Assessment of point cloud analysis in improving object-based agricultural land cover classification using discrete lidar data in Cabadbaran, Agusan del Norte, Phillippines

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Rollan, T.A.M. and Blanco, A.C. (2016) Assessment of point cloud analysis in improving object-based agricultural land cover classification using discrete lidar data in Cabadbaran, Agusan del Norte, Phillippines. In: GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC) .

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Event: GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC)
Abstract:Cabadbaran City is the capital of Agusan del Norte which is located at the north eastern portion of Mindanao, Philippines. Almost 30% of its land area is devoted to agriculture (mainly rice, corn, coconut, banana, vegetables and abaca). Currently, the city government and agriculture office are implementing programs focusing on improving coconut and vegetable productivity, controlling banana disease and infestation, and enhancing abaca production industry. In support of decision making, the current situation must first be assessed by answering the basic questions what and where through detailed and accurate resource mapping. In this study, only discrete LiDAR datasets were utilized. Corresponding orthophotos were used only for training and validation. Land cover classification was done using two workflows using Support Vector Machines (SVM) as the classifier. In the first workflow, land cover classes were classified using rasterized point cloud metrics such as minimum, maximum, standard deviation, skewness, kurtosis, quartile average, mode and median. In the second workflow, point cloud analysis was used to derive additional features for classification which was integrated and executed in the same object-based software through Cognition Network Language (CNL). The derivations of the additional features were conducted after running an initial segmentation which means that the distribution of points was analysed within the delineated objects. Classes that benefited to point cloud-based metrics are mostly non-ground agricultural classes namely coconut, mango and palm trees. These classes have obtained increase in accuracies by an average of 11.2% using validation sample set 1 and an average of 18.2% using validation sample set 2. Ground classes, particularly barren land and rice, appeared to be incompatible to these point cloud metrics as shown by the decrease in accuracies for Methods 2 and 3 by about 18.1% using validation sample set 1 and about 16.4% using validation sample set 2. Exploring other useful point cloud-based metrics and testing on sites with other land cover classes are highly recommended.
Item Type:Conference or Workshop Item (Paper)
Link to this item:https://doi.org/10.3990/2.412
Conference URL:https://www.geobia2016.com/
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