2016 esri user san diego, california, usa conference · 2016 esri user conference presentation...

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: [email protected]; Phone: +63 49 501 6503; Fax: +63 49 536 2893

mailto:[email protected]

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

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

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


AGRICULTURAL

LAND COVER MAP

of VICTORIA, LAGUNA


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


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


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

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


• 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


• 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


Sample Run for 2 Days (2008)

Continuous Processing, Training, and Map Turn-over up to May 2017

www.phil-lidar.uplb.edu.ph

2016 esri user san diego, california, usa conference · 2016 esri user conference presentation...

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