FOREST CARBON ESTIMATION USING REMOTE SENSING

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FOREST CARBON ESTIMATION USING REMOTE SENSING
By Jwan Aldoski
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Outline
Introduction Methods for Forest Carbon Measuring
Study Area Methodology Data used Data
process Data Analysis
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Introduction

• Carbon is one of the most common elements on
  earth, and is found in all living organisms
• Carbon is the basis of most molecules found in
  vegetation
• Carbohydrates, Sugars, Fats, Proteins, Alcohols,
  DNA, Chlorophyll
• Big problem now its high level as co2 in
  atmosphere

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Introduction

• Forest trees take co2 from atmosphere and stored
  in five pools within and around vegetation
• Above-ground biomass stems, bark, leaves, etc
  of tree and non tree plants
• Below-ground biomass roots of all sizes of tree
  and non tree plants
• Dead wood
• Litter
• Soil organic carbon (SOC)

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Introduction
Accurate forest biomass and carbon Measurement
are necessary for

• managing forest resources, informing climate
  change modeling studies, and meeting national and
  international reporting requirements for
  greenhouse gas inventories IPCC and REDD .
• also necessary at the sub-national level for
  purposes such as completing the Malaysia Forest
  Service Climate Change Scorecard that
  necessitates annual estimates of carbon stocks
  and fluxes for each National Forest , and for
  quantifying changes in forest biomass on regional
  scales in response to disturbance.

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Forest Carbon Measurement Methods
Pool Methods Suitability
Above-ground tree biomass Plot Very suitable and cost-effective, commonly adopted and familiar. Plot selection is key to the method
Above-ground tree biomass Plot-less, transect Good but not suitable in dense vegetation
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Forest Carbon Measurement Methods
Pool Methods Suitability
Above-ground tree biomass Harvest Expensive, time consuming, not appropriate all the time. Used to develop allometric equations
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Forest Carbon Measurement Methods
Pool Methods Suitability
Above-ground Tree Biomass Modeling or Allometric Method Suitable for projections, requires basic input parameters from field measurements
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Forest Carbon Measurement Methods
Pool Methods Suitability
Above-ground Tree Biomass Carbon flux measurements Expensive and needs skilled human resources
Eddy covariance instrumentation
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Forest Carbon Measurement Methods
Pool Methods Suitability
Above-ground Tree Biomass Remote sensing Needs field measurements for calibration. Data are usually at large spatial scales, needs expertise to be used and can be expensive
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Forest Carbon Measurement Methods
Pool Methods Suitability
Below-ground Tree Biomass Root extraction and mass measurement Expensive and not suitable at large scales
Below-ground Tree Biomass Root to shoot ratio This study Most commonly used Requires AGB measurement
Below-ground Tree Biomass Biomass equations Requires input data e.g. height, diameter, girth
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Recently Forest Carbon Measurement Method
Pool Methods Suitability
Above-ground Tree Biomass Below-ground Tree Biomass Remote sensing data ground data Needs plots measurements for calibration. Data are usually at large to small spatial scales, needs expertise to be used and can be expensive for large area.
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two primary methods of mapping aboveground tree
forest biomass or carbon from combing remote
sensing data ground data

• First approach is stratify and multiply
• assigns a biomass value, or a range of biomass
  values, to areas of land distinguished by
  characteristics such as vegetation type or land
  use.
• Limitation
• uses ground-based measurements to determine
  biomass values
• the ambiguities present in land area
  classification
• the wide range of variability in aboveground
  biomass within a given land cover type
• Most country (such as Malaysia) under redd
  program using this methods (Lu et al.,2017)

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• Second approach is Direct Mapping Approach
• It employs a set of spatially continuous
  variables to predict biomass values or carbon at
  unobserved locations.
• The direct mapping approach takes advantage of a
  variety of geospatial variables, such as,
  spectral , vegetation indices, backscattered
  energy, climate and topography, and other
  information from remote sensing platforms like
  Optical Data, Radar and LiDAR
• Advantage
• Resulting map are more accurate across the
  landscape
• Update changes are easier
• Method For This Study

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Remote sensing Data
Different types of RS images are used
Multispectral images - capture data at specific
frequencies across the electromagnetic
spectrum Radar images - capture elevation data,
biomass applications are being developed Lidar
images - capture three dimensional information
about surface features
(Lu et al.,2017)
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Most of country under redd program using these
Remote Sensing Imagery
Spatial resolution Sensor
Coarse MODIS
gt250 m LANDSAT MSS
Medium Landsat ETM 7
20-250m ASTER
30m LANDSAT
RADARSAT
Fine IKONOS
lt20m SPOT-5
Lidar
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Challenges for using remote sensing for forest
biomass and carbon estimation

• RS needs to be calibrated with field measurements
• Some satellite imagery is very expensive
• RS data requires technical expertise to be
  interpreted
• Clear and practical methodologies are needed not
  only in field measurements, but also in the
  application of remote sensing
• New technology and methodologies (e.g. Lidar
  technique data acquisition, radar data) could
  contribute further to improve precision and
  accuracy of assessment, if their costs could be
  brought down.
• However , with availability of two new freely
  satellite base data Landsat 8 and sentinel open
  a new technology for forest biomass and carbon
  estimation it needs to be applied in different
  area and different types for forest

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Objectives

• Main objective of this is estimation Total Live
  Tree Carbon in tropical Forest using remote
  sensing data.
• Other more specific objectives are the
  following
• 1. Develop Total Live Tree Carbon Model by
  Combining Landsat 8 Data and Recent Forest
  Inventory Data
• 2 . Enhancing Total Live Tree Carbon Estimation
  Accuracy Using LANDSAT-8 OLI Sensor Texture
  Metrics
• 3. The impact of integrating LANDSAT-8 OLI
  Sensor Data and environmental variables in
  estimating total live tree carbon
• 4. Develop an algorithm for Estimating total
  live tree carbon by Combining LANDSAT-8 OLI
  Sensor and Sentinel -2 Data

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Study Area Kelantan sate
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Methodology and Techniques
Remote sensing (RS) techniques

Field measuring techniques for estimating Live
Tree carbon stocks
Forest biomass or carbon stocks
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Biomass or carbon estimation techniques base on
variables extract from remote sensing data and
combine with ground base

• Direct Measure (Spectral Base Model )
• Biophysical Model
• Spectral Vegetation Indices Model
• Forest Canopy Height Model ( For LIDAR And RADAR)
  
• Image Texture Metrics Model
• for this study
• Direct Measure (Spectral Base Model )
• Spectral Vegetation Indices Model
• Image Texture Metrics Model

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Data Used

• NFI Data (2011-2013)
• Remote Sensing Data
• a. Landsat 8 (2013 and 2016)
• 1. OIL sensor L1T product
• 2.TIRS Sensor L1T product
• b. sentinel 2 (2016)
• c. ASTER-GDEM)

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Satellite Data
Lansat-8 22/4/2013 14/4/2016
Sentinel 2 30/30/2016
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Landsat 8 Scenes (22/4/2013)
Field Scene 1 Scene 2
Landsat Scene Identifier LC81270562013112LGN02 LC81270572013112LGN02
Acquisition Date 4/22/2013 4/22/2013
Collection Category T1 T1
WRS Path 127 127
WRS Row 56 57
Land Cloud Cover 25.49 14.69
Scene Cloud Cover 30.11 14.49
Ground Control Points Model 228 204
Ground Control Points Version 4 4
Geometric RMSE Model (Meters) 7.495 7.453
Geometric RMSE Model X 5.463 5.132
Geometric RMSE Model Y 5.131 5.404
Image Quality 9 9
Sun Elevation 64.263198 63.62659674
Sun Azimuth 73.73885301 71.02902756
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Sentinel_ 2 ) 30/30/2016)
T47NQG T47NRH T47NQF T48NTM T47NRF T47NRG
Acquisition Start Date 2016-03-30T034406.616Z 2016-03-30T034406.616Z 2016-03-30T034406.616Z 2016-03-30T034406.616Z 2016-03-30T034406.616Z 2016-03-30T034406.616Z
Cloud Cover 3.4527 1.8676 5.1664 5.7392 11.4197 9.1715
Agency ESA ESA ESA ESA ESA ESA
Platform SENTINEL-2A SENTINEL-2A SENTINEL-2A SENTINEL-2A SENTINEL-2A SENTINEL-2A
Product Type S2MSI1C S2MSI1C S2MSI1C S2MSI1C S2MSI1C S2MSI1C
Processing Level LEVEL-1C LEVEL-1C LEVEL-1C LEVEL-1C LEVEL-1C LEVEL-1C
Datum WGS84 WGS84 WGS84 WGS84 WGS84 WGS84
Map Projection UTM UTM UTM UTM UTM UTM
UTM Zone 47N 47N 47N 48N 47N 47N
EPSG Code 32647 32647 32647 32648 32647 32647
Resolution 10, 20, 60 10, 20, 60 10, 20, 60 10, 20, 60 10, 20, 60 10, 20, 60
Units METER METER METER METER METER METER
Sun Zenith Angle Mean 23.15110658 22.39924621 23.05207183 21.6952372 22.16510178 22.26548367
Sun Azimuth Angle Mean 93.68887064 96.08306288 91.58265046 94.07835604 91.71141179 93.90944121
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Landsat-2 (14/4/2016 )
Field Scene 1 Scene 2
Landsat Product Identifier LC08_L1TP_127056_20160414_20170326_01_T1 LC08_L1TP_127057_20160414_20170326_01_T1
Acquisition Date 4/14/2016 4/14/2016
WRS Path 127 127
WRS Row 56 57
Land Cloud Cover 7.9 10.34
Sun Elevation 63.72898378 63.24344887
Sun Azimuth 80.15464531 77.37417541
Data Type Level-1 Level 1TP Level 1TP
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NFI data (2011-2013)
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Locating of NFI Plots
NFI data 1. Forest mapping/stratification 2.
Number of stems per ha (N) 3. Basal area per
hectare (m2) 4. Volume per ha (V) and 5. Dry
biomass (tones per ha) 6. carbon (tones per ha)
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Plot types

• Temporary, Permanent data tree pools

precision in inventory 5 of the mean at 95 CI
Calculate number of plots needed
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forest type maps and plots
Forest Type code plot number kelantan area for each strata in ha
Virgin Inland Forest lowland Hill Forest 1 3 24,664
Virgin Inland Forest High Hill Forest 2 2 6,803
Logging Forest lowland Hill Forest (1-10 years ) 3 13 67,918
Logging Forest High Hill Forest (1-10 years ) 4 10 3,798
Logging Forest lowland Hill Forest (11-20 years ) 5 6 63,310
Logging Forest High Hill Forest (11-20 years ) 6 8 10,776
Logging Forest lowland Hill forest (21-30 years ) 7 6 172,298
Logging Forest High Hill Forest (21-30 years ) 8 4 14,164
Logging Forest lowland Hill Forest (gt30 years ) 9 3 123,874
Logging Forest High Hill Forest (gt30 years ) 10 5 13,661
Non-Reserved Inland Forest Forest 14 1 24,778
Protection Forest lowland Hill forest 16 1 110,621
Protection Forest High Hill forest 17 1 61,218
Protection Forest Mountain Forest 18 3 73,373
Total 66 771,256
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Forest classes for forest type mapping in this
study
Name Forest Type Strata
1 Virgin Inland Forest lowland ,Hill Forest High Hill Forest 1,2
2 Logging Forest lowland Hill Forest 3,5,7,9
3 Logging Forest High Hill Forest 4,6,8,10
4 Non-Reserved Inland Forest 14
5 Protection Forest lowland Hill forest High Hill forest Mountain Forest 16-17-18
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Aboveground Biomass _tree (AGB_tree)
Biomass Measure
( keto et al, 1978)
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Belowground Biomass _tree (BGB _tree)
Biomass Measure
Root Shoot method (BGB/AGB)

• BGB AGB (t/ha) 0.235
• 
  (Mokany et al 2006)
• Total  Live Tree Biomass (TLTB) (t /ha)
  AGB_tree BGB_tree

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Carbon Measurement

• Carbon stocks within aboveground tree pool (t C
  ha-1) CAG-tree
• CAG-tree(t C /ha) AGB_tree 0.47
• Carbon stock within belowground tree pool (tC
  ha-1) CBG-tree
• CBG-tree(tC /ha) BGB _tree 0.47
• Total Carbon stocks within tree pool (C total )
  (tC ha-1 )
• C total C GB_tree C GB_tree
• (IPCC defaults 2011)

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CO2 emissions and carbon credit

• CO2 emissions are sometimes expressed in units of
  t CO2 instead of t C
• To convert C stock ? CO2
• CO2 emissions C total
  44/12

Scaling-up plot data Pixel size (30 m 30 m)
?10 000/ Plot size as expansion factor PEF
1 ton of con2 e 5 carbon credit c total
5 Price carbon credit in each forest c
total 5
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Descriptive Statistics Descriptive Statistics Descriptive Statistics Descriptive Statistics Descriptive Statistics Descriptive Statistics Descriptive Statistics Descriptive Statistics Descriptive Statistics Descriptive Statistics
Field Measurement  Plot No. Range   Minimum   Maximum   Sum   Mean Mean Std. Deviation Variance
Field Measurement  Plot No. Range   Minimum   Maximum   Sum   Statistic Std. Error Statistic Statistic
TD 66 50 43 93 4233 64.14 1.373 11.151 124.335
TLTV 66 55.56 40.52 96.08 4056.06 61.4555 1.80372 14.65352 214.726
BA 66 4.81 3.26 8.07 350.77 5.3147 .14035 1.14017 1.300
AGB_TREE 66 70.60 46.70 117.30 4740.59 71.8271 2.00857 16.31767 266.266
BGB_TREE 66 15.57 10.77 26.34 1128.26 17.0948 .52838 4.29257 18.426
TLTB 66 83.97 57.68 141.65 5868.85 88.9220 2.47160 20.07936 403.181
TLTC 66 39.47 27.11 66.58 2758.37 41.7935 1.16166 9.43735 89.064
eco2 66 19.74 13.55 33.29 1379.20 20.8970 .58086 4.71890 22.268
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Satellite Data Process
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Lansat8 pr-prosesing for OLI Sensor L1T product
Radiometric correction

• 1. Converts to Spectral Radiance
• using the radiance scaling factors
• L?    MLQcal AL
• where
• L?  Spectral radiance (W/(m2 sr
  µm))ML  Radiance multiplicative scaling factor
  for the band.AL  Radiance additive scaling
  factor for the band.
• Qcal                L1 pixel value in DN
• 2. OLI Top of Atmosphere Reflectance
• Equation converts Level-1 DN values to TOA
  reflectance
• ??'    M?Qcal A?
• where
• ??'  TOA Planetary Spectral Reflectance, without
  correction for solar angle.  (Unitless)M?  Refle
  ctance multiplicative scaling factor for the
  band.A?  Reflectance additive scaling factor
  for the band.Qcal  L1 pixel value in DN
•  

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Geometric correction
images were geometrically corrected using 23
ground control points (GCPs) of major features
(e.g. roads and buildings ) and digital elevation
models (DEM) to attain improved geodetic accuracy
and a geometrically rectified product free from
distortions (NASA, 2013), The first order
polynomial function was used and a
nearest-neighbour resampling protocol was applied
to correct for systematic shifts occurring in a
few cases between neighboring images. The total
transformation root mean square error (RMSE) of
less than a pixel was attained.
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Atmospheric correction and re-projected
Lansat8 pr-prosesing for OLI Sensor L1T product

• Lansat8 images were Atmospheric ally using the
  MODTRAN based on the Fast Line-of-sight
  Atmospheric Analysis of Spectral Hypercube
  (FLAASH) radiative transfer algorithm (Matthew et
  al., 2000 Perkins et al., 2005), topographic
  correction using ccorrection method, Then the
  Landsat images re-projected to the Universal
  Transverse Mercator (UTM) coordinate system with
  datum WGS 1984 and zone 47 north using the
  nearest neighbor resampling method. The final
  image mosaic of the Kelantan state

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Variables measurement from Landsat-8 OLI
Code Landsat 8---Band Name Bandwidth (µm) Resolution (m)
PS2 Band 2 Blue 0.45 0.51 30
PS3 Band 3 Green 0.53 0.59 30
PS4 Band 4 Red 0.64 0.67 30
PS5 Band 5 NIR 0.85 0.88 30
PS6 Band 6 SWIR 1 1.57 1.65 30
PS7 Band 7 SWIR 2 2.11 2.29 30
PS9 Band 9 Cirrus 1.36 1.38 30
Raw Landsat-8 OLI Spectral bands
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Spectral Vegetation Indices,
code Abbrev. Name Formula
V1 CIgreen Chlorophyll Index Green NIR /Green-1
V2 NDVI Normalized Difference Vegetation Index NIR - Red /NIR Red
V3 GNDVI Green Normalized Difference Vegetation Index NIR -Green/ NIR Green
V4 GSAVI Green Soil Adjusted Vegetation Index NIR -Green/ NIR Green L(1L)
V5 (SAVI) Soil Adjusted Vegetation Index (Red -Green)(1L)/( Red Green L) L 0.5
V6 GRNDVI Green-Red NDVI NIR-(Green Red) / NIR (Green Red)
V7 (NDII) Normalized difference infrared index Red - NIR / Red NIR
V8 RI Normalized Difference Red Green Index Red-Green / Red Green
V9 NGRDI Normalized green red difference index Green-Red/Green Red
V10 EVI2 Enhanced Vegetation Index 2 2.5 NIR Red/ NIR 2.4Red1
V11 WDRVI Wide Dynamic Range Vegetation Index 0.1 NIR -Red/0.1 NIR Red
V12 Norm G Norm G Green/ NIR Red Green
V13 Norm NIR Norm NIR NIR / NIR Red Green
V14 Norm R Norm R Red/ NIR Red Green
V15 TNDVI Transformed NDVI v-(NDVI)0,5
V16 MSRNir/Red Modified Simple Ratio NIR/RED (NIRRED)1/ v (NIRRED)1
(Dube and Mutanga, 2015 Güneralp et al., 2014
Mutanga et al., 2012 Robinson et al., 2016
Sibanda et al., 2015b)
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Spectral Band Ratios, Spectral Band Differencing
D1 Difference Green Red Index Green - Red
D2 ------ Difference Green NIR Index Green NIR
D3 GDVI Difference NIR/Green Index NIR -Green
D4 RDVI Red Difference Vegetation Index NIR-Red
D5 DVI Difference Vegetation Index Red -Green
D6 --- Difference Red NIR Index Red NIR
R1 G/RSR Simple Ratio Green Red Green / Red
R2 G/ NIR SR Simple Ratio Green Near- Infrared Green / NIR
R3 GRVI Green Ratio Vegetation Index NIR / Green
R4 RRVI RED Ratio Vegetation-Index NIR / Red
R5 R/G SR Red/Green Ratio Vegetation-Index Red / Green
R6 R/NIR SR Red/NIR Ratio Vegetation-Index Red / NIR
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Tasseled Cap indices using reflectance-based
transformation (M.H.A. Baig et al., 2014)
Tasseled cap indices
Landsat TCT Landsat TCT (Blue)Band2 (Green)Band 3 (Red)Band 4 (NIR)Band 5 (SWIR1)Band 6 (SWIR1)Band 6 SWIR2)Band7
Bright Index (BI) Bright Index (BI) 0.3029 0.2786 0.4733 0.5599 0.508 0.508 0.1872 (M.H.A. Baig et al., 2014)
Green Vegetation Index(GVI) Green Vegetation Index(GVI) -0.2941 -0.243 -0.5424 0.7276 0.0713 0.0713 -0.1608 (M.H.A. Baig et al., 2014)
Wetness Index(WI) Wetness Index(WI) 0.1511 0.1973 0.3283 0.3407 -0.7117 -0.7117 -0.4559 (M.H.A. Baig et al., 2014)
Tasseled Cap Tasseled Cap Tasseled Cap Tasseled Cap Tasseled Cap Tasseled Cap Tasseled Cap Tasseled Cap Tasseled Cap Tasseled Cap
BI 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7 0.3029B20.2786B30.4733B40.5599B50.508B60.1872B7
GI -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7 -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7 -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7 -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7 -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7 -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7 -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7 -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7 -0.2941 B2-0.243B3-0.5424B40.7276B50.0713B6-0.1608B7
WI 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7 0.1511 B20.1973B30.3283B40.3407B5-0.7117B6-0.4559B7
Ratio greenness (TCG)/ brightness ((TCB Ratio greenness (TCG)/ brightness ((TCB Ratio greenness (TCG)/ brightness ((TCB Ratio greenness (TCG)/ brightness ((TCB TCG/TCB TCG/TCB TCG/TCB TCG/TCW this study TCG/TCW this study TCG/TCW this study
Tasseled Cap distance Tasseled Cap distance Tasseled Cap distance Tasseled Cap distance Squar root TCB2 TCG2 Squar root TCB2 TCG2 Squar root TCB2 TCG2 Duane et al.2013 Duane et al.2013 Duane et al.2013
Tasseled Cap disturbance index Tasseled Cap disturbance index Tasseled Cap disturbance index Tasseled Cap disturbance index (TCB)- (TCG TCW)) (TCB)- (TCG TCW)) (TCB)- (TCG TCW)) Healey et al., 2005) Healey et al., 2005) Healey et al., 2005)
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Image Texture variables
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Topographical Variables

• ASTER Global Digital Elevation Data (ASTER-GDEM)
  with 30 m. a fill-sink process as pre-processing
  was applied ASTER-GDEM data in a GIS environment
  ,Then topographical variables in this study,
  altitude information (elevation) (m) ,aspect (in
  azimuth degrees) slope (in percentage), land
  curvature (concave, convex or ?at) ,a measure of
  potential relative radiation, and Insolation (W
  h/m2), were derived at 30 m spatial resolution
  using surface analysis tools in a GIS
  environment)

(Holmgren, 1994 White and Running, 1994 Beven
and Kirkby, 1979 Güntner et al., 2004 Pierce et
al., 2005 Sörensen et al., 2006)
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Precipitation Map

• Precipitation rainfall (mm datasets) was derived
  from Meteorological Data Malaysia acquired from
  Meteorology Department. There are total of 73
  weather stations that can provide annual
  meteorological records in Kelantan.
• Inverse Distance Weighting (IDW) and Kriging

Average rainfall map (22-4-2013)
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Weather stations, Kelantan state, Malaysia
Station No Station Code Station Name State District Latitude Longitude Rainfall mm 22/4/2013
1 4614001 Brook Kelantan Gua Musang 04 40 35 101 29 05 20.5
2 4614002 Lojing Kelantan Gua Musang 04 36 00 101 24 00 29
3 4717001 Blau Kelantan Gua Musang 04 46 00 101 45 25 0
4 4720026 Ldg. Mentara Kelantan Gua Musang 04 45 20 102 01 00 8
5 4721001 Upper Chiku Kelantan Gua Musang 04 45 55 102 10 25 0.5
6 4726001 Gunung Gagau Kelantan Gua Musang 04 45 25 102 39 20 25
....... ....... ....... ...... ..... ......
72 6121067 Stn. Keretapi Tumpat Kelantan Tumpat 06 11 55 102 10 10 5.9
73 6122064 Stor JPS Kota Bharu Kelantan Kota Bharu 06 06 30 102 15 25 3.2
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TIRS Sensor L1T product

• TIRS Sensor L1T product
• 1.Converts to Spectral Radiance
• using the radiance scaling factors
• L?    MLQcal AL
• where
• L?  Spectral radiance (W/(m2 sr
  µm))ML  Radiance multiplicative scaling factor
  for the band.AL  Radiance additive scaling
  factor for the band.
• Qcal    L1 pixel value in DN
• 2. Atmosphere Brightness Temperature (LST) is
• The conversion formula is as follows
• TK2/In (K1/ L? 1)
• where
• T    TOA Brightness Temperature, in Kelvin.L?  
   Spectral radiance (Watts/(m2 sr µm))K1  
    Thermal conversion constant for the band
• K2    Thermal conversion constant for
  the band
• 3. Atmosphere Brightness Temperature , in
  Kelvin convert to Celsius
• LST T-273

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Extract from All Images

• In order to ensure resolution consistency and for
  easy compatibility and comparison, all the
  variables were standardized into the same
  resolution (30 M) in a GIS environment using the
  nearest neighbour resampling technique. Finally,
  the values from the variables for each field plot
  (n 66) were extract then exported into an
  excel spread as a table and these were then
  associated with their respective plot forest
  carbon or biomass

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Data analysis

• Statistical analysis is done by ArcGIS , envi
  software's and SPSS statistic analysis
• Statistical analysis methods
• Simple and multilinear regression
  methods
• Stochastic gradient boosting (SGB)
  algorithm
• Radom Forest algorithm

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Experimental phases
Experimental phases Different variables groupings
Exp.1 Spectral Bands (Most important Variables selected)
Exp.2 Spectral Bands Env. variables
Exp.3 Spectral Bands Env. Variables topography variables
Exp.4 Vegetation Indices (VIs) (Most important Variables selected)
Exp.5 Vegetation Indices (VIs) Env. variables
Exp.6 Vegetation Indices (VIs) Env. variables topography variables
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References

• Beuchle, R., et al. 2011. A Satellite Dataset
  for Tropical Forest Change Assessment.
  International Journal of Remote Sensing 32
  70097031. doi10.1080/01431161.2011.611186.
• Bodart, C., et al. 2011. Pre-processing of a
  Sample of Multi-scene and Multi-date Landsat
  Imagery used to Monitor Forest Cover Changes over
  the Tropics. ISPRS Journal of Photogrammetry and
  Remote Sensing 66 555563.
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Thank you