Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field data.

Index terms:
forest measurement; laser scanners; LiDAR

Authorship

Diogo Nepomuceno Cosenza

Universidade de Lisboa, Instituto Superior de Agronomia, Centro de Estudos Florestais, Tapada da Ajuda, CP 1349-017 Lisboa, Portugal. E-mail: dncosenza@gmail.com, paulasoares@isa.ulisboa.pt, magatome@isa.ulisboa.ptUniversidade de LisboaPortugalLisboa, PortugalUniversidade de Lisboa, Instituto Superior de Agronomia, Centro de Estudos Florestais, Tapada da Ajuda, CP 1349-017 Lisboa, Portugal. E-mail: dncosenza@gmail.com, paulasoares@isa.ulisboa.pt, magatome@isa.ulisboa.pt

Vicente Paulo Soares

Universidade Federal de Viçosa, Departamento de Engenharia Florestal, Avenida Purdue, s/no, Campus Universitário, Edifício Reinaldo de Jesus Araújo, CEP 36570-900 Viçosa, MG, Brazil. E-mail: vicentepsoares@gmail.com, hgleite@ufv.br, gleriani@ufv.br, chamaral@ufv.brUniversidade Federal de ViçosaBrazilViçosa, MG, BrazilUniversidade Federal de Viçosa, Departamento de Engenharia Florestal, Avenida Purdue, s/no, Campus Universitário, Edifício Reinaldo de Jesus Araújo, CEP 36570-900 Viçosa, MG, Brazil. E-mail: vicentepsoares@gmail.com, hgleite@ufv.br, gleriani@ufv.br, chamaral@ufv.br

Helio Garcia Leite

José Marinaldo Gleriani

Cibele Hummel do Amaral

Joel Gripp Júnior

Universidade Federal de Viçosa, Departamento de Engenharia Civil, Avenida P.H. Rolfs, s/no, Campus Universitário, CEP 36570-900 Viçosa, MG, Brazil. E-mail: jgripp@ufv.brUniversidade Federal de ViçosaBrazilViçosa, MG, BrazilUniversidade Federal de Viçosa, Departamento de Engenharia Civil, Avenida P.H. Rolfs, s/no, Campus Universitário, CEP 36570-900 Viçosa, MG, Brazil. E-mail: jgripp@ufv.br

Antonilmar Araújo Lopes da Silva

Celulose Nipo-Brasileira S/A, Rodovia BR 381, Km 172, Distrito de Perpétuo Socorro, CEP 35196-000 Belo Oriente, MG, Brazil. E-mail: antonilmar@gmail.comCelulose Nipo-BrasileiraBrazilBelo Oriente, MG, BrazilCelulose Nipo-Brasileira S/A, Rodovia BR 381, Km 172, Distrito de Perpétuo Socorro, CEP 35196-000 Belo Oriente, MG, Brazil. E-mail: antonilmar@gmail.com

Paula Soares

Margarida Tomé

SCIMAGO INSTITUTIONS RANKINGS

Figures | Tables | Formulas

Figures (4)
Tables (4)
Formulas (7)

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Figure 1.
Digital model image of the normalized forest before (A) and after (B) the application of the smoothing filter and after the local maxima filter (C).

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Figure 2.
Histograms and residual dispersion and graphs of the relationship between observed and estimated values for tree diameter at breast height (dbh), height, and volume.

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Figure 3.
Relationship between the age of the area and the error related to number of trees (A) and wood stock (B) for the high forest and coppice systems, as well as relationship between the number of trees (C) and wood stock (D) estimated from field sampling and airborne laser scanning (ALS).

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Figure 4.
Distribution of tree heights estimated from field sampling and airborne laser scanning (ALS) in high forest and coppice areas.

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Table 1.
Number of sampling plots per forest area.

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Table 2.
Fitted equations and respective precision statistics.

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Table 3.
Number of trees (NT) and wood stock (WS) estimated from field sampling (FS) and airborne laser scanning (ALS).

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Table 4.
Results of the Kolmogorov-Smirnov test for two samples⁽¹⁾.

Thumbnail

Figure 1. Digital model image of the normalized forest before (A) and after (B) the application of the smoothing filter and after the local maxima filter (C).

Figure 2. Histograms and residual dispersion and graphs of the relationship between observed and estimated values for tree diameter at breast height (dbh), height, and volume.

Figure 3. Relationship between the age of the area and the error related to number of trees (A) and wood stock (B) for the high forest and coppice systems, as well as relationship between the number of trees (C) and wood stock (D) estimated from field sampling and airborne laser scanning (ALS).

Figure 4. Distribution of tree heights estimated from field sampling and airborne laser scanning (ALS) in high forest and coppice areas.

Table 1. Number of sampling plots per forest area.

Area	Age (years)	Area (ha)	Number of plots	Average plot area (m²)
P7	7	477	24	292
P6	6	105	5	303
P5	5	124	6	305
P4	4	132	7	306
C7	7	318	31	302
C6	6	195	20	293
C5	5	254	27	292
C4	4	76	8	293
General		1,681	128	298

Table 2. Fitted equations and respective precision statistics.

Area	n⁽¹⁾	Diametric equations⁽²⁾				Hypsometric equations⁽³⁾				Volumetric equations⁽⁴⁾
Area	n⁽¹⁾	b₀	b₁	Bias	RMSE (%)	b₀	b₁	Bias	RMSE (%)	b₀	b₁	b₂	Bias	RMSE (%)
P4	25	1.47	0.06	0.12	9	0.65	7.39	-0.02	8	2.36x10^-6	1.11	2.69	<0.01	10
P5	79	1.14	0.07	0.07	10	1.31	7.76	0.02	4	2.09x10^-5	2.03	1.03	<0.01	0
P6	49	1.42	0.05	0.16	10	1.08	9.19	0.01	6	1.18x10^-5	0.67	2.52	<0.01	18
P7	88	1.90	0.04	0.00	16	-1.35	9.62	-0.04	12	3.74x10^-6	3.02	0.75	0.01	17
C4	21	1.49	0.06	0.10	9	0.26	7.75	-0.03	7	2.47x10^-4	2.07	0.33	<0.01	11
C5	18	1.81	0.04	0.05	10	-2.00	8.95	-0.04	9	4.64x10^-5	1.78	1.11	<0.01	6
C6	13	1.80	0.04	0.08	11	-1.34	9.23	-0.05	10	2.83x10^-5	2.07	1.01	<0.01	16
C7	62	1.83	0.04	0.04	13	-4.02	10.26	-0.06	10	3.38x10^-5	2.12	0.91	<0.01	9

⁽¹⁾Number of measured trees. ⁽²⁾dbh=eb0+b1Ht⁽³⁾H⌢t=b0+b1ln(dbh).⁽⁴⁾V⌢=b0dapb1Htb2. dbh, diameter at breast height, in centimeters; Ht, tree height, in meters; V, tree volume in cubic meters; b₀ and b₁, model parameters; and RMSE, root mean square error.

Table 3. Number of trees (NT) and wood stock (WS) estimated from field sampling (FS) and airborne laser scanning (ALS).

Area	NT from FS	CI⁽¹⁾ (%)	NT from ALS	Error for NT		WS from FS inventory (m³)	CI (%)	WS from ALS (m³)	Error for WS (m³)
P4	142,010	±4	111,734	-30,276	(-21%)	18,319	±27	8,045	-10,274	(-56%)
P5	122,047	±8	111,734	-10,313	(-8%)	16,631	±3	11,103	-5,529	(-33%)
P6	113,894	±17	86,415	-27,479	(-24%)	32,901	±14	23,647	-9,254	(-28%)
P7	456,389	±4	356,658	-99,731	(-22%)	113,265	±12	98,312	-14,953	(-13%)
C4	70,933	±8	51,633	-19,300	(-27%)	12,675	±10	7,865	-4,810	(-38%)
C5	231,488	±4	186,768	-44,720	(-19%)	48,082	±8	34,744	-13,338	(-28%)
C6	178,772	±6	145,140	-33,632	(-19%)	45,772	±11	34,033	-11,739	(-26%)
C7	313,800	±4	202,716	-111,084	(-35%)	82,948	±10	62,985	-19,964	(-24%)

⁽¹⁾CI, confidence interval.

Table 4. Results of the Kolmogorov-Smirnov test for two samples⁽¹⁾.

Area	Observed D (Maximum D)	Calculated D
P4	0.16	0.05*
P5	0.09	0.11*
P6	0.49	0.10^ns
P7	0.47	0.09*
C4	0.16	0.04*
C5	0.19	0.06*
C6	0.18	0.05*
C7	0.28	0.09*

⁽¹⁾D, calculated statistic for the Kolmogorov-Smirnov test. *Significant at 5% probability. ^nsNonsignificant.

dbh = e^{b_{0} + b_{1} H}^{t}

ln (dbh) = b_{0} + b_{1} Ht

Ht = \frac{ln (dbh)}{b_{1}} - \frac{b_{0}}{b_{1}}

Ht = b_{2} ln (dbh) + b_{3}

RMSE (%) = 100 {\bar{Y}}^{- 1} . \sqrt{n^{- 1} . \sum_{i = 1}^{n} {(Y_{i} - {\overset{⌢}{Y}}_{i})}^{2}}

bias = n^{- 1} . \sum_{i = 1}^{n} (Y_{i} - {\overset{⌢}{Y}}_{i})

[\begin{array}{l} 1 2 1 \\ 2 4 2 \\ 1 2 1 \end{array}] \times \frac{1}{16}

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Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

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[TFN1] ⁽¹⁾Number of measured trees. ⁽²⁾dbh=eb0+b1Ht⁽³⁾H⌢t=b0+b1ln(dbh).⁽⁴⁾V⌢=b0dapb1Htb2. dbh, diameter at breast height, in centimeters; Ht, tree height, in meters; V, tree volume in cubic meters; b₀ and b₁, model parameters; and RMSE, root mean square error.