Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

Sarzynski, Thuan; Giam, Xingli; Carrasco, Luis; Lee, Janice Ser Huay

doi:10.3390/rs12071220

Cited by 43 publications

(45 citation statements)

References 58 publications

(88 reference statements)

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“…Mananze et al [36] derived a Land Cover map of a study area in Mozambique from Landsat 7 and Landsat 8 bands, vegetation indices, and textural features extracted by GLCM. Radar and optical imagery were combined to map oil palm plantations in Sumatra, Indonesia, using GLCM textural features, derived from SAR (Synthetic Aperture Radar) data, to improve the classification [37].…”

Section: Introductionmentioning

confidence: 99%

Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

2020

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Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO approaches, including both object segmentation and object textural analysis, are still not common in the GEE environment, probably due to the difficulties existing in concatenating the proper functions, and in tuning the various parameters to overcome the GEE computational limits. In this context, this work is aimed at developing and testing an OO classification approach combining the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices, and two ML algorithms (Random Forest (RF) or Support Vector Machine (SVM)) to perform the final classification. A Principal Components Analysis (PCA) is applied to the main seven GLCM indices to synthesize in one band the textural information used for the OO classification. The proposed approach is implemented in a user-friendly, freely available GEE code useful to perform the OO classification, tuning various parameters (e.g., choose the input bands, select the classification algorithm, test various segmentation scales) and compare it with a PB approach. The accuracy of OO and PB classifications can be assessed both visually and through two confusion matrices that can be used to calculate the relevant statistics (producer’s, user’s, overall accuracy (OA)). The proposed methodology was broadly tested in a 154 km2 study area, located in the Lake Trasimeno area (central Italy), using Landsat 8 (L8), Sentinel 2 (S2), and PlanetScope (PS) data. The area was selected considering its complex LULC mosaic mainly composed of artificial surfaces, annual and permanent crops, small lakes, and wooded areas. In the study area, the various tests produced interesting results on the different datasets (OA: PB RF (L8 = 72.7%, S2 = 82%, PS = 74.2), PB SVM (L8 = 79.1%, S2 = 80.2%, PS = 74.8%), OO RF (L8 = 64%, S2 = 89.3%, PS = 77.9), OO SVM (L8 = 70.4, S2 = 86.9%, PS = 73.9)). The broad code application demonstrated very good reliability of the whole process, even though the OO classification process resulted, sometimes, too demanding on higher resolution data, considering the available computational GEE resources.

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Section: Introductionmentioning

confidence: 99%

Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

2020

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“…Using GEE saves a lot of processing time and also allows users to process very large data sizes. By utilizing GEE, researchers from developing countries can research the same quality as researchers from developed countries (Cassol et al, 2020;Kumar & Mutanga, 2018;Mondal et al, 2019;Sarzynski et al, 2020;Traganos et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Monitoring Mangrove Forest Cover Using Palsar/Palsar- 2 Mosaic Imagery, and Google Earth Engine Algorithm for Entire Mahakam Delta Indonesia

Nugroho

2021

SNG

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Many natural mangrove forests have been converted into ponds or settlements. This study shows a method for monitoring mangrove areas at a spatial resolution of 25 meters using the 2007 and 2017 PALSAR/PALSAR-2 mosaic dataset in Delta Mahakam, Indonesia. To carry out this monitoring process, the Google Earth Engine tool is used. From the research area of 1505.25 km2, there was a reduction in the mangrove area of 30.76 km2, but there was also an increase of 29.5 km2.

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“…These indices have been proved to be effective in earlier work classifying plantation and natural forest [23,43,44]. Further, we used a form of the radar vegetation index (RVI) [45] We used the results from the time-series (see Section 2.3.1 above) to select 2 vegetation indices: first, the Normalised Burn Ratio (NBR) vegetation index:…”

Section: Sentinel-1 Processingmentioning

confidence: 99%

Synergistic Use of Sentinel-1 and Sentinel-2 to Map Natural Forest and Acacia Plantation and Stand Ages in North-Central Vietnam

Spracklen

2021

Remote Sensing

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Many remote sensing studies do not distinguish between natural and planted forests. We combine C-Band Synthetic Aperture Radar (Sentinel-1, S-1) and optical satellite imagery (Sentinel-2, S-2) and examine Random Forest (RF) classification of acacia plantations and natural forest in North-Central Vietnam. We demonstrate an ability to distinguish plantation from natural forest, with overall classification accuracies of 87% for S-1, and 92.5% and 92.3% for S-2 and for S-1 and S-2 combined respectively. We found that the ratio of the Short-Wave Infrared Band to the Red Band proved most effective in distinguishing acacia from natural forest. We used RF on S-2 imagery to classify acacia plantations into 6 age classes with an overall accuracy of 70%, with young plantation consistently separated from older. However, accuracy was lower at distinguishing between the older age classes. For both distinguishing plantation and natural forest, and determining plantation age, a combination of radar and optical imagery did nothing to improve classification accuracy.

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Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

Cited by 43 publications

References 58 publications

Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

Monitoring Mangrove Forest Cover Using Palsar/Palsar- 2 Mosaic Imagery, and Google Earth Engine Algorithm for Entire Mahakam Delta Indonesia

Synergistic Use of Sentinel-1 and Sentinel-2 to Map Natural Forest and Acacia Plantation and Stand Ages in North-Central Vietnam

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