Monitoring tea plantations during 1990-2022 using multi-temporal satellite data in Assam (India).

IF 1.1 4区 环境科学与生态学 Q4 ECOLOGY Tropical Ecology Pub Date : 2023-05-24 DOI:10.1007/s42965-023-00304-x
Bikash Ranjan Parida, Trinath Mahato, Surajit Ghosh
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引用次数: 1

Abstract

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.

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利用多时相卫星数据监测阿萨姆邦(印度)1990-2022年的茶园。
背景:茶叶是一种珍贵的经济作物,在亚洲许多国家广泛种植。准确绘制茶园地图对茶叶产业的增长和发展至关重要。在印度东部,茶园在其经济中发挥着重要作用。Sonitpur、Jorhat、Sibsagar、Dibrugarh和Tinsukia是阿萨姆邦的主要茶叶产区。由于茶园的快速增长和人口的迅速增长,对茶园进行详细的测绘和定期监测对于了解土地利用变化至关重要。目的:本研究旨在使用Landsat-5和Sentinel-2等卫星数据,在十年尺度上分析1990年至2022年茶园的动态。方法:在谷歌地球引擎(GEE)平台中部署了一个名为随机森林(RF)的监督分类器,对茶园进行分类。结果:结果显示,Dibrugarh地区的茶园显著增长(112%),而其余地区的增长率为45-89%。在32年(1990-2022年)期间,阿萨姆邦五个区的茶园面积约为1280.47平方公里(78.71%)。精度和召回率用于衡量茶园分类的准确性,其F1得分相当高(0.80至0.96)。结论:本研究有助于证明遥感技术在评估茶园动态方面的应用,这有助于决策者管理茶园和土地覆盖的潜在变化。
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来源期刊
Tropical Ecology
Tropical Ecology ECOLOGY-
CiteScore
3.30
自引率
6.20%
发文量
71
审稿时长
>12 weeks
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
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