Evaluation of the Ecological Environment Quality of the Kuye River Source Basin Using the Remote Sensing Ecological Index

Synergizing remote sensing and ecological indicators (RSEIs) for evaluating ecological environmental quality (EEQ) in Asansol Municipal Corporation: an integrated approach

Human activities have dramatically affected global ecology over the past few decades. Geospatial technologies provide quick, efficient, and quantitative evaluation of spatiotemporal changes in eco-environmental quality (EEQ). This study focuses on a novel approach called remote sensing-based ecological indicators (RSEIs), which has used Landsat imagery data to assess environmental conditions and their changing trends. Four ecological indicators, mainly heatness, dryness, wetness, and greenness, have been used to assess the EEQ in Asansol Municipal Corporation Region (AMCR). Assembling all the indicators to generate RSEI, the principal component analysis (PCA) approach was applied. Our findings show that wetness and greenness favorably impact the province's EEQ, whereas dryness and heat create a negative impact. The RSEI assessment revealed that 24.53 to 28.83% of the area was poor and very poor, whereas the areas with very good decreased from 18.80 to 4.01% from 2001 to 2021 due to urban expansion and industrialization. The relative importance analysis indicates that greenness has a positive relation with RSEI, and dryness and heatness have a negative relation with RSEI. Finally, the receiving operating characteristic (ROC) was used for validation (AUC-0.885) of the RSEI. This study offers valuable insights for ecological management decision-making, guiding planners, and policymakers.

Application of a novel remote sensing ecological index (RSEI) based on geographically weighted principal component analysis for assessing the land surface ecological quality

In evaluating the integrated remote sensing-based ecological index (RSEIPCA), principal component analysis (PCA) has been extensively utilized. However, the conventional PCA-based RSEI (RSEIPCA) cannot accurately evaluate component indicators' spatially shifting relative significance. This study presented a novel RSEI evaluation strategy based on geographically weighted principal component analysis (RSEIGWPCA) to address this deficiency. Second, compared to the classic RSEIPCA, RSEIGWPCA was tested at English Bazar and surrounding areas using two-fold validation. In this regard, the Jaccard test from a different setting and correlation analysis were utilized to examine the geographical distribution of RSEI derived by PCA and GWPCA. The validation output revealed better effectiveness of GWPCA over PCA in assessing the RSEI. The findings revealed that (i) in RSEI assessment, the spatial heterogeneity of the dataset helped to formulate individual weights by GWPCA that was not performed by PCA; and (ii) the areas having higher RSEI were primarily located around the Chatra wetland of this study area, and the areas with lower RSEI were located mainly in the industrial part. It has been concluded that RSEIGWPCA is a helpful approach in the RSEI evaluating for the regional and local scale like English bazaar city and its neighbourhood.

Quantifying the coal mining impact on the ecological environment of Gobi open-pit mines

Xinjiang is a coal-rich region with many bare rocky and gravelly areas with a delicate and fragile ecology. What is the ecological impact of mining activities? In this study, the Salinity Index (SI-T), New Gravel Land Index (NGLI), and Land Deterioration Index (LD) were used to establish an improved remote sensing ecological index (IRSEI) for the HongShaQuan open-pit coal mine (HSQ). The spatial and temporal evolution of the ecological environment of the HSQ was revealed by IRSEI and unary linear regression analysis. Moreover, the influence of mining on the ecological environment of the Gobi mining area was quantitatively evaluated by the random forest model (RF) and difference-in-difference (DID) approach. The results indicated that the value of IRSEI in HSQ had typically decreased over the last 30a. The ecological environment in most areas of the study area was poor and fair levels. The ecological environment of the whole study area showed a decreasing pattern from southeast to northwest. The proportion of degraded area (52.33 %) was much higher than that of improved area (0.39 %). The average residual before and after mining in HSQ were -0.1011 and -0.2323, respectively, which were much higher than that of the whole study area (-0.0330 and -0.0658, respectively), indicating that the mining activities in HSQ harmed the ecological environment and aggravated the degradation of the ecological environment. The impact of mining activities on the ecological environment from 2000 to 2021 was -0.138 using DID. The results from the multiple regression model (MR), RF, and DID during the pre-mining period (2000-2011) were -0.0709, -0.1011, and -0.1345, respectively, while they became -0.1765, -0.2323, and -0.1963 during the post-mining period (2012-2020), respectively. The latter was worse than the former, all showing that the mining activities of HSQ had resulted in a negative effect on the ecological environment. It also demonstrated that the negative value of mining from the MR, RF, and DID have a very similar change trend and was near in value. This verified the feasibility of DID and RF in quantitative analysis of the ecological environment. The ecological environment quality of HSQ was mainly affected by climate change, and less influenced by mining, the contribution rates of both in the IRSEI improved area were 88.48 % and 11.52 %, respectively; and in the degraded area were 69.45 % and 30.55 %, respectively. Therefore, the Gobi mining area should pay attention to the protection of the ecosystem while developing coal. Planting vegetation can promote the governance and restoration of the ecological environment in the mining area.

Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains' Northern Slopes, Xinjiang, China

Accurately capturing the changing patterns of ecological quality in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) and researching its significant impacts responds to the requirements of high-quality sustainable urban development. In this study, the spatial and temporal distribution patterns of remote sensing ecological index (RSEI) were obtained by normalization and PCA transformation of four basic indicators based on Landsat images. It then employed geographic detectors to analyze the factors that influence ecological change. The result demonstrates that: (1) In the distribution of land use conversions and degrees of human disturbance, built-up land, principally urban land, and agricultural land, represented by dry land, are rising, while the shrinkage of grassland is the most substantial. The degree of human disturbance is increasing overall for glaciers. (2) The overall ecological environment of the northern slopes of Tianshan is relatively poor. Temporally, the ecological quality changes and fluctuates, with an overall rising trend. Spatially, ecological quality is low in the north and south and high in the center, with high values concentrated in the mountains and agriculture and low values in the Gobi and desert. However, on a large scale, the ecological quality of the Urumqi-Changji-Shihezi metropolitan area has worsened dramatically compared to other regions. (3) Driving factor detection showed that LST and NDVI were the most critical influencing factors, with an upward trend in the influence of WET. Typically, LST has the biggest influence on RSEI when interacting with NDVI. In terms of the broader region, the influence of social factors is smaller, but the role of human interference in the built-up area of the oasis city can be found to be more significant at large scales. The study shows that it is necessary to strengthen ecological conservation efforts in the UANSTM region, focusing on the impact of urban and agricultural land expansion on surface temperature and vegetation.