Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform

Aryo Adhi Condro, Yudi Setiawan, Lilik Budi Prasetyo, Rahmat Pramulya and Lasriama Siahaan
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Aryo Adhi Condro: Tropical Biodiversity Conservation Program, Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry, IPB University (Bogor Agricultural University), Kampus IPB Darmaga Bogor 16680, Indonesia
Yudi Setiawan: Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry, IPB University (Bogor Agricultural University), Kampus IPB Darmaga Bogor 16680, Indonesia
Lilik Budi Prasetyo: Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry, IPB University (Bogor Agricultural University), Kampus IPB Darmaga Bogor 16680, Indonesia
Rahmat Pramulya: Faculty of Agriculture, University of Teuku Umar, Meulaboh, Aceh Barat, Aceh 23681, Indonesia
Lasriama Siahaan: Natural Resources and Environmental Management Studies Program, Graduate School of IPB University (Bogor Agricultural University), Kampus IPB Baranangsiang Bogor 16129, Indonesia

Land, 2020, vol. 9, issue 10, 1-15

Abstract: Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia.

Keywords: commodity; classification; Random Forest; Indonesia (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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