Development of Mapping Design for Agricultural Features Extracted
from LiDAR Datasets
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
1,3
1
Phil-LiDAR 2 Agricultural Resources Extraction from LiDAR Surveys, Training Center for Applied Geodesy and
Photogrammetry, University of the Philippines, Diliman, Quezon City, Philippines
2
Phil-LiDAR 1 Data Archiving and Distribution Component, Training Center for Applied Geodesy and Photogrammetry,
University of the Philippines, Diliman, Quezon City, Philippines
3
Department of Geodetic Engineering, University of the Philippines, Diliman, Quezon City, Philippines
Keywords: Agriculture, LULC, Mapping Design, Post-classification, Geodatabase Schema, LiDAR, Minimum Mapping
Unit, Resource Maps, Models, Automated Workflows.
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.
1 INTRODUCTION
Agricultural land use/land cover (LULC) maps are
produced by utilizing Light Detection and Ranging
(LiDAR) Technology under the Phil-LiDAR 2
Program Project 1, funded by the Philippines’
Department of Science and Technology (DOST) and
monitored/co-managed by the Philippine Council for
Industry, Energy and Emerging Technology Research
and Development. This is to complement programs of
the Department of Agriculture and to provide
accurate, reliable, and detailed agricultural maps at
the crop level. The Project utilizes LiDAR data
acquired by the DREAM/Phil-LiDAR 1 Program,
other remote sensing systems, and field
measurements.
LiDAR, or 3D laser scanning, is an active remote
sensing which measures point cloud data at a rate of
100,000 to 500,000 points per second. These high
accuracy datasets and derivative layers enable
accurate and detailed classification of features on the
ground. As of February 22, 2017, available LiDAR
data covers 42.43 percent (125,200.00 sq.km.) of the
Philippines’ land area. LiDAR datasets, which
include Digital Surface Models (DSM), Digital
Terrain Models (DTM), Orthophotos, and Classified
LAZ, are available for distribution.
Agricultural resource maps produced by the Phil-
LiDAR 2 Program are disseminated to local
government units (LGUs), national government
agencies (NGAs) and other partners in order to
strengthen collaboration. These maps are used for
planning, decision making and development needs.
Combined with hazard maps and other thematic
maps, vulnerabilities of agricultural resources are
assessed.
The Program started in July 2014 with the
University of the Philippines Diliman, through the
276
Gatdula, N., Jerez, M., Rollan, T., Jose, R., Caranza, C., Laurente, J. and Blanco, A.
Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets.
DOI: 10.5220/0006365902760283
In Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017), pages 276-283
ISBN: 978-989-758-252-3
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Training Center for Applied Geodesy and
Photogrammetry, leading fourteen universities in the
nationwide inventory of natural resources. Processes
implemented in relation to agricultural feature
extraction and resource mapping generate various
data layers and outputs in different forms. It was
deemed necessary to develop a map design in the
mapping of agricultural resources in the Philippines
for harmonization and standardization of maps
produced by agencies and universities.
2 RELATED LITERATURE
The development of mapping design intends to
standardize the output agricultural resource maps of
implementing institutions involved in the Program. It
is important that maps, through the map design, are
able to effectively show the results of analysis.
Developer must establish a ‘good design’ and
consider the objective/s and end use of maps. Maps
can represent and communicate the results of the
analysis to wide range of users. Maps interact with
users through the use of map products and how it is
represented (Longley et al, 2005). Theoretically, map
information is communicated to users through map
designs. In practical terms, however, this is not easily
achieved (ESRI, 1996).
In a map design process, considerations are
enumerated by Robinson et al (1995) as follows: (1)
purpose, determines what is to be mapped and how
message is to be represented; (2) reality, defines the
phenomena being mapped by limiting the design of
the map; (3) available data, the specific data type or
format affects the design; (4) map scale, controls how
data should appear; (5) audience, wide range of user
sees information differently; (6) conditions of use,
environment on which map is to be used will define
the design of the map; and (7) technical limits, digital
and printed formats are usually processed and
represented differently.
The standardization of output maps start with the
standardization of procedure for output generation. In
this light, development of algorithms for mapping
design are considered.
2.1 Minimum Mapping Unit
Post-processing of initial classified maps include the
determination of spatial grain or Minimum Mapping
Unit (MMU) (Rutchey et al, 2009). MMU is the
smallest entity size shown in a map. Several factors
are considered in determining the smallest map unit:
(1) data resolution, correponds to the ground
dimension of a single pixel; (2) map scale, refers to
the ratio between the map distance and ground
distance; (3) classification, refers to the specific class
type of an object; (4) print size, corresponds the
physical dimension of the map paper; (5) PPI, the
number of pixels within an inch of printed material;
and (6) viewing distance, considers the distance of a
person looking at a printed map, poster, signage, etc.
on display (Spangrud, 2015).
Identified MMU should provide information
without losing significant spatial information
(Rutchey et al, 2009). In Phil-LiDAR 2 Program,
MMU is applied to digital and printed formats, in
custom-scale and 1:10,000 scale based on NAMRIA
map index.
2.2 Agricultural LULC Schema and
Classes
The Department of Agriculture - Bureau of Soils and
Water Management (DA-BSWM) released in 2009
the standard codes for thematic mapping, including
the classes for LULC maps. Mapping codes are
grouped based on the most extensive dominated land
use, percent dominant land use, most extensive
associated land use, and percent associated land use.
Percent distribution ranges from 50% to 100% for the
dominant and below 5% to above 30% for the
associated land use.
Geodatabase schema stores the spatial attribute
data in table and polygon geometry which is
maintained through structured query language (SQL)
approach, a series of relational functions and
operators. Schema is documented in a data dictionary
wherein objects in a database, tables, fields in the
table, and the relationship between fields and tables
are well-defined. Attribute domains are applied to
enforce the integrity of the dataset. (ESRI, 2016).
Implementation of proposed map design entails
the use of Geographic Information System (GIS),
models and automated workflows in order to
standardize map production across universities.
3 METHODOLOGY
Mapping design for agricultural LULC maps,
including the algorithm for post-classification,
development of geodatabase schema, and map
layouts, are considered. For coastal municipalities
and cities, mangrove and aquaculture classes are
integrated into the agricultural maps.
Models and automation workflows were
developed to improve the implementation of the map
design.
Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets
277
The general workflow for agricultural map design
is shown in Figure 1, beginning from the initial
classified image to the maps that will handed over to
stakeholders. The classification image, showing the
agricultural features extracted from LiDAR dataset
using object based image analysis (OBIA), is
exported as vector file from eCognition and post-
classified in ArcGIS. Refinement and post-
classification are done by determining the MMU and
smoothening LULC polygons. Template geodatabase
schema are applied to the refined shapefile to produce
a series of LULC maps.
Figure 1: General workflow for the mapping design of
agricultural LULC maps.
Definition of LULC classes and types followed
the standard codes of Bureau of Soils and Water
Management. The standard 1:10,000 scale maps are
based on the National Mapping and Resource
Information Authority map indexes. These were used
so as not to deviate largely from the government’s
mapping standards.
Test data were used to show the performance of
the developed mapping design.
3.1 Refinement/Post-classification
Procedure
Initial LULC shapefiles were exported from
eCognition and added as layers in ArcGIS.
Determination of the MMU depends on the selection
of the significant feature with the smallest area. A
feature, or class, is considered significant when (1) a
class is surrounded by other classes but are near
clusters of the same class; (2) a class is abundant
alongside a river or road; (3) a class serves as
boundaries of a parcel; (4) a class is abundant even
within built-up areas; and other related scenarios.
Figure 2 shows the general workflow for LULC
post-classification and refinement. Implementation of
MMU was carried out through the Elimination and
Smoothing tools in ArcGIS.
Figure 2: Workflow for the refinement/post-classification
of LULC.
Objects are eliminated by determining the
smallest significant unit and merging the insignificant
objects with neighbouring polygons. Non-
agricultural features equal to the identified MMU and
features less than the identified MMU are removed.
For printed maps, MMU can be computed by
applying Equation 1.




(1)
As for the smoothing of LULC shapefiles, the
maximum smoothing tolerance is set at 3 meters as
higher tolerance can alter the shape and area of
significant objects. Percent changes in number of
objects and polygonal areas for the initial and post-
processed/refined LULCs were computed.
3.2 Geodatabase Schema
A geodatabase schema was developed to standardize
the output maps of agricultural feature extraction and
LULC mapping of fourteen (14) implementing
universities.
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3.2.1 LULC Classes
Definition of LULC classes is based on the mapping
standards developed by government agencies. Each
class has assigned code, defined in the table domain
and concatenated to come up with the unique
identifier.
3.2.2 LULC Schema and Domains
Schema refers to the structure of the agricultural
LULC dataset. Table structure consists of class, types
and corresponding unique identifier. Data values are
restricted by creating domains for ‘Classification
Types, ‘LULC Classes’, ‘LULC Types, ‘Crops,
‘Farming System, ‘Water body Types, and ‘Road
Types’.
3.2.3 Automation of Schema
Implementation
Automation workflow for schema implementation
was developed in order to generate series of maps
more efficiently. Model is based on Python and
utilizes the ArcGIS geoprocessing toolbox.
Required parameters are the Excel file, where
LULC classes are specified, and template
geodatabase schema, where LULC data is loaded
(See Figure 3).
Figure 3: Automation workflow for schema
implementation.
3.3 Data Integration
Agricultural and aquaculture resource datasets are
combined for coastal areas. There are two cases in
data integration: (1) coastal classification was not
used as thematic layer in agricultural classification;
and (2) coastal classification was used as thematic
layer in agricultural LULC classification.
The integration aims to remove the overlapping
agricultural and coastal classes. The developed
workflow ensured that significant classes in coastal
areas are retained. Insignificant classes, mostly
‘Road’, ‘Bare/Fallow’ and ‘Building’, are reclassified
as auxiliary layer. Result of data integration is
appended to the geodatabase schema (See Figure 4).
Figure 4: Workflow for agricultural and coastal LULC
integration.
3.4 Map Layout
Final agricultural LULC files should be represented
in a map series. Mapping templates in custom-scale
and 1:10,000 were developed to standardize series of
layouts for agricultural LULC maps. Maps in
1:10,000 scale are produced through Data Driven
Pages (DDP) and ArcPy, while, maps in custom-scale
are generated through ArcMap layout page.
Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets
279
3.4.1 DDP and ArcPy for 1:10,000 Scale
Maps
DDP was used in the creation of series of layout pages
from a single map document. The capabilities of DDP
has been extended using ArcPy, a Python scripting
module that automates the exporting and printing of
maps. The iteration of map production is based on the
selected 1:10,000 indexes with available LULC files
(See Figure 5).
Figure 5: Automation workflow for the mapping of
1:10,000 scale maps.
3.4.2 Template Layout for Custom Scale
Maps
Maps are also represented in custom-scale layout.
Template was produced and distributed to
universities to harmonize map production.
3.5 Production of GIS Files
The final and completed LULC files are handed over
to LGUs, NGAs and other stakeholders. Vector files
are provided in 1:10,000 scale shapefiles and custom-
scale KML/KMZ file formats.
3.5.1 Clip per 1:10,000 Scale Model
Smaller regions of the final LULC files are produced
for easier data handling. For better visualization,
these shapefiles can be loaded in Google Earth Pro.
Figure 6 shows the model for the clipping of 1:10,000
LULC files.
Figure 6: Automation workflow for the clipping of LULC
in 1:10,000 scale grids.
3.5.2 LULC Shapefile to KML/KMZ
Conversion Model
Keyhole Markup Language is an XML-based format
used by Google Earth. Files can be in KML/KMZ file
formats. Model for the conversion of shapefiles to
KML/KMZ are developed (See Figure 7).
Figure 7: Automation workflow for the conversion of
shapefile to KMZ/KML.
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4 RESULTS AND DISCUSSION
The initial classified shapefile has been refined to
remove unnecessary objects and to set the minimum
spatial grain. In MMU identification, the first
agricultural class with the smallest area is observed.
Table 1 shows the list of agricultural and non-
agricultural classes commonly present in the
classification.
Table 1: Common LULC classes.
Non-agricultural
Class
Agricultural and other
Significant Classes
Water
Crop Fields
(i.e. Rice, Corn, Sugarcane)
Bare/Fallow
Crop Trees
(i.e. Coconut, Banana, Mango)
Building
Mangroves
Road
Shrubland
Grassland
The MMU should be the smallest significant
object in LULC map. Figures 8, 9, 10, and 11 show
the common scenarios where class is considered
important.
Figure 8: Banana object surrounded by other trees and
shrubs but cluster of banana are present nearby (left);
sugarcane within Barren parcel (right).
Figure 9: Abundant banana objects alongside rivers (left);
abundant non-agricultural trees along roads (right).
Figure 10: Coconut trees used as boundaries in a Mango
plantation.
Figure 11: Abundant coconut objects in built-up area (left);
abundant non-agricultural trees in built-up area (right).
The iterative application of the refinement process
has been tested. Tables 2 and 3 show the percent
change in number of objects and area. The percent
change may vary depending on how segmentation in
eCognition was done. Test Area A (Block 8H) has
lower values compared to Test Area B (Block 1C)
which may imply that segmentation is relatively good
and removal of the salt and pepper effect was done
prior to refinement.
Table 2: Percent change in number of objects after
refinement.
LULC Class
Test Area A
Test Area B
Bare/fallow
1.61%
-34.45%
Building
0.84%
-29.33%
Developed
2.09%
-29.66%
Grassland
1.22%
0.44%
Mango
1.16%
0.00%
Non-agri
0.81%
-44.28%
Rice
5.00%
-14.77%
Road
1.85%
-45.49%
Water
3.99%
-60.44%
Table 3: Percent change in area after refinement.
LULC Class
Test Area A
Test Area B
Bare/fallow
0.04%
-0.11%
Building
-0.01%
2.02%
Developed
-0.01%
1.09%
Grassland
0.03%
0.17%
Mango
-0.08%
-0.23%
Non-agri
-0.09%
-0.74%
Rice
0.01%
0.04%
Road
0.10%
1.32%
Water
0.00%
0.06%
Development of Mapping Design for Agricultural Features Extracted from LiDAR Datasets
281
The developed geodatabase schema is applied to
the post-classified/refined LULC shapefile. Table 4
shows the structure of the LULC datasets.
Table 4: Schema of the agricultural LULC map.
Name
Description
CLASSIFICATION
Classification types
RESOURCE_TYPE
Resource map types
ID_CLASS
ID of LULC class
MAIN_CLASS
Main LULC class
OTHER_CLASS1
Other LULC class
OTHER_CLASS2
Other LULC class
CLASS_DESCRIPTION
LULC class
description
ID_TYPE
ID of LULC type
MAIN_TYPE
Main LULC type
OTHER_TYPE1
Other LULC type
OTHER_TYPE2
Other LULC type
TYPE_DESCRIPTION
LULC type description
DATA_SOURCE
Source of dataset (e.g.
LiDAR, Landsat)
DATASET_
ACQUIRED
Acquisition date of
dataset
FARMING_SYSTEM
Farming system
CROP_PLANTING_PER
IOD
Farming period of
crops (e.g. Dec-Feb)
JAN
Crop planted in Jan
FEB
Crop planted in Feb
MAR
Crop planted in Mar
APR
Crop planted in Apr
MAY
Crop planted in May
JUN
Crop planted in Jun
JUL
Crop planted in Jul
AUG
Crop planted in Aug
SEP
Crop planted in Sep
OCT
Crop planted in Oct
NOV
Crop planted in Nov
DEC
Crop planted in Dec
AREA
Area of LULC
REGION
Region of the main
City/Muni
PROVINCE
Province of the main
City/Muni
CITYMUNI
Main City/Muni
BARANGAY
Main Barangay of the
main City/Muni
REMARKS
Remarks
Identification of the main class should be based on
the dominant crop in the area. Dominance is based on
height for intercropping systems and on hectarage for
mixed cropping systems.
The manual implementation of schema requires
doing some of the processes repeatedly. Thus,
automation workflow for the application of schema
were developed. Based on benchmark testing,
approximately 1,500 to 1,800 features were updated
per hour. This translates to 25 to 30 features per
minute. Processing time was observed to be highly
dependent on the size and number of features.
In agricultural and coastal data integration,
insignificant objects found in the coastal area are
reclassified as auxiliary layer (See Figure 12).
Figure 12: Roads observed in fishpond area (left) and
overlapping fishponds and bare objects (right).
Final LULC maps are produced in 1:10,000 scale
and custom-scale JPEG files. Figure 13 shows sample
agricultural and coastal LULC maps in custom-scale
layout.
Figure 13: Custom scale layout of agricultural and coastal
LULC map.
Vector files, in 1:10,000 scale shapefiles and
custom-scale KML/KMZ file formats, are also
generated (See Figures 14 and 15).
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282
Figure 14: KML/KMZ maps in custom-scale.
Figure 15: LULC shapefiles in 1:10,000 subset files.
5 SUMMARY AND
CONCLUSIONS
The development of mapping design was considered
in the production of agricultural LULC to ensure the
standardization of maps disseminated to various
stakeholders. The design has been useful in the
management of spatial information and maintenance
of LULC database. Model and scripts using ArcGIS,
ArcPy and Python are utilized in the production of
LULC maps, resulting in faster turn-around from data
to map products.
ACKNOWLEDGEMENTS
The research is made possible through the funding of
Philippines’ Department of Science and Technology
and monitoring of the Philippine Council for
Industry, Energy and Emerging Technology Research
and Development. Technical facilities and support
were provided by the UP College of Engineering, the
UP Department of Geodetic Engineering and the UP
Training Center for Applied Geodesy and
Photogrammetry. The research is also supported by
the Philippines’ Department of Agriculture through
the Information Technology Center for Agriculture
and Fisheries.
The authors acknowledge everyone who has
contributed to the research.
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