geomaticsengineering - universidad de almería · geomaticsengineering ... 2008. digital earth in...

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


PhotogrammetrySatellite imageryLiDAR

� Information & Knowledge extraction� Final Recap

� Introduction


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


)]()([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 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


DATA CAPTURE

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MODELING

ANALYSIS

APPLICATION

TIN DTM

CUTS & FILLS

SETTING OUT ON SITE



5http://www.globallandcover.com/GLC30Download/index.aspx



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� Data capture: Photogrammetry


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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%



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



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


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� Data capture: Spectral information from satellite imagery


“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).



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Normalized Burn Ratio (NBR) = (Nir-Swir)/(Nir+Swir)

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∆NBR = prefire NBR – postfire NBR (EMERGENCY REHABILITATION AREAS)

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Source: United States Geological Survey



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


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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)


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



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� Data capture: multispectral and LiDAR(data fusion)


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� Data capture: Terrestrial Laser Scanning


Study case: Civil Engineering works upon the Cañarete cliff (Almeria) to control rock stripping and slope landslides risk

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Study case: Zagzal cave. Berkane (Morocco)

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Study case: San Felipe Fortress (Almería, Spain)

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� Information & Knowledge Extraction


Object Based Image Analysis (OBIA): A Game Changer

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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



OBIA principles

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OBIA principles

Normalized Surface Feature HeightsImagery Layers (RGB – 15cm)

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DATA FUSION



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



Data Fusion: Maybe the master piece?

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Previously extracted greenhousesWorldView-2 segmented orthoimage




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Data Fusion: Precision

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Precision Farming



Regional scale studies: medium resolution satellite imagery

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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




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




41SEGMENTATION WITH RED, NIR & SWIR1




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




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%




Random Forest 1984-2013. Increase in Forest close to 8800 has (∆ 5.3%)

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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



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 %



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



Forest Monitoring (LiDAR + imagery)

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Forest Monitoring (LiDAR + imagery)

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Photogrammetry

LiDAR



Forest Monitoring (Filtering non-ground points)

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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



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.))




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Fieldheight

Heightdifference

Lidarheight

CANOPY TREE OVERESTIMATION (STEEP TERRAIN)

CANOPY TREE UNDERESTIMATION (GENERAL CASE)



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



Forest Monitoring (Forest regional inventory)

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� Final Recap


DATA

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Features vector ( )ini xxxv ...21=

Biomass (kg/tree, kg/ha)

Multiple Stepwise Regression/ Support Vector Regression

( )nk xxxF ...21Ψ=


Many thanks for your kind attentionkind attention

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