Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis
Kuan
Song, Department of Geography, & Global Land Cover Facility, University of Maryland, songkuan@yahoo.com
(Presenting)
Chengquan
Huang, Department of Geography, & Global Land Cover Facility, University of Maryland, cqhuang@umd.edu
John
R. G.
Townshend, Department of Geography, & Global Land Cover Facility, University of Maryland, jtownshe@umd.edu
Paul
E
Davis, Department of Geography, & Global Land Cover Facility, University of Maryland, pdavis@geog.umd.edu
Saurabh
Schannan, Department of Geography, & Global Land Cover Facility, University of Maryland, schannan@umiacs.umd.edu
Mathew
Smith, Department of Geography, & Global Land Cover Facility, University of Maryland, smithm@umiacs.umd.edu
Vegetation phenology is a major consideration in acquiring satellite images for forest change analysis, especially in regions having deciduous forests and/or high inter-annual climate variability. When trees have little or no leaves during the leaf-off season, spectrally deciduous forests are difficult to be distinguished from non-forested surfaces, including disturbed forests. Therefore, use of images acquired during or near the leaf-off season in forest change analysis can result in substantial errors in the derived change products. In this study, we evaluate the phenological suitability of two global Landsat data sets centered around 1990 and 2000 for forest change analysis. These two data sets, together with a third one centered around 2006, which is being assembled, will be used to produce an Earth Science Data Record of global forest cover change. For each
image in these data sets, we use the vegetation canopy field information from MODIS products and the AVHRR NDVI information from the GIMMS dataset to determine if that image was acquired during the leaf-on or leaf-off season. Because phenology is a concern more
for deciduous forests than for evergreen forests in forest change studies, for images having deciduous forests we will use the NDVI values of deciduous forests in this analysis. Here the deciduous forest is identified according to the percent deciduous data set
of the MODIS VCF products. Results from this study will reveal the percentage of Landsat images in the two global Landsat sets suitable for forest change analysis. The approach developed in this study can be used to guide the image selection process in assembling future global satellite data sets intended for forest change analysis. The approach can also be used by researchers to precisely target other
particular periods in the forest growing season such as senescence.
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