Exploiting fullwaveform lidar signals to estimate timber volume and above-ground biomass of individual trees

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1 Exploiting fullwaveform lidar signals to estimate timber volume and above-ground biomass of individual trees Tristan Allouis 1, Sylvie Durrieu 1 Cédric Véga 2 Pierre Couteron 3 1 Cemagref/AgroParisTech, UMR TETIS, Montpellier, France 2 French Institute of Pondicherry, Pondicherry, India 3 Institut de Recherche pour le Développement, UMR AMAP, Montpellier, France 2011 IEEE IGARSS, Vancouver, Canada 1/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

2 Introduction: Context Why assessing forest biomass? Estimating forest productivity and carbon sequestration rate Defining strategies for sustainable forest management and climate change mitigation How? Through allometric equations using field-measured trunc diameter at breast height (DBH) Cost and assess issues Through remote sensing techniques Do not give access to the DBH 2/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

3 Introduction: Background Lidar technique overview Light detection and ranging 1 Emission/reception of laser pulses 2 Signal processing 3 Signal and echoes geo-positioning Advantages: High resolution products (several pt/m 2 ) Ground echoes under the canopy 3/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

4 Introduction: Background State of the art 3D information derived from lidar data: Scope: Height, basal area, volume (direct or indirect methods) Topography under cover Timber inventory and management Habitat monitoring Ecosystem modelling 4/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

5 Introduction: Aim of the study Questions Can other tree metrics replace DBH in allometric equations? Can full-waveform signals improve volume/biomass estimates? What is the accuracy of such estimates at tree level? 5/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

6 Material: Study site Study area Located in the French Alps (mountainous) Planted with Black Pine Field data 6 circular plots of 15 m radius (61 trees) Tree DBH, total height, crown base height 6/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

7 Material: Study site Reference Volume Equation by the French Institute for Agricultural Research for Black Pine within France (C=trunc circonference; H=total height): Volume = H C C C H C H H 2 Reference Biomass Equation by Gil et al. (2011) for Black Pine within Spain: Biomass = DBH DBH Gil, Blanco, Carballo, Calvo, Carbon stock estimates for forests in the Castilla y León region, Spain. A GIS based method for evaluating spatial distribution of residual biomass for bio-energy, Biomass and Bioenergy, vol. 35, pp /18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

8 Material: Lidar data Characteristics Small-footprint size ( 25 cm) Density = 5 shots/m 2 Sample rate of 98% per surface unit 2 types of lidar data Canopy Height Model (CHM): classical lidar data derived from discrete returns Full-Waveform lidar signals 8/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

9 Method: Deriving metrics from the CHM CHM metrics Segmentation of individual trees (Véga and Durrieu, 2011) and extraction of: Total tree height (Ht CHM ) Crown projected area (Acrown CHM ) Tree bounding volume (BV CHM = Acrown CHM Ht CHM ) Véga, Durrieu, Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: application to a mountainous forest with heterogeneous stands, International Journal of Applied Earth Observations and Geoinformation 13, /18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

10 Method: Deriving metrics from full-waveform lidar signals Method Aggregation of signals falling inside modeled tree crowns One aggregrated signal corresponds to one individual tree Vegetation profile calculation (correction of signal attenuation, more details in Allouis et al. 2010) Allouis, Durrieu, Cuesta, Chazette, Flamant, Couteron, Assessment of tree and crown heights of a maritime pine forest at plot level using a fullwaveform ultraviolet lidar prototype, International Geoscience and Remote Sensing Symposium (IGARSS), pp /18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

11 Method: Deriving metrics from full-waveform lidar signals FW metrics Curve integral (I SIG, I PROF, I2 SIG, I2 PROF ) Ratio beween I and ground component integral (R SIG, R PROF ) Maximum signal amplitude except ground (Max SIG ) Crown base height (Hcrown PROF ) Height of maximum profile amplitude except ground (Hmax PROF ) Aggregated waveform Range Power Vegetation profile Range Density 11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

12 Method: Deriving metrics from full-waveform lidar signals FW metrics Curve integral (I SIG, I PROF, I2 SIG, I2 PROF ) Ratio beween I and ground component integral (R SIG, R PROF ) Maximum signal amplitude except ground (Max SIG ) Crown base height (Hcrown PROF ) Height of maximum profile amplitude except ground (Hmax PROF ) Aggregated waveform Range Power Vegetation profile Range Density 11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

13 Method: Deriving metrics from full-waveform lidar signals FW metrics Curve integral (I SIG, I PROF, I2 SIG, I2 PROF ) Ratio beween I and ground component integral (R SIG, R PROF ) Maximum signal amplitude except ground (Max SIG ) Crown base height (Hcrown PROF ) Height of maximum profile amplitude except ground (Hmax PROF ) Aggregated waveform Range Power Vegetation profile Range Density 11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

14 Method: Deriving metrics from full-waveform lidar signals FW metrics Curve integral (I SIG, I PROF, I2 SIG, I2 PROF ) Ratio beween I and ground component integral (R SIG, R PROF ) Maximum signal amplitude except ground (Max SIG ) Crown base height (Hcrown PROF ) Height of maximum profile amplitude except ground (Hmax PROF ) Aggregated waveform Power Range MaxSIG Vegetation profile Density HmaxPROF HcrownPROF Range 11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

15 Method: Building estimation models Process Building volume and biomass estimation models: 1 Selection of significant metrics (stepwise algorithm) 2 Construction of final models (10 subsamples for calibration/validation) 3 Comparision of model performance (for CHM-only, CHM+FW and benchmark models) 12/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

16 Results: Replacing DBH in allometric equations Strong relationship between DBH and crown projected area. Perspectives Using crown area in traditional DBH models Building new models with other metrics West, Enquist, Brown, A general quantitative theory of forest structure and dynamics, Proceedings of the National Academy of Sciences of the United States of America, vol. 106, pp /18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

17 Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunk REF, DBH REF, Ht REF CHM-only Volume: BVcrown CHM, Ht CHM, Acrown CHM Biomass: BVcrown CHM, Ht CHM CHM+FW Volume: BVcrown CHM, Acrown CHM, I2 SIG, Ht CHM Biomass: I2 SIG, BVcrown CHM, Acrown CHM, Ht CHM, R PROF Volume Biomass AdjR 2 Error AdjR 2 Error Benchmark 1 1 % 1 8 % CHM-only % % CHM+FW % % 14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

18 Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunk REF, DBH REF, Ht REF CHM-only Volume: BVcrown CHM, Ht CHM, Acrown CHM Biomass: BVcrown CHM, Ht CHM CHM+FW Volume: BVcrown CHM, Acrown CHM, I2 SIG, Ht CHM Biomass: I2 SIG, BVcrown CHM, Acrown CHM, Ht CHM, R PROF Volume Biomass AdjR 2 Error AdjR 2 Error Benchmark 1 1 % 1 8 % CHM-only % % CHM+FW % % 14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

19 Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunk REF, DBH REF, Ht REF CHM-only Volume: BVcrown CHM, Ht CHM, Acrown CHM Biomass: BVcrown CHM, Ht CHM CHM+FW Volume: BVcrown CHM, Acrown CHM, I2 SIG, Ht CHM Biomass: I2 SIG, BVcrown CHM, Acrown CHM, Ht CHM, R PROF Volume Biomass AdjR 2 Error AdjR 2 Error Benchmark 1 1 % 1 8 % CHM-only % % CHM+FW % % 14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

20 Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunk REF, DBH REF, Ht REF CHM-only Volume: BVcrown CHM, Ht CHM, Acrown CHM Biomass: BVcrown CHM, Ht CHM CHM+FW Volume: BVcrown CHM, Acrown CHM, I2 SIG, Ht CHM Biomass: I2 SIG, BVcrown CHM, Acrown CHM, Ht CHM, R PROF Volume Biomass AdjR 2 Error AdjR 2 Error Benchmark 1 1 % 1 8 % CHM-only % % CHM+FW % % 14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

21 Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunk REF, DBH REF, Ht REF CHM-only Volume: BVcrown CHM, Ht CHM, Acrown CHM Biomass: BVcrown CHM, Ht CHM CHM+FW Volume: BVcrown CHM, Acrown CHM, I2 SIG, Ht CHM Biomass: I2 SIG, BVcrown CHM, Acrown CHM, Ht CHM, R PROF Volume Biomass AdjR 2 Error AdjR 2 Error Benchmark 1 1 % 1 8 % CHM-only % % CHM+FW % % 14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

22 Results: Estimation models Estimation error (%) Estimation error (%) Benchmark CHM CHM+FW Volume estimation Benchmark CHM CHM+FW Biomass2 estimation 15/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

23 Conclusion Crown area is a good predictor of DBH Tree bounding volume (height x crown area) is one of the most efficient lidar metric for volume and biomass estimation Slight improvement using FW lidar metrics in biomass estimation models but no improvement in volume estimations Approach limited to monospecific and single-storey forests Future work: evaluating FW metrics worth at plot level 16/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

24 Thank you for your attention 17/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

25 Exploiting fullwaveform lidar signals to estimate timber volume and above-ground biomass of individual trees Tristan Allouis 1, Sylvie Durrieu 1 Cédric Véga 2 Pierre Couteron 3 1 Cemagref/AgroParisTech, UMR TETIS, Montpellier, France 2 French Institute of Pondicherry, Pondicherry, India 3 Institut de Recherche pour le Développement, UMR AMAP, Montpellier, France 2011 IEEE IGARSS, Vancouver, Canada 18/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals