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.

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.

Assessment on 2000-2010 Ecosystem Function Changes of Yiwulvshan National Nature Scenic Area

2014 ◽  
Vol 1051 ◽  
pp. 489-494
Author(s):  
Xiao Chen Wang ◽  
Jing Hai Zhu ◽  
Yuan Man Hu ◽  
Wei Ling Liu
Keyword(s):  
Remote Sensing ◽  
Ecosystem Function ◽  
Primary Productivity ◽  
Water Conservation ◽  
Net Primary Productivity ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
The Past ◽  
Remote Sensing Technology ◽  
Sensing Technology

Based on the remote-sensing data and ground data, this study is conducted on the ecosystem function of Yiwulvshan National Nature Scenic Area (hereinafter as “Yiwulvshan Scenic Area”) from 2000 to 2010 with the GIS (geographic information system) and RS (remote sensing) technology, so as to provide reference for better environmental protection of the scenic area. It is shown from the results that there is no obvious change of land use in Yiwulvshan Scenic Area; while the capacity for soil and water conservation is slightly improved mainly due to increase of vegetation coverage; the vegetation net primary productivity declines somewhat about 5.27% in past 10 years; and biodiversity is slightly increased. As a whole, the ecosystem function of Yiwulvshan Scenic Area basically kept stable in the past 10 years, which indicated that the existing regulations can effectively protect the ecological function of the Scenic Area.

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.

scholarly journals Predicting the Distribution of Oxytropis ochrocephala Bunge in the Source Region of the Yellow River (China) Based on UAV Sampling Data and Species Distribution Model

2021 ◽  
Vol 13 (24) ◽  
pp. 5129
Author(s):  
Xinyu Zhang ◽  
Yaxin Yuan ◽  
Zequn Zhu ◽  
Qingshan Ma ◽  
Hongyan Yu ◽  
...  
Keyword(s):  
Species Distribution ◽  
Yellow River ◽  
Source Region ◽  
Regional Scale ◽  
Animal Husbandry ◽  
Aerial Photographs ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Observation Data ◽  
Distribution Ranges

Oxytropis ochrocephala Bunge is an herbaceous perennial poisonous weed. It severely affects the production of local animal husbandry and ecosystem stability in the source region of Yellow River (SRYR), China. To date, however, the spatiotemporal distribution of O. ochrocephala is still unclear, mainly due to lack of high-precision observation data and effective methods at a regional scale. In this study, an efficient sampling method, based on unmanned aerial vehicle (UAV), was proposed to supply basic sampling data for species distribution models (SDMs, BIOMOD in this study). A total of 3232 aerial photographs were obtained, from 2018 to 2020, in SRYR, and the potential and future distribution of O. ochrocephala were predicted by an ensemble model, consisting of six basic models of BIOMOD. The results showed that: (1) O. ochrocephala mainly distributed in the southwest, middle, and northeast of the SRYR, and the high suitable habitat of O. ochrocephala accounted for 3.19%; (2) annual precipitation and annual mean temperature were the two most important factors that affect the distribution of O. ochrocephala, with a cumulative importance of 60.45%; and (3) the distribution probability of O. ochrocephala tends to increase from now to the 2070s, while spatial distribution ranges will remain in the southwest, middle, and northeast of the SRYR. This study shows that UAVs can potentially be used to obtain the basic data for species distribution modeling; the results are both beneficial to establishing reasonable management practices and animal husbandry in alpine grassland systems.

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