首页|Quantitative inverse modeling of nitrogen content from hyperion data under stress of exhausted coal mining sites

Quantitative inverse modeling of nitrogen content from hyperion data under stress of exhausted coal mining sites

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Monitoring and evaluating the nutritional status of vegetation under stress from exhausted coal mining sites by hy-per-spectral remote sensing is important in future ecological restoration engineering. The Wangpingcun coal mine, located in the Mentougou district of Beijing, was chosen as a ease study. The ecological damage was analyzed by 3S technology, field investiga-tion and from chemical data. The derivative spectra of the diagnostic absorption bands are derived from the spectra measured in the field and used as characteristic spectral variables. A correlation analysis was conducted for the nitrogen content of the vegetation samples and the first derivative spectrum and the estimation model of nitrogen content established by a multiple stepwise linear regression method. The spatial distribution of nitrogen content was extracted by a parameter mapping method from the Hyperion data which revealed the distribution of the nitrogen content. In addition, the estimation model was evaluated for two evaluation indicators which are important for the precision of the model. Experimental results indicate that by linear regression and parameter mapping, the estimation model precision was very high. The coefficient of determination, R2, was 0.795 and the standard deviation of residual (SDR) 0.19. The nitrogen content of most samples was about 1.03% and the nitrogen content in the study site seems inversely proportional to the distance from the piles of coal waste. Therefore, we can conclude that inversely modeling nitrogen content by hyper-speetral remote sensing in exhausted coal mining sites is feasible and our study can be taken as reference in spe-cies selection and in subsequent management and maintenance in ecological restoration.

Hyperionnitrogen contentestimation modellinear regression

LU Xia、HU Zhen-qi、GUO Li

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Department of Marine Technology, Huaihai Institute of Technology, Lianyungang, Jiangsu 222001, China

College of Resources & Safety Engineering, China University of Mining & Technology, Beijing 100083, China

Beijing Institute of Geological Survey, Beijing 100083, China

2009

矿业科学技术学报(英文版)
中国矿业大学

矿业科学技术学报(英文版)

CSTPCDEI
影响因子：1.222
ISSN：2095-2686
年,卷(期)：2009.19(1)
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