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National Forest Monitoring System: Shifting from Visual to Digital Classification of National Forests, Indonesia Presented by Yudi 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 Forestry Bogor Agricultural University, INDONESIA

National Forest Monitoring System Shifting from …lcluc.umd.edu/sites/default/files/Setiawan.pdfIPB, Hatfield, Daemeter, MoEF & LAPAN Improved speed and accuracy of national forest

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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


Mozaic SPOT-6/7

Mozaic Landsat

Period 5


6-4 years periods 3 years periods annual +SPOT Data+ Burnscar

+SPOT Data+ Burnscar Montly


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


Data Source: Landsat 5 TM SPOT Vegetation Landsat 7 ETM+ Landsat 8 OLI (since 2013)

















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


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


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)





Make 5bands 8 bits for mosaic processing (654 &


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


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-


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


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.



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



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


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







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