By Topic

Sub-Pixel Mapping of Tree Canopy, Impervious Surfaces, and Cropland in the Laurentian Great Lakes Basin Using MODIS Time-Series Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yang Shao ; U.S. Environmental Protection Agency, National Research Council, National Exposure Research Laboratory, Research Triangle Park, NC, USA ; Ross S. Lunetta

This research examined sub-pixel land-cover classification performance for tree canopy, impervious surface, and cropland in the Laurentian Great Lakes Basin (GLB) using both time-series MODIS (Moderate Resolution Imaging Spectro radiometer) NDVI (Normalized Difference Vegetation Index) and surface reflectance data. Classification training strategies included both an entire-region approach and an ecoregion-stratified approach, using multi-layer perceptron neural network classifiers. Although large variations in classification performances were observed for different ecoregions, the ecoregion-stratified approach did not significantly improve classification accuracies. Sub-pixel classification performances were largely dependent on different types of MODIS input datasets. Overall, the combination of MODIS surface reflectance bands 1-7 generated the best sub-pixel estimations of tree canopy (R2 = 0.57), impervious surface (R2 = 0.63) and cropland (R2 = 0.30), which are considerable higher than those derived using only MODIS-NDVI data (tree canopy R2 = 0.50, impervious surface R2 = 0.51, and cropland R2 = 0.24). Also, sub-pixel classification accuracies were much improved when the results were aggregated from 250 m to 500 m spatial resolution. The use of individual date MODIS images were also examined with the best results being achieved for Julian days 185 (early July), 217 (early August), and 113 (late April) for tree canopy, impervious surface, and cropland, respectively. The results suggested the relative importance of the image data input selection, spatial resolution, and acquisition dates for the sub-pixel mapping of major cover types in the GLB.

Published in:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  (Volume:4 ,  Issue: 2 )