Download GV2M: Global Vegetation Monitoring and Modeling

GV2M: Global Vegetation Monitoring and Modeling
A DESCRIPTION OF WEST AFRICAN LAND COVER AND ITS SEASONAL DYNAMICS
BASED ON MULTI-SENSOR REMOTE SENSING TIME SERIES
Abstract type : Oral presentation
Session : S2: Land cover and use cover change
Submitted by : Ursula Gessner
Authors and Speakers : Ursula Gessner
Information about other authors :
Gessner, U. (1), Machwitz, M. (2), Knauer, K. (1,3), Kuenzer, C. (1), Dech, S. (1)
(1). German Aerospace Center, German Remote Sensing Data Center (DLR-DFD), Germany;
(2). CRP Gabriel Lippmann, Luxembourg; (3). University of Wuerzburg, Germany
Land cover is closely linked to various biogeophysical characteristics of the land surface. The
information on spatial and temporal distribution of land cover classes is therefore a crucial input
and basis of evaluation in earth system modeling. Land cover datasets allow conclusions on
structural and functional vegetation characteristics such as leaf area and growth form
composition, and on physical properties such as albedo and roughness length. Space-borne
remote sensing, in combination with in-situ measurements is the optimal basis for reliable and
area-wide consistent land cover mapping and monitoring. In this context, the use of multitemporal satellite data is decisive not only for tracking land cover changes but also for
capturing seasonal cycles. To date, a number of global land cover datasets are available.
However, major discrepancies between these datasets can be observed in regions where land
cover mapping is particularly challenging. Difficulties arise in landscapes of strong spatial and
temporal variability and in regions were reliable ground truth data is scarce. Several
comparative studies have shown that these kinds of mapping discrepancies are typical for large
parts of the African continent. This presentation focuses on West Africa, a region that covers
the major African vegetation zones, and is thus representative for the continent in many
respects. Major challenges in mapping the land cover of this region are presented and
discussed. These are for example related to the prevalent small-scale land use and resulting
landscape heterogeneity, to strong seasonal dynamics of vegetation, and to high cloud cover
that hinders remote sensing at optical wavelengths. Approaching these challenges, a land
cover mapping procedure is presented that relies on annual time series of MODIS (Moderate
Resolution Imaging Spectroradiometer) reflectances and ASAR (Advanced Synthetic Aperture
Radar) Wide Swath data in combination with detailed in-situ observations and high resolution
Landsat imagery. While vegetation and agricultural classes are derived from MODIS data,
permanent waterbodies and seasonally flooded areas are delineated from ASAR time series.
The novel land cover map of West Africa discriminates 13 classes at a spatial resolution of
250 m. Each land cover class features detailed descriptions with respect to life form
composition and land use. In order to improve the usability of the dataset as evaluation and
input data for modeling, seasonal dynamics of land cover (leaf area development and albedo)
are described in additional information layers. The novel land cover map of West Africa is
compared to available global datasets, differences in class assignments are highlighted, and
their relevance with respect to biogeophysical land surface properties is discussed.
References :
Keywords :
land cover, seasonal dynamics, remote sensing, Africa
land cover, seasonal dynamics, remote sensing, Africa
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