Land Degradation Detection, Mapping, and monitoring in the Northwestern part of Hebei Province, China, Using RS and GIS Technologies Ayad Mohammed Fadhil Al-Quraishi 1/2, Guang Dao Hu 1, Jian Guo Chen 1 1 Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China. 2 Ministry of Higher Education and Scientific Research, Foundation of Technical Education, Baghdad, Iraq. Email: ayad@cug.edu.cn, ayad_alquraishi@hotmail.com Introduction Land degradation is a complex ensemble of surface processes (e.g. wind erosion, water erosion, soil compaction, salinisation, and soil water-logging). These can ultimately lead to "desertification". As the increasing world population places more demands on land for food production etc., many marginal arid and semiarid lands will be at risk of degradation. The need to maintain sustainable use of these lands requires that they be monitored for the onset of land degradation so that the problem may be addressed in its early stages. Monitoring will also be required to assess the effectiveness of measures to control land degradation. The most typical and serious form of land degradation in China is desertification. Desertified land covers an area of 3.3 million km2, accounting for 34% of the total territory or 79% of the entire arid land in China (Chen et al., 1996). Over 100 million ha of grassland, 7.7 million ha of farmland and 0.1 million ha of woodland have been affected by degradation (Sun et al., 1998). Desertified sandy land increased by 25,200 km2 for the period from 1975 to 1987 about 40.5% of which was distributed in the semi-arid agropastoral regions of northern China (Zhu and Wang, 1993). At present, desertification is spreading with an annual growth of 10,400 km2, with 400,000,000 population affected. Annual direct economic loss caused by desertification is approximately 6,500,000,000 US Dollars (UNCCD, 2002). The basic premise in using remote sensing data for change detection is that changes in land cover result in changes in radiance values, which can be remotely sensed. Techniques to perform change detection with satellite imagery have become numerous as a result of increasing versatility in manipulating digital data and increasing computing power. Image differencing procedure is to register simply two images and prepare a temporal difference image by subtracting the digital numbers (DN) for one date from those of the other. The difference in the areas of no change will be very small, and areas of change will reveal larger positive or negative values (Lillesand and Kiefer 1987). The objective of this study is to detect, assess, mapping, and monitoring the land degradation risk in the study area in the northwestern part of Hebei Province, China, at county level using Remote Sensing 'RS" and Geographical Information System 'GIS' technologies. Study Area Hebei Province is situated in temperate and warm temperate zones. The study area extends between latitude N 39° 27' to N 41° 11', longitude E 114° 24' to 115° 55'. It covers an area of 20,828 km2, accounting 11.1% of the total area of Hebei Province. Northern and northwestern parts of Hebei are located in the temperate continental monsoon climate zone. Cold and windy winters and warm and dry summers are the general characteristics of the climate. The annual average rainfall ranges between 300 mm and 600 mm (Ministry of Civil Affairs and Ministry of Construction, 1992). Most of the rain comes between May and August. Figure 1 shows the location map of the study area in the Northwestern part of Hebei Province, China.  Figure 1. Location map of the study area in the Northwestern part of Hebei Province Materials and Methods Remote Sensing Data A Landsat-5 thematic mapper (TM) imagery remotely sensed dataset (124/32) was assembled for this study, the period analysed was 1987 and 1996. NDVI The Normalized Difference Vegetation Index (NDVI) was initially proposed by Rouse et al. (1974). NDVI derived from the ratio of band 3 and band 4 in Landsat TM images data was applied for monitoring vegetation changes in the study area within the years of 1987 and 1996. NDVI = (TM4-TM3) / (TM4-TM3) Tasseled Cap Transformation (TCT) Tasseled Cap transformation is one of the available methods for enhancing spectral information content of Landsat TM data. Tasseled Cap transformation especially optimizes data viewing for vegetation studies. Tasseled Cap index was calculated from data of the related six TM bands. Three of the six tasseled cap transformation bands are often used:
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