2021 |
Purwonegoro, Benni; Liyantono, ; Setiawan, Yudi WEBGIS APPLICATION FOR MONITORING LAND USE CHANGE IN INDONESIA Unpublished Forthcoming Forthcoming. Abstract | Links | BibTeX | Tags: land use, webGIS @unpublished{Purwonegoro2021, title = {WEBGIS APPLICATION FOR MONITORING LAND USE CHANGE IN INDONESIA}, author = {Benni Purwonegoro and Liyantono and Yudi Setiawan}, url = {https://lulcc.ipb.ac.id/home/wp-content/uploads/2021/09/Draft_01_WebGIS-Application-for-Monitoring-Land-Use-Change-in-Indonesia_Jurnal-Globe_Benny_YS_01.pdf}, year = {2021}, date = {2021-09-14}, abstract = {Indonesia is home to the world’s third largest tropical forest, It’s unfortunate that forest in Indonesia has been reported as the highest deforestation. Agriculture and plantation activities considered as one of factors that cause deforestration, this research focus on monitoring 5 Commodities (Cacao, Coffe, Palm Oil, Paddy and Rubber) as a factor that causes forest loss in Indonesia. Remote Sensing (RS) and Geographic Information System (GIS) is state of the art tools widely used for the monitoring landuse change. Development of WebGIS on this research designed to be user friendly, up to date and can be easily integrated with other GIS system. The goverment, stakeholders and public users can monitor deforestation that caused by agriculture activities each of region in Indonesia quickly, furthermore help policy makers in providing more appropriate regulations on landuse management. From the secondary data that has been analyzed, the result show that Indonesia has forest lost 12.3 million hectares from 2000 to 2018, of which an estimated 4.1 million hectares become plantations and agricultural areas. This means, about 33.5% of those 5 commodities activities have an impact on forest loss, and the remaining around 8.2 million hectares caused by other factors. }, keywords = {land use, webGIS}, pubstate = {forthcoming}, tppubtype = {unpublished} } Indonesia is home to the world’s third largest tropical forest, It’s unfortunate that forest in Indonesia has been reported as the highest deforestation. Agriculture and plantation activities considered as one of factors that cause deforestration, this research focus on monitoring 5 Commodities (Cacao, Coffe, Palm Oil, Paddy and Rubber) as a factor that causes forest loss in Indonesia. Remote Sensing (RS) and Geographic Information System (GIS) is state of the art tools widely used for the monitoring landuse change. Development of WebGIS on this research designed to be user friendly, up to date and can be easily integrated with other GIS system. The goverment, stakeholders and public users can monitor deforestation that caused by agriculture activities each of region in Indonesia quickly, furthermore help policy makers in providing more appropriate regulations on landuse management. From the secondary data that has been analyzed, the result show that Indonesia has forest lost 12.3 million hectares from 2000 to 2018, of which an estimated 4.1 million hectares become plantations and agricultural areas. This means, about 33.5% of those 5 commodities activities have an impact on forest loss, and the remaining around 8.2 million hectares caused by other factors. |
Pramulya, Rahmat; Pulunggono, Heru Bagus; Bantacut, Tajudin; Setiawan, Yudi; Zulfajrin, Moh Assesing Suitability Evaluation of Arabica Coffee on the Gayo Highlands Unpublished Forthcoming Forthcoming. Abstract | Links | BibTeX | Tags: coffee, gayo @unpublished{Pramulya2021, title = {Assesing Suitability Evaluation of Arabica Coffee on the Gayo Highlands}, author = {Rahmat Pramulya and Heru Bagus Pulunggono and Tajudin Bantacut and Yudi Setiawan and Moh Zulfajrin}, url = {https://lulcc.ipb.ac.id/home/wp-content/uploads/2021/09/Draft_01_Assesing-Suitability-Evaluation-of-Arabica-Coffee-on-the-Gayo-Highlands_Agriculture-2072021.pdf}, year = {2021}, date = {2021-09-14}, abstract = {The growing interest for the arabica coffee cultivation as one of the mainstay commodities and income sources for people in Gayo Highlands requires consideration of the suitability aspect between the coffee plant requirement with the climate and land biophysical constraints. However, the most current Indonesian Land Suitability adopted The1976’s Framework for Land Evaluation (FAO) which is containing less centralized consideration of ecological aspects. This study aims to discover the land suitability classes for coffee arabica within the Gayo Highlands and provide recommendations for each land unit with limiting factors oriented to ecological aspects, based on the Indonesian land suitability guidelines using a combination of recent and legacy field survey data, modeling, and remote sensing.}, keywords = {coffee, gayo}, pubstate = {forthcoming}, tppubtype = {unpublished} } The growing interest for the arabica coffee cultivation as one of the mainstay commodities and income sources for people in Gayo Highlands requires consideration of the suitability aspect between the coffee plant requirement with the climate and land biophysical constraints. However, the most current Indonesian Land Suitability adopted The1976’s Framework for Land Evaluation (FAO) which is containing less centralized consideration of ecological aspects. This study aims to discover the land suitability classes for coffee arabica within the Gayo Highlands and provide recommendations for each land unit with limiting factors oriented to ecological aspects, based on the Indonesian land suitability guidelines using a combination of recent and legacy field survey data, modeling, and remote sensing. |
2020 |
Condro, Aryo Adhi; Setiawan, Yudi; Prasetyo, Lilik Budi; Pramulya, Rahmat; Siahaan, Lasriama Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform Journal Article Land, 9 (10), pp. 377, 2020, ISSN: 2073-445X. Abstract | Links | BibTeX | Tags: cloud computing, GEE @article{Condro2020, title = {Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform}, author = {Aryo Adhi Condro and Yudi Setiawan and Lilik Budi Prasetyo and Rahmat Pramulya and Lasriama Siahaan}, url = {https://www.mdpi.com/2073-445X/9/10/377/htm}, doi = {10.3390/land9100377}, issn = {2073-445X}, year = {2020}, date = {2020-10-08}, journal = {Land}, volume = {9}, number = {10}, pages = {377}, 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 = {cloud computing, GEE}, pubstate = {published}, tppubtype = {article} } 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. |
2021 |
WEBGIS APPLICATION FOR MONITORING LAND USE CHANGE IN INDONESIA Unpublished Forthcoming Forthcoming. |
Assesing Suitability Evaluation of Arabica Coffee on the Gayo Highlands Unpublished Forthcoming Forthcoming. |
2020 |
Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform Journal Article Land, 9 (10), pp. 377, 2020, ISSN: 2073-445X. |
