Search Results (4)

Open-pit coal mining inevitably damages the soil and vegetation in mining areas. Currently, the restoration of cold and arid open-pit mines in Xinjiang, China, is still in the initial exploratory stage, especially the changes in soil nutrients in spoil dumps over time. Dynamic remote sensing monitoring of vegetation in mining areas and their correlation are relatively rare. Using the Heishan Open Pit in Xinjiang, China, as a case, soil samples were collected during different discharge periods to analyze the changes in soil nutrients and uncover the restoration mechanisms. Based on four Landsat images from 2018 to 2023, the remote sensing ecological index (RSEI) and fractional vegetation cover (FVC) were obtained to evaluate the effect of mine restoration. Additionally, the correlation between vegetation changes and soil nutrients was analyzed. The results indicated that (i) the contents of nitrogen (N), phosphorus (P), potassium (K), and organic matter (OM) in the soil increased with the duration of the restoration period. (ii) When the restoration time of the dump exceeds 5 years, N, P, K, and OM content is higher than that of the original surface-covered vegetation area. (iii) Notably, under the same restoration aging, the soil in the artificial mine restoration demonstration base had significantly higher contents of these nutrients compared to the soil naturally restored in the dump. (iv) Over the past five years, the RSEI and FVC in the Heishan Open Pit showed an overall upward trend. The slope remediation and mine restoration project significantly increased the RSEI and FVC values in the mining area. (v) Air humidity and surface temperature were identified as key natural factors affecting the RSEI and FVC in cold and arid open pit. The correlation coefficients between soil nutrient content and vegetation coverage were higher than 0.78, indicating a close and complementary relationship between the two. The above results can clarify the time–effect relationship between natural recovery and artificial restoration of spoil dumps in cold and arid mining areas in Xinjiang, further promoting the research and practice of mine restoration technology in cold and arid open pits. Full article

Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection. Full article

One essential part of transportation infrastructure is highways. The surrounding eco-environment is greatly impacted by the construction of highways. However, few studies have investigated changes in eco-environmental quality during highway construction, and the main impact areas of the construction have not been clarified. The highway from Sunit Right Banner to Huade (Inner Mongolia–Hebei border) was used as the study area. GEE was used to establish RSEI. During highway construction, Sen + M-K trend analysis, Hurst analysis, and Geodetector were employed to assess RSEI changes and driving factors. The results show the following: (1) An area of 1500 m around the highway is where the ecological impact of highway construction will be the greatest. (2) The curve of the annual mean of the RSEI from 2016 to 2021 is V-shaped. From northwest to southeast, there is an increasing trend in spatial distribution. (3) The largest environmental degradation during highway construction occurred during the first year of highway construction. (4) The factor detector results indicate that DEM, precipitation, distance from the administrative district, and FVC were the main RSEI drivers in the research region. The interaction detector’s findings show that the drivers’ combined influence on the RSEI was greater than that of their individual components. (5) Compared to the 2016–2021 trend, the proportion of future degraded areas in terms of eco-environmental quality will increase by 3.16%, while the proportion of improved areas will decrease by 2.99%. Full article

With the rapid development of urbanization and population growth, the ecological environment in the Yellow River Delta has undergone significant changes. In this study, Landsat satellite data and Google Earth Engine (GEE) were utilized to dynamically evaluate the changes in eco-environmental quality in the Yellow River Delta region using the remote sensing ecological index (RSEI). Additionally, the CASA model was used to estimate net primary productivity (NPP) and explore the relationship between vegetation NPP, land-use and land-cover change (LUCC), and eco-environmental quality to reveal the complexity and related factors of eco-environmental quality changes in this region. The results show that: (1) Over the past 20 years, the eco-environmental quality in the Yellow River Delta region has changed in a “V” shape. The eco-environmental quality near the Yellow River Basin is relatively better, forming a diagonal “Y” shape, while the areas with poorer eco-environmental quality are mainly distributed in the coastal edge region of the Yellow River Delta. (2) The response of vegetation NPP to eco-environmental quality in the Yellow River Delta region is unstable. (3) Urban construction land in the Yellow River Delta region is strongly correlated with RSEI, and the absolute value of the dynamic degree of land use is as high as 8.78%, with significant land transfer changes. The correlation between arable land and RSEI is weak, while coastal mudflats are negatively correlated with RSEI, with the minimum absolute value of the dynamic degree of land use being −1.01%, and significant land transfer changes. There is no correlation between forest land and RSEI. Our research results can provide data support for the eco-environmental protection and sustainable development of the Yellow River Delta region and help local governments to take corresponding measures. Full article