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Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat Images 4 October 2014 | GE Theater, UP Diliman | Speaker: Imee A. Saladaga

Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

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Page 1: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap)

Estimation of Biomass Available Potential From Crop Areas

Identified Using Landsat Images

4 October 2014 | GE Theater, UP Diliman | Speaker: Imee A. Saladaga

Page 2: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASSOutline

• Objectives of biomass component• General framework of RE Map for Biomass• Expected outputs• Theory• Mathematical model• Work flow• Initial results• Summary

Page 3: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASS

1. Develop algorithms and work flow for biomass energy resource assessment and site suitability analysis

2. Evaluate biomass resource available in the Philippines with the use of LIDAR data

3. Perform a site suitability analysis for biomass resource extraction and site location

4. Generate biomass resource and site potential maps

Objectives for Biomass Component

Page 4: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASSFramework of Biomass Mapping

Page 5: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASS

Expected Outputs of Biomass

Biomass Theoretical Resource Map (ton)

Page 6: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Expected Outputs of Biomass

Biomass Available Resource Map (MJ)

Page 7: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASS

Site Suitability Map

Expected Outputs of Biomass

Page 8: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Potential Site Map for Biomass Development

Expected Outputs of Biomass

Page 9: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASS

Work flow and algorithms for biomass resource assessment and site suitability analysis

Expected Outputs of Biomass

Page 10: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

TheoryBIOMASS

Biomass – a biological material that can be found from living organisms

Sources of Biomass:

Page 11: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

TheoryBIOMASS

Primary Feed stocks in the Philippines

Page 12: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

TheoryBIOMASS

Primary Feed stocks in the Philippines

Page 13: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

TheoryBIOMASS

Primary Feed stocks in the Philippines

Page 14: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

TheoryBIOMASS

Primary Feed stocks in the Philippines

Page 15: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

TheoryBIOMASS

Primary Feed stocks in the Philippines

Page 16: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

TheoryBIOMASS

Crop residue Metric ton/MW

Rice Hull 8,150

Coconut Husk 7,372

Sugarcane Bagasse 6,832

Sugarcane Trash 7,000

Corn Cobs 6, 378

Estimated Biomass Feedstock Requirement per Year (DOE)

Page 17: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

6 wheeler = 8 tons

1 day: 22.3 metric tons rice hull

1 MW Power plant requirement per day

Rice Hull 8,150 MT/MW

Page 18: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Mathematical ModelBIOMASS

Theoretical Potential - is the total annual production of residues in a region. It is a function of cultivated area and the biomass production yield of each crop:

Where:

Page 19: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Mathematical ModelBIOMASS

Available Potential - refers to the energy content of the biomass potential.

Where:

Page 20: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Mathematical ModelBIOMASS

Biomass Resource

Assumption Used in 2000 Biomass Atlas

Basis

Rice hull 1 ton = 225 kg Rice hull DOE-CBRED

Coconut waste

1000 whole nuts of coconut

United Coconut Association of the Philippines

= 0.180 tons cocoshell

= 0.4 tons cocohusk

= 0.28 tons coir dust

BagasseCane contains 29% Bagasse (50% mc) with 20-25% recovery

Sugar Regulatory Administration

Corn cob 27% of grain weight DOE-CBRED

Page 21: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Workflow

Page 22: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Work Flow

Page 23: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Negros Occidental

Landsat imageLandsat image after

classification

Initial Results

Page 24: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Initial ResultsNegros Occidental Land Cover Map

Page 25: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASSInitial Results of Model

SUGARCANE (NEGROS OCCIDENTAL)

Year

Crop Density Yield

(metric tons/ha)

Residue Density Yield

(metric tons/ha)

Biomass Theoretical

Potential (metric tons)

Biomass Available

Potential (MJ)

2006 68.31 19.81 2,783,779 14,7652007 59.76 17.33 2,435,348 12,9172008 71.51 20.74 2,914,185 15,4572009 64.14 18.60 2,613,842 13,8642010 57.43 16.65 2,340,395 12,413

Parameter ValuesCrop Area (ha) 140,525

Efficiency Factor 25%Availability for Energy Production 100%

Lower Heating Value (MJ/kg) 16.56Provincial Area (ha) 780,548

Bagasse = 29% of Sugarcane

Bureau of Agricultural Statistics

Page 26: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASSInitial Results

Please note:Data used to fom this map is very old (1980’s DA) and may no longer be applicable to the present. We just used this to test our work flow and mathematical model since lidar derived crop areas are not yet available to us as of now.

Page 27: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASSInitial Results

RICE HULL (TARLAC)

Year

Crop Density Yield

(metric tons/ha)

Residue Density Yield

(metric tons/ha)

Biomass Theoretical

Potential (metric tons)

Biomass Available

Potential (MJ)2006 4.52 1.02 131,272 7,2002007 4.59 1.03 133,305 7,3112008 4.52 1.02 131,272 7,2002009 4.22 0.95 122,560 6,7222010 4.34 0.98 126,045 6,913

Parameter ValuesCrop Area (ha) 129,078

Efficiency Factor 100%Availability for Energy Production 100%

Lower Heating Value (MJ/kg) 16.50Provincial Area (ha) 300,849

Page 28: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Biomass Available Potential (MJ)from Rice Hulls (Tarlac)

Page 29: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

BIOMASSSummary

• Crop areas were identified using Landsat images.

• From the delineated crop areas, the validity of the mathematical model was tested together with other parameters derived from statistical data.

• LiDAR-derived crop areas, once available, will be used for the final assessment of biomass available potential.

Page 30: Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap) Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

Philippine Renewable Energy Resource Mapping from LiDAR Surveys (REMap)

Thank you!

Estimation of Biomass Available Potential From Crop Areas Identified Using Landsat

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