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RSS Remote Sensing Solutions GmbH 2012

Application of multisourceremote sensing data for

estimating and monitoring tropical forests

carbon stocks

Dr. Vanessa Keuck

Prof. Dr. Florian SiegertPeter NavratilSandra EnglhartDr. Jonas FrankeDr. Uwe Ballhorn

RSS Remote Sensing Solutions GmbH


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Forest Inventories, plot sampling

Collection ofdata of

forest type

tree species

dbh

biomass per ha

carbon per ha

geo-location

AGBcalculation

allometricequation formoist tropicalforests (Chaveet al. 2005)


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LiDAR derived products

250

t/ha

250

t/ha


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Photo: F. Siegert

Assessment of carbon stock using SARdata

Field

inventoryLiDAR

Modeling Upscaling(SVM, NN, Regression)

SAR


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Upscaling using SAR

LiDAR is used to up-scaleforest inventories

Dipterocarp - 3

80

90

100

110

120

130

140

830300 830350 830400 830450 830500 830550

X(m)

Z(m)

140forest inventoryplots

3,970LiDAR biomassestimations


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Multifrequency SAR biomass maps

Calibration R=0.79 The model is valid up to

300 t/ha Independent validation using

10% of the LiDAR estimates

(randomly distributed)(R=0.55)Published: Englhart et al. (2011) in RSE


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Photo: F. Siegert

Assessment of carbon stock usingRapidEye data

Fieldinventory LiDAR RapidEye

Stratification

Modeling

Upscaling

Classification



Land Cover map based on RapidEye imagery

Overall accuracy: 87.8%



From land cover to carbon stock

Establishment of a reference emission level by the use of the Carbon Calculator: Database contains biomass values from over 200 publications LiDAR based land cover specific AGBs (in progress) Open for integration of in-situ biomass values

Carbon Calculator



AGB map based on RapidEye imagery


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Forest Disturbance AssessmentChange Detection

22.05.200910.20.201021.06.201029.07.2012


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Forest Disturbance AssessmentSMA Results

Soil Fraction

RGB: 3/5/2

22/05/2009

GV Fraction

NPV Fraction

1000 m

RGB: Soil/NPV/GV



Forest MonitoringDeforestation and forest degradation mapping

Published: Franke et al. 2012 in JSTAR

validation using video data from flight campaigns:Overall accuracy: 91.5%



Conclusion

Precise, reliable and consistent forest inventory data are

most important

Combined with LiDAR data estimation of abovegroundbiomass for small areas is possible, also change detection

LiDAR based forest disturbance measurements have a high

potential

Large area up-scaling using multi-frequency SAR data iswith limitations possible (estimation accuracy)

High resolution satellite data, such as RapidEye, allowstimely and accurate detection of deforestation anddegradation

For forest disturbance measurements the crucial point is the

temporal resolution


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Thank you for your attention

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