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Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets

Topics: Cartography and Geodesy; Geospatial Information and Technologies ; Query Processing and Optimization; Spatial Analysis and Integration; Spatial Modeling and Reasoning; Standardization and Interoperability; Storage, Indexing and Searching

Authors: Nerissa B. Gatdula 1 ; Mylene V. Jerez 1 ; Therese Anne M. Rollan 1 ; Ronalyn P. Jose 1 ; Coleen Dorothy U. Caranza 1 ; Joyce Anne Laurente 2 and Ariel C. Blanco 1

Affiliations: 1 University of the Philippines, Philippines ; 2 Phil-LiDAR 1 Data Archiving and Distribution Component and Training Center for Applied Geodesy and Photogrammetry, Philippines

ISBN: 978-989-758-252-3

Keyword(s): Agriculture, LULC, Mapping Design, Post-classification, Geodatabase Schema, LiDAR, Minimum Mapping Unit, Resource Maps, Models, Automated Workflows.

Related Ontology Subjects/Areas/Topics: Data Engineering ; Databases and Data Security ; Query Processing and Optimization

Abstract: Methods for agricultural feature extraction were developed to produce detailed (crop-level) agricultural land use/land cover (LULC) maps from high resolution LiDAR datasets. As of February 2017, available LiDAR data in the Philippines covers 125,200.00 sq.km. or 42.43% of the land area of the Philippines. As part of product generation, definition of mapping design was considered. This includes algorithm for post-classification, development of geodatabase schema, and map layouts. Output maps in custom and 1:10,000 scale JPEG maps, shapefiles and KMZ files are distributed to local government units, national government agencies and other stakeholders for use in planning and other applications. Definition of LULC classes and types is in accordance with the standard codes of Bureau of Soils and Water Management while 1:10,000 is based on National Mapping and Resource Information Authority map indexes. Initial classified maps are maintained in high resolution layers. Detailed objects are re fined by determining the Minimum Mapping Unit (MMU). The use of mapping design has standardized the output agricultural resource maps of implementing universities involved in the Phil-LiDAR 2 Program. Models and automated workflows were developed to improve the implementation of the map design. (More)

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Paper citation in several formats:
Gatdula N., Jerez M., Rollan T., Jose R., Caranza C., Laurente J. and Blanco A. (2017). Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets.In Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-252-3, pages 276-283. DOI: 10.5220/0006365902760283

@conference{gistam17,
author={Nerissa B. Gatdula and Mylene V. Jerez and Therese Anne M. Rollan and Ronalyn P. Jose and Coleen Dorothy U. Caranza and Joyce Anne Laurente and Ariel C. Blanco},
title={Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets},
booktitle={Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2017},
pages={276-283},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006365902760283},
isbn={978-989-758-252-3},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets
SN - 978-989-758-252-3
AU - Gatdula N.
AU - Jerez M.
AU - Rollan T.
AU - Jose R.
AU - Caranza C.
AU - Laurente J.
AU - Blanco A.
PY - 2017
SP - 276
EP - 283
DO - 10.5220/0006365902760283

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