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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 1

An Integrated Optical Remote Sensing System forEnvironmental Perturbation Research

Cho-ying Huang, Ching-Wen Chai, Chao-ming Chang, Jr-Chuan Huang, Kai-Ting Hu, Ming-Lun Lu, andYuh-Lurng Chung

Abstract—Remote sensing is the only technology that can sys-tematically monitor physical properties of the biosphere over avast region. However, it is still a challenge to make these measuresmeaningful for assessing the impacts of environmental perturba-tion. Here, we integrate an optical remote sensing system termedEcoiRS (Ecosystem observation by an integrated Remote Sensingsystem) specifically for this purpose. EcoiRS consists of three sub-systems: an off-the-shelf atmospheric correction model (ACORN),a cloud/shadow removal model, and an advanced spectral mixtureanalysis model (AutoMCU). The core of ACORN is a set of ra-diative transfer codes that can be used to remove the effects ofmolecular/aerosol scatterings and water vapor absorption from re-motely sensed data, and to convert these digital signals to surfacereflectance. Shadow and cloud cover that would obscure the reflec-tive properties of land surfaces in an image can beminimized by re-ferring to their optical and thermal spectral profiles. AutoMCU ex-ecutes iterative unmixing for each pixel using selected spectral end-members based upon the rule ofMonte Carlo simulation. Themainoutcomes of EcoiRS include cover fractions of green vegetation,non-photosynthetically active vegetation and bare soils, along withuncertaintymeasures for each pixel. The dynamics of these derivedproducts are significant indicators for monitoring the change ofstates of terrestrial environments, and they can be used for inves-tigating different environmental perturbations. Here, we demon-strate studies of implementing EcoiRS to map three major but rel-atively less studied cases in a western Pacific island (Taiwan): ty-phoons, tree diseases and alien plant invasion.

Index Terms—Biological invasion, climate change, EcoiRS, land-slide, spectral mixture analysis, tree mortality, typhoon.

I. INTRODUCTION

G LOBAL climate change is a phenomenon that is be-lieved to threaten human population, biodiversity, and

the health of the planet. Many studies have revealed that thechanges (especially elevated temperature) may accelerate water

Manuscript received July 02, 2012; revised November 08, 2012 and Feb-ruary 20, 2013; accepted February 26, 2013. This project was sponsored by theNational Science Council of Taiwan grants (NSC 98-2221-E-002-198-, NSC98-2313-B-002-062-MY2, NSC 100-2621-B-002-001-MY3) and NationalTaiwan University (10R70604-2). Part of the work was presented at theInternational Conference on Earth Observations and Social Impacts (ICEO-SI)2011.C. Huang, C. Chang, J.-C. Huang, and K.-T. Hu are with the Department of

Geography, National Taiwan University, Taipei, Taiwan (e-mail: [email protected]).C.-W. Chai is with the Far Eastern Group, Taipei, Taiwan.M.-L. Lu is with the Graduate Institute of Bioresources, National Pingtung

University of Science and Technology, Pingtung, Taiwan.Y.-L. Chung is with the Department of Forestry, National Pingtung Univer-

sity of Science and Technology, Pingtung, Taiwan.Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/JSTARS.2013.2250489

cycling and thus modify spatiotemporal patterns of evapo-transpiration, snow melt, runoff and ground water recharge[see a review by [1]]. These alterations in hydrological andenergy cycles would not only directly affect plant physiologybut may feedback to the atmosphere as a consequence ofextreme weather events [2]. In humid bioclimatic regions,climatic anomalies such as amplified tropical cyclones wouldcause catastrophes that would severely damage biomes and an-thromes [3]–[5]. Tree die-off and alien species invasion are twomajor biological perturbations that cause tremendous changeto natural and human habitats and may be directly or indirectlyrelated to climate change. Since early 2000, insect and diseaseattacks have caused tremendous damage to forests worldwide[6], [7]. The physiological mechanisms resulting in these out-breaks and tree die-off events are complex. Recent studies haverevealed that these events may be affected by the combinationof consecutive drought and elevated temperature [8], [9]. Alienspecies (especially plants) invasion can influence species com-position and ecosystem function, and alter disturbance regimesresulting tremendous impacts on natural environments andhuman societies [10], [11]. Anthropogenic perturbations (usedinterchangeably with “disturbance” through the article; gen-erally defined as processes altering environmental conditionswith consequences for biotic and abiotic interactions [12]) andclimate change may dramatically change physical settings andfacilitate the proliferation of alien invasive species that canadapt to a new environment more quickly than native species[12]–[14]. Biological invasion induced ecosystem feedbackscan also have direct collateral impacts on regional and globalenvironmental changes [15].Limited manpower and wide spatial extent of disturbed

areas are the two main constraints for the frequent monitoringand assessment of perturbations in most countries. Millionsof hectares of lands are usually managed by only a handful ofstaff. Therefore, an effective monitoring protocol is needed.Remote sensing is the only technology that can systematicallymonitor the physical properties of the biosphere over a vastregion, and it has been heavily utilized in the recent decades[16]–[20]. However, it remains a challenge to make these mea-sures meaningful for terrestrial ecological research, especiallyto assess the impacts of perturbations on natural environments[21]. Here we outline a remote sensing methodology that canproduce a set of land surface cover fractions that are useful formonitoring the impacts of perturbations on natural settings.

II. SYSTEM REQUIREMENTS

We integrate an optical remote sensing (defined as measuringthe reflective/absorptive characteristics of an objective within

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2 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

the approximate spectral range of 350–2500 nm. Hereafter,“remote sensing”) pre-processing/analysis system termedEcoiRS (the Ecosystem observation by an integrated RemoteSensing system). This system is specifically designed formapping perturbations in natural environments (defined asareas with a minimum number of man-made structures) oftropical and subtropical regions. A related system called CLAS(the Carnegie Landsat Analysis System), developed by Asneret al. [21], was used for the rapid mapping of forest cover,deforestation and disturbance over vast tropical forested re-gions. EcoiRS implements an improved data preprocessingprocedure and expands the applications for several differentenvironmental perturbations, such as typhoon-induced land-slides, tree pathogens and alien plant invasion in tropical andsubtropical regions (see the case studies below). The mainrequirements (and strengths) for EcoiRS are (i) methodicaldata processing protocol, (ii) flexibility of satellite imagesand (iii) applicability to a desktop personal computer (PC). Asystematic data processing procedure is essential for effectiveanalysis of a large volume of remotely sensed data, and it wouldsignificantly reduce arbitrary human error, which can causecascading effects on outcomes. For the second requirement,open source spaceborne images are the only available remotelysensed data in many undeveloped and developing countries inthe tropical and subtropical zones. Therefore, EcoiRS shouldbe able to process different types of satellite images, especiallythe Thematic Mapper (TM) and Enhanced Thematic Mapperplus (ETM+) on the Landsat platforms and the ModerateResolution Imaging Spectroradiometer (MODIS) on the Terraand Aqua satellites. These satellite sensors collect land surfacereflectance frequently and systematically, and the data are madeavailable for the public regardless of nationality. Finally, withthe advancement of modern technology, the performance ofPCs with a 64-bit Windows operating system (e.g., WindowsXP 64-bit, Windows 7) is not far from a high-performanceLinux computer cluster [22] when dealing with a certain size(e.g., 1 GB) of remotely sensed images, and at much lower cost.Therefore, it is crucial to select or design each component ofEcoiRS to be compatible with PCs to make it a feasible tool forresearch institutes with limited funding sources and facilities.

III. MODEL DESCRIPTION

EcoiRS consists of three main functions: (i) atmosphericcorrection to reduce atmospheric effects, such as molecular andaerosol scattering and water vapor absorption and to retrievetrue land surface reflective properties; (ii) cloud and shadowmasking to filter out thick cloud cover and shadows generatedby terrain and cloud; (iii) spectral mixture analysis to extractsub-pixel information of the abundance of photosyntheticallyactive vegetation (PV), non-photosynthetically active vege-tation (NPV, such as senescent vegetation and coarse woodydebris) and soil substrate cover fractions (Fig. 1). The selectionof analytical tools to accomplish each task was based uponthe principles of simplicity, automation and accuracy. Theatmospheric correction subsystem is low cost, off-the-shelfatmospheric correction software, and the other two subsystemswere coded by IDL (Interactive Data Language, v. 7.x and

above) with ENVI (ENvironment for Visualizing Images, v.4.7 and above) modules (Exelis Visual Information Solutions,Inc., Boulder, Colorado, USA) that can be exported to self-ex-ecutable files. These systems can be executed separately toevaluate the performance of each step, or be integrated as wholeby a controller program written in. NET in Windows operatingsystem (Microsoft Corp., Redmond, Washington, USA). Thethree sub-systems are introduced below.

A. Atmospheric Correction (Landsat Only)

The software selected to integrate into EcoiRS to min-imize atmospheric effects on an image was AtmosphericCOrrection Now (ACORN, ImSpec, Palmdale, California,USA) version 6b. ACORN is a MODTRAN 4 (MODeratespectral resolution atmospheric TRANSsmittance algorithmand computer model version 4)-based atmospheric correctiontool [23] used to produce high quality surface reflectancewithout ground measurements. It has been widely utilizedin environmental research [e.g., [4], [24] and many others].We note that atmospheric correction only applies to LandsatTM and ETM+ images, and EcoiRS bypasses the MODIS8-Day 500 m surface reflectance product (MOD09A1) be-cause it was atmospherically corrected before data acquisition(http://www.ladsweb.nascom.nasa.gov/).ACORN requires that the input images be converted

as 16-bit radiance before furtherprocessing. Landsat images obtained from internet dataservers (e.g., the USGS Global Visualization Viewer:http://www.glovis.usgs.gov/) are usually in the form of adigital number (DN) and need to be converted to radianceby referring to coefficients provided by the official website(http://www.landsathandbook.gsfc.nasa.gov/). Most informa-tion required for performing atmospheric correction can befound in metadata, except for atmospheric water vapor andvisibility. For the ACORN multispectral mode, only one valueof each parameter can be assigned for each image. Accordingto the user manual [25], a typical water vapor amount forarid and humid regions is 15 mm and 25 mm, respectively,and average visibility is approximately 100 km and 20 kmfor clear and hazy conditions, respectively. Therefore, valueswithin these ranges should be provided by users. In manycases, users can refer to an international network of precipitablewater vapor estimation to set up proper parameters (SuomiNet,http://www.suominet.ucar.edu/).

B. Cloud and Shadow Mask

Cloud and shadow cause the main uncertainty in remotesensing land surface research, and they are commonly ob-served in tropical/subtropical zones and mountainous regions,respectively. EcoiRS implements a modified/simplified versionof Irish et al. [26] that is suitable for tropical and subtropicalenvironments to mask cloud cover from Landsat images. Thereflectance of cloud cover is relatively high in the visible region(400–700 nm), especially within the red region (approximately600–700 nm) (Table I). This property is unique and has beenutilized as one of the indicators in EcoiRS to distinguish cloudsfrom other land cover types. Cloud temperatures are usually


HUANG et al.: AN INTEGRATED OPTICAL REMOTE SENSING SYSTEM FOR ENVIRONMENTAL PERTURBATION RESEARCH 3

Fig. 1. A conceptual model of EcoiRS (Ecosystem observation by an integrated Remote Sensing system), which consists of image pre-processing: atmosphericcorrection and cloud/shadow cover removal ((a) and (b)), and an automated, probability based spectral mixture analysis model (an Automated Monte CarloUnmixing [AutoMCU]). (c) A prerequisite of AutoMCU is a comprehensive set of spectral endmember bundles of photosynthetically active vegetation (PV),non-photosynthetically active vegetation (NPV) and soil measured by spaceborne imaging spectroscopy or a portable spectroradiometer (solid, long-dashed andshort-dashed lines are mean, mean standard deviation [SD] and minimum/maximum reflectance values, respectively, for each band). These high resolutionspectral endmember bundles (upper panel) are convolved to multispectral forms (lower panel) to match the targeted images (in this case, a Landsat ETM+ [theEnhanced Thematic Mapper plus] image). (d) Main products of EcoiRS: fractional cover of PV, NPV and soil; the pixel values range from 0% (dark) to 100%(bright).

lower than land surface temperatures, and they can be retrievedfrom Landsat thermal band 6:

(1)

where , are temperature (K) and radiance, respectively. The coefficients

and are 607.76 and 1260.56, and 666.09 and 1282.71 forLandsat TM and ETM+, respectively. A constant emissivityof 0.95 is usually applied to modify when using atmo-spherically uncorrected radiance data for ground temperatureestimation [27]. Because the target is cloud cover and theabove cloud atmospheric profiles are various and extremelydifficult to characterize, emissivity was not taken into accountin EcoiRS for the sake of simplicity. A Landsat pixel waslabeled “cloud” if its temperature lower than 290 K and thereflectance value more than 0.35 in band 3 (630–690 nm) (abright object) [26]. Because the majority of monitored naturalareas (in tropical and subtropical regions) are highly vegetatedand a commonly used greenness index the NormalizedDifference Vegetation Index (NDVI) is highly sensitive tocloud contamination [28], an additional threshold of NDVI

less than 0.5 was set to enhance the algorithm. This is a newfeature for cloud removal. We note that the proposed method isonly feasible for thick but not thin cloud layers such as cirrusor the edge of cloud. Overall, the results are satisfactory basedupon visual assessment (Fig. 1(a) and (b)). For the MODIS8-day surface reflectance product, only the ranges of cloudendmembers in the optical region (350–2500 nm)for each band (Table I) and the NDVI ( 0.5) were used to maskcloud cover, as there was a lack of spatially correspondingin-sync 500 m thermal data. The criterion for assigning thethreshold values (one standard deviation [SD] above the meansof endmember values for all bands) is relatively conservativefor EcoiRS to preserve more potentially useful pixels.Shadows can suppress the reflectance of land surfaces and in-

troduce uncertainty to the analysis particularly in a mountainousregion (Fig. 2(a)). Instead of correcting for the effect [e.g., [29]],a conservative strategy was carried out to remove shaded pixelssince it was extremely difficult to evaluate the accuracy of mod-ified data. More than 1000 shadow endmembers were selectedfrom Landsat TM and ETM+ from more than 20 images ac-quired in different seasons and regions (Table I). Values belowone SD below the means of the shadow endmembers for allbands are defined as shadow pixels. In many cases, water bodies



TABLE IREFLECTANCE VALUES USED FOR IDENTIFYING AND MASKING LANDSAT TM (THE THEMATIC MAPPER)/ETM+ (THE ENHANCED THEMATIC MAPPER PLUS) ANDMODIS (THE MODERATE RESOLUTION IMAGING SPECTRORADIOMETER) CLOUD CONTAMINATED AND SHADED PIXELS. PIXELS CAN BE LABELED AS CLOUD OR

SHADOW IF THE REFLECTANCE VALUES FOR ALL BANDS ARE GREATER THAN OR LESS THAN THE THRESHOLD VALUES, RESPECTIVELY

Band widths of TM are not shown but very similar to those of ETM+.NA: not applicable.

Fig. 2. Contemporary fractional cover maps of Taiwan at a spatial resolutionof 30 m and 500 m derived from (a) Landsat ETM+ and (b) the Moderate Reso-lution Imaging Spectroradiometer (MODIS) images by EcoiRS. Abundances ofPV, NPV and soil cover are illustrated by inputting these into green, red and bluechannels. The legend is an abstract of a 3-D color cube that applies to Figs. 5–7as well.

can be masked out as well because they behave similarly toshadows in the spectral space (low reflectance in the visual re-gion and nearly zero reflectance in the near-infrared [700–1300nm] and shortwave infrared regions [1300–2500 nm]). Note thatno shadow mask was applied to MODIS images because theshaded areas are usually not discernible at a spatial resolutionof 500 m (Fig. 2(b)).

C. Spectral Mixture Analysis

The core of EcoiRS is AutoMCU (Automated Monte CarloUnmixing), which is a probability-based spectral mixture anal-ysis model [30], [31]. Spectral mixture analysis is a mathe-matical approach often used to derive sub-pixel cover fractions

of land surface materials acquired from remotely sensed data[32]. This method is ideal for use in disturbed settings wheresub-pixel cover variation is high. Each endmember componentcontributes to the pixel-level spectral reflectance as thelinear combination of endmember (e) spectra:

(2)

(3)

where and are the reflectance and cover fraction of eachendmember (PV, NPV and soil), respectively, and is theerror term (root-mean-square error [RMSE], (2)). Equation (3)indicates that the endmembers sum to unity. Asner et al. [33]suggested that there are a number of endmember combinationsthat can produce a particular spectral signal, so a wide rangeof numerically acceptable unmixing results for any imagepixel is possible. Hence, this probabilistic spectral mixtureanalysis technique was implemented to account for this naturalvariability [34] through the iterative random selection of end-member reflectance from ‘bundles’ (a.k.a. spectral libraries)[35]. Endmember bundles for NPV and soils can be directlyacquired using a field spectroradiometer at a 1 nm spectralresolution [36]. There are two approaches to acquiring PV end-members. In some cases, top-layer canopy sunlit leaves may becollected by a tree climber or using a shotgun [37]. Hemisphericleaf spectra are acquired by a field spectroradiometer connectedwith an integrating sphere [38]; these spectra can then beconverted to canopy reflectance using canopy radiative transfer[e.g., [39]]. However, in most cases, it is extremely difficultto collect PV spectra from the field due to the high stature ofthe vegetation canopy (e.g., tropical rainforest). Therefore, PVendmembers were extracted from spaceborne hyperspectralHyperion images (http://www.edcsns17.cr.usgs.gov/eo1/acqui-sition/hyperion) acquired for these settings with high canopyclosure by referring to high spatial resolution (e.g., 5 m



Fig. 3. Six bands ((a)–(c)) of the Landsat Thematic Mapper (TM) and seven bands ((d)–(f)) of MODIS 8-day surface reflectance product spectral profiles for PV,NPV and soil, respectively. Solid, long-dashed and short-dashed lines are mean, mean SD and minimum/maximum reflectance values, respectively, for eachband.

to sub-meter) remotely sensed images from Google Earth(http://www.google.com/earth/index.html). Acquisition datesfor both systems (Hyperion vs. Google Earth) should be asclose as possible for consistency. ACORN was also used forhyperspectral atmospheric correction. High resolution spectrawere convolved to match the spectral profiles of the selectedimages (TM/ETM+, MODIS) (Fig. 3). According to Asner etal. [40] and Huang et al. [36], a 250 times repetition for eachpixel should be sufficient for multispectral data. Histograms ofPV, NPV and soil fraction abundance are produced to examinethe accuracy of cover estimation, which should be in the shapeof a normal distribution with high kurtosis (a measure ofpeakedness), or AutoMCU will reject the process and requestanother round of unmixing.

IV. CASE STUDIES

A. Site Description

Taiwan, a 36,000 island (Fig. 4(a)), is selected as thestudy region for testing EcoiRS. Taiwan is situated between theworld’s largest terrestrial (Asia) and oceanic (the Pacific Ocean)divisions (Fig. 4(b)) overlapping the Tropic of Cancer, whichdivides the island into two climatic regions: tropical and sub-tropical zones. The mean annual precipitation and temperatureare approximately 2500 mm and 22 , respectively, and theyvary pronouncedly across regions [41]. The mean el-evation of Taiwan is 778 ( 845) m a.s.l., with steep slopesin the mountainous regions. The dominant land cover type ofTaiwan is forest, which occupies 58% of the land area and can



Fig. 4. (a) Elevation map of Taiwan and (b) its location.

be generally categorized as natural forests (73%), forest planta-tions (20%) and bamboo forests (7%). Non-irrigated fields andpaddy fields occupy 14% and 11% of the land area, respectively,and are distributed mostly below 800 m a.s.l. Approximately6% of the land is urbanized, and these areas are mostly locatedbelow 300m a.s.l. Themajor natural perturbations in Taiwan aretropical cyclones, insect and disease infestation and alien plantspecies invasion, which were much less studied using moderateresolution optical remote sensing.

B. Case I: Landslide

Landslides are one of the most destructive disturbances inTaiwan. These events are mainly triggered by heavy rainfall de-livered by tropical cyclones, which can have tremendous im-pacts on forested lands and enormous damage to human habi-tats [5]. One key step of landslide research is the precise delin-eation of altered land patches, which have been commonly usedfor landslide detection, monitoring and prediction. Metternichtet al. [42] summarized remote sensing techniques applied forlandslide mapping, and the acquisition of data with the easiestaccess is multispectral imaging with systematic data acquisitioncycles, such as Landsat TM and ETM+ and MODIS, which arethe primary data sources for EcoiRS.The main products of EcoiRS include a set of PV, NPV and

soil cover fractions (Fig. 1), and the combinations of these spa-tial layers can reveal the components and processes of land-slides. A case study described here focuses on a mountainousregion in Taroko National Park in eastern Taiwan (Fig. 5) (24.1374 N, 121.3841 E). The size of the park is 92,000 ha. Land-slide patches derived from a Landsat TM image (World Ref-erence System [WRS] Path [P] 117-Row [R] 43, image date:

2005/09/17) by EcoiRS have been compared with those de-rived from manual digitization on high spatial resolution falsecolor aerial photographs (1 m) and Formosat-2 satellite images(2 m) acquired in the same time period [43]. A strong agreementwas observed. In addition, EcoiRS-processed coarse resolutionLandsat landslide coverage may outperform a typical high spa-tial resolution remote sensing approach with much greater flex-ibility, a larger spatial extent, more efficiency and lower dataacquisition cost. In most cases, the visual interpretation of land-slides is based upon the shape and color tone of the land sur-faces. Therefore, only continuous and severely impacted areas(brownish patches, Fig. 5(d)) can be outlined, though the un-avoidable weakness of this approach is the lack of detailed in-formation of land surface materials. Such information is partic-ularly important for investigating this natural hazard. Fig. 5(a)demonstrates that injecting the EcoiRS extracted abundance offractional cover in the Red-Green-Blue (RGB) color model canprovide new perspectives for observing the spatial arrangementand characteristics of landslides. The blue patches indicate areascovered mainly by bare soils ( 95%) with a minimum amount( 10%) of PV and NPV (e.g., coarse woody debris) cover, thereddish and pinkish areas reveal bare soil surfaces ( 80%) cov-ered by a substantial amount of NPV (40–50%); and the yellowcolored areas indicate the coexistence of PV (80%) and NPV(20%) (Fig. 5(c) and 5(d)). A preliminary study [44] showed thatthere was a very good match (90% overall accuracy) by com-paring landslide coverage derived from manual digitalizationand EcoiRS with a threshold ( ,according to sensitivity analysis). Information retrieved fromthe fractional cover may help us understand the ages and con-ditions of these disturbed areas. Investigation of the spatial andtemporal dynamics of land cover fractions by integrating sea-sonal time-series Landsat images and digital elevation modelsmay shed some light on the evolution of landslides [45], [46].With the availability of time-series images, the spatio-temporaldynamics of landslides (with information of severity and re-covery) can be monitored.

C. Case II: Tree Mortality

In the past decade (2000–present), drought has occurred morefrequently than before [47], resulting in severe impacts suchas drought-induced tree mortality [48]. These events have beenrecognized as one of the major potential sources of carbon emis-sions in terrestrial environments [49], [50]. Tree die-off canhappen within a wide spectrum of settings, ranging from aridto humid environments, and such events have been recorded inall continents [6], [7] except Antarctica and Greenland, wherethe majority of land is covered by ice year round. Althoughwater limitation is not a crucial factor for the growth of forests intropical and subtropical regions of Taiwan, several tree die-offevents have been reported locally possibly related to insect orpathogen outbreaks. In terms of the intensity and spatial cov-erage, the most severe tree mortality events recorded in Taiwanare Casuarina spp. die-offs.Casuarina spp. is a fast growing tree species that is often

planted along coasts as windbreaks to prevent land degradation.Field observations have revealed that the life span of Casua-rina spp. is short (20–30 years), and the species is difficult to



Fig. 5. An EcoiRS derived Landsat TM image (image date: 2005/09/17) highlighting landslides (blue and reddish pixels) in the mountainous region of (b) TarokoNational Park in eastern Taiwan. The white color outlined polygons are landslide spatial coverage generalized by on-screen digitizing by referring to high spatialresolution aerial photographs (1 m) or Formosat-2 satellite images (2 m) acquired in the same time period. (c) A close-up look of a landslide (24.2050 N, 121.4259E, indicated by an arrow in (a)) is encompassed by a box, and (d) a snapshot of landslide for (c) was taken by J.-C. Huang on August 25, 2009.

Fig. 6. (a) Major disease infected sites (indicated by arrows, reddish and yellowish Landsat TM pixels [image date: 2010/02/10]) in Casuarina spp. plantationafter tree eradication in (b) Taichung Harbor (a snapshot from Google Earth). (c) An index map depicts the location of Taichung Harbor.

regenerate naturally in the coastal zones of Taiwan due to theharsh climatic and biophysical conditions and tropical cycloneassociated perturbations (e.g., windstorms, heavy rainfall) [51],[52]. According to local reports, a substantial number of treeshave been infected and killed by diseases (possibly brown rootrot [dark brown crust formed by the fungus Phellinus noxiuson exposed roots and lower plant stems]) [53]. To date, thereis no treatment for the disease, and the most commonly prac-ticed strategy in Taiwan is to remove the infected individualsimmediately. In many cases, the tree eradicated sites were soonoccupied by herbaceous plants, which would turn senescent inwinter. Fig. 6 shows an example in Taichung Harbor, centralTaiwan (24.2995 N, 120.5311 E). A large area ( 400 ha) ofa Casuarina spp. plantation was reported as suffering differentdegrees of infection with brown root rot disease. A recent winterLandsat TM image (WRS P118-R43, image date: 2010/02/10)was processed using EcoiRS. The patches after tree removalwere soon occupied by grasses [51], and they were clearly de-lineated by EcoiRS. The image shows these patches as reddishand yellowish pixels (high litter cover) displayed by injectingthe abundance of PV, NPV and soil substrate into the RGB chan-nels, respectively. Although insect and pathogen outbreaks have

pervasive impacts on natural environments, a patchy spatial pat-tern is commonly observed [[54], [55], and this case study]. Inmany cases (such as brown root rot disease), the spatial cov-erage of each damaged patch is relatively small. The abilityof the sub-pixel analysis of EcoiRS may permit the effectivemonitoring of these subtle changes (e.g., in Fig. 6(a), yellowishpixels: high cover of PV [30–40%] and NPV [20–30%]; red-dish/pinkish pixels: a mixture of high NPV [30–40%] and soil[30–40%] cover with a small amount of PV [20%]). This casestudy shows the capability of EcoiRS to map the impacts of dis-ease outbreaks regionally in forested lands. With a set of followup images, the trajectory of recovery of disturbed ecosystem canbe monitored.

D. Case III: Alien Plant Invasion

In Taiwan, there are approximately 279 introduced plantspecies that are well adapted to the natural environments,and more than 50 of them are invasive. One of the mosttroublesome non-native species is Leucaena leucocephala(Fig. 7(d)), which is a Central American Mimosoid tree thatcan now be commonly found in lowlands across regions ofTaiwan. Leucaena leucocephala is a fast growing plant that



Fig. 7. Spatial distribution of (d) Leucaena leucocephala (taken by C. Huangon September 01, 2010) in (c) Kenting National Park in southern Taiwan delin-eated by comparing Landsat images acquired in (a) summer (2004/07/12) and(b) winter (2002/02/05). Dark green pixels in the (b) winter image indicate thesites invaded by L. leucocephala, which are within white hatch pattern poly-gons generated via on-screen digitizing by referring to high spatial resolutionfalse color aerial photographs (1 m) or a Formosat-2 (2 m) satellite image. Asmall patch of natural tropical rainforest is indicated by the arrow for seasonalcomparison of different vegetation types (a and b).

can reproduce via seeds or from sprouting and form densethickets within just a few months. It is a nitrogen fixer thatcan survive on nutrient-poor soils [56]. The litterfall of L.leucocephala contains toxic elements that prohibit the growthof other species (known as allelopathy) [57]. Anecdotal reportsreveal that L. leucocephala was introduced to Taiwan in the1970s. The physical environments of Taiwan at low elevation( 500 m a.s.l.) are well-suited for the plant’s growth. Dueto the aforementioned growing strategies and lack of naturalenemies on the island, the spread of L. leucocephala has beenrapid and extremely difficult to control.The case study concentrates on the 18,084 ha Kenting

National Park (21.9507 N, 120.7852 E), which is locatedin southern Taiwan (Fig. 7(c)). Lu et al. [58] found that the

areal size of the L. leucocephala infestation has doubledbetween 1988 and 2007 (from 4,210 ha to 8,553 ha). Oneunique phenological characteristic of L. leucocephala inthis tropical region is its defoliation during the dry seasonfrom January to April, averaging total 112 75 mmduring these four months (mean annual precipitation is

), according to more than a century ofmeteorological records (1897–2010) (Central Weather Bureau,http://www.cwb.gov.tw/V7/index.htm). Therefore, it should berelatively simple to differentiate the L. leucocephala invadedvegetation from the surrounding tropical rainforests by com-paring EcoiRS derived cover fractions from the wet summerseason (Fig. 7(a)) and the dry season (Fig. 7(b)). Tropicalrainforests stay relatively green year round, in contrast of thedistinct defoliation pattern of L. leucocephala vegetation. Fig. 7demonstrates that the pixel colors of infested sites becomedarker/bluish in the dry season, mainly due to the substantialdecrease ( 29% in L. leucocephala invaded sites vs. 11%in natural tropical rainforests) of PV (green channel) and thesequential increase (36% [L. leucocephala] vs. 27% [trop-ical rainforests]) of the background soil cover (blue channel)(Fig. 7(b)).

E. Summary

The three case studies (landslide, tree mortality and alienplant invasion) demonstrated the strength of EcoiRS for rapidmapping of perturbed sites. Results showed that cover frac-tions of NPV and/or soil were key indicators for identifying thelocations and possibly the intensity of disturbances. With theproper manipulation of color scales in the RGB color model,the region of interest can be highlighted. Patch dynamics [59]of single or even multiple perturbations related ecological pro-cesses through time such as response, recovery and feedbackmay be investigated with the availability of time-series data.

V. PERFORMANCE AND FUTURE CHALLENGES FOR ECOiRS

In terms of time integration, it only takes approximately anhour or even less for a well-trained technician to complete theimage pre-processing steps of EcoiRS (atmospheric correctionand cloud/shadow mask) with quality assurance/control usinga standard PC (e.g., Intel Core 2 Quad Central ProcessingUnit [CPU] [email protected] GHz, 8 gigabytes of Random AccessMemory [RAM]). The majority of the computation time is spenton the execution of AutoMCU because this module calculatescover fractions pixel by pixel. The computer processing timefor each image greatly depends on the computation power of thehardware, such as CPU and RAM, the number of iterations foreach pixel, the collection and selection of endmembers, and thequality of the input data (e.g., cloud coverage, proportion of theurbanized area). In general, it takes approximately 3–4 hours tounmix a full Landsat scene using a high performance PC (IntelCore i7 CPU [email protected] GHz, 24 gigabytes of RAM) if thenumber of iterations is set to 30, which should be acceptablefor most applications [21]. However, approximately 40 hoursmay be needed for a full process of 250 iterations per pixel[36], [40]. The computer processing time can be significantlyshortened if EcoiRS can be installed in a Linux-based computercluster and if jobs can be divided by a parallel processing



algorithm [22]. With the increase of PC computational power,EcoiRS will be a feasible and highly cost-efficient choice formost small research institutions.Accuracy is another crucial indicator for evaluating the per-

formance of EcoiRS, although it is highly dependent upon thequality of input data, which can be affected by atmosphericconditions, image uncertainty (e.g., cloud and shadow), dataspatial and spectral resolutions, endmember selection for un-mixing (see Somers et al. [60] for a detailed review), struc-ture complexity of vegetation, and the research of interest. Ac-cording to Asner et al. [21], the accuracies of CLAS and itssister systems (CLASlite and AutoMCU) in mapping the de-forestation cover of different biomes across continents were ap-proximately 85–95%. Our previous studies showed that the un-certainties were approximately 40% when map-ping shrub cover in semi-arid Prosopis woodlands with a his-tory of recent wildfire [36], and 36% and 46% when estimatinglive carbon losses using rapidly declining time-series of PV inpinyon-juniper ecosystems [61] and post tree die-off NPV inaspen forests [55], respectively. Overall, the performance ofEcoiRS is satisfactory considering the quantification of thesedisturbed, heterogeneous land surfaces at the regional scale.Although only limited quantitative (% cover) error assess-

ment has been carried out for EcoiRS in tropical and subtropicalforests when applying it to investigate the impacts of land-slides, insect/pathogen outbreaks or biological invasions, wedemonstrate that, with ground knowledge, simply ingestingcover fractions of NPV, PV and soil into the RGB channels,respectively, may be effective to assess damages qualitativelyover a vast region. This can be useful from a natural resourcemanagement perspective, and the impact would be even morepronounced with interpretation by field ecologists. The perfor-mance should be promising based upon the visual comparisons(patch level) with the outcomes derived from high spatialresolution remotely sensed images (Figs. 5–7). Very limitedvalidation (if any) has been applied to the MODIS-derivedresults (Fig. 2(b)) in the current work and in previous studies.This is particularly important because these results are probablythe only data suitable for research at the continental and globalscales, and they are ideal for tropical and subtropical regions,where cloud free days are infrequent. However, its coarsespatial resolution would make the taskincredibly challenging, especially in mountainous and remoteareas. In the future, conducting quantitative evaluations usingfield surveys or alternative spatial analysis techniques (e.g.,using sub-meter high spatial resolution satellite and/or airborneimages with a statistical model such as a receiver operatingcharacteristic [ROC] curve) for EcoiRS will be the top priorityin studying these aforementioned perturbations.

VI. CONCLUSIONS

Combining systematic atmospheric correction andcloud/shadow mask methods with an advanced probabilitybased spectral mixture analysis model (AutoMCU) has beenwidely implemented to assess the impacts of deforestation andto estimate carbon stocks in tropical rainforests [21]. This study

integrates a similar system, EcoiRS, with more systematicatmospheric correction and cloud/shadow mask algorithms,and it demonstrates broader management applications.In the future, the EcoiRS will be compatible with Landsat 8,

the Landsat Data Continuity Mission, which has been launchedon February 11 2013. The sensor is the successor for the de-commissioned 27 year-old Landsat TM (in November 2011 dueto hardware deterioration), and degraded ETM+ (the failure ofthe scan line corrector in June 2003). EcoiRS can be utilized asa rapid landscape scale monitoring tool for studying the rami-fications of landslides, tree mortality and alien plant invasionson tropical and subtropical environments. Quantitative assess-ments of the system uncertainties for the presented case studiesshould be prioritized for EcoiRS development to enhance therobustness of the system and to ensure a broader contribution toenvironmental perturbation research.

ACKNOWLEDGMENT

The authors thank the anonymous reviewers and the editor forproviding suggestions that greatly improved the manuscript.

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Cho-ying Huang received the B.S. degree in MarineEnvironment from National Sun Yat-sen University,Taiwan, in 1996, the M.S. degree in Watershed Sci-ence from Colorado State University in 2000, and thePh.D. degree in Natural Resources from Universityof Arizona in 2006.His post-doctoral training (2007–2008) was in

the Department of Global Ecology at CarnegieInstitution. He has been an Assistant Professor since2009 at the Department of Geography at NationalTaiwan University (NTU). His research interests

include global ecology, terrestrial biogeochemistry, remote sensing of theenvironment and alien plant invasion.

Ching-Wen Chai received the B.S. degree infinance from National Sun Yat-sen University in1996, and the M.S. degree in Computer Science andEngineering from University of Texas at Arlingtonin 2001.He was a programmer in Dr. C.Y. Huang’s labo-

ratory and works for Far Eastern Group as a seniorsystem architect.

Chao-ming Chang received the LL.B. degree fromTunghai University in 2008.He has worked in Dr. C.Y. Huang’s laboratory

since 2011. His research focuses on atmosphericcorrection and image processing.

Jr-Chuan Huang received the B.S., M.S.,and Ph.D.degrees from the Department of Geography at Na-tional Taiwan University (NTU) in 1997, 1999, and2003, respectively.His research interests focus on the modeling

of hydro-geomorphic processes to understand thewater, sediment and nutrient transport in subtropicalcatchments.

Kai-Ting Hu is a third year undergraduate studentin the Department of Geography at National TaiwanUniversity (NTU). Her research focuses on assessingthe impacts of landslides on ecosystem productivity.

Ming-Lun Lu is pursuing the Ph.D. degree in theGraduate Institute of Bioresources at the NationalPingtung University of Science and Technology(NUPST). He works for Taiwan Endemic SpeciesResearch Institute as an Assistant Researcher. Hisresearch interests are remote sensing and biologicalinvasion.

Yuh-Lurng Chung is recently retired (2011) andwas a Professor in the Department of Forestry,National Pingtung University of Science and Tech-nology (NUPST). His research interests are spatialinformation science, forest mensuration, forestresources management and biostatistics.

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