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2016 Esri User
Conference
San Diego,
California, USA
June 27 – July
1, 2016
Damasa B. Magcale-Macandog1, Milben A. Bragais2, Ozzy Boy S. Nicopior2, Mic
Ivan V. Sumilang2, Lester Ryan P. Mojica2, Donald A. Luna2, Precious R. Zara2, Marlon A. Reblora2, Jan Martin G. Magcale2, Ricajay C. Dimalibot2, Davies V. Ocampo2, Pristine Pearl H. Arelas2, Iana Mariene A. Silapan2, Vanessa Gail L. Borja2, Sarena Grace L. Quiñones2 and Randy P. Porcioncula2
1Project Leader, Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna, Philippines2Researcher, Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna, Philippines*Corresponding author’s email: dmmacandog@gmail.com; Phone: +63 49 501 6503; Fax: +63 49 536 2893
Hazards
Disasters
Located at the Pacific Ring of Fire , Philippines is known to be prone in natural such as
which leads to like
EARTHQUAKE 7.2-mag. Earthquake in Bohol last Oct 15, 2013 © Michael Poole
VOLCANIC ERUPTION Mt. Mayon last May 7, 2013
STORM SURGE Typhoon Yolanda last Nov. 14, 2013
TYPHOONNASA image of Typhoon Yolanda by Jeff Schmaltz
© http://gongjumonica.com/
©www.gmanetwork.com©www.nationalturk.com© earthobservatory.nasa.gov
© Erik de Castro©www.mwebantu.com ©www.mwebantu.com
Loss of lives and properties…
And…Destruction of resources
Guinsaugon , Leyte landslide, Feb. 26, 2006 ©CBS Interactive.Inc
Mangrove and coastal destruction in Coron, Palawan, 2012 © travelfoodguru.wordpress.com
Coconut plantation in Samar, Leyte after typhoon Yolanda, 2013 © Erik de Castro
Massive Coral damage in Apo Island after typhoon Sendong,2011 & typhoon Pablo,2012 © Steve De Neef
The need for detailed resource assessment
HazardsExposure
VulnerabilityDisasters
Loss of lives and properties
Destruction of resources
Severe Impacts on:POVERTY ALLEVIATIONFOOD SECURITYECONOMIC GROWTHENERGY SUPPLY
To help mitigate disasters, we need to answer the following questions:
What resources exist where?
Which resources are exposed and vulnerable to hazards?
How to protect and conserve resources?
Characteristics and status of these resources?
THESE DETAILED INFORMATION ARE NEEDED BY GOVERNMENT AGENCIES AND LOCAL GOVERNMENT UNITS FOR BETTER PLANNING AND DECISION MAKING.
Objectives of the project
Produce high-resolution national resource maps;
Produce vulnerability assessment maps for high-value crops and coastal resources;
Formulate recommendations to help address future local supply and demand in agriculture, coastal, forest, and renewable resources.
Nationwide resource inventory project
Flood modelling
(18 river basins)
Flood modelling Resources inventory
DREAM
Program
Phil-LIDAR 1 Phil-LIDAR 2
Five components of the project
1. Philippine Agricultural Resources and Facilities
Inventory from LIDAR Survey
2. Coastal Resources Inventory from
LIDAR Surveys
3. Forest Resource Extraction from LIDAR Surveys
4. Development of the Philippine Hydrologic
Dataset from LIDAR
5. Philippine Renewable Energy
Resource Inventory from LIDAR
PARMap
CoastMap
FRExLS
PHD
REMap
Phil-LiDAR 2: Implementing Agencies
Luzon
UP Diliman
UPB / MMSU
CLSU
Isabela SU
UPLBMapua
AdNU
Visayas
UP Cebu
Univ. of San Carlos
Visayas State University
Mindanao
CARAGA State U
CMU
MSU-IIT
UP Min
AdZU
Phil-LiDAR 2 UPLB: STUDY AREAS
Mindoro
Marinduqu
e
Romblon
Palawan
Laguna
What is LiDAR?
Light Detection And Ranging
Similar to
RADAR (radio waves)
SONAR (sound waves)
LiDAR uses laser instead
Pictures © Isenburg, M. [powerpoint presentation]
LiDAR equipment consist of a vehicle (Airplane or Helicopter for broad surfaces), a laser, a scanner, a GPS (Global Positioning System), and an INS (inertial navigation system).
How does LiDAR work?
No. of points per square meter can be
increased.
Point density
No. of points per square meter can be
increased.
Point density
LiDAR data = “point cloud”
Collection of training points on field
1. handheld GPS
2. compass
3. camera
4. field data sheet
Field Instruments
Collection of training points on field
Collection of training points on field
Sample Field-collected training points
Processing
1. ArcMap 10.2
2. ENVI 5
3. eCognition Definiens
4. LAStools
Processing Software
Points from Land Cover 2010 Points from Google Earth
Generation of Level 2 sampling points
PARMap: Processing
Object-Based Image Analysis (OBIA)
1 sub-block9 individual tiles
PARMap: Initial Output
Aerial photo
classificatio
n
PARMap: Initial Output
AGRICULTURAL
LAND COVER MAP
of VICTORIA, LAGUNA
PARMap: Initial Output
AGRICULTURAL
LAND COVER MAP
of PILA, LAGUNA
CoastMap: Processing
Processing Workflow:
Aquaculture
Multi-Threshold using hillshade
DSM, DTM, SLOPE, SLOPE OF SLOPE
NDSM, HILLSHADE
eCognitionProject
No Data w/ Data
Multi-Threshold using dsm
Land class
Water class
Is the aquaculture in one class?
NOYES
Multi-Threshold using slope
Ground
Non-Ground
Assign Class (refinements for
aquaculture)
Are all aquaculture extracted?NO
YES Export shapefile
Aquaculture shapefiles
Refinements (Generalization, Smooth Polygon, Simplify Building
Tool)
Submission
CoastMap: Processing
Processing Workflow:
Mangroves
Aquaculture workflow until refined land vs
water
All Derivatives & Aquaculture, train, & valid, Shapefile
Low High
Multi-Threshold / Contrast split
using slope + ndsm
Accuracy about .90?
NO
YES
Aquaculture shapefile
Refinements
Multiscale Segmentation (Separately)
Mangrove + Aquaculture shapefiles
Address revisions
Extract Water(Ruleset based)
Assign Class(assign class by thematic layer)
Classification(SVM using S
EATH features)
Accuracy Assessment
SEATH(export object
statistics)
Export Shapefiles(mangrove only)
In-house QC
Okay?
Submission
YES
NO
CoastMap: Processing
Processing Workflow - Mangrove
PARMap points Specific classes Reclass to CoastMap
Sample Processing Outputs
Fish pens and fish cages, Boilinao, Pangasinan
Mangrove Map,San Juan, Batangas
Courtesy of UPD CoastMap
FRExLS: Processing
Workflow and Target Parameters
Las File: Processing and Application of various Algorithms to develop CHM, DEM, DSM (ArcGIS,
LASTools, Global Mapper)
TREE HEIGHT
ABOVEGROUNDBIOMASS
CANOPY COVER &CANOPY DENSITY
VEGETATION POINTS: Number, Spacing and Intensity
LiDAR DATA: Acquisition, Validation and Processing (DREAM)
CANOPY HEIGHT MODEL
TREE COUNT, CANOPY GAP, & CROWN DIAMETER
TREE DELINEATIONAllometric
Relationships of Field and LiDAR Parameters
Field Data Collection
Courtesy of UPD FRExLS
Sample Processing Outputs
Makiling, Los Banos
Courtesy of UPD FRExLS
Sample Processing Outputs
Makiling, Los Banos
Courtesy of UPD FRExLS
Sample Processing Outputs
Makiling, Los Banos
Courtesy of UPD FRExLS
Sample Processing Outputs
Makiling, Los Banos
Courtesy of UPD FRExLS
FRExLS: Initial Output
FRExLS: Initial Output
REMap: Processing
ComponentsSolar Wind BiomassHydro
REMap: Initial Processing Output
• The Solar Energy component is expected to produce suitability maps for Solar Farms.
• For UPLB, the team is expected to run the ‘r.sun’ model in Grass GIS to Produce:
1) Clear Sky GHI
2) Real Sky GHI
*Used SAR DEM with a 10m by 10m resolution as a base input
REMap: Solar
REMap: Initial Processing Output
REMap: Initial Processing Output
• The Biomass Energy component is expected to produce suitability maps for Biomass Powerplants.
• For UPLB, the team is expected to compute (Using ArcGIS) for the Theoretical Potential and Available Potential for the following crops:
1) Rice
2) Coconut
3) Corn
4) Sugar
*Please take note that the area of crops used for the computation came from the final land cover maps from PARMap.
REMap: Solar
REMap: Processing
Computation of Biomass and Map Layout were done in ArcGIS 10.2
REMap: Initial Processing Output
• The Biomass Energy component is expected to produce suitability maps for Wind Farms.
• For UPLB, the team is expected to run the Weather Research and Forecasting (WRF) Model in for the years:
A. 2008
B. 2010
C. 2014
D. 2015
REMap: Wind
REMap: Initial Processing Output
Sample Run for 2 Days (2008)
Continuous Processing, Training, and Map Turn-over up to May 2017
www.phil-lidar.uplb.edu.ph
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