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Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring

Hüttich, Christian and Herold, Martin and Wegmann, Martin and Cord, Anna and Strohbach, Ben and Schmullius, Christiane and Dech, Stefan (2011) Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring. Remote Sensing of Environment (115), pp. 2445-2459. DOI: 10.1016/j.rse.2011.05.005.

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Abstract

Land-cover is an important parameter in analyzing the state and dynamics of natural and anthropogenic terrestrial ecosystems. Land-cover classes related to semi-arid savannas currently exhibit among the greatest uncertainties in available global land cover datasets. This study focuses on the Kalahari in northeastern Namibia and compares the effects of different composite lengths and observation periods with class-wise mapping accuracies derived from multi-temporal MODIS time series classifications to better understand and overcome quality gaps in mapping semi-arid land-cover types. We further assess the effects of precipitation patterns on mapping accuracy using Tropical Rainfall Measuring Mission (TRMM) observation data. Botanical field samples, translated into the UN Land Cover Classification System (LCCS), were used for training and validation. Different sets of composites (16-day to three-monthly) were generated from MODIS (MOD13Q1) data covering the sample period from 2004 to 2007. Land-cover classifications were performed cumulatively based on annual and inter-annual feature sets with the use of random forests. Woody vegetation proved to be more stable in terms of omission and commission errors compared to herbaceous vegetation types. Generally, mapping accuracy increases with increasing length of the observation period. Analyses of variance (ANOVA) verified that inter-annual classifications significantly improved class-wise mapping accuracies, and confirmed that monthly composites achieved the best accuracy scores for both annual and inter-annual classifications. Correlation analyses using piecewise linear models affirmed positive correlations between cumulative mapping accuracy and rainfall and indicated an influence of seasonality and environmental cues on the mapping accuracies. The consideration of the inter-seasonal variability of vegetation activity and phenology cycling in the classification process further increases the overall classification performance of savanna classes in large-area land-cover datasets. Implications for global monitoring frameworks are discussed based on a conceptual model of the relationship between observation period and accuracy.

Document Type:Article
Title:Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring
Authors:
AuthorsInstitution or Email of Authors
Hüttich, Christianchristian.huettich@uni-wuerzburg.de
Herold, MartinCenter for Geoinformation, Wageningen University, The Netherlands
Wegmann, Martinmartin.wegmann@uni-wuerzburg.de
Cord, Annaanna.cord@dlr.de
Strohbach, BenNational Botanical Research Institute of Namibia, Winhoek, Namibia
Schmullius, ChristianeDepartment of Earth Observation, Institute of Geography, Friedrich-Schiller-University Jena, Germany
Dech, Stefanstefan.dech@dlr.de
Date:2011
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
In Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1016/j.rse.2011.05.005
Page Range:pp. 2445-2459
Status:Published
Keywords:MODIS, LCCS, TRMM, Land cover, Savanna
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: Carina Kübert
Deposited On:29 Sep 2011 09:34
Last Modified:19 Apr 2013 08:51

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