<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices"^^ . "Accurate and up-to-date spatial agricultural information is essential for applications including agro-environmental\r\nassessment, crop management, and appropriate targeting of agricultural technologies. There is growing\r\nresearch interest in spatial analysis of agricultural ecosystems applying satellite remote sensing technologies.\r\nHowever, usability of information generated from many of remotely sensed data is often constrained by accuracy\r\nproblems. This is of particular concern in mapping complex agro-ecosystems in countries where small farm\r\nholdings are dominated by diverse crop types. This study is a contribution to the ongoing efforts towards\r\novercoming accuracy challenges faced in remote sensing of agricultural ecosystems. We applied time-series\r\nanalysis of vegetation indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index\r\n(EVI)) derived from the Moderate Resolution Imaging Spectrometer (MODIS) sensor to detect seasonal patterns\r\nof irrigated and rainfed cropping patterns in five townships in the Central Dry Zone of Myanmar, which is an\r\nimportant agricultural region of the country has been poorly mapped with respect to cropping practices. To\r\nimprove mapping accuracy and map legend completeness, we implemented a combination of (i) an iterative\r\nparticipatory approach to field data collection and classification, (ii) the identification of appropriate size and\r\ntypes of predictor variables (VIs), and (iii) evaluation of the suitability of three Machine Learning algorithms:\r\nSupport Vector Machine (SVM), Random Forest (RF), and C5.0 algorithms under varying training sample sizes.\r\nThrough these procedures, we were able to progressively improve accuracy and achieve maximum overall accuracy\r\nof 95% When a small sized training dataset was used, accuracy achieved by RF was significantly higher\r\ncompared to SVM and C5.0 (P < 0.01), but as sample size increased, accuracy differences among the three\r\nmachine learning algorithms diminished. Accuracy achieved by use of NDVI was consistently better than that of\r\nEVI (P < 0.01). The maximum overall accuracy was achieved using RF and 8-days NDVI composites for three\r\nyears of remote sensing data. In conclusion, our findings highlight the important role of participatory classification,\r\nespecially in areas where cropping systems are highly diverse and differ over space and time. We also\r\nshow that the choice of classifiers and size of predictor variables are essential and complementary to the participatory\r\nmapping approach in achieving desired accuracy of cropping pattern mapping in areas where other\r\nsources of spatial information are scarce."^^ . "2020-06" . . . "175" . . "Elsevier"^^ . . . "Computers and Electronics in Agriculture (TSI)"^^ . . . "01681699" . . . . . . . . . . . . . . . . . . . . . . . . . . . . "M K"^^ . "Gumma"^^ . "M K Gumma"^^ . . "D E"^^ . "Johnson"^^ . "D E Johnson"^^ . . "K H"^^ . "Nge"^^ . "K H Nge"^^ . . "A"^^ . "Nelson"^^ . "A Nelson"^^ . . "L K"^^ . "Palao"^^ . "L K Palao"^^ . . "A"^^ . "Paliwal"^^ . "A Paliwal"^^ . . "K T"^^ . "Win"^^ . "K T Win"^^ . . "G L"^^ . "Feyisa"^^ . "G L Feyisa"^^ . . . . . . "Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices (PDF)"^^ . . . . . "05_Participatory mapping _ML Techniques.pdf"^^ . . . "Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices (Other)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #11541 \n\nCharacterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices\n\n" . "text/html" . . . "Participatory Modeling"@en . . . "GIS Techniques/Remote Sensing"@en . .