scholarly journals Eco-Economic Environment Coupling Based on Urban RSEI Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yitong Lu ◽  
Minghang Li
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Remote Sensing Data ◽  
Urban Agglomeration ◽  
Ecological Environment ◽  
Coordinated Development ◽  
Research Results ◽  
Sensing Data ◽  
Ecological Economic ◽  
Environment Quality

Introduction. Ecological environment is the foundation of social and economic development, and the coordinated development of social economy and ecological environment is one of the hotspots in urban sustainable development research. Traditional ecological economic measurement methods usually have problems such as difficulty in data acquisition and difficulty in pasteurization. In recent years, with the rise of global remote sensing technology, remote sensing has also been used to observe human social and economic activities. Remote sensing data have been created for the limitations of traditional statistical data with the advantages of being independent in the field of ecological and economic measurement. Methodology. This paper uses luminous remote sensing technique to obtain the visible-near-infrared electromagnetic wave information emitted from the surface at night under cloudless conditions and MODIS data combined with urban RSEI theory to quantitatively invert the ecological environment quality of the study area in 2019 based on a remote sensing perspective, taking a certain urban agglomeration in China as the research area. And it is continuous in time and space. Therefore, the coordination degree of remote sensing data should be fully explored in quantitative research. For proper analysis, regression was used. Research Content and Results. 2019 MODIS data were used to retrieve the six vegetation-related ecological environment factors in the study area and combine the urban RSEI theory to construct the remote sensing ecological comprehensive index of the study area, and the vegetation ecological environment quality and spatial agglomeration characteristics were evaluated. The results showed that vegetation coverage, leaf area index, total primary productivity, and surface moisture make positive contributions to the ecosystem of the study area. Based on the theoretical basis of coupling coordination theory, ecological economic theory, and sustainable development, we measured the degree of coordinated development between the social economic system and the ecological environment system in the study area from 2018 to the future period and combined the research results with the study area. The actual situation explores the practice path of benign coupling of the ecological economy. Conclusion. This paper is completely based on the research ideas of remote sensing data to measure the socioeconomic level and ecological environment quality and proves that remote sensing data and urban RESI theory are efficient and reliable new tools for the coordinated development of ecological economics. The research results can provide a development plan for urban agglomeration.

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

2019 ◽  
Vol 239 ◽  
pp. 118126 ◽  
Author(s):  
Wei Shan ◽  
Xiaobin Jin ◽  
Jie Ren ◽  
Yongcai Wang ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Quality Assessment ◽  
Remote Sensing Data ◽  
Land Consolidation ◽  
Ecological Environment ◽  
Sensing Data ◽  
Environment Quality

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.

scholarly journals Environmental degradation in the transnational area of Changbai Mountain based on multiple remote sensing data

2021 ◽  
Author(s):  
Yaohang Sun ◽  
Ying Nan ◽  
Da Zhang ◽  
Xuegang Gan ◽  
Lichen Piao
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Environmental Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Environmental Changes ◽  
Remote Sensing Data ◽  
Changbai Mountain ◽  
Sensing Data ◽  
Regional Sustainable Development

Rapidly and effectively assessing environmental degradation is essential for promoting regional sustainable development in the transnational area of Changbai Mountain (TACM). However, comprehensively understanding environmental degradation in the TACM is still inadequate. In this study, we developed an environmental degradation index (EDI) by using multiple remote sensing data, including enhanced vegetation index (EVI), gross primary productivity (GPP), land surface temperature (LST), and MODIS surface reflectance products. We then evaluated its performance comparing with the remote sensing ecological index (RSEI), and assessed the environmental degradation across the whole TACM, in the subregions of China, the Democratic People’s Republic of Korea (DPRK), and Russia during 2000-2019. The results indicated that the EDI had the advantages of simplicity and rapidity, which can assess the environmental degradation in the TACM across long-time scales and large spatial extent. The TACM experienced a downward trend of environmental changes from 2000 to 2019. Degraded environment areas (49,329.50 km2) accounted for 30.09% of the entire TACM. The largest area of the degraded environment was on the DPRK’s side (i.e., 25,395.00 km2), which was 5.6 times larger than that on the Russian side and 1.3 times larger than that on the Chinese side. Hotspot areas that experienced significant environmental degradation just covered 17.69% of the land area of the TACM, the area of environmental degradation in them accounted for 33.89% of the total degraded environment across the whole TACM. We suggest that international cooperation policies and measures ought to be enacted to promote regional sustainable development.

scholarly journals The Coupling Coordinated Relationship Between New-type Urbanization, Eco-Environment and its Driving Mechanism: A Case of Guanzhong, China

2021 ◽  
Vol 9 ◽  
Author(s):  
Liyuan Dong ◽  
Juan Shang ◽  
Rizwan Ali ◽  
Ramiz U Rehman
Keyword(s):  
Driving Force ◽  
Driving Forces ◽  
Urban Agglomeration ◽  
Ecological Environment ◽  
Driving Mechanism ◽  
Coordinated Development ◽  
Environment Quality ◽  
New Type ◽  
Coordination Degree

As an important platform for participating in international competition and cooperation, supporting economic growth and promoting coordinated regional development, urban agglomeration plays an important role in China’s economic, social and urbanization development. At this time, the Guanzhong Plain urban agglomeration (GZPUA), as the second largest urban agglomeration in western China, has a moderate population density. The high demand and high input of resources for population growth make the regional ecological destruction and environmental pollution more prominent. Therefore, it is of great practical significance to study the coordinated development of urbanization and ecological environment in GZPUA. By using the panel data of the GZPUA of China between 2008 and 2017, this study constructed evaluation index system of new-type urbanization and ecological environment quality and calculated the weights of the indices within the evaluation system via the improved entropy weight method, finally determined the new-type urbanization and ecological environment quality of each city. Then the coupling coordination degree model was used to analyze the coupling coordination relationship between two systems of GZPUA and their coupling stages and levels. In addition, the driving mechanism of their coordination degree was explored by using geographic detector method. The results show that: 1) The GZPUA new-type urbanization quality is characterized by both slow growth except Xi’an by a rapid increase. The ecological environment quality is characterized by both slow growth and fluctuations, except Qingyang by a decrease. There are spatial differences between the quality of new-type urbanization and the quality of ecological environment. 2) The 11 cities can be divided into high-high type (Xi’an), high-low type (Xianyang, Yuncheng, Linfen), low-low type (Pingliang, Weinan), and low-high type (Shangluo, Tianshui, Qingyang), different types should take different development paths. 3) The coordination degree between urbanization and ecological environment quality in GZPUA showed an upward trend, and formed a spatial distribution pattern with Xi’an as the core and decreasing to the outer circle cities, with regional differences. 4) The coordinated development of new-type urbanization and ecological environment is a process in which various driving factors act on different driving forces. These driving forces can be summarized as market driving force, endogenous driving force, outward driving force and administrative driving force. Based on the current situation of coordinated development of new-type urbanization and ecological environment in the GZPUA, it is recommended to promote the coordinated development of urbanization and ecological environment according to local conditions, strengthen the urbanization market mechanism, and optimize the industrial layout. Further, guide the flow of various factors across regions, strengthen technological innovation on the basis of breaking regional divisions, narrow the gap between urban and rural areas, establish the concept of coordinated development, and give play to the government’s “visible hand” role.

scholarly journals Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

2018 ◽  
Vol 10 (9) ◽  
pp. 1365 ◽  
Author(s):  
Jacinta Holloway ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Remote Sensing Data ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Sensing Data ◽  
Machine Learning Methods ◽  
Development Goals

Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.

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