Journal of Atmospheric and Environmental Optics ›› 2026, Vol. 21 ›› Issue (5): 841-856.doi: 10.3969/j.issn.1673-6141.2026.05.011

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Remote sensing monitoring and evaluation of ecological environment of the main urban area of Guangyuan City in the mountain-basin transition zone

LI Jinzhi1, WANG Shuguo1*, SHEN Qian2,3, LI Wenxin4, ZHANG Yuting2,3, HUANG Ruolong5   

  1. 1 School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China; 2 Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 3 International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; 4 College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China; 5 College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
  • Received:2023-09-07 Revised:2023-11-06 Online:2026-09-28 Published:2026-09-30
  • About author:李金枝 (1998- ), 女, 四川广元人, 硕士研究生, 主要从事水环境遥感及生态环境遥感方面的研究。E-mail: lijinzhi0812@163.com

Abstract: Objective Mountain-basin transition zones are ecologically sensitive geomorphic regions characterized by pronounced to pographic gradients and strong exchanges of material and energy between mountainous and basin ecosystems. However, systematic long-term assessments of ecological environmental quality in mountain-basin transition zones remain limited, particularly those addressing both temporal dynamics and spatial heterogeneity. Furthermore, ecological environmental quality is inherently multidimensional, and assessments based on individual remote-sensing indicators may inadequately capture the comprehensive effects of remote sensing ecological index (RSEI). Against this background, the main urban area of Guangyuan, located within the mountain-basin transition zone in northern Sichuan Province, China, was selected to quantitatively assess the spatiotemporal variations of eco-environmental quality from 2000 to 2021 using the RSEI. The applicability of the RSEI framework assessment method in mountain-basin transition environments was also evaluated, which will provide a scientific basis for local ecological conservation, urban environmental management, and sustainable planning in Guangyuan. Methods Five phase Landsat satellite datasets were used, comprising Landsat 5 TM imagery for 2000, 2007 and 2011, and Landsat- 8 OLI/TIRS images for 2017 and 2021. A total of 20 scenes with cloud coverage below 15% were screened for subsequent processing. Four ecological components were retrieved from satellite imagery: greenness represented by the Normalized Difference Vegetation Index (NDVI); wetness (WET) derived using the tasseled-cap transformation; dryness represented by the Normalized Difference Built-up and Soil Index (NDBSI); and heat characterized by Land Surface Temperature (LST). Following normalization of the four ecological indicators, Principal Component Analysis (PCA) was performed to integrate their information objectively. The first principal component (PC1), which retained most of the original ecological information, was normalized to obtain RSEI values ranging from 0 to 1, with higher values indicating better ecological environmental quality. The resulting RSEI values were classified into five ecological-quality categories using equal intervals: poor, relatively poor, moderate, good, and excellent. The water area of Jialing River was masked using the Modified Normalized Difference Water Index (MNDWI), in order to eliminate the interference of large-scale water areas on the RSEI inversion results. To examine the characteristics of ecological environmental quality changes in the transition zone, the relationships between terrain characteristics and RSEI were statistically evaluated by combining digital elevation model (DEM), slope data and RSEI values in the study area. Statistical data of vegetation coverage, precipitation, and urban planning documents were comprehensively analyzed to explore the driving factors that affect eco-environment changes. Results and Discussion Temporally, the mean RSEI values in 2000, 2007, 2011, 2017, and 2021 were 0.603, 0.821, 0.548, 0.565, and 0.595, respectively. These values indicate a pronounced increase from 2000 to 2007, followed by a substantial decline in 2011 and a gradual recovery till 2021, with a slight overall declining trend over the whole research period. Among the four ecological components, NDVI, representing greenness, increased by 27.55% over the study period, whereas the WET component initially increased and subsequently declined. NDBSI, representing surface dryness, increased by 15.90% and was negatively correlated with wetness, while LST (heat) exhibited an inverted-M-shaped fluctuation. And RSEI showed the strongest sensitivity to greenness among the four components. Spatially, areas with low RSEI values were concentrated primarily within the built-up areas of Lizhou District of Guangyuan City, which experienced a trend of decreasing first and then expanding, whereas high RSEI values occurred predominantly in mountainous forest areas. The excellent ecological-quality class dominated in 2007, the good class was predominant in 2000, 2011, and 2017, whereas the moderate class accounted for the largest proportion of the study area in 2007. Both the values of DEM and slope showed significant positive correlations with RSEI. Specifically, valley-plain zones, characterized by low elevation and intensive human activity, exhibited poorer ecological-environment quality, while mountainous areas with limited human disturbance maintained better ecological conditions. The ecological improvement observed in 2007 is consistent with the timing of the implementation of natural forest protection and grain-for-green projects, while the subsequent degradation appears associated with a combination of urban expansion and climate fluctuation.It should be pointed out that due to the frequent clouds and fogs in humid mountainous terrain, slight temporal inconsistency among between spliced satellite scenes introduces some uncertainty into the results. Compared with standard-based ecological indices that require multi-source ground-based statistical datas, the RSEI based assessment method only relies on remote-sensing observations and is suitable for long-sequence monitoring. However, while the field quantitative verification for this composite index still needs further exploration. Conclusion From 2000 to 2021, the ecologica-environmental quality of Guangyuan's main urban area presented an"rising falling rising" trajectory, with a slight net decline over the full period. Spatially, urban construction areas consistently exhibited low ecological quality, while mountain-forest areas maintained high RSEI values. Terrain conditions, vegetation coverage, urbanization progress and local climate jointly affected the eco-environmental pattern of this mountain-basin transition zone. Although regional vegetation coverage continued to increase, urban expansion caused a continuous shrinkage of high quality ecological space. RSEI is shown to be feasible tool for eco-environmental assessment in mountain-basin transition cities. Targeted ecological restoration and urban-boundary control measures are therefore recommended for valley-plain urban zones to mitigate ecological pressure induced by human construction activities.

Key words: mountain-basin transition zone, Guangyuan City, remote sensing ecological index, Landsat

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