Use of Remote Sensing in Estimating Density of Forest Settlements / Uso do Sensoriamento Remoto na Estimativa da Densidade de Povoamentos Florestais
Abstract
This work aimed to test the efficiency of the eCognition software in high resolution images to perform the crown counting of Pinus elliotti trees. The study area is located in the municipality of Fazenda Rio Grande-PR, Brazil, with a pine plantation within the Experimental Farm Gralha Azul (FEGA). The area was flown over with a drone, model Phantom 4, to obtain high resolution images, later the software Agisoft PhotoScan was used for the generation of orthomosaic. And finally, with the eCognition software, via object-oriented classification, the selection of Pinus crown samples was carried out to define a model, which better delineated them. Thus, aiming at the detection and counting of individuals, the Template matching tool was used in the total area of the image, computing 530 trees. In the field survey, 839 Pinus elliotti trees were measured. Thus, when comparing the number of trees detected by the software and the survey in the field, it was observed that the software measured only 63% of the real population in the plot. This difference between the results can be justified, due to the proximity between the crowns, which made it difficult to differentiate between individuals, since the crowns ended up merging in the images due to the occurrence of wind during the flight, which explains the crown overlap, hindering the detection and counting of individuals so that it was possible to estimate the density of the stand. It is concluded that the next studies can present better results, applying the methodology in different areas, climatic situations and planting stages, and that from this study further research can be carried out increasing the methodology and involving other species, mainly with regard to native species.References
AGISOFT, L. L. C. Agisoft Photoscan Pro. 2014. Disponível em: < https://www.researchgate.net/profile/Daniel_Silva19/publication/316527115_QUALIDADE_DE_ORTOMOSAICOS_DE_IMAGENS_DE_V
ANT_PROCESSADOS_COM_OS_SOFTWARES_APS_PIX4D_E_PHOTOSCAN/links/590240c94585156502a2fce3/QUALIDADE-DE-ORTOMOSAICOS-DE-IMAGENS-DE-VANT-PROCESSADOS-COM-OS-SOFTWARES-APS-PIX4D-E-PHOTOSCAN.pdf> Acesso em: 25 de mar. de 2019.
ANTUNES, A.F.B. 2003. Object oriented analysis and semantic network for high resolution image classification. Boletim de Ciências Geodésicas. Volume 9, n 2.
ANTUNES, A. F. B e STURM, U. 2005. Segmentação orientada a objeto aplicado ao monitoramento de ocupações irregulares em áreas de proteção ambiental. Anais XII Simpósio Brasileiro de Sensoriamento Remoto, Goiânia, Brasil, 16-21 abril 2005, INPE, p. 2019-2026.
BERNI, J. et al. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an Unmanned Aerial Vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722–738.
BOLFE, É. L. Levantamento e monitoramento dos recursos florestais dos tabuleiros costeiros do nordeste do brasil. In: I Simpósio Regional de Geoprocessamento e Sensoriamento Remoto, 2002, Aracaju - SE.
BOSAK, K. Secrets of UAV photomapping. 2009. Disponível em: <http://ww38.aerialrobotics.eu/pteryx/pteryx-mapping-secrets.pdf>. Acesso em: 03 de abr. de 2019.
BOYD, D. S.; DANSON, F. M. Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography, v. 29, n.1, p. 1-26, 2005.
BRAUN-BLANQUET J. Fitosociologia: bases para el estudio de las comunidades vegetales. 3. ed. Madrid: Aum. Blume; 1979.
CAUGHLEY, G. J. & SINCLAIR, A. R. E. 1994. Wildlife Ecology and Management. Blackwell Scientific Publications, Oxford.
CASSEMIRO, G, H, M.; PINTO, H, B. Composição e processamento de imagens aéreas de alta-resolução obtidas com Drone. Trabalho de Conclusão de Curso, Universidade de Brasília, Brasília, 2014. Disponível em: < https://fga.unb.br/articles/0000/7686/TCC2_GuilhermeCassemiro_090115465_e_HugoBorges_090116461_v2.pdf> Acesso em: 25 mar. 2019.
CLARK, M. L.; CLARK, D. B.; ROBERTS, D. A. Small footprint lidar estimation of subcanopy elevation and tree height in a tropical rain forest landscape. Remote Sensing of Environment, v. 91, n. 1, p. 68-89, 2004.
DEFINIENS, 2002. eCognition version 2.0. Trimble Germany GmbH, Arnulfstrasse 126, 80636 Munich, Alemanha.
DEFINIENS, 2009. eCognition Developer 8. Trimble Germany GmbH, Arnulfstrasse 126, 80636 Munich, Alemanha
DUCATI, et al. Inventário Florestal do Rio Grande do Sul. Porto Alegre: CEPSRM/UFRGS, 1999. (Série Relatórios Técnicos, 4/1999).
DUNFORD, R. et al. Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest. Int. J. Remote Sens. 2009, 30, 4915–4935.
FERREIRA, L. G.; FERREIRA, N. C.; FERREIRA, M. E. Sensoriamento remoto da vegetação: evolução e estado-da-arte. Acta Scientiarum. Biological Sciences, v. 30, n. 4, 2008. Disponível em: < http://www.redalyc.org/html/1871/187116040006/> Acesso em: Acesso em: 25 mar. 2019.
FREITAS, W. K; MAGALHÃES, L. M. S. Métodos e Parâmetros para Estudo da Vegetação com Ênfase no Estrato Arbóreo. Floresta e Ambiente, [s.l.], v. 19, n. 4, p.520-540, out. 2012. FapUNIFESP (SciELO). http://dx.doi.org/10.4322/floram.2012.054.
GAIOTTO, D.F.; ACRA, L. A. Levantamento qualitativo de epífitos da Fazenda Gralha Azul–Fazenda Rio Grande–Paraná. Estudos de Biologia, v. 27, n. 60, 2005 Disponível em: <https://periodicos.pucpr.br/index.php/estudosdebiologia/article/view/21926> Acesso em: 09 abr. 2019.
GONZÁLEZ-FERREIRO, E.; DIÉGUEZ-ARANDA, U.; BARREIRO-FERNÁNDEZ, L.; BUJÁN, S.; BARBOSA, M.; SUÁREZ, J.C.; BYE, I.J.; MIRANDA, D. (2013) A mixed pixel- and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations Int. J. Remote Sens., 34, pp. 7671-7690.
HYYPPÄ, J.; HYYPPÄ, H.; INKINEN, M.; ENGDAHL, M.; LINKO, S.; ZHU, Y. (2000).” Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes”. Canadian journal of remote sensing, v. 128, p. 109-120.
HUSCH, B.; MILLER, C.I; KERSHAW, J. Forest mensuration. 4. ed. New Jersey: John Willey e Sons, Inc, 2003. 443 p.
KLEINA, N. Review: drone DJI Phantom 4. 2016. Disponível em: <https://www.tecmundo.com.br/drones/105226-analise-drone-dji-phantom-4.htm>. Acesso em: 15 mai. 2019.
LAMPRECHT, H. Silvicultura nos trópicos: ecossistemas florestais e respectivas espécies arbóreas – possibilidades e métodos de aproveitamento sustentado. Rossdorf: República Federal da Alemanha: Dt. Ges. Für Techn. Zusammenarbeit; 1990.
LEE, H.; SLATTON, K.C.; ROTH, B.E.; CROPPER, W.P. Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests Int. J. Remote Sens., 31 (2010), pp. 117-139.
MARTINS F.R. Estrutura de uma floresta mesófila. Campinas: Ed. UNICAMP; 1991.
McALLISTER, D. M.; VALEO, C. A robust new method for the remote sensing of LAI in montane and boreal forests. International Journal of Remote Sensing, v. 28, n. 8, p.1891-1905, 2007.
MENESES, P.R.; ALMEIDA, T. (Org.). Introdução ao processamento de imagens de sensoriamento remoto. Brasília, 2012. Disponível em: < https://s3.amazonaws.com/academia.edu.documents/33333156/M
ENESES___ALMEIDA_2012_INTRO_AO_PROCESSAMENTO_DE_IMAGEM.pdf> Acesso em: 01 abr. 2019.
MOREIRA, M. Identificação de reflorestamentos por meio da análise quantitativa de imagens orbitais Landsat.1984. 65 f. Dissertação (Mestrado) – Universidade Federal de Viçosa, Viçosa, 1984.
MUELLER-DOMBOIS, D.; ELLENBERG, H. Aims and methods of vegetation ecology. New York: John Wiley & Sons; 1974.
OLIVEIRA L.T., CARVALHO L.M.T., FERREIRA M.Z., OLIVEIRA T.C.A., ACERBI JUNIOR F.W. (2012). Application of LIDAR to forest inventory for tree count in stands of Eucaliptus sp. CERNE (UFLA), v. 18, p. 175/ 1-184.
OZDEMIR, I. Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpeded Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing, v. 29, n. 19, p. 5643-5655, 2008.
PARANHOS FILHO, A. C. 2008. Sensoriamento remoto ambiental aplicado: introdução às geotecnologias: material didático – Campo Grande/ MS: Ed UFMS; p 103 -140.
PÉLLICO NETO, S.; BRENA, D. A. Inventário Florestal. Curitiba: UFPR/UFSM, 1993.
READ, J. M.; CLARK, D. B.; VENTICINQUE, E. M.; MOREIRA, M. P. Application of merged 1-m and 4-m resolution satellite data to research and management in tropical forests. Journal of Applied Ecology, v. 40, n. 3, p. 592-600, 2003.
SILVA, A. A.; OLIVEIRA, A. P. G.; PARANHOS FILHO, A. C. Uso do eCognition 8.9 Para Classificação de Imagem de Alta Resolução. Mossoró: UERN, 2016. 106 p. Disponível em: <http://bit.ly/2gxQPoi>. Acesso em: 27 mar. 2019.
SILVA NETO, Manoel. Modelos 3D: Como são gerados através dos drones? 2017. Disponível em: <http://blog.droneng.com.br/modelos-3d-como-sao-gerados/>. Acesso em: 01 out. 2019.
SOLOMON, D.; LEHMANN, J.; MAMO, T.; FRITZSCHE, F.; ZECH, W. Phosphorus forms and dynamics as influenced by land use changes in the sub-humid Ethiopian highlands. Geoderma, v. 105, n. 1–2, p. 21–48, 2002.
SONG, C.; DICKINSON, M. B. Extracting forest canopy structure from spatial information of high-resolution optical imagery: tree crown size versus leaf area index. International Journal of Remote Sensing, v. 29, n. 19, p. 5605-5622, 2008.
SOUZA, C. C. et al. Técnicas de Sensoriamento Remoto Como Subsídios aos Estudos de Florestas Implantadas no Brasil – Uma Revisão Bibliográfica. Revista Ciência Florestal, Santa Maria, v. 17, n. 4, p.409-417, out. 2007.
TOMMASELLI, A. M. G. Fotogrametria Básica. [S.l.]: UNESP - Universidade Estadual Paulista, 2009.
TRIMBLE. 2014. Trimble Geospatial Image. eCognition Developer. Disponível em:
<http://www.ecognition.com/products/ecognition-developer>. Acesso em: 27 de mar. de 2019
WULDER, M; NIEMANN, K. O.; GOODENOUGH, D. G. Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sensing of Environment, New York, v. 73, n. 1, p. 103-114, 2000.
