ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLII-3-W2-163-2017 QUALITY ASSESSMENT AND CONTROL OF OUTPUTS OF A NATIONWIDE AGRICULTURAL LAND COVER MAPPING PROGRAM USING LIDAR: PHIL-LIDAR 2 PARMAP EXPERIENCE Pagkalinawan H. M. 1 Gatdula N. B. 1 Jose R. P. 1 Rollan T. M. 1 Tañada E. M. 1 Magtalas S. Y. 1 Dela Torre D. G. 1 Dela Cruz P. A. 1 Aves J. L. 1 Tan G. D. 1 Apura R. A. 1 Dimalanta C. P. 1 Blanco A. C. 1 2 Phil-LiDAR 2 Project 1 Agricultural Resources Extraction from LIDAR Surveys (PARMap), Training Center for Applied Geodesy and Photogrammetry, National Engineering Center, University of the Philippines, Diliman, Quezon City, Philippines Department of Geodetic Engineering, College of Engineering, University of the Philippines, Diliman, Quezon City, Philippines 16 11 2017 XLII-3/W2 163 170 Copyright: © 2017 H. M. Pagkalinawan et al. 2017 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLII-3-W2/163/2017/isprs-archives-XLII-3-W2-163-2017.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLII-3-W2/163/2017/isprs-archives-XLII-3-W2-163-2017.pdf

The Agricultural Resources Extraction from LiDAR Surveys (PARMAP) project component of the Nationwide Detailed Resources Assessment using LiDAR (Phil-LiDAR 2) Program aims to produce detailed agricultural maps using LiDAR. Agricultural land cover at crop level was classified through object based image analysis using Support Vector Machine as classifier and LiDAR derivatives from point cloud (2 points per sq.m.) and orthophoto (0.5-meter resolution) as inputs. An accuracy of at least 90 %, assessed using validation points from the field and through image interpretation, was required before proceeding to post-processing and map lay-out. Knowledge sharing and capacity development facilitated by the University of the Philippines Diliman (UPD) enabled partner universities across the Philippines to produce outputs for their assigned region. Considering output layers were generated by multiple teams working on different landscape complexities with some degree of data quality variability, quality checking is crucial to ensure accuracy standards were met. UPD PARMap devised a centralized and end-to-end scheme divided into four steps – land classification, GIS post-processing, schema application, and map lay-out. At each step, a block is reviewed and, subsequently, either approved or returned with documentation on required revisions. Turnaround time of review is at least one block (area ranging from 10 to 580 sq. km.) per day. For coastal municipalities, an additional integration process to incorporate mapped coastal features was applied. Common problems observed during quality checking include misclassifications, gaps between features, incomplete attributes and missing map elements. Some issues are particular to specific blocks such as problematic LiDAR derivatives. UPD addressed these problems through discussion and mentoring visits to partner universities. As of March 2017, a total of 336 municipal agricultural maps have been turned-over to various stakeholders. For the remaining months of the program, an additional 360 maps are expected to be distributed.