Abstract
Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.
Highlights
Land cover products are fundamental to many applications in environmental monitoring, land management, and global change studies [1,2]
The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system, respectively
It can be seen that bare areas, grassland, cropland, and forest are among the most abundant land-cover types, a result that is consistent with the true spatial patterns of land-cover types in China
Summary
Land cover products are fundamental to many applications in environmental monitoring, land management, and global change studies [1,2]. They are an important input to climate change modeling, greenhouse gas inventories, and biodiversity conservation planning [3]. Due to differences in spatial resolution, classification schemes, thematic detail, and classification accuracy, the land cover datasets are barely good enough to meet the needs of various user communities [5,6,7,8,9,10,11]. FROM-GLC and GlobeLand are two representative global 30-m land-cover products that use Landsat imagery [4,12]. The GlobeLand classification system is simpler, and the interpretation of training samples in FROM_GLC is time-consuming
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