Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets

Nerissa B. Gatdula, Mylene V. Jerez, Therese Anne M. Rollan, Ronalyn P. Jose, Coleen Dorothy U. Caranza, Joyce Anne Laurente, Ariel C. Blanco

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 refined 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.

References

  1. Buckley, A. (2008). Guidelines for minimum size for text and symbols on maps. Retrieved from https://blogs.esri.com/esri/arcgis/2008/01/16/sizeforte xtandsymbolsonmaps/
  2. Bureau of Soils and Water Management (2009). Manual on Map Standards & Symbols for Soil & Water GIS.
  3. Carating, R., Manguerra, J., Samalca, I., Pascual, N., Albano, F. (2009). Manual on Map Standard & Symbols for Soil & Water GIS. Bureau of Soils and Water Management.
  4. Conley, E. P. (2013). 2013 ESRI International User Conference. Generalization for Multi-scale Mapping. San Diego, California.
  5. Environmental Systems Research Institute, Inc. (1996). Introduction to Map Design. Retrieved from http://healthcybermap.org/HGeo/res/intrcart.pdf
  6. Environmental Systems Research Institute, Inc. (2007). ArcGIS Desktop Help. Retrieved from http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm? TopicName=Simplifying_and_smoothing_features.
  7. Environmental Systems Research Institute, Inc. (2016). The architecture of a geodatabase. Retrieved from http://desktop.arcgis.com/en/arcmap/10.3/managedata/geodatabases/the-architecture-of-ageodatabase.htm
  8. Environmental Systems Research Institute, Inc. (2016). An overview of attribute domains. Retrieved from https://pro.arcgis.com/en/proapp/help/data/geodatabases/an-overview-of-attributedomains.htm
  9. Longley, P., Goodchild, M., Maguire, D., & Rhind, D. (2005). Geographic Information Systems and Science. ISBNs: 0-470-87000-1.
  10. Philippe Rigaux, M. S. (2002). Spatial Database with Application to GIS
  11. Rutchey, K., & Godin, J. (2009). Determining an appropriate minimum mapping unit in vegetation mapping for ecosystem restoration: a case study from the Everglades, USA. Landscape Ecol 24:1351-1362. Springer Science+Business Media B.V
  12. Spangrud, D. (2015). A Question of Scale, Resolution, and MMU. Retrieved from https://blogs.esri.com/esri/esriinsider/2015/05/21/a-question-of-scale-resolution-andmmu/ on 21 Jan 2016
  13. Stohlgren, T.J. (2006). Measuring Plant Diversity: Lessons from the Field. Oxford University Press, USA.
  14. Stocksigns (2012). What size sign should I use? A viewing distance guide. Retrieved from http://blog.stocksigns.co.uk/size-sign-viewingdistance-guide/
  15. T. W. Lillesand and R. W. Kiefer (2004). Remote Sensing and Image Interpretation. New York: Wiley, p. 157.
  16. USGS Land Cover Institute. NLCD 92 Land Cover Class Definitions. Retrieved from http://landcover.usgs.gov/classes.php.
Download


Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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