{"title":"利用多时相卫星数据监测阿萨姆邦(印度)1990-2022年的茶园。","authors":"Bikash Ranjan Parida, Trinath Mahato, Surajit Ghosh","doi":"10.1007/s42965-023-00304-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration.</p><p><strong>Objectives: </strong>The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2.</p><p><strong>Methods: </strong>A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations.</p><p><strong>Results: </strong>The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45-89%. During 32 years (1990-2022), about 1280.47 km<sup>2</sup> (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96).</p><p><strong>Conclusions: </strong>This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover.</p>","PeriodicalId":54410,"journal":{"name":"Tropical Ecology","volume":" ","pages":"1-12"},"PeriodicalIF":1.1000,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206575/pdf/","citationCount":"1","resultStr":"{\"title\":\"Monitoring tea plantations during 1990-2022 using multi-temporal satellite data in Assam (India).\",\"authors\":\"Bikash Ranjan Parida, Trinath Mahato, Surajit Ghosh\",\"doi\":\"10.1007/s42965-023-00304-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration.</p><p><strong>Objectives: </strong>The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2.</p><p><strong>Methods: </strong>A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations.</p><p><strong>Results: </strong>The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45-89%. During 32 years (1990-2022), about 1280.47 km<sup>2</sup> (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96).</p><p><strong>Conclusions: </strong>This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover.</p>\",\"PeriodicalId\":54410,\"journal\":{\"name\":\"Tropical Ecology\",\"volume\":\" \",\"pages\":\"1-12\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206575/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tropical Ecology\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s42965-023-00304-x\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tropical Ecology","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s42965-023-00304-x","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECOLOGY","Score":null,"Total":0}
Monitoring tea plantations during 1990-2022 using multi-temporal satellite data in Assam (India).
Background: Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration.
Objectives: The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2.
Methods: A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations.
Results: The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45-89%. During 32 years (1990-2022), about 1280.47 km2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96).
Conclusions: This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover.
期刊介绍:
Tropical Ecology is devoted to all aspects of fundamental and applied ecological research in tropical and sub-tropical ecosystems. Nevertheless, the cutting-edge research in new ecological concepts, methodology and reviews on contemporary themes, not necessarily confined to tropics and sub-tropics, may also be considered for publication at the discretion of the Editor-in-Chief. Areas of current interest include: Biological diversity and its management; Conservation and restoration ecology; Human ecology; Ecological economics; Ecosystem structure and functioning; Ecosystem services; Ecosystem sustainability; Stress and disturbance ecology; Ecology of global change; Ecological modeling; Evolutionary ecology; Quantitative ecology; and Social ecology.
The Journal Tropical Ecology features a distinguished editorial board, working on various ecological aspects of tropical and sub-tropical systems from diverse continents.
Tropical Ecology publishes:
· Original research papers
· Short communications
· Reviews and Mini-reviews on topical themes
· Scientific correspondence
· Book Reviews