Remote Sensing as a model and monitoring tool for Land Health
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Remote Sensing as a model and monitoring tool for Land Health

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Remote Sensing as a model and monitoring tool for Land Health

Remote Sensing as a model and monitoring tool for Land Health

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Remote Sensing as a model and monitoring tool for Land Health Presentation Transcript

  • 1. PART 1 - Data
  • 2. Weather satellite dataAvailable for each 15 minutes Sep Aug Illustrated with May NOAA-AVHRR available since Feb 1981 online
  • 3. Vegetation change derived from weathersatellite data (1981-2009) (each 10 day)
  • 4. MODIS data We have weekly MODIS data onvegetation and reflectance for the last 10 years. About 10000 Scenes.
  • 5. The Landsat programWe have about 10 000 Landsat scenes, from all the sensors: MSS 1 MSS 2 MSS 3 MSS 4 MSS 5 TM 4 TM 5 ETM 7
  • 6. Other satellite data sourcesWe also process data from ASTER Rapid Eye Quickbird TOMS SeaWifs and more sensors
  • 7. PART 2 - Processing
  • 8. Downloading and organizing Download from FTP servers, organizing into folders and register to database isautomated by using scripting (Tcl-Expect,applescript and shell commands). Serversnot allowing FTP but delivering data upon request must be visited manually at present.
  • 9. Importing and projecting Satellite images come in an incredible number of different formats and projections. And not seldom is the geo- registering a bit out of place. This step can not be fully automated. The mosttedious part of identifying ground control points (points with an exactly knownposition) is, however automated. But the correctness of the geo-registering must still be manually checked.
  • 10. Reflectance correction Of the satellites we use, only the data from MODIS is delivered as ground reflectance data. For other sensors we need to convert the data from the registered electromagnetic signal at the sensor to ground reflectance. This demands detailed knowledge about the sensor calibration, the distance to the sun, the suns elevation at the time ofimage acquisition, and the transparency of the atmosphere at the time of acquisition.
  • 11. Terrain correction For high resolution imagery it is essential to correct for terrain shading and shadows. For medium to low resolutiondata it is not that important. This demands a detail Digital Elevation Model (DEM). For this we use the Shuttle Radar Topography Mission (SRTM) or the ASTER DEM. The former is better overflatter areas, whereas the latter is better in very steep terrain (e.g. Tibet).
  • 12. Spectral classificationThe automated processing chain includes the option of automated Spectral Angle Mapping (SAM) of any feature in the scene. By default water, forests, grasslands and some other features are classified by using global spectrallibraries. The spectral data is extracted to fit the individual sensors used in the processing chain. This data is used to support some of the subsequent processing, and can also be used for feature extraction.
  • 13. Band transformation The satellite sensors register electromagnetic radiation in different wavelength bands. The datacan not be directly compared To overcome this we use a pre-determined Principal ComponentAnalysis methods called Tasseled Cap. It was first defined for Landsat data, but is now also defined for e.g. MODIS, ASTER and Quickbird. Byapplying this transformation we derive 4 physically related indexes (brightness, greenness, wetness and yellowness) that are comparable. These 4 indexes are then used in most of the subsequent processing, which is thus the same for data derived from all sensors.
  • 14. Cloud indexing and masking Identifying clouds and cloud shadows is crucial for getting the correct information from the satellite images. The hithertopublished cloud identification methods did not meet the standard we needed. We have hence developed a set of different cloud detection routines. This turned out to be the most difficult task in thedevelopment of the automated processing chain.
  • 15. Water indexing and masking Water is not trivial to detect accurately in satellite imagery. But without an accurate water mask the detection of forests, clouds and cloud shadows becomes biased. The surface wetness is also initself an important indicator. Adopting timeseries analysis it can for instance be used for predicting soil water conditions and forecast crop and vegetation production.Again we were forced to develop our ownwater indexing and masking algorithms to get the quality we desired.
  • 16. Woody biomass indexing and forest masking Woody biomass reflect electromagnetic radiation differently compared to non- woody vegetation. We used this well known difference to design an index forwoody biomass, which we then threshold to automatically derive forests from the satellite images. Preliminary resultsindicate that this index is well correlated with stem density on the ground.
  • 17. Bare soil and organic matter indexingAreas without photosynthetic pigmentsare automatically extracted (excluding clouds and water). These NonPhotosynthetic areas are then indexedand divided into area with and without organic residue.
  • 18. The processingunmixing Pixel chain includes an automatic forward (or data) driven pixel unmixing. As we do not have information on the spectral end-members for each scene we adopt a method identifying the spectral signal from one material (e.g. vegetation) based on an index (e.g. vegetation index). We then use the index value in each pixel to extract the part ofthe reflectance in that pixel that is derived from the identified material, and hypothetically we can then unmix the reflectance from the material and other stuff.
  • 19. Feature structural indexing If the processing chain is set to producefeature classes as outcomes, we can use the features as objects and calculate patch, class and regional indexes. Patchindexes describe the structure of a single feature (size, perimeter, core area, edgecontrast etc). Class indexes describe the features of the same class within a defined region (total number of features, total area, relative area, density etc).Region indexes describe the matrix of the entire landscape under study.
  • 20. PART 3 - Processing examplenot fit! Clouds! The scenes did The colors were not the same! Terrain shadows! Tasseled Cap from Landsat DifferenceWater Tasseledcap Wetnessscene (senescentCloud Natural Raw landsat after from 1972 corrected TasselledCap emissivity (surface 1972 Woody SoilThermallightnesson image asskinspectral Tasseled Capsignal Tasseled Capbased spectral Normalised vegetation and 2001 colors Brightnessreflectance image colorscenecolor Difference Water CapNormalized (19950121) Yellowness MSS Normalized Difference Normalized DifferenceTasseledand featurenatural(vegetation)backdrop Near infraredCap Bare (20030119) SAM water cloud shadows mask Clouds landsat vegetation) image TCNDCIcorrected classification Terrain Cap Greenness (Water) Tasseledwith reflectance (corrected) TasseledCap Tasseled natural Index Raw (soil) color temperature) Index Index bands Index) Index unmixing
  • 21. PART 4 Applications
  • 22. Mount Kilimanjaro
  • 23. Mount Kilimanjaro
  • 24. Mount Kilimanjaro
  • 25. Mount Kilimanjaro
  • 26. Zanzibar1975 1986 2001 2009
  • 27. Rwenzori
  • 28. Rwenzori Glaciers in the Rwenzori Mountains: a reinterpretation Satellite generated imagePhotograph of the peaks of theby Sella Rwenzori Mountainstaken the 12th (2005), also showingof July 1906 glacial extents in 1906 andfrom Stairs 1955.Peak,showingMount Bakerand MountStanley. Mount Speke Mount Stanley 1906 1987 2005 Mount Baker
  • 29. Rwenzori Driving forces contributing to glacier retreata) Global changes in temperature and atmospheric circulation patterns.b) Continental drying (less precipitation and more sunshine)c) Local changes in land use and land cover Landcover changes – Adjusted NDVI trend 1973-2005
  • 30. Lake Naivasha
  • 31. Ivory Coast Vegetation and land cover changes in the cocoa belt in Ivory Coastusing time series of high resolution satellite images The illustration site was chosen to represent the transition zone between open land and forest. Thomas Gumbricht, ICRAF
  • 32. This image Landsat TM image from to 2002 Forest losses from1988 1988 2002 illustrates the forest This image is The image is cover in 1988 takencorrected later, but in 14 years for sun- (green-yellow)earth geometry 2002and Fire = slash and burn? comparedseason the same withatmospheric The (green). the (December).disturbances. The yellowish areas anniversary imagecolorsforested in for were are then pair can be usedderived from the in 1988, but not in studying changescorrected image 2002. vegetation (e.g.bands. and tree forests The forest other cover) and cover is Village expansion = population growth?The reflectance data calculated changes. land cover fromof the image can be global standards inused for mappingTo forest look at some Let us reflection.biophysical ground evaluate the changes that can beconditionsof notably accuracy - the easily detected fromin combination with forest cover maps, this image pair....a spectral library site specific fieldderived needed. It data is from groundsampling in also be would then theregion of to estimate The small insets show the 1988 image possible study. the losses in biomass - and in Thomas Gumbricht, ICRAF
  • 33. PART 5 Sharing and Dissemination
  • 34. Freeware GIS
  • 35. ICRAF DATA Online DATA Extraction and Automatic download SQL and extraction quality control (e.g. python/delphi) (e.g. TCL-expect) PostgreSQL DB Geospatial Statisticalinterpolation indicators(e.g. surfer/TNT- mips) Apache (e.g. R) PHP User interface (HTML)
  • 36. ICRAF DATA Online DATA Extraction and Automatic download SQL and extraction quality control (e.g. python/delphi) (e.g. TCL-expect) PostgreSQL DB Geospatial Statisticalinterpolation indicators(e.g. surfer/TNT- mips) GIS (e.g. R) XML User interface (HTML)