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Review ArticleSignificant Remote Sensing Vegetation IndicesA Review of Developments and Applications

Jinru Xue and Baofeng Su

College of Mechanical and Electronic Engineering Northwest AampF University Yangling Shaanxi 712100 China

Correspondence should be addressed to Baofeng Su bfsnwsuafeducn

Received 24 January 2017 Accepted 13 April 2017 Published 23 May 2017

Academic Editor Chenzong Li

Copyright copy 2017 Jinru Xue and Baofeng Su This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative andqualitative evaluations of vegetation cover vigor and growth dynamics among other applications These indices have been widelyimplemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned AerialVehicles (UAV) Up to date there is no unified mathematical expression that defines all VIs due to the complexity of different lightspectra combinations instrumentation platforms and resolutions used Therefore customized algorithms have been developedand tested against a variety of applications according to specific mathematical expressions that combine visible light radiationmainly green spectra region from vegetation and nonvisible spectra to obtain proxy quantifications of the vegetation surface Inthe real-world applications optimization VIs are usually tailored to the specific application requirements coupled with appropriatevalidation tools and methodologies in the ground The present study introduces the spectral characteristics of vegetation andsummarizes the development of VIs and the advantages and disadvantages from different indices developed This paper reviewsmore than 100VIs discussing their specific applicability and representativeness according to the vegetation of interest environmentand implementation precision Predictably research and development ofVIs which are based onhyperspectral andUAVplatformswould have a wide applicability in different areas

1 Introduction

Remote sensed information of growth vigor and theirdynamics from terrestrial vegetation can provide extremelyuseful insights for applications in environmental monitoringbiodiversity conservation agriculture forestry urban greeninfrastructures and other related fields Specifically thesetypes of information applied to agriculture provide not onlyan objective basis (depending on resolution) for the macro-and micromanagement of agricultural production but also inmany occasions the necessary information for yield estima-tion of crops [1] These latter applications have been devel-oped to be a well-known discipline category precision agri-culture which could be tracked back to three decades ago [1]However the applicability of remote sensing and its differentVIs extracted from these techniques usually relies heavily onthe instruments and platforms to determine which solutionis best to get a particular issue

11 Remote Sensing Platform Considerations In terms ofplatforms the advantages of satellite based remote sensinginclude high spatial resolution which makes possible theextraction of long time data series of consistent and compa-rable data which can be cost effective [2] Furthermore somesatellite platforms have free access to visible andmultispectraldata such as Landsat 7-8 However there are two main prob-lems with these platforms for precision agriculture applica-tions which are related to the per pixel resolution (30m2 perpixel for Landsat and 500m2 forMODIS) and the orbit period(16 d for Landsat and 26 d for SPOT) More recently pixelresolution has been increased by newer satellites such asWorldView-2 and -3 (DigitalGlobe Longmont CO USA)WorldView-2 was the first commercial high resolution satel-lite to provide eight spectral sensors in the visible to nearinfrared range Along with the four typical multispectralbands blue (450ndash510 nm) green (510ndash580 nm) red (630ndash690 nm) and near infrared (NIR) (770ndash895 nm) each sensor

HindawiJournal of SensorsVolume 2017 Article ID 1353691 17 pageshttpsdoiorg10115520171353691

2 Journal of Sensors

is narrowly focused on a particular range of the electromag-netic spectrum that is sensitive to a particular feature fromthe ground or a property of the atmosphere However imagesfrom this platform can be cost prohibitive for long time dataseries studies

The second problem with satellite based remote sensingis the revisitation time which is 16 days in average whichmakes the agricultural applications difficult specifically thoserelated to water and nutrient management Moreover passivesensors cannot penetrate clouds therefore there is no usabledata capture for overcast days

To solve these two main problems airborne and morerecently UAV platforms can be used The former can also becost prohibitive due to the requirement of expensive aircraftsand pilots The latter has become almost of ubiquitous use inthe last five years with affordable aircrafts and camera pay-loads ranging from visible near and thermal infrared and 3DLIDAR which has been referred to as Unmanned AerialSystem (UAS) Among UAS platforms there are mainly fixedwing andmultirotor options availableThere is a compromiseusing these UAS platforms in relation to payload weightversus flying time In general longer flying time achievedby fixed wing systems demands lighter weight payloads Forexample small high definition visible cameras weighting lessthan 300 grams as payload of a fixed wing UAS allow it tofly for around two hours using currently available battery-power [3] On the contrary battery-powered multirotor UASwith higher payload capacity have reduced fly time that at themoment is around 15- to 25-minute duration Using theseUAS higher spatial and temporal data resolution can beachieved which makes possible precision agriculture appli-cations to the submeter resolution per pixel This allowsresearch and practical applications applied to growth andvigor dynamic assessment plant water status sensing forirrigation scheduling applications and evapotranspirationmodelling among others [4ndash9]

12 Remote Sensing and Vegetation Indices Remote sensingof vegetation is mainly performed by obtaining the electro-magnetic wave reflectance information from canopies usingpassive sensors It is well known that the reflectance of lightspectra from plants changes with plant type water contentwithin tissues and other intrinsic factors [10]The reflectancefrom vegetation to the electromagnetic spectrum (spectralreflectance or emission characteristics of vegetation) is deter-mined by chemical and morphological characteristics of thesurface of organs or leaves [3] The main applications forremote sensing of vegetation are based on the following lightspectra (i) the ultraviolet region (UV) which goes from10 to 380 nm (ii) the visible spectra which are composedof the blue (450ndash495 nm) green (495minus570 nm) and red(620ndash750 nm) wavelength regions and (iii) the near and midinfrared band (850ndash1700 nm) [11 12] The emissivity rate ofthe surface of leaves (equivalent to the absorptivity in thethermal waveband) of a fully grown green plant without anybiotic or abiotic stress is generally in the range of 096ndash099and ismore often between 097 and 098 [13] On the contraryfor dry plants the emissivity rate generally has a larger rangegoing from 088 to 094 [13] Vegetation emissivity in the near

andmid infrared regions has beenwidely studiedwithin plantcanopies Indices extracted from this light spectra range canbe attributed to a range of characteristics beyond growth andvigor quantification of plants related to water content pig-ments sugar and carbohydrate content protein content andaromatics among others [2 14] Different applications aredependent on the reflectivity peaks or overtones for specificcompounds within the visible and nearmid infrared regionsof light spectra [14 15] Plant reflectivity in the thermal infra-red spectral range (8ndash14 120583m) follows the blackbody radiationlaw [16] which allows interpreting plant emission as directlyrelated to plant temperature Hence indices obtained fromthis spectra range can be used as a proxy to assess stomatadynamics that regulates transpiration rate of plants There-fore the later indices can be used as indicator of plant waterstatus [17ndash19] and abioticbiotic stress levels [20 21]

The latter considerations demonstrate that the quantita-tive interpretation of remote sensing information from vege-tation is a complex task Many studies have limited this inter-pretation by extracting vegetation information using individ-ual light spectra bands or a group of single bands for dataanalysis Thus researchers often combine the data from nearinfrared (07ndash11m) and red (06ndash07m) bands in differentways according to their specific objectives [2] These typesof combinations present many disadvantages (eg lack ofsensitivity) by using single or limited group of bands to detectfor example vegetation biomass These limitations are par-ticularly evident when trying to apply these types of VI onheterogeneous canopies such as horticultural tree planta-tions Amixed combination of soils weeds cover crops in theinterrow and the plants of interest makes the discriminationregions of interest and extraction of simple VI very difficultspecifically when the vegetation of interest has differentVIs due to spatial variability or VIs corresponding to othervegetation (weeds and cover crop) which can be similar tothose of interest The later will complicate imaging denoisingand filtering processes Several image analysis techniques andalgorithms have been developed to go around these issueswhich will be described later Even though there are manyconsiderations as described before the construction of simpleVI algorithm could many times render simple and effectivetools to measure vegetation status on the surface of the Earth[6]

2 Vegetation Indices and Validation Process

With the use of high resolution spectral instrumentation thenumber of bands obtained by remote sensing is increasingand the bandwidth is getting narrower [7] One of the mostused and implemented indices calculated from multispectralinformation as normalized ratio between the red and nearinfrared bands is theNormalizedDifferenceVegetation Index(NDVI) [22] A direct use of NDVI is to characterize canopygrowth or vigor hence many studies have compared it withthe Leaf Area Index (LAI) [23] where LAI is defined as thearea of single sided leaves per area of soil [24]

Vegetation information from remote sensed images ismainly interpreted by differences and changes of the greenleaves from plants and canopy spectral characteristics The

Journal of Sensors 3

most common validation process is through direct or indirectcorrelations between VIs obtained and the vegetation charac-teristics of interest measured in situ such as vegetation coverLAI biomass growth and vigor assessment More estab-lished methods are used to assess VIs using direct and geo-referencedmethods bymonitoring sentinel plants to be com-pared with VIs obtained from the same plants for calibrationpurposes

The later process is known as allometric measurementsand requires destructivemethods to scan specific area of totalleaves per plant or tree in the case of LAI [25] Indirect vali-dationmethods are based on proximal instrumentation usingthe same or similar spectral instrumentation to assess georef-erenced sentinel plants at the same angle as the aerial plat-forms The latter method is useful to compare VIs obtainedfrom satellite that are sensitive to atmospheric effects andserve as amean to obtain correction factorsMore recent indi-rect methods based on cover photography to estimate canopycover LAI porosity and clumping index have used auto-mated analysis methods

For this purpose upward looking cover photogrammetryat zero zenith angle is taken with visible cameras to obtaincanopy architecture parameters calculated using computervision algorithms An automated image acquisition and cal-culation method was proposed by Fuentes et al 2008 appliedto Eucalyptus trees [26] and it has been successfully appliedfor other crops such as grapevines compared to allometricmeasurements and to validate NDVI calculated from satelliteinformation (WorldView-2) [27] apple trees with increasedaccuracy by using a variable light extinction coefficient (119896)[28] and cherry trees improving the method by extractingnonleaf material such as branches for tall trees [29 30] Inlate 2015 a computer application (App) for smartphones andtablet PCs called VitiCanopy was released for free use toassess canopy architecture parameters using the cover pho-tography automated algorithms which can be applied to anyother tree crop by changing to a specific 119896 value [31 32] OtherApps using RGB photogrammetry to assess LAI have beenlater developed such as PoketLAI [33ndash35]

21 Basic Vegetation Indices Jordan [36] proposed in 1969one of the first VIs named Ratio Vegetation Index (RVI)which is based on the principle that leaves absorb relativelymore red than infrared light RVI can be expressed mathe-matically as

RVI = 119877NIR

(1)

where NIR is the near infrared band reflectance and 119877 isred band reflectance According to the spectral characteristicsof vegetation bushy plants have low reflectance on the redband and have shown a high correlation with LAI Leaf DryBiomass Matter (LDBM) and chlorophyll content of leaves[37] The RVI is widely used for green biomass estimationsand monitoring specifically at high density vegetation cov-erage since this index is very sensitive to vegetation and has agood correlation with plant biomass However when the veg-etation cover is sparse (less than 50 cover) RVI is sensitive

to atmospheric effects and their representation of biomass isweak

The Difference Vegetation Index (DVI) was proposedlater [38] and can be expressed as

DVI = NIR minus 119877 (2)

The DVI is very sensitive to changes in soil background itcan be applied to monitoring the vegetation ecological envi-ronment Thus DVI is also called Environmental VegetationIndex (EVI)

The Perpendicular Vegetation Index (PVI) [38] is asimulation of theGreenVegetation Index (GVI) in119877 NIR 2Ddata In the NIRminus119877 coordinate system the spectral responsefrom soil is presented as a slash (soil brighten line)The lattereffect can be explained as the soil presents a high spectralresponse in the NIR and 119877 bands The distance between thepoint of reflectivity (119877 NIR) and the soil line has been definedas the Perpendicular VI which can be expressed as follows

PVI = radic(120588soil minus 120588veg)2119877 minus (120588soil minus 120588veg)2NIR (3)

where 120588soil is the soil reflectance 120588veg is the vegetationreflectivity PVI characterizes the vegetation biomass in 120588redthe soil background the greater the distance the greater thebiomass

PVI can also be quantitatively expressed as

PVI = (DNNIR minus 119887) cos 120579 minus DN119877 lowast sin 120579 (4)

where DNNIR and DN119877 are the radiation reflected luminancevalues from the NIR and 119877 respectively 119887 is the interceptof the soil baseline and the vertical axis of NIR reflectivityand 120579 is the angle between the horizontal axis of 119877 reflectivityand soil baseline PVI filters out in this way the effects ofsoil background in an efficient manner PVI also has lesssensitivity to atmospheric effects and it is mainly used forthe inversion of surface vegetation parameter (grass yieldchlorophyll content) the calculation of LAI and vegetationidentification and classification [39 40] However PVI issensitive to soil brightness and reflectivity especially in thecase of low vegetation coverage and needs to be adjusted forthis effect [41]

As mentioned before the Normalized Difference Vege-tation Index (NDVI) is the most widely used as VI it wasproposed by Rouse Jr et al [42] which can be expressed as

NDVI = (120588NIR minus 120588119877)120588NIR+ 120588119877 (5)

Since the index is calculated through a normalization proce-dure the range of NDVI values is between 0 and 1 having asensitive response to green vegetation even for low vegetationcovered areas This index is often used in research related toregional and global vegetation assessments and was shown tobe related not only to canopy structure and LAI but also tocanopy photosynthesis [43 44] However NDVI is sensitiveto the effects of soil brightness soil color atmosphere cloudand cloud shadow and leaf canopy shadow and requiresremote sensing calibration


22 Vegetation Indices considering Atmospheric Effects Giventhe limitations of NDVI under atmospheric effects Kaufmanand Tanre [40] proposed the Atmospherically Resistant Veg-etation Index (ARVI) This index is based on the knowledgethat the atmosphere affects significantly 119877 compared to theNIR Thus Kaufman and Tanre modified the radiation valueof 119877 by the difference between the blue (119861) and 119877 ThereforeARVI can effectively reduce the dependence of this VI toatmospheric effects which can be expressed as

ARVI = (NIR minus 119877119861)(NIR + 119877119861) 120588lowast119903119887 = 120588lowast120591 minus 120574 (120588lowast119887 minus 120588lowast120591 )

(6)

where 119877119861 is the difference between 119861 and 119877 is the reflectivityrelated to the molecular scattering and gaseous absorptionfor ozone corrections and represents the air conditioningparameters

The ARVI is commonly used to eliminate the effects ofatmospheric aerosols The aerosols and ozone effects in theatmosphere still need to be eliminated by the 5S atmospherictransport model [45] However to implement the 5S atmo-spheric transmission model actual atmospheric parametersmust be considered which are difficult to obtain If the ARVIindex is not calculated using the 5S model this index is notexpected to perform much better than NDVI consideringatmospheric effects or large dust particles in the atmosphereThus Zhang et al [46] proposed a new atmospheric effectresistant vegetation index namely IAVI that can eliminateatmospheric interference without the use of the 5S model

IAVI = 120588nir minus [120588119903 minus 120574 (120588119887 minus 120588119903)]120588nir + [120588119903 minus 120574 (120588119887 minus 120588119903)] (7)

where the range of 120574 values is between 065 and 112 asignificant value of 120574 is close to 1 for ARVI After testing theerror caused in IAVI by the atmosphere effect is between 04and 37 which is less than those found when using NDVIin the same conditions (14ndash31)

23 Adjusted-Soil Vegetation Index The distinction of veg-etation from the soil background was originally proposedby Richardson and Wiegand [47] by analyzing the soil linewhich can be considered as a linear relationship on the 2Dplane of the soil spectral reflectance values between the NIRand 119877 Therefore it can be considered as a comprehensivedescription of a large number of soil spectral informationfrom a number of environments [48] Many VIs that takeinto account the effect of soil background have been basedon this principle In addition to PVI ((3)-(4)) the Soil LineAtmospheric Resistance Index (SLRA) was developed basedon the improvement of the soil line principle The SLRA wasthen combined with the Transformed Soil-Adjusted Vegeta-tion Index (TSAVI) to develop the Type Soil AtmosphericImpedance Vegetation Index (TSARVI) [49] which will bediscussed later

As shown before NDVI (5) is very sensitive to back-ground factors such as the brightness and shade of the veg-etation canopies and soil background brightness Researches

have shown thatwhen the backgroundbrightness is increasedNDVI also increases systematically Given the effect of soilbackground119877 radiation increases significantly when the veg-etation cover is sparse conversely NIR radiation is reducedto make the relationship between vegetation and soil moresensible Many VIs have been developed to adjust to the soileffect

SinceNDVI and PVI have some deficiencies in describingthe spectral behavior of vegetation and soil backgroundHuete [50] established the Soil-Adjusted Vegetation Index(SAVI) which can be expressed as follows

SAVI = (120588119899 minus 120588119903) (1 + 119871)(120588119899 + 120588119903 + 119871) (8)

The above model of a soil vegetation system was estab-lished to improve the sensitivity ofNDVI to soil backgroundswhere 119871 is the soil conditioning index which improves thesensitivity of NDVI to soil backgroundThe range of 119871 is from0 to 1 In practical applications the values of 119871 are determinedaccording to the specific environmental conditionsWhen thedegree of vegetation coverage is high 119871 is close to 1 showingthat the soil background has no effect on the extraction ofvegetation informationThis kind of ideal conditions is rarelyfound in natural environments and can be applicable only inthe case of a large canopy density and coverage [40]The valueof 119871 is around 05 undermost common environmental condi-tionsWhen119871 is close to 0 the value of SAVI is equal toNDVIHowever 119871 factor should vary inversely with the amount ofvegetation present to obtain the optimal adjustment for thesoil effect Thus a modified SAVI (MSAVI) replaces 119871 factorin the SAVI equation (8) with a variable 119871 function In thisway MSAVI [51] reduces the influence of bare soil on SAVIwhich can be expressed as follows

MSAVI = 05 lowast 2119877800 + 1minus SQRT [(2119877800 + 1)2 minus 8 (119877800 minus 119877670)]

(9)

The SAVI is much less sensitive than the RVI (1) tochanges in the background caused by soil color or surfacesoil moisture content Three new versions of SAVI (SAVI2SAVI3 and SAVI4) were developed based on the theoreticalconsiderations of the effects of wet and dry soils [41] SAVI2SAVI3 and SAVI4 reduce the effect of background soilbrightness by taking into account the effect of the variationof the solar incidence angle and changes in the soil physicalstructure

Based on the implementation of the MSAVI Richardsonand Wiegand (1977) proposed a Modified Secondary Soil-Adjusted Vegetation Index (MSAVI2) [47] which can beexpressed as

MSAVI2= 05lowast [(2NIR + 1) minus radic(2NIR + 1)2 minus 8 (NIR minus 119877)]

(10)


MSAVI2 does not rely on the soil line principle and has asimpler algorithm it is mainly used in the analysis of plantgrowth desertification research grassland yield estimationLAI assessment analysis of soil organic matter droughtmonitoring and the analysis of soil erosion [39]

Baret et al studied the sensitivity of fiveVIs (NDVI SAVITransformed Soil-Adjusted Vegetation Index (TSAVI) Mod-ified Soil-Adjusted Vegetation Index (MSAVI) and GlobalEnvironment Monitoring Index (GEMI)) to the soil back-groundThey simulated the performance of the VIs for differ-ent soil textures moisture levels and roughness by using theScattering from Arbitrarily Inclined Leaves (SAIL) modelThey determined an optimum value of SAIL = 016 to reducethe effects of soil background and then proposed an Opti-mized Soil-Adjusted Vegetation Index (OSAVI) [48] that canbe expressed as follows

OSAVI = (NIR minus 119877)(NIR + 119877 + 119883) (11)

where SAIL is 016 and OSAVI does not depend on the soilline and can eliminate the influence of the soil backgroundeffectively However the applications of OSAVI are notextensive it ismainly used for the calculation of abovegroundbiomass leaf nitrogen content and chlorophyll contentamong others [52]

24 Tasseled Cap Transformation of Greenness VegetationIndex (GVI YVI and SBI) Kauth and Thomas studied thespectral pattern of the vegetation growth process and called itthe ldquospike caprdquo pattern including the soil background reflec-tivity and brightness lineTheTasseled Cap Transformation isa conversion of the original bands of an image into a new setof bands with defined interpretations that are useful for vege-tationmapping ATasseled CapTransformation is performedby taking ldquolinear combinationsrdquo of the original image bandswhich is similar in concept to the multivariate data analysistechnique called principal components analysis (PCA) [53]

The Tasseled Cap can convert Landsat MSS Landsat TMand Landsat 7 ETMdata For LandsatMSS data furthermorethe Tasseled Cap performs orthogonal transformation on theoriginal data which converts it into a 4D space This con-version includes the Soil Brightness Index (SBI) (14) degreeof Green Vegetation Index (GVI) (12) and the degree ofYellow Vegetation Index (YVI) (13) It also includes NonsuchIndex (NSI) mainly for noise reduction The NSI is closelyrelated to atmospheric effects For the Landsat 5 TM datathe Tasseled Cap results consist of three factors brightnessgreenness and a third component related to soil Amongthem the brightness and the greenness are equivalent to SBIand GVI in the MSS Tasseled Cap The third component isrelated to soil characteristics and humidity For Landsat 7ETM data the Tasseled Cap Transformation generates sixbands namely brightness greenness humidity the fourthcomponent (noise) a fifth component and a sixth compo-nent

GVI = minus0290MSS4 minus 0562MSS5 + 0600MSS6

+ 0491MSS7 (12)

YVI = minus0829MSS4 minus 0522MSS5 + 0039MSS6

+ 0149MSS7 (13)

SBI = +0433MSS4 minus 0632MSS5 + 0586MSS6

+ 0264MSS7 (14)

The GVI YVI and SBI ignore the interaction and effects ofthe atmosphere soil and vegetation SBI andGVI can be usedto evaluate the behavior of vegetation and bare soil [54] TheGVI has a strong correlation with different vegetation coversThus GVI increases the processing of atmospheric effectsJackson et al (1980) proposed the Adjust Soil BrightnessIndex (ASBI) and Adjust Green Degree Vegetation Index(AGVI) [55] which can be expressed as follows

ASBI = 20YVIAGVI = GVI minus (1 + 0018GVI)YVI minus NSI2 (15)

Misra and Wheeler (1977) performed PCA of Landsatimages and computed the multiple factors of these indexesThis analysis was the basis of the development of the MisraSoil Brightness Index (MSBI) Misra Green Degree Vege-tation Index (MGVI) and Misra Yellow Degree VegetationIndex (MYVI) [56] which can be expressed as follows

MSBI = 0406MSS4 + 060MSS5 + 0645MSS6

+ 0243MSS7MGVI = minus0386MSS4 minus 053MSS5 + 0535MSS6

+ 0532MSS7MYVI = 0723MSS4 minus 0597MSS5 + 0206MSS6

minus 0278MSS7

(16)

Since NDVI has been found to be affected only by soilbrightness it presents a negative correlation between NDVIand soil brightness A positive correlation is found when onlyatmospheric effects affect NDVI Under natural conditionsthe soil and atmosphere influence NDVI in a complexmanner which interacts with the vegetation cover influenceTherefore atmosphere and vegetation have a collective effecton NDVI based on the soil characteristics and exposure Liuand Huete comprehensively analyzed multiple soil types andatmospheric enhanced VIs They developed the AtmosphereAntivegetation Index (ARVI) and Soil-Adjusted VegetationIndex (SAVI) for a comprehensive analysis of vegetation inthese conditionsThey found that as a result of the interactionbetween the soil and the atmosphere reducing one of themmay increase the other They introduced a feedback mecha-nism by building a parameter to simultaneously correct soil


and atmospheric effectsThis parameter is the EnhancedVeg-etation Index (EVI) [57] that can be expressed as follows

EVI = (TM4 minus TM3) (1 + 119871)TM4 minus 1198621TM3 + 1198622TM + 119871

EVI = 25 lowast 120588lowast119899 minus 120588lowast119903120588lowast119899 + 1198621120588lowast119903 minus 1198622120588lowast119887 + 119871(17)

which includes the values of NIR R and B which arecorrected by the atmosphere L represents soil adjustmentparameters and its value is equal to 1 and parameters corre-spond to constant values equivalent to 6 and 75 respectively

25 Vegetation Indices Based on UAS Remote Sensing in theVisible Spectra Region UAS remote sensing is a low altituderemote sensing technology (50ndash100m) which is less affectedby atmospheric factors during the data acquisition processIt has the advantages of affordability simple operation fastimaging speed and high spatial and temporal resolutionswhich is unparalleled compared with traditional [58] remotesensing technologies based on satellites At present the UASremote sensing technology plays a crucial role in the field ofaerial remote sensingwith increased interest in applying theseplatforms on different studies of vegetation assessment [59]Thepractical applications ofUAS aremainly related to imagesacquisition in the visible bands (RGB) due to easy access ofubiquitous high resolution cameras at low price and weightHowever due to rapid advances in technology multispectraland infrared thermal cameras are becoming increasinglycheaper and miniaturized

As previously shown through different VIs most of themare based on the mixture of visible light bands and NIR togenerate algorithms and those based only on the visible lightspectra are not common However weightless high definitioncameras are appearing in the market that includes the NIRband which will enhance the practical applicability of UASin the near future These types of reflectance are commonlymeasured using visible multispectral and hyperspectralcameras [5] According to Gago et al (2015) NDVI is oneof the most employed indices for UAS applications and isdefined specifically as

NDVI = (119877800 minus 119877680)(119877800 + 119877680) (18)

where 119877800 is the reflectance at 800 nm and 119877680 at 680 nmDue to the high NIR reflectance of chlorophyll this index isused to detect plants greenness [60] Some studies describedthe use of UAS with multispectral cameras and high resolu-tionmultispectral satellites to estimate LAI (Leaf Area Index)through NDVI [27 61]

The optimized index transformed chlorophyll absorp-tion in reflectance Transformed Chlorophyll Absorption inReflectance IndexOptimized Soil-AdjustedVegetation Index(TCARIOSAVI) was proposed as more sensitive VI to chlo-rophyll content In this way avoiding other factors that couldaffect the reflectance values such as canopy reflectance andsoil reflectance among others [5] Another index evaluated

by Zarco-Tejada et al (2013) was the PRInorm which is animprovement of the Photochemical Reflectance Index (PRI)This index considers xanthophyll changes related to waterstress but also generates a normalization considering chloro-phyll content and canopy leaf area reduction which is mainlyaffected by water stress [62] However by obtaining a quickand effective method to extract vegetation information basedon UAS visible images it will enhance and popularize thescope of application of UAS immediately [63] In this senseWang et al (2015) comprehensively considered the spectralcharacteristics of healthy green vegetation and the spectralcharacteristics of typical features of UAS imagery [63] Theyuse green (119866) band instead of the NIR band to calculateNDVI (120588red + 120588blue) compared to 119877 for NDVI and the 119866band multiplied by 2 for (120588red + 120588blue) Thus a Visible-BandDifference Vegetation Index (VDVI) is created based on thethree bands of visible light which can be expressed as follows

VDVI = (2 lowast 120588green minus 120588red minus 120588blue)(2 lowast 120588green + 120588red + 120588blue) (19)

The values of VDVI are within [minus1 1] and the accuracy ofthe vegetation extraction based on VDVI is higher than othervisible light band-based VIs and 119866 band Furthermore theaccuracy of VDVI has been reported to be over 90 [63]

26 Vegetation Indices Related to Vegetation Status TheNDVI as shown previously enhances the contrast of thereflectivity of the NIR and 119877 channels (5) Therefore it isa nonlinear extension of NIR and 119877 ratios resulting in theenhancement of the lower part of these values (higher valuesare suppressed) Hence NDVI reaches saturation in this waymore easilyThus Gitelson [64] proposed theWide DynamicRange Vegetation Index (WDRVI) which can be expressedas follows

WDRVI = (120572120588nir minus 120588red)(120572120588nir + 120588red) (20)

WDRVI enhances the dynamic range of NDVI by applyinga weighting parameter to the NIR reflectance If 120572 equals 1WDRVI is equivalent to NDVI If 120572 is equal to the ratio(120588red120588NIR) WDRVI is zero After validation procedures acoefficient value of 020 for appears to be generally effectivefor the WDRVI calculations According to Gitelson (2004)[64] WDRVI offers a simple way to enhance the dynamicrange for high biomass environments (LAI gt 2) Howeverwhen biomass is low (LAI lt 1) NDVI is still the best choicefor the plant classification

According to the spectral reflectance of plant leaves(between 550 nm and 700 nm) it can be considered constanteven if the chlorophyll content of leaves is variable Basedon this relationship Kim et al (1994) measured the level ofabsorption at 670 nmand linked the reflection peak at 700 nm


and 550 nm the Chlorophyll Absorption Ratio Index (CARI)was then developed [65] and can be expressed as

CAR lowast (119877700119877670) CAR = 1003816100381610038161003816(119886 lowast 670 + 119877670 + 119887)1003816100381610038161003816(1198862 + 1)05

119886 = (119877700 minus 119877500)150 119887 = 119877550 minus (119886 lowast 550)

(21)

Later Daughtry et al improved the CARI by proposing amodified CARI (MCARI) [66] which can be expressed as

MCARI

= 15 lowast [25 (119877800 minus 119877670) minus 13 (119877800 minus 119877550)]radic(2119877800 + 1)2 minus (6119877800 minus 5119877670) minus 05

(22)

The MCARI is more sensitive to leaf chlorophyll concentra-tions Daughtry et al (2000) found that LAI chlorophyll andthe chlorophyll-LAI interaction accounted for 60 27 and 13of the MCARI variation Even though the MCARI formulais not related to the NIR bands good predictions were stillfound

In agriculture crop growth is directly linked to watersupply and plant water status When the soil water supplyis insufficient plants will be under water stress which leadsto reduced crop yield and even crop failures under extremedrought conditions So it is very important to evaluate thecrop water status in a timely and accurate manner whichhas direct implications on crop growth yield and quality ofproduce [67] In recent years the development of infraredthermal remote sensing technology made it possible to mea-sure canopy temperature changes and dynamics from croppopulations These changes are related to the transpirationrate of plants and stomatal conductance Hence crop leaf andcanopy temperature have been used for the determination ofcrop water status [68] In order to make the canopy temper-ature measurements consistent Idso et al (1981) establishedthe Crop Water Stress Index (CWSI) to monitor crop waterstatus [69]

CWSI = (119879canopy minus 119879nws)(119879dry minus 119879nws) (23)

where 119879canopy is the temperature of fully sunlit canopy leaves(∘C) 119879nws is the temperature of fully sunlit canopy leaves (∘C)when the crop is non-water-stressed (well-watered) 119879dry isthe temperature of fully sunlit canopy leaves (∘C) when thecrop is severely water stressed due to low soil water availabil-ity 119879nws and 119879dry are the lower and upper baselines used tonormalize CWSI for the effects of environmental conditions(air temperature relative humidity solar radiation and wind

speed) on 119879canopy The CWSI has two models an empiricalmodel and a theoretical model however the theoreticalmodel involves too many parameters and these parametersare not easy to obtain Therefore the theoretical model isonly used for research purposes [70ndash72]The empiricalmodelcan be obtained only by using crop canopy temperature airtemperature and air saturation difference so the empiricalmodel has been further studied and used in many cropapplications [73]

Besides the use of infrared thermal radiation to detectplant water stress detection the visible part of the spectrumhas also been useful for early water stress detection Thisinvolves using indices focused on bands at specific wave-lengths where photosynthetic pigments are affected by waterstress conditions such as chlorophyll The PhotochemicalReflectance Index (PRI) has been used as a stress index ofstress based on this principle with initial developments tobe applied to disease symptoms detection which can beexpressed as

PRI = (119877531 minus 119877570)(119877531 minus 119877570) (24)

It has been shown that the Light Use Efficiency (LUE) is akey variable to estimate Net Primary Productivity (NPP) [7475]When obtaining reliable accuracy in LUEmeasurementsit is possible to study the distribution of energy and globalclimate change The PRI is a normalized difference VI ofreflectivity at 531 nm and 570 nm and the reflectance of thesetwo bands is affected by the xanthophyll cycle and is closelyrelated to LUE of leaves Therefore PRI provides a goodestimation of leaf LUE

27 Summary of Vegetation Indices A summary of the mainVIs discussed in this paper can be found in Table 1 with theirrespective citations

3 Conclusions

Simple VIs combining visible and NIR bands have signif-icantly improved the sensitivity of the detection of greenvegetation Different environments have their own variableand complex characteristics which needs to be accountedwhen using different VIs Therefore each VI has its specificexpression of green vegetation its own suitability for specificuses and some limiting factors Therefore for practicalapplications the choice of a specific VI needs to bemade withcaution by comprehensively considering and analyzing theadvantages and limitations of existing VIs and then combinethem to be applied in a specific environment In this way theusage of VIs can be tailored to specific applications instru-mentation used and platforms With the development ofhyperspectral and multispectral remote sensing technologynewVIs can be developed which will broaden research areasIt is envisioned that these new developments will be readilyapplied and adopted by UAS platforms and will become oneof the most important research areas in aerospace remotesensing in the near future


Table 1 Summary of vegetation index expression

Index Definition Reference

AGVI GVI minus (1 + 0018GVI) lowast YVI minus NSI2 [76]

ARI ( 1119877550 ) minus (1119877700 ) [76]

ARI2 119877800 [( 1119877550 ) minus (1119877700 )] [76]

ARVI (NIR minus 119877119861)(NIR + 119877119861) [40]

ASBI 02YVI [77]

ATSAVI[119886 (NIR minus 119886Red minus 119887)][119886NIR + Red minus 119886119887 + 119883 (1 + 1198862)] [78]

AVI 20MSS7 minusMSS5 [79]

AVI tanminus1 [(1205823 minus 1205822)1205822 ] (NIR minus 119877)minus1 + tanminus1 [(1205822 minus 1205821)1205822 ] (119866 minus 119877)minus1 [80]

BGI1119877400119877550 [81]

BGI2119877450119877550 [81]

BRI1119877400119877690 [60]

BRI2119877450119877690 [60]

CAI 05 (1198772000 + 1198772200) minus 1198772100 [82]

CARI CAR lowast (119877700119877670 ) [65]

CCCI(NDRE minusNDREmin)(NDREmax minusNDREmin) [83]

CRCWD 1 minus 120588119903min [84]

CRI500(1119877515)(1119877550) [85]

CRI700(1119877515)(1119877700) [85]

CWSI((119879119888 minus 119879119886) minus (119879119888 minus 119879119886)119897119897)((119879119888 minus 119879119886)119906119897 minus (119879119888 minus 119879119886)119897119897) [69]

DI1 119877800 minus 119877550 [86]

DVI int12058211205821

(119889120588119889120582)119889120582 [87]

DVI 24MSS7 minusMSS5 [38]

EVI[(TM4 minus TM3) (1 + 119871)](TM4 minus 1198621TM3 + 1198622TM + 119871) [88]

EXG 2 lowast 120588green minus 120588red minus 120588blue [89 90]

GARINIR minus [Green minus 120574 (Blue minus Red)]NIR + [Green minus 120574 (Blue minus Red)] [24]

GDVI NIR minus Green [23]


Table 1 Continued


GEMI120578(1 minus 025120578) minus (119877 minus 0125)(1 minus 119877)

120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]

GNDVI(120588NIR minus 120588119866)(120588NIR + 120588119866) [91]

GRABS GVI minus 009178SBI + 558959 [93]

GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]

GVI (minus0283MSS4 minus 066MSS5 + 0577MSS6 + 0388MSS7) [53]

GVSB GVISBI

[95]

LIC3119877400119877740 [96]

HJVI[2 (120588nir minus 120588red)](120588nir + 6120588red minus 75120588blue + 1) [97]

HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]

IAVI120588nir minus [120588119903 minus 120574 (120588119887 minus 120588119903)]120588nir + [120588119903 minus 120574 (120588119887 minus 120588119903)] [46]

IITM5TM7

[99]

IPVITM4(TM4 + TM3) [100]

MCARI [(119877700 minus 119877670) minus 02 (119877700 minus 119877550)] (119877700119877670 ) [66]

MCARI15 lowast [25 (119877800 minus 119877670) minus 13 (119877800 minus 119877550)]radic(2119877800 + 1)2 minus (6119877800 minus 5119877670) minus 05 [101]

MGVI (minus0386MSS4 minus 053MSS5 + 0535MSS6 + 0532MSS7) [102]

MNLI[(NIR2 minus Red) (1 + 119871)](NIR2 + Red + 119871) [103]

MNSI (0404MSS4 minus 0039MSS5 minus 0505MSS6 + 0762MSS7) [102]

MRENDVI(120588750 minus 120588705)(120588750 + 120588705 minus 2 lowast 120588445) [104 105]


Table 1 Continued


MRESR(120588750 minus 120588445)(120588705 minus 120588445) [104 105]

MSAVI2 05 lowast [(2NIR + 1) minus radic(2NIR + 1)2 minus 8 (NIR minus 119877)] [106]

MSBI (0406MSS4 + 060MSS5 + 0645MSS6 + 0243MSS7) [102]

MSAVI 05 lowast 2119877800 + 1 minus SQRT [(2119877800 + 1)2 minus 8 (119877800 minus 119877670)] [51]

MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]

MTVI 12 lowast [12 (119877800 minus 119877550) minus 25 (119877670 minus 119877550)] [101]

MTVI215 lowast [12 (119877800 minus 119877550) minus 25 (119877670 minus 119877550)]

radic(2 lowast 119877800 + 1)2 minus (6 lowast 119877800 minus 5 lowast radic119877670) minus 05 [101]

MYVI (0723MSS4 minus 0597MSS5 + 0206MSS6 minus 0278MSS7) [102]

NDGI (119866 minus 119877)(119866 + 119877) [78]

NDI (NIR minusMIR)(NIR +MIR) [108]

NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]

NDNI[log (11205881510) minus log (11205881680)][log (11205881510) + log (11205881680)] [111]

NDLI[log (11205881754) minus log (11205881680)][log (11205881754) + log (11205881680)] [111]

NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]

NDVI(120588NIR minus 120588119877)(120588NIR + 120588119877) [42]

NDWI (Green minus NIR)(Green + NIR) [112]

NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]

NMDI[120588860 minus (1205881640 minus 1205882130)][120588860 + (1205881640 minus 1205882130)] [115]

NLI(NIR2 minus Red)(NIR2 + Red) [116]


Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]

PVI radic(120588soil minus 120588veg)2119877 minus (120588soil minus 120588veg)2NIR[38]

PVI (NIR minus 119886119877 minus 119887)radic1198862 + 1 [121]

RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]

RDVI radicNDIVI sdot DVI [121]

RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]

SAVI(120588NIR minus 120588119866)(120588NIR + 120588119866 + 119871) + (1 + 119871) [50]

SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]

SBI (minus0283MSS4 minus 066MSS5 + 0577MSS6 + 0388MSS7) [53]

SBL MSS7 minus 24MSS5 [38]

SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]

TCARI 3 lowast [(119877700 minus 119877670) minus 02 lowast (119877700 minus 119877550) (119877700119877670 )] [126]

TDVI radic05 + [ (NIR minus Red)(NIR + Red) ] [132]

TSARVI[119886119903119887 (NIR minus 119886119903119887119877119861 minus 119887119903119887)][119877119861 + 119886119903119887NIR minus 119886119903119887119861119903119887 + 119883 (1 + 1198861199031198872)] [49]

TSAVI [119886 (NIR minus 119886119877 minus 119861)][119877 + 119886NIR minus 119886119887] [133]

TVI radicNDVI + 05119871 [134]

VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]

VCI(NDVI119894 minusNDVImin)(NDVImax minusNDVImin) [136]

VDVI(2 lowast 120588green minus 120588red minus 120588blue)(2 lowast 120588green + 120588red + 120588blue) [63]

VHI 119886 lowast VCI + (1 minus 119886) lowast TCI [136]

VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]

YVI (minus0283MSS4 minus 066MSS5 + 0577MSS6 + 0388MSS7) [53]

WBI119877970119877900 [115]

WDRVI(120572120588nir minus 120588red)(120572120588nir + 120588red) [64]

WV-VI (NIR2 minus Red)(NIR2 + Red) [137]

ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]


Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This study was supported by the National Natural Sci-ence Foundation of China (no 41401391) Key Scienceand Technology Program of Shaanxi Province China (noS2016YFNY0066) the Scientific Research Foundation for theReturned Overseas Chinese Scholars State Education Min-istry the National Key Research and Development Programof China (no 2016YFD0200601) the Fundamental ResearchFunds for the Central Universities of China (no 2014YB071)and the Exclusive Talent Funds of Northwest AampF Universityof China (no 2013BSJJ017)

References

[1] D J Mulla ldquoTwenty five years of remote sensing in precisionagriculture key advances and remaining knowledge gapsrdquoBiosystems Engineering vol 114 no 4 pp 358ndash371 2013

[2] W J Foley AMcIlwee I Lawler L Aragones A PWoolnoughandN Berding ldquoEcological applications of near infrared reflec-tance spectroscopy - A tool for rapid cost-effective predictionof the composition of plant and animal tissues and aspects ofanimal performancerdquo Oecologia vol 116 no 3 pp 293ndash3051998

[3] C Zhang and JM Kovacs ldquoThe application of small unmannedaerial systems for precision agriculture a reviewrdquo PrecisionAgriculture vol 13 no 6 pp 693ndash712 2012

[4] R Ballesteros J F Ortega D Hernandez and M MorenoldquoCharacterization of vitis vinifera l Canopy using unmannedaerial vehicle-based remote sensing and photogrammetry tech-niquesrdquoAmerican Journal of Enology andViticulture vol 66 no2 pp 120ndash129 2015

[5] J Gago C Douthe R E Coopman et al ldquoUAVs challengeto assess water stress for sustainable agriculturerdquo AgriculturalWater Management vol 153 pp 9ndash19 2015

[6] H Hoffmann H Nieto R Jensen R Guzinski P J Zarco-Tejada and T Friborg ldquoEstimating evapotranspiration withthermal UAV data and two source energy balance modelsrdquoHydrology and Earth System Sciences Discussions vol 12 no 8pp 7469ndash7502 2015

[7] E Honkavaara H Saari J Kaivosoja et al ldquoProcessing andassessment of spectrometric stereoscopic imagery collectedusing a lightweight UAV spectral camera for precision agricul-turerdquo Remote Sensing vol 5 no 10 pp 5006ndash5039 2013

[8] T XiaW P KustasM C Anderson et al ldquoMapping evapotran-spiration with high-resolution aircraft imagery over vineyardsusing one-and two-source modeling schemesrdquo Hydrology andEarth System Sciences vol 20 no 4 pp 1523ndash1545 2016

[9] S Ortega-Farıas S Ortega-Salazar T Poblete et al ldquoEstimationof energy balance components over a drip-irrigated oliveorchard using thermal and multispectral cameras placed ona helicopter-based unmanned aerial vehicle (UAV)rdquo RemoteSensing vol 8 no 8 article no 638 2016

[10] L Chang S Peng-Sen and Liu Shi-Rong ldquoA review of plantspectral reflectance response to water physiological changesrdquoChinese Journal of Plant Ecology vol 40 no 1 pp 80ndash91 2016

[11] H R Bin Abdul Rahim M Q Bin Lokman S W Harun etal ldquoApplied light-side couplingwith optimized spiral-patternedzinc oxide nanorod coatings for multiple optical channel alco-hol vapor sensingrdquo Journal of Nanophotonics vol 10 no 3Article ID 036009 2016

[12] B A Cruden D Prabhu and R Martinez ldquoAbsolute radiationmeasurement in venus and mars entry conditionsrdquo Journal ofSpacecraft and Rockets vol 49 no 6 pp 1069ndash1079 2012

[13] J Hatfield J Baker and T J Arkebauer ldquoLeaf radiative proper-ties and the leaf energy budgetrdquo in Micrometeorology in Agri-cultural Systems Agronomy Monograph American Society ofAgronomy Crop Science Society of America and Soil ScienceSociety of America Madison Wis USA 2005

[14] G D Batten ldquoPlant analysis using near infrared reflectancespectroscopy The potential and the limitationsrdquo AustralianJournal of Experimental Agriculture vol 38 no 7 pp 697ndash7061998

[15] D A Burns and E W Ciurczak Handbook of Near-InfraredAnalysis CRC Press 2007

[16] R Karwa ldquoLaws of thermal radiationrdquo in Heat and MassTransfer pp 665ndash696 Springer 2017

[17] A Prashar and H G Jones ldquoAssessing drought responses usingthermal infrared imagingrdquo Methods in Molecular Biology vol1398 pp 209ndash219 2016

[18] AMartynenko K Shotton T Astatkie et al ldquoThermal imagingof soybean response to drought stress the effect of ascophyllumnodosum seaweed extractrdquo SpringerPlus vol 5 no 1 no 13932016

[19] S Fuentes R de Bei J Pech and S Tyerman ldquoComputationalwater stress indices obtained from thermal image analysis ofgrapevine canopiesrdquo Irrigation Science vol 30 no 6 pp 523ndash536 2012

[20] A-K Mahlein E-C Oerke U Steiner and H-W DehneldquoRecent advances in sensing plant diseases for precision cropprotectionrdquo European Journal of Plant Pathology vol 133 no 1pp 197ndash209 2012

[21] E-C Oerke A-K Mahlein and U Steiner ldquoProximal sensingof plant diseasesrdquo in Detection And Diagnostics of Plant Patho-gens pp 55ndash68 Springer 2014

[22] A Karnieli N Agam R T Pinker et al ldquoUse of NDVI andland surface temperature for drought assessment merits andlimitationsrdquo Journal of Climate vol 23 no 3 pp 618ndash633 2010

[23] R P Sripada R W Heiniger J G White and R Weisz ldquoAerialcolor infrared photography for determining late-season nitro-gen requirements in cornrdquo Agronomy Journal vol 97 no 5 pp1443ndash1451 2005

[24] B Zhang D Wu L Zhang Q Jiao and Q Li ldquoApplication ofhyperspectral remote sensing for environment monitoring inmining areasrdquo Environmental Earth Sciences vol 65 no 3 pp649ndash658 2012

[25] A N Ganeshamurthy V Ravindra R Venugopalan M Mathi-azhagan and R M Bhat ldquoBiomass distribution and develop-ment of allometric equations for non-destructive estimationof carbon sequestration in grafted mango treesrdquo Journal ofAgricultural Science vol 8 no 8 201 pages 2016

[26] S Fuentes A R Palmer D Taylor M Zeppel R Whitley andD Eamus ldquoAn automated procedure for estimating the leafarea index (LAI) of woodland ecosystems using digital imageryMATLAB programming and its application to an examinationof the relationship between remotely sensed and field measure-ments of LAIrdquo Functional Plant Biology vol 35 no 10 pp 1070ndash1079 2008


[27] S Fuentes C Poblete-Echeverrıa S Ortega-Farias S Tyermanand R de Bei ldquoAutomated estimation of leaf area index fromgrapevine canopies using cover photography video and com-putational analysis methodsrdquo Australian Journal of Grape andWine Research vol 20 no 3 pp 465ndash473 2014

[28] C Poblete-Echeverrıa S Fuentes S Ortega-Farias J Gonzalez-Talice and J A Yuri ldquoDigital cover photography for estimatingLeaf area index (LAI) in apple trees using a variable lightextinction coefficientrdquo Sensors (Switzerland) vol 15 no 2 pp2860ndash2872 2015

[29] M Carrasco-Benavides S Ortega-Farıas L O Lagos et alldquoCrop coefficients and actual evapotranspiration of a drip-irrigated Merlot vineyard using multispectral satellite imagesrdquoIrrigation Science vol 30 no 6 pp 485ndash497 2012

[30] M Mora F Avila M Carrasco-Benavides G Maldonado JOlguın-Caceres and S Fuentes ldquoAutomated computation ofleaf area index from fruit trees using improved image processingalgorithms applied to canopy cover digital photograpiesrdquo Com-puters and Electronics in Agriculture vol 123 pp 195ndash202 2016

[31] R De Bei S Fuentes M Gilliham et al ldquoViticanopy A freecomputer app to estimate canopy vigor and porosity for grape-vinerdquo Sensors (Switzerland) vol 16 no 4 article no 585 2016

[32] S Fuentes R De Bei C Pozo and S Tyerman ldquoDevelopmentof a smartphone application to characterise temporal and spatialcanopy architecture and leaf area index for grapevinesrdquoWine ampViticulture Journal vol 20 no 6 pp 56ndash60 2012

[33] C Francone V PaganiM Foi G Cappelli andR ConfalonierildquoComparison of leaf area index estimates by ceptometer andpocketlai smart app in canopies with different structuresrdquo FieldCrops Research vol 155 pp 38ndash41 2014

[34] F Orlando E Movedi L Paleari et al ldquoEstimating leaf areaindex in tree species using the PocketLAI smart apprdquo AppliedVegetation Science vol 18 no 4 pp 716ndash723 2015

[35] M Campos-Taberner F J Garcıa-Haro R Confalonieri et alldquoMultitemporal monitoring of plant area index in the valenciarice district with PocketLAIrdquo Remote Sensing vol 8 no 3article no 202 2016

[36] C F Jordan ldquoDerivation of leaf-area index from quality of lighton the forest floorrdquo Ecology vol 50 no 4 pp 663ndash666 1969

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[38] A J Richardson and C Weigand ldquoDistinguishing vegetationfrom soil background informationrdquo Photogrammetric Engineer-ing and Remote Sensing p 43 1977

[39] X D L Wenlong ldquoVegetation index controlling the influenceof soil reflectionrdquo 2009 httpwwwpapereducnreleasepapercontent200906-376

[40] Y J Kaufman and D Tanre ldquoAtmospherically Resistant Vege-tation Index (ARVI) for EOS-MODISrdquo IEEE Transactions onGeoscience and Remote Sensing vol 30 no 2 pp 261ndash270 1992

[41] D J Major F Baret and G Guyot ldquoA ratio vegetation indexadjusted for soil brightnessrdquo International Journal of RemoteSensing vol 11 no 5 pp 727ndash740 1990

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[43] J A Gamon C B Field M L Goulden et al ldquoRelationshipsbetween NDVI canopy structure and photosynthesis in threeCalifornian vegetation typesrdquo Ecological Applications vol 5 no1 pp 28ndash41 1995

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[45] D Tanre C Deroo P Duhaut et al ldquoDescription of a computercode to simulate the satellite signal in the solar spectrum the 5Scoderdquo International Journal of Remote Sensing vol 11 no 4 pp659ndash668 1990

[46] R-H Zhang N X Rao and K N Liao ldquoApproach for a vegeta-tion index resistant to atmospheric effectrdquoActa Botanica Sinicavol 38 no 1 pp 53ndash62 1996

[47] A J Richardson and C Wiegand ldquoDistinguishing vegetationfrom soil background informationrdquo Photogrammetric Engineer-ing amp Remote Sensing vol 43 no 12 pp 1541ndash1552 1977

[48] F Baret S Jacquemoud and J F Hanocq ldquoThe soil line conceptin remote sensingrdquo Remote Sensing Reviews vol 7 no 1 pp 65ndash82 1993

[49] A Bannari D Morin and D He ldquoHigh spatial and spectralresolution remote sensing for the management of the urbanenvironmentrdquo in Proceedings of the 1st International AirborneRemote Sensing Conference and Exhibition pp 12ndash15 Stras-bourg France 1994

[50] A R Huete ldquoA soil-adjusted vegetation index (SAVI)rdquo RemoteSensing of Environment vol 25 no 3 pp 295ndash309 1988

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[55] R D Jackson P Pinter Jr R J Reginato and S B IdsoldquoHand-held radiometry a set of notes developed for use at theworkshop of hand-held radiometryrdquo in Proceedings of the EarlyWarning and Crop Condition Assessment Phoenix Ariz USAFebruary 1980

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[57] H Q Liu and A Huete ldquoFeedback based modification of theNDVI tominimize canopy background and atmospheric noiserdquoIEEETransactions onGeoscience andRemote Sensing vol 33 no2 pp 457ndash465 1995

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[59] B Li R Liu S Liu Q Liu F Liu andG Zhou ldquoMonitoring veg-etation coverage variation of winter wheat by low-altitude UAVremote sensing systemrdquo Nongye Gongcheng XuebaoTransac-tions of the Chinese Society of Agricultural Engineering vol 28no 13 pp 160ndash165 2012

[60] P J Zarco-Tejada V Gonzalez-Dugo and J A J Berni ldquoFluo-rescence temperature and narrow-band indices acquired froma UAV platform for water stress detection using a micro-hyper-spectral imager and a thermal camerardquo Remote Sensing ofEnvironment vol 117 pp 322ndash337 2012


[61] J A J Berni P J Zarco-Tejada G Sepulcre-Canto E Fereresand F Villalobos ldquoMapping canopy conductance and CWSI inolive orchards using high resolution thermal remote sensingimageryrdquo Remote Sensing of Environment vol 113 no 11 pp2380ndash2388 2009

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[63] X Wang M Wang S Wang and Y Wu ldquoExtraction of vegeta-tion information from visible unmanned aerial vehicle imagesrdquoNongye Gongcheng XuebaoTransactions of the Chinese Societyof Agricultural Engineering vol 31 no 5 pp 152ndash159 2015

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[65] M S Kim C Daughtry E Chappelle J McMurtrey and CWalthall ldquoThe use of high spectral resolution bands for esti-mating absorbed photosynthetically active radiation (a par)rdquo inProceedings of the 6th International Symposium on PhysicalMea-surements and Signatures in Remote Sensing CNES PhoenixAriz USA January 1994

[66] C S T Daughtry C L Walthall M S Kim E B De Colstounand J E McMurtrey III ldquoEstimating corn leaf chlorophyll con-centration from leaf and canopy reflectancerdquo Remote Sensing ofEnvironment vol 74 no 2 pp 229ndash239 2000

[67] Z Funian Z Hong C JiayuW Ruijun and Y Fuklin ldquoPrelimi-nary investigation on difference of crop water stress index base-line for maizerdquo Chinese Agricultural Science Bulletin vol 29 pp46ndash53 2013

[68] S A OrsquoShaughnessy S R Evett P D Colaizzi and T A HowellldquoUsing radiation thermography and thermometry to evaluatecrop water stress in soybean and cottonrdquo Agricultural WaterManagement vol 98 no 10 pp 1523ndash1535 2011

[69] S B Idso R D Jackson P J Pinter Jr R J Reginato and JL Hatfield ldquoNormalizing the stress-degree-day parameter forenvironmental variabilityrdquoAgricultural Meteorology vol 24 pp45ndash55 1981

[70] B B D Silva J A Ferreira and T V R Rao ldquoCrop water stressindex and water-use efficiency for melon (Cucumis melo l) ondifferent irrigation regimesrdquo Agricultural Journal 2007

[71] G Kar and A Kumar ldquoSurface energy fluxes and crop waterstress index in groundnut under irrigated ecosystemrdquo Agricul-tural and Forest Meteorology vol 146 no 1-2 pp 94ndash106 2007

[72] V Lebourgeois J-L Chopart A Begue and L Le MezoldquoTowards using a thermal infrared index combined with waterbalance modelling to monitor sugarcane irrigation in a tropicalenvironmentrdquoAgriculturalWaterManagement vol 97 no 1 pp75ndash82 2010

[73] A Anda ldquoIrrigation timing in maize by using the crop waterstress index (CWSI)rdquo Cereal Research Communications vol 37no 4 pp 603ndash610 2009

[74] A Ruimy L Kergoat and A Bondeau ldquoComparing globalmodels of terrestrial net primary productivity (NPP) Analysisof differences in light absorption and light-use efficiencyrdquoGlobal Change Biology vol 5 no 1 pp 56ndash64 1999

[75] A Haxeltine and I C Prentice ldquoA general model for the light-use efficiency of primary productionrdquo Functional Ecology vol10 no 5 pp 551ndash561 1996

[76] A A Gitelson M NMerzlyak and O B Chivkunova ldquoOpticalproperties and nondestructive estimation of anthocyanin con-tent in plant leavesrdquo Photochemistry and Photobiology vol 74no 1 pp 38ndash45 2001

[77] R D Jackson ldquoSpectral indices in N-Spacerdquo Remote Sensing ofEnvironment vol 13 no 5 pp 409ndash421 1983

[78] F Baret and G Guyot ldquoPotentials and limits of vegetationindices for LAI and APAR assessmentrdquo Remote Sensing ofEnvironment vol 35 no 2-3 pp 161ndash173 1991

[79] P Ashburn ldquoThe vegetative index number and crop identifica-tionrdquo NASA Proc of Tech vol 1 Johnson Space Center 1979

[80] S Plummer P North and S Briggs ldquoThe angular vegetationindex An atmospherically resistant index for the second alongtrack sacnning radiometer (atsr-2)rdquo in Proceedings of the SixthInternational Symposium of Physical Measurements and Signa-tures in Remote Sensing Val DrsquoIsere France 1994

[81] P J Zarco-Tejada A Berjon R Lopez-Lozano et al ldquoAssess-ing vineyard condition with hyperspectral indices Leaf andcanopy reflectance simulation in a row-structured discontinu-ous canopyrdquo Remote Sensing of Environment vol 99 no 3 pp271ndash287 2005

[82] C S T Daughtry ldquoAgroclimatology Discriminating crop resi-dues from soil by shortwave infrared reflectancerdquo AgronomyJournal vol 93 no 1 pp 125ndash131 2001

[83] F Li Y Miao G Feng et al ldquoImproving estimation of summermaize nitrogen status with red edge-based spectral vegetationindicesrdquo Field Crops Research vol 157 pp 111ndash123 2014

[84] N H Broge and E Leblanc ldquoComparing prediction powerand stability of broadband and hyperspectral vegetation indicesfor estimation of green leaf area index and canopy chlorophylldensityrdquo Remote Sensing of Environment vol 76 no 2 pp 156ndash172 2001

[85] A A Gitelson G P Keydan and M N Merzlyak ldquoThree-bandmodel for noninvasive estimation of chlorophyll carotenoidsand anthocyanin contents in higher plant leavesrdquo GeophysicalResearch Letters vol 33 no 11 Article ID L11402 2006

[86] C Buschmann and E Nagel ldquoIn vivo spectroscopy and internaloptics of leaves as basis for remote sensing of vegetationrdquoInternational Journal of Remote Sensing vol 14 no 4 pp 711ndash722 1993

[87] T H Demetriades-Shah M D Steven and J A Clark ldquoHighresolution derivative spectra in remote sensingrdquoRemote Sensingof Environment vol 33 no 1 pp 55ndash64 1990

[88] A Huete K Didan T Miura E P Rodriguez X Gao and L GFerreira ldquoOverview of the radiometric and biophysical perfor-mance of the MODIS vegetation indicesrdquo Remote Sensing ofEnvironment vol 83 no 1-2 pp 195ndash213 2002

[89] J Torres-Sanchez J M Pena A I de Castro and F Lopez-Granados ldquoMulti-temporal mapping of the vegetation fractionin early-seasonwheat fields using images fromUAVrdquoComputersand Electronics in Agriculture vol 103 pp 104ndash113 2014

[90] B Pinty and M M Verstraete ldquoGEMI a non-linear index tomonitor global vegetation from satellitesrdquoVegetatio vol 101 no1 pp 15ndash20 1992

[91] M Louhaichi M M Borman and D E Johnson ldquoSpatiallylocated platform and aerial photography for documentation ofgrazing impacts on wheatrdquoGeocarto International vol 16 no 1pp 65ndash70 2001

[92] A A Gitelson and M N Merzlyak ldquoSignature analysis of leafreflectance spectra Algorithm development for remote sensingof chlorophyllrdquo Journal of Plant Physiology vol 148 no 3-4 pp494ndash500 1996


[93] E T Kanemasu J L Hellman J O Bagley and W L PowersldquoUsing Landsat data to estimate evapotranspiration of winterwheatrdquo Environmental Management vol 1 no 6 pp 515ndash5201977

[94] R C G Smith J Adams D J Stephens and P T Hick ldquoFore-casting wheat yield in a Mediterranean-type environment fromtheNOAA satelliterdquoAustralian Journal of Agricultural Researchvol 46 no 1 pp 113ndash125 1995

[95] G Badhwar ldquoThe use of parameters to separate and identifyspring small grainsrdquo in Proceedings of the Quarterly TechnicalInterchange Meeting NASA Johnson Space Flight Center Hous-ton Tex USA 1981

[96] H K Lichtenthaler M Lang M Sowinska F Heisel and J AMiehe ldquoDetection of vegetation stress via a new high resolutionfluorescence imaging systemrdquo Journal of Plant Physiology vol148 no 5 pp 599ndash612 1996

[97] Y Zhang Q-Y Meng J-L Wu and F Zhao ldquoStudy of environ-mental vegetation index based on environment satellite CCDdata and LAI inversionrdquo Guang Pu Xue Yu Guang Pu Fen XiSpectroscopy and Spectral Analysis vol 31 no 10 pp 2789ndash27932011

[98] A-K Mahlein T Rumpf P Welke et al ldquoDevelopment ofspectral indices for detecting and identifying plant diseasesrdquoRemote Sensing of Environment vol 128 pp 21ndash30 2013

[99] H B Musick and R E Pelletier ldquoResponse to soil moisture ofspectral indexes derived from bidirectional reflectance in the-matic mapper wavebandsrdquo Remote Sensing of Environment vol25 no 2 pp 167ndash184 1988

[100] R E Crippen ldquoCalculating the vegetation index fasterrdquo RemoteSensing of Environment vol 34 no 1 pp 71ndash73 1990

[101] D Haboudane J R Miller E Pattey P J Zarco-Tejada and I BStrachan ldquoHyperspectral vegetation indices and novel algo-rithms for predicting green LAI of crop canopies modeling andvalidation in the context of precision agriculturerdquo Remote Sens-ing of Environment vol 90 no 3 pp 337ndash352 2004

[102] P Misra and S Wheeler ldquoLandsat data from agricultural sites-crop signature analysisrdquo in Proceedings of the InternationalSymposium on Remote Sens pp 1473ndash1482 Environ 1977

[103] Z Yang P Willis and R Mueller ldquoImpact of band-ratioenhanced awifs image on crop classification accuracyrdquo in Pro-ceedings of the 17th William Pecora Memorial Remote SensingSymposium Denver Colo USA 2008

[104] B Datt ldquoA new reflectance index for remote sensing of chloro-phyll content in higher plants Tests using Eucalyptus leavesrdquoJournal of Plant Physiology vol 154 no 1 pp 30ndash36 1999

[105] D A Sims and J A Gamon ldquoRelationships between leaf pig-ment content and spectral reflectance across a wide range ofspecies leaf structures and developmental stagesrdquo Remote Sens-ing of Environment vol 81 no 2-3 pp 337ndash354 2002

[106] J M Chen ldquoEvaluation of vegetation indices and a modifiedsimple ratio for boreal applicationsrdquo Canadian Journal ofRemote Sensing vol 22 no 3 pp 229ndash242 1996

[107] E RHunt Jr and BN Rock ldquoDetection of changes in leaf watercontent using Near- and Middle-Infrared reflectancesrdquo RemoteSensing of Environment vol 30 no 1 pp 43ndash54 1989

[108] H Mcnairn and R Protz ldquoMapping corn residue cover onagricultural fields in oxford county ontario using thematicmapperrdquo Canadian Journal of Remote Sensing vol 19 no 2 pp152ndash159 1993

[109] B Datt ldquoVisiblenear infrared reflectance and chlorophyll con-tent in Eucalyptus leavesrdquo International Journal of Remote Sens-ing vol 20 no 14 pp 2741ndash2759 1999

[110] J E Vogelmann B N Rock andDMMoss ldquoRed edge spectralmeasurements from sugar maple leavesrdquo International Journalof Remote Sensing vol 14 no 8 pp 1563ndash1575 1993

[111] L Serrano J Penuelas and S LUstin ldquoRemote sensing of nitro-gen and lignin in Mediterranean vegetation from AVIRIS datadecomposing biochemical from structural signalsrdquo RemoteSensing of Environment vol 81 no 2-3 pp 355ndash364 2002

[112] S K McFeeters ldquoThe use of the Normalized Difference WaterIndex (NDWI) in the delineation of open water featuresrdquo Inter-national Journal of Remote Sensing vol 17 no 7 pp 1425ndash14321996

[113] J Verrelst M E Schaepman B Koetz and M KneubuhlerldquoAngular sensitivity analysis of vegetation indices derived fromCHRISPROBA datardquo Remote Sensing of Environment vol 112no 5 pp 2341ndash2353 2008

[114] C J Tucker ldquoRed and photographic infrared linear combina-tions for monitoring vegetationrdquo Remote Sensing of Environ-ment vol 8 no 2 pp 127ndash150 1979

[115] LWang and J J Qu ldquoNMDIAnormalizedmulti-band droughtindex for monitoring soil and vegetation moisture with satelliteremote sensingrdquo Geophysical Research Letters vol 34 no 20Article ID L20405 2007

[116] N S Goel and W Qin ldquoInfluences of canopy architecture onrelationships between various vegetation indices and LAI andFPAR a computer simulationrdquo Remote Sensing Reviews vol 10no 4 pp 309ndash347 1994

[117] G Rondeaux M Steven and F Baret ldquoOptimization of soil-adjusted vegetation indicesrdquo Remote Sensing of Environmentvol 55 no 2 pp 95ndash107 1996

[118] J A Gamon J Penuelas and C B Field ldquoA narrow-wavebandspectral index that tracks diurnal changes in photosyntheticefficiencyrdquo Remote Sensing of Environment vol 41 no 1 pp 35ndash44 1992

[119] M N Merzlyak A A Gitelson O B Chivkunova and V YRakitin ldquoNon-destructive optical detection of pigment changesduring leaf senescence and fruit ripeningrdquo Physiologia Plan-tarum vol 106 no 1 pp 135ndash141 1999

[120] G A Blackburn ldquoQuantifying chlorophylls and carotenoids atleaf and canopy scales An evaluation of some hyperspectralapproachesrdquo Remote Sensing of Environment vol 66 no 3 pp273ndash285 1998

[121] J-L Roujean and F-M Breon ldquoEstimating PAR absorbedby vegetation from bidirectional reflectance measurementsrdquoRemote Sensing of Environment vol 51 no 3 pp 375ndash384 1995

[122] A Gitelson and M N Merzlyak ldquoSpectral relfectance changesassociated with autumn senescence of Aesculus hippocastanumL and Acer platanoides L leaves Spectral features and relationto chlorophyll estimationrdquo Journal of Plant Physiology vol 143no 3 pp 286ndash292 1994

[123] J A Gamon and J S Surfus ldquoAssessing leaf pigment contentand activity with a reflectometerrdquo New Phytologist vol 143 no1 pp 105ndash117 1999

[124] R Escadafal and A Huete ldquoEtude des proprietes spectrales dessols arides appliquee a lrsquoamelioration des indices de vegetationobtenus par teledetectionrdquo Comptes Rendus de lrsquoAcademie desSciences vol 312 no 2 pp 1385ndash1391 1991

[125] R L Pearson and L D Miller ldquoRemote mapping of standingcrop biomass for estimation of the productivity of the shortgrassprairierdquo Remote Sensing of Environme vol 8 p 1355 1972

[126] D Haboudane J R Miller N Tremblay P J Zarco-TejadaandLDextraze ldquoIntegrated narrow-band vegetation indices for


prediction of crop chlorophyll content for application to pre-cision agriculturerdquo Remote Sensing of Environment vol 81 no2-3 pp 416ndash426 2002

[127] R Pu and P Gong Advances in Environmental Remote SensingHigher Education Beijing 2011

[128] G S Birth and G R McVey ldquoMeasuring the color of growingturf with a reflectance spectrophotometerrdquo Agronomy Journalvol 60 no 6 p 640 1968

[129] J E McMurtrey III E W Chappelle M S Kim J J Meisingerand L A Corp ldquoDistinguishing nitrogen fertilization levels infield corn (Zea mays L) with actively induced fluorescence andpassive reflectance measurementsrdquo Remote Sensing of Environ-ment vol 47 no 1 pp 36ndash44 1994

[130] EW ChappelleM S Kim and J EMcMurtrey ldquoRatio analysisof reflectance spectra (RARS) an algorithm for the remote esti-mation of the concentrations of chlorophyll A chlorophyll Band carotenoids in soybean leavesrdquo Remote Sensing of Environ-ment vol 39 no 3 pp 239ndash247 1992

[131] B Datt ldquoRemote sensing of chlorophyll a chlorophyll b chloro-phyll a+b and total carotenoid content in eucalyptus leavesrdquoRemote Sensing of Environment vol 66 no 2 pp 111ndash121 1998

[132] A BannariHAsalhi andPMTeillet ldquoTransformeddifferencevegetation index (TDVI) for vegetation cover mappingrdquo in Pro-ceedings of the IEEE International Geoscience and Remote Sens-ing Symposium IGARSS rsquo02 pp 3053ndash3055 Toronto Canada2002

[133] F Baret G Guyot and D J Major ldquoTSAVI A vegetation indexwhich minimizes soil brightness effects on LAI and APARestimationrdquo in Proceedings of the 12th Canadian Symposium onRemote Sensing Geoscience and Remote Sensing Symposium pp1355ndash1358 IEEE 1989

[134] J Rouse Jr ldquoMonitoring the vernal advancement and retrogra-dation (green wave effect) of natural vegetationrdquo NASA Techni-cal Reports Server Tex USA 1974 httpsntrsnasagovsearchjspR=19740022555

[135] A A Gitelson Y J Kaufman R Stark and D RundquistldquoNovel algorithms for remote estimation of vegetation fractionrdquoRemote Sensing of Environment vol 80 no 1 pp 76ndash87 2002

[136] F N Kogan ldquoApplication of vegetation index and brightnesstemperature for drought detectionrdquoAdvances in Space Researchvol 15 no 11 pp 91ndash100 1995

[137] A FWolf ldquoUsingWorldView-2 Vis-NIRmultispectral imageryto support land mapping and feature extraction using normal-ized difference index ratiosrdquo in Proceedings of the 18th AnnualConference on Algorithms and Technologies for MultispectralHyperspectral and Ultraspectral Imagery Baltimore Md USA2012

[138] C D Elvidge and Z Chen ldquoComparison of broad-band andnarrow-band red and near-infrared vegetation indicesrdquo RemoteSensing of Environment vol 54 no 1 pp 38ndash48 1995

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of


International Journal of

RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



is narrowly focused on a particular range of the electromag-netic spectrum that is sensitive to a particular feature fromthe ground or a property of the atmosphere However imagesfrom this platform can be cost prohibitive for long time dataseries studies

The second problem with satellite based remote sensingis the revisitation time which is 16 days in average whichmakes the agricultural applications difficult specifically thoserelated to water and nutrient management Moreover passivesensors cannot penetrate clouds therefore there is no usabledata capture for overcast days

To solve these two main problems airborne and morerecently UAV platforms can be used The former can also becost prohibitive due to the requirement of expensive aircraftsand pilots The latter has become almost of ubiquitous use inthe last five years with affordable aircrafts and camera pay-loads ranging from visible near and thermal infrared and 3DLIDAR which has been referred to as Unmanned AerialSystem (UAS) Among UAS platforms there are mainly fixedwing andmultirotor options availableThere is a compromiseusing these UAS platforms in relation to payload weightversus flying time In general longer flying time achievedby fixed wing systems demands lighter weight payloads Forexample small high definition visible cameras weighting lessthan 300 grams as payload of a fixed wing UAS allow it tofly for around two hours using currently available battery-power [3] On the contrary battery-powered multirotor UASwith higher payload capacity have reduced fly time that at themoment is around 15- to 25-minute duration Using theseUAS higher spatial and temporal data resolution can beachieved which makes possible precision agriculture appli-cations to the submeter resolution per pixel This allowsresearch and practical applications applied to growth andvigor dynamic assessment plant water status sensing forirrigation scheduling applications and evapotranspirationmodelling among others [4ndash9]

12 Remote Sensing and Vegetation Indices Remote sensingof vegetation is mainly performed by obtaining the electro-magnetic wave reflectance information from canopies usingpassive sensors It is well known that the reflectance of lightspectra from plants changes with plant type water contentwithin tissues and other intrinsic factors [10]The reflectancefrom vegetation to the electromagnetic spectrum (spectralreflectance or emission characteristics of vegetation) is deter-mined by chemical and morphological characteristics of thesurface of organs or leaves [3] The main applications forremote sensing of vegetation are based on the following lightspectra (i) the ultraviolet region (UV) which goes from10 to 380 nm (ii) the visible spectra which are composedof the blue (450ndash495 nm) green (495minus570 nm) and red(620ndash750 nm) wavelength regions and (iii) the near and midinfrared band (850ndash1700 nm) [11 12] The emissivity rate ofthe surface of leaves (equivalent to the absorptivity in thethermal waveband) of a fully grown green plant without anybiotic or abiotic stress is generally in the range of 096ndash099and ismore often between 097 and 098 [13] On the contraryfor dry plants the emissivity rate generally has a larger rangegoing from 088 to 094 [13] Vegetation emissivity in the near

andmid infrared regions has beenwidely studiedwithin plantcanopies Indices extracted from this light spectra range canbe attributed to a range of characteristics beyond growth andvigor quantification of plants related to water content pig-ments sugar and carbohydrate content protein content andaromatics among others [2 14] Different applications aredependent on the reflectivity peaks or overtones for specificcompounds within the visible and nearmid infrared regionsof light spectra [14 15] Plant reflectivity in the thermal infra-red spectral range (8ndash14 120583m) follows the blackbody radiationlaw [16] which allows interpreting plant emission as directlyrelated to plant temperature Hence indices obtained fromthis spectra range can be used as a proxy to assess stomatadynamics that regulates transpiration rate of plants There-fore the later indices can be used as indicator of plant waterstatus [17ndash19] and abioticbiotic stress levels [20 21]

The latter considerations demonstrate that the quantita-tive interpretation of remote sensing information from vege-tation is a complex task Many studies have limited this inter-pretation by extracting vegetation information using individ-ual light spectra bands or a group of single bands for dataanalysis Thus researchers often combine the data from nearinfrared (07ndash11m) and red (06ndash07m) bands in differentways according to their specific objectives [2] These typesof combinations present many disadvantages (eg lack ofsensitivity) by using single or limited group of bands to detectfor example vegetation biomass These limitations are par-ticularly evident when trying to apply these types of VI onheterogeneous canopies such as horticultural tree planta-tions Amixed combination of soils weeds cover crops in theinterrow and the plants of interest makes the discriminationregions of interest and extraction of simple VI very difficultspecifically when the vegetation of interest has differentVIs due to spatial variability or VIs corresponding to othervegetation (weeds and cover crop) which can be similar tothose of interest The later will complicate imaging denoisingand filtering processes Several image analysis techniques andalgorithms have been developed to go around these issueswhich will be described later Even though there are manyconsiderations as described before the construction of simpleVI algorithm could many times render simple and effectivetools to measure vegetation status on the surface of the Earth[6]

2 Vegetation Indices and Validation Process

With the use of high resolution spectral instrumentation thenumber of bands obtained by remote sensing is increasingand the bandwidth is getting narrower [7] One of the mostused and implemented indices calculated from multispectralinformation as normalized ratio between the red and nearinfrared bands is theNormalizedDifferenceVegetation Index(NDVI) [22] A direct use of NDVI is to characterize canopygrowth or vigor hence many studies have compared it withthe Leaf Area Index (LAI) [23] where LAI is defined as thearea of single sided leaves per area of soil [24]

Vegetation information from remote sensed images ismainly interpreted by differences and changes of the greenleaves from plants and canopy spectral characteristics The






RVI = 119877NIR

(1)


















(6)









SAVI = (120588119899 minus 120588119903) (1 + 119871)(120588119899 + 120588119903 + 119871) (8)



(9)




(10)









+ 0491MSS7 (12)


+ 0149MSS7 (13)


+ 0264MSS7 (14)




MSBI = 0406MSS4 + 060MSS5 + 0645MSS6



minus 0278MSS7

(16)









NDVI = (119877800 minus 119877680)(119877800 + 119877680) (18)














(21)


MCARI


(22)







PRI = (119877531 minus 119877570)(119877531 minus 119877570) (24)



3 Conclusions






ARI ( 1119877550 ) minus (1119877700 ) [76]

ARI2 119877800 [( 1119877550 ) minus (1119877700 )] [76]


ASBI 02YVI [77]




BGI1119877400119877550 [81]

BGI2119877450119877550 [81]

BRI1119877400119877690 [60]

BRI2119877450119877690 [60]

CAI 05 (1198772000 + 1198772200) minus 1198772100 [82]

CARI CAR lowast (119877700119877670 ) [65]


CRCWD 1 minus 120588119903min [84]

CRI500(1119877515)(1119877550) [85]

CRI700(1119877515)(1119877700) [85]


DI1 119877800 minus 119877550 [86]

DVI int12058211205821

(119889120588119889120582)119889120582 [87]







Table 1 Continued



120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]



GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]


GVSB GVISBI

[95]

LIC3119877400119877740 [96]


HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]


IITM5TM7

[99]









Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














RVI = 119877NIR

(1)


















(6)









SAVI = (120588119899 minus 120588119903) (1 + 119871)(120588119899 + 120588119903 + 119871) (8)



(9)




(10)









+ 0491MSS7 (12)


+ 0149MSS7 (13)


+ 0264MSS7 (14)




MSBI = 0406MSS4 + 060MSS5 + 0645MSS6



minus 0278MSS7

(16)









NDVI = (119877800 minus 119877680)(119877800 + 119877680) (18)














(21)


MCARI


(22)







PRI = (119877531 minus 119877570)(119877531 minus 119877570) (24)



3 Conclusions






ARI ( 1119877550 ) minus (1119877700 ) [76]

ARI2 119877800 [( 1119877550 ) minus (1119877700 )] [76]


ASBI 02YVI [77]




BGI1119877400119877550 [81]

BGI2119877450119877550 [81]

BRI1119877400119877690 [60]

BRI2119877450119877690 [60]

CAI 05 (1198772000 + 1198772200) minus 1198772100 [82]

CARI CAR lowast (119877700119877670 ) [65]


CRCWD 1 minus 120588119903min [84]

CRI500(1119877515)(1119877550) [85]

CRI700(1119877515)(1119877700) [85]


DI1 119877800 minus 119877550 [86]

DVI int12058211205821

(119889120588119889120582)119889120582 [87]







Table 1 Continued



120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]



GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]


GVSB GVISBI

[95]

LIC3119877400119877740 [96]


HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]


IITM5TM7

[99]









Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












(6)









SAVI = (120588119899 minus 120588119903) (1 + 119871)(120588119899 + 120588119903 + 119871) (8)



(9)




(10)









+ 0491MSS7 (12)


+ 0149MSS7 (13)


+ 0264MSS7 (14)




MSBI = 0406MSS4 + 060MSS5 + 0645MSS6



minus 0278MSS7

(16)









NDVI = (119877800 minus 119877680)(119877800 + 119877680) (18)














(21)


MCARI


(22)







PRI = (119877531 minus 119877570)(119877531 minus 119877570) (24)



3 Conclusions






ARI ( 1119877550 ) minus (1119877700 ) [76]

ARI2 119877800 [( 1119877550 ) minus (1119877700 )] [76]


ASBI 02YVI [77]




BGI1119877400119877550 [81]

BGI2119877450119877550 [81]

BRI1119877400119877690 [60]

BRI2119877450119877690 [60]

CAI 05 (1198772000 + 1198772200) minus 1198772100 [82]

CARI CAR lowast (119877700119877670 ) [65]


CRCWD 1 minus 120588119903min [84]

CRI500(1119877515)(1119877550) [85]

CRI700(1119877515)(1119877700) [85]


DI1 119877800 minus 119877550 [86]

DVI int12058211205821

(119889120588119889120582)119889120582 [87]







Table 1 Continued



120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]



GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]


GVSB GVISBI

[95]

LIC3119877400119877740 [96]


HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]


IITM5TM7

[99]









Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation

















+ 0491MSS7 (12)


+ 0149MSS7 (13)


+ 0264MSS7 (14)




MSBI = 0406MSS4 + 060MSS5 + 0645MSS6



minus 0278MSS7

(16)









NDVI = (119877800 minus 119877680)(119877800 + 119877680) (18)














(21)


MCARI


(22)







PRI = (119877531 minus 119877570)(119877531 minus 119877570) (24)



3 Conclusions






ARI ( 1119877550 ) minus (1119877700 ) [76]

ARI2 119877800 [( 1119877550 ) minus (1119877700 )] [76]


ASBI 02YVI [77]




BGI1119877400119877550 [81]

BGI2119877450119877550 [81]

BRI1119877400119877690 [60]

BRI2119877450119877690 [60]

CAI 05 (1198772000 + 1198772200) minus 1198772100 [82]

CARI CAR lowast (119877700119877670 ) [65]


CRCWD 1 minus 120588119903min [84]

CRI500(1119877515)(1119877550) [85]

CRI700(1119877515)(1119877700) [85]


DI1 119877800 minus 119877550 [86]

DVI int12058211205821

(119889120588119889120582)119889120582 [87]







Table 1 Continued



120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]



GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]


GVSB GVISBI

[95]

LIC3119877400119877740 [96]


HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]


IITM5TM7

[99]









Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
















NDVI = (119877800 minus 119877680)(119877800 + 119877680) (18)














(21)


MCARI


(22)







PRI = (119877531 minus 119877570)(119877531 minus 119877570) (24)



3 Conclusions






ARI ( 1119877550 ) minus (1119877700 ) [76]

ARI2 119877800 [( 1119877550 ) minus (1119877700 )] [76]


ASBI 02YVI [77]




BGI1119877400119877550 [81]

BGI2119877450119877550 [81]

BRI1119877400119877690 [60]

BRI2119877450119877690 [60]

CAI 05 (1198772000 + 1198772200) minus 1198772100 [82]

CARI CAR lowast (119877700119877670 ) [65]


CRCWD 1 minus 120588119903min [84]

CRI500(1119877515)(1119877550) [85]

CRI700(1119877515)(1119877700) [85]


DI1 119877800 minus 119877550 [86]

DVI int12058211205821

(119889120588119889120582)119889120582 [87]







Table 1 Continued



120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]



GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]


GVSB GVISBI

[95]

LIC3119877400119877740 [96]


HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]


IITM5TM7

[99]









Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













(21)


MCARI


(22)







PRI = (119877531 minus 119877570)(119877531 minus 119877570) (24)



3 Conclusions






ARI ( 1119877550 ) minus (1119877700 ) [76]

ARI2 119877800 [( 1119877550 ) minus (1119877700 )] [76]


ASBI 02YVI [77]




BGI1119877400119877550 [81]

BGI2119877450119877550 [81]

BRI1119877400119877690 [60]

BRI2119877450119877690 [60]

CAI 05 (1198772000 + 1198772200) minus 1198772100 [82]

CARI CAR lowast (119877700119877670 ) [65]


CRCWD 1 minus 120588119903min [84]

CRI500(1119877515)(1119877550) [85]

CRI700(1119877515)(1119877700) [85]


DI1 119877800 minus 119877550 [86]

DVI int12058211205821

(119889120588119889120582)119889120582 [87]







Table 1 Continued



120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]



GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]


GVSB GVISBI

[95]

LIC3119877400119877740 [96]


HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]


IITM5TM7

[99]









Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













ARI ( 1119877550 ) minus (1119877700 ) [76]

ARI2 119877800 [( 1119877550 ) minus (1119877700 )] [76]


ASBI 02YVI [77]




BGI1119877400119877550 [81]

BGI2119877450119877550 [81]

BRI1119877400119877690 [60]

BRI2119877450119877690 [60]

CAI 05 (1198772000 + 1198772200) minus 1198772100 [82]

CARI CAR lowast (119877700119877670 ) [65]


CRCWD 1 minus 120588119903min [84]

CRI500(1119877515)(1119877550) [85]

CRI700(1119877515)(1119877700) [85]


DI1 119877800 minus 119877550 [86]

DVI int12058211205821

(119889120588119889120582)119889120582 [87]







Table 1 Continued



120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]



GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]


GVSB GVISBI

[95]

LIC3119877400119877740 [96]


HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]


IITM5TM7

[99]









Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Table 1 Continued



120578 = [2(NIR2 minus 1198772) + 15NIR + 05119877](NIR + 119877 + 05)[90]

GLI(2119877119892 minus 119877119903 minus 119877119887)(2119877119892 + 119877119903 + 119877119887) [91]

GM1119877750119877550 [92]

GM2119877750119877700 [92]



GRVI NIRGreen

[23]

Greenness index (119866) 119877554119877677 [94]


GVSB GVISBI

[95]

LIC3119877400119877740 [96]


HI(119877534 minus 119877698)(119877534 + 119877698) minus 05119877704 [98]


IITM5TM7

[99]









Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Table 1 Continued






MSR[(119877800119877670) minus 1][SQRT (119877800119877670 + 1)] [106]

MSI1205881599120588819 [107]





NDGI (119866 minus 119877)(119866 + 119877) [78]


NDI1(119877780 minus 119877710)(119877780 minus 119877680) [109]

NDI2(119877850 minus 119877710)(119877850 minus 119877680) [109]

NDI3(119877734 minus 119877747)(119877715 minus 119877726) [110]



NDVI(119877800 minus 119877680)(119877800 + 119877680) [96]



NGBDI (119866 minus 119877)(119866 + 119861) [113]

NGRDI (119866 minus 119877)(119866 + 119877) [114]




Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Table 1 Continued


OSAVI(1 + 016) (119877800 minus 119877670)(119877800 + 119877670 + 061) [117]

PRI(119877531 minus 119877570)(119877531 + 119877570) [118]

PSRI(119877680 minus 119877500)119877750 [119]

PSNDc(119877800 minus 119877470)(119877800 + 119877470) [120]

PSSRa119877800119877680 [120]

PSSRb119877800119877635 [120]

PSSRc119877800119877470 [120]



RARS119877746119877513 [85]

RDVI(119877800 minus 119877670)[SQRT (119877800 + 119877670)] [121]


RENDVI(119877750 minus 119877705)(119877750 + 119877705) [122]

RGRI(sum690119868=600 119877119868)(sum599119868=500 119877119869) [123]

RI (119877 minus 119866)(119877 + 119866) [124]

RVI 119877NIR

[125]


SIPI(119877800 minus 119877445)(119877800 + 119877680) [126]



SDr sum119873

1205881015840 (120582119894) [127]

SGI NIRRed

[128]

SR119877800119877670 [36]

SR2119877800119877550 [86]


Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Table 1 Continued


SR3119877700119877670 [129]

SR4119877740119877720 [110]

SR5119877675(119877700119877650) [130]

SR6119877672(119877550119877708) [126]

SR7119877860(119877550119877708) [131]






VARI (119866 minus 119877) (119866 + 119877 minus 119861) [135]




VREI1119877740119877720 [110]

VREI2(119877734 minus 119877747)(119877715 + 119877726) [110]


WBI119877970119877900 [115]



ZM119877750119877710 [81]

1DZ -DGVI120582119899sum1205821

100381610038161003816100381610038161205881015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]

2DZ -DGVI120582119899sum1205821

1003816100381610038161003816100381612058810158401015840 (120582119894)10038161003816100381610038161003816 Δ120582119894 [138]




Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












Acknowledgments


References
















































































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation






























































































































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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