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 land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia.
Highlights
Tropical forests are important ecosystems for biodiversity and carbon storage and they provide numerous material and non-material benefits to people [1]
This is especially pronounced in tropical Southeast Asia, where one of the major drivers for this land-use change is the expansion of the oil palm (Elaeis guineensis), an economically important vegetable oil crop for the purpose of food, oleochemical industries, and biofuel [4]
We aim to address the following research questions: (i) does the use of radar and optical imagery for oil palm classification outperform radar-only and optical-only classification?; (ii) how does our 30 m semi-automated map of oil palm plantations compare with two maps a 250 m semi-automated map and a 30 m digitized map of oil palm plantations for the same year? The study proposes improved methods to map oil palm, using a combination of radar and optical imagery on the Google Earth Engine and a systematic comparison of previous oil palm maps to identify similarities to drive advancement of new methods
Summary
Tropical forests are important ecosystems for biodiversity and carbon storage and they provide numerous material and non-material benefits to people [1]. Recent global assessments on the drivers of forest degradation and loss showed that commodity-driven deforestation, which is the loss of forest cover for commodity crops, such as palm oil and soy, was the major driver for deforestation in the tropics [2,3]. This is especially pronounced in tropical Southeast Asia, where one of the major drivers for this land-use change is the expansion of the oil palm (Elaeis guineensis), an economically important vegetable oil crop for the purpose of food, oleochemical industries, and biofuel [4]. It is crucial to map and monitor oil palm plantations for evaluating and managing their social and environmental effects [13,14,15]
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