The document discusses remote sensing as a landscape inventory tool, detailing a hierarchical approach across various ecological scales including ecotopes, patches, and basins. It emphasizes the use of satellite imagery for monitoring vegetation phenology and extracting key metrics for analyzing annual vegetation growth and land cover classification. Additionally, it addresses the development of phenology libraries and databases for improved data sharing and analysis.

1. Remote Sensing as
landscape inventory tool
Thomas Gumbricht (ICRAF)
Thomas Gumbricht
Sentinel landscapes, CIFOR 2011

2. PART 1 – A hierarchical approach
Ecotope
Thomas Gumbticht
Sentinel landscapes, CIFOR 2011

3. PART 1 – A hierarchical approach
Patch and hillslope
Thomas Gumbticht
Sentinel landscapes, CIFOR 2011

4. PART 1 – A hierarchical approach
Basin
Thomas Gumbticht
Sentinel landscapes, CIFOR 2011

5. PART 1 – A hierarchical approach
Continental
Thomas Gumbticht
Sentinel landscapes, CIFOR 2011

6. PART 1 – A hierarchical approach
Africa Soil Information Service (AfSIS) – sentinel sites
Thomas Gumbticht
Sentinel landscapes, CIFOR 2011

7. PART 1 – A hierarchical approach
Sentinel site
design
Sentinel landscapes, CIFOR 2011

8. PART 2 – phenology monitoring
Monitoring vegetation annual phenology
from time series of satellite imagery
Thomas Gumbticht
Sentinel landscapes, CIFOR 2011

9. PART 2 – phenology monitoring
Deriving vegetation density data form satellite data – basic principles

10. PART 2 – phenology monitoring
Method: Capturing the raw data
To do phenology studies requires a large amount of input data. At HQ we are
using an automated FTP engine (Expect) to search the MODIS Data Pool
https://lpdaac.usgs.gov/get_data/data_pool
For the data we need.
Sentinel landscapes, CIFOR 2011

11. PART 2 – phenology monitoring
Cleaning and smoothing the annual time-series
Sentinel landscapes, CIFOR 2011

12. PART 2 – phenology monitoring
Extracting annual phenology
For the annual vegetation phenology, we extract 11 indexes:
1. The annual average vegetation density
2. The annual maximum vegetation density
3. The annual minimum vegetation density
4. The annual limit for vegetation green up
5. The accumulated vegetation growth over the growing season(s)
6. The incremental vegetation growth over the growing seasons(s)
7. The length of the growing season(s)
8. The length of the green up phase of the growing season
9. The annual day of year for the start of the first growing season
10. The annual day of year for the peak of the vegetation density
11. The number of growing seasons
The first three indexes are based on the total annual vegetation cycle. The limit for
vegetation green up is calculated per annum, and based on a ratio definition:
EVIratio = (EVI - EVImin)/(EVImax – EVImin),
Sentinel landscapes, CIFOR 2011

13. PART 2 – phenology monitoring
Method: Extracting annual phenology
The annual average vegetation density
The annual maximum vegetation density
Annual average vegetation density Annual maximum vegetation density
Sentinel landscapes, CIFOR 2011

14. PART 2 – phenology monitoring
Method: Extracting annual phenology
The annual day of year for the start of the first growing season
The annual day of year for the peak of the vegetation density
Length of growing season Length of greening up period
Sentinel landscapes, CIFOR 2011

15. PART 2 – phenology monitoring
Method: Land use and land cover mapping
The phenology data generated from annual time series of satellite images
can be used for mapping land cover and land use. The phenology curve can
be be used to differentiate vegetation types that can not be distinguished in a
single scene of multi-spectral image data. I.e. Forests of different types, as
well as grasslands and various agricultural crops have different phenology.
To actual classify land use and land cover from phenology, we need to
develop a library of typical phenology patterns. For this we need to develop
field surveys or use phenology patterns reported in the literature.

16. Other indexes that could be used for analyzing annual variations like phenology
Rainfall (can be obtained from a combination of station data and Remote Sensing)
Temperature (available from the MODIS sensor)

Surface wetness (index can be generated from MODIS reflectance and emissivity data)

Sentinel landscapes, CIFOR 2011

17. PART 3 – biophysical indexing
Method
summary
Sentinel landscapes, CIFOR 2011

18. PART 3 – Biophysical indexing
Lake Naivasha - Kenya
Sentinel landscapes, CIFOR 2011

19. PART 3 – Biophysical indexing
Lake Naivasha - Kenya
Sentinel landscapes, CIFOR 2011

20. PART 3 – Biophysical indexing
Lake Naivasha - Kenya
Sentinel landscapes, CIFOR 2011

21. PART 3 – Biophysical indexing
Lake Naivasha - Kenya
Sentinel landscapes, CIFOR 2011

22. PART 3 – Biophysical indexing
Lake Naivasha - Kenya
Sentinel landscapes, CIFOR 2011

23. PART 3 – Biophysical indexing
Lake Naivasha - Kenya
Sentinel landscapes, CIFOR 2011

24. PART 3 – Biophysical indexing
Lake Naivasha - Kenya
Sentinel landscapes, CIFOR 2011

25. Mount Kilimanjaro - Kenya

26. Mount Kilimanjaro - Kenya
Sentinel landscapes, CIFOR 2011

27. PART 4 – Databases and data sharing
Web client 1:
Google Earth
Sentinel landscapes, CIFOR 2011

28. PART 4 – Databases and data sharing
Web client 2:
Openlayers
Sentinel landscapes, CIFOR 2011

29. PART 4 – Databases and data sharing
Desktop client
QGIS
Sentinel landscapes, CIFOR 2011