Evaluation of ecological changes based on a remote sensing ecological index in a Manas Lake wetland, Xinjiang

2019 ◽  
Vol 39 (8) ◽  
Author(s):  
王丽春 WANG Lichun ◽  
焦黎 JIAO Li ◽  
来风兵 LAI Fengbing ◽  
张乃明 ZHANG Naiming
Keyword(s):  
Remote Sensing ◽  
Ecological Index ◽  
Ecological Changes

scholarly journals Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis

2019 ◽  
Vol 11 (20) ◽  
pp. 2345 ◽  
Author(s):  
Hanqiu Xu ◽  
Yifan Wang ◽  
Huade Guan ◽  
Tingting Shi ◽  
Xisheng Hu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Change Detection ◽  
Ecological Status ◽  
Vector Analysis ◽  
Ecological Index ◽  
Ecological Conditions ◽  
Change Vector Analysis ◽  
Ecological Changes ◽  
Change Vector

Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.

scholarly journals Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

2021 ◽  
Vol 10 (7) ◽  
pp. 475
Author(s):  
Ting Zhang ◽  
Ruiqing Yang ◽  
Yibo Yang ◽  
Long Li ◽  
Longqian Chen
Keyword(s):  
Remote Sensing ◽  
Quality Assessment ◽  
Urban Expansion ◽  
Assessment Method ◽  
Landsat Images ◽  
Ecological Index ◽  
Environment Quality ◽  
Quality Assessment Method ◽  
The Impact

The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method where the impact of RSEI indicators on the eco-environment quality and the seasonal change of RSEI are examined and considered. The northern Chinese municipal city of Tianjin was selected as a case study to test the proposed method. Landsat images acquired in spring, summer, autumn, and winter were obtained and processed for three different years (1992, 2005, and 2018) for a multitemporal analysis. Results from the case study show that both the contributions of RSEI indicators to eco-environment quality and RSEI values vary with the season and that such seasonal variability should be considered by normalizing indicator measures differently and using more representative remote-sensing images, respectively. The assessed eco-environment quality of Tianjin was, overall, improving owing to governmental environmental protection measures, but the damage caused by rapid urban expansion and sea reclamation in the Binhai New Area still needs to be noted. It is concluded that our proposed urban eco-environment quality assessment method is viable and can provide a reliable assessment result that helps gain a more accurate understanding of the evolution of the urban eco-environment quality over seasons and years.

scholarly journals A Multi-Criteria Evaluation of the Urban Ecological Environment in Shanghai Based on Remote Sensing

2021 ◽  
Vol 10 (10) ◽  
pp. 688
Author(s):  
Yuxiang Yan ◽  
Xianwen Yu ◽  
Fengyang Long ◽  
Yanfeng Dong
Keyword(s):  
Remote Sensing ◽  
Evaluation Method ◽  
Projection Pursuit ◽  
Detection Methods ◽  
Ecological Environment ◽  
Ecological Index ◽  
Environment Quality ◽  
Shanghai City ◽  
Projection Pursuit Model ◽  
The Impact

The urban ecological environment is related to human health and is one of the most concerned issues nowadays. Hence, it is essential to detect and then evaluate the urban ecological environment. However, the conventional manual detection methods have many limitations, such as the high cost of labor, time, and capital. The aim of this paper is to evaluate the urban ecological environment more conveniently and reasonably, thus this paper proposed an ecological environment evaluation method based on remote sensing and a projection pursuit model. Firstly, a series of criteria for the urban ecological environment in Shanghai City are obtained through remote sensing technology. Then, the ecological environment is comprehensively evaluated using the projection pursuit model. Lastly, the ecological environment changes of Shanghai City are analyzed. The results show that the average remote sensing ecological index of Shanghai in 2020 increased obviously compared with that in 2016. In addition, Jinshan District, Songjiang District, and Qingpu District have higher ecological environment quality, while Hongkou District, Jingan District, and Huangpu District have lower ecological environment quality. In addition, the ecological environment of all districts has a significant positive spatial autocorrelation. These findings suggest that the ecological environment of Shanghai has improved overall in the past five years. In addition, Hongkou District, Jingan District, and Huangpu District should put more effort into improving the ecological environment in future, and the improvement of ecological environment should consider the impact of surrounding districts. Moreover, the proposed weight setting method is more reasonable, and the proposed evaluation method is convenient and practical.

scholarly journals A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks

Land ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 46
Author(s):  
Xincheng Zheng ◽  
Zeyao Zou ◽  
Chongmin Xu ◽  
Sen Lin ◽  
Zhilong Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Road Network ◽  
Vegetation Index ◽  
Road Networks ◽  
Bare Soil ◽  
Coupling Mechanism ◽  
Ecological Index ◽  
The Impact ◽  
The Relationship

Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments.

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