This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, September 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including nine 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 eight non-grassland types: active cropland, harvested cropland, urban area, wetland, desertified land, saline and alkaline land, cloud, water body + cloud shadow. To eliminate the classification error existing among different sub-types of the same gross type, the 17 sub-class land cover types were grouped into five gross types: meadow grassland, temperate grassland, desert grassland, cropland and non-grassland. The overall classification accuracy of the five land cover types was 81.0% for 1987, 81.7% for 1991, 80.1% for 1997 and 78.2% for 2000.

Characterization of land cover types in Xilin River Basin using multi-temporal Landsat images

The Xilin River Basin, Inner Mongolia, China is dominated by grassland ecosystems Human activities (eg, crop cultivation, livestock grazing, urban development, construction of infrastructure) in the basin have resulted in substantial changes in land cover, particularly since late 1980s In this paper we used Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data to study the land use/cover change in the Xilin River Basin, Inner Mongolia High-quality (less than 10% cloud cover) TM/ETM+ images (WRS124-29 and WRS124-30) acquired on four different dates (July 31, 1987, August 11, 1991, August 27, 1997, and May 23, 2000) were collected for this study The Xilin River Basin is within two Landsat images (WRS124-29 and WRS124-30), and therefore, we mosaic WRS124-29 and WRS124-30 images and then cookie-cut it for the Xilin River Basin after preprocessing of the images Based on the vegetation and land use characteristics of the basin, a hierarchical land cover classification scheme was created, including identification of major grassland types (eg, Stipa grandis, Leymus chinensis) Then we conducted image classification using both supervised and unsupervised classification methods Ancillary data such as vegetation map, soil map, topography maps, field survey data and literature of the Xilin River Basin were used for interpretation and labeling of spectral clusters as well as accuracy assessment of land cover classification Finally, the resultant classification results of those four dates were used to quantify the spatial extent of land use/cover change in the basin and to develop the transitional matrix of land use/cover changes since 1987 The results derived from this study could be linked to biogeochemical models for assessing the consequence of land use/cover change on carbon and nitrogen dynamics of terrestrial ecosystems in the basin

Observation of land use/cover change of the Xilin River Basin, inner Mongolia using multitemporal Landsat images

The authors outline a method for employing space photography in the classification and mapping of different types of steppe and semidesert plant cover according to their value as a fodder resource. Considerable attention is devoted to describing a computer algorithm for clustering groups of plant species and their cover densities. Cross checking of the identified clusters in the field for ground truth provided the basis for the development of a series of remote sensing interpretation keys and the compilation of maps of pasture types. A concluding section presents recommendations for the replication of such work in other settings. Translated by Jay K. Mitchell, PlanEcon, Inc., Washington, DC 20005 from: Izvestiya Akademii Nauk SSSR, seriya geograficheskaya, 1990, No. 1, pp. 101–111.

V. S. Khrutskiy

Papers

Classification of steppe pasture resources from remote sensing data