Landsat TM/ETM+ sensor data has proven to be a highly effective data source for vegetation and land use classification at both global and regional scales. In this study, based on land cover classification, we conducted computer-aided analysis of degradation sequence of the meadow grassland in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images (WRS 124-39 and 124-30) acquired on Jul.31, 1987, Aug.11, 1991, Sep. 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including 9 grassland types at community level: F. sibiricum steppe, S. baicalensis steppe, A. chinensis + forbs steppe, A. chinensis + bunchgrass steppe, A. chinensis + Ar. frigida steppe, S. grandis + A. chinensis steppe, S. grandis + bunchgrass steppe, S. krylavii steppe, Ar. frigida steppe and 8 non-grassland types: active cropland, harvested cropland, urban area, wetland, desertilized land, saline and alkaline land, cloud, water body + cloud shadow. Then we created thematic maps of the areal change and spatial variation of the meadow grassland in Xilin River Basin, Inner Mongolia. We used Geographical Information System (GIS) tools to create thematic maps of the meadow grassland and then analyzed its degradation sequence (or the evolution route). Driven by overgrazing, the meadow grassland ecosystem in Xilin River Basin, Inner Mongolia had undergone and was undergoing degradation evolution; the evolution route was from meadow grassland (F. sibiricum steppe, S. baicalensis steppe), via temperate grassland (A. lymus + bunchgrass steppe, A. lymus + forbs Steppe, A. lymus + S. grandis steppe, S. grandis + bunchgrass steppe, S. grandis + forbs steppe and A. lymus + Ar. frigida steppe) to desert grassland (S. krylavii steppe and Ar. frigida steppe). Results of this study show that increasing human population and accelerated social-economic development has caused dramatic degradation and fragmentation to the grassland ecosystems in Xilin River Basin.
Land cover information is important for the study of physical, chemical, biological and anthropological process on the surface of earth. Remote sensing data has been used to produce the land cover map by visual interpretation or automatic classification method in the past years. IGBP DISCover land cover dataset is a global land cover dataset based on remote sensing method in recent years. Firstly, we present a method to compare different land cover dataset based on invariant reliable land unit. Secondly, we compare IGBP Discover land cover dataset with Chinese land cover dataset. Finally, we analyze the possible reasons impacting the differences among the land cover classifications. The comparison results show that most of the land surface in China was identified as different types in those two datasets. For example, 63.7% of the deciduous needleleaf forest units in CLCD are mapped to the mixed forest by IDLCD. The different classification scheme and method used in these datasets are most likely the reasons to explain the differences between them.
Vegetation phenology is an important variable in a wide variety of Earth and atmospheric science applications. The role of remote sensing in phenological studies is increasingly regarded as a key to understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for forest phenology analysis. The phenology of forest covering Northeast China and its spatial characteristics were investigated using MODIS normalized difference vegetation index (NDVI) data. Threshold-based method was used to estimate three key forest phenological variables: start of growing season (SOS), end of growing season (EOS) and the growing season length (GSL). The spatial pattern of key phenological stages were mapped and analyzed. The derived phenological variables were validated by referring to previous research achievements in this study area. The phenological pattern of Changbaishan Reserve was compared with the distribution of forest types. Results indicate that spatial characteristics of vegetation phenology are corresponding with the distribution of vegetation types and the phenology information can be used to improve vegetation classification accuracy as an auxiliary variable.