Change vector analysis (CVA) and principal component analysis in NDVI time-trajectories space are powerful tools to analyze land-cover change. The magnitude of the change vector indicates amplitude of the change, while its direction indicates the nature of the change. CVA is applied to two remotely sensed indicators of land surface conditions, NDVI and spatial structure, in order to improve the capability to detect and categorize land-cover change. The magnitude and type of changes are calculated in China from 1989 to 1999. Through the research, the main conclusions are drawn as follows: 1) The changes of NDVI are quite different between eastern China and western China, and the change range in the east is bigger than that in the west. The trend in NDVI time series is smoothly increasing, the increases happen mostly in Taiwan, Fujian, Sichuan and Henan provinces and the decreases occur in Yunnan and Xinjiang. 2) The spatial structure index can indicate changes in the seasonal ecosystem dynamics for spatially heterogeneous landscapes. Most of spatial structure changes, which occurred in southern China, correlated with vegetation growth processes and strike of mountains.

Hydrologic data derived from digital elevation models (DEM) has been regarded as an effective method in the spatial analysis of geographical information systems (GIS). However, both DEM resolution and terrain complexity has impacts on the accuracy of hydrologic derivatives. In this study, a multi-resolution and multi-relief comparative approach was used as a major methodology to investigate the accuracy of hydrologic data derived from DEMs. The experiment reveals that DEM terrain representation error affects the accuracy of DEM hydrological derivatives (drainage networks and watershed etc.). Coarser DEM resolutions can usually cause worse results. However, uncertain result commonly exists in this calculation. The derivative errors can be found closely related with DEM vertical resolution and terrain roughness. DEM vertical resolution can be found closely related with the accuracy of DEM hydrological derivatives, especially in the smooth plain area. If the mean slope is less than 4 degrees, the derived hydrologic data are usually unreliable. This result may be helpful in estimating the accuracy of the hydrologic derivatives and determining the DEM resolution that is appropriate to the accuracy requirement of a particular user. By applying a threshold value to subset the cells of a higher accumulation flow, a stream network of a specific network density can be extracted. Some very important geomorphologic characteristics, e.g., shallow and deep gullies, can be separately extracted by means of adjusting the threshold value. However, such a flow accumulationbased processing method can not correctly derive those streams that pass through the working area because it is hard to accumulate enough flow direction values to express the stream channels at the stream's entrance area. Consequently, errors will definitely occur at the stream’s entrance area. In addition, erroneous derivatives can also be found in deriving some particular rivers, e.g., perched (hanging up) rivers, anastomosing rivers and braided rivers. Therefore, more work should be done to develop and perfect the algorithms.

The sediments of the Dongting Lake come from four channels (one of them was closed in 1959), connected with the Yangtze River, four tributaries (Lishui, Yuanjiang, Zishui and Xiangjiang) and local area, and some of them are transported into the Yangtze River in Chenglingji, which is located at the exit of the Dongting Lake, some of them deposit into drainage system in the lake region and the rest deposit into the lake. The annual mean sediment is 166,555x10⁴ t, of which 80% come from the four channels, 18% from the four tributaries and 2% from local area, whereas 26% of the total sediments are transported into the Yangtze River and 74% deposited into the lake and the lake drainage system. Based on topographic maps of 1974, 1988 and 1998, and the spatial analysis method with geographic information system (GIS), changes in sediment deposition and erosion are studied in this paper. By overlay analysis of 1974 and 1988, 1988 and 1998, erosion and sediments deposition areas are defined. The main conclusions are: (1) sediment rate in the lake is larger than erosion rate from 1974 to 1998. The mean deposition in the lake is 0.43 m; (2) annual sediment deposition is the same between 1974-1988 and 1988-1998, but the annual volume of deposition and erosion of 1988-1998 is bigger than that in 1974-1988; (3) before the completion of the Three Gorges Reservoir, there will be 7.82x10⁸ m³ of sediments deposited in the lake, which would make the lake silted up by 0.33 m; (4) in the lake, the deposition area is found in the north of the east Dongting Lake, the south-west of the south Dongting Lake, and the east of the west Dongting Lake; while the eroded area is in the south of the east Dongting Lake, the middle of the south Dongting Lake, the west of the west Dongting Lake, as well as Xiangjiang and Lishui river flood channels.

This paper describes the spatial and temporal relationship between AVHRR/NDVI (Normalized Difference Vegetation Index) and climatological parameters (temperature and precipitation), which, in some sense, is influenced by topographical factors and land-cover types in Colorado. The correlation coefficients and partial correlation coefficients have been computed pixel by pixel over Colorado in order to analyze the relationship. The temporal variation and correlation of AVHRR/NDVI, temperature and precipitation were analyzed with a sampling method. The study reveals that there exists a close correspondence between monthly NDVI and temperature, which has strong impact from temperature on the changes of NDVI in Colorado. The spatial changes of NDVI are not influenced obviously by the precipitation since these two variables are different from each other in time series in Colorado. The study clearly revealed the spatial variation and its distribution patterns of relationship between NDVI and climatic parameters (temperature and precipitation) in Colorado.