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Vegetation Stress Level Monitoring in Mine Area Based on HJ-1 Hyperspectral Data

Author: HuYuLing
Tutor: WangPing；ZhanLiLi
School: Shandong University of Science and Technology
Course: Photogrammetry and Remote Sensing
Keywords: vegetation’s pollution monitoring hyperspectral remote sensing HJ-1A satellite data heavy metal stress spectrum variation factor
CLC: TP751
Type: Master's thesis
Year: 2011
Downloads: 143
Quote: 2
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Abstract


Taking Yanzhou coal mine area as research area, this article studied the mining area vegetation heavy metal coercion situation by comprehensive using of ground data collection and hyperspectral remote sensing processing method. This article realized vegetation’s pollution monitoring in mine area based on the HJ-1A high spectrum remote sensing phantom, and provided the technical support for the mining area environmental pollution and governs.This paper focused on the following aspects of research:1. The author gathered the plant spectrum curve and the plant sample in the model district, tested Cu, Pb, Ni, Cr, Mn, and Zn heavy metal content, the moisture content and the chlorophyll content in the plant sample, and analyzed vegetation’s heavy metal pollution situation. That results show that six kind of heavy metal element’s synthesis pollution index from high to low is: Mn>Ni>Zn>Cu>Pb>Cr, the Jier mining area vegetation heavy metal pollution degree is more serious than the Jisan mining areas.2. This paper analyzed coercion vegetation’s spectral signatures, optimized the coercion vegetation’s best spectrum variation factor:554nm,631nm, the 557nm place index of reflection, R695/R670, R605/R760, R710/R760,1/R550 vegetation indices, constructed the vegetation heavy metal synthesis pollution index, the biochemistry parameter and between the spectrum characteristic factor multi-dimensional linear regression model. The best model estimation precision is above 80%.3. Based on the high spectrum remote sensing image HJ-1A/HSI data, this paper inverted the vegetation’s pollution index, the moisture content and the chlorophyll content. The best precision arrives 78.24%. The inversion result indicated that when the vegetation’s pollution became heavy, the vegetation water content from high become low and the vegetation chlorophyll content also changes. This result is consistent with previous studies.The practice proved that the data based on the HJ-1A high spectrum can select the coercion vegetation effectively, monitor the vegetation pollution situation. This paper provided the new mentality for discriminating the ecological environment condition through the vegetation growing trend and the pollution, provided the science reference for other similar satellite datum’s application.

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CLC: > Industrial Technology > Automation technology,computer technology > Remote sensing technology > Interpretation, identification and processing of remote sensing images > Image processing methods
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