|
|
|
Environmental Change Monitoring - A Case Study in the Region of Yinchuan,
Ningxia, China
Weicheng Wu
PRODIG/Ecole Pratique des Hautes Etudes, 191 Rue St-Jacques, 75005 Paris, France
Tel.: +33 1 44 32 14 88 Fax: +33 1 43 29 63 83
Email: E-mail: wuwc@univ-paris1.fr,
1. Introduction
Change monitoring by remote sensing has been one of the environment research foci since 1960s, when Verstappen used aerial photographs to measure the coastal evolution (Wu et al., 2002b). Other two widely applied tools occurred in the last decade are Geographical Information System (GIS) and Global Positioning System (GPS). These three techniques, termed "3S" in the 1990s and recently replaced by "geomatics" or "geoinformatics", have played an important role in the researches in environment. A recently occurred hotspot is the human-environment interaction modelling which aims at understanding the driving forces and mechanism of the environmental evolution based on GIS and linear/non-linear statistic analysis (Lambin, 1994, Mertens et al., 1997; Wu et al., 2002a, 2003 and Wu 2003a and b). This paper summarises such an interdisciplinary research by linking environment evolution with human activity, taking the region of Yinchuan, Ningxia, China, for example.
The study site, administratively located in the north part of Ningxia and surrounded by Inner Mongolia on the east, north and west, is one of the arid regions in northwest China (figure 1). It consists of a half of the Helan Mountains and most part of the Yinchuan Plain. The annual precipitation ranges from 78 to 295mm, annual evaporation from 1473 to 2318mm and annual average temperature from 8.2°C to 9.6°C in the recent decades (Wu et al. 2002a). The analysis on meteorological data in the past half century indicates that the annual temperature has been increasing and precipitation decreasing. The climate has been getting dryer and warmer. Since 1999, the Chinese government has inaugurated a middle to long term development strategy on the northwestern regions in China. The region of Yinchuan becomes one of the critical areas. It is thus of importance to undertake an interdisciplinary research on the evaluation of the environmental changes due to human activities and provide a monitoring prototype and useful references to the local governments for their sustainable development planning and environmental management. The initial study result has been reported in the conference Map Asia 2002 (Wu et al., 2002a). This paper presents a more profound analysis on the relative subject as a continued part of the former.
Multitemporal remote sensing data (Landsat TM dated Sept.20, 1987, Sept.17, 1989, and ETM Aug.12, 1999), county-level socio-economic and meteorological data and software such as PCI, ER Mapper, SYSTAT/SAS and ArcView GIS were utilised in this research.
2. METHODOLOGY
As mentioned above, the method adopted in the change monitoring is precisely shown in figure 2.
3. CHANGE DISCRIMINATION
The procedures to distinguish the changes shown in figure 2 are depicted as follows:
- Image-to-image registration
The remotely sensed data (TM1987, 1989 and ETM1999) were geometrically corrected by the topographic maps on the scale of 1/200,000 to 1/300,000 in the datum WGS84 and projection UTM (48) using polynomial model (3rd order) and bilinear re-sampling. The RMS error of the image-to-image rectification comes between 0.53 and 0.58 pixels.
- Atmospheric correction
A completely image-based approach was introduced for atmospheric correction based on the researches of Crist et al. (1984a, 1984b and 1986a) and Chavez (1988 and 1996).
Chavez proposed a DOS (dark-object subtraction) model (1975, 1988) and its ameliorated version - COST model (1996). A key point in his method is to measure the haze value to be removed. The traditional way to obtain the haze value is to measure the radiance in some deep clear water or shaded areas in image where the radiance in near infrared bands is zero or near to zero. Any over-zero value is considered to be a result of scattering and path radiation. Such haze removal often produces over-correction and is not applicable to the image where dark-object does not exist (Chavez, 1996). A potential approach to acquire this value is by Tasseled Cap transformation. According to Crist et al. (1986a), the 4th component of this transformation is a haze indicator, which can be expressed as:
H = 0.8832+B1 - 0.0819+B2 - 0.4580+B3 - 0.0032+B4 - 0.0563+B5 + 0.0130+B7 (1)
where H is the haze value of pixel.
The equation (1) produces a total haze value for each pixel. On the assumption that whole scene should have the same haze background, the mean haze value derived from this equation was thus used to remove the scattering effect supposing that it was very clear when the images were being sensed (see Wu 2003b).
|
|
|