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Time series of coarse resolution satellite imagery: some experiences and caveats Agustín Lobo A contribution to the GLOBAL LAND COVER.

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Presentation on theme: "Time series of coarse resolution satellite imagery: some experiences and caveats Agustín Lobo A contribution to the GLOBAL LAND COVER."— Presentation transcript:

1 Time series of coarse resolution satellite imagery: some experiences and caveats Agustín Lobo A contribution to the GLOBAL LAND COVER 2000

2 A. Lobo. Time series of coarse-resolution satellite imagery 2 Classification of time series of Vegetation Index have produced vegetation charts at regional to global scales that are in general agreement with charts produced by compilation.

3 A. Lobo. Time series of coarse-resolution satellite imagery 3

4 A. Lobo. Time series of coarse-resolution satellite imagery 4 Summer-peaking vegetation Spring-peaking vegetation

5 A. Lobo. Time series of coarse-resolution satellite imagery 5 Summer-peaking vegetation Spring-peaking vegetation

6 A. Lobo. Time series of coarse-resolution satellite imagery 6

7 A. Lobo. Time series of coarse-resolution satellite imagery 7 Summer-peaking vegetation Spring-peaking vegetation

8 A. Lobo. Time series of coarse-resolution satellite imagery 8 Summer-peaking vegetation Spring-peaking vegetation

9 A. Lobo. Time series of coarse-resolution satellite imagery 9

10 A. Lobo. Time series of coarse-resolution satellite imagery 10

11 A. Lobo. Time series of coarse-resolution satellite imagery 11

12 A. Lobo. Time series of coarse-resolution satellite imagery 12 Single-date imagery would never produce such a result, no matter how many spectral bands would be considered...

13 A. Lobo. Time series of coarse-resolution satellite imagery 13...because time series of Vegetation Index are an estimate of the phenolgy of fPAR, which is a fundamental property of vegetation.

14 A. Lobo. Time series of coarse-resolution satellite imagery 14 Phenolgy of fPAR is shaped by climatic constraints (temperature and water availability) acting on the trade-offs of leaf maintenance, which implies that time series of Vegetation Index respond to climate.

15 A. Lobo. Time series of coarse-resolution satellite imagery 15 Nevertheless, there are two important shortcomings to be considered: 1. Phenology is also fine-tuned by meteorological conditions, which implies that there is significant inter-annual variation and, therefore, mean annual series should be preferred for land-cover classification.

16 A. Lobo. Time series of coarse-resolution satellite imagery 16

17 A. Lobo. Time series of coarse-resolution satellite imagery 17

18 A. Lobo. Time series of coarse-resolution satellite imagery Although an important property, Phenology of fPAR is not enough to discriminate among some important land- cover types. Other properties should be measured from RS, such are:

19 A. Lobo. Time series of coarse-resolution satellite imagery 19 Leaf/wood biomass ratio (“woodiness”) Leaf type and size Total biomass Height of dominant canopy “Layering” (vertical profile of leaf biomass)

20 A. Lobo. Time series of coarse-resolution satellite imagery 20 Leaf type and size Important to estimate the relative abundance of coastal conifers (i.e. P. halepensis) and evergreen oaks, because of their different behavior against fire. Note that vegetation changes due to increased aridity in the Mediterranean will be mediated through wildfires. RS Methods: angular effects? Total biomass Height of dominant canopy “Layering” (vertical profile of leaf biomass) RS Methods: Perhaps with SAR?

21 A. Lobo. Time series of coarse-resolution satellite imagery 21 Other important properties for vegetation functioning (but not for land-cover discrimination): Phenology of photosynthetic activity (PRI) Phenology of evapotranspiration Canopy roughness

22 A. Lobo. Time series of coarse-resolution satellite imagery 22 Leaf/wood biomass ratio Important for the C budget, to estimate fuel load, and for land-cover identification. Methods: SWIR ?

23 A. Lobo. Time series of coarse-resolution satellite imagery 23 In High resolution imagery, SWIR has been found to be important to discriminate forest types CAN-1 (%) CAN-2 (%) green (16.4) (14.3) red (34.5) ( 6.1) nir (16.8) (64.6) swir (32.2) (15.0)

24 A. Lobo. Time series of coarse-resolution satellite imagery 24 Using VEGETATION imagery, SWIR has been found also to be important to discriminate land cover types: burned forest arid vegetation forest irrigated fields

25 A. Lobo. Time series of coarse-resolution satellite imagery 25

26 A. Lobo. Time series of coarse-resolution satellite imagery 26

27 A. Lobo. Time series of coarse-resolution satellite imagery 27

28 A. Lobo. Time series of coarse-resolution satellite imagery 28

29 A. Lobo. Time series of coarse-resolution satellite imagery 29 Pastures

30 A. Lobo. Time series of coarse-resolution satellite imagery 30 Cistus and broom dry garrigues

31 A. Lobo. Time series of coarse-resolution satellite imagery 31 Quercus ilex forest and shrubland

32 A. Lobo. Time series of coarse-resolution satellite imagery 32 Quercus rotundifolia shrubland

33 A. Lobo. Time series of coarse-resolution satellite imagery 33 P. halepensis with Q. rotundifolia understorey

34 A. Lobo. Time series of coarse-resolution satellite imagery 34 P. uncinata mountain forests

35 A. Lobo. Time series of coarse-resolution satellite imagery 35 Therefore, although there is some sensitivity to the wood/leaf ratio, the eventual presence of water complicates the problem.

36 A. Lobo. Time series of coarse-resolution satellite imagery 36 P-OVNI, S-NDVI, S-OVNI? 21 images to Burkina-Faso (Lobo & Bartholome)

37 A. Lobo. Time series of coarse-resolution satellite imagery 37 S-NDVIP-OVNI

38 A. Lobo. Time series of coarse-resolution satellite imagery 38 S-NDVIP-OVNI “S-OVNI”

39 A. Lobo. Time series of coarse-resolution satellite imagery 39

40 A. Lobo. Time series of coarse-resolution satellite imagery 40


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