Ecological Environment Quality Assessment Research Articles

Spatial-temporal characteristics analysis and ecological environment quality evaluation of forest health care bases in Yunnan, Guizhou and Sichuan provinces

As the forest health care in China is still in the early stage of development, the construction standards of forest health care base are not unified yet, resulting in large differences in the evaluation criterion for the ecological environment of forest health care bases. So, it is urgent to develop a new forest health care ecological environment quality assessment method. Yunnan, Guizhou and Sichuan provinces of China were selected as the study area, the previous 6 batches of 165 national forest health care pilot construction bases were selected as the main data source. This study explored the spatial and temporal distribution characteristics of forest health care bases in the study area using standard deviation ellipses, kernel density estimation method and cold and hot spot analysis. Furthermore, this study evaluated the ecological environment quality of the forest health care bases with a new ecological environment quality evaluation index model, which assembled Fraction Vegetation Coverage (FVC), Wetness (WET), Evapotranspiration (ET), Land Surface Temperature (LST) and Normalized Difference Bare Soil Index (NDBSI). The results are as follows: (1) the forest health bases in the study area are mainly located by the east of the Hu Line with a northeast-southwest distribution direction characteristics, and gradually expanded into a shape of “high in the east and low in the west, multi-point development”. (2) the area with ecological environment quality in excellent, good and medium grade accounts for about 87.73 % in the study area, indicating that most of the study area is suitable for the construction of forest health care base. These results can provide a practical guidance for the further rational layout and balanced development of forest health care bases in the study area.

Spatial-temporal Evolution and Driving Factors Analysis of Ecological Environment Quality in Daihai Basin based on AWRSEI

Watersheds are an important ecological security barrier and social and economic development area. In order to evaluate the ecological environment quality of arid and semi-arid watersheds more objectively and accurately, based on the remote sensing ecological index (RSEI), the salinity index was introduced to construct a remote sensing ecological index (AWRSEI) suitable for arid and semi-arid watersheds, and the Daihai watershed was taken as an example to analyze its applicability. Based on the AWRSEI model, four phases of Landsat TM/OLI composite images were used to quantitatively evaluate the ecological and environmental quality of the Daihai Basin from 2001 to 2020. The spatio-temporal evolution of the ecological and environmental quality of the Daihai Basin was analyzed using the coefficient of variation and spatial autocorrelation, and the cause analysis and driving factor explanation power analysis were carried out using geographic detectors. The results showed that:① the average correlation coefficient between AWRSEI and various ecological factors was 0.860, which was more representative than that of a single index component. The load positive and negative values and ranking of the first principal component were consistent with those of RSEI, the contribution rate of eigenvalues was 3.69% higher than those of RSEI, and the evaluation results were closer to the real surface conditions, which is suitable for the ecological environment quality assessment of arid watersheds. ② The annual average of the AWRSEI index in the Daihai Basin from 2001 to 2020 was 0.427, which indicated a poor ecological environment quality basis. During the study period, the average of AWRSEI showed a fluctuating trend and reached the highest value of 0.502 in 2020. The overall ecological environment quality improved significantly, the deterioration area decreased by 20.51%, and the improvement area increased by 12.71%. In terms of spatial distribution, the ecological environment quality of forest land in the southern and northwestern high-altitude areas of the Daihai Basin was good, whereas that in the northern and southern mid-altitude areas was poor, and that in the northern area was better than that in the southern area. ③ The average variation coefficient of AWRSEI in the Daihai Basin was 0.280, the ecological environment quality was stable, and the overall change fluctuation was small; the high fluctuation was mainly concentrated in the southern part of the lake and the residential area. There was a significant spatial autocorrelation in the ecological environment quality of the Daihai Basin, and the high-high agglomeration area was mainly distributed in the forest area at high altitude and the cultivated land area at low altitude. Low-low concentration areas were scattered in the middle altitude area. ④ The improvement of the ecological environment in Daihai Basin from 2001 to 2020 was mainly due to the increase in NDVI and the decrease in NDBSI and NDSI. NDVI and NDBSI were the combination with the strongest interaction and the strongest interpretation of the ecological environment. Land use was the dominant factor of AWRSEI and had the strongest explanatory power. The combination of land use and meteorological factors was the strongest interaction, and the relationship between each driving factor was enhanced.

Ecological environment quality assessment and spatial autocorrelation of northern Shaanxi mining area in China based-on improved remote sensing ecological index

The northern Shaanxi coal mining area is an important coal production base in China and an ecologically fragile area, and it is of great significance to explore the ecological environment quality and spatial evolution trend of the mining area. Based on Sentinel-2 and MODIS image data, an improved remote sensing ecological index was constructed by principal component analysis method: normalized difference vegetation index, normalized difference water index, normalized differential buildup and bare soil index, and net primary productivity. The ecological environment quality of the northern Shaanxi coal mining area and its influencing factors, as well as the spatial autocorrelation analysis of ecological environment quality, were discussed. The results showed that: 1) The vegetation coverage in the study area showed an overall increasing trend, but it was greatly affected by the average annual temperature. The NDVI index at the mine area is higher than the NDVI index at the non-mine area. 2) The water area gradually decreases with the year, which has a certain negative correlation with the total raw coal production. 3) The NDBB index showed a decreasing trend with the year, and compared with the non-mining area. 4) The coupling of year-by-year precipitation and temperature leads to interannual fluctuation of NPP value. 5) The change of ecological environment quality in the study area is the result of the comprehensive effect of natural factors and human factors. The implementation of ecological protection projects such as geological environmental protection and land reclamation in mining areas also has a certain impact on the trend of ecological environment quality. 6) There was a significant spatial autocorrelation in the quality of the ecological environment in the study area. There are significant “High-High” gathering areas of ecological environment quality within the coal mining area.

Comprehensive Ecological Environment Quality Assessment Based on AHP and CRITIC Weight Method—Taking Turpan and Hami Cities as Examples

Comprehensive Ecological Environment Quality Assessment Based on AHP and CRITIC Weight Method—Taking Turpan and Hami Cities as Examples

Assessment of optimal seasonal selection for RSEI construction: a case study of ecological environment quality assessment in the Beijing-Tianjin-Hebei region from 2001 to 2020

Timely and objective assessment of the optimal season for the construction of remote sensing ecological index (RSEI) is of great significance for accurate and effective assessment of ecological environment quality. We manipulated RSEI in to monitor seasonal variations in ecological environment quality (EEQ) in the Beijing-Tianjin-Hebei (JJJ) region from 2001 to 2020. First, we evaluated image quality across all four seasons and filled in missing observations through liner interpolation. Second, Seasonal RSEI was constructed using MODIS and compared across different years. Third, temporal and spatial variations within the same seasons in EEQ. Additionally, Moran’s I was utilized to evaluate spatial autocorrelation of EEQ, and the stability of the correlation between RSEI and four indicators seasonal indicators was compared. The results showed that: 1) the PC1 component concentrates most of the characteristics of the four indicators, especially in summer (over 71%); 2) the Moran’ I in the summer of 2001, 2006, 2011, 2016 and 2020 are 0.909, 0.898, 0.917, 0.921 and 0.892, respectively, which indicated that the EEQ has a strong positive spatial correlation. 3) the correlation between the four indicators and summer RSEI showed high correlation in different years, and the standard deviation of the correlation between the four indicators and RSEI fluctuated most slightly in summer, which the std of NDVI, WET, LST and, NDBSI were 0.005, 0.052, 0.026 and 0.017, respectively. This study theoretically demonstrates that summer is the optimal season for constructing RSEI, filling the research gap in previous studies regarding the rationale for selecting images from periods of vigorous vegetation growth for RSEI construction, which can provide a reference for selecting the optimum season for the ecological quality monitoring of urban in the future.

Integrating three-dimensional greenness into RSEI improved the scientificity of ecological environment quality assessment for forest

Normalized Difference Vegetation Index (NDVI) is widely used to represent the greenness indicator for the Ecological Environment Quality (EEQ) assessment based on the traditional Remote Sensing Ecological Index (RSEI). However, NDVI saturation issues are reported in agriculture and forest ecosystems at high greenness and biomass, creating a challenge when using NDVI to reflect the greenness components for forest EEQ assessment. In this paper, three-dimensional greenness (TDG) was obtained by the Forest Canopy Height (FCH) and Fractional Vegetation Cover (FVC) to quantify the forest greenness. The NDVI of RSEI was replaced with TDG to establish an improved remote sensing ecological index (TDRSEI) for the forest EEQ evaluation in the Central Yunnan, China. Moreover, we analyzed the difference between RSEI and TDRESI by qualitative and quantitative means and discussed deeply the optical saturation of NDVI by the quadratic function. The results shown that there were similar spatial distribution patterns and a strong correlation between TDG and FCH, Leaf Area Index (LAI), FVC, and NDVI, the TDG can be used to replace NDVI for reflecting forest greenness. The standard deviation of TDRSEI from the same FCH pixels was all less than that of RSEI, the absolute correlation coefficient between TDRSEI and ecological components were all greater than 0.63, and the mean values of TDRSEI (0.73, 0.84, and 0.90) from the relatively high FCH (20 m, 25 m, and 30 m) were greater than RSEI (0.71, 0.78, and 0.85), showing that the TDRSEI were more stable than RSEI for forest EEQ assessment and it can improve the EEQ in the high FCH. In Central Yunnan, China, the forest EEQ from TDRSEI and RSEI maps both increased with the growth of FCH, the distribution of RSEI had no significant difference between the low FCH and the high FCH, while the mean values of TDRSEI increased linearly with FCH growth. Besides, the ecological saturation points of TDRSEI and RSEI corresponding to the FCH were 19.50 m and 34.02 m, respectively. Therefore, TDRSEI method integrates the three-dimensional greenness to evaluate the forest EEQ objectively.

Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau

Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.

Ecological environmental quality assessment of Chinese estuarine wetlands during 2000–2020 based on a remote sensing ecological index

Coastal estuarine wetlands are important transition zones between rivers and oceans and are extremely rich in biodiversity. In recent years in China, large-scale reclamation and development of coastal cities have imposed serious pressures on coastal ecosystems. Thus, assessing the ecological quality of estuarine wetlands is extremely important for sustainable development. Our study focuses on four typical estuarine wetlands at the mouths of the Yangtze, Yellow, Liaohe and PearRivers. Their ecological quality between 2000 and 2020 was assessed using a remote sensing ecological index (RSEI), which was derived from several remote sensing indexes processed via the online Google Earth Engine platform. From 2000 to 2020, the RSEIs of coastal wetland increased from 0.42 to 0.63 in the Liao River estuary, 0.27 to 0.45 in the Pearl River estuary and from 0.47 to 0.54 in the Yangtze River estuary, and decreased from 0.56 to 0.49 in the Yellow River estuary. The spatial distribution in ecological environmental quality was significantly clustered. High-high clusters occurred mainly in areas of lush vegetation, while low-low clusters were mostly found in built-up areas or coastal zones. Aquacultural and built-up areas had negative impacts on the ecological environment, while vegetation cover had a positive influence. The quality of the ecological environment within these wetlands has gradually improved due to the environmental management policies of national and local governments. This approach to evaluating the ecological quality of estuarine wetlands using the RSEI and Google Earth Engine provides critical information to inform sustainable development policy.

Eco-environmental quality assessment based on pressure-state-response framework by remote sensing and GIS

Ecological environment quality assessment (EEQ) is an important parameter for sustainable development and sometimes it's more valuable during industrialization and urbanization progress of the region. This study compares remote sensing ecological index (RSEI) and ecological index (EI) and assesses EEQ with the most popular pressure-state-response (PSR) method based on a set of remote sensing and statistical indexes through a weight system in Samara region Russia. EI and RSEI framed from 15 toand 4 indicators respectively and in the PSR framework natural-human pressure and health state assessed based on 12 and 3 indicators respectively and in the last response index developed from pressure and state indicators. Results indicate that RSEI and EI showed very strong comparability in the ecological sense but EI reflect more effectively EEQ changes than RSEI. Finally this study explores all important effective indicators developed from remote sensing for eco-environment quality assessment, which provide a strong decision making base for sustainable development as well as provide a new and latest technological support for long term comprehensive mapping, monitoring and assessment of environment.

Research on Evaluation Method of Ecological Environment Quality Based on the Improvement of Human Settlement Environment

Against the background that ecological environment quality has become a hot research topic in recent years, ecological environment quality assessment has emerged as an emerging discipline. By adopting scientific methods, it objectively and quantitatively reveals the real situation of ecological environment quality, and is committed to providing support basis for environmental protection and comprehensive prevention and control. Therefore, how to develop a reasonable eco-environmental quality assessment system has become the focus of attention. Based on this, this paper summarizes the research results in this field at home and abroad, and discusses and illustrates the main existing evaluation methods and evaluation models through horizontal and vertical analysis. The development status of ecological environment quality evaluation research field and the application characteristics of different evaluation methods are analyzed, some problems in theoretical innovation, model construction and application of conclusions are discussed, and the development trend of ecological environment quality evaluation research is proposed based on this.

Ecological environment quality assessment of Xishuangbanna rubber plantations expansion (1995–2018) based on multi-temporal Landsat imagery and RSEI

The specific impact of ecological environment quality at a regional scale due to the rubber plantations expansion is still unclear in Xishuangbanna, Yunnan province, China. First, we used a pixel and phenology-based multiple normalization approach to map rubber plantations over six time periods during 1995–2018 with the available high-quality Landsat imagery. Second, a pixel-based optimized algorithm was developed to reduce the commission and omission errors of the rubber plantations maps and we mapped the rubber plantations expansion area. Third, a remote sensing-based ecological index (RSEI) was employed to assess the rubber plantations expansion area ecological environment quality. RSEI value decreased with the lowest value in 2007 (0.511) and increased with the highest value in 2016 (0.727). This study demonstrated that the ecological environment quality degraded from 1995 to 2007 with an increase of rubber plantations expansion, but improved gradually from 2007 to 2018 with the slow growth of rubber.

Improvement of remote sensing ecological index in arid regions: Taking Ulan Buh Desert as an example

The arid area is mainly composed of desert, with fragile eco-environment and being extremely vulnerable to the influence of natural and human perturbations. Based on the remote sen-sing ecological index (RSEI), the arid remote sensing ecological index (ARSEI) was formed to improve the remote sensing ecological index for arid area, which was coupled with the information of greenness, humidity, salinity, heat and land degradation to quantitatively evaluate the eco-environment quality. We used ARSEI and RSEI to dynamically monitor and evaluate the eco-environment quality of Ulan Buh Desert from 2000 to 2019, and analyzed their differences and their applicability in arid area. We further examined the characteristics and reasons of the temporal and spatial variations of the eco-environment quality of Ulan Buh Desert. The results showed that the ARSEI index had better applicability to the eco-environment quality in arid area than the RSEI, and it enhanced the role of land use changes in the ecological environment quality assessment. From 2000 to 2019, the overall eco-environmental quality of Ulan Buh Desert was worse. The parts under better, good, and medium grades were mainly distributed in the northern region, the parts with worse grades were mainly concentrated in the gobi and sandy land, and the poor ones were mainly located in area with low coverage vegetation. From 2000 to 2019, the overall quality of the eco-environment in the Ulan Buh Desert were becoming better. Meanwhile, the eco-environment quality of towns and farms in the northern part of the desert changed complexly, with deterioration and improvement alternately distributed. The main reason for the changes in the eco-environment of Ulan Buh Desert was the positive effects of ecological agriculture and sand industry.

基于Yaahp软件的铜陵市生态环境质量评价研究

基于Yaahp软件的铜陵市生态环境质量评价研究

Water Ecological Environment Protection under Changing Environment: A Systematic Review and Bibliometric Analysis

Li, W.; Jiang, R.; Zhao, Y.; Xie, J.; Zhu, J., and Cao, R., 2019. Water ecological environment protection under changing environment: A systematic review and bibliometric analysis. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 9–15. Coconut Creek (Florida), ISSN 0749-0208.Systematic review is usually used to reveal the change characteristics of the quantity and law of the citations focus on one research theme. The paper investigated the research status, development trend and hotspots of water ecological environment protection using bibliometric analysis. Based on bibliometric, mathematics and statistics, articles related to the water ecological environment protection in Web of Science for the period of 2008-2019 were selected. CiteSpace software was used to conduct the annual frequency analysis of papers, countries and research institutions analysis, co-occurring keywords analysis, clustering analysis and research frontier. Results show that: (1) The number of published papers and cited papers have increased for the past 12 years. Especially, the number of published papers has increased sharply since 2015, indicating that the research on water ecological environment protection has been transferred from the exploration stage to rapid development stage, which needs to be analyzed from the aspects of national policy. (2) China has more publications than other countries. The research institutions are mainly concentrated on universities, the cooperation among them is not close. It shows the characteristics of small distribution and large concentration. Keywords such as “model” and “water quality” are research hotspots. (3) The results of clustering analysis show that water ecological environment quality assessment, water and soil conservation, ecosystem services and aquatic ecosystem are four main research directions of water ecological environment protection. (4) Using method of burst detection, nine keywords are obtained, which indicate that water ecological environment protection has rich research perspectives. The burst strength of land use is high, it will become the research hotspot of water ecological environment protection in future. The results should be beneficial to systematically understand research progress and future trends of water ecologic environment under changing environment, and therefore it promotes ecological civilization construction.

Ecological environment quality assessment based on remote sensing data for land consolidation

Abstract Ecological transformation is inevitable in development of land consolidation (LC) in China. An appropriate approach for assessing ecological environmental quality (EEQ) is fundamental for the analysis of LC efficiency. However, previous studies generally rely on statistical data or land use data, and the objectivity of these data and research periods are generally limited at the project scale. Based on the remote sensing data 6 years before and after LC, this study developed the RS-based theoretical research framework, provided a contrastive analysis between Remote Sensing Ecological Index (RSEI) and Ecological Index (EI) and expanded spatio-temporal pattern study to assess EEQ in a typical land consolidation project area (LCPA) of China, with the application of RSEI method. Results suggested that RSEI and EI showed strong comparability in the ecological sense, and RSEI can reflect the changes in EEQ in LCPA more effectively than EI. Wetness and greenness indicators had positive effects on EEQ promotion, while heat and dryness indicators had negative effects, and the effect of dryness indicator was more significant than that of other indicators. Before, during, and after LC, EEQ decreased first, increased later, and decreased overall, with the characteristics of degenerating during the construction period, improving during the restoration period, as well as degenerating in the overall process. LC caused persistent disturbance to EEQ, but there was a lag for restoration for more than 5 years. Prediction indicated that the selected indicators were the key indicators of EEQ, among which wetness and greenness were essential for EEQ improvement. This study provides certain theoretical guidance and method reference for the scientific, objective, direct, and long-term comprehensive monitoring and assessment of EEQ at the project scale.

A new approach of deriving indicators and comprehensive measure for ecological environmental quality assessment

The key indicators’ identifications and assessments of ecological environmental quality (EEQ) are very important for management policies and strategy. In this paper, founding upon the facts that the key indicators of EEQ from the experts’ selections are hidden in and improved by the constantly additions of new publications, a new approach of deriving indicators and comprehensive measure for ecological environmental quality assessment (EEQA) is developed from dynamic co-word network. We elaborate the new approach and the modeling construction roadmap: the key indicators of EEQ emerge from the dynamic co-word network; basing on clustering and K-core analysis of the co-word network domains, four levels of indicators for EEQA are deduced; based on the similarity or relatedness between the indicators in the co-word network, nodal degrees are introduced to calculate indicators’ weights and derive a comprehensive measure for EEQ, finally forming a rigorous co-word network model of EEQA. This model is applied to some typical cases of EEQA (a town, a city and two specific cases from Chongqing and Beijing) and the reasonable assessment results are obtained. The results are compared with other models to show the features of the model. The co-word network model for EEQA is a potential and universal function derived from traditional co-word methods of bibliometrics. Beyond the EEQA, the assessment of many other complex phenomena can be similarly conducted in terms of the given technical roadmap.

The synthetic geo-ecological environmental evaluation of a coastal coal-mining city using spatiotemporal big data: A case study in Longkou, China

The geological and ecological (geo-ecological) environment of the coastal coal-mining city is a basic element for human subsistence, and it connects the regional economy with social sustainable development. Using Longkou, a coastal mining city, as an example, a geological and ecological environmental quality assessment was conducted based on spatiotemporal big data. Remote sensing images, a digital elevation model (DEM), and precipitation and interpolation processing were used to generate factor layers. A synthetic evaluation index system was set up, including physical geography, geological conditions, mining intensity, ecological environmental recovery and geological hazards associated with mining. Moreover, an analytical hierarchy process was used to calculate the factor weight of each evaluation factor, and a consistency check was performed to build an assessment model of the geo-ecological environment of Longkou. The results indicate that multi-factor spatiotemporal big data provide a scientific assessment of the geo-ecological environmental quality with indispensable data and methods. The spatial distribution of geo-ecological environmental quality presented clear specialization of zonality, showing poor quality in the coastal coal mine ore concentration area and good quality in the inland and mountainous areas of Nanshan Mountain. The geo-ecological environmental quality of Longkou was divided into 5 levels as worst, poor, middle, good and better districts. The good and better districts accounted for 76.763% of the total area of the assessment region, indicating that the geo-ecological environmental quality of the study area was in good condition. The mining intensity and ecological environment recovery were major factors in determining the regional variation of the geo-ecological environment of Longkou. The possible causes inducing uncertainties and limitations in evaluation of the geo-ecological environmental quality were discussed. The model combining AHP with GIS proposed in this paper is an effective means of evaluating regional geo-ecological environmental quality.

The studies of ecological environmental quality assessment in Anhui Province based on ecological footprint

Under the dual pressures of the socio-economic development and the increase in population density, the ecological environmental quality issues in Anhui Province have become increasingly prominent, which seriously handicap the sustainable development of regional economy, and it is more important to evaluate ecological environmental quality. By using the data of ecological environment, society, economy and population in Anhui Province, this paper measures ecological footprint, ecological environmental carrying capacity and ecological deficit and surplus in Anhui Province based on ecological footprint. The results show that ecological environmental quality in Anhui Province is not in the best state of the coordinated development, and the relationship between the supply and the demand of per ecological footprint almost is not in balance.