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

LAPAN’s 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 LAPAN’s 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)

Lapan’s System

IPB’s 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 <5 m were likely due to the presence of poor quality LiDAR retrievals of low cover conditions.

Mean absolute errors from LiDAR-derived height and Landsat-estimated height by the log-linear model

LiDAR tree height (CHM)

Errors from Landsat 8-estimated height

0 – 5 m 1.465 – 10 m 0.61

10 – 15 m -1.6315 – 20 m -4.94

> 20 m -7.99

Validation of Landsat-estimated height

FRCI and Landsat 8 bands

Cor(y,ŷ): 0.69 RSE : 0.21

Cor(y, ŷ): 0.77RSE : 0.19

Cor(y, ŷ): 0.74RSE : 0.19

Cor(y, ŷ): 0.73RSE : 0.2

Cor(y, ŷ): 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,ŷ): 0.63RSE: 0.19

Cor(y,ŷ): 0.83RSE: 0.19

Cor(y,ŷ): 0.45RSE: 0.28

Cor(y,ŷ): 0.81RSE: 0.18

Cor(y,ŷ): 0.79RSE: 0.19

Cor(y,ŷ): 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