# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 1 Introduction  
Land cover data is essential for a variety of studies such as global change [1; 2], ecological environments [3; 4] and resource management. It plays a critical role in improving the performance of hydrological, ecological, biogeochemical and atmospheric models [5]. Satellite remote sensing has been widely applied and recognized as a powerful and effective tool for producing land cover products [6]. A series of land cover products at global and regional scales, such as the IGBP-DIS global 1-km land cover product [7], MODIS global 500-m land cover product (MCD12Q1) [8], GlobCover 2009 at 300 m-resolution [9], global 30-m land cover products (FROM-GLC [5; 10] and GlobeLand30 [11]) and Chinese land use datasets at 1:100,000 scale (CLUDs) [12; 13], have been derived from remotely sensed data in response to the policy- and science-driven need for land cover data. Currently, land cover products at 30 m-resolution over large regions or even the globe are highlighted, since most significant human activities on the land surface can be captured at this scale [11; 14]. However, the spatial agreement of multiple existing land cover products tends to be low in regions with complex, heterogeneous land cover [15]. Especially in mountainous areas, severe challenges arise for land cover mapping, such as the rugged terrain, the high spatial heterogeneity of land surfaces and the frequent cloud contamination of satellite imagery [16], having an enormous impact on scientific research and applications related to land cover over mountainous areas. Multi-temporal satellite imagery can improve our ability to distinguish between vegetation types by leveraging variability in phenological patterns across the landscape [17; 18], such as distinguishing between evergreen forests and deciduous forests [19] and identifying rubber plantations from natural forests [20]. It is natural to consider the use of multi-temporal satellite imagery in enhancing the quality of land cover products over mountainous areas with a wide variety of vegetation heterogeneities. The Landsat TM image is ideal input data for 30 m-resolution land cover mapping due to its long-term archive (over four decades) [21] and free availability. The native HJ-CCD sensors have a distinct advantage to acquire more high-quality satellite images due to the wide range (700 km \(\times\) 700 km) and short repeat cycle (2-4 days) [22; 23]. Meanwhile, they have the same spatial resolution (30 m) and similar spectrum range in the visible and near-infrared band [24]. Therefore, the combination of Landsat TM images and native HJ-CCD images offers a feasible approach for the collection of multi-temporal satellite images needed for 30 m-resolution land cover mapping over largely mountainous areas. The spatial distribution of some land cover classes in mountainous areas often follow certain geographical rules and is constrained by various topographical factors (such as elevation, aspect and slope). For example, the distribution of natural vegetation displays distinct vertical stratification in mountainous areas, while the distribution of cropland is constrained by terrain slope, and the tree line represents the highest elevation that forests can grow. These rules and topographical factors play an important role in improving the accuracy of land cover mapping in mountainous areas [16]. Meanwhile, some existing thematic maps (such as land use maps, vegetation maps and soil maps), field photos and records, as well as relevant research results and statistical data could also provide some useful information for enhancing the quality of land cover products from different perspectives. We refer to these rules and information as geo-knowledge in this paper. Although geo-knowledge is central to decision-making and discovering classification errors for land cover mapping in mountainous areas, how to translate this geo-knowledge into computer language and maximize its potential is a practical problem.