Area-based fuzzy membership forest cover comparison between MODIS NPP and Forest Inventory and Analysis (FIA) across eastern U.S. forest
This research assessed the accuracy of the moderate resolution imaging spectroradiometer’s (MODIS) land cover classification of softwood and hardwood using a fuzzy-based approach for 31 easternmost states in the U.S. Our main objective was to quantitatively evaluate spatially explicit land cover classifications of MODIS net primary product (NPP) scheme using the USDA Forest Service’s (FS) field-based, tree-specific Forest Inventory Analysis (FIA). We used a grid of 648 km2 hexagons as base mapping units and interpreted our results at the USDA FS level IV ecological regions. Forest area was calculated for both MODIS and FIA and were found to be strongly correlated (Pearson’s r = 0.875, p < 0.01), which suggests the two classifications are comparable. Area-based fuzzy memberships of softwood and hardwood forest were determined for both MODIS and FIA for each hexagon. We used cross-entropy (H c) to evaluate the accuracy of the MODIS classification. Our results determined that the accuracy of MODIS forest cover classification was not uniform for all ecological regions. Tree species importance values (IV) and Shannon’s diversity index (H s) were calculated to examine species abundance and heterogeneity, which may partially explain discrepancies between MODIS and FIA classifications. The greatest misclassifications were due to (1) MODIS underestimating softwood forest cover and (2) MODIS confusing forest cover with other land covers such as grassland, cropland, or woody savanna. Our results provide a guideline for users to understand the degree of uncertainty of MODIS forest cover classifications in the eastern USA.
KeywordsFuzzy classification MODIS FIA Field data Cross-entropy
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