Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image

The aim of this study is to find out the vegetation monitoring in mining areas which is of great significance in grasping the ecological environment status of mining areas. As the first target of spectral reflection, the vegetation canopy directly affects the results of spectral reception. Leaf Angle Distribution (LAD) is a key canopy structural parameter, which directly determines the amount of solar radiation intercepted by the canopy. Taking the relationship between the effect of leaf inclination angle on vegetation canopy reflectance and NDVI as an entry point, the study monitored and analyzed the vegetation in Dexing mining area, China, from 2013 to 2018 by NDVI, based on which the Copper Stress Vegetation Index (CSVI), which is applicable to the narrow wavelength band of remote sensing imagery, was evaluated whether it could be applied to the wide wavelength band. The results show that the NDVI value of the horizontal distribution is significantly higher than that of other leaf inclination distributions, and the NDVI in the direction of the horizontal distribution is 0.82, while the NDVI in the direction of the hi-straight distribution is 0.75; the NDVI of the mining area decreases gradually with the increase of the area of the mining area, and the correlation coefficients of the two groups of CSVI based on the wide and narrow bands are not large, which is not suitable for the vegetation monitoring by using the Landsat8 OLI imagery. Wide band is less applicable for vegetation monitoring. Bangladesh J. Bot. 53(3): 835-847, 2024 (September) Special

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Publication Details

Journal
Bangladesh Journal of Botany
Published
2024-10-14
DOI
https://doi.org/10.3329/bjb.v53i30.76631
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
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article

Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image

Quan Fu, Runqing Tian, Kun Wang, Ling Chen et al.
Bangladesh Journal of Botany
Remote Sensing and Land Use
article

Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image

Quan Fu, Runqing Tian, Kun Wang, Ling Chen, Tao Wang, Pan Wu
article en

Abstract

The aim of this study is to find out the vegetation monitoring in mining areas which is of great significance in grasping the ecological environment status of mining areas. As the first target of spectral reflection, the vegetation canopy directly affects the results of spectral reception. Leaf Angle Distribution (LAD) is a key canopy structural parameter, which directly determines the amount of solar radiation intercepted by the canopy. Taking the relationship between the effect of leaf inclination angle on vegetation canopy reflectance and NDVI as an entry point, the study monitored and analyzed the vegetation in Dexing mining area, China, from 2013 to 2018 by NDVI, based on which the Copper Stress Vegetation Index (CSVI), which is applicable to the narrow wavelength band of remote sensing imagery, was evaluated whether it could be applied to the wide wavelength band. The results show that the NDVI value of the horizontal distribution is significantly higher than that of other leaf inclination distributions, and the NDVI in the direction of the horizontal distribution is 0.82, while the NDVI in the direction of the hi-straight distribution is 0.75; the NDVI of the mining area decreases gradually with the increase of the area of the mining area, and the correlation coefficients of the two groups of CSVI based on the wide and narrow bands are not large, which is not suitable for the vegetation monitoring by using the Landsat8 OLI imagery. Wide band is less applicable for vegetation monitoring. Bangladesh J. Bot. 53(3): 835-847, 2024 (September) Special

Bangladesh Journal of Botany
Chang'an University (CN), China Land Surveying and Planning Institute (CN), China XD Group (China) (CN), Shaanxi Provincial Land Engineering Construction Group
Shaanxi Provincial Land Engineering Construction Group
Life in Land
Openalex Percentile: Top 78%
Remote Sensing and Land Use
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