Mapping large area tea plantations using progressive random forest and Google Earth Engine

Qu, Le’an; Li, Manchun; Chen, Zhenjie; Liu, Wangbing; Zhi, Junjun; Zhang, Lechun

doi:10.1117/1.jrs.16.024509

Cited by 9 publications

(1 citation statement)

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“…Few studies have employed satellite data and machine learning (ML) algorithms to examine tea plantations (Phan et al 2020 ; Qu et al 2022 ). In this context, the ML algorithm, especially Random Forest (RF), was utilised in the Google Earth Engine (GEE) cloud platform to map tea plantations in the Anhui Province of eastern China (Qu et al 2022 ). The RF is a well-suited and dependable classifier for extracting tea plantations during the spring tea flushes in March and April (Wang et al 2019 ).…”

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confidence: 99%

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

2023

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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 km 2 (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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