Characterization of Brazilian forest types utilizing canopy height profiles derived from airborne laser scanning

Applied Vegetation Science

Volumn 19, Issue 3, 2016, Pages 518-527

  Görgens, Eric B ^a   Soares, Carlos P B ^b   Nunes, Matheus H ^a   Rodriguez, Luiz C E ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b FEDERAL UNIVERSITY OF VIÇOSA   (Brazil)

Author keywords

ALS; Foliage profile; Forest plantation; LiDAR; Light detection and ranging; Ombrophilous; Rupestrian field; Semi deciduous

Indexed keywords

EID: 84954504864     PISSN: 14022001     EISSN: 1654109X     Source Type: Journal    
DOI: 10.1111/avsc.12224     Document Type: Article

References (45)

1. Alves, R.J.V. & Kolbek, J. 2010. Can campo rupestre vegetation be floristically delimited based on vascular plant genera? Plant Ecology 207: 67–79.
2. Asner, G.P., Mascaro, J., Muller-Landau, H.C., Vieilledent, G., Vaudry, R., Rasamoelina, M., Hall, J.S. & van Breugel, M. 2012. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168: 1147–1160.
3. Aubry, P. & Debouzie, D. 2000. Geostatistical estimation variance for the spatial mean in two-dimensional systematic sampling. Ecology 81: 543–553.
4. Aubry, P. & Debouzie, D. 2001. Estimation of the mean from a two-dimensional sample: the geostatistical model-based approach. Ecology 82: 1484–1494.
5. Bailey, R.L. & Dell, T. 1973. Quantifying diameter distributions with the Weibull function. Forest Science 19: 97–104.
6. Baker, P.J. & Wilson, J.S. 2000. A quantitative technique for the identification of canopy stratification in tropical and temperate forests. Forest Ecology and Management 127: 77–86.
7. Benites, V.M., Schaefer, C.E.G., Simas, F.N. & Santos, H.G. 2007. Soils associated with rock outcrops in the Brazilian mountain ranges Mantiqueira and Espinhaço. Brazilian Journal of Botany 30: 569–577.
8. Chambers, J.Q. & Silver, W.L. 2004. Some aspects of ecophysiological and biogeochemical responses of tropical forests to atmospheric change. Philosophical Transactions of the Royal Society of London, Series B-Biological Sciences 359: 463–476.
9. Conceição, A.A. & Pirani, J.R. 2007. Diversidade em quatro áreas de campos rupestres na Chapada Diamantina, Bahia, Brasil: espécies distintas, mas riquezas similares. Rodriguésia 58: 193–206.
10. Coops, N.C., Hilker, T., Wulder, M.A., St-Onge, B., Newnham, G., Siggins, A. & Trofymow, J.A. 2007. Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees- Structure and Function 21: 295–310.
11. Coops, N.C., Varhola, A., Bater, C.W., Teti, P., Boon, S., Goodwin, N. & Weiler, M. 2009. Assessing differences in tree and stand structure following beetle infestation using lidar data. Canadian Journal of Remote Sensing 35: 497–508.
12. Da Silva, J.M.C. & Bates, J.M. 2002. Biogeographic patterns and conservation in the South American Cerrado: a tropical Savanna hotspot. BioScience 52: 225–233.
13. de Carvalho, F., de Souza, F.A., Carrenho, R., de Souza Moreira, F.M., Jesus, E.D.C. & Fernandes, G.W. 2012. The mosaic of habitats in the high-altitude Brazilian rupestrian fields is a hotspot for arbuscular mycorrhizal fungi. Applied Soil Ecology 52: 9–19.
14. Dean, T.J., Cao, Q.V., Roberts, S.D. & Evans, D.L. 2009. Measuring heights to crown base and crown median with LiDAR in a mature, even-aged loblolly pine stand. Forest Ecology and Management 257: 126–133.
15. Drake, J.B., Dubayah, R.O., Knox, R.G., Clark, D.B. & Blair, J.B. 2002. Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. Remote Sensing of Environment 81: 378–392.
16. Fox, T.R. 2000. Sustained productivity in intensively managed forest plantations. Forest Ecology and Management 138: 187–202.
17. Giulietti, A., Harley, R., Queiroz, L.D., Wanderley, M. & Pirani, J. 2000. Caracterização e endemismos nos campos rupestres da Cadeia do Espinhaço. Tópicos Atuais em Botânica. Brasília, SBB/Embrapa BR 31: 1–318.
18. He, F.L., LaFrankie, J.V. & Song, B. 2002. Scale dependence of tree abundance and richness in a tropical rain forest, Malaysia. Landscape Ecology 17: 559–568.
19. Higuchi, P., Oliveira-Filho, A.T., Bebber, D.P., Brown, N.D., Silva, A.C. & Machado, E.L.M. 2008. Spatio-temporal patterns of tree community dynamics in a tropical forest fragment in South-east Brazil. Plant Ecology 199: 125–135.
20. IBA. 2014. Anuário estatístico da indústria brasileira de árvores: ano base 2013. IBA, Brasília, DF, BR: 97p.
21. Jacobi, C.M., Carmo, F.F.D. & Vincent, R.D.C. 2008. Estudo fitossociológico de uma comunidade vegetal sobre canga como subsídio para a reabilitação de áreas mineradas no Quadrilátero Ferrífero, MG. Revista Árvore 32: 345–353.
22. Kraus, K. & Pfeifer, N. 2001. Advanced DTM generation from LIDAR data. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences 34: 23–30.
23. Lefsky, M.A. 1997. Application of Lidar remote sensing to the estimation of forest canopy and stand structure (Thesis Ph.D.). University of Virginia, VA, US.
24. Lefsky, M.A., Cohen, W.B., Acker, S.A., Parker, G.G., Spies, T.A. & Harding, D. 1999. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment 70: 339–361.
25. Lefsky, M.A., Cohen, W.B., Parker, G.G. & Harding, D.J. 2002. Lidar Remote Sensing for Ecosystem Studies Lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. BioScience 52: 19–30.
26. Leitão Filho, H.D.F. 1987. Considerações sobre a florística de florestas tropicais e subtropicais do Brasil. IPEF 35: 41–46.
27. Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M. 2003. LiDAR remote sensing of forest structure. Progress in Physical Geography 27: 88–106.
28. Medeiros, M. 2009. Caracterização fitofisionômica e estrutural de áreas de Floresta Ombrófila Densa Montana no Parque Estadual da Serra do Mar, SP, Brasil, Dissertação de Mestrado. Instituto de Botânica da Secretaria do Estado do Meio Ambiente. SP, Brasil São Paulo, BR. 85p.
29. Murthy, D.P., Xie, M. & Jiang, R. 2004. Weibull models. John Wiley & Sons, Chichester, UK.
30. Oliveira-Filho, A.T. & Fontes, M.A.L. 2000. Patterns of floristic differentiation among Atlantic forests in southeastern Brazil and the influence of climate. Biotropica 32: 793–810.
31. Parker, G.G., Harmon, M.E., Lefsky, M.A., Chen, J.Q., Van Pelt, R., Weis, S.B., Thomas, S.C., Winner, W.E., Shaw, D.C. & Frankling, J.F. 2004. Three-dimensional structure of an old-growth Pseudotsuga tsuga canopy and its implications for radiation balance, microclimate, and gas exchange. Ecosystems 7: 440–453.
32. Rapini, A., Ribeiro, P.L., Lambert, S. & Pirani, J.R. 2008. A flora dos campos rupestres da Cadeia do Espinhaço. Megadiversidade 4: 15–23.
33. Reutebuch, S.E., Andersen, H.E. & McGaughey, R.J. 2005. Light detection and ranging (LIDAR): an emerging tool for multiple resource inventory. Journal of Forestry 103: 286–292.
34. Shiels, A.B., Zimmerman, J.K., Garcia-Montiel, D.C., Jonckheere, I., Holm, J., Horton, D. & Brokaw, N. 2010. Plant responses to simulated hurricane impacts in a subtropical wet forest, Puerto Rico. Journal of Ecology 98: 659–673.
35. Souza, A.A., Galvao, L.S. & Santos, J.R. 2010. Relationships between Hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment. Remote Sensing Letters 1: 55–64.
36. Tsui, O.W., Coops, N.C., Wulder, M.A. & Marshall, P.L. 2013. Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass. Remote Sensing of Environment 139: 340–352.
37. Valle, D., Schulze, M., Vidal, E., Grogan, J. & Sales, M. 2006. Identifying bias in stand-level growth and yield estimations: a case study in eastern Brazilian Amazonia. Forest Ecology and Management 236: 127–135.
38. Vasconcelos, M. 2000. Reserva do Caraça: história, vegetação e fauna. Aves 1: 3–7.
39. Vastaranta, M. 2012. Forest mapping and monitoring using active 3D remote sensing. Dissertationes Forestales.
40. Veloso, H.P., Rangel Filho, A.L.R. & Lima, J.C.A. 1991. Classificação da vegetação brasileira, adaptada a um sistema universal. Ministério da Economia, Fazenda e Planejamento, Fundação Instituto Brasileiro de Geografia e Estatística, Diretoria de Geociências, Departamento de Recursos Naturais e Estudos Ambientais, Rio de Janeiro, BR.
41. Veloso, H.P., Oliveira-Filho, L.D., Vaz, A., Lima, M., Marquete, R. & Brazão, J. 1992. Manual técnico da vegetação brasileira. IBGE, Rio de Janeiro, BR.
42. Viana, V.M. & Tabanez, A.A.J. 1996. Biology and conservation of forest fragments in the Brazilian Atlantic moist forest. In: Schelhas, J. & Greenberg, R. (eds.) Forest patches in tropical landscapes, pp. 151–167. Island Press, Washington, DC.
43. Wulder, M.A., Han, T., White, J.C., Sweda, T. & Tsuzuki, H. 2007. Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization. Remote Sensing of Environment 110: 123–137.
44. Wulder, M.A., White, J.C., Nelson, R.F., Naesset, E., Orka, H.O., Coops, N.C., Hilker, T., Bater, C.W. & Gobakken, T. 2012. Lidar sampling for large-area forest characterization: a review. Remote Sensing of Environment 121: 196–209.
45. Xavier, A.C., Soares, J.V. & Almeida, A.C. 2002. Variação do índice de área foliar em clones de eucalipto ao longo de seu ciclo de crescimento. Revista Árvore 26: 421–427.