[vc_empty_space][vc_empty_space]
Mapping the distribution of coffee plantations from multi-resolution, multi-temporal, and multi-sensor data using a random forest algorithm
Tridawati A.a,b, Wikantika K.a, Susantoro T.M.c, Harto A.B.a, Darmawan S.d, Yayusman L.F.a, Ghazali M.F.b
a Remote Sensing and Geographic Information Science Research Group, Department of Geodesy and Geomatics, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia
b Department of Geodesy and Geomatics Engineering, Faculty of Engineering, Universitas Lampung, Bandar Lampung, 35141, Indonesia
c Research and Development Center for Oil and Gas Technology “LEMIGAS”, Ministry of Energy and Mineral Resources, Jakarta, 12230, Indonesia
d Institut Teknologi Nasional Bandung, Bandung, 40124, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 by the authors. Licensee MDPI, Basel, Switzerland.Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Accuracy of classifications,Agroforestry system,Coffee plantation,Kappa statistic,Multi-sensor data,Overall accuracies,Random forest algorithm,Remote sensing data[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Multi-resolution,Multi-temporal,Random forest algorithm,Tree[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][{‘$’: ‘This research received no external funding and the APC was funded by Center for Remote Sensing, Institut Teknologi Bandung-Indonesia. The authors would like to thank Digital Globe for providing data support for this research. The authors would also like to thank the Department of Geomatics and Engineering of Bandung Institute of Technology (ITB), the Center for Remote Sensing-Institut Teknologi Bandung (ITB), Indonesia, Institut Teknologi Nasional, Universitas Lampung, and KJSKB Ketut Tomy Suhari for their support. Special thanks also to Yohannes Dian Aditya, M. Ali, Haikal, Efrila Aji, Sapta Yonda, and Nilton for assisting in the field survey.’}, {‘$’: ‘Funding: This research received no external funding and the APC was funded by Center for Remote Sensing, Institut Teknologi Bandung-Indonesia.’}][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.3390/rs12233933[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]