Mapping palm oil expansion using SAR to study the impact on the CO<sub>2</sub>cycle

Pohl, Christine

doi:10.1088/1755-1315/20/1/012012

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…Looking forward, a potential solution is to utilize tree structural information to distinguish different plantation species (e.g., rubber as a broad-leaved species may be structurally different with other species, while coconut palm trees usually grow taller than oil palm trees). Indeed, previous research has had success in mapping mature oil palm using structural information from radar data [74]. In addition, the spectral information available at higher resolution, e.g., Landsat 8 or Sentinel-2 satellites, could also help to better distinguish among species.…”

Section: Tree Plantation Species-specific Mappingmentioning

confidence: 99%

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

et al. 2020

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Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is technically challenging. Thus, most available plantation maps rely on visual inspection of imagery, and many of them are limited to small areas for specific years. Here, we present an automated approach, which we call Plantation Analysis by Learning from Multiple Classes (PALM), for mapping plantations on an annual basis using satellite remote sensing data. Due to the heterogeneity of land cover classes, PALM utilizes ensemble learning to simultaneously incorporate training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. We implement the proposed automated approach using MODIS data in Sumatra and Indonesian Borneo (Kalimantan). To validate the classification, we compare plantations detected using our approach with existing datasets developed through visual interpretation. Based on random sampling and comparison with high-resolution images, the user’s accuracy and producer’s accuracy of our generated map are around 85% and 80% in our study region.

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Section: Tree Plantation Species-specific Mappingmentioning

confidence: 99%

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

et al. 2020

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“…They have been implemented in determining the land cover of large extensions of land [21] and focused on determining areas associated with oil palm growing [23]. In addition, research has been carried out to discriminate the coverages related to this same crop from other types of coverage such as forests and secondary vegetation using SAR images [24], where the potential of using these sources for this purpose is demonstrated, such as the studies carried out by [7], [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of optical and synthetic aperture radar image fusion methods: a case study applied to Sentinel imagery

Monsalve-Tellez

Garcés-Gómez

Torres-León

2023

IJECE

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<p>This paper evaluates different optical and synthetic aperture radar (SAR) image fusion methods applied to open-access Sentinel images with global coverage. The objective of this research was to evaluate the potential of image fusion methods to get a greater visual difference in land cover, especially in oil palm crops with natural forest areas that are difficult to differentiate visually. The application of the image fusion methods: Brovey (BR), high-frequency modulation (HFM), Gram-Schmidt (GS), and principal components (PC) was evaluated on Sentinel-2 optical and Sentinel-1 SAR images using a cloud computing environment. The results show that the application of the implemented optical/SAR image fusion methods allows the creation of a synthetic image with the characteristics of both data sources. The multispectral information provided by the optical image and information associated with the geometry and texture/roughness of the land covers, provided by the SAR image, allows a greater differentiation in the visualization of the various land covers, achieving a better understanding of the study area. The fusion methods that visually presented greater characteristics associated with the SAR image were the BR and GS methods. The HFM method reached the best statistical indicators; however, this method did not present significant visual changes in the SAR contribution.</p>

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“…By implementing optical and synthetic aperture radar (SAR) image fusion methods, it is possible to visually improve the differentiation of land cover, especially land cover with the presence of water bodies [17], and increase classification accuracy by using supervised classification algorithms [18,19]. Studies have been conducted to map oil palm plantations using a combination of optical and SAR information [20] and using SAR images only [21], demonstrating the potential for the microwave range to discriminate the areas associated with this coverage [13,22,23]. In particular, SAR images have been used in areas with high cloud cover since the information captured in the microwave range is not affected by weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm

Monsalve-Tellez

Torres-León²,

Garcés-Gómez

2022

Agriculture

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This paper presents an evaluation of land cover accuracy, particularly regarding oil palm crop cover, using optical/synthetic aperture radar (SAR) image fusion methods through the implementation of the random forest (RF) algorithm on cloud computing platforms using Sentinel-1 SAR and Sentinel-2 optical images. Among the fusion methods evaluated were Brovey (BR), high-frequency modulation (HFM), Gram–Schmidt (GS), and principal components (PC). This work was developed using a cloud computing environment employing R and Python for statistical analysis. It was found that an optical/SAR image stack resulted in the best overall accuracy with 82.14%, which was 11.66% higher than that of the SAR image, and 7.85% higher than that of the optical image. The high-frequency modulation (HFM) and Brovey (BR) image fusion methods showed overall accuracies higher than the Sentinel-2 optical image classification by 3.8% and 3.09%, respectively. This demonstrates the potential of integrating optical imagery with Sentinel SAR imagery to increase land cover classification accuracy. On the other hand, the SAR images obtained very high accuracy results in classifying oil palm crops and forests, reaching 94.29% and 90%, respectively. This demonstrates the ability of synthetic aperture radar (SAR) to provide more information when fused with an optical image to improve land cover classification.

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Mapping palm oil expansion using SAR to study the impact on the CO₂cycle

Cited by 12 publications

References 24 publications

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

Evaluation of optical and synthetic aperture radar image fusion methods: a case study applied to Sentinel imagery

Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm

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Mapping palm oil expansion using SAR to study the impact on the CO2cycle

Cited by 12 publications

References 24 publications

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

Evaluation of optical and synthetic aperture radar image fusion methods: a case study applied to Sentinel imagery

Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm

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Mapping palm oil expansion using SAR to study the impact on the CO₂cycle