Analysis of driving factors that influence the pattern and quality of the ecosystem in Ordos

Tian, 董天 Dong; Yang, 肖洋 Xiao; Lu, 张路 Zhang; Yi, 肖燚 Xiao; Zheng, Hua; Zhiyun, 欧阳志云 Ouyang

doi:10.5846/stxb201804080788

Cited by 3 publications

(2 citation statements)

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“…For instance, Xiao et al [61] found a clear inverse association between precipitation and ecological quality in Inner Mongolia with regard to road density. Conversely, in a separate study conducted by Dong et al [62], a clear positive relationship was observed between the ecological quality of Ordos City and road density. This study revealed a significant negative correlation between road density and ecological quality.…”

Section: Driving Forces Of Ecological Qualitymentioning

confidence: 75%

Spatio-Temporal Variations in Ecological Quality and Its Response to Topography and Road Network Based on GEE: Taking the Minjiang River Basin as a Case

Zuo,

Li,

Zhang

et al. 2023

Land

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Urbanization has rapidly increased, leading to a wide range of significant disruptions to the global ecosystem. Road construction has emerged as the primary catalyst for such ecological degradation. As a result, it is imperative to develop efficient technological approaches for quantifying and tracking alterations in the ecological environment. Additionally, it is crucial to delve deeper into the spatial correlation between the quality of the ecosystem and the urban road network. This is of crucial importance in promoting sustainable development within the region. In this study, the research area selected was the Minjiang River Basin (MRB). We made optimal use of the Google Earth Engine (GEE) cloud platform to create a long-term series of remote sensing ecological index (RSEI) data in order to assess the quality of the ecological environment in the area. Additionally, we integrated digital elevation data (DEM) and OpenStreetMap (OSM) road network data to investigate the response mechanisms of RSEI with regard to elevation, slope, and the road network. The findings were as follows: (1) There were two distinct phases observed in the average value of RSEI: a slow-rising phase (2000–2010) with a growth rate of 1.09% and a rapidly rising phase (2010–2020) with a growth rate of 5.36%; the overall 20-year variation range fell between 0.575 and 0.808. (2) During the period of 2000 to 2010, approximately 41.6% of the area exhibited enhanced ecological quality, whereas 17.9% experienced degradation. Subsequently, from 2010 to 2020, the proportion of the region with improved ecological quality rose to 54.0%, while the percentage of degraded areas declined to 3.8%. (3) With increasing elevation and slope, the average value of RSEI initially rose and then declined. Specifically, the regions with the highest ecological quality were found in the areas with elevations ranging from 1200 to 1500 m and slopes ranging from 40 to 50°. In contrast, areas with an elevation below 300 meters or a slope of less than 10° had the poorest ecological quality. (4) The RSEI values exhibited a rapid ascent within the 1200 m buffer along the road network, while beyond this threshold, the increase in RSEI values became more subdued. (5) The bivariate analysis found a negative correlation between road network kernel density estimation (KDE) and RSEI, which grew stronger with larger scales. Spatial distribution patterns primarily comprised High–Low and Low–High clusters, in addition to non-significant clusters. The southeastern region contained concentrated High–Low clusters which covered approximately 10% of the study area, while Low–High clusters accounted for around 20% and were predominantly found in the western region. Analyzing the annual changes from 2000 to 2020, the southeastern region experienced a decrease in the number of High–Low clusters and an increase in the number of High–High clusters, whereas the northwestern region showed a decline in the number of Low–High clusters and an increase in the number of non-significant clusters. This study addresses a research gap by investigating the spatial correlation between road distribution and RSEI, which is vital for comprehending the interplay between human activities and ecosystem services within the basin system.

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Section: Driving Forces Of Ecological Qualitymentioning

confidence: 75%

Spatio-Temporal Variations in Ecological Quality and Its Response to Topography and Road Network Based on GEE: Taking the Minjiang River Basin as a Case

Zuo,

Li,

Zhang

et al. 2023

Land

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“…Some domestic scholars have studied the macroscopic pattern evolution and the scale of soil-wind erosion sample points of the Kubuqi Desert ecosystem. However, there is still a lack of research on desertification process monitoring and spatiotemporal evolution of soil-wind erosion based on the regional scale (Meng et al, 2014;Zhao et al, 2017;Dong et al, 2019). Based on this study, a new research method is proposed to carry out sand classification in the Kubuqi Desert.…”

mentioning

confidence: 98%

Spatio-Temporal Evolution of Sandy Land and its Impact on Soil Wind Erosion in the Kubuqi Desert in Recent 30 Years

Dang

Chi

et al. 2022

Front. Environ. Sci.

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Continuous remote-sensing monitoring of sand in desert areas and the exploration of the spatio–temporal evolution characteristics of soil–wind erosion has an important scientific value for desertification prevention and ecological restoration. In this study, the Kubuqi Desert was selected as the study area, and the Landsat series satellite remote sensing data, supplemented by satellite remote sensing data such as GE images, SPOT-5, ZY-3, GF-1/2/6, etc., integrated object-oriented, decision tree, and auxiliary human–computer interaction interpretation methods, developed the Kubuqi Desert area dataset from 1990 to 2020, and established a soil erosion intensity database of the past 30 years based on the soil–wind erosion correction equation. The results show that the application of the training samples obtained by a high-score collaborative ground sampling to land use/cover classification in desert areas can effectively improve the efficiency of remote-sensing mapping of sand changes and the accuracy of change information identification, and the overall accuracy of the classification results is 95%. In general, the sandy area of the Kubuqi Desert area has decreased year by year, during which the mobile sand in the hinterland of the desert has expanded in a scattered distribution. The overall soil–wind erosion intensity showed a downward trend, especially since 2000; the ecological improvement trend after the implementation of desertification control projects is obvious. Changes in the sand type contributed the most to the reduction of soil–wind erosion intensity (contribution 81.14%), ecological restoration played a key role in reducing the soil–wind erosion intensity (contribution 14.42%), and the increase of forest and grass vegetation covers and agricultural oases played a positive role in solidifying the soil- and wind-proof sand fixation. The pattern of sandy land changes in desert areas is closely related to the national ecological civilization construction policy and the impact of climate change.

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Relationships Between Key Dryland Ecosystem Services: A Case Study in Ordos, China

Zhao

Ding

et al. 2022

Front. Earth Sci.

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Dryland ecosystem services (ESs) have been severely harmed by global environmental changes and increased human activities. To improve ESs, it is necessary to understand how they interact in drylands. In this study, we selected Ordos dryland, which is situated in northern China, as the study area to assess its four key ESs—food supply (FS), carbon storage (CS), water yield (WY), and habitat quality (HQ)—and to identify the hotspots of multiple ES supply. Furthermore, we studied the constraint effects between ESs in Ordos in 2000, 2010, and 2020 and used a spatial trade-off model to map the trade-off and synergy areas of ESs from 2000 to 2010 and from 2010 to 2020. The results indicated that all four ESs in Ordos increased significantly over the study period. The hotspots for the supply of multiple ESs also increased in areal extent during this period, and the state of the regional ecological environment continued to improve. The constraint effect between ESs showed that as the CS increased, its constraint effect on WY and FS decreased and then increased, whereas its constraint effect on HQ only decreased; as the WY increased, its constraint effect on HQ decreased and then increased, and its constraint effect on FS continued to decrease; as the FS increased, its constraint effect on HQ continued to increase. From the change in the area of ESs trade-offs and synergies, there was an increase in the area of positive synergy for four pairs of ESs in Ordos, which were CS-WY, CS-HQ, WY-HQ, and FS-HQ. These findings help in establishing a scientific foundation for the management and optimization of ESs in drylands.

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Analysis of driving factors that influence the pattern and quality of the ecosystem in Ordos

Cited by 3 publications

References 0 publications

Spatio-Temporal Variations in Ecological Quality and Its Response to Topography and Road Network Based on GEE: Taking the Minjiang River Basin as a Case

Spatio-Temporal Variations in Ecological Quality and Its Response to Topography and Road Network Based on GEE: Taking the Minjiang River Basin as a Case

Spatio-Temporal Evolution of Sandy Land and its Impact on Soil Wind Erosion in the Kubuqi Desert in Recent 30 Years

Relationships Between Key Dryland Ecosystem Services: A Case Study in Ordos, China

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