Eco-environment quality evaluation based on remote sensing over Qingjiang, Hubei Province

2008 ◽  
Author(s):  
Yan Sun ◽  
Cheng Wang ◽  
Lijun Li ◽  
Shanzhen Yi
Keyword(s):  
Remote Sensing ◽  
Quality Evaluation ◽  
Hubei Province ◽  
Environment Quality

scholarly journals THE ANALYSE OF AIR ENVIRONMENT QUALITY MONITORING IMPROVEMENT POSSIBILITIES

2015 ◽  
Vol 1 ◽  
pp. 273
Author(s):  
Gotfrīds Noviks ◽  
Andris Skromulis
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Dust Particles ◽  
Action Program ◽  
Air Quality Monitoring ◽  
Environment Quality ◽  
Air Quality Measurements ◽  
Quality Measurements

Paper presents the results of air pollution analyses during last 8 years in Rezekne city. There is carried out a research of atmospheric dust particles, found correlations between concentrations of different air pollutants. Is given overview about air quality measurements in other countries, pointed out air ionization importance on air quality evaluation. The aim of the research – to ground the extension of air quality monitoring indicators including parameters of the air ionisation and to work out an action program to improve an air quality in working areas and recreating zones.

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 METHODOLOGY DEVELOPMENT FOR CALCULATING THE AREA OF GREENERY IN A CITY, USING REMOTE SENSING DATA

2021 ◽  
pp. 170-181
Author(s):  
Olga S. Sergeeva ◽  
◽  
Semen P. Pirozhkov ◽  
Keyword(s):  
Remote Sensing ◽  
Urban Environment ◽  
Remote Sensing Data ◽  
Green Spaces ◽  
Space Images ◽  
Important Indicator ◽  
Sensing Data ◽  
Environment Quality ◽  
Gis Technologies ◽  
Green Areas

This article discusses the possibility of using Earth remote sensing data and GIS technologies for assessing the area of green spaces in a city. Green areas are an important indicator of the urban environment quality. Quantitative information on such areas is necessary to calculate the total index of the urban environment quality, which is provided annually to the state statistics authorities. Various methods of obtaining such data are possible, including by decoding orthophotomaps, aerial photography, and mobile laser scanning. GIS technologies provide ample opportunities in this area: they allow one to create electronic maps, attributive databases, and maintain up-to-date information. The paper provides an example of using space images to calculate green areas in one of the microdistricts in the city of Perm. We describe the technique for recalculating the number of trees in a landscaping area; assess the planting areas of general and limited use and the total area of green spaces in the microdistrict; calculate the share of green areas and the greening level of the microdistrict, which are necessary for calculating the urban environment quality index. The technique proposed in this work can significantly reduce the time and labor costs for finding indicators of the urban environment greening.

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