Using machine learning algorithm and landsat time series to identify establishment year of para rubber plantations: a case study in Thalang district, Phuket Island, Thailand

Somching, Natthaphon; Wongsai, Sangdao; Wongsai, Noppachai; Koedsin, Werapong

doi:10.1080/01431161.2020.1799450

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…Since satellite images have multiple spectra, different photo indices are presented to distinguish harvesting areas [21], and they play an important role in mapping local and regional rubber plantations [22][23][24]. Furthermore, these tools have provided a real-time understanding of changes in rubber plantation area [25,26]. This will reduce the time, labor, and cost of inspecting rubber plantations in the field, where access is limited [27,28].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Rubber Yield Using Sentinel-2 Satellite Data

et al. 2023

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Rubber is a perennial plant grown to produce natural rubber. It is a raw material for industrial and non-industrial products important to the world economy. The sustainability of natural rubber production is, therefore, critical for smallholder livelihoods and economic development. To maintain price stability, it is important to estimate the yields in advance. Remote sensing technology can effectively provide large-scale spatial data; however, productivity estimates need to be processed from high spatial resolution data generated from satellites with high accuracy and reliability, especially for smallholder livelihood areas where smaller plots contrast with large farms. This study used reflectance data from Sentinel-2 satellite imagery acquired for the 12 months between December 2020 and November 2021. The imagery included 213 plots where data on rubber production in smallholder agriculture were collected. Six vegetation indices (Vis), namely Green Soil Adjusted Vegetation Index (GSAVI), Modified Simple Ratio (MSR), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Normalized Green (NR), and Ratio Vegetation Index (RVI) were used to estimate the rubber yield. The study found that the red edge spectral band (band 5) provided the best prediction with R2 = 0.79 and RMSE = 29.63 kg/ha, outperforming all other spectral bands and VIs. The MSR index provided the highest coefficient of determination, with R2 = 0.62 and RMSE = 39.25 kg/ha. When the red edge reflectance was combined with the best VI, MSR, the prediction model only slightly improved, with a coefficient determination of (R2) of 0.80 and an RMSE of 29.42 kg/ha. The results demonstrated that the Sentinel-2 data are suitable for rubber yield prediction for smallholder farmers. The findings of this study can be used as a guideline to apply in other countries or areas. Future studies will require the use of reflectance and vegetation indices derived from satellite data in combination with meteorological data, as well as the application of complex models, such as machine learning and deep learning.

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Section: Introductionmentioning

confidence: 99%

Estimation of Rubber Yield Using Sentinel-2 Satellite Data

et al. 2023

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“…However, these studies only concentrated on a few coral islands, and it was hard to go further in such studies due to the difficulty in data collection and equipment maintenance. Satellite remote sensing data, which consider the vegetation and land use type changes at a long temporal scale [20,21], could be convenient to study hydrological processes on remote islands; however, it has rarely been used in ET a evaluation on coral islands.…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration on Natural and Reclaimed Coral Islands in the South China Sea

Han

Liu

et al. 2021

Remote Sensing

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Studies of evapotranspiration on remote tropical coral islands are important to explore and sustain scarce freshwater resources. However, there is a significant knowledge gap between research to evaluate evapotranspiration based on remote sensing methods and the influences of different land use types on water dynamics on reclaimed coral islands. This study applied the remote-sensing-based Vegetation Interfaces Processes (VIP-RS) model to estimate actual evapotranspiration (ETa) on Zhaoshu Island, Yongxing Island, and Yongshu Island in the South China Sea from 2016 to 2019. The results showed that the average annual ETa of Zhaoshu Island, Yongxing Island, and Yongshu Island was 685 mm, 530 mm, and 210 mm, respectively. Annual transpiration (Ec) and soil evaporation (Es) exhibited similar patterns on the natural islands; however, Es controlled the water consumption on the reclaimed islands. Water dynamics exhibited seasonal fluctuations due to the uneven distribution of precipitation (PRP). However, ETa of the natural islands was higher than PRP in the dry season, indicating vegetation has to absorb water from the groundwater to sustain growth. The results also agreed with the analysis of dominant driving factors based on partial correlation analysis, which demonstrated that the Normalized Difference Vegetation Index (NDVI) is the most important factor that influences ETa, while relative humidity (RH) controlled the bare land or sparsely vegetated areas on the reclaimed islands. The setting of different land use types showed that vegetation and built-up or hardened roads took control of evapotranspiration and rainwater collection, respectively, which play important roles in water dynamics on corals islands. The evaluation of ETa based on a remote-sensing-based model overcame the difficulty in fieldwork observation, which improves the certainty and accuracy at a spatial scale. In addition, it gave us a new reference to protect and manage scarce freshwater resources properly.

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Deep learning-based prediction for stand age and land utilization of rubber plantation

Mahakalanda

Demotte²,

Perera³

et al. 2022

Application of Machine Learning in Agriculture

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Using machine learning algorithm and landsat time series to identify establishment year of para rubber plantations: a case study in Thalang district, Phuket Island, Thailand

Abstract: 2020) Using machine learning algorithm and landsat time series to identify establishment year of para rubber plantations: a case study in Thalang district,

Cited by 11 publications

References 36 publications

Estimation of Rubber Yield Using Sentinel-2 Satellite Data

Estimation of Rubber Yield Using Sentinel-2 Satellite Data

Evapotranspiration on Natural and Reclaimed Coral Islands in the South China Sea

Deep learning-based prediction for stand age and land utilization of rubber plantation

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