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Understorey identification through the generation of canopy base - - PowerPoint PPT Presentation

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Understorey identification through the generation of canopy base height models based on LiDAR data Luka Jurjevic Croatian Forest Research Institute, Croatia 1. Introduction Forest three-dimensional (3D) distribution is transcendent in

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Understorey identification through the generation of canopy base height models based

  • n LiDAR data

Luka Jurjevic Croatian Forest Research Institute, Croatia

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Forest three-dimensional (3D) distribution is transcendent in ecosystem management Knowledge understorey information is a challenge Most of the research focus on the tree layer or shrubs Only a few studies evaluate understorey structural variables

Depiction of possible understory components that airborne LiDAR pulses can intersect (Wing et al. 2012)

  • 1. Introduction
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These researches considered an initial threshold of shrub heights and evaluated LiDAR metrics in a defined range This approach does not work in complex forest environment Our goal is to find a methodology to define the limit between overstorey and understorey in complex forest environment.

An ideal irregular forest (Roth, 1935)

  • 1. Introduction
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  • 2. Study area

Study area covers 411.30 ha of pedunculate

  • ak forest.

Study area terrain is flat (elevations in range 105m to 118m a.s.l). Presence of other species e.g. common hornbeam, black alder and narrow-leaved ash. Two understorey species: common hazel and common hawthorn

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Field data

  • 2. Study area

Data collection in 2017.g.

  • Q. Robur Stands

165 measured / 112 LiDAR coverage circular plots r = 8 m or 15 m Measured and recorded: h (50% of the trees), crown base height (cbh), species, dbh.

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LiDAR data

  • 2. Study area

Optech ALTM Gemini 167 laser scanner mounted on the Pilatus P6. Acquisition took time between 29 June and 25 August 2016. Density per m2 All returns – 13,64 points·m2 Last return – 9,71 points·m2 7% of points were classified as “ground”.

h = 112-144 m a.s.l.

LiDAR

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LiDAR data Field data hcb model Individual Tree Identification Thinning cloud HCB value Tree variables Metrics per plot

Optimal heightbreak

HCB model

Understory evaluation Filtering cloud

  • 3. Methodology
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  • 3. Methodology

Hypothetical heightbreak from hcb model Hypothetical heightbreak from HCB model

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  • One model per species
  • Logistic function:

Where: K = number of parameters to be estimated bi = parameter estimates xi = independent variables

SPECIES TREES Alnus glutinosa 284 Carpinus betulus 876 Fraxinus angustifolia 237 Quercus robur 1549 Tilia sp. 140 Other 70

hcb= H 1+EXP ∑ bixi K i=1

  • 3. Methodology

Individual Tree Identification Height to Canopy Base model Height to crown base model

Walters and Hann (1986)

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  • Thinning cloud with

Buján et al. (2019) algorithm to 5 pulse per square meter

  • LiDAR metrics with

FUSION v3.80 program (McGaughey 2018)

Original plot with 14 returns/m2 5 returns/m2

  • 3. Methodology

Individual Tree Identification Height to Canopy Base model Height to crown base model

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  • 3. Methodology
  • The variability of the hcb values measured in

the field was studied

  • Several metrics were calculated for each plot

(minimum, maximum, mean, median, mode, percentiles)

  • These were the dependent variables of the

HCB models

  • Independent variables were LiDAR metrics
  • Regression Models were applied

Individual Tree Identification Height to Canopy Base model Height to crown base model

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  • 3617 trees measured in the field, classified according to their visibility (No visible,

Probably visible, Visible and Unclassified)

  • Trees identification with Li et al. (2012) algorithm
  • In the identification snag, tree top broken, cut and fallen trees were removed
  • 3. Methodology

Individual Tree Identification Height to Canopy Base model Height to crown base model

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  • 4. Results

hcb MODEL RMSE RMSE (%) BIAS BIAS (%) R2 (%) EFFICIENCY (%) Alnus glutinosa 1.88 12.75

  • 0.01163

0.08 83.30 69.05 Carpinus betulus 2.56 35.17 0.01655 0.23 65.82 43.11 Fraxinus angustifolia 2.73 18.69

  • 0.05919

0.41 79.40 62.53 Quercus robur 2.76 18.05

  • 0.00743

0.05 78.37 61.39 Tilia sp. 2.11 24.03

  • 0.05307

0.60 88.13 77.06 Other 1.93 22.93

  • 0.01757

0.21 90.28 80.67

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

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  • 4. Results

mean min max sd hcb 14.77 0.30 21.70 3.38 DG 26.58 17.00 58.20 8.15 dbh 23.58 10.25 42.70 7.24 dhratio 1.12 0.63 1.87 0.27 balmod_g 0.67 0.00 1.00 0.22 hcb MODEL Equation

  • A. glutinosa

hcb = H / (1 + exp(-1.71 + 0.0406*DG - 0.0417*dbh + 1.154*dhratio - 0.837*balmod_g))

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

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  • 4. Results

mean min max sd hcb 7.29 0.50 20.80 3.40 DM 26.37 15.70 49.50 5.53 G 33.98 15.40 67.00 9.80 dhratio 1.09 0.54 3.18 0.28 bar 0.12 0.01 0.58 0.10 hcb MODEL Equation

  • C. betulus

hcb = H / (1 + exp(-1.095 + 0.0312*DM - 0.0118*G + 1.308*dhratio - 2.114*bar))

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

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  • 4. Results

mean min max sd hcb 14.59 2.11 25.80 4.45 bar 0.30 0.03 2.29 0.30 rbad 0.38 0.07 18.29 1.32 hcb MODEL Equation

  • F. angustifolia

hcb = H / (1 + exp(-0.593 + 0.708*bar - 0.0623*rbad))

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

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  • 4. Results

mean min max sd hcb 15.30 2.00 32.10 4.44 HL 26.14 17.40 37.90 4.47 N 546.12 55.70 1691.00 279.93 dbh 38.86 10.15 120.10 17.70 bar 0.46 0.02 3.65 0.45 hcb MODEL Equation

  • Q. robur

hcb = H / (1 + exp(1.692 - 0.0918*HL - 0.000741*N + 0.0284*dbh - 0.5499*bar))

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

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  • 4. Results

mean min max sd hcb 8.78 1.50 19.90 4.41 DM 26.33 20.80 31.70 3.10 HL 25.81 23.10 32.80 1.79 AGE 85.57 63.00 163.00 17.56 dhratio 1.17 0.59 3.27 0.31 hcb MODEL Equation Tilia sp. hcb = H / (1 + exp(-2.786 - 0.101*DM + 0.223*HL - 0.009102*AGE + 0.497*dhratio))

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

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  • 4. Results

mean min max sd hcb 8.42 1.80 17.30 4.39 IH 17.89 12.30 34.15 4.27 AGE 98.86 33.00 163.00 47.94 dbh 18.49 10.00 38.30 7.33 dhratio 1.16 0.65 2.15 0.30 hcb MODEL Equation Other hcb = H / (1 + exp(-1.207 + 0.0695*IH - 0.00379*AGE - 0.0398*dbh + 0.957*dhratio))

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

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  • 4. Results

PRELIMINARY RESULTS PRELIMINARY RESULTS

Model RMSE RMSE (%) BIAS BIAS (%) R2adj (%) EFFIENCY (%) HCB_MEAN 1.05 9.23 0.00 0.02 46.01 47.35 HCB_MIN 1.43 38.03

  • 0.06

1.68 14.46 16.65 HCB_MEDIAN 1.68 15.07

  • 0.01

0.07 38.89 39.11 HCB_P05 1.48 29.99

  • 0.03

0.63 14.49 15.95 HCB_P10 1.31 23.04

  • 0.02

0.29 16.07 18.90 HCB_P20 1.02 14.93 0.00 0.03 22.46 24.42 HCB_P25 1.05 14.17 0.00 0.00 28.08 34.50 HCB_P30 1.30 16.21 0.00 0.00 27.34 33.34 HCB_P40 1.73 17.34 0.00 0.02 44.47 46.55 HCB_P50 1.65 14.35

  • 0.01

0.11 55.52 54.71

Individual Tree Identification Height to Canopy Base model Height to crown base model

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…………………………………..

  • 4. Results

Model Equation HCB_MEAN 3.8728*exp(2.2135*LH_L_CV)*exp(-0.7908*LH_L_SK)*exp(0.0239*LH_P40) HCB_P50 249.7569*LH_KUR^-0.1629*LH_P30^-0.355*TR_LH_L3^-1.6901

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

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  • 4. Results

20 30 40 50 60 70 80 5 10 15 20 25

Identification success (%) Pulse per square meter

Individual Tree Identification

Individual Tree Identification Height to Canopy Base model Height to crown base model PRELIMINARY RESULTS PRELIMINARY RESULTS

Not identified Wrongly identified Correctly identified Not Visible 36.23 26.23 33.13 Probably 35.86 13.47 49.83 Visible 18.57 5.64 74.15 Unclassified 36.81 20.39 40.43 Total 30.16 16.93 50.14

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  • 4. Results

PRELIMINARY RESULTS PRELIMINARY RESULTS

Legend (m)

´

Digital Canopy Model, DCM Height to Canopy Base, HCB

´

Legend (m)

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  • 5. Discussion
  • Goodness of fit statistics show that hcb models work correctly
  • Further variables should be tested for HCB modeling
  • The ITI method does not yield the expected results, high LiDAR return densities

introduce noise in the results

  • Using the species as covariates instead of taking a model by species could be

tested

  • Building a crown diameter model to weight the calculation of HCB based on the

surface of each crown would improve the predictive capacity of the model

  • It is mandatory to contrast the processing of the point cloud under the predicted
  • ptimum heightbreak obtained with understorey field data
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  • 6. Conclusions

 It would be essential:

  • to validate with the field work
  • to identify a more correct method to delineate

crowns in irregular forests

  • to test different densities of LiDAR returns to check

the accuracy increase in the estimations  The stand method works better than the individual tree method  It is feasible to assess the structural variables of understorey by filtering the point cloud

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Collaborators

Saray Martín-García1 Ivan Balenović2 Luka Jurjević2 Iñigo Lizarralde1,4 Sandra Bujan3 Rafael Alonso-Ponce1,4

1 föra forest technologies SLL 2 Croatian Forest Research Institute, Division for

Forest Management and Economics

3 Land Laboratory, Department of Agroforestry

Engineering University of Santiago de Compostela

4 Institute for Research in Sustainable Forest

Management (iuFOR)

Thank you!

FUNDING This research has ben supported by :

  • the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776045; project “Operational sustainable forestry

with satellite-based remote sensing (My Suistanable Forest)“.

  • the Croatian Science Foundation under the project IP-2016-06-7686 “Retrieval of Information from Different Optical 3D Remote Sensing Sources for

Use in Forest Inventory (3D-FORINVENT)”, The work of doctoral student Luka Jurjević has been fully supported by the “Young researchers' career development project – training of doctoral students”

  • f the Croatian Science Foundation funded by the European Union from the European Social Fund.

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