Remote sensing as landscape inventory tool

Remote Sensing as landscape inventory tool

Thomas Gumbricht (ICRAF)

Thomas Gumbricht Sentinel landscapes, CIFOR 2011

PART 1 – A hierarchical approach

Thomas Gumbticht Sentinel landscapes, CIFOR 2011

Ecotope



Patch and hillslope



Basin



Continental



Africa Soil Information Service (AfSIS) – sentinel sites

Sentinel landscapes, CIFOR 2011


Sentinel site design

Monitoring vegetation annual phenology from time series of satellite imagery


PART 2 – phenology monitoring

Deriving vegetation density data form satellite data – basic principles


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.



https://lpdaac.usgs.gov/get_data/data_pool

https://lpdaac.usgs.gov/get_data/data_pool

Cleaning and smoothing the annual time-series



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),



Annual average vegetation density Annual maximum vegetation density

Method: Extracting annual phenology The annual average vegetation density The annual maximum vegetation density



Length of growing season Length of greening up period

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



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.


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)


PART 3 – biophysical indexing


Method summary

PART 3 – Biophysical indexing


Lake Naivasha - Kenya

Mount Kilimanjaro - Kenya

Web client 1: Google Earth

PART 4 – Databases and data sharing


Web client 2: Openlayers



Desktop client QGIS



Remote sensing as landscape inventory tool

Education

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