Remote Sensing-based Ecological Index Research Articles

The Combined Effects of Topography and Climate Factors Dominate the Spatiotemporal Evolution of the Ecological Environment in the Yangtze River Economic Belt

ABSTRACTThe Yangtze River Economic Belt (YREB) is a key eco‐environmental protection barrier and economic engine in China. Understanding the characteristics of its eco‐environmental spatiotemporal evolution and mechanisms driving these changes is important for revealing shifts in ecosystem structure and function. However, the governing elements of the ecological environment and how these components interact remain unclear due to the strong coupling impact of urbanization and climate change. This paper constructed a long‐term series remote sensing based ecological index (RSEI) to evaluate the characteristics of eco‐environmental variation of YREB during 2000–2024. Subsequently, the fundamental reasons of the eco‐environmental evolution were quantitatively analyzed using optimal parameters‐based geographical detector (OPGD). Results indicated: (1) The average RSEI of YREB was 0.68, indicating a rather high overall eco‐environmental quality (EEQ), with 73% of the region having RSEI values more than 0.60. The regions with excellent conditions were concentrated in Zhejiang, Jiangxi, Guizhou, and Hunan, while the poorer areas were in northwestern Yunnan, southwestern Sichuan, and western Hubei. (2) The EEQ of YREB mainly showed a degradation trend, with degraded areas exceeding 80% over the past 25 years. In the future, most areas might face higher risks of ecological degradation. (3) Digital elevation model (DEM), temperature (Tem), and population (POP) were key driving factors of the ecological environment. There were significant interactions between certain factors, with the interaction between DEM and potential evapotranspiration (Pet) explaining 62.2% of the ecological quality distribution. The findings assisted the creation of an ecological civilization by offering policy proposals for managing and conserving ecological environments.

An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality

ABSTRACT Remote sensing-based ecological index (RSEI) is a widely used index for monitoring eco-environmental quality (EEQ). However, the traditional estimations usually face poor stability in tracking the continuous EEQ changes. To overcome this issue, this study introduces an improved framework for RSEI estimation to enhance the temporal comparability of EEQ assessments. It incorporates annual mean values and temporal normalization of key ecological indicators to address limitations, including susceptibility to seasonal variability and reliance on single-phase data. Through applying to Liangshan Prefecture, a mountainous region in Southwest China, the improved RSEI captures both long-term trends and short-term ecological dynamics from 2001 to 2020, revealing relatively stable EEQ, with mean values ranging from 0.465–0.470. Regions experiencing significant improvement or degradation accounted for less than 10% of the total area. Environmental improvements were largely attributed to human disturbance reduction and ecological restoration projects in high-altitude areas, while degraded regions were concentrated in the Anning River Valley, driven by population growth and urban expansion, affecting 51% of the area. The assessments demonstrate that the improved method shows greater stability and spatial coherence than the traditional ones, providing a robust metric for tracking EEQ trends for environmental monitoring and sustainable development planning.

Response of Ecological Quality to Land Use/Cover Change During Rapid Urbanization of Xiong’an New Area

Rapid urbanization facilitates socioeconomic development but also exacerbates land use/cover change (LUCC), significantly impacting ecological environments. Timely, objective, and quantitative assessments of ecological quality changes resulting from LUCC are essential for safeguarding the natural environment and managing land resources. However, limited research has explored the potential interrelationships between the spatio-temporal heterogeneity of LUCC and ecological quality during urbanization. This study focuses on the Xiong’an New Area, a region experiencing rapid urbanization, utilizing the remote sensing-based ecological index (RSEI) to monitor ecological quality dynamics from 2017 to 2023. To address the computational challenges associated with large-scale regions, a streamlined RSEI construction method was developed using Landsat imagery and implemented via Google Earth Engine (GEE). A geographically weighted regression (GWR) analysis, integrated with Sentinel-2 land use data, was employed to examine the influence of LUCC on ecological quality. The findings reveal the following: (1) Ecological quality in the Xiong’an New Area has exhibited an overall positive trajectory, with improvements elevating the ecological status to above moderate levels. (2) Urban expansion resulted in a 17% reduction in farmland, primarily converted into construction land, which expanded by approximately 12%. (3) Ecological protection policies have facilitated the conversion of farmland into wetlands and urban green areas, which emerged as the principal contributors to ecological quality enhancement. (4) A positive correlation was observed between changes in ecological land and ecological quality, while a negative correlation was identified between shifts in the construction land and farmland and ecological quality. This research provides valuable scientific insights into ecological conservation and land use management, thereby establishing a foundation for the development of rational land resource planning and sustainable ecological development strategies in the Xiong’an New Area.

Spatial Relationship between Eco-environment Quality and Land Use Change in Guangdong Province-Hong Kong-Macao, China Based on Remote Sensing-based Ecological Index

The relationship between eco-environment quality and land use change is an important guarantee for regional sustainable development. Guangdong Province-Hong Kong-Macao have always been closely linked and are now entering a period of accelerated development, but there is a lack of research on the spatial relationship between eco-environment quality and land use change integrated among the three places. Based on historical land use data and Google earth engine （GEE）, MODIS images from the summer of 2000 to 2020 （from June 1 to September 1） were selected （with a time span of five years）. The remote sensing-based ecological index （RSEI） was calculated to characterize the status of eco-environment quality and explore the spatial relationship between eco-environment quality and land use change. The results showed that： ① The land use type in the study area was mainly forest land, which was the base of the northern ecological development area （ecological area） and the northwestern landscape of the Guangdong-Hong Kong-Macao Greater Bay Area （the Greater Bay Area）. The land use degree presented a spatial pattern of "high in the middle, low in the periphery," "high in the south and low in the north," and "high in the southwest and northeast." The intensity of land use change was the least in the ecological area. ② According to RSEI, the regions with an eco-environment quality from low to high were the Greater Bay Area, the eastern wing of the coastal economic belt （east wing）, the western wing of the coastal economic belt （west wing）, and the ecological area. ③ There was a strong negative correlation between eco-environment quality and land use degree. The distribution range of high to low （H-L） cluster was relatively stable, mainly concentrated in the center of the Greater Bay Area, while the distribution of low to high （L-H） cluster was relatively unstable, mainly distributed in the ecological region, and there was no significant correlation between the eastern and western counties, Hong Kong and Macao. ④ The low-value patches of ecological environment quality in Guangdong-Hong Kong-Macao centers were consistent with artificial surface expansion. The center of Foshan in Guangzhou, the eastern part of Shantou, and the southwestern part of Zhanjiang were mainly coupled, while the ecological area, the central part of the east wing, the eastern part of the west wing, and the northwestern part of the Greater Bay Area were mainly uncoupled. Land use change and eco-environment quality were closely related in space. The eco-environment quality of the study area mainly presented a spatial pattern of "high in the middle and low in the north" and "a certain low value in the east." The Greater Bay Area had the lowest eco-environment quality, followed by that of the eastern wing. In the past 20 years, the expansion of low-value eco-environment quality in the center of the Greater Bay Area had formed a continuous landscape base of low-value eco-environment quality. The ecological area had most of the high value areas of eco-environment quality, which provided a large area of ecological environment of good patches for the study area. From 2000 to 2020, there was a significant negative correlation between land use degree and eco-environment quality in the study area, with regional heterogeneity. Land use change is an important factor in the change in eco-environment quality, so it is necessary to focus on the whole planning and layout of land use structure to improve the eco-environment quality.

Impact of Forest Landscape Patterns on Ecological Quality in Coastal Cities of Fujian, China, from 2000 to 2020

The Fujian coastal zone, a key region in China’s coastal belt, has experienced significant landscape and ecological changes due to intense human activities. Understanding the relationship between landscape patterns and ecological quality is critical for sustainable development and ecological protection. Taking the coastal cities, including Fuzhou, Xiamen, and Ningde in Fujian Province of China, as a case, the spatio–temporal changes in landscape patterns and the remote sensing-based ecological index (RSEI) during 2000 and 2020 were explored by the Google Earth Engine (GEE) cloud platform, and then their spatial relationships were identified through Pearson correlation analysis and bivariate spatial autocorrelation analysis. The findings reveal that (1) forest land was the dominant landscape in Fuzhou and Ningde, while cropland prevailed in Xiamen. Significant changes occurred in the land use landscape patterns of the three cities, mainly due to a substantial increase in the built-up land and varying degrees of reduction in arable and forest land. At the landscape level, both Fuzhou and Xiamen exhibited increased landscape fragmentation, while Ningde showed a trend of landscape aggregation; at the class level, forest land in Fuzhou and Xiamen exhibited increased fragmentation, whereas in Ningde, it showed an aggregation trend. (2) Between 2000 and 2020, the ecological–environmental quality of Fuzhou and Ningde continuously improved, while the improvement in Xiamen was less significant. Poor and fair ecological environments in the three cities were mainly concentrated in city centers and coastal zones, and areas of ecological quality degradation were primarily concentrated in coastal zones. (3) Correlation analysis indicates that, whether at the landscape level or the class level, the ecological quality of the three cities is significantly negatively correlated with the fragmentation index and significantly positively correlated with the aggregation index. Moreover, the positive correlation between ecological quality and the forest landscape aggregation index, as well as the negative correlation with the forest landscape fragmentation index, are both significantly stronger than those at the landscape level. As urbanization progresses, forest landscape fragmentation intensifies, especially in city centers and coastal areas, having a significant negative impact on ecological quality. These results highlight the importance of landscape pattern management in maintaining ecological quality. This paper provides insights for coastal cities on balancing urban development with ecological preservation in the context of rapid urbanization.

Evaluation and driving force analysis of ecological environment in low mountain and hilly regions based on optimized ecological index.

In low mountain and hilly regions, vegetation cover is higher and plant growth has an accumulative effect, sequestering carbon more strongly. The traditional remote sensing based ecological index (RSEI) lacks the consideration of vegetation productivity, and using it to evaluate ecological environment in low mountain and hilly regions will be biased. In this study, the vegetation productivity was introduced to construct a natural remote sensing based ecological index (NRSEI) that responds to the low mountain and hilly regions, as an example of Gaizhou City, China. Additionally, this study explored the spatiotemporal evolution of ecological environment quality from 2014 to 2020 and quantified the influences of factors. The results show that the first principal component (PC1) increased from 56 to 67% to 65-87% and considered the accumulation process in the ecosystem. NRSEI was more valid. From 2014 to 2020, the quality of the ecological environment generally declined and then increased. The area with "Excellent" increased from 23 to 38%. The quality of ecosystems in the west, northwest, and south deteriorated significantly, a distribution pattern of "high in the center, low in the north and south". Landuse and topographic conditions dominate the impacts on the ecosystem in the context of social, economic and policy influences. The interactions of the factors were two-factor enhancement that together affect the ecological environment. The results contribute to the development of urban conservation policies in low mountain and hilly regions.

Spatial and Temporal Characteristics and Driving Force Analysis of Ecological Environmental Quality in Fengfeng Mining Area with Remote Sensing Ecological Index of PM2.5 Concentration

The Fengfeng mining area is an important coal-producing area in China and crucial environmental problems have been caused by large-scale exploitation of coal mines. The spatio-temporal evolution and driving factors of the ecological environment quality in this area must be explored for promoting the transformation of coal-based cities. Based on Landsat data of the Google earth engine （GEE） platform, this study constructed a new remote sensing-based ecological index （RSEInew） for the Fengfeng mining area from 2000 to 2020. The spatial and temporal evolution of RSEInew and its driving factors were evaluated by using trend analysis and geographic detector methods. The results showed that： ① From 2000 to 2020, the RSEInew of the Fengfeng mining area presented a fluctuating increasing trend （trend = 0.002 2）, and the proportion of good and excellent ecological environmental quality showed an increasing trend, rising from 24.80% in 2000 to 65.54% in 2020. ② The change in the RSEInew grade indicated that the proportion of significant improvement （3 and 4） of ecological environment quality grade in the Fengfeng Mining area from 2000 to 2020 was 10.21%, which was mainly distributed in Hexun town and Yijing town in the northwest of the Fengfeng mining area. The proportion of significant degradation （-3 and -4） was only 1.58%, mainly scattered in Linshui town and Dashe town. ③ RSEInew values increased significantly during 2000-2020 in the area accounting for 18.29%, mainly distributed in the central and northern areas and the western fringe of the Fengfeng mining area. The significantly reduced area accounted for 9.25%, mainly concentrated in the eastern area of the Fengfeng mining area. The coefficient of variation results showed that the areas with high fluctuation of RSEInew were mainly concentrated in Pengcheng town and Linshui town in the middle and eastern Fengfeng mining area. ④ From the perspective of influencing factors, the average q value of land use type （X6） during 2000-2020 was 0.290, which was much higher than other factors. The q value of social and economic factors showed an increasing trend, indicating that the spatial distribution of ecological environment quality in this region was increasingly strongly influenced by human activities. The interaction results showed that land use change was the key factor influencing ecological environment quality in the Fengfeng mining area.

Assessment and simulation of eco-environmental quality changes in rapid rural urbanization: Xiong’an New Area, China

Xiong’an New Area was established as a state-level new area in 2017 and serves as a typical representative area for studying the ecological evolution of rural areas under rapid urbanization in China. Remote sensing-based ecological index (RSEI) is a regional eco-environmental quality (EEQ) assessment index. Many studies have employed RSEI to achieve rapid, objective, and effective quantitative assessment of the spatio-temporal changes of regional EEQ. However, research that combines RSEI with machine learning algorithms to conduct multi-scenario simulation of EEQ is still relatively scarce. Therefore, this study assessed and simulated EEQ changes in Xiong’an and revealed that: (1) The large-scale construction has led to an overall decline in EEQ, with the RSEI decreasing from 0.648 in 2014 to 0.599 in 2021. (2) Through the multi-scenario simulation, the non-unidirectional evolution of RSEI during the process of urban-rural construction has been revealed, specifically characterized by a significant decline followed by a slight recovery. (3) The marginal effects of urban-rural construction features for simulated RSEI demonstrate an inverted “U-shaped” curve in the relationship between urbanization and EEQ. This indicates that urbanization and EEQ may not be absolute zero-sum. These findings can provide scientific insights for maintaining and improving the regional EEQ in urban-rural construction.

Remote sensing ecological index (RSEI) affects microbial community diversity in ecosystems of different qualities

Soil microorganisms are key to ecological environment stability, but climate change and human activities exacerbate ecological environment changes. Therefore, assessment of ecological environment quality impacts on microbial diversity is needed. The Tarim River is the largest inland river in China and plays a crucial role in supporting regional biodiversity, maintaining ecological balance, and preventing desertification. In this study, we used the Remote Sensing-based Ecological Index (RSEI) to assess the ecological quality of habitats in the Tarim River Basin and explore the effects of habitat quality (extreme, semi-extreme, and general) on the structural diversity of microbial (bacterial and fungal) communities, biogeographic patterns, co-occurrence networks, and community assembly processes. Study results show that soil physicochemical characteristics varied significantly with habitat quality; highly resilient microorganisms are more abundant in habitats with low ecological quality. RSEI affects changes in microbial communities, and the positive correlation ratio of the network is inversely proportional to RSEI. The interspecific relationships of microbial communities in the Tarim River Basin are dominated by positive correlations, and community assembly is strongly influenced by stochastic processes. RSEI directly affects soil microbial diversity, with its contribution to both bacterial and fungal diversity being 0.27. Total nitrogen (TN) also directly affects microbial diversity, with effects of 0.11 on bacteria and 0.07 on fungi, respectively. This study provides scientific evidence and technical support for understanding microbial diversity in environments and for the development of regional sustainable development policies.

Spatial-temporal response of the regional ecological quality to urban settlement development

ABSTRACT Based on the objective evaluation of the regional ecological quality (Urban Cluster in Mid-inner Zhejiang) by Remote Sensing based Ecological Index (RSEI), it was proposed to study the spatial-temporal response of the regional ecological quality to the urban built-up areas, impervious surface, land use and “production-living-ecological” space under urban settlement change to quantitatively describe the mechanism of human activities’ influence on the regional ecology. The results showed that: (1) From 1985 to 2020, the RSEI of the Urban Cluster in Mid-inner Zhejiang was above 0.50 as a whole, showing a trend of first decreasing and then increasing with a slight decrease in some parts. (2) From the perspective of the urban built-up areas, it was dominated by ecological level 2–3, gradually changing from ecological level 3 to ecological level 2. (3) From the perspective of the impervious surface, it was dominated by ecological level 2–3, accounting for more than 75.00% of the total study area. (4) From the perspective of land use, the cropland, forestland, water and construction land were mainly ecological level 2–4, 3–4, 3–4 and 2–3. (5) From the perspective of “production-living-ecological” space, production space, ecological space and living space were dominated by ecological level 3, 3–4 and 2–3. The results can deepen the understanding of the impact of urban settlement development on the regional ecological quality, avoid the unbalanced situation of blindly pursuing socio-economic development while ignoring the ecological environment, which scientifically helps the sustainable construction and high-quality development of the Urban Cluster in Mid-inner Zhejiang.

Spatio-Temporal Evolution of Ecological Environment Quality Based on High-Quality Time-Series Data Reconstruction: A Case Study in the Sanjiangyuan Nature Reserve of China

The Sanjiangyuan Nature Reserve of China (SNRC) is recognized as one of the most fragile and sensitive terrestrial ecosystems in China, posing challenges for obtaining reliable and complete Moderate Resolution Imaging Spectro Radiometer (MODIS) data for ecological environment quality (EEQ) monitoring due to adverse factors like clouds and snow. In this study, a complete high-quality framework for MODIS time-series data reconstruction was constructed utilizing the Google Earth Engine (GEE) cloud platform. The reconstructed images were used to compute the Remote Sensing based Ecological Index (RSEI) on a monthly scale in the SNRC from 2001 to 2020. The results were as follows: The EEQ of the study area exhibited a “first fluctuating decline, then significant improvement” trend, with the RSEI values increasing at a rate of 0.84%/a. The spatial pattern of the EEQ displayed significant spatial heterogeneity, characterized by a “low in the west and high in the east” distribution. The spatial distribution pattern of the RSEI exhibited significant clustering characteristics. From 2001 to 2020, the proportion of “high–high” clustering areas exceeded 35%, and the proportion of “low–low” clustering areas exceeded 30%. Poor ecological conditions are mainly associated with population agglomerations, cultivated land, unutilized land, and bare ground, while grasslands and forests have higher RSEI values. The result of the trend analysis revealed a significant trend in RSEI change, with 62.96% of the area significantly improved and 6.31% significantly degraded. The Hurst Index (HI) results indicated that the future trend of the RSEI is predominantly anti-persistence. The proportion of areas where the EEQ is expected to continue improving in the future is 33.74%, whereas 21.21% of the area is forecasted to transition from improvement to degradation. The results showed that the high-quality framework for MODIS time-series data reconstruction enables the effective continuous monitoring of EEQ over long periods and large areas, providing robust scientific support for long time-series data reconstruction research.

Quantitatively exploring the influence of geographical conditions on ecological quality using a novel remote sensing model: a comparison between two geographical disparity regions in China

ABSTRACT The ecological quality of a region is significantly influenced by its geographical conditions, which can yield different effects on ecosystems. Nevertheless, the lack of adequate technology has impeded quantitative investigations into these differences. Consequently, there is an increasing demand for effective techniques to quantitatively measure differences in ecological quality resulting from variations in geographical conditions. This study applied the novel Remote Sensing-based Ecological Index (RSEI) concurrently to two distinct provincial-level regions in China, Fujian and Ningxia, to quantitatively detect their ecological differences. These two regions possess contrasting geographical conditions, with Fujian having high forest coverage and abundant rainfall, while Ningxia features low forest coverage and extensive loess plateau and desert terrain. By linking geographical factors with their corresponding ecological responses, we conducted a comprehensive analysis to determine whether the contrasting geographical conditions between the two regions had caused significant disparities in their ecological status. The results indicate that the contrasting geographical conditions have indeed led to marked ecological differences, with Fujian exhibiting excellent ecological status, while Ningxia lags behind due to unfavorable geographical conditions. In terms of RSEI scores, Fujian consistently achieved higher RSEI values (>0.8) in the study years, reaching an excellent ecological level, whereas Ningxia recorded scores lower than 0.45 during the comparable years, corresponding to a poor to moderate ecological level. Regarding the impact of geographical factors on ecological conditions, the positive contributions of greenness and wetness indicators to the ecology in Fujian were significantly greater than those in Ningxia (58% vs. 39%), whereas the contributions of negative indicators, dryness and hotness, were notably higher in Ningxia compared to Fujian (|–61|% vs. |–42|%). The successful concurrent application of RSEI to these two geographically distant regions also demonstrates the robustness of the RSEI technique.

Coupling eco-environmental quality and ecosystem services to delineate priority ecological reserves—A case study in the Yellow River Basin

Priority ecological reserves (PER) aim to protect areas with significant ecological value and crucial ecological functions, optimizing resource allocation to maximize the benefits of ecological conservation. However, most previous studies have considered only ecosystem services (ESs) in delineating PER, neglecting eco-environmental quality (EEQ). This study used the Remote Sensing-based Ecological Index (RSEI) to represent EEQ and combined it with ESs to delineate PER at the county scale in the Yellow River Basin (YRB). Additionally, it employed Multiscale Geographically Weighted Regression to identify the driving factors influencing the ESs and EEQ of PER. The results showed that: (1) From 2000 to 2020, both RSEI and the Comprehensive ESs (CES) in the YRB exhibited a fluctuating upward trend; (2) Three types of PER were extracted, with ESs reserve mainly distributed in the upstream region, EEQ reserve primarily in the middle and lower reaches, and integrated ecological reserve mainly in the midstream region, all dominated by vegetation land-use types; (3) Within the extracted PER, RSEI was mainly influenced by soil, aspect, population (pop), PM2.5, temperature (tmp), and potential evapotranspiration (pet), while CES was affected by soil, pop, PM2.5, slope, tmp, precipitation, and pet. To enhance the EEQ and ESs of the YRB, it was recommended to incorporate at least 105,379 km2 into the existing protected areas in the YRB. These areas should be subdivided based on their ecological status, with specific management measures for different types of PER. This study provides recommendations for environmental protection and land planning in the YRB, actively responding to current policies on high-quality development and ecological environmental protection in the YRB.

Reconnaissance of spatio-temporal variability in ecological stability with local adaptability based on multi-temporal LANDSAT archive in ganga river basin

Assessment of ecological environment is an indispensable part of the eco-environment protection and restoration. This study utilizes a remote sensing-based ecological index (RSEI) to better understand the environmental scenario in the Ganga basin. RSEI has been computed using five parameters: Wetness, Dryness, Greenness, Heat, and a newly incorporated parameter Albedo representative of land degradation. Median-based RSEI maps have been constructed using LANDSAT archives in Google Earth Engine (GEE) platform, covering three decades (1990–2021). The Ganga basin has been divided into five agro-climatic zones. For each zone and time frame (1990–99, 2000–09, 2010–19, and 2020–21), a median-based RSEI map has been generated. The analysis reveals that RSEI becomes poorer for sub-basin 1 (SB1), sub-basin 2 (SB2), and sub-basin 5 (SB5) in the 2010–19 period compared to the 1990–99 and 2000–09 periods. On the other hand, RSEI for sub-basin 3 (SB3) improved in the 2010–19 period compared to the previously mentioned periods. Sub-basin 4 (SB4) remained the least fluctuated region compared to the other sub-basins. The Global Moran's I value is highest for SB3 for the 1990–99 and 2000–09 periods, while for the 2010–19 and 2020–21 periods, SB2 has the highest Global Moran's I. This study incorporates big data analysis and can be indispensable in exploring the interactions between ecosystem services and anthropogenic activities in river basin systems.

Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

Spatial and Temporal Heterogeneity of Eco-Environmental Quality in Yanhe Watershed (China) Using the Remote-Sensing-Based Ecological Index (RSEI)

The long-term impacts of climate change and human activities have resulted in the Yanhe watershed, a typical watershed in the Loess Plateau region, exhibiting a high degree of vulnerability and significant heterogeneity in ecological environmental quality. This has led to environmental degradation and complex socio-ecological challenges. Consequently, there is an urgent need to carry out research on the spatial and temporal differentiation patterns of ecological environment quality. By utilizing remote sensing data spanning 21 years, this study evaluated the evolutionary trends and consistency of ecological environment quality (EEQ) within the Yanhe watershed based on the remote-sensing-based ecological index (RSEI). Furthermore, it examined global and local spatial autocorrelation of the RSEI by constructing a hexagonal grid, thereby revealing the spatiotemporal characteristics of EEQ at different scales within the Yanhe watershed. The results were as follows: (1) The EEQ has exhibited an overall upward trend in the past two decades, while it has displayed significant fluctuations; (2) the Global Moran’s I values for the years 2000, 2010, and 2020 were 0.18, 0.32, and 0.21, respectively, indicating a presence of spatial autocorrelation within the RSEI; (3) the overall EEQ of the Yanhe watershed will continue to improve, although the ecological quality in certain areas remains unstable due to local natural conditions and human activities. This research not only contributes to the technical framework for analyzing the spatiotemporal heterogeneity of EEQ but also provides actionable insights for ecosystem restoration and sustainability within the Loess Plateau watershed. Our work advances the understanding of ecological dynamics in semi-arid regions and offers a model for assessing ecological quality in similar environmental contexts.

Spatiotemporal Evolution of Ecological Environment Quality and Driving Factors in Jiaodong Peninsula, China

To monitor the Ecological Environment Quality (EEQ) of the Jiaodong Peninsula and provide a scientific basis for ecological environment governance and sustainable development in the region, this study evaluates the EEQ of Jiaodong using the Remote Sensing-based Ecological Index (RSEI) model and analyzes its spatiotemporal evolution patterns, building upon single-factor correlation analysis using the Random Sample Consensus (RANSAC) algorithm, using GeoDetector to analyze the driving mechanisms of human activities and natural factors in EEQ. The results indicate the following: (1) The average RSEI values for 2000, 2010, and 2020 are 0.60, 0.57, and 0.66, with Good or Excellent areas accounting for 56.48%, 51.02%, and 67.17%. From 2000 to 2020, RSEI strong improvement areas were predominantly distributed in the eastern hilly areas of the Jiaodong Peninsula. The RSEI of the Jiaodong Peninsula showed significant spatial autocorrelation. (2) The RANSAC algorithm effectively reduces noise interference in remote sensing data, thereby improving the accuracy of single-factor correlation analysis. (3) In 2000, the importance of natural factors exceeds that of human activity factors. The Standardized Precipitation Evapotranspiration Index (SPEI) is the most important driving factor; while in 2010 and 2020, human activity factors surpass natural factors in importance, with the Land Use Composite Index (LUCI) being the most significant driving factor. The driving factors exhibited double-factor and nonlinear enhancement. The most significant interaction factors affecting the EEQ of the Jiaodong Peninsula in 2000, 2010, and 2020 are SPEI∩GDP, LUCI∩SPEI, and LUCI∩GDP.

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.

Ecological health assessment of Tibetan alpine grasslands in Gannan using remote sensed ecological indicators

ABSTRACT Ecosystem health assessments are crucial to protect the ecological environment and ensure the sustainable ecological functions of alpine ecoregions. At present, few studies evaluating the ecosystem health of the Gannan alpine grassland, China, an ecologically fragile area, based on a remote sensing theoretical framework exist. As such, this study assessed the ecosystem health of the Gannan alpine grassland based on the Remote Sensing-based Ecological Index (RSEI) and provided a comparative analysis of the RSEI and Gross Primary Productivity (GPP), extending the study of their spatiotemporal patterns and influencing factors. The results suggested that RSEI and GPP showed strong comparability in an ecological sense, with the RSEI better reflecting changes in ecosystem health of the Gannan alpine grassland than the GPP. Overall, the health of the Gannan alpine grassland ecosystem was good (RSEI of 0.61–0.76) and a slow, fluctuating upward trend was seen from 2000 (RSEI = 0.66) to 2020 (RSEI = 0.72). Notably, the RSEI was high in the south and low in the north of the region. Over the past 21 years, 43.92% of the ecologically healthy grassland in the southwest of Gannan has been degrading, while the poor ecological health of 39.04% of the grasslands in the southeast and northeast improved. The model test results show that RSEI could reasonably evaluate the ecosystem health of Gannan alpine grassland. Our assessment results provide important scientific data and information on health monitoring and targeted ecological restoration efforts in the Gannan region.

Remote sensing perspective in exploring the spatiotemporal variation characteristics and post-disaster recovery of ecological environment quality, a case study of the 2010 Ms7.1 Yushu earthquake

Natural hazards usually cause heavy casualties and vast economic losses, as well as severe damage to the ecological environment. Quantitative and scientific evaluations of the ecological environment quality (EEQ) and its recovery trend after hazards can provide valuable insights for disaster risk reductions. This study takes the 2010 Ms7.1 Yushu earthquake as an example to explore the spatiotemporal changes and driving mechanisms of EEQ before and after the earthquake using remote sensing and GIScience. First, the Moderate-resolution Imaging Spectroradiometer (MODIS) data was selected to establish the remote sensing based ecological index (RSEI). Then, we analyzed the spatiotemporal characteristics of Yushu’s EEQ from 2001 to 2020 and explored spatial autocorrelation relationships. Last, the driving mechanism of EEQ recovery in Yushu was analyzed using the GeoDetector model. The main conclusions are as follows: (1) From the perspective of the RSEI time series, the EEQ in Yushu County was strongly negatively affected during the earthquake and recovered well after the earthquake. (2) Based on a spatial distribution analysis, it can be observed that the regions in Yushu with relatively high EEQ are primarily concentrated in the central and southern areas. Conversely, the northwestern and southeastern areas display relatively lower quality. Moreover, the EEQ in Yushu County has a strong correlation and clustering, as evidenced by Moran’s I value exceeding 0.7 over the years. (3) Based on the GeoDetector results, elevation and population were found to be the key driving factors affecting changes in post-disaster EEQ. The interaction between elevation and slope plays the most critical role in the process of ecological recovery. This study provides a theoretical basis for the evolution of the post-disaster ecological environment in Yushu County and helps decision-makers better balance the relationship between social development and environmental protection in disaster management and urban planning. It also provides useful reference and guidance for future ecological protection and recovery under similar disasters.