Remote Sensing Monitoring of Soil Erosion in Hunyuan County

2013 ◽  
Vol 295-298 ◽  
pp. 2404-2408 ◽  
Author(s):  
Li Li Feng ◽  
Xiu Ming Jia
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Research Result ◽  
Vegetation Coverage ◽  
Affecting Factors ◽  
Northern Area ◽  
Remote Sensing Monitoring ◽  
Erosion Area ◽  
Remote Sensing Techniques ◽  
Land Using

Combining remote sensing techniques with GIS, choosing land using, vegetation coverage and slope as the main affecting factors of soil erosion to monitor and evaluate the soil erosion of Hunyuan County. The research result shows that the soil erosion was seriously, and the soil erosion area that intensity was greater than mild erosion in 2009 is 1635km2, occupies 83.2% totally. The severely erosion area is including Northern area, Southeastern mountains and the area between the alluvial plains and the mountains.

scholarly journals A Conversion Method to Determine Regional Vegetation Cover Factor from Standard Plots based on Large Sample Theory and TM Images: A Case Study in the Eastern Farming-pasture Ecotone of Northern China

2017 ◽  
Author(s):  
Degen Lin ◽  
Yuan Gao ◽  
Yaoyao Wu ◽  
Peijun Shi ◽  
Huiming Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Vegetation Cover ◽  
Regional Scale ◽  
Strong Support ◽  
Vegetation Coverage ◽  
Large Sample ◽  
Coverage Ratio ◽  
Conversion Method ◽  
Large Sample Theory

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiment, and needs the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

scholarly journals A Conversion Method to Determine Regional Vegetation Cover Factor from Standard Plots based on Large Sample Theory and TM Images: A Case Study in the Eastern Farming-pasture Ecotone of Northern China

2017 ◽  
Author(s):  
Degen Lin ◽  
Yuan Gao ◽  
Yaoyao Wu ◽  
Peijun Shi ◽  
Huiming Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Vegetation Cover ◽  
Regional Scale ◽  
Strong Support ◽  
Vegetation Coverage ◽  
Large Sample ◽  
Coverage Ratio ◽  
Conversion Method ◽  
Large Sample Theory

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot be directly using the C factor formula. That is because the C factor formula obtain by experiment, and need the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improved the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

A METHODOLOGY FOR MAPPING AND MONITORING CROPLAND SOIL EROSION

1987 ◽  
Vol 67 (3) ◽  
pp. 433-444 ◽  
Author(s):  
JOSEF CIHLAR
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Satellite Remote Sensing ◽  
Field Evaluation ◽  
Rill Erosion ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Long Term Trends ◽  
Remote Sensing Techniques

A methodology is described for mapping and monitoring the erosion of soil by water, using remote sensing techniques and the universal soil loss equation as the primary tools. Four aspects are covered: mapping baseline sheet and rill erosion, monitoring actual rill and gully erosion, estimating changes in potential sheet and rill erosion, and determining long-term trends. A successful field evaluation of the methodology was undertaken in a potato-growing area of New Brunswick. The implementation of the procedure using state-of-the-art microcomputer and satellite remote sensing technology is proposed. Key words: Soil erosion, remote sensing, geographic information systems

scholarly journals Remote Sensing Monitoring of Vegetation Coverage of Hunshandake Sand Based on HJ-1A

2021 ◽  
Vol 783 (1) ◽  
pp. 012139
Author(s):  
Tianshi Feng ◽  
Zhiguo Wang ◽  
Zhiguo Pang ◽  
Wei Jiang ◽  
Hao Li
Keyword(s):  
Remote Sensing ◽  
Vegetation Coverage ◽  
Remote Sensing Monitoring

scholarly journals Comprehensive Research on Remote Sensing Monitoring of Grassland Degradation: A Case Study in the Three-River Source Region, China

2019 ◽  
Vol 11 (7) ◽  
pp. 1845 ◽  
Author(s):  
Ying Zhang ◽  
Chaobin Zhang ◽  
Zhaoqi Wang ◽  
Ru An ◽  
Jianlong Li
Keyword(s):  
Remote Sensing ◽  
Net Primary Productivity ◽  
Comprehensive Evaluation ◽  
Yellow River ◽  
Source Region ◽  
Regional Scale ◽  
Vegetation Coverage ◽  
Grassland Degradation ◽  
Annual Change ◽  
Remote Sensing Monitoring

In this study, we proposed climate use efficiency (CUE), a new index in monitoring grassland ecosystem function, to mitigate the disturbance of climate fluctuation. A comprehensive evaluation index (EI), combining with actual vegetation net primary productivity (NPP), CUE, vegetation coverage, and surface bareness, was constructed for the dynamic remote sensing monitoring of grassland degradation/restoration on a regional scale. By using this index, the grassland degradation/restoration in the Three-River Source Region (TRSR) was quantitatively evaluated during 2001–2016, which has been an important ecological barrier area in China. Results showed the following: During the study period, the grassland of Yellow River source (SRYe) had high vegetation coverage, NPP, CUE, and low bareness, whereas Yangtze River source (SRYa) had low vegetation coverage, NPP, CUE, and high bareness. The vegetation coverage and CUE of the grassland showed upward trends, with annual change rates of 0.75% and 0.45% year −1. The surface bareness and NPP showed downward trends, with annual change rates of −0.37% year−1 and −0.24 g C m−2 yr−2, respectively. Assessment of EI revealed that 67.18% of the grassland of TRSR showed a recovery trend during the study period. The overall restoration of the SRYe was the best, followed by SRYa. However, the status of Lancang River source (SRLa) was poor.

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