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
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 r
efined 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)