Remote sensing for spatial ecology
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1 Séminaire : Quels outils pour un changement d'échelle dans la gestion des insectes d intérêt économique? Remote sensing for spatial ecology Agnès BEGUE (CIRAD, UMR TETIS) Atelier CIRAD Oct 2011 Agnès BEGUE
2 Many references on remote sensing for spatial ecology «Remote sensing» and «ecology» (121) «Remote sensing» and «habitat» (108) «Remote sensing» and «biodiversity» (37) «Remote sensing» and «pest» (8) «Remote sensing» and «insect» (7) Applications in spatial ecology Land cover classification (qualitative RS) and spatial analysis Land surface parameters (quantitative RS) and modeling Land surface change (change detection and trend analysis) Remote sensing offer Satellite remote sensing / Aerial remote sensing Remote sensing information content Spectral, spatial, temporal dimensions 2
3 The remote sensing offer 3
4 A more than 150-year old technique! Boston, à partir d un ballon 1860, J. W. Black 1859 Invention of photography (1839) - First known aerial photo (Tournachon - Nadar, France) - First known saved aerial photo ( James Wallace Black.) 1 ère image CORONA - USSR 18 août s First meteorological and military satellites : Nimbus (1964) / Corona (1960) 4
5 Nombre satellites lancés Satellite remote sensing (1/2) Satellites privés THRS Satellite SPOT 8 Satellite Landsat Année de lancement In the optical domain : (janv. 2010) 5
6 Log(Résolution spatiale (m)) Satellite remote sensing (2/2) Multispectral 1 Panchromatique Hyperspectral Thermique Année lancement Optical domain : (janv. 2010) 6
7 How to choose a satellite image (which images for which application )? Study site Image size Objects/classes to identify Surface parameters to quantify Spatial resolution Spectral band Time period and frequency Archive/Programming (tasking) Budget Image cost Partnership Image licence Technical skills / ancillary data Image level 7
8 How to choose a satellite image (which images for which application )? Study site Image size Objects/classes to identify Surface parameters to quantify Spatial resolution Spectral band Time period and frequency Archive/Programming (tasking) Budget Image cost Partnership Image licence Technical skills / ancillary data Image level 8
9 VEGETATION (3200 km) QuickBird (15 km) Echelle régionale (106 km 2) SPOT/VEGETATION MODIS LAN DSAT (180 km) SPOT (60 km) Echelle locale ( 10 -> 104 km2) SPOT/LANDSAT (103 / 10 4 km2) QuickBird/Ikonos (centaine km2) Photos aériennes (dizaine km2)
10 Spatial resolution SPOT XS = 20m Ikonos MS = 4m Ikonos P = 1m 10 palm trees 1-2 palm trees <1 palm tree For a thematic question, the best spatial resolution is not always the finest. 10
11 Résolution spatiale (m) Spatial resolution vs Image size Modis Landsat Aster SPOT Formosat Ikonos IRS QuickBird Photo aérienne Taille image (km) 11
12 How to choose a satellite image (which images for which application )? Study site Image size Objects/classes to identify Surface parameters to quantify Spatial resolution Spectral band Time period and frequency Archive/Programming (tasking) Budget Image cost Partnership Image licence Technical skills / ancillary data Image level 12
13 Spectral bands (1/2) THERMAL INFRARED VISIBLE 13
14 Spectral bands (2/2) Surface parameters Biomass, Leaf area, vegetation cover Spectral band Visible + Near Infrared (large spectral bands) = multi-spectral Plant N content, soil organic matter, soil components Visible + Near Infrared (narrow spectral bands) = Super hyper-spectral Evapo-transpiration Urban temperature Thermal Infrared Soil moisture Surface roughness Tree height, DEM DSM Micro-waves = radar Radar altimetry MNT images MNT terrain naturel 14
15 How to choose a satellite image (which images for which application )? Study site Image size Objects/classes to identify Surface parameters to quantify Spatial resolution Spectral band Time period and frequency Archive/Programming (tasking) Budget Image cost Partnership Image licence Technical skills / ancillary data Image level 15
16 Archive or tasking Archives : Landsat (1972), SPOT (1986), SPOT5 (2002) QuickBird (2001), Ikonos (1999) NOAA (1982), VEGETATION (1998), MODIS (1999) Aerial photos Tasking : Only some satellites are programmable : SPOT, THRS Cost > Not garanteed (tasking conflicts, clouds ) 16
17 Acquisition frequency The acquisition frequency depends on : Satellite orbital parameters + Target latitude Sensor field of view + Sensor depointing capacities Different time scales according to the process : Daily monitoring (low resolution satellites) Natural hazards, water stress Seasonnal monitoring (low and high resolution satellites) Primary production, fraction of soil covered by vegetation Annual monitoring (high and very high resolutions satellites) Change in land use/ land cover Ground Track after 1 Day Ground Track after 7 Days 17
18 How to choose a satellite image (which images for which application )? Study site Image size Objects/classes to identify Surface parameters to quantify Spatial resolution Spectral band Time period and frequency Archive/Programming (tasking) Budget Image cost Partnership Image licence Technical skills / ancillary data Image level 18
19 Coût (Euros/km²) Image cost Cost = f(archive/tasking, Commande resolution, min image en Euros size, pre-processing level ) 25 Quickbird 15 IKONOS 5 SPOT Landsat Résolution (m) Satellite image cost (tasking) (the size of the circle is proportional to the minimum order in ) 19
20 Aerial remote sensing (1/5) Ultra-light aircraft 20
21 Aerial remote sensing (2/5) VISIBLE PROCHE INRAROUGE INDICE DE VEGETATION Site de La Mare, le 19 avril 2006 RED-EDGE (ROUGE/PIR) INFRAROUGE THERMIQUE V. Lebourgeois et al. (2006) 21
22 Aerial remote sensing (3/5) Sugarcane V. Lebourgeois et al. (2006) 22
23 Detection of weeds Aerial remote sensing (4/5) V. Lebourgeois et al. (2006) 23
24 Precision farming Aerial remote sensing (5/5) Characterizing the INSIDE PLOT HETEROGENEITY V. Lebourgeois et al. (2006) 24
25 The remote sensing information content 25
26 Spectral information 26
27 Textural Information B QuikBird Panchromatic 0.6m B B SC Source : G. Lainé, CIRAD Natural vegetation Sugarcane Banana trees 27
28 Structural information Source : Quickbird P Mechanized banana field Source : Quickbird MS+P Not mechanized banana field 28 Source : Gérard Lainé, CIRAD
29 Organisation level Domaine forestier Domaine agricole Boisement lâche Boisement dense Reboisement Cultures et terrains nus Ombre feuillages Sol nu TRES HAUTE RESOLUTION C. Puech (2001) 29
30 Temporal information (seasonal) juillet 4 décembre octobre janvier février juillet septembre avril juin août mai mars septembre mai Paysage agricole Ile de La Réunion nuages sol nu Activité chlorophylienne + Bégué et al. (2009) 30 6 km
31 Indice de végétation (1-1000) Temporal information (seasonal) 800 repousse Plantation 2003 Plantation avr-02 oct-02 mars-03 sept-03 mars-04 sept-04 31
32 Temporal information (annual) Bruzzone (2003) 32
33 Temporal information (annual) Bruzzone (2003) 33
34 Temporal information (mid/long term trend) Jong et al., 2011 Bruzzone (2003) Bruzzone (2003) 34
35 Image pre-processing Not to be underestimated!!! 35
36 Conclusions A very large offer in terms of spatial data (satellite and/or aerial images) : resolution, image size, spectral bands, repetitivity, length of time series Increasing number of Very High Resolution satellites; New satellite concepts (daily visit in High Resolution); A «democratisation» of the image (Google Earth ) For the «democratisation» of the costs be patient Most of the remote sensing applications are of interest for ecology : Qualitative and quantitative description of the main landscape components (vegetation, soil, water, altiutude ), and their respective spatial distributions. 36
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