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 Silva 1 , Eric Bastos Gorgens 1 , Luiz Carlos Estraviz Rodriguez 1 , Carlos Alberto Silva 1 , Clayton Alcarde Alvares 2 , Otávio Camargo Campoe 2 , José Luiz Stape 3 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 . 16 th , October, 2012 Curitiba, Paraná X- Seminário de Atualização em Sensoriamento Remoto e Sistemas de Informações Geográficas Aplicados à Engenharia Florestal
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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.,
•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);