Abstract
Abstract. Knowing the detailed error structure of a land cover map is crucial for area estimation. Facilitated by the opening of the Landsat archive, global land cover mapping at 30-m resolution has become possible in recent years. Two global Landsat-based continuous fields of tree cover maps have been generated by Sexton et al. (2013) and Hansen et al. (2013) but the accuracy of which have not been comprehensively evaluated. Here we used canopy cover derived from airborne small-footprint Lidar data as a reference to evaluate the accuracy of these two datasets as well as the National Land Cover Database 2001 canopy cover layer (Homer et al. 2004) in two entire counties in Maryland, United States. Our results showed that all three Landsat datasets captured well the spatial variations of tree cover in the study area with an r2 ranging between 0.54 and 0.58, a mean bias error ranging between -15% and 5% tree cover, and a root mean square error ranging between 27% and 29% tree cover. When the continuous tree cover maps were converted to binary forest/nonforest maps, all three products were proved to have an overall accuracy >= 80% but with significant differences in producer’s accuracy and user’s accuracy. Data users are thus suggested to beware of these accuracy patterns when selecting the most appropriate dataset for their specific applications.
Highlights
Changes in forest cover significantly affect the global carbon cycle, the hydrological cycle and biodiversity richness (Foley et al, 2005)
We have demonstrated the applicability of small-footprint airborne light detection and ranging (Lidar) data as reference to evaluate the accuracy of land cover products generated from optical satellite data
Using wallto-wall Lidar-derived canopy cover as reference, we estimated the accuracy of three Landsat-based continuous fields of tree cover datasets (Sexton et al 2013; Hansen et al 2013; Homer et al 2004) in Howard County and Anne Arundel County, Maryland, USA
Summary
Changes in forest cover significantly affect the global carbon cycle, the hydrological cycle and biodiversity richness (Foley et al, 2005). The proliferation of available datasets provides users rich alternatives yet simultaneously creates some confusion as to which data to choose for their specific applications. This confusion is mainly caused by the lack of a comprehensive accuracy assessment of each available product. Many datasets have not been comprehensively evaluated For those validated maps, the accuracy numbers are often generated using diverse reference data and are not directly comparable (Fritz and See, 2008; Pflugmacher et al, 2011; Zhao et al, 2014). Absolute error estimation of a land cover map is still and always needed because accuracy information is a crucial input for subsequent applications, such as area and associated uncertainty estimation and land cover change detection (Olofsson et al, 2013; Sexton et al, 2015; Song et al, 2014b)
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