scholarly journals Land Cover Mapping and Ecological Risk Assessment in the Context of Recent Ecological Migration

2021 ◽  
Vol 13 (7) ◽  
pp. 1381
Author(s):  
Tingting Zhang ◽  
Zhenrong Du ◽  
Jianyu Yang ◽  
Xiaochuang Yao ◽  
Cong Ou ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Ecological Risk ◽  
Supervised Machine Learning ◽  
Ecological Environment ◽  
Monitoring Method ◽  
Construction Land ◽  
Ecological Migration ◽  
Unused Land ◽  
The Impact

In order to protect the ecological environment and solve the poverty problem in the western region, China has established an ecological migration (EM) policy. This policy aims to relocate populations from poverty-stricken areas with fragile ecological environments, which inevitably leads to changes in land cover and the ecological environment. The objective of this study was to identify the effects of EM in a typical region (Wuwei), including changes in the land cover and ecological risk (ER). A land cover change monitoring method was implemented for the 2010–2019 period for six land cover classes using random forest, which is an effective supervised machine learning method. The land cover change patterns were analyzed by determining the area changes of the six classes and applying a land use transition matrix, and a landscape ecological risk model based on landscape disturbance and fragility was used. Our results demonstrate that the increase and decrease in the area of cultivated land, unused land, and construction land can be divided into two stages (2010–2015 and 2015–2019). The area of water and perennial snow doubled during the study periods. The major land cover transitions were between unused land and construction land and between unused land and crop land. In addition, the ER value for the Qilian Mountain National Nature Reserve decreased because of the implementation of EM in the study area, indicating that the ecological environment was effectively improved. The results demonstrate the advantage of the proposed approach in understanding the impact of EM on regional land cover changes and the ecological environment so as to provide guidance for follow-up planning and development.

scholarly journals Correlation Studies between Land Cover Change and Baidu Index: A Case Study of Hubei Province

2020 ◽  
Vol 9 (4) ◽  
pp. 232 ◽  
Author(s):  
Yongqing Zhao ◽  
Rendong Li ◽  
Mingquan Wu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Driving Forces ◽  
Research Area ◽  
Hubei Province ◽  
Cultivated Land ◽  
Land Occupation ◽  
Construction Land ◽  
The Impact

Current land cover research focuses primarily on spatial changes in land cover and the driving forces behind these changes. Among such forces is the influence of policy, which has proven difficult to measure, and no quantitative research has been conducted. On the basis of previous studies, we took Hubei Province as the research area, using remote sensing (RS) images to extract land cover change data using a single land use dynamic degree and a comprehensive land use dynamic degree to study land cover changes from 2000 to 2015. Then, after introducing the Baidu Index (BDI), we explored its relationship with land cover change and built a tool to quantitatively measure the impact of changes in land cover. The research shows that the key search terms in the BDI are ‘cultivated land occupation tax’ and ‘construction land planning permit’, which are closely related to changes in cultivated land and construction land, respectively. Cultivated land and construction land in all regions of Hubei Province are affected by policy measures with the effects of policy decreasing the greater the distance from Wuhan, while Wuhan is the least affected region.

Modelling long-term soil organic carbon dynamics under the impact of land cover change and soil redistribution

CATENA ◽  
2017 ◽  
Vol 151 ◽  
pp. 63-73 ◽  
Author(s):  
Samuel Bouchoms ◽  
Zhengang Wang ◽  
Veerle Vanacker ◽  
Sebastian Doetterl ◽  
Kristof Van Oost
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Dynamics ◽  
Soil Redistribution ◽  
The Impact ◽  
Soil Organic Carbon Dynamics

scholarly journals Land cover change dominates decadal trends of biogenic volatile organic compound (BVOC) emission in China

2020 ◽  
Author(s):  
Hui Wang ◽  
Qizhong Wu ◽  
Alex B. Guenther ◽  
Xiaochun Yang ◽  
Lanning Wang ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Correlation Coefficients ◽  
Anthropogenic Sources ◽  
The South ◽  
High Positive Correlation ◽  
Positive Correlation ◽  
Isoprene Emission ◽  
Increasing Trends ◽  
The Impact

Abstract. Satellite observations reveal that China has been leading the global greening trend in the past two decades. We assessed the impact of land cover change on total BVOC emission in China during 2001–2016 and found a significant increasing trend of 1.09 % yr−1 with increases of 1.35, 1.25 and 1.43 % yr−1 for isoprene, monoterpenes and sesquiterpenes, respectively. Comparison of different scenarios showed that vegetation change is the main driver of BVOC emission change in China. Considerable heterogeneity was observed on regional scales, with the highest increasing trends of BVOC emission found in the Qinling Mountains and in the south of China. The BVOC emission for the year 2016 in these two regions was enhanced by 61.89 and 67.64 % compared to that of 2001, respectively. We compared the long-term HCHO vertical columns (VC) from the satellite-based Ozone Monitoring Instrument (OMI) with the estimation of isoprene emission in summer. The results showed statistically significant positive correlation coefficients over the regions with high vegetation cover fractions. In addition, the isoprene emission and HCHO VC both showed statistically significant increasing trends in the south of China where these two variables have high positive correlation coefficients. This result supports our estimation of the variability and trends of BVOC emission in China. Although anthropogenic sources comprise ∼63 % NMVOC emissions in China, the continued increase of BVOC will enhance the importance of considering BVOC when making policies for controlling ozone pollution in China along with ongoing efforts to reduce anthropogenic emissions.

The impact of land cover change on storms in the Sydney Basin, Australia☆

2006 ◽  
Vol 54 (1-2) ◽  
pp. 57-78 ◽  
Author(s):  
A GERO ◽  
A PITMAN ◽  
G NARISMA ◽  
C JACOBSON ◽  
R PIELKE
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
The Impact

scholarly journals A spatial resolution threshold of land cover in estimating terrestrial carbon sequestration in four counties in Georgia and Alabama, USA

2010 ◽  
Vol 7 (1) ◽  
pp. 71-80 ◽  
Author(s):  
S. Q. Zhao ◽  
S. Liu ◽  
Z. Li ◽  
T. L. Sohl
Keyword(s):  
Carbon Sequestration ◽  
Land Cover ◽  
Land Cover Change ◽  
Spatial Resolution ◽  
Spatial Patterns ◽  
Global Scale ◽  
Terrestrial Carbon ◽  
Carbon Modeling ◽  
Terrestrial Carbon Sequestration ◽  
The Impact

Abstract. Changes in carbon density (i.e., carbon stock per unit area) and land cover greatly affect carbon sequestration. Previous studies have shown that land cover change detection strongly depends on spatial scale. However, the influence of the spatial resolution of land cover change information on the estimated terrestrial carbon sequestration is not known. Here, we quantified and evaluated the impact of land cover change databases at various spatial resolutions (250 m, 500 m, 1 km, 2 km, and 4 km) on the magnitude and spatial patterns of regional carbon sequestration in four counties in Georgia and Alabama using the General Ensemble biogeochemical Modeling System (GEMS). Results indicated a threshold of 1 km in the land cover change databases and in the estimated regional terrestrial carbon sequestration. Beyond this threshold, significant biases occurred in the estimation of terrestrial carbon sequestration, its interannual variability, and spatial patterns. In addition, the overriding impact of interannual climate variability on the temporal change of regional carbon sequestration was unrealistically overshadowed by the impact of land cover change beyond the threshold. The implications of these findings directly challenge current continental- to global-scale carbon modeling efforts relying on information at coarse spatial resolution without incorporating fine-scale land cover dynamics.

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