Abstract
Various subpixel mapping (SPM) methods have been proposed as downscaling techniques to reduce uncertainty in classifying mixed pixels. Such methods can provide category maps of a higher spatial resolution than the original input images. The aim of this study was to explore and validate the potential of SPM as an alternative method for obtaining land use/land cover (LULC) maps of regions where high-spatial-resolution LULC maps are unavailable. An experimental design was proposed to evaluate the feasibility of SPM for providing the alternative LULC maps. A case study was implemented in the Jingjinji region of China. SPM results for spatial resolutions of 500–100 m were derived from a single 1-km synthetic fraction image using two representative SPM methods. The 1-km synthetic fraction image was assumed to be error free. Accuracy assessment and analysis showed that overall accuracies of the SPM results were reduced from about 85% to 75% with increasing spatial resolution, and that producer’s accuracies varied considerably from about 62% to 93%. SPM performed best when handling areal features in comparison with linear and point features. The highest accuracies were achieved for areas with the lowest complexity. The study concluded that the results from SPM could provide an alternative LULC data source with acceptable accuracy, especially in areas with low complexity and with a large proportion of areal features.
Highlights
Various aspects of land use and land cover (LULC) are important in geo-information, environmental, and socioeconomic applications [1]
This study shows that overall accuracy (OA) and Kappa coefficient (KP) generally decrease as the spatial resolution of the subpixel mapping (SPM) results
The objective of this study was to investigate the feasibility of using subpixel mapping (SPM)
Summary
Various aspects of land use and land cover (LULC) are important in geo-information, environmental, and socioeconomic applications [1]. Remote sensing is a cost-effective and efficient means to obtain data for LULC monitoring over large land surfaces at a variety of spatial and temporal scales [2]. The presence of mixed pixels in remote sensing images, can cause difficulties in the extraction of accurate LULC information because their spectral characteristics reflect the composite signature of different LULC classes [3]. Soft classification (or spectral unmixing) methods have been proposed to address this problem by estimating the memberships of LULC classes within a pixel [4]. Such techniques might be unable to provide any indication of the spatial distribution of such LULC classes within each mixed pixel [5]. Subpixel mapping (SPM) may be Remote Sens. 2016, 8, 360; doi:10.3390/rs8050360 www.mdpi.com/journal/remotesensing
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