{"title":"Adaptive methods of detecting environmental changes using multispectral satellite images on the Earth for example territory Solotvyno","authors":"V. Yakimchuk, K. Sukhanov, O. Tomchenko","doi":"10.36023/UJRS.2021.8.1.187","DOIUrl":null,"url":null,"abstract":"The methods for detecting environmental changes using two multispectral multispectral space images of the Earth, which can be used to assess changes in the ecological and geological environment to control the dynamics of processes in real time, in order to prevent environmental emergencies. The creation and operation of a system of continuous geoecological monitoring based on space information involves the detection of relative changes in the environment on two multispectral space images of the Earth, obtained after a certain period of time. To do this, it is necessary to develop and apply adaptive methods (indices) for detecting relative changes. Three methods are proposed that are adapted to specific images to find the optimal solution that maximizes the relative changes in two different time multispectral space images of the Earth. The first method selects one optimal channel from all channels of the space image, the second method - two optimal channels from all channels and the third - four optimal channels from all channels. There are known methods that assess the presence or absence of changes in two space images at different times, but they do not provide information about the direction of changes. The proposed indices can take positive and negative values, reflecting the trend of changes on the ground. The negative values of the indices obtained in the area near the village of Solotvyno, Tyachiv district, Zakarpattia region, correspond to the areas with the most dynamic changes in the environment of such exogenous processes as karst funnels and lakes, and positive values correspond to increasing vegetation areas. The results of comparing the application of the change detection index on two different multispectral space images of the Earth on four, two channels and one channel showed that increasing the number of channels can give a more reasonable picture of changes, but to concretize these changes requires ground-based observations.","PeriodicalId":38304,"journal":{"name":"遥感学报","volume":"8 1","pages":"10-17"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"遥感学报","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.36023/UJRS.2021.8.1.187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The methods for detecting environmental changes using two multispectral multispectral space images of the Earth, which can be used to assess changes in the ecological and geological environment to control the dynamics of processes in real time, in order to prevent environmental emergencies. The creation and operation of a system of continuous geoecological monitoring based on space information involves the detection of relative changes in the environment on two multispectral space images of the Earth, obtained after a certain period of time. To do this, it is necessary to develop and apply adaptive methods (indices) for detecting relative changes. Three methods are proposed that are adapted to specific images to find the optimal solution that maximizes the relative changes in two different time multispectral space images of the Earth. The first method selects one optimal channel from all channels of the space image, the second method - two optimal channels from all channels and the third - four optimal channels from all channels. There are known methods that assess the presence or absence of changes in two space images at different times, but they do not provide information about the direction of changes. The proposed indices can take positive and negative values, reflecting the trend of changes on the ground. The negative values of the indices obtained in the area near the village of Solotvyno, Tyachiv district, Zakarpattia region, correspond to the areas with the most dynamic changes in the environment of such exogenous processes as karst funnels and lakes, and positive values correspond to increasing vegetation areas. The results of comparing the application of the change detection index on two different multispectral space images of the Earth on four, two channels and one channel showed that increasing the number of channels can give a more reasonable picture of changes, but to concretize these changes requires ground-based observations.
遥感学报Social Sciences-Geography, Planning and Development
CiteScore
3.60
自引率
0.00%
发文量
3200
期刊介绍:
The predecessor of Journal of Remote Sensing is Remote Sensing of Environment, which was founded in 1986. It was born in the beginning of China's remote sensing career and is the first remote sensing journal that has grown up with the development of China's remote sensing career. Since its inception, the Journal of Remote Sensing has published a large number of the latest scientific research results in China and the results of nationally-supported research projects in the light of the priorities and needs of China's remote sensing endeavours at different times, playing a great role in the development of remote sensing science and technology and the cultivation of talents in China, and becoming the most influential academic journal in the field of remote sensing and geographic information science in China.
As the only national comprehensive academic journal in the field of remote sensing in China, Journal of Remote Sensing is dedicated to reporting the research reports, stage-by-stage research briefs and high-level reviews in the field of remote sensing and its related disciplines with international and domestic advanced level. It focuses on new concepts, results and progress in this field. It covers the basic theories of remote sensing, the development of remote sensing technology and the application of remote sensing in the fields of agriculture, forestry, hydrology, geology, mining, oceanography, mapping and other resource and environmental fields as well as in disaster monitoring, research on geographic information systems (GIS), and the integration of remote sensing with GIS and the Global Navigation Satellite System (GNSS) and its applications.