[PDF] Forest height monitoring and aboveground biomass variability in

4 nov 2016 · RSS Remote Sensing Solutions GmbH 2016 Forest height monitoring and aboveground biomass variability in Indonesia's tropical forests

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[PDF] Forest height monitoring and aboveground biomass variability in

41763_3FSiegert1.pdf

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Forest height monitoring and

aboveground biomass variability in

Indonesia's tropical forests

Florian Siegert, Sandra Lohberger, Kristina Konecny, Matthias Stängel,

Werner Wiedemann, Uwe Ballhorn, Peter Navratil

GFOI R&D and GOFC-GOLD Land Cover

Science Meeting

The Hague, The Netherlands

31 October ² 4 November, 2016

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Tropical lowland forest in Indonesia

Most species-rich ecosystem in SE Asia

Mainly dipterocarps (60% endemic)

Forest height: 45 m, max: 70 m

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Land cover and land use 2013 (MoEF based on Landsat)

Lowland Dipterocarp forest

Montane Dipterocarp forest

Peat swamp forest

Heath forest (kerangas)

Mangrove forest

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High AGB variability (data from GlobBiomass)

Lowland Dipterocarp forest

AGB 200 -800 t/ha

Montane Dipterocarp forest

AGB 150 -300 t/ha

Peat swamp forest

AGB 10 -350 t/ha

Heath forest (kerangas)

AGB 5 -200 t/ha

Mangrove forest

AGB 70 -200 t/ha

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Low pole 10-100 t/ha

Medium tall

100-200 t/ha

Photo: F. Siegert

Tall

200-350 t/ha

Peat swamp forest

Natural variability of AGB

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Human induced variability of AGB

Intensive managed logging

Illegal logging

Disturbance by wildfires

Shifting cultivation

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Methodological approach (CARL research)

Field

inventory

LiDAR

Modelling

SAR

Forest height and

biomass estimation

Large scale

biomass estimations

Drones

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Forest inventories and allometric modelling

Nested circular plots

Forest type - tree species ² DBH ²

(tree height)

Estimation of biomass and carbon

per ha using allometric models (Chave et al. 2005/2015)

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LiDAR acquisition and data products

3D point

cloud

Aerial image

True ortho image

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60 m

LiDAR acquisition and data products

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Digital terrain model

Lowland Dipterocarp Peat Swamp Forest

LiDAR acquisition and data products

Digital surface model

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SUMATRA

Overview of field and LiDAR data

Central Kalimantan

²165 field inventory plots

²6.000 km² full-coverage LiDAR

West Kalimantan (Kapuas Hulu)

²84 field inventory plots

²1200 km LiDAR transects

East Kalimantan (Berau & Malinau)

²78 field inventory plots

²1500 km LiDAR transects

South Sumatra

²115 field inventory and

biodiversity plots

²1850 km of LiDAR transects

Data collected by:

Bioclime

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LiDAR based aboveground biomass model

AGB model was created at 5m

spatial resolution (i.e. each pixel represents an area of 0.1ha)

Cell values were scaled to represent

aboveground biomass in tons per hectare

High aboveground biomass

variability within classes could be identified (e.g. Primary Dryland

Forest)

Areas with the highest

aboveground biomass were located around the Kerinci Sebelat National Park

Lowland

Dipterocarp

Peat swamp

forest

Mangrove

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Forest types in Lidar 3D point clouds

Lowland Dipterocarp

Peat swamp forest

Mangrove

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Forest degradation in Lidar 3D point clouds

Lowland Dipterocarp Peat swamp forest

undisturbed logged

Severely degraded by

recurrent fire

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LiDAR point cloud

Aerial photo

Illegal logging

Logging in Lidar 3D point clouds

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Representative samples of Lidar and field plots

Terrain

height

Canopy

height Tree height

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Canopy

Height

70 m
0 m 025m

Ø Terrain

Height:

40.0 m

AGB:

373.7 t/ha

Pristine Lowland Dipterocarp Forest

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Canopy

Height

70 m
0 m 025m

Plot ID:

01_06

Ø Terrain

Height:

196.5 m

AGB:

273.2 t/ha

Disturbed Lowland Dipterocarp Forest

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Canopy

Height

70 m
0 m 025m

Plot ID:

01_02

Ø Terrain

Height:

434.2 m

AGB:

374.8 t/ha

Sub-montane Dipterocarp Forest

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Canopy

Height

70 m
0 m 025m

Plot ID:

01_01

Ø Terrain

Height:

325.6 m

AGB:

166.3 t/ha

Disturbed Sub-montane Dipterocarp Forest

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LiDAR height metrics for AGB estimation

LiDAR height metrics:

Centroid Height (CH)

Quadratic mean canopy height (QMCH)

Published: Jubanski et al. 2013, Englhart et al. 2013

R2=0.84

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LiDAR transects show high AGB variability

Undisturbed

Lowland Dipterocarp

Logged in

2008/2009

Logged in

2011/2012

RapidEye

18.09.2012

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0 750

AGB [t/ha]

545 t/ha

300 t/ha

220 t/ha

Undisturbed

Lowland Dipterocarp

Logged in

2008/2009

Logged in

2011/2012

LiDAR transects show high AGB variability

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LiDAR transects to estimate AGB variability

Recent logging activities (2011/12)

RapidEye 18.09.2012

0 750

AGB [t/ha]

RapidEye 06.02.2014

LiDAR transects show high AGB variability

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Mean AGB:

155.7 t/ha

Orthophoto (10/2012) RapidEye (09/2012) AGB Model (10/2012) CHM (10/2012)

0 750 t/ha

0 65 m

LiDAR transects show high AGB variability

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RapidEye [4-5-3]

18.09.2012

0 750

AGB [t/ha]

LiDAR transects show high AGB variability

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Mean AGB:

176.8 t/ha

Trees up to 45 meters

tall Orthophoto (10/2012) RapidEye (09/2012) AGB Model (10/2012) CHM (10/2012)

0 750 t/ha

0 65 m

LiDAR transects show high AGB variability

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RapidEye [4-5-3]

18.09.2012

0 750

AGB [t/ha]

LiDAR transects show high AGB variability

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Mean AGB:

444.9 t/ha

Trees up to 55 meters

tall Orthophoto (10/2012) RapidEye (09/2012) AGB Model (10/2012) CHM (10/2012)

0 750 t/ha

0 65 m

LiDAR transects show high AGB variability

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22.05.2009

02.10.2010

21.06.2010

29.07.2012

RapidEye time series showing the progress of selective logging and fast regrowth.

Monitoring forest degradation

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CHM 2007 CHM 2011 DTM 2011

Monitoring forest degradation by repeated LiDAR

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CHM 2011

Monitoring forest degradation by repeated LiDAR

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Moitoring AGB change (decrease)

LiDAR 2007

LiDAR 2011

Rapideye 2009

Rapideye 2011

Englhart S., Jubanski J. & Siegert F. (2013). Quantifying dynamics in tropical peat swamp forest biomass with multi-temporal LiDAR datasets. Remote Sensing, 5, 2368²2388

Orthophoto

Average CH

height: 15.5 m

Average CH

height: 17.3 m Gap:

Average height: 8.9 m

AGB=227 t/ha

Average height: 12.9 m

AGB=248 t/ha

Monitoring AGB change (increase)

Methodological approach (CARL research)

Field

inventory

LiDAR

Modelling

SAR

Forest height and

biomass estimation

Large scale

biomass estimations

Drones

Drone data acquisition

https://vimeo.com/rssgmbh

Canopy height models from drones and LiDAR

LiDAR 3D point cloud

Aerial photo and point cloud

acquired by drone

SAR Interferometry ² a promising methodology

InSAR has the potential to measure tree height, which then can be used to estimate AGB in tropical forests

Research project:

Pol-InSAR 4 AGB ²Forest height retrieval and AGB estimation using polarimetric SAR Interferometry (TerraSAR-X, TanDEM-X, RADARSAT-2, Sentinel-1)

²Accuracy analysis

²Development of operational methods

funded by

Methodological approach (CARL research)

Field

inventory

LiDAR

Modelling

Modellig

SAR

Forest height and

biomass estimation Large scale biomass estimations

Drone

GlobBIOMASS - regional AGB estimation

ALOS PALSAR mosaic

²25 m spatial resolution

²acquired in 2009

²HH, HV polarization

²Acquired during dry season

(May-October)

SRTM

²30m spatial resolution

Additional data

²LiDAR AGB estimations

R²=0.65

RMSE=86 t/ha

Multiple linear regression

model based on -backscatter -ratios -textures

GlobBIOMASS - regional AGB estimation

AGB estimation up to 200 t/ha showing variability in lower and higher AGB ranges Change assessment and emission estimation feasible

GlobBIOMASS - regional AGB estimation

Logged forests

AGB & carbon stock (CARL pre-operational)

Photo: F. Siegert

Field

inventory

LiDAR data acquisition

RapidEye imagery

Stratification

Modeling

Upscaling

Land cover classification

Carbon stock

map

Determination of local AGB values

²Intersection of AGB model with land cover classification ²Different forest types and degradation stages ²Descriptive statistics for each class: Minimum, Maximum, Mean, Standard deviation

CARL Framework Implementation

CARL Definition Supporting information Practicalities 1

Research

Basic principles

observed and technology concept formulated. Lowest level of readiness. Scientific research begins to be translated into applied research and development (R&D). Applications are speculative, and there may be no proof or detailed analysis to support the assumptions. Published research exists that identifies the principles that underlie the technology. Promising case studies exist.

2 Analytical and

experimental function (calibration) and/or proof of concept (validation). Active R&D is initiated. This includes analytical and laboratory studies to measure parameters of interest. Method still needs to be tested as repeatable or applicable in the REDD+ MRV context. Validation data may not be available. 3

Pre-operational

Demonstration in small-

scale environment. The basic technological components are integrated with reasonably realistic supporting elements so they can be tested in a small-scale environment. End-to-end processing demonstrated. Data processing methods have been documented in peer review publications. Methods have been assessed for applicability in different forest monitoring contexts.

4 Demonstration in a

larger-scale environment. Representative model or prototype method (near the desired performance), which is well beyond that of level 3, is tested in a larger-scale environment. Processing workflows and methods demonstrated in large scale processing systems or national programs. Basic data may not necessarily yet be available for routine monitoring. 5

Operational

Demonstration in an

operational environment (national level).

Represents the end of true method development.

Technology/method has been proven to work in its

final form. Application of the technology/method in its final form with at least one MRV cycle completed. Product/technology/method fits within a context or system, optimised to meet monitoring requirements with documented uncertainties. Processing workflows and methods are robust and repeatable. Methods are demonstrated to be applicable in forest monitoring contexts. Documented peer reviewed guidance is available. Core data is available for monitoring at relevant spatial and temporal resolutions, and scales. Includes a clear pathway for continuous improvement.

6 Actual technology

proven through successful use in operational environments. Application of the technology/method in its final form with at least two MRV cycle completed. Method implemented in country(ies) as part of national environmental and forest monitoring programs. TTE Technical Assessment or Technical Analysis process, or equivalent, completed successfully. Advantages and trade-offs of the method are well documented, notably in terms of adequacy, technical complexity, performance, and costs. Includes a clear pathway for continuous improvement.

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Comparison with other AGB maps

SAR AGB model Baccini Saatchi

LiDAR

Central

Kalimantan

With FORCLIME Germany supports

Indonesia's efforts to reduce

greenhouse gas emissions from the forestry sector, and to implement sustainable forest management.

[PDF] Forest height monitoring and aboveground biomass variability in