In order to have an objective understanding of the status that vegetation is resumed and the progress that the ecology environment construction project makes in Shaanxi province, we utilized the year of 1997 as a benchmark in our investigation and did the dynamic supervising for the variations in the north part of Shaanxi province under the support of 3S technology. The major task of the dynamic supervising had been completed by the software of ERDAS IMAGINE. Three dynamic supervising methods are used: (1) Difference value or ratio value of TM images from two different periods was calculated and the area of variation was obtained. (2) TM remote sensing images from the two different periods are merged, based on which the corresponding relationship between the zone where the light spectrum property changed abruptly and the variation of practical use of land was found. (3) Light spectrum curves for different ground objects in different areas were established on the basis of the classified result of 1997. According to those curves, the present classified diagram was made and the variation diagrams of the land using were obtained after comparing these two classified diagrams.
Now, the recovering and reconstruction of ecology environment are more and more focused by people all the nation, when government began to develop the west of nation. But, how to make the exact evaluation to the construction work of ecology environment, and how to make the best out of the limited resource of constructing work, these need to dynamic monitor the variation of the land-use in research region. In this paper, we mainly work over the method witch using remote sensing technology and working with LANDSAT TM satellite RS images, digital evolution model data, RS base situation investigation data, vectorized data of terrain map and outside investigation information etc., to establish the models based on judgment rules, acquired from expert experiment, and form the expert classification system. The research results indicate that (1) it is precise method to classify the RS images because of combined use of all kinds of data and science geographic model, especially, the variation of land-use can be cleanly interpreted from RS images. (2) It can be used to classify the same objects with same spectral feature which can not be classified by traditional methods just like supervised and unsupervised classification. (3) By using this method, the precise of classification can be improved because it excludes something being classified unreasonable.
The hill and ravine area on the Loess Plateau is the typical region of serious soil erosion, excess reclamation, and deteriorated eco-environment in middle reaches of Yellow River. The main project of eco-environment construction is that recover vegetation, and quit high sloping cultivated land to forest or meadow. The local government in the concerned region poses that sloping cultivated land higher than 15 degree should quit. How many are there qualified and how about their distribution? These are the basis problems of the execution of the eco-environment construction project. In this paper, using TM image and digital relief map, the interpretation of land use classification and the calculation of slope are made for Baota , Yan’an, with the software of ARC/INFO and ERDAS IMAGINE. And also the sloping cultivated land is mapped, basing on the composite analysis of land use map and slope map.
In the semi-arid and arid region in northern Shaanxi Province, 11 TM images were selected as information source. On the platform of ERDAS IMAGINE, based on the classification signatures established according to the training fields by in situ surface investigations and with the support of various information from GIS, the computer-supervised classification was used to extensively investigate the surface eco-environmental factors, such as the vegetation, land use type and vegetation coverage. The total area covers 84000 Km2. The average precision of classification is 83.78%, Kappa coefficient 0.819. Thematic maps have been compiled at the scale of 1:100,000. The features of surface eco-environment in this area are briefly analyzed on the basis of the remote sensing investigation results.