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Dr. Yudi Setiawan
Department of Forest Resource Conservation and Ecotourim, Faculty of Forestry and Environment, IPB University

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Research Keywords & Expertise

0 Landscape Ecology
0 Remote sensing & GIS applications in Agriculture and Forestry
0 land use / cover change
0 forestry conservation
0 wetland ecosystem

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Journal article
Published: 08 October 2020 in Land
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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.

ACS Style

Aryo Condro; Yudi Setiawan; Lilik Prasetyo; Rahmat Pramulya; Lasriama Siahaan. Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform. Land 2020, 9, 377 .

AMA Style

Aryo Condro, Yudi Setiawan, Lilik Prasetyo, Rahmat Pramulya, Lasriama Siahaan. Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform. Land. 2020; 9 (10):377.

Chicago/Turabian Style

Aryo Condro; Yudi Setiawan; Lilik Prasetyo; Rahmat Pramulya; Lasriama Siahaan. 2020. "Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform." Land 9, no. 10: 377.

Journal article
Published: 01 December 2018 in Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)
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Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots.

ACS Style

Yudi Setiawan; Lilik Budi Prasetyo; Hidayat Pawitan; Prita Ayu Permatasari; Desi Suyamto; Arif Kurnia Wijayanto; Fakultas Kehutanan Departemen Konservasi Sumberdaya Hutan Dan Ekowisata; Fakultas Matematika Dan Ilmu Pengetahuan Alam Departemen Geofisika Dan Meteorologi; Lembaga Penelitian Dan Pengembangan Kepada Masyarakat (Pplh- Lppm) Pusat Penelitian Lingkungan Hidup. IDENTIFYING AREAS AFFECTED BY FIRES IN SUMATRA BASED ON TIME SERIES OF REMOTELY SENSED FIRE HOTSPOTS AND SPATIAL MODELING. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) 2018, 8, 420 -427.

AMA Style

Yudi Setiawan, Lilik Budi Prasetyo, Hidayat Pawitan, Prita Ayu Permatasari, Desi Suyamto, Arif Kurnia Wijayanto, Fakultas Kehutanan Departemen Konservasi Sumberdaya Hutan Dan Ekowisata, Fakultas Matematika Dan Ilmu Pengetahuan Alam Departemen Geofisika Dan Meteorologi, Lembaga Penelitian Dan Pengembangan Kepada Masyarakat (Pplh- Lppm) Pusat Penelitian Lingkungan Hidup. IDENTIFYING AREAS AFFECTED BY FIRES IN SUMATRA BASED ON TIME SERIES OF REMOTELY SENSED FIRE HOTSPOTS AND SPATIAL MODELING. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). 2018; 8 (3):420-427.

Chicago/Turabian Style

Yudi Setiawan; Lilik Budi Prasetyo; Hidayat Pawitan; Prita Ayu Permatasari; Desi Suyamto; Arif Kurnia Wijayanto; Fakultas Kehutanan Departemen Konservasi Sumberdaya Hutan Dan Ekowisata; Fakultas Matematika Dan Ilmu Pengetahuan Alam Departemen Geofisika Dan Meteorologi; Lembaga Penelitian Dan Pengembangan Kepada Masyarakat (Pplh- Lppm) Pusat Penelitian Lingkungan Hidup. 2018. "IDENTIFYING AREAS AFFECTED BY FIRES IN SUMATRA BASED ON TIME SERIES OF REMOTELY SENSED FIRE HOTSPOTS AND SPATIAL MODELING." Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) 8, no. 3: 420-427.

Journal article
Published: 25 October 2016 in Geoplanning: Journal of Geomatics and Planning
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Peat swamp area is an essential ecosystem due to high vulnerability of functions and services. As the change of forest cover in peat swamp area has increased considerably, many studies on peat swamp have focused on forest conversion or forest degradation. Meanwhile, in the context of changes in the forestlands are the sum of several processes such as deforestation, reforestation/afforestation, regeneration of previously deforested areas, and the changing spatial location of the forest boundary. Remote sensing technology seems to be a powerful tool to provide information required following that concerns. A comparison imagery taken at the different dates over the same locations for assessing those changes tends to be limited by the vegetation phenology and land-management practices. Consequently, the simultaneous analysis seems to be a way to deal with the issues above, as a means for better understanding of the dynamics changes in peat swamp area. In this study, we examined the feasibility of using MODIS images during the last 14 years for detecting and monitoring the changes in peat swamp area. We identified several significant patterns that have been assigned as the specific peat swamp ecosystem. The results indicate that a different type of ecosystem and its response to the environmental changes can be portrayed well by the significant patterns. In understanding the complex situations of each pattern, several vegetation dynamics patterns were characterized by physical land characteristics, such as peat depth, land use, concessions and others. Characterizing the pathways of dynamics change in peat swamp area will allow further identification for the range of proximate and underlying factors of the forest cover change that can help to develop useful policy interventions in peatland management.

ACS Style

Yudi Setiawan; Hidayat Pawitan; Lilik Budi Prasetyo; May Parlindungan; Prita Ayu Permatasari. TEMPORAL VEGETATION DYNAMICS IN PEAT SWAMP AREA USING MODIS TIME-SERIES IMAGERY: A MONITORING APPROACH OF HIGH-SENSITIVE ECOSYSTEM IN REGIONAL SCALE. Geoplanning: Journal of Geomatics and Planning 2016, 3, 137-146 .

AMA Style

Yudi Setiawan, Hidayat Pawitan, Lilik Budi Prasetyo, May Parlindungan, Prita Ayu Permatasari. TEMPORAL VEGETATION DYNAMICS IN PEAT SWAMP AREA USING MODIS TIME-SERIES IMAGERY: A MONITORING APPROACH OF HIGH-SENSITIVE ECOSYSTEM IN REGIONAL SCALE. Geoplanning: Journal of Geomatics and Planning. 2016; 3 (2):137-146.

Chicago/Turabian Style

Yudi Setiawan; Hidayat Pawitan; Lilik Budi Prasetyo; May Parlindungan; Prita Ayu Permatasari. 2016. "TEMPORAL VEGETATION DYNAMICS IN PEAT SWAMP AREA USING MODIS TIME-SERIES IMAGERY: A MONITORING APPROACH OF HIGH-SENSITIVE ECOSYSTEM IN REGIONAL SCALE." Geoplanning: Journal of Geomatics and Planning 3, no. 2: 137-146.