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Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia

Hüttich, Christian and Herold, Martin and Strohbach, Ben and Dech, Stefan (2010) Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia. Environmental Monitoring and Assessment, pp. 1-17. Springer Verlag. DOI: DOI 10.1007/s10661-010-1602-5.

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Official URL: http://www.springerlink.com/content/8274n71420g2t2ww/

Abstract

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional landcover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of lifeform composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.

Document Type:Article
Title:Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia
Authors:
AuthorsInstitution or Email of Authors
Hüttich, Christianchristian.huettich@uni-wuerzburg.de
Herold, MartinUniversität Wageningen
Strohbach, BenNational Botanical Research Institute of Namibia
Dech, Stefanstefan.dech@dlr.de
Date:2010
Journal or Publication Title:Environmental Monitoring and Assessment
Refereed publication:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:DOI 10.1007/s10661-010-1602-5
Page Range:pp. 1-17
Publisher:Springer Verlag
Status:Published
Keywords:Harmonization, Standardization, Time series, Random forest, Remote sensing, Phenology
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W EO - Erdbeobachtung
DLR - Research area:Space
DLR - Program:W EO - Erdbeobachtung
DLR - Research theme (Project):W - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Dr. Tanja Kraus
Deposited On:16 Sep 2010 12:32
Last Modified:19 Apr 2013 08:52

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