Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.8 Overview

Module 2.8 Overview and status of evolving technologiesREDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF

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Module 2.8 Overview and status of evolving technologies

Module developers:

Brice Mora, Wageningen University

Erika Romijn, Wageningen University

Country examples:

1. Tropical biomass mapping in Kalimantan by integrating ALOS PALSAR and LIDAR data

2. Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area

Source: US Forest Service.

V1, May 2015

Creative Commons License


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1. Tropical biomass mapping in Kalimantan by integrating ALOS PALSAR and LIDAR data

Study from Quinones et al. (2014) on estimating tropical forest biomass in Kalimantan using a combination of RADAR and LIDAR

Advantage of RADAR: works under cloudy conditions

Limitations of RADAR: saturation effects and speckle

Using RADAR in combination with LIDAR can help to overcome the limitations


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Classification of forest structural types using RADAR data

Image processing chain:

●Data import and metadata extraction, radiometric calibration, coarse geocoding, fine geocoding, and geometric and radiometric terrain correction

Preprocessing:

●Strip selection, radiometric correction, ortho-rectification, slope correction, and mask preparation

Classification 17 strata

●Unsupervised segmentation, postprocessing, validation, and LCCS labelling


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Vegetation structural type, Kalimantan


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Generation of vegetation height map through fusion of LIDAR and RADAR data

Extraction of vegetation height from LIDAR data for 100,000 points: histogram with distribution of heights for each vegetation structure type (stratum)

Matching of LIDAR height histograms with ALOS PALSAR HV histograms for each vegetation structure type height map for whole Kalimantan

LIDAR height histograms for each stratum

RADAR HV histograms


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Tropical biomass mapping

Use of 3 different equations to calculate biomass based on the height map:- Bio1 = Height^1.68

- Bio2 = 0.06328*(Height^2.4814)

- Bio3 = 9.875+0.04552*(Height^2.5734)

Map validation with biomass estimates from field data

RMSE Ketterings et al. 2001

Kenzo et al. 2009

Brown 1997

Bio1 10.47 10.69 10.37

Bio2 10.97 10.27 12.37

Bio3 10.51 10.28 11.27

Use of 3 different equations to calculate biomass based on field data:

- Ketterings et al. (2001) BIO =

0.066*D^2.59

- Kenzo et al. (2009) BIO = 0.0829*D^2.43

- Brown (1997) BIO = 0.118*D^2.53


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Tropical biomass mapping

Use of 3 different equations to calculate biomass based on the height map:- Bio1 = Height^1.68

- Bio2 = 0.06328*(Height^2.4814)

- Bio3 = 9.875+0.04552*(Height^2.5734)

Map validation with biomass estimates from field data

RMSE Ketterings et al. 2001

Kenzo et al. 2009

Brown 1997

Bio1 10.47 10.69 10.37

Bio2 10.97 10.27 12.37

Bio3 10.51 10.28 11.27

Use of 3 different equations to calculate biomass based on field data:

- Ketterings et al. (2001) BIO =

0.066*D^2.59

- Kenzo et al. (2009) BIO = 0.0829*D^2.43

- Brown (1997) BIO = 0.118*D^2.53


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2. Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area

Naesset et al. (2011),“Model-assisted Regional Forest Biomass Estimation Using LIDAR and InSAR as Auxiliary Data: A Case Study From a Boreal Forest Area”

Enhancing biomass estimation with input from forest structure parameters, which were measured with LIDAR and InSAR techniques


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Use of LIDAR and InSAR as auxiliary data to

estimate forest biomass in a boreal forest area

Methodology

Stratification of forest land into four strata, through interpretation of aerial photographs (photogrammetry)

Collecting field data:

● For sample survey plots and large field plots

● For measurements of tree diameter (dbh) and tree height

● Computed from field measurements: Lorey’s mean height hL, basal area (G), number of trees per hectare (N)

Acquiring LIDAR and InSAR data


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Acquiring LIDAR and InSAR data

Acquiring LIDAR data for each grid cell of the study area:

●Canopy height distributions, including order statistics: height deciles and maximum height value

●Canopy density distributions

Acquiring SRTM InSAR (X-band) data to produce a digital surface model (DSM) and digital height error model (HEM) and two datasets of pixel-level canopy heights:

●Subtracting the LIDAR terrain model from InSAR DSM

●Subtracting the terrain model generated from official topographic map from the InSAR DSM


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Estimation of aboveground biomass

Estimation of aboveground biomass (AGB) from field data:

● Using dbh and tree height as independent variables to estimate the mean biomass per hectare for each stratum, which is called “observed biomass”

Model-assisted and model-based regression to estimate AGB, using LIDAR and InSAR as auxiliary data:

● Using variables from canopy height distributions obtained with LIDAR for 4 forest strata

● Using the 2 InSAR height variables for 4 forest strata

Difference between observed biomass and model-assisted estimation of biomass using LIDAR and InSAR data was calculated


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Comparison of model-assisted estimation of biomass and observed biomass

Source: Naesset et al. 2011, fig. 2.

LIDAR estimates InSARTopo estimates InSARLIDAR estimates

LIDAR InSARTOPO InSARLIDAR

Using unadjusted synthetic estimator)

RMSE: 17.3MD: -4.6

RMSE: 53.2MD: -20.6

RMSE: 44.1MD: -21.0

Using adjusted synthetic estimator

RMSE:17.7MD: -4.1

RMSE: 52.7MD: -19.6

RMSE: 42.6MD: -18.4

Predicted biomass (Mg/ha) Predicted biomass (Mg/ha) Predicted biomass (Mg/ha)

Obse

rved b

iom

ass

(M

g/h

a)

Obse

rved b

iom

ass

(M

g/h

a)

Obse

rved b

iom

ass

(M

g/h

a)


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Conclusions: Use for tropical biomass estimation

LIDAR:

●Promising for tropical biomass estimation

●High accuracy and high precision of estimates

●However, monitoring costs are high

InSAR:

●Moderate accuracy and precision

●RADAR: ability to see through clouds

●Frequent updates at low costs

●Useful when accurate terrain model is used—however, these are not widely available in the tropics


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Recommended modules as follow-up

Modules 3.1 to 3.3 to proceed with REDD+ assessment and reporting


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References

Brown, S., 1997. Estimating Biomass and Biomass Change of Tropical Forests: a Primer (FAO Forestry

Paper-134), FAO, United Nations, Rome.

Di Gregorio, A., and Louisa J.M. Jansen. 2000. Land Cover Classification System (LCCS): Classification

Concepts and User Manual. Rome: Food and Agricultural Organization.

http://www.fao.org/docrep/003/x0596e/X0596e00.htm#P-1_0.

IPCC (Intergovernmental Panel on Climate Change). 2000. Good Practice Guidance and Uncertainty

Management in National Greenhouse Gas Inventories. (Often IPCC GPG.) Geneva, Switzerland: IPCC.

http://www.ipcc-nggip.iges.or.jp/public/gp/english/.

Kenzo, T., R. Furutani, D. Hattori, J. J. Kendawang, S. Tanaka, K. Sakurai, and I. Ninomiya. 2009.

“Allometric Equations for Accurate Estimation of Aboveground Biomass in Logged-over Tropical

Rainforests in Sarawak, Malaysia.” Journal of Forest Research 14 (6): 365–372. doi:10.1007/s10310-009-

0149-1

Ketterings Q. M., R. Coe, M. van Noordwijk. 2001. “Reducing Uncertainty in the Use of Allometric Biomass

Equations for Predicting Aboveground Tree Biomass in Mixed Secondary Forests.” Forest Ecology and

Management 146: 199–209.


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Næsset, E., Gobakken, T., Solberg, S., Gregoire, T.G., Nelson, R., Ståhl, G., Weydahl, D., 2011. “Model-

assisted Regional Forest Biomass Estimation Using LiDAR and InSAR as Auxiliary Data: A Case Study from

a Boreal Forest Area.” Remote Sensing of Environment 115 (12): 3599-3614.

Quinones, M., C. Van der Laan, D. Hoekman, and V. Schut., 2014. Integration of Alos PalSAR and LIDAR

IceSAT data in a multistep approach for wide area biomass mapping. Presentation Living Planet,

Edinburg, September 2013.

Documents

Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.8 Overview