WETLAND AND WATER RESOURCE MODELING AND ASSESSMENT: A Watershed Perspective - Chapter 15 ppt

179 15 Development of Geospatial Ecological Indicators in Jiangxi Province, China Peng Guo and Xiaoling Chen 15.1 INTRODUCTION Comprehensive evaluation of ecological environments is necessary for environmen- tal sustainability and management planning, which can provide quantitative docu- ments as scientic guides for informed decision making. The existence of several environmental impact assessment (EIA) methods makes it difcult to make on an appropriate choice (Sankoh 1996). Remote sensing, being a very useful observational tool, has been integrated with GIS (geographic information system) to monitor and evaluate environmental condi- tions (Shen et al. 2004, Wang et al. 2002, 2004). In these studies, Landsat TM, ETM+ and NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer) images were used to obtain the vegetation cover status derived from NDVI (Normalized Distance Vegetative Index). In this paper, we demonstrate the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) for monitoring ecological and environmental conditions. MODIS is own on two NASA satellites (Terra/morning pass: 1999–present and Aqua/afternoon pass: 2002–present). MODIS data are collected continuously and are available for public use. Poyang Lake, located in the middle reach of the Yangtze River and the subtropi- cal wet monsoon zone, is the largest freshwater lake in China. The area of Poyang Lake basin is 16.22 × 10 4 km 2 (9% of the Yangtze River basin), and 97% of the basin is located in Jiangxi Province. Over many years, the total area of forest and lake land cover has been reduced due to human activities such as increasing popula- tion, which has resulted in the deterioration of the ecological environment in the Poyang Lake basin. The area of soil and water being damaged by erosion in Jiangxi Province is 3.52 × 10 4 km 2 (Guan 2001). This process of erosion has been seriously destroying the quality of the ecological environment and impacting the sustainable development of economic growth in Jiangxi Province and the Yangtze River basin. This paper focuses on developing geospatial ecological indicators and quantitatively analyzing environmental factors of Jiangxi Province based on remote sensing and GIS methods. © 2008 by Taylor & Francis Group, LLC 180 Wetland and Water Resource Modeling and Assessment 15.2 MATERIALS AND METHODS 15.2.1 B UILDING AN EVALUATION INDICATOR SYSTEM Many factors, including biophysical and anthropogenic factors, may inuence the ecology and environment in the basin. In order to quantitatively depict the char- acteristics of ecology and environment, an evaluation indicator system needs to be developed in a comprehensive and concise way for factor selection. In this chapter, various factors, such as characteristics and spatial scale of the Jiangxi Province, climate, terrain, soil erosion, and vegetation cover status, were selected to build the indicator system that can better reect the ecological and environmental characteris- tics of the study area. The data mainly includes ≥0° accumulated temperature, ≥10° accumulated temperature, average annual temperature, precipitation and evapo- transpiration, surface humid index, vegetation index, elevation, slope, aspect, soil erosion, and MODIS image. 15.2.2 EXTRACTION OF WATER BODIES AND NDVI In the process of evaluation, a water body was regarded as an individual land object to be distinguished from others. According to the characteristics of spectral reec- tance of a water body and vegetation, the near-infrared band is the most useful in distinguishing the land-and-water boundary and ground vegetation (Zhen and Chen 1995, Tan et al. 2004). Bands 1 and 2 of the MODIS images, whose wavelengths range from 0.62 to 0.67 μm and 0.84 to 0.87 μm, respectively, match bands 3 and 4 of the Landsat TM images. These two bands were used to identify vegetation and water body. The NDVI is adopted to synthesize vegetation index diagram as follows: NDVI = (NIR − Red)/(NIR + Red) = (Band2 − Band1)/ (Band2 + Band1) The NIR and Red are are digital numbers (DNs) of the near-infrared band and red band, respectively. The analysis showed that the area of NDVI <0.25 was cov- ered by water bodies in the wetlands of the Poyang Lake basin. 15.2.3 STATISTICAL METHODS It is important and difcult to integrate the multi-indices into an integrative indicator when the ecological environment is evaluated. Some methods such as weighted index, AHP (analytical hierarchy process), and GEM (group eigenvalue method) have been ordinarily used. In this chapter, PCA (principal component analysis) was performed on the ecological parameters to evaluate the ecological environment in Jiangxi Province. 15.2.4 PRINCIPAL COMPONENT ANALYSIS Principal component analysis is a powerful technique for pattern recognition that attempts to explain the variance of a large set of intercorrelated variables and trans- form them into a smaller set of independent (uncorrelated) variables (principal com- ponents). The rst principal component accounts for as much of the variability in the dataset as possible, and each succeeding component accounts for as much of the © 2008 by Taylor & Francis Group, LLC Development of Geospatial Ecological Indicators in Jiangxi Province, China 181 remaining variability as possible. Principal component analysis provides informa- tion on the most meaningful parameters, which describe whole datasets, then render data reduction with minimum loss of original information. Performed with GIS software, spatial PCA is used to transform the data in a stack from the input multivariate attribute space into a new multivariate attribute space whose axes are rotated with respect to the original space. The axes (attributes) in the new space are uncorrelated. The main reasons for transforming the data in a principal component analysis are to “compress” data by eliminating redundancy, to emphasize the variance within the grids of a stack, and to make the data more interpretable. In the evaluation of an ecological environment, the integrative indicator can be dened as a weighted sum of M principal components. The weight is the component loading of principal components. The function is expressed as: where E is the result of assessment, a is the component loading, and Y is the principal component. For excluding the effect of different scalars and dimensions of variables, the data were standardized by the follow equation: where X aver is the average of X, and is the standard deviation of X. 15.2.5 QUALITY INDEX OF ECOLOGICAL ENVIRONMENTAL BACKGROUND Based on the result of an ecological environmental synthetic evaluation, we used the following function to calculate the quality index of the ecological environment background of Jiangxi Province: where I j is the quality index of the ecological environment background in the region j; E i is the value of eco-environmental classes, as there are six environmental classes; the eco-environment with the best situation is assigned as 6, and the one with the worst situation is assigned as 1; A i is the area of class i and S i is the area of region j. 15.3 RESULTS AND DISCUSSION By processing data with GRID model in the ArcInfo workstation, we calculated the climate index using the following parameters: ≥0° accumulated temperature, ≥10° © 2008 by Taylor & Francis Group, LLC E aY a Y a Y M M = + + + 1 1 2 2  (15.1) D X X i i aver = −( ) σ (15.2) I E A S j i i i i = × × = ∑ 100 1 6 ( / ) (15.3) s 182 Wetland and Water Resource Modeling and Assessment accumulated temperature, the average annual temperature, precipitation and evapo- transpiration, surface humid index, and terrain index from the following parameters: elevation, slope, aspect. The equations are as follows: I climate = 0.5149 × ClimateP1 + 0.3040 × ClimateP2 + 0.1721 × ClimateP3 (15.4) I terr = 0.6385 × TerrP1 + 0.2582 × TerrP2 (15.5) where I climate is the climate index, ClimateP i is the ith principal component of climate index; I terr is the topographical index, and TerrP i is the ith principal component of topographical index. After analyzing the vegetation index, soil erosion index, topographical index, and climate index by using the PCA method, we calculated the ecological environ- mental assessment index in Jiangxi Province. Table 15.1 shows the principal compo- nent Eigen values of various principal components. From the above analysis, we derived the linear equation of the ecological envi- ronmental assessment index: I eco = 0.7806 × EcoP 1 + 0.1407 × EcoP 2 (15.6) where I eco is the ecological environmental assessment index, EcoP 1 is the rst PC (principal component) of four indices, and EcoP 2 is the second PC. We then calculated the integrated ecological environmental evaluation for Jiangxi Province (Table 15.2) using the environmental evaluation classication map (Figure 15.1). The ecological environment was classied into six classes by the eco-environ- ment indicator. Table 15.2 shows that the largest area occurs in class 5, while the sum of classes 3, 4, and 5 covers 64.05% of the total area. The sixth class, describ- ing the optimal situation of a quality eco-environment, covers 16.70% of the total province, which is a class characterized by less soil erosion and higher vegetation coverage. Figure 15.1 shows that the quality of the ecological environment in the upper reaches of the ve rivers in the Poyang Lake basin is worse than the lower reaches of those rivers in the basin, which are near Poyang Lake and mainly consist of ood- plains. The environment in the upper reaches of the ve rivers in the Poyang Lake basin has a great potential for affecting the water quality of Poyang Lake. Figure 15.2 is the quality index map of the eco-environmental background of Jiangxi Province, which was calculated based on equation 15.3, which can be used to spatially identify the environmental quality in Jiangxi Province. The results revealed that the Nan- chang area has good eco-environmental quality. In addition, we determined that Ganzhou in the southern part of the Poyang Lake basin, and Pingxiang in the north- ern part, have poor eco-environmental quality. © 2008 by Taylor & Francis Group, LLC Development of Geospatial Ecological Indicators in Jiangxi Province, China 183 TABLE 15.1 The eigenvalues and attribute ratio in the principal components analysis. Ecological environment index Terrain index Climate index PC Eigen values Deviation loading (%) Accumulated de viation (%) E igen values Deviation loading (%) Accumulated deviation (%) Eigen values Deviation loading (%) Accumulated deviation (%) I 1.37968 78.06 78.06 1.02512 63.85 63.85 1.42617 0.5149 51.49 II 0.24869 14.07 92.13 0.43053 25.82 89.67 0.59126 0.3040 81.89 III 0.12187 6.90 99.03 0.15990 10.33 100 0.49594 0.1721 99.10 © 2008 by Taylor & Francis Group, LLC 184 Wetland and Water Resource Modeling and Assessment TABLE 15.2 The integrated ecological environmental evaluation of Jiangxi Province, China. Eco-environmental class Grid number Area (km 2 ) Percent (%) 1 775080 7750.8 4.64 2 2152866 21528.66 12.89 3 3199463 31994.63 19.16 4 3684927 36849.27 22.07 5 3810588 38105.88 22.82 6 2788534 27885.34 16.70 Water 284414 2844.14 1.70 Total 16765872 167658.72 100.00 water 1 2 3 4 5 6 FIGURE 15.1 The integrated eco-environmental classes of Jiangxi Province, China. (Source: Chinese State Bureau of Surveying and Mapping) © 2008 by Taylor & Francis Group, LLC Development of Geospatial Ecological Indicators in Jiangxi Province, China 185 15.4 CONCLUSIONS It is obvious that remote sensing and geographic information science (RSGIS) are useful tools for studying the status of eco-environmental quality for diverse regions. RSGIS has many advantages such as lower cost, faster information collection, and more efcient investigation and assessment of the eco-environment on a large spatial scale. With the development of a new generation of remote sensing sensors, RSGIS will provide us with more feasible and cheaper means for studying the environmen- tal problem. PCA, as a very useful analysis method, can be commendably used in the assessment. Combining the PCA method with RSGIS, this study performs primary research on the eco-environment in Jiangxi Province, which produced meaningful results. In next stage, we will study the change of eco-environment over the past 30 years by using this study frame. ACKNOWLEDGMENTS This work was supported by the National Key Basic Research and Development Pro- gram: 2003CB415205 and the Opening Foundation of the Key Lab of Poyang Lake Ecological Environment and Resource Development at Jiangxi Normal University, Grant No.200401006(1). High : 509 Fu zhou Jiu jiang Jing de zhen Gan zhou Ji an Ping xiang Xin yu Ying tan Shang rao Nan chang Yi chun Low : 346 FIGURE 15.2 The quality index map of the eco-environmental background of Jiangxi Province, China. (Source: Chinese State Bureau of Surveying and Mapping) © 2008 by Taylor & Francis Group, LLC 186 Wetland and Water Resource Modeling and Assessment REFERENCES Bernard, P., and L. Antoine. 2004. Principal component analysis: An appropriate tool for water quality evaluation and management-application to a tropical lake system. Eco- logical modeling 295–311. Guan, R. S., 2001. 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