National Forest Monitoring System:Shifting from Visual to
Digital Classification of National Forests, Indonesia
Presented byYudi Setiawan, Lilik B. Prasetyo
Land Cover/Land Use Changes (LC/LUC) and Impacts on Environment
in South/Southeast Asia - International Regional
Science Meeting, 28-30th May, 2018, Philippines
Dep. of Forest Resource Conservation and Ecotourism Faculty of
ForestryBogor Agricultural University, INDONESIA
Forests 2020A collaborative program to advance Earth Observation
applications to forests monitoring, supported by the UK Space
Agency (UKSA) - coordinated by Ecometrica, Ltd.
- UK partners: Univ. of Edinburgh, Univ. of Leicester, &
Carbomap
- Indonesia partners: IPB, Hatfield, Daemeter, MoEF &
LAPAN
Improved speed and accuracy of national forest monitoring
system
National priorities and expected benefits:
Mozaic SPOT-6/7
Mozaic Landsat
Period 4
2014-2015
Mozaic SPOT-6/7
Mozaic Landsat
Period 5
2017
6-4 years periods 3 years periods annual +SPOT Data+
Burnscar
+SPOT Data+ Burnscar Montly
Fire
Requirement of providing data and information quickly
Historical of Indonesia Land Cover Map
Flowchart of Land Cover Classification
Landsat data catalogue
(Indonesia)http://landsat-catalog.lapan.go.id
# Cloud free mosaic productLANDSAT-8 MOSAIC, 2016
# National Land Cover product2016
LUC: 23 classes(source: MoEF)
Covered by 225 scenes
Landsat Mosaic Land Cover 1990-2017
1990
Data Source: Landsat 5 TM SPOT Vegetation Landsat 7 ETM+ Landsat
8 OLI (since 2013)
19901996
20002003
20062009
2011
2012
7
20132014
20162015
2017
19962000
20032006
20092011
20122013
2014
20162015
2017
Indonesia Land Cover Data (by MoEF)
Easily accessible data for governments, community and public
Transparent Method, published (data type, delineate technique,
verification, and quality control (QC)
Consistent Definition, class, method, data source, etc. Required
for FREL, GHG, moratorium map, etc
Accurate and up-to-date Accuracy assessment.Required for:
recalculation, giving permission, area conflict
Land Cover Data Requirements
IMPROVING NATIONAL FOREST MONITORING SYSTEM
Need innovations:VisualDigital
Forest Monitoring:- Half-year- Quarterly- Monthly
1. Estimation of tree height based on multi sensor and image
fusion: To estimate forest tree with > 5m height, retrieval
algorithm from LiDAR data and optic sensors
2. Estimation of forest canopy cover: To estimate forest canopy
cover > 30%, retrieval algorithm from LiDAR data and optic
sensors (Landsat & Sentinel 2) for each pixel as a real number
between zero and 100 (%)
3. Assessment of minimum mapping unit size: To estimate forest
minimum area > 0.25 ha
Pre-processing TOA, BRDF, Cloud/shadow, Topographic corr.,
image
normalization, and mosaicking
Research Needs
Digital forest classification
Receiving and Processing Facilities LAPANGS ParepareGS RumpinGS
Jakarta
Data Processing Centre,Jakarta
Data Landsat 8 (Server) Copy Data
Local Storage
Checking Area
Remove Data
Data has been processed
Remove Data
Conversion from TIFF to ERS
Conversion from DN to TOA
& BDRF
Re-projection from UTM to Geographic
Cloud masking area based on RBI (divided
land and sea)Cirrus removal Quick look from the temporary result
(Res 30m)
NO
YES
YES
NO
Make 5bands 8 bits for mosaic processing (654 &
432).
Stacking layer multispectral &
ThermalReport process
LAPANs AlgorithmExisting system
Pre-processing stepsAutomatic digital pre-processing (using
Python, NetLogo, R and PERL)
1. TOA and BRDF2. Cloud/Shadow masking (including
land/water, etc)3. Topographic correction4. Image
normalization5. Mosaicking
Pre-processing (flow)
Innovation-1 Automatic digital
pre-processing
Input Data for the Digital Forest Monitoring
Collaboration of LAPAN-IPB (Hosted by LAPAN)
Existing automatic pre-processing system (LAPAN)
Python source code for Topographic Correction
Solar Position Calculation
Illumination Condition Calculation : : From
Average to every pixels
Rotation Model
READY FOR INTEGRATION into LAPANs system of image
pre-processing
BDPJNNational Remote Sensing Databank
L8 datasetsFor all Indonesia
(225 ti les)-Temporal Storage-
Pre-processing system- Cloud/cloud shadow
masking- Land-sea masking
- TOA & BRDF- Topographic corr.
- Image Normalization
Processing database
Information related to image processing &
history(passes & failed)
Image results(Corrected image
products)-Temporal Storage-
Processing system1. Automatic Classification
System2. Automatic forest cover change detection System
Monitoring system
OutputsResult of classification
and forest cover changes
Web Platform(EO Labs)
Modul to select L8 data covers Indonesia area
- Real Time-
SchedulerProcess runs every 5 pm
Collaboration of IPB-LAPANHosted by LAPAN
FORESTS2020: Nationwide Forest Monitoring SystemHosted by
IPB
Cloud/shadow masking image Land-sea
masking image
Digital Forest Monitoring System: Design System
Innovation-1 Automatic digital pre-
processing
Innovation-2 Forest classification
system Innovation-3 Forest cover change
detection system
Collaboration of LAPAN-IPB (Hosted by LAPAN)
FORESTS2020: Nationwide Forest Monitoring System (Hosted by
IPB)
Lapans System
IPBs System
Forest canopy height and canopy coverDatasets: LIDAR (as the
reference of our algorithm developed using the optics/SAR data
(Landsat and Sentinel), source: MoEF, LAPAN and Forests2020
Methods: LidR (R), 3D Voxel (NetLogo), and LiDAR360/LiMapper
LIDAR data (las format) Canopy Height Model
Flight parameters of the LIDAR scanning: - flying height 70 m
above ground, - laser time gap 2 s, - drone speed 6-8 m/s, - flight
side lap - 50%. RTK GNSS LiDAR antenna
TOPCON GR 5 YellowScan LiDAR systemDJI Matrice 600
Methodology
5 locations selected for LiDAR data acquisition Overlaid with a
30 m fishnet The final number of grid samples in the reference
dataset was 981 grids.
The LiDAR points cloud data were processed using LidR package in
R, to create canopy height model (CHM) directly with a 2-m pixel
size image.
https://github.com/Jean-Romain/lidR/wiki/Rasterizing-perfect-canopy-height-models.
Methodology
POINTS CLOUD (LIDAR) Canopy cover by Ma et al.s formula
(2017)
which defined as a percentage of CHM pixel values above 2 m
(represents tree heights > 2 m) over all CHM pixels
Data processing was done by using lidR package (Roussel and
Auty, 2017) and R statistical software (R Core Team, 2013).
Canopy Height Model derived from LiDAR data
RGB orthophoto image
The black line represent the grid for 30-m Landsat pixel
Revealed to the mixed height-pixel issue
Canopy Height Model
Red band was found to be the best band, followed by swir band.
Nirband was not significantly correlated with the tree height
attribute.
Red, swir and nir reflectance metrics accounted for 59.4%, 53.7%
and 0.04% of Landsat log-linear models.
Red reflectance SWIR reflectance NIR reflectance
Log(tree height) = 3.848 35.82*redR2 = 0.594
Log(tree height) = 4.473 15.23*swirR2 = 0.537
Log(tree height) = 2.023 + 0.746*nirR2 = 0.0049
Height variable of the multispectral metric
Estimated tree height from red reflectance of Landsat
Height variable of the multispectral metric
Mean absolute errors were highest in the tree height >20 m,
as a mean of underestimate in the tree height >20 m. A lack of
signal to differentiate taller canopies is a likely reason, as
shown in Figure above.
Meanwhile, overestimates in the tree height 20 m -7.99
Validation of Landsat-estimated height
FRCI and Landsat 8 bands
Cor(y,): 0.69 RSE : 0.21
Cor(y, ): 0.77RSE : 0.19
Cor(y, ): 0.74RSE : 0.19
Cor(y, ): 0.73RSE : 0.2
Cor(y, ): 0.77RSE : 0.18
y = 2.2e(-34.06x) y = 2.4e(-11.46x) y = 2.6e(-51.83x)
y = 1.9e(-14.09x)y = 3.4e(-10.46x)
Cor(y,): 0.63RSE: 0.19
Cor(y,): 0.83RSE: 0.19
Cor(y,): 0.45RSE: 0.28
Cor(y,): 0.81RSE: 0.18
Cor(y,): 0.79RSE: 0.19
Cor(y,): 0.53RSE: 0.27
y = e(-6.5+7.2x) y = e(-6.0+7.0x) y = e(-2.3+1.7x)
y = e(-2.1+0.5x) y = e(-2.7+1.0x) y = e(-1.5+0.2x)
FRCI and Vegetation Indices
Visual and Digital Classification (Comparison)
Land cover 2016(visual classification)
LiDAR FRCI & Landsat 8 NCDVI
-based Classification
HVR Image (2015) from Google Earth
LiDAR Coverage
To collect ground reference datasets To test and evaluate the
algorithm to other
ecosystem types (mangrove, swamp forests, peat swamp, lowland
and mountainous forest ecosystems.
To evaluate the algorithm at operational level
(national-scale).
To disaggregate forest classes (forest types and degradation
levels)
Further works
Thank you very much Salamat
(Cidanau, Dec 2017)
Measuring accuracyMethods:Canopy closure/canopy cover
Hemispherical Densitometer/densiometer
Landsat GRID-based Hemisperical photo
30m
30m
The windows also cover different regions of Indonesia tosecure
political buy-in to the improved systems, and includethe provinces
of Riau, Jambi, South Sumatra, West Java,Banten, and Central
Kalimantan (6 provinces).
The windows cover different types of forests (ecosystems)such
as:
1. mangrove forests,2. peat/swamp forests,3. lowland forests,4.
montane/sub-montane forests,5. timber forest plantation,6. complex
multi-strata agroforestry systems, and7. simple tree-crop systems
(e.g. oil palm plantation).
Window Areas
NATIONAL FOREST INVENTORY(NFI)
1990-1996 ( 2.735 cluster plots) 1996-2000 ( 1.145 cluster
plots) 2000-2006 ( 485 cluster plots) 2006-2014 (>3.000 cluster
plots)
Systematic Stratified Sampling 20 km x 20 km
Grid UTM Forest state area 6 forest classifications
PSP
TSP
TSP TSP
TSP
TSPTSPTSP
TSP
Timber stock
Slide Number 1Forests 2020Slide Number 3Slide Number 4Landsat
data catalogue (Indonesia)# Cloud free mosaic productLandsat Mosaic
Land Cover 1990-2017Slide Number 8Slide Number 9Research
NeedsReceiving and Processing Facilities LAPANExisting
systemPre-processing stepsPre-processing (flow) Python source code
for Topographic CorrectionSlide Number 16Digital Forest Monitoring
System: Design SystemForest canopy height and canopy
coverMethodologyMethodologyPOINTS CLOUD (LIDAR)Canopy Height
ModelHeight variable of the multispectral metricHeight variable of
the multispectral metricValidation of Landsat-estimated heightFRCI
and Landsat 8 bandsFRCI and Vegetation IndicesVisual and Digital
Classification (Comparison)Further worksThank you very much
SalamatMeasuring accuracySlide Number 32Slide Number 33Slide Number
34Slide Number 35
