2020
DOI: 10.3390/rs12071220
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

3
27
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 40 publications
(30 citation statements)
references
References 57 publications
(88 reference statements)
3
27
0
Order By: Relevance
“…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%
“…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%
“…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%
“…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%