Environmental Change Monitoring - A Case Study in the Region of Yinchuan, Ningxia, China  Figure 1: Location, administrative and geomorphologic units of the study area - the region of Yinchuan, Ningxia, China  Figure 2: Approaches adopted in the case study Viewing that the recent and ancient images were acquired respectively on Aug.12, 1999 and Sept.20, 1987/Sept.17, 1989. There are about 32-35 days' difference in Day of Year between the ancient and recent images. The effect caused by the difference of the sun-earth distance and sun elevation angle should be also corrected. Thus the COST model was used to correct such effect and at the same time transform the at-satellite radiance into the surface feature reflectance according to the following formula:  where Rs - spectral reflectance of the surface; Lhaze - haze effect, for example, path radiance (Wm-2 sr-1mm-1); Eo - solar spectral irradiance on a surface perpendicular to the sun's rays outside the atmosphere (Wm-2 mm-1). Eo contains the Earth-Sun distance term that is in astronomical units (AUs are a function of time of year and range from about 0.983 to 1.017) (see Chander et al., 2003) q - solar zenith angle Lsat - at-satellite spectral radiance for the given band (Wm-2 sr-1mm-1). It has the following relationship with the digital counts of pixel (Chavez 1996 and NASA, 2000): Lsat = ((Lmax - Lmin)/Maximum DC)* DC + Lmin where Lmax represents the spectral radiance scaled to the maximum digital count (DC), Lmin is the spectral radiance to the minimum DC (see NASA, 2000 and Chander et al., 2003). A Tasseled Cap transformation (Crist et al. 1984a, b and 1986a) was conducted again on the atmospherically corrected ETM and TM images to reduce the data volume and convert the land cover information included in the six bands into three indicators: Brightness (B), Greenness (G) and Wetness (W), which mean respectively land bareness, vegetation vigour and soil moisture. Reflectance-based Tasseled Cap features (B, G and W) range from -0.5 to 1.4. To facilitate the calculation, they were normalised to the extent from 0 to 255. As differencing technique provides lower change detection errors when compared against other approaches (Jensen et al., 1982), it is applied to the change discrimination between the same land cover indicator (e.g., G) of different dates. The differenced values (D) vary from -255 to 255. They were normalised to the range of 0 to 255. Thresholding produces change maps containing three levels of information: negative change, no change and positive change. Field investigation shows that these "changes" are capable for highlighting where the real modifications have taken places. However, three classes are not enough to illustrate the concrete change types. Based on the field trip, a further identification was carried out. The changes as farmland extension, urban extension, rural built-up increase, land degradation, land to water depression, water-body to land, river narrowing, etc., were discriminated. Finally, each type of change was quantified to county-level. The results are shown in table 1 and figure 3.
4. HUMAN-NATURE INTERACTION ANALYSIS Environmental changes, mainly in forms of land use changes, are caused by human activity and mutations in natural conditions (e.g., climate change). The monitoring of such changes would be most relevant and useful when it is accompanied by the understanding of the forces driving change processes. The question is how to link the changes in environment with the human activity. As reviewed by Lambin (1994), this task could be calibrated by multivariate regression models, namely either panel analysis or cross-sectional analysis. The former links the changes in dependent variables (e.g., changes in environment) during a certain interval of time with the changes in independent variables (e.g., human activity) in the corresponding period of time and across a large number of localities. The latter associates the dependent variable (e.g., land use pattern) at one point of time with the independent variables (e.g., spatial determinants) at the same point of time across a large number of localities. The panel analysis allows us to understand the driving forces governing change progress and cross-sectional analysis aims at discriminating the spatial determinants for the environmental situation and structure at the observed point of time. These two analyses can be expressed as follows:  Figure 3: Environmental changes in the region of Yinchuan from 1987 to 1999 |
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