{"title":"Aerospace monitoring of ecosystem dynamics and ecological prognoses","authors":"B.V. Vinogradov","doi":"10.1016/0031-8663(88)90034-8","DOIUrl":null,"url":null,"abstract":"<div><p>The paper presents Soviet experience with aerospace monitoring of ecosystem dynamics based on a comparison of repeated aerial and/or space images. The dynamics of single ecosystems are described by linear or, more commonly, non-linear exponential or parabolic functions. The dynamics of simple two-component ecosystems are described by the interrelation of “reserve” vs. “resource” trends. The dynamics of composite ecosytems are represented by transition matrices and graphs of transition probabilities, using Markovian chains. Provided the areal dimensions and identification accuracy are sufficient, a normative long-term forecast may be calculated for 5–20 years ahead.</p></div>","PeriodicalId":101020,"journal":{"name":"Photogrammetria","volume":"43 1","pages":"Pages 1-16"},"PeriodicalIF":0.0000,"publicationDate":"1988-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0031-8663(88)90034-8","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photogrammetria","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0031866388900348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The paper presents Soviet experience with aerospace monitoring of ecosystem dynamics based on a comparison of repeated aerial and/or space images. The dynamics of single ecosystems are described by linear or, more commonly, non-linear exponential or parabolic functions. The dynamics of simple two-component ecosystems are described by the interrelation of “reserve” vs. “resource” trends. The dynamics of composite ecosytems are represented by transition matrices and graphs of transition probabilities, using Markovian chains. Provided the areal dimensions and identification accuracy are sufficient, a normative long-term forecast may be calculated for 5–20 years ahead.