Remote Sensing-based Ecological Index Research Articles

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.

Cross-comparison of Landsat-8 and Landsat-9 data: a three-level approach based on underfly images

ABSTRACT The recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites’ SR data, with an average absolute percent change (PC) of 1.88% and an average R 2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R 2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R 2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R 2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24°C lower than Landsat-8 LST across the five test areas but can be 0.58°C lower in built-up land-dominated areas and 0.42°C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

Study on the factors influencing ecological environment and zoning control: a study case of the Dongting Lake area

IntroductionProtecting the ecosystem of the Dongting Lake area is of utmost importance for maintaining ecological balance and achieving human well-being.MethodsThis study identifies the key factors influencing the remote sensing based ecological index (RESI) in the Dongting Lake area based on the spatial and temporal evolution characteristics of the RESI and environmental changes and anthropogenic disturbance factors. The priority zoning for ecological restoration was delineated in combination with the anthropogenic composite index (ACI). By exploring the influence of anthropogenic disturbances on RESI, the zoning locations were determined using spatial statistics and linear regression methods.ResultsThe overall RESI of the Dongting Lake area showed a decline from 2001 to 2020, with the mean value decreasing from 0.52 to 0.48. High-quality zones were mainly located in mountainous and forested areas, while low-quality zones were mainly distributed in more developed cities in the east urban area. Anthropogenic factors were the main reasons for the decline in the ecological environment, while natural factors showed a positive correlation with RESI. Based on the RESI and ACI, four ecological control zones (H-H, H-L, L-H, and L-L) were delineated, which accounted for a total of 45.66% of the Dongting Lake area. Among them, 3.91% required immediate control and management, while 17.80% required artificial maintenance. This study explores the influencing factors and mechanisms of the ecological environment quality in the Dongting Lake area, and explores the effective spatial paths for the implementation of ecological restoration zoning control and differentiated restoration strategies in the Dongting Lake area.DiscussionThis study provides a scientific basis for mitigating ecological and environmental problems in the Dongting Lake area, and provides a reference for ecological restoration and regulation and the realization of sustainable development goals in China and global regions with complex environmental problems.

Post-earthquake recovery and its driving forces of ecological environment quality using remote sensing and GIScience, a case study of 2015 Ms8.1 Nepal earthquake

Natural hazards always have significant impacts on the regional ecological environment, causing huge damages to biodiversity, soil erosion, and impoverishment. This study aimed to analyze the variation of ecological environment quality in Nepal from 2014 to 2018, with a specific focus on the impact and recovery of the 2015 Ms8.1 Nepal earthquake. We utilized Google Earth Engine (GEE) and remote sensing based ecological index (RSEI) to evaluate the ecological environment quality. Moreover, Moran’s I and Geodetector model were employed to explore spatial clustering and driving forces of post-earthquake recovery. The results indicate that after the 2015 Nepal earthquake, there was a decrease of 0.0225 in the mean RSEI in Nepal. The monitoring of the severe-affected areas showed that the regional environmental quality gradually improved within three years after the earthquake. After a span of three years, the ecological environment managed to recover to its pre-earthquake level. In terms of spatial distribution, there is a notable correlation in Nepal, with the ecological environment exhibiting higher quality in the southern regions and lower quality in the northern regions. By examining the driving forces behind the changes in ecological environment quality during the post-earthquake recovery period, it has been determined that altitude, precipitation, and their interaction serve as influential factors. These findings provide valuable insights into the state of ecological environment recovery after the 2015 Nepal earthquake. Furthermore, this study establishes a robust theoretical foundation for local decision-makers to develop ecological protection measures and enhance local disaster risk management.

Google Earth Engine for improved spatial planning in agricultural and forested lands: A method for projecting future ecological quality

This research aims to use Google Earth Engine and the CA algorithm in open-source software to generate multiple scenarios for predicting future ecological quality and evaluating the spatial planning of agricultural and forested lands. The goal is to determine the most accurate scenario and use it to assess a spatial planning map of these lands. Three different Remote Sensing-based Ecological Index (RSEI) scenarios were simulated using Landsat images and MOLUSCE (Modules for Land Use Change Evaluation) was used to predict and select the most accurate scenario. The results showed that a future RSEI projection based on data from a 7-year interval (2007 and 2014) had the highest accuracy, as indicated by 83.79 percent correctness and an overall Kappa value of 0.72, compared to the other two alternative projections (based on data from a 4-year interval: 2013 and 2017; and a 12-year interval: 1997 and 2009). The selected scenario was then used to project an RSEI map for 2028, which is expected to show an expansion of areas with poor ecological quality, particularly near urban centers. The “poor” and “very poor” areas of the projected RSEI map were used to assess a spatial planning map of agricultural and forested lands for 2031, resulting in the identification of planned agricultural and forested areas at high risk for poor ecological quality in the future. Local governments and urban planners are advised to prioritize the conservation of these areas.

How do natural and socio-economic factors influence the sustainable development of the ecological environment in the World Natural Heritage Sites? Evidence from the Jiuzhaigou, China

With the rapid development of tourism in the World Natural Heritage Sites (WNHSs), it made ecological environment issues increasingly become prominent while boosting regional economic development. Exploring the influence and evolution of natural and socio-economic factors will help to maintain the authenticity and integrity of the WNHSs, and promote sustainable development. Here, to better understand the sustainable development situation of the WNHSs, we construct a nature-society-based comprehensive evaluation framework, and combining the remote sensing-based ecological index (RSEI) with the spatial autocorrelation model, and employed the obstacle factor diagnostic model, to reveal the spatial–temporal characteristics and the key driving factor of ecological environment in the Jiuzhaigou. Meanwhile, we assess the integrated ecological sensitivity of the Jiuzhaigou and determine the key conservation area. Through the analysis of results, we find that RSEI from 2013 to 2020 exhibits a fluctuation trend resembling an ”N-shape”, high RSEI points tend to cluster in the southern part of the study area, while low RSEI points cluster in the northern part, it has a significant spatial heterogeneity. We shed further light on that when the number of tourists exceeds the population carrying capacity of the heritage site, the negative impacts of tourism development on the natural environment will outweigh the positive impacts of tourism revenue growth on regional economic development and constrain the sustainable development of the site as a whole. Specifically, dissolved oxygen concentration in water, nitrogen dioxide concentration, and noise are the main drivers of nature. The proportion of tertiary industry, fixed asset investment, and total tourism consumption are the main drivers of society. Furthermore, the ecological sensitivity of Jiuzhaigou is moderately low. Therefore, we suggest that the government should improve and refine the existing disaster early warning mechanism, dynamically adjust and control the number of tourists received per day, harmonize the relationship between economic development and environmental protection, and strengthen the protection of authenticity and integrity in areas of high ecological sensitivity.

Ecological restoration evaluation of afforestation in Gudao Oilfield based on multi-source remote sensing data

The growing petroleum industry poses significant ecological challenges, prompting an increased focus on ecological restoration projects in onshore oilfields. Current efforts focus on revegetation in deforested oilfields, while research remains limited on alternative ecological restoration strategies aimed at establishing new ecosystems in oilfields. This study employed multi-source remote sensing data from 1985 to 2022 to calculate a remote sensing-based ecological index (RSEI) and constructed an integrated forest health index (IFHI), in order to evaluate the ecological restoration effects in the Gudao shelterbelt of Shengli Oilfield in the Yellow River Delta, and investigated the impact of oil extraction by considering forest phenology. The RSEI of the shelterbelt showed an upward trend and reached a Good level of ecological environment quality from 1990 to 2003, but it declined after that, indicating the potential of RSEI to quickly assess ecological restoration effects and guide management at different stages. Comparing the restoration effects of different tree species, a Robinia pseudoacacia L. (RP) and Fraxinus velutina Torr. (FV) mixed forest demonstrated the greatest capacity to improve environmental quality, with the most years (25 years) of the Good and Excellent levels and the highest IFHI value (1.52). In contrast, Ulmus pumila L. (UP) and Sophora japonica L. (SJ) were unsuitable for mixed planting for ecological restoration. The study also found that monospecific RP forests within 30 m of oil wells were significantly impacted by oil extraction (P ≤ 0.05), necessitating tailored forest management. The research aims to serve as a reference for ecological restoration in global onshore oil production areas, particularly in delta regions and sparsely vegetated areas.

Exploring the impact of urbanization on ecological quality in the middle reaches of the Yangtze River Urban Agglomerations, China

Rapid urbanization in urban agglomeration areas puts tremendous pressure on the eco-environment, and balancing urbanization development and ecological quality protection is crucial for regional high-quality development. However, current research lacks a multi-scale assessment of ecological quality and urbanization in urban agglomeration areas. Here, we assessed the multi-scale ecological quality and urbanization level of the Middle Reaches of the Yangtze River Urban Agglomerations (MRYRUA) from 2001 to 2021 based on multi-source remote sensing imagery by constructing the remote sensing-based ecological index (RSEI) and the comprehensive nighttime light index (CNLI), respectively, and analyzed the coupling coordination degree (CCD) of the two using the CCD model. The following conclusions were obtained: (1) There are spatial differences in the ecological quality of pixel, county, and municipal scales. Multi-dimensional analysis indicates that the RSEI shows an increasing trend, and the ecological quality of the MRYRUA has generally improved in the past 20 years. (2) The DN values of nighttime light and the three light indices have increased to varying degrees at different scales, among which the average value of CNLI has increased from 0.02 in 2000 to 0.12 in 2021. The urbanization level of the MRYRUA is constantly improving, but there is an obvious development imbalance. (3) The ecological quality and urbanization level developed in a more coordinated direction at different scales, but the urbanization level lagged behind ecological quality as a whole, and the two were negatively correlated in space. The results of this study can guide the formulation of ecological protection and high-quality development policies in the MRYRUA.

Mapping of Ecological Environment Based on Google Earth Engine Cloud Computing Platform and Landsat Long-Term Data: A Case Study of the Zhoushan Archipelago

In recent years, with the rapid advancement of China’s urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor and evaluate the ecological environment using a Google Earth Engine cloud-based platform and Landsat time series. Firstly, a long-term series of Landsat images was obtained to construct and calculate the remote sensing-based ecological index (RSEI). Then, the Theil–Sen median estimation and the Mann–Kendall test were used to evaluate the trend and significance of the RSEI time series and combined with the Hurst index to predict the future development trend of the ecological environment in the study area. Finally, the coefficient of variation method was used to determine the temporal stability of the ecological environment. Taking Zhoushan Archipelago, China, as the study area, we mapped the distribution of the ecological environment using a spatial resolution of 30 m and evaluated the ecological environment from 1985 to 2020. The results show that (1) from 1985 to 2020, the average RSEI in the Zhoushan Archipelago decreased from 0.7719 to 0.5817, increasing at a rate of −24.64%. (2) The changes in the areas of each level of ecological environmental quality show that the ecological environment in the Zhoushan Archipelago generally exhibited a decreasing trend. During the study period, the proportion of the areas with excellent ecological environmental quality decreased by 38.83%, while the proportion of areas with poor and relatively poor ecological environmental quality increased by 20.03%. (3) Based on the overall change trend, the degradation in the ecological environment in the Zhoushan Archipelago was greater than the improvement, with the degradation area accounting for 84.35% of the total area, the improvement area accounting for 12.61% of the total area, and the stable area accounting for 3.05% of the total area. (4) From the perspective of the sustainability of the changes, in 86.61% of the study area, the RSEI exhibited positive sustainability, indicating that the sustainability of the RSEI was relatively strong. (5) The coefficient of variation in the RSEI was concentrated in the range of 0–0.40, having an average value of 0.1627 and a standard deviation of 0.1467, indicating that the RSEI values in the Zhoushan Archipelago during the study period were concentrated, the interannual fluctuations of the data were small, and the time series was relatively stable. The results of this study provide theoretical methods and a decision-making basis for the dynamic monitoring and regional governance of the ecological environment in island areas.

Dynamic Evaluation of Ecological Environment Quality in Coastal Cities from the Perspective of Water Quality: The Case of Fuzhou City

Urbanization has led to enormous pressure on the urban ecological environment, especially in coastal cities. It is important to evaluate the ecological quality of coastal cities. We compared the remote sensing-based ecological index (RSEI) and the water benefit-based ecological index (WBEI) indices of Fuzhou City, and selected the WBEI to assess the changes in the quality of the ecological environment in Fuzhou City from 2000 to 2020 and analyzed the relevant changes in land intensity. The results show that (1) the Fuzhou WBEI outperforms the RSEI in the evaluation of ecological quality in the study area, since the WBEI takes into account water efficiency and can more accurately represent the ecological environment of coastal cities; (2) during 2000–2020, the overall trend of ecological quality in Fuzhou is better, with nonsignificant decreasing areas in the western and northern mountainous areas and a significant increasing trend in the southeast coast; and (3) different land use types influence the urban ecological environment quality, with forests and man-made surfaces having a good correlation with the WBEI. The increase in forests contributes to the improvement of urban ecological environment quality, and the conversion of high-intensity arable land and construction land decreases urban ecological environment quality. This study can provide a basic reference for the assessment of ecological environment quality in Fuzhou City and provide experience for the ecological environment assessment of coastal cities.

Quantifying impacts of natural and anthropogenic factors on urban ecological quality changes: A multiscale survey in Xi’an, China

Over the past decades, cities worldwide have experienced a sharp decline in urban ecological quality (UEQ) due to rapid urbanization accompanied. However, the spatiotemporal dynamics of UEQ and its associated multi-scale impacts of natural and human factors on UEQ changes were still unknown. Thus, based on the Remote Sensing-based Ecological Index, Random Forest regression, and Geographical Detector, taking the downtown area of Xi’an City, China, as an example, we revealed the spatiotemporal dynamics of UEQ from 2000 to 2019 and quantified the impacts of 19 natural and human factors on UEQ changes at 30 scales. Results showed that 1) from 2000 to 2019, the UEQ in the downtown area of Xi'an experienced a sharp decrease at first, and then gradually recovered. But the recovered UEQ was much lower than the lost UEQ, indicating the big loss of UEQ. 2) The identified driving factors had significant impacts on local UEQ within 1300 m. 3) Elevation, temperature, precipitation, slope, and GDP were more important than other factors in impacting UEQ changes. 4) Natural conditions and climate change had the greatest impact on the regional UEQ, followed by social changes and urban built environment. Altogether, our results revealed the complex impacts of natural-anthropogenic factors on UEQ changes, including impact scale, impact strength, and impact interaction, thus can assist local government in optimizing urban planning and improving livable cities.

Detection of spatiotemporal changes in ecological quality in the Chinese mainland: Trends and attributes

Global climate change and revegetation programs have significantly changed the ecological quality (EQ) in the Chinese mainland after 1999. Monitoring and assessing the changes in the regional EQ and analyzing their drivers are crucial for ensuring ecological restoration and rehabilitation. However, it is challenging to carry out a long-term and large-scale quantitative assessment of the EQ of a region based on traditional field investigations and experiment methods alone; notably, in previous studies, the effects of carbon and water cycles and human activities on the variations in EQ have not been studied comprehensively. Therefore, in addition to remote sensing data and principal component analysis, we used the remote sensing-based ecological index (RSEI), to assess the EQ changes in the Chinese mainland during 2000–2021. Additionally, we also analyzed the impacts of carbon and water cycles and anthropological activities on the changes in the RSEI. The main conclusions of this study were: since the beginning of the 21st century, we observed a fluctuating upward trend in the EQ changes in the Chinese mainland and eight climatic regions. From 2000 to 2021, in terms of the EQ, North China (NN) portrayed the highest increase rate (2.02 × 10−3 year−1, P < 0.05). There was a breaking point in 2011, the EQ in the region experienced a change, from a downward trend to an upward one. Northwest China, Northeast China, and NN portrayed an overall significant increasing trend in the RSEI, whereas the southwest part of the Southwest Yungui Plateau (YG) and a part of the plain region of the Changjiang (Yangtze) River (CJ) river region portrayed a significant decreasing trend in the EQ. Overall, the carbon and water cycles and human activities played a pivotal role in determining the spatial patterns and trends of the EQ in the Chinese mainland. In particular, the self-calibrating Palmer Drought Severity Index, actual evapotranspiration (AET), gross primary productivity (GPP), and soil water content (Soil_w) were identified as the key drivers of the RSEI. In the central and western Qinghai–Tibetan Plateau (QZ) and the northwest region of NW, the changes in RSEI were dominated by AET; however, in central NN, southeastern QZ, northern YG, and central NE, the changes were driven by GPP, and in the southeast region of NW, south region of NE, northern region of NN, middle YG region, and a part of the middle CJ region, the changes were driven by Soil_w. The population-density-related change in the RSEI was positive in the northern regions (NN and NW) but negative in the southern regions (SE), whereas the RSEI change related to ecosystem services was positive in the NE, NW, QZ, and YG regions. These results are beneficial for the adaptive management and protection of the environment and the realization of green and sustainable developmental strategies in the Chinese mainland.

Coupling Coordination Degree between Ecological Environment Quality and Urban Development in Chengdu–Chongqing Economic Circle Based on the Google Earth Engine Platform

Rapid urbanization often exerts massive pressure on the resources relied upon by the ecological environment. It is necessary to quickly evaluate the interaction and mutual influence between regional urbanization and the ecological environment. This paper uses the Google Earth Engine (GEE) platform, integrates MODIS and night light remote sensing data sets, and computes the remote sensing-based ecological index (RSEI) and the coupling coordination degree (CCD) to measure the coupling coordination and analyze the spatiotemporal changes in the Chengdu–Chongqing Economic Circle (CCEC) for 2010, 2015, and 2020. Our results demonstrate four key findings. Firstly, the CCD varies spatially; it peaks at the Chengdu and the West Chongqing Plains, decreasing outwards along the mountains, with the lowest degree of coupling in the central, southern, and northern edge areas of the CCEC. Additionally, it has shown a trend of maintaining unchanged first and then increasing, mainly responding to policy decisions. Secondly, the changes between the different coupling levels were almost stable and mainly occurred between adjacent levels. Thirdly, the coupling level of towns spreads outwards from the centers at Chengdu and Chongqing and has an overall upward trend in time. Fourthly, in the most recent year, the coupling types present a distribution pattern of one developing axis connected with two peaks. Specifically, the environment system lagging type aggregates in Chengdu, Chongqing, and their surrounding areas, and the others mainly are economic system lagging type. The high internal coupling type also mainly occurs in the high and low coupling levels. Under this context, constructive suggestions for developmental optimization in the study area were proposed.

Coupling coordination analysis of urbanization and the ecological environment based on urban functional zones.

Urbanization is an inevitable process in human social progress; additionally, the ecological environment is the carrier and foundation of human social development. Considering central Shanghai, China, as an example, this study quantitatively analyzed the coupling coordination relationship between urbanization and the ecological environment based on urban functional zones; remote sensing images, Open Street Map roads, and point of interest data were analyzed for the urban functional zones via the remote sensing-based ecological index (RSEI), comprehensive nighttime light index (CNLI), and coupling coordination degree (D). The results revealed that urban functional zones in central Shanghai were mainly mixed functional zones and comprehensive functional zones, which formed a spatial structure that gradually radiated outward from the urban core. Additionally, CNLI values were high; the proportion of CNLI between 0.6 and 1 was 94.37%. Moreover, the RSEI showed spatial differentiation; it was low in the center and gradually increased outward. Additionally, D was at the primary coordination level. The coupling coordination type in the core area corresponded to an ecological environment lag, which gradually transitioned to a state of systematic balanced development from the core area outward, but showed sluggish urbanization in some areas. This quantitative analysis of the coupling coordination between urbanization and the ecological environment based on urban functional zones provides effective scientific references for optimization of spatial planning.

What Are the Correlations between Human Disturbance, the Spatial Pattern of the Urban Landscape, and Eco-Environmental Quality?

Human transformation of the landscape is reflected in its composition and spatial pattern. Therefore, exploring the response of the eco-environment to the composition and spatial pattern of the landscape is beneficial to providing a theoretical basis for urban planners. In this study, we take a typical oil city in China as an example and introduce the hemeroby index, landscape metrics, and a remote sensing-based ecological index (RSEI) to calculate and evaluate the urban landscape pattern, human disturbance, and eco-environmental quality, as well as exploring the relationships between them. The results demonstrate that the mean RSEI value of the study area was 0.4866, indicating that its eco-environmental quality was relatively moderate. The whole study area had a relatively high degree of human disturbance (hemeroby index = 7.4498), where the effect of human disturbance on the eco-environment was more intense in natural ecosystems, such as forest and grasslands, but less intense in artificial landscapes, such as built-up areas and farmlands. The urban landscape pattern was significantly correlated with eco-environmental quality, among which the proportion of green space and impervious surface had the strongest correlations with the mean RSEI, with correlation coefficients of 0.538 and −0.577, respectively. In addition, the correlation between the landscape pattern and the RSEI presented obvious spatial heterogeneity.

Dynamic Changes, Spatiotemporal Differences, and Ecological Effects of Impervious Surfaces in the Yellow River Basin, 1986–2020

Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries.

Index-based impact monitoring of water infrastructures in climate change mitigation projects: A case study of MGNREGA-IWMP projects in Maharashtra

Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is currently the largest funded rural development program in India to ensure rural livelihood by providing employment through different activities. Owing to climate change impacts and water scarcity for the last 5 years, approximately more than 70% of activities (budget of 5 billion USD per year) are focused on water issues. For better utilization of resources, MGNREGA has been implementing these works in convergence with many other schemes like the integrated watershed management programme (IWMP), with the aim of restoring rural natural resources, like soil, vegetation, and water. Although the largest Rural Development Scheme in India, the monitoring and evaluation (M&amp;amp;E) does not focus on the actual impact on water resources but only focuses on the amount spent. This can lead to only information on funds spent but does not have much an impact on water resources and earmarking data for monitoring. The recent advancements in remote sensing techniques have provided open access to high-resolution satellite data along with the processing platform. Considering the available technologies and gaps in the existing M&amp;amp;E framework, the current study developed a low-cost, comprehensive, robust, and near real-time M&amp;amp;E framework to assess the impact of water projects. A novel remote sensing-based ecological index (RSEI) has been developed to assess the overall ecological status of land surfaces due to the water infrastructures developed. The performance of RSEI has also been assessed using the secondary data of crop production, which shows excellent results (adjusted R2 value of 82% and Pearson's correlation coefficient of 0.92). In addition, RSEI results were compared against the results of government audits (e.g., by the Comptroller Auditor General of India), wherein RSEI found similar areas of increased water status and crop productivity. The results indicate that the use of the RSEI-based M&amp;amp;E framework will allow for an impact assessment of water infrastructure and also aid to identify the priority areas demanding immediate intervention. Thus, RSEI can be used as a decision support system for stakeholders, including for smooth planning, designing, implementation, monitoring, and evaluation of water infrastructure for combating climate change extremes such as droughts.