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Based on remote sensing data and investigations, this paper focuses mainly on a general evaluation of environmental quality in the middle-upper areas of the Yellow River region using GIS technology, Arc/Info, and ArcView. In this paper, the upper areas of the Yellow River regions are divided into three parts: the north-east Qinghai-Tibet Plateau ecosystem areas, the Erdos Plateau-Hetao Plain ecosystem areas, and the Loess-Plateau ecosystem areas. The study analyses the current environmental situation and the overall environmental quality in each ecosystem area and determines the law of distribution of environmental quality in the three areas. Finally, the paper provides some suggestions to improve the ecological environment in each area, in the hope of yielding a scientific foundation based on planning ecological environment protection in the Yellow River basin. [ABSTRACT FROM AUTHOR]

Owing to the influence of global change, land cover and land use have changed significantly over the last decade in the cold and arid regions of China, such as Madoi County which is located in the source area of the Yellow River. In this paper, land-use/cover change and landscape dynamics are investigated using satellite remote sensing (RS) and a geographical information system (GIS). The objectives of this paper are to determine land-use/cover transition rates between different cover types in the Madoi County over 10 years e.g., from 1990 to 2000. Second, the changes of landscape metrics using various indices and models are quantified. The impact factors of LUCC (Land-Use land cover Change) are systematically identified by integrating remote sensing as well as statistical data, including climate, frozen soil, hydrological data and the socio-economic data. Using 30 m×30 m spatial resolution Landsat (Enhanced) Thematic Mapper (TM/ETM+) data in our study area, nine land cover classes can be discriminated. Our results show that Grassland, Marshes and Water Bodies decrease notably, while oppositely, Sands - Gobi and Barren land increase significantly. The number of lakes with an acreage larger than six hectares decreased from 405 in 1990 to 261 in 2000. Numerous small lakes dried out. The area of grassland with a high cover fraction decreased as well, while the surface area of grassland with a medium level of cover fraction increased. The medium cover fraction grassland mainly originates from high cover fraction grassland. The desertification of land is a serious issue. (ii) The inter-transformations between Grasslands, Barren Land, Sands, Gobi, Water Bodies and Marshes are remarkable. The Shannon-Weaver Diversity Index (SWDI), the Evenness Index (EI) and the extent of Landscape Heterogeneity (LH) has improved. Marshes have become more fragmented hence, with less connected patches. (iii) In the recent 30 years, average annual temperature, the power of evaporation and the index of dryness did increase significantly. Moreover, soil moisture content (SMC) decreased and the drought trend accelerated. The degradation of frozen soil has impacted on the decrease of surface water area and induced a drop in groundwater levels. Monitoring LUCC in sensitive regions would not only benefit from a study of vulnerable ecosystems in cold and high altitude regions, but would provide scientifically based decision-making tools for local governments as well. [ABSTRACT FROM AUTHOR]

It is important to estimate land surface evapotranspiration (ET) for water resources evaluation, drought monitoring and crop production simulation. In this paper, a relationship between annual ET, integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) and Relative Moisture Index (RMI) was established. Based on this relationship, the spatial distribution and dynamic change of annual ET were estimated for the Yellow River Basin, China from 1982 to 2000. Our analyses involved the use of integrated NDVI data, monthly mean air temperature, and precipitation. Our results showed that the integrated AVHRR NDVI can be used to effectively estimate annual ET in the Yellow River Basin, with an accuracy over 90% for the whole basin. [ABSTRACT FROM AUTHOR]

The Loess Plateau located in the upper and middle reaches of the Yellow River is the most serious soil erosion region in China. Check-dams (also known as silt trappers) have been demonstrated to be an effective soil and water conservation measure in the region and a traditional workflow for check-dam planning and engineering is time-consuming and cannot meet the requirement of efficiently locating optimal check-dam sites. Fine resolution satellite imagery and analysis can play a key role in screening and determining special river bed segments that can be candidates for check-dam sites. In this research, HongShiMao watershed of the Loess Plateau was selected as our case study area. Based on a detailed analysis of spectral characteristics of a fused SPOT-5 imagery for dominant land covers and geomorphological features of a constructed digital elevation model, the river bed of key channels within the watershed was automatically identified. Then we selected four check-dam sites on the river bed and four orientations of a check-dam site to explore locational and directional profiles. Such profile information is most useful for locating optimal check-dam sites in a cost-effective manner and reducing associated expenditures surrounding check-dam constructions. This remote sensing application demonstrates the latest spatial information technologies such as fine resolution satellite imagery and 3D geospatial visualisation hold promises for changing traditional workflows and advancing scientific decision making of environmental conservation projects. [ABSTRACT FROM AUTHOR]

There has been growing interest in the use of reflectance spectroscopy as a rapid and inexpensive tool for soil characterization. In this study, we collected 95 soil samples from the Yellow River Delta of China to investigate the level of soil salinity in relation to soil spectra. Sample plots were selected based on a field investigation and the corresponding soil salinity classification map to maximize variations of saline characteristics in the soil. Spectral reflectances of air-dried soil samples were measured using an Analytical Spectral Device (ASD) spectrometer (350-2500 nm) with an artificial light source. In the Yellow River Delta, the dominant chemical in the saline soil was NaCl and MgCl2. Soil spectra were analysed using two-thirds of the available samples, with the remaining one-third withheld for validation purposes. The analysis indicated that with some preprocessing, the reflectance at 1931-2123 nm and 2153-2254 nm was highly correlated with soil salt content (S SC). In the spectral region of 1931-2123 nm, the correlation R ranged from -0.80 to -0.87. In the region of 2153-2254 nm, the S SC was positively correlated with preprocessed reflectance (0.79-0.88). The preprocessing was done by fitting a convex hull to the reflectance curve and dividing the spectral reflectance by the value of the corresponding convex hull band by band. This process is called continuum removal, and the resulting ratio is called continuum removed reflectance (CR reflectance). However, the S SC did not have a high correlation with the unprocessed reflectance, and the correlation was always negative in the entire spectrum (350-2500 nm) with the strongest negative correlation at 1981 nm (R = -0.63). Moreover, we found a strong correlation (R = 0.91) between a soil salinity index (S SI: constructed using CR reflectance at 2052 nm and 2203 nm) and S SC. We estimated S SC as a function of S SI and S SI' (S SI': constructed using unprocessed reflectance at 2052 nm and 2203 nm) using univariate regression. Validation of the estimation of S SC was conducted by comparing the estimated S SC with the holdout sample points. The comparison produced an estimated root mean squared error (RMSE) of 0.986 (S SC ranging from 0.06 to 12.30 g kg-1) and R 2 of 0.873 for S SC with S SI as independent variable and RMSE of 1.248 and R 2 of 0.8 for S SC with S SI' as independent variable. This study showed that a soil salinity index developed for CR reflectance at 2052 nm and 2203 nm on the basis of spectral absorption features of saline soil can be used as a quick and inexpensive method for soil salt-content estimation. [ABSTRACT FROM AUTHOR]

The change history of vegetation cover and its relations to growing season precipitation (GSP) and average growing season temperature (AGST) in the source region of the Yellow River (SRYR) during 1990-2000 was retrieved based on the 1 km Advanced Very High-Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and meteorological records. The results show an overall warming and drying trend of the climate and a common degradation tendency of the ecosystem, with a greening trend in higher rugged regions. The pixel-by-pixel correlations between NDVI and climate factors indicate that a decrease in GSP mainly affects ecosystems with low precipitation and worse vegetation condition, and superimposes on the effects of increasing AGST which further deteriorate the climate background of these ecosystems. However, the positive correlations between AGST and NDVI in some higher/rugged regions suggest that the raising temperature can ameliorate vegetation growth conditions in these areas. Comparison and combination of the results of three change detection algorithms, i.e. post-classification comparison (PCC), principal components analysis (PCA) and a newly developed multi-temporal image difference (MTID) method, show that the integration of different methods can give a more comprehensive understanding of vegetation changes than any single method. [ABSTRACT FROM AUTHOR]

Preservation of cultivated land is one of China's four basic strategic policies. Timely land use monitoring is a prerequisite of cultivated land management and sustainable utilization. Using digital satellite remote sensing techniques, the intention is to develop a workable procedure for cultivated land change detection in the Yellow River delta region. Four detection methods were assessed. The assessment, in general, suggests that it is difficult to find a single efficient method for cultivated land change detection in the study area, where spectral confusion is widespread due to soil salinization and juxtaposed land use patterns. The knowledge-based visual change detection and classification-result overlay methods are more appropriate than the multi-temporal composite and classification, and image ratioing. The latter two methods are useful and efficient when remote sensing data acquired in the suitable season are available, and distinct spectral characteristics of different land use types exist. The results show that the area of cultivated land in this region decreased by 5321.8 ha over the period 1987 to 1998, i.e. 483.8 ha every year, mainly concentrated in the central paddy field region and northeast dry land region. [ABSTRACT FROM AUTHOR]