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Geomatics Engineeringa key tool for Environmental Monitoring
Fernando J. AguilarFull Professor at Engineering Department
University of Almería, Spain
Politecnico di Bari, May 2017
Part of the material presented in this seminar has been developed in the context of the Research Project GREENHOUSESAT, supported by the Spanish Ministry of Economy and Competitiveness (Spain) and the European Union (FEDER founds) under Grant Reference AGL2014-56017-R
Further information: https://www.ual.es/Proyectos/GreenhouseSat/
Agenda:
� Brief introduction
� A matter of scale� Data capture
Photogrammetry
Geomatics Engineeringa key tool for Environmental Monitoring
PhotogrammetrySatellite imageryLiDAR
� Information & Knowledge extraction� Final Recap
� Introduction
Geomatics Engineeringa key tool for Environmental Monitoring
Digital Earth: an emerging concept
Baba Dioum: “In the end, we will conserve only what we love. We will love only what we understand. We
1Gore, A., 1998. The Digital Earth: Understanding our planet in the 21st Century. Speech presented at the Californian Science Center, Los Angeles, California, January 31, 1998.
love only what we understand. We will understand only what we have been taught.”
International Union for Conservation of Nature, 1968.
� Introduction
Geomatics Engineeringa key tool for Environmental Monitoring
)]()([21
)( hxZxZVarh +−=γ
Spatial Autocorrelation Principle (weaving reality): All things in the world are related, but the nearest things are more similar than distant ones (First law of Geography or Tobler’s law)
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Tobler, W. R (1970). A computer model simulation of urban growth in the Detroit region. Economic Geography 46(2).
Negreiros, J.G., Aguilar, F.J., Aguilar, M.A., 2011. Lectures on Spatial Statistics. Saint Joseph Academic Press, 206 pp.
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� Introduction
Geomatics Engineeringa key tool for Environmental Monitoring
Geomatics is an integral discipline (geodesy, topography, cartography, remote sensing) dealing with the capture, analysis and application of descriptive data and localization of georeferenced objects (geospatial data)
� Data capture
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� Data capture� Information and knowledge extraction (Data mining, Geostatistics,
OBIA, GIS, Time series, Point clouds, Structural equation modeling…)
� Modeling� Decision making
Shupeng, C., van Genderen, J.L., 2008. Digital Earth in support of global change research, International Journal of Digital Earth, 1(1):43-65.
� A matter of scale
Geomatics Engineeringa key tool for Environmental Monitoring
DATA CAPTURE
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MODELING
ANALYSIS
APPLICATION
TIN DTM
CUTS & FILLS
SETTING OUT ON SITE
� A matter of scale
Geomatics Engineeringa key tool for Environmental Monitoring
5http://www.globallandcover.com/GLC30Download/index.aspx
� A matter of scale
Geomatics Engineeringa key tool for Environmental Monitoring
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� A matter of scale
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: Photogrammetry
Geomatics Engineeringa key tool for Environmental Monitoring
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Flight Height (AGL) = 1000 m
Flight strips = 4
Number of photographs = 86
DMC (Digital Mapping Camera) Intergraph
GSD = 10 cm
RGB+Nir (12 bits)
Forward overlap = 65%
Side overlap = 60%
� Data capture: Photogrammetry
Geomatics Engineeringa key tool for Environmental Monitoring
SfM (Structure from Motion)
SIFT (scale invariant feature
transform) o ASIFT (Affine SIFT)
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D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004
GUOSHEN YU, AND JEAN-MICHEL MOREL, ASIFT: An Algorithm for Fully Affine Invariant
Comparison, Image Processing On Line,1 (2011). http://dx.doi.org/10.5201/ipol.2011.my-asift
Bundle Adjustment
Dense Matching
� Data capture: Photogrammetry
Geomatics Engineeringa key tool for Environmental Monitoring
RPAS (Remotely Piloted Aircraft System):
• UFOCAM XXL V2. Payload 2.1 kg.
• SONY α6000 camera, f = 30 mm. 24.3 Mp, sensor APS-C CMOS (4.19 µm/pixel). RGB images GSD = 1.5 cm (flight height 120 m).
• 97 photographs comprising 3 strips.
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• 97 photographs comprising 3 strips. Forward overlap = 90% and side overlap = 50%.
� Data capture: spectral information from satellite imagery
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: Spectral information from satellite imagery
Geomatics Engineeringa key tool for Environmental Monitoring
“Super-spectral” VHR satellite
WorldView-3.
0.31 m GSD (PAN channel)
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0.31 m GSD (PAN channel)
8 MS bands (1.24 m GSD)
8 SWIR bands (3.7 m GSD)
Several additional bands to optimize
atmospheric correction (30 m GSD).
� Data capture: Spectral information from satellite imagery
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: Spectral information from satellite imagery
Geomatics Engineeringa key tool for Environmental Monitoring
Normalized Burn Ratio (NBR) = (Nir-Swir)/(Nir+Swir)
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� Data capture: Spectral information from satellite imagery
Geomatics Engineeringa key tool for Environmental Monitoring
∆NBR = prefire NBR – postfire NBR (EMERGENCY REHABILITATION AREAS)
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Source: United States Geological Survey
� Data capture: Spectral information from satellite imagery
Geomatics Engineeringa key tool for Environmental Monitoring
SENTINEL-2A
Revisit time at mid latitudes 2-3 days (when working together with SENTINEL-2B, launched recently; March 7, 2017)
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� Data capture: Digital Surface Models from satellite imagery
Geomatics Engineeringa key tool for Environmental Monitoring
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M.A. Aguilar, M.M. Saldaña, F.J. Aguilar, 2014. Generation and Quality Assessment of Stereo-Extracted DSM from GeoEye-1 and WorldView-2 Imagery. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 52, NO. 2, 2014
� Data capture: Airborne Laser Scanning (LiDAR)
Geomatics Engineeringa key tool for Environmental Monitoring
Digital cameras
2 HASSELBLAD H3D 39 (Digital 39 y 22
Mp) GSD 10 cm RGB+Nir (12 bits)
LiDAR sensor
RIEGL LMS Q240i 60
FOV = 30º, 10000 Hz, maximum range
650 m
Average point density 2 points/m2
Average point space 0.7 m
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Average point space 0.7 m
Estimated height accuracy (m) better
than 0,08 m
� Data capture: Airborne Laser Scanning (LiDAR)
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: Airborne Laser Scanning (LiDAR)
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: Airborne Laser Scanning (LiDAR)
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: Airborne Laser Scanning (LiDAR)
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: Airborne Laser Scanning (LiDAR)
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: multispectral and LiDAR(data fusion)
Geomatics Engineeringa key tool for Environmental Monitoring
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� Data capture: Terrestrial Laser Scanning
Geomatics Engineeringa key tool for Environmental Monitoring
Study case: Civil Engineering works upon the Cañarete cliff (Almeria) to control rock stripping and slope landslides risk
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� Data capture: Terrestrial Laser Scanning
Geomatics Engineeringa key tool for Environmental Monitoring
Study case: Zagzal cave. Berkane (Morocco)
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� Data capture: Terrestrial Laser Scanning
Geomatics Engineeringa key tool for Environmental Monitoring
Study case: San Felipe Fortress (Almería, Spain)
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Object Based Image Analysis (OBIA): A Game Changer
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
OBIA principles
1. Color
2. Texture
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1. Statistics from color (and
others) features
2. Shape
3. Size
4. Texture
5. Neigborhood
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
OBIA principles
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
OBIA principles
Normalized Surface Feature HeightsImagery Layers (RGB – 15cm)
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DATA FUSION
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Object Based Point Cloud Analysis (OBPCA)
Return
Pulse
Object
Mean, Standard deviation, Minimum, Maximum, Median, Mode, etc.
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All returns First return Last return
returnslast ofNumber returns totalofNumber
R = R>1.1? VegetationYes
Non classified
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Data Fusion: Maybe the master piece?
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Data Fusion: Maybe the master piece?
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Data Fusion: Maybe the master piece?
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Previously extracted greenhousesWorldView-2 segmented orthoimage
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Data Fusion: Maybe the master piece?
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Data Fusion: Precision
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Precision Farming
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Regional scale studies: medium resolution satellite imagery
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest land cover evolution in the Moulouya river watershed
1984 Landsat 5 Orthomosaic 2013 Landsat 8 Orthomosaic
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Radiometric correction (digital value to radiance at sensor) and atmospheric correction (“6S” model, Vermote et al., 1997) to obtain ground reflectance images
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest land cover evolution in the Moulouya river watershed
Spectral Mixture Analysis (Automated Montecarlo Unmixingalgorithm (1))
Vegetation Indices
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1) Asner 1998, Asner y Lobell 2000, Asner y Heidebrecht 2002, Asner et al. 2004 & 2005)
Endmember fractions
PV
NPV
BS
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest land cover evolution in the Moulouya river watershed
41SEGMENTATION WITH RED, NIR & SWIR1
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest land cover evolution in the Moulouya river watershed
Random Forest (2013) (ensemble learning supervised classifier)
ImportanceNDVI_2013 1,000000
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NDVI_2013 1,000000frac_PV_2013_cor 0,977034frac_soil_2013_cor 0,834373
VIgreen_2013 0,773432MSR_2013 0,753162
NDSVI_2013 0,748575frac_NPV_2013_cor 0,615447
F.J. Aguilar, A. Nemmaoui, M.A. Aguilar, M. Chourak , Y. Zarhloule and A.M. García Lorca, 2016. A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data. Forests, 7(1), 23: 1-19
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest land cover evolution in the Moulouya river watershed
Year 2013: accuracy assessment estimated from the out-of-bag samples
Classification data predicted by Random
Forest ModelTotal
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Forest ModelForest Non
ForestTraining data
(Ground Truth)Forest 127 15 142
Non Forest 16 247 263Total 143 262 405
User’s accuracy
Producer’s accuracy
Overall accuracy
Forest 88.81% 89.43%92.34%
Non Forest 94.27% 93.91%
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest land cover evolution in the Moulouya river watershed
Random Forest 1984-2013. Increase in Forest close to 8800 has (∆ 5.3%)
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
SPOT 6 TRIPLET. GRID FORMAT 8 M. HYDROLOGICALLY CORRECTED
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Check Points: 23Minimum vertical error -1.171 mMaximum vertical error 0.901 mMean error -0.169 mStandard deviation 0.527 mRMSEz 0.553 m
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
year)
Mean erosion rate for the whole watershed12.1 t/ha year
Sediment delivery
USLE MODEL
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Ero
sio
nra
te(t
/ha
year)Sediment delivery
ratio contributed to the dam Mohamed V turns out to be 19.17 %
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
FOREST MONITORING: PRECISION FORESTRY
Accurate and up-to-date information on the spatial distribution of
forest type, forest cover and tree species composition is a key
factor for sustainable forest management and a central
component of forest monitoring
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component of forest monitoring
How to deal with efficient,
accurate and integrated
methods to monitor forests
evolution?
Traditional forest inventory
is time-consuming and
cost-intensive
GEOMATICS
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (LiDAR + imagery)
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (LiDAR + imagery)
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Photogrammetry
LiDAR
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (Filtering non-ground points)
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (Filtering non-ground points)
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P. Axelsson, 2000. DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing, vol. 33(B4/1), pp. 110-117.
Filtering non-ground points
Digital Terrain Model
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (Canopy Height Computation)
DSM
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DTM
CHMComputation of many metrics related
to height distribution (mean, mode,
Sd, maximum & minimum,
percentiles, gaps (% of first returns
below a threshold with respect to the
whole first returns, etc.))
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (Canopy Height Computation)
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� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (Canopy Height Computation)
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Fieldheight
Heightdifference
Lidarheight
CANOPY TREE OVERESTIMATION (STEEP TERRAIN)
CANOPY TREE UNDERESTIMATION (GENERAL CASE)
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (Modeling forest relevant variables)
� Average canopy height (mean)� Gap fraction (P)
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� Gap fraction (P)� Variance in canopy height (σ2 )
Beech and Maple forest stands
HL
� Information & Knowledge Extraction
Geomatics Engineeringa key tool for Environmental Monitoring
Forest Monitoring (Forest regional inventory)
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� Final Recap
Geomatics Engineeringa key tool for Environmental Monitoring
DATA
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Features vector ( )ini xxxv ...21=
Biomass (kg/tree, kg/ha)
Multiple Stepwise Regression/ Support Vector Regression
( )nk xxxF ...21Ψ=
Geomatics Engineeringa key tool for Environmental Monitoring
Many thanks for your kind attentionkind attention