INFLUENCE OF GROUND FILTER WINDOW IN LIDAR DATA UNDER TWO TYPES OF

FOREST COVER

INFLUÊNCIA DA JANELA DO FILTRO DE TERRENO SOB DUAS CONDIÇÕES DE DENSIDADE DE COPA

André Gracioso Peres da Silva1, Eric Bastos Gorgens1, Luiz Carlos Estraviz Rodriguez1, Carlos Alberto Silva1, Clayton Alcarde Alvares2, Otávio Camargo Campoe2, José Luiz Stape3

1 Universidade de São Paulo – USP, Escola Superior de Agricultura “Luiz de Queiroz”

2 Instituto de Pesquisas e Estudos Florestais – IPEF 3 North Carolina State University – NCSU

.

16th, October, 2012

Curitiba, Paraná

X- Seminário de Atualização em Sensoriamento

Remoto e Sistemas de Informações Geográficas

Aplicados à Engenharia Florestal

Introduction

1- Active sensors revolution in remote sensing technology, over the last 10 years.

Direct measuring of three-dimensional structures (REUTEBUCH et al., 2005);

2- LIDAR advantages are: more technically mature and widely available (REUTEBUCH et al.,

2005);

3- Airborne Laser Scanning (ALS) equipaments (McGAUGHEY, 2012):

•Laser emitter-receiver scanning unit ;

•Inertial measurement unit (IMU);

•GPS on the aircraft and on the ground;

•Computer to process and store data;

Source: Reutebuch et al. (2005).

Introduction

4- The time between the pulse emission and its return to the aircraft is used to associate a “z”

coordinate to an object as a consequence 3D cloud points with all returns containing a “x”,

“y” and “z” position (RODRIGUEZ et al., 2010);

5- Possible forest products derived from LiDAR: biomass, canopy structure parameters, digital

terrain model , other (RODRIGUEZ et al., 2010);

Introduction

6- Digital Terrain Model (DTM) main error types according to Hodgson e Bresnahan (2004);

a) Elevation error caused by the sensor system measurement;

b) Horizontal error caused by the sensor system measurement;

c) Labeling process error, when returns associated to the ground are split up from the ones

which belong to the vegetation;

Introduction

7- Kraus and Pfeifer (1998) filtering algorithm (BRIESE, PFEIFER and NORBERT, 2001; KRAUS

and PFEIFER, 2001; KRAUS, KARL and RIEGER, 1999; MENG et al., 2010). Interpolation filter:

pi = weight attributed to a point i;

v i = deviation between the point i and the reference surface;

g = threshold, which a point i receive maximum weight (= 1);

w = tolerance relative to the threshold for calculation of proportional weights;

a and b = parameters of the sigmoid function that calculates proportional weights for points

between the threshold and the threshold + tolerance.

Introduction

8- Kraus and Pfeifer (1998) filtering algorithm – example.

g -2

w 2.5

a 1

b 4

Height (m) Reference surface (m) Deviation (vi) Weight Height (m) Reference surface (m) Deviation (vi) Weight Height (m) Reference surface (m) Deviation (vi) Weight

0.50 5.37 -4.87 1.000 0.50 1.07 -0.57 0.191 0.10 0.09 0.413 0.029

5.50 5.37 0.13 0.046 0.25 1.07 4.43 0.000

2.00 5.37 -3.37 1.000 2.00 1.07 0.93 0.000

4.00 5.37 -1.37 0.864 3.46 1.07 2.93 0.000

25.00 5.37 19.63 0.000

10.00 5.37 4.63 0.000

0.30 5.37 -5.07 1.000 0.30 1.07 -0.77 0.302 0.09 0.09 0.213 0.040

0.40 5.37 -4.97 1.000 0.40 1.07 -0.67 0.240 0.10 0.09 0.313 0.034

1.00 5.37 -4.37 1.000 1.00 1.07 -0.07 0.067 0.07 0.09 0.913 0.000

5.00 5.37 -0.37 0.124 0.62 1.07 3.93 0.000

Height (m) Reference surface (m) Deviation (vi) Weight Height (m) Reference surface (m) Deviation (vi) Weight Height (m) Reference surface (m) Deviation (vi) Weight

0.003 0.003 0.497 0.025 0.00007 0.000097 0.4999 0.025 0.0000017 0.000003 0.499997 0.025

0.004 0.003 0.297 0.035 0.00013 0.000097 0.2999 0.035 0.0000043 0.000097 0.2999 0.035

0.003 0.003 0.397 0.029 0.00010 0.000097 0.3999 0.029 0.0000028 0.000097 0.3999 0.029

Step 5 Step 6….Step n

Step 1 Step 2 Step 3

Step 4

Objective

To assess the influence of the ground filter window size on the DTM

generation under two types of forest cover: plantation and natural forest.

Avaliar a influência da dimensão da janela de filtro de pontos do terreno em dois tipos de

cobertura florestal: floresta estacional semidecidual e plantios florestais de eucalipto.

Material and Method

1- Experimental area:

•Name: Cooperative program of IPEF: EUCLFUX (http://www.ipef.br/eucflux/)

•Location = Itatinga-SP, Brazil; Area = 19.89 Km²; Terrain: flat to soft wavy (up to 8% of inclination);

•Plantation: Eucalyptus grandis (descendant from seeds; Coff’s Harbour) with 6.5 years old

(planted in 2002) and tree spacing of 6m² (3,75 x 1,60 m);

•Natural Forest: Floresta Estacional Semidecidual (Atlantic Forest);

Source: Google images

Material and Method

Source: Google Earth.

Material and Method

Material and Method

2- LiDAR flight (parameters):

•Enterprise: Esteio Engenharia e Aerolevantamentos S.A.

•Date: April, 2009;

•Laser Scanner: LEICA ALS60;

•Flight Height: 1600 m of altitude (~ 850 m regarding the gorund);

•Flight speed: 140 Km.h-1;

•Scan angle: 15 degrees;

•Scan frequency: 74.1 Hz;

•Pulse frequency: 136.3 Khz

•Swath width: 6 strips of 447 m each;

•Swath overlap: 30%

•Returns density: 4/m²;

•Horizontal datum: SAD 69; Vertical datum: Imbituba-SC.

Material and Method

3- Plots:

•3 square plots with size of 2000 m² for Eucalyptus grandis;

•3 square plots with size of 2000 m² for natural forest;

•4- Software:

•FUSION 3.21 – U.S. Forest Service (McGAUGHEY, 2012) Free Software;

•5- Ground Filter Window Size:

•According to Kraus and Pfeifer’s algorithm, with the following parameters:

g= -2; w = 2.5; a = 1; b = 4 (suggested standard)

Treatments:

•Window size (J) with 1 meter of intervals, ranging from 1 x 1 meter to 10 x 10 meters;

•Ground Filter Code: FOR %%J IN (1,2,3,4,5,6,7,8,9,10) DO CALL GroundFilter

cgf_eucflux4_w%%J.las %%W EUCFLUX_4PTS.las.

Material and Method

5- DTM generation:

•Command: GridSurfaceCreate (raster image);

•Pixel: 1 x 1 meter;

•No usage of smoothing switches (to isolate the ground filter window size effect on the DTM

generation);

6- Counting of peaks on DTM:

•Command: CanopyMaxima (algorithm which identifies local maxima);

•Fixed window evaluation size: 2 x 2 meters.

Results and Discussion

Results and Discussion

Results and Discussion

Results and Discussion

•Umbrella effect: higher forest cover densities decrease the chance of pulses to be reflected on

the ground. Forest returns are labeled as terrain, and for this reason, they cause noise on the

Digital Terrain Model.

•Similar result was observed in recent researche with forest cover density gradient

(HODGSON e BRESNAHAN, 2004);

Conclusions

1- The type of vegetation influences the DTM quality, when it is considered a filter to classify

ground returns;

2- The quality of a ground filter can be evaluated by counting the number of peaks resulted on

the Digital Terrain Model;

3- The number of peaks on a DTM can be used as na indicator to define the best ground filter

window size.

References

BRIESE, C.; PFEIFER, N. Airborne Laser Scanning and Derivation of Digital Terrain Models. Fifth Conference on Optical 3-D Measurement Techniques. Vienna, Austria: [s.n.], 2001. .

HODGSON, M. E.; BRESNAHAN, P. Accuracy of airborne lidar-derived elevation: empirical assessment and error budget. Photogrammetric engineering and remote sensing, v. 70, n. 3, p. 331–340, 2004.

KRAUS, K.; PFEIFER, N. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, v. 53, p. 193-203, 1998.

KRAUS, K.; PFEIFER, N. Advanced DTM Generation from LiDAR Data. International Archives Of Photogrammetry Remote Sensing And Spatial Information Sciences, v. 34, n. 3/W4, p. 23-30, 2001.

KRAUS, K.; RIEGER, W. Processing of laser scanning data for wooded areas. Photogrammetric Week. Anais... Wien: [s.n.]. , 1999

MCGAUGHEY, R. J. FUSION / LDV : Software for LIDAR Data Analysis and Visualization. 3.10. ed. Washington DC: USDA/Forest Service, 2012. p. 170

MENG, X.; CURRIT, N.; ZHAO, K. Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues. Remote Sensing, v. 2, n. 3, p. 833-860, 22 mar 2010.

REUTEBUCH, S. E.; ANDERSEN, H. E.; MCGAUGHEY, R. J. Light detection and ranging (LIDAR): an emerging tool for multiple resource inventory. Journal of Forestry, v. 103, n. 6, p. 286–292, 2005.

RODRIGUEZ, L. C. E.; POLIZEL, J. L.; FERRAZ, S. F. B.; ZONETE, M. F.; FERREIRA, M. Z. Inventário florestal com tecnologia laser aerotransportada de plantios de Eucalyptus spp no Brasil. Ambiência, Guarapuava-PR, v. 6, p. 67 - 80, 2010.

Acknowledgements

• X SENGEF’s organizing committee;

• GET-LiDAR group http://cmq.esalq.usp.br/LIDAR/doku.php?id=equipe:team

• Professor Luiz Carlos Estraviz Rodriguez;

• Instituto de Pesquisas e Estudos Florestais (IPEF);

Obrigado!!

Laboratório de Métodos Quantitativos – LMQ, ESALQ/USP.

andregracioso@gmail.com

skype: andre.gracioso

X- Seminário de Atualização em Sensoriamento

Remoto e Sistemas de Informações Geográficas

Aplicados à Engenharia Florestal

16th, October, 2012

Curitiba, Paraná