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Ecological quality is a critical factor affecting the livability of urban areas. Remote sensing technology enables the rapid assessment of ecological quality (EQ), providing scientific theoretical support for the maintenance and management of urban ecology. This paper evaluates and analyzes the EQ and its driving factors in the city of Wuhan using remote sensing data from five periods: 2001, 2006, 2011, 2016, and 2021, supported by the Google Earth Engine (GEE) platform. By employing principal component analysis, a Remote Sensing Ecological Index (RSEI) was constructed to assess the spatiotemporal differences of EQ in Wuhan City. Furthermore, the study utilized the optimal parameter-based geographical detector model to analyze the influence of factors such as elevation, slope, aspect, population density, greenness, wetness, dryness, and heat on the RSEI value in 2021 and further explored the impact of changes in precipitation and temperature on the EQ in Wuhan. The results indicate that (1) principal component analysis shows that greenness and wetness positively affect Wuhan's EQ, while dryness and heat have negative impacts; (2) spatiotemporal analysis reveals that from 2001 to 2021, the EQ in Wuhan showed a trend of initial decline followed by improvement, with the classification grades evolving from poor and average to good and better; (3) the analysis of driving factors shows that all nine indicators have a certain impact on the EQ in Wuhan, with the influence ranking as NDVI > NDBSI > LST > WET > elevation > population density > GDP > slope > aspect; (4) the annual average temperature and precipitation in Wuhan have a non-significant impact on the EQ. The EQ in Wuhan has improved in recent years, but comprehensive management still requires enhancement. [ABSTRACT FROM AUTHOR]

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China's EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors. [ABSTRACT FROM AUTHOR]

The quantitative evaluation of the coupling coordination degree (CCD) between the regional economy and eco-environment systems is of great importance for the realization of sustainable development goals, which could identify economic or eco-environmental cold areas. To date, traditional evaluation frameworks mainly include the indicator system construction based on statistical data, which seldom utilize the geo-spatiotemporal datasets. Hence, this study aimed to evaluate the CCD change trend of the Yangtze River Delta (YRD) and explore the relationship between the CCD, economy, and eco-environment on the county scale. In this study, YRD was selected as the study area to evaluate its level of CCD at different periods, and then the nighttime difference index (NTDI) and eco-environmental comprehensive evaluation index (ECEI) were calculated to represent the difference in the development of the regional economy and the eco-environmental quality (EEQ). The CCD between the two systems was then calculated and analyzed using global, local, and Geary's C spatial autocorrelation indicators, in addition to change trend methods. The main findings showed that: (1) During the period 2000–2020, the economic system in YRD showed a continuously upward trend (0.0487 a−1), with average NTDI values of 0.2308, 0.2964, 0.3223, 0.3971, and 0.4239, respectively. In spatial terms, the economy system showed a distribution of "high in the east and low in the west". (2) YRD's EEQ indicated a gradual upward trend (from 0.3590 in 2000 to 0.3970 in 2020), with a change trend value of 0.0020 a−1. Spatially, the regions with high ECEI were mainly located in southwestern counties. (3) In the past 20 years, the CCD between economic and eco-environment systems showed an increased change trend, with a change trend value of 0.0302 a−1. The average CCD values for the five periods were 0.3992, 0.4745, 0.4633, 0.5012, and 0.5369. The overall level of CCD improved from "moderate incoordination" to "low coordination". (4) Both NTDI and ECEI indexes have a positive effect on the improvement of regional CCD. However, the contribution of NTDI is a little higher than that of ECEI. [ABSTRACT FROM AUTHOR]

The effective refuge area is a key indicator in the study of emergency shelters. Accurately extracting the effective refuge area and analyzing the effectiveness of emergency shelters are of great significance for site selection, spatial distribution, and the evaluation of suitability. Beijing is one of only three capitals in the world located in a high-seismic-intensity zone of magnitude 8. The fast and accurate monitoring of effective refuge areas and an analysis of the effectiveness of emergency shelters are conducive to evacuation planning and disaster prevention and mitigation, and they promote the construction of a resilient city. However, the extraction of effective refuge areas in existing studies is not only a time-consuming and labor-intensive task but also has accuracy and efficiency problems, resulting in less precise validity analyses. In this paper, a remote sensing monitoring technology system for the effective refuge areas of emergency shelters is proposed based on multi-source data. Different methods were used to extract various land features, such as buildings and collapsed areas, water, dense areas of understory vegetation, and steep slope areas that cannot be evacuated, to obtain the effective refuge area at a detailed scale, in combination with the service radius of emergency shelters, the population distribution, and the actual road network, the criteria for effectiveness analysis were established for the effective open space ratio, capacity, per capita accessible effective refuge area, and population allocation gap. Taking the area within the Fifth Ring Road of Beijing as an example, the effectiveness of emergency shelters was analyzed at both the whole scale and a local scale. The results show that the effective refuge areas of different emergency shelters in Beijing vary significantly, with the smallest effective refuge area being located in Rings 2–3 and the largest one being located in Rings 4–5; between different regions, there are differences in the effectiveness. This study provides a feasible method for the fast, accurate, and detailed extraction of the effective refuge areas of emergency shelters and also provides a reference for emergency planning for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Under the background of intensified human activities and global climate warming, the frequency and intensity of flood disasters have increased, causing many casualties and economic losses every year. Given the difficulty of mountain shadow removal from large-scale watershed flood monitoring based on Sentinel-1 SAR images and the Google Earth Engine (GEE) cloud platform, this paper first adopted the Support Vector Machine (SVM) to extract the water body information during flooding. Then, a function model was proposed based on the mountain shadow samples to remove the mountain shadows from the flood maps. Finally, this paper analyzed the flood disasters in the middle and lower basin of the Yangtze River (MLB) in 2020. The main results showed that: (1) compared with the other two methods, the SVM model had the highest accuracy. The accuracy and kappa coefficients of the trained SVM model in the testing dataset were 97.77% and 0.9521, respectively. (2) The function model proposed based on the samples achieved the best effect compared with other shadow removal methods with a shadow recognition rate of 75.46%, and it alleviated the interference of mountain shadows for flood monitoring in a large basin. (3) The flood inundated area was 8526 km2, among which, cropland was severely affected (6160 km2). This study could provide effective suggestions for relevant stakeholders in policy making. [ABSTRACT FROM AUTHOR]

Timely and quantitatively evaluating regional eco-environmental quality (EEQ) is of great significance for realizing regional sustainable development goals. Especially for cloudy areas, it was a great challenge to construct a regional EEQ dataset with high quality and high resolution. However, existing studies failed to consider the influence of land surface and season elements in evaluating regional EEQ. Therefore, this study aimed to promote an accurate EEQ-evaluating framework for cloudy areas. Zhaotong city, a typical karst and cloudy region, was chosen as the study area. First, we integrated multi-source spatiotemporal datasets and constructed a novel eco-environmental comprehensive evaluation index (ECEI) to assess its EEQ from 2000 to 2020. Next, standard deviation ellipse (SDE) and trend analysis methods were applied to investigate regional EEQ's change trends. Finally, ecological index (EI) values for different years were calculated to validate the effectivity of the ECEI. The main findings were as follows: (1) The EEQ of Zhaotong showed an upward-fluctuating trend (0.0058 a−1), with average ECEI values of 0.729, 0.693, 0.722, 0.749, and 0.730. (2) The spatial distribution pattern of the EEQ showed high values in the north and low values in the south, with Zhaoyang district having the lowest ECEI value. (3) From 2000 to 2020, the standard deviation of the major axis of the ellipse moved northeast of Zhaotong city with θ of SDE changing from 57.06° to 62.90°, thus, indicating the improvement of northeastern regions' EEQ. (4) The coefficients of the determinant (R2) between the EI and ECEI were 0.84, which was higher than that of EI–RSEI (R2 = 0.56). This indicated that our promoted framework and the ECEI could acquire more accurate EEQ results and provide suggestions for relevant policymakers. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of the coupling coordination degree (CCD) between regional haze the disaster risk index (HRI) and urbanization development index level (UDI) is of great significance for the realization of regional sustainable development goals. Given the lack of the combination of remote sensing and statistical data to evaluate the CCD between two systems, the Chinese mainland's 31 provinces and autonomous regions were taken to evaluate their HRI and UDI by building index systems. Then, an entropy method and one improved coupling coordination model were used to calculate and analyze the spatiotemporal characteristics of CCD between HRI and UDI during 2000–2020. The results showed that: (1) From 2000 to 2020, the value of HRI in China showed a "W" type change trend with its value increased from 0.7041 in 2000 to 0.8859 in 2020, indicating that haze pollution level showed a fluctuating downward trend; (2) From 2000 to 2020, China's UDI values showed a gradual upward trend with its value increased from 0.1647 in 2000 to 0.4640 in 2020, with an average annual growth rate of 8.63%; (3) From 2000 to 2020, CCD values between HRI and UDI showed a fluctuating upward trend with its value increased from 0.5374 in 2000 to 0.7781 in 2020, with an average annual growth rate of 2.13%; the overall level of China's CCD had raised from low coordination to moderate coordination, and eastern coastal provinces had higher CCD values, while those of central and western provinces had lower CCD values; (4) HRI, UDI and CCD could be well fitted with the R2 of 0.9869. Specifically, UDI had a higher contribution to improving the CCD than the HRI. [ABSTRACT FROM AUTHOR]

There were significant differences in the dominant driving factors of the change process of different types of wetlands in the Yellow River delta. In addition, to our knowledge, the optimal classification feature sets with the Random Forest algorithm for wetlands in the Yellow River delta were least explored. In this paper, the wetland information in the study area was extracted based on a Random Forest algorithm with de-feature variable redundancy, and then the change process of wetland and its dominant factors from 2015 to 2021 was monitored and analyzed using the Geodetector and gravity center model. The results showed that (1) the optimal variable sets composed of red edge indexes based on the Random Forest algorithm had the highest classification accuracy, with the overall accuracy and Kappa coefficient of 95.75% and 0.93. (2) During 2015–2021, a large area of natural wetland in the Yellow River delta was transformed into an artificial wetland. The wetlands showed an overall development direction of "northwest–southeast" along the Yellow River. (3) The interaction between vegetation coverage and accumulated temperature had the largest explanatory power of the change in the natural wetland area. The interaction between solar radiation and DEM had the largest explanatory power for the change in the artificial wetland area. The research results could better provide decisions for wetland protection and restoration in the Yellow River delta. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) is significant for disaster prevention and mitigation, land use management, and as a reference for decision-making. Convolutional neural networks (CNNs) in deep learning have been successfully applied to LSM studies and have been shown to improve the accuracy of LSM. Although optimizing the quality of negative samples at the input step of a deep learning model can improve the accuracy of the model, the risk of model overfitting may increase. In this study, an LSM method based on the Gaussian heatmap sampling technique and a CNN was developed from the perspective of LSM dataset sampling. A Gaussian heatmap sampling technique was used to enrich the variety of landslide inventory at the input step of the deep learning model to improve the accuracy of the LSM results. This sampling technique involved the construction of a landslide susceptibility Gaussian heatmap neural network model, LSGH-Net, by combining a CNN. A series of optimization strategies such as attention mechanism, dropout, etc., were applied to improve the model structure and training process. The results demonstrated that the proposed approach outperformed the benchmark CNN-based algorithm in terms of metrics (Accuracy = 95.30%, F1 score = 95.13%, and Sensitivity = 91.79%). The Gaussian heatmap sampling technique effectively improved the accuracy of frequency histograms of the landslide susceptibility index, which provided finer-grained mapping details and more reasonable landslide density. By analyzing Gaussian heatmap at different scales, the approach proposed in this paper is an important reference for different regions and other disaster susceptibility studies as well. [ABSTRACT FROM AUTHOR]

Evaluating and exploring regional eco-environmental quality (EEQ), economic development equality (EDE) and the coupling coordination degree (CCD) at multiple scales is important for realizing regional sustainable development goals. The CCD can reflect both the development level and the interaction relationship of two or more systems. However, relevant previous studies have ignored non-statistical data, lacked multiscale analyses, misused the coupling coordination degree model or have not sufficiently considered economic development equality. In response to these problems, this study integrated multisource remote sensing datasets to calculate and analyse the remote sensing ecological index (RSEI) and then used nighttime light data and population density data to calculate the proposed nighttime difference index (NTDI). Next, a modified coupling coordination degree (MCCD) index was proposed to analyse the MCCD between EEQ and EDE. Then, spatiotemporal and multiscale analyses at the county, city, province, urban agglomeration and country levels were performed. Global and local spatial autocorrelation and trend analyses were performed to evaluate the spatial aggregation degree and change trends from 2001 to 2020. The main conclusions are as follows: (1) The EEQ of China displayed a fluctuating upwards trend (0.0048 a−1), with average RSEI values of 0.5950, 0.6277, 0.6164, 0.6311 and 0.6173; the EDE of China showed an upwards trend (0.0298 a−1), with average NTDI values of 0.1271, 0.1635, 0.1642, 0.2181 and 0.2490; and China's MCCD indicated an upwards trend (0.0220 a−1), with values of 0.4614, 0.5027, 0.4978, 0.5401 and 0.5525. (2) The highest global Moran's I of NTDI and MCCD was achieved at the city scale, while the highest RSEI was achieved at the county scale. From 2001 to 2020, the spatial agglomeration effect of the RSEI decreased, while that of the NTDI and MCCD increased. (3) A power function relationship occurred between NTDI and MCCD at different scales. Furthermore, the NTDI had a higher contribution to improving the MCCD than the RSEI and the R2 of the fitted curve at different scales ranged from 0.8183 to 0.9915. [ABSTRACT FROM AUTHOR]

Measuring the regionally coordinated development degree quantitively at an urban agglomeration scale is vital for regional sustainable development. To date, existing studies mainly utilized statistical data to analyze coordinated development degrees between different subsystems, which failed to measure the development gap of subsystems between cities. This study integrated remote sensing and statistical data to evaluate the development degree from six subsystems. The coordinated index (CI) and coordinated development index (CDI) were then promoted to assess the coordinated degree and coordinated development degree. The main findings were: (1) The coordinated development degree of Jing-Jin-Ji (JJJ) had increased from 0.4616 in 2000 to 0.6099 in 2015, with the corresponding grade improvement from "moderate" to "good"; (2) JJJ and six subsystems' development degree showed an increasing trend. JJJ's whole development degree had improved from 0.34 to 0.52, and the grade had changed from "fair" to "moderate"; (3) The coordinated degree of JJJ displayed a "V" shape. However, the coordinated degree was lower in 2015 than in 2000. [ABSTRACT FROM AUTHOR]

With the development of remote sensing techniques and the increasing need for soil contamination monitoring, we estimated soil heavy metal zinc (Zn) content using hyperspectral imaging. Geographically weighted regression (GWR), an extension of the ordinary least squares (OLS) regression framework, was proposed. By estimating a set of parameters for any number of locations in a study area, GWR can probe the spatial heterogeneity in data relationships, whereas the regression parameters of an OLS model are global and aspatially-varied. The objectives of this study were: (1) To find the possible relationships between hyperspectral data and soil Zn content, and (2) to investigate the existence of their spatial heterogeneity. In this study, 67 soil samples collected from Pingtan Island, Fujian Province, China, were used to conduct laboratory hyperspectral modeling for soil Zn content estimation. Four transformations of square root, logarithm, reciprocal of logarithm, and reciprocal, as well as the fractional-order differential operations were applied to increase the amount of reflectance data in which the effective variables for modeling might be involved, and to enhance the spectral characteristics of soil Zn content. To find sensitive variables and to remove redundancy and multicollinearity in the spectra, a data sifting process was applied by selecting wavelengths with local maximum in the absolute values of the correlation coefficients with Zn content in one type of spectral data and by employing Variance Inflation Factors. Since a modeling sample size of 46 is insufficient to construct the appropriate OLS and GWR models, four methods are proposed using all 67 samples to choose explanatory variables. A random process to select 57 samples for modeling and 10 samples for validation was applied to assess model performance, in which the mean verification R2 (Rv2) was used as an indicator. The results show that GWR stepwise regression is the most effective method to select better variables. As the mean Rv2 converges toward the OLS value when the bandwidth of the GWR model increases, the four variables selected by the GWR stepwise regression were used to establish the representative OLS and GWR models. The representative OLS model has the best mean verification effect among all studied models, which had a mean Rv2 value that is 44.6% higher than the OLS model constructed using OLS stepwise regression. [ABSTRACT FROM AUTHOR]