Exploiting fullwaveform lidar signals to estimate         timber volume and above-ground biomass of                       ...
Introduction: Context       Why assessing forest biomass?           Estimating forest productivity and carbon sequestratio...
Introduction: Background       Lidar technique overview              Light detection and ranging         1   Emission/rece...
Introduction: Background       State of the art       3D information derived from lidar data:           Height, basal area...
Introduction: Aim of the study       Questions           Can other tree metrics replace           DBH in allometric equati...
Material: Study site       Study area           Located in the French Alps           (mountainous)           Planted with ...
Material: Study site       Reference Volume       Equation by the French Institute for Agricultural Research for       Bla...
Material: Lidar data       Characteristics           Small-footprint size (                25 cm)           Density = 5   ...
Method: Deriving metrics from the CHM       CHM metrics       Segmentation of individual trees       (Véga and Durrieu, 20...
Method: Deriving metrics from full-waveform lidar signals        Method            Aggregation of signals falling inside  ...
Method: Deriving metrics from full-waveform lidar signals        FW metrics            Curve integral (ISIG , IPROF ,     ...
Method: Deriving metrics from full-waveform lidar signals        FW metrics            Curve integral (ISIG , IPROF ,     ...
Method: Deriving metrics from full-waveform lidar signals        FW metrics            Curve integral (ISIG , IPROF ,     ...
Method: Deriving metrics from full-waveform lidar signals        FW metrics            Curve integral (ISIG , IPROF ,     ...
Method: Building estimation models        Process        Building volume and biomass estimation models:          1   Selec...
Results: Replacing DBH in allometric equations                                                                        → St...
Results: Estimation models        Metrics selected in linear models            Benchmark                  Volume and bioma...
Results: Estimation models        Metrics selected in linear models            Benchmark                  Volume and bioma...
Results: Estimation models        Metrics selected in linear models            Benchmark                  Volume and bioma...
Results: Estimation models        Metrics selected in linear models            Benchmark                  Volume and bioma...
Results: Estimation models        Metrics selected in linear models            Benchmark                  Volume and bioma...
Results: Estimation models                                                          q                                     ...
Conclusion        Crown area is a good predictor of DBH        Tree bounding volume (height x crown area) is one of the   ...
Thank you for your attention17/18   Tristan Allouis, S. Durrieu, C. Véga, P. Couteron   Estimation of individual tree biom...
Exploiting fullwaveform lidar signals to estimate          timber volume and above-ground biomass of                      ...
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EXPLOITING FULLWAVEFORM LIDAR SIGNALS TO ESTIMATE TIMBER VOLUME AND ABOVE-GROUND BIOMASS OF INDIVIDUAL TREES.pdf

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EXPLOITING FULLWAVEFORM LIDAR SIGNALS TO ESTIMATE TIMBER VOLUME AND ABOVE-GROUND BIOMASS OF INDIVIDUAL TREES.pdf

  1. 1. Exploiting fullwaveform lidar signals to estimate timber volume and above-ground biomass of individual trees Tristan Allouis1 , Sylvie Durrieu1 Cédric Véga2 Pierre Couteron3 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, Canada1/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  2. 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 DBH2/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  3. 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/m2 ) Ground echoes under the canopy3/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  4. 4. Introduction: Background State of the art 3D information derived from lidar data: Height, basal area, volume (direct or indirect methods) Topography under cover Scope: Timber inventory and management Habitat monitoring Ecosystem modelling4/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  5. 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. 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 height6/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  7. 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 = 34111.14 + 0.020833846 · H · C 2 − 1486.2307 · C + 2.2695012·C ·H +15.664201·C 2 −56.250923·H −0.0061317691·H 2 Reference Biomass Equation by Gil et al. (2011) for Black Pine within Spain: Biomass = 0.6073 · DBH 2 − 5.0998 · DBH − 23.729 Gil, Blanco, Carballo, Calvo, 2011. 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. 243-2527/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  8. 8. Material: Lidar data Characteristics Small-footprint size ( 25 cm) Density = 5 shots/m2 ⇒ 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 signals8/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  9. 9. Method: Deriving metrics from the CHM CHM metrics Segmentation of individual trees (Véga and Durrieu, 2011) and extraction of: Total tree height (HtCHM ) Crown projected area (AcrownCHM ) Tree bounding volume (BVCHM = AcrownCHM · HtCHM ) Véga, Durrieu, 2011. 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, 646–656.9/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  10. 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, 2010. 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. 1382-138510/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  11. 11. Method: Deriving metrics from full-waveform lidar signals FW metrics Curve integral (ISIG , IPROF , Aggregated waveform Vegetation profile I2SIG , I2PROF ) Power Density Ratio beween I and ground component integral (RSIG , RPROF ) Maximum signal amplitude except ground (MaxSIG ) Crown base height (HcrownPROF ) Height of maximum profile Range Range amplitude except ground (HmaxPROF )11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  12. 12. Method: Deriving metrics from full-waveform lidar signals FW metrics Curve integral (ISIG , IPROF , Aggregated waveform Vegetation profile I2SIG , I2PROF ) Power Density Ratio beween I and ground component integral (RSIG , RPROF ) Maximum signal amplitude except ground (MaxSIG ) Crown base height (HcrownPROF ) Height of maximum profile Range Range amplitude except ground (HmaxPROF )11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  13. 13. Method: Deriving metrics from full-waveform lidar signals FW metrics Curve integral (ISIG , IPROF , Aggregated waveform Vegetation profile I2SIG , I2PROF ) Power Density Ratio beween I and ground component integral (RSIG , RPROF ) Maximum signal amplitude except ground (MaxSIG ) Crown base height (HcrownPROF ) Height of maximum profile Range Range amplitude except ground (HmaxPROF )11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  14. 14. Method: Deriving metrics from full-waveform lidar signals FW metrics Curve integral (ISIG , IPROF , Aggregated waveform Vegetation profile I2SIG , I2PROF ) Power Density Ratio beween I and ground component integral (RSIG , RPROF ) HmaxPROF MaxSIG Maximum signal amplitude HcrownPROF except ground (MaxSIG ) Crown base height (HcrownPROF ) Height of maximum profile Range Range amplitude except ground (HmaxPROF )11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  15. 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. 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, 2009. 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. 7040-704513/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  17. 17. Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunkREF , DBHREF , HtREF CHM-only Volume: BVcrownCHM , HtCHM , AcrownCHM Biomass: BVcrownCHM , HtCHM CHM+FW Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF Volume Biomass AdjR2 Error AdjR2 Error Benchmark 1 1% 1 8% CHM-only 0.93 15 % 0.87 30 % CHM+FW 0.95 17 % 0.91 25 %14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  18. 18. Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunkREF , DBHREF , HtREF CHM-only Volume: BVcrownCHM , HtCHM , AcrownCHM Biomass: BVcrownCHM , HtCHM CHM+FW Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF Volume Biomass AdjR2 Error AdjR2 Error Benchmark 1 1% 1 8% CHM-only 0.93 15 % 0.87 30 % CHM+FW 0.95 17 % 0.91 25 %14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  19. 19. Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunkREF , DBHREF , HtREF CHM-only Volume: BVcrownCHM , HtCHM , AcrownCHM Biomass: BVcrownCHM , HtCHM CHM+FW Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF Volume Biomass AdjR2 Error AdjR2 Error Benchmark 1 1% 1 8% CHM-only 0.93 15 % 0.87 30 % CHM+FW 0.95 17 % 0.91 25 %14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  20. 20. Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunkREF , DBHREF , HtREF CHM-only Volume: BVcrownCHM , HtCHM , AcrownCHM Biomass: BVcrownCHM , HtCHM CHM+FW Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF Volume Biomass AdjR2 Error AdjR2 Error Benchmark 1 1% 1 8% CHM-only 0.93 15 % 0.87 30 % CHM+FW 0.95 17 % 0.91 25 %14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  21. 21. Results: Estimation models Metrics selected in linear models Benchmark Volume and biomass: BVtrunkREF , DBHREF , HtREF CHM-only Volume: BVcrownCHM , HtCHM , AcrownCHM Biomass: BVcrownCHM , HtCHM CHM+FW Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF Volume Biomass AdjR2 Error AdjR2 Error Benchmark 1 1% 1 8% CHM-only 0.93 15 % 0.87 30 % CHM+FW 0.95 17 % 0.91 25 %14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  22. 22. Results: Estimation models q q 60 150 q q q 40 q 100 q q q q q 20 Estimation error (%) Estimation error (%) q q q q 50 q q 0 q q q q q q q −20 q q 0 q −40 −50 q q −60 q −100 q q Benchmark CHM CHM+FW Benchmark CHM CHM+FW Volume estimation Biomass2 estimation15/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  23. 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 level16/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  24. 24. Thank you for your attention17/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals
  25. 25. Exploiting fullwaveform lidar signals to estimate timber volume and above-ground biomass of individual trees Tristan Allouis1 , Sylvie Durrieu1 Cédric Véga2 Pierre Couteron3 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, Canada18/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

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