scholarly journals A Model for Incorporated Measurement of Sustainable Development Comprising Remote Sensing Data and Using the Concept of Biodiversity

2010 ◽  
Vol 3 (2) ◽  
Author(s):  
Azniv Felix Petrosyan
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Remote Sensing Data ◽  
Sensing Data

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 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.

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.

SUSTAINABLE DEVELOPMENT OF THE AGRICULTURAL REGION BY THE APPLICATION OF SPACE MONITORING TECHNOLOGIES

2020 ◽  
Author(s):  
I.Y. Grishin ◽  
◽  
R.R. Timirgaleeva ◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Remote Sensing Data ◽  
Information Resource ◽  
Earth Remote Sensing ◽  
Agricultural Region ◽  
Use Of Space ◽  
Sensing Data ◽  
Monitoring Technologies ◽  
Soil Status

The article reveals the need to systematize the requirements for solving the targets for the development of the agricultural region. The necessity of monitoring soil status indicators based on the use of space monitoring technologies is substantiated. It is shown that agriculture is the most promising field of application of Earth remote sensing data. It is proposed to use the aggregate regional information resource

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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