Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon

Global Biogeochemical Cycles

Volumn 30, Issue 11, 2016, Pages 1639-1660

  Longo, Marcos ^a   Keller, Michael ^a,b,c   dos Santos, Maiza N ^a   Leitold, Veronika ^d   Pinagé, Ekena R ^a,e   Baccini, Alessandro ^f   Saatchi, Sassan ^c   Nogueira, Euler M ^g   Batistella, Mateus ^h   Morton, Douglas C ^d  

^a EMBRAPA INFORMÁTICA AGROPECUÁRIA   (Brazil)

^b INTERNATIONAL INSTITUTE OF TROPICAL FORESTRY   (Puerto Rico)

^c CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^d NASA GODDARD SPACE FLIGHT CENTER   (United States)

^e UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^f WOODS HOLE RESEARCH CENTER   (United States)

^g INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA   (Brazil)

^h EMBRAPA CERRADOS   (Brazil)

Author keywords

airborne lidar; Amazon; biomass; fire; forest degradation; land use change

Indexed keywords

ABOVEGROUND BIOMASS; AIRBORNE SENSING; CARBON EMISSION; DATA SET; DEFORESTATION; ENVIRONMENTAL DEGRADATION; FOREST FIRE; FOREST INVENTORY; LAND USE CHANGE; LIDAR; LOGGING (TIMBER);

AMAZONAS [BRAZIL]; BRAZIL;

EID: 85001000721     PISSN: 08866236     EISSN: 19449224     Source Type: Journal    
DOI: 10.1002/2016GB005465     Document Type: Article

References (126)

1. Aguiar, A. P. D., et al. (2012), Modeling the spatial and temporal heterogeneity of deforestation-driven carbon emissions: The INPE-EM framework applied to the Brazilian Amazon, Glob. Change Biol., 18(11), 3346–3366, doi:10.1111/j.1365-2486.2012.02782.x.
2. 2 inversion reveals seasonal and geographic differences in Amazon net biome exchange, Glob. Change Biol., 22, 3427–3443, doi:10.1111/gcb.13305.
3. Alder, D., J. N. M. Silva, J. O. P. de Carvalho, J. C. Lopes, and A. R. Ruschel (2012), The cohort-empirical modelling strategy and its application to forest management for Tapajós forest, Pará, Brazilian Amazon, Bois For. Trop., 314(4), 17–23.
4. Almeida, C. A., A. C. Coutinho, J. C. D. M. Esquerdo, M. Adami, A. Venturieri, C. G. Diniz, N. Dessay, L. Durieux, and A. R. Gomes (2016), High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data, Acta Amazonica, 46(3), 291–302, doi:10.1590/1809-4392201505504.
5. Andersen, H.-E., S. E. Reutebuch, R. J. McGaughey, M. V. N. d'Oliveira, and M. Keller (2014), Monitoring selective logging in western Amazonia with repeat lidar flights, Remote Sens. Environ., 151, 157–165, doi:10.1016/j.rse.2013.08.049.
6. Anderson, L. O., L. E. O. C. Aragão, M. Gloor, E. Arai, M. Adami, S. Saatchi, Y. Malhi, Y. E. Shimabukuro, J. Barlow, E. Berenguer, and V. Duarte (2015), Disentangling the contribution of multiple land covers to fires-mediated carbon emissions in Amazonia during the 2010 drought, Global Biogeochem. Cycles, 29(10), 1739–1753, doi:10.1002/2014GB005008.
7. Aragão, L. E. O. C., Y. Malhi, R. M. Roman-Cuesta, S. Saatchi, L. O. Anderson, and Y. E. Shimabukuro (2007), Spatial patterns and fire response of recent Amazonian droughts, Geophys. Res. Lett., 34(7), L07701, doi:10.1029/2006GL028946.
8. Aragão, L. E. O. C., B. Poulter, J. B. Barlow, L. O. Anderson, Y. Malhi, S. Saatchi, O. L. Phillips, and E. Gloor (2014), Environmental change and the carbon balance of Amazonian forests, Biol. Rev., 89(4), 913–931, doi:10.1111/brv.12088.
9. Asner, G. P. (2001), Cloud cover in Landsat observations of the Brazilian Amazon, Int. J. Remote Sens., 22(18), 3855–3862, doi:10.1080/01431160010006926.
10. Asner, G. P., and J. Mascaro (2014), Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric, Remote Sens. Environ., 140, 614–624, doi:10.1016/j.rse.2013.09.023.
11. Asner, G. P., M. Keller, R. Pereira, J. C. Zweede Jr., and J. N. M. Silva (2004), Canopy damage and recovery after selective logging in Amazonia: Field and satellite studies, Ecol. Appl., 14(SP4), 280–298, doi:10.1890/01-6019.
12. Asner, G. P., D. E. Knapp, E. N. Broadbent, P. J. C. Oliveira, M. Keller, and J. N. Silva (2005), Selective logging in the Brazilian Amazon, Science, 310(5747), 480–482, doi:10.1126/science.1118051.
13. Asner, G. P., et al. (2010), High-resolution forest carbon stocks and emissions in the Amazon, Proc. Natl. Acad. Sci. U.S.A., 107(38), 16,738–16,742, doi:10.1073/pnas.1004875107.
14. Asner, G. P., J. Mascaro, H. C. Muller-Landau, G. Vieilledent, R. Vaudry, M. Rasamoelina, J. S. Hall, and M. Breugel (2012), A universal airborne LiDAR approach for tropical forest carbon mapping, Oecologia, 168(4), 1147–1160, doi:10.1007/s00442-011-2165-z.
15. Baccini, A., and G. P. Asner (2013), Improving pantropical forest carbon maps with airborne LiDAR sampling, Carbon Manage., 4(6), 591–600, doi:10.4155/cmt.13.66.
16. Baccini, A., et al. (2012), Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nat. Clim. Change, 2(3), 182–185, doi:10.1038/nclimate1354.
17. Balch, J. K., D. C. Nepstad, P. M. Brando, L. M. Curran, O. Portela, O. de Carvalho, and P. Lefebvre (2008), Negative fire feedback in a transitional forest of southeastern Amazonia, Glob. Change Biol., 14(10), 2276–2287, doi:10.1111/j.1365-2486.2008.01655.x.
18. Barber, C. P., M. A. Cochrane, C. M. Souza Jr., and W. F. Laurance (2014), Roads, deforestation, and the mitigating effect of protected areas in the Amazon, Biol. Conserv., 177, 203–209, doi:10.1016/j.biocon.2014.07.004.
19. Barlow, J., C. A. Peres, B. O. Lagan, and T. Haugaasen (2003), Large tree mortality and the decline of forest biomass following Amazonian wildfires, Ecol. Lett., 6(1), 6–8, doi:10.1046/j.1461-0248.2003.00394.x.
20. Batistella, M., E. L. Bolfe, and E. F. Moran (2013), Agroforestry in Tomé-Açu: An alternative to pasture in the Amazon, in Human-Environment Interactions: Current and Future Directions, edited by E. S. Brondízio and E. F. Moran, pp. 321–342, chap 14, Springer Netherlands, Dordrecht, Netherlands, doi:10.1007/978-94-007-4780-7.
21. Berenguer, E., J. Ferreira, T. A. Gardner, L. E. O. C. Aragão, P. B. de Camargo, C. E. Cerri, M. Durigan, R. C. D. Oliveira, I. C. G. A. Vieira, and J. Barlow (2014), A large-scale field assessment of carbon stocks in human-modified tropical forests, Glob. Change Biol., 20(12), 3713–3726, doi:10.1111/gcb.12627.
22. Blanc, L., M. Echard, B. Hérault, D. Bonal, E. Marcon, J. Chave, and C. Baraloto (2009), Dynamics of aboveground carbon stocks in a selectively logged tropical forest, Ecol. Appl., 19(6), 1397–1404, doi:10.1890/08-1572.1.
23. Boyd, D. S., R. A. Hill, C. Hopkinson, and T. R. Baker (2013), Landscape-scale forest disturbance regimes in southern Peruvian Amazonia, Ecol. Appl., 23(7), 1588–1602, doi:10.1890/12-0371.1.
24. Brando, P. M., et al. (2014), Abrupt increases in Amazonian tree mortality due to drought–fire interactions, Proc. Natl. Acad. Sci. U.S.A., 111(17), 6347–6352, doi:10.1073/pnas.1305499111.
25. Breiman, L. (2001), Random forests, Mach. Learn., 45(1), 5–32, doi:10.1023/A:1010933404324.
26. Bustamante, M. M. C., et al. (2016), Towards an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity, Glob. Change Biol., 22(1), 92–109, doi:10.1111/gcb.13087.
27. Chambers, J. Q., N. Higuchi, J. P. Schimel, L. V. Ferreira, and J. M. Melack (2000), Decomposition and carbon cycling of dead trees in tropical forests of the central Amazon, Oecologia, 122(3), 380–388, doi:10.1007/s004420050044.
28. Chauvel, A., Y. Lucas, and R. Boulet (1987), On the genesis of the soil mantle of the region of Manaus, Central Amazonia, Brazil, Experientia, 43(3), 234–241, doi:10.1007/BF01945546.
29. Chave, J., R. Condit, S. Aguilar, A. Hernandez, S. Lao, and R. Perez (2004), Error propagation and scaling for tropical forest biomass estimates, Philos. Trans. R. Soc. London, Ser. B, 359(1443), 409–420, doi:10.1098/rstb.2003.1425.
30. Chave, J., D. Coomes, S. Jansen, S. L. Lewis, N. G. Swenson, and A. E. Zanne (2009), Towards a worldwide wood economics spectrum, Ecol. Lett., 12(4), 351–366, doi:10.1111/j.1461-0248.2009.01285.x.
31. Chave, J., et al. (2014), Improved allometric models to estimate the aboveground biomass of tropical trees, Glob. Change Biol., 20(10), 3177–3190, doi:10.1111/gcb.12629.
32. Coe, M. T., et al. (2013), Deforestation and climate feedbacks threaten the ecological integrity of South–Southeastern Amazonia, Philos. Trans. R Soc. Lond. B Biol. Sci., 368(1619), 20120155, doi:10.1098/rstb.2012.0155.
33. de Castilho, C. V., W. E. Magnusson, R. N. O. de Araújo, R. C. C. Luizão, F. J. Luizão, A. P. Lima, and N. Higuchi (2006), Variation in aboveground tree live biomass in a central Amazonian forest: Effects of soil and topography, Forest Ecol. Manage., 234(1–3), 85–96, doi:10.1016/j.foreco.2006.06.024.
34. De'ath, G. (2007), Boosted trees for ecological modeling and prediction, Ecology, 88(1), 243–251, doi:10.1890/0012-9658(2007)88[243:BTFEMA]2.0.CO;2.
35. d'Oliveira, M. V. N., S. E. Reutebuch, R. J. McGaughey, and H.-E. Andersen (2012), Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre state, western Brazilian Amazon, Remote Sens. Environ., 124, 479–491, doi:10.1016/j.rse.2012.05.014.
36. dos-Santos, M. N., and M. Keller (2016a), CMS: Forest Inventory and Biophysical Measurements, Pará, Brazil, 2012–2014, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tenn.
37. dos-Santos, M. N., and M. Keller (2016b), CMS: LiDAR Data for Forested Areas in Paragominas, Pará, Brazil, 2012–2014, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tenn.
38. EBATA (2012), Plano operacional anual—UMF II, Floresta Nacional Saracá-Taquera, Oriximiná, Pará, Activities Rep., Ebata Produtos Florestais Ltda., Belém, PA, Brazil.
39. Efron, B., and R. Tibshirani (1997), Improvements on cross-validation: The 632+ bootstrap method, J. Am. Stat. Assoc., 92(438), 548–560, doi:10.2307/2965703.
40. Feldpausch, T. R., et al. (2012), Tree height integrated into pantropical forest biomass estimates, Biogeosciences, 9(8), 3381–3403, doi:10.5194/bg-9-3381-2012.
41. Ferraz, A., S. Saatchi, C. Mallet, and V. Meyer (2016), Lidar detection of individual tree size in tropical forests, Remote Sens. Environ., 183, 318–333, doi:10.1016/j.rse.2016.05.028.
42. Gatti, L. V., et al. (2014), Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements, Nature, 506(7486), 76–80, doi:10.1038/nature12957.
43. Gerwing, J. J., and C. Uhl (2002), Pre-logging liana cutting reduces liana regeneration in logging gaps in the Eastern Brazilian Amazon, Ecol. Appl., 12(6), 1642–1651, doi:10.1890/1051-0761(2002)012[1642:PLLCRL]2.0.CO;2.
44. Gibbs, H. K., B. Sandra, J. O. Niles, and J. A. Foley (2007), Monitoring and estimating tropical forest carbon stocks: Making REDD a reality, Environ. Res. Lett., 2(4), 045023, doi:10.1088/1748-9326/2/4/045023.
45. Goodman, R. C., O. L. Phillips, D. C. Torres, L. Freitas, S. T. Cortese, A. Monteagudo, and T. R. Baker (2013), Amazon palm biomass and allometry, Forest Ecol. Manag., 310, 994–1004, doi:10.1016/j.foreco.2013.09.045.
46. Goodman, R. C., O. L. Phillips, and T. R. Baker (2014), The importance of crown dimensions to improve tropical tree biomass estimates, Ecol. Appl., 24(4), 680–698, doi:10.1890/13-0070.1.
47. Guimarães, J., A. Veríssimo, P. Amaral, and A. Demachki (2011), Municípios Verdes: Caminhos Para a Sustentabilidade, Instituto do Homem e Meio Ambiente da Amazônia – Imazon, Belém, PA, Brazil.
48. Guitet, S., B. Hérault, Q. Molto, O. Brunaux, and P. Couteron (2015), Spatial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome, PLoS One, 10(9), E0138456, doi:10.1371/journal.pone.0138456.
49. Hansen, M. C., J. R. G. Townshend, R. S. DeFries, and M. Carroll (2005), Estimation of tree cover using MODIS data at global, continental and regional/local scales, Int. J. Remote Sens., 26(19), 4359–4380, doi:10.1080/01431160500113435.
50. Hansen, M. C., et al. (2013), High-resolution global maps of 21st-century forest cover change, Science, 342(6160), 850–853, doi:10.1126/science.1244693.
51. Harmon, M. E., D. F. Whigham, J. Sexton, and I. Olmsted (1995), Decomposition and mass of woody detritus in the dry tropical forests of the Northeastern Yucatan Peninsula, Mexico, Biotropica, 27(3), 305–316, doi:10.2307/2388916.
52. Hawbaker, T. J., N. S. Keuler, A. A. Lesak, T. Gobakken, K. Contrucci, and V. C. Radeloff (2009), Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design, J. Geophys. Res., 114, G00E04, doi:10.1029/2008JG000870.
53. Hess, L. L., J. M. Melack, A. G. Affonso, C. Barbosa, M. Gastil-Buhl, and E. M. L. M. Novo (2015), Wetlands of the lowland Amazon Basin: Extent, vegetative cover, and dual-season inundated area as mapped with JERS-1 synthetic aperture radar, Wetlands, 35(4), 745–756, doi:10.1007/s13157-015-0666-y.
54. Hudak, A. T., N. L. Crookston, J. S. Evans, M. J. Falkowski, A. M. S. Smith, P. E. Gessler, and P. Morgan (2006), Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data, Can. J. Remote Sens., 32(2), 126–138, doi:10.5589/m06-007.
55. Hudak, A. T., E. K. Strand, L. A. Vierling, J. C. Byrne, J. U. Eitel, S. Martinuzzi, and M. J. Falkowski (2012), Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys, Remote Sens. Environ., 123, 25–40, doi:10.1016/j.rse.2012.02.023.
56. Hunter, M. O., M. Keller, D. Vitoria, and D. C. Morton (2013), Tree height and tropical forest biomass estimation, Biogeosciences, 10(6), 10,491–10,529, doi:10.5194/bg-10-8385-2013.
57. IBAMA (2004), Floresta Nacional do Tapajós: Plano de manejo, Government Rep., Brazilian Ministry of Envrionment.
58. a edição, Tech. Rep., Fundação Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro, Brazil.
59. INPE (2014), Mapeamento da degradação florestal na Amazônia brasileira (DEGRAD). [Available at http://www.obt.inpe.br/degrad/.]
60. Jakubowski, M. K., Q. Guo, and M. Kelly (2013), Tradeoffs between lidar pulse density and forest measurement accuracy, Remote Sens. Environ., 130, 245–253, doi:10.1016/j.rse.2012.11.024.
61. Joshi, N., E. T. A. Mitchard, N. Woo, J. Torres, J. Moll-Rocek, A. Ehammer, M. Collins, M. R. Jepsen, and R. Fensholt (2015), Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data, Environ. Res. Lett., 10(3), 034014, doi:10.1088/1748-9326/10/3/034014.
62. Karsten, R. J., H. Meilby, and J. B. Larsen (2014), Regeneration and management of lesser known timber species in the Peruvian Amazon following disturbance by logging, Forest Ecol. Manage., 327, 76–85, doi:10.1016/j.foreco.2014.04.035.
63. Keller, M., G. P. Asner, N. Silva, and M. Palace (2004a), Sustainability of selective logging of upland forest in the Brazilian Amazon, in Working forests in the Neotropics: Conservation through sustainable management?, edited by D. J. Zarin et al., pp. 41–63, chap 4., Columbia Univ. Press, New York.
64. Keller, M., et al. (2004b), Ecological research in the large-scale biosphere–atmosphere experiment in Amazonia: Early results, Ecol. Appl., 14(SP4), 3–16, doi:10.1890/03-6003.
65. Keller, M., M. Palace, G. P. Asner, R. Pereira, and J. N. M. Silva (2004c), Coarse woody debris in undisturbed and logged forests in the Eastern Brazilian Amazon, Glob. Change Biol., 10(5), 784–795, doi:10.1111/j.1529-8817.2003.00770.x.
66. Langner, A., F. Achard, and G. Grassi (2014), Can recent pan-tropical biomass maps be used to derive alternative Tier 1 values for reporting REDD+ activities under UNFCCC?, Environ. Res. Lett., 9(12), 124008, doi:10.1088/1748-9326/9/12/124008.
67. Lapola, D. M., et al. (2014), Pervasive transition of the Brazilian land-use system, Nat. Clim. Change, 4(1), 27–35, doi:10.1038/nclimate2056.
68. Le Quéré, C., et al. (2015), Global carbon budget, Earth Syst. Sci. Data, 7(2), 349–396, doi:10.5194/essd-7-349-2015.
69. Leitold, V., M. Keller, D. Morton, B. Cook, and Y. Shimabukuro (2015), Airborne lidar-based estimates of tropical forest structure in complex terrain: Opportunities and trade-offs for REDD+, Carbon Balance Manage., 10(3), 1–12, doi:10.1186/s13021-015-0013-x.
70. Lewis, S. L., P. M. Brando, O. L. Phillips, G. M. F. van der Heijden, and D. Nepstad (2011), The 2010 Amazon drought, Science, 331(6017), 554, doi:10.1126/science.1200807.
71. Liaw, A., and M. Wiener (2002), Classification and regression by random forest, R News, 2(3), 18–22.
72. Liu, Z., D. Ostrenga, W. Teng, and S. Kempler (2012), Tropical Rainfall Measuring Mission (TRMM) precipitation data and services for research and applications, Bull. Am. Meteorol. Soc., 93(9), 1317–1325, doi:10.1175/BAMS-D-11-00152.1.
73. Lloyd, A. H., P. A. Duffy, and D. H. Mann (2013), Nonlinear responses of white spruce growth to climate variability in interior Alaska, Can. J. Forest Res., 43(4), 331–343, doi:10.1139/cjfr-2012-0372.
74. MADEFLONA (2010), Plano operacional anual 2010—UMF I. Floresta Nacional Jamari, Itapuã d'Oeste, Rondônia, Activities Rep., MADEFLONA Industrial Madeireira Ltda., Itapuã d'Oeste, RO, Brazil.
75. Maltamo, M., O. M. Bollandsås, E. Næsset, T. Gobakken, and P. Packalén (2011), Different plot selection strategies for field training data in ALS-assisted forest inventory, Forestry, 84(1), 23–31, doi:10.1093/forestry/cpq039.
76. Marvin, D. C., G. P. Asner, D. E. Knapp, C. B. Anderson, R. E. Martin, F. Sinca, and R. Tupayachi(2014), Amazonian landscapes and the bias in field studies of forest structure and biomass, Proc. Natl. Acad. Sci. U.S.A., 111(48), E5224—E5232, doi:10.1073/pnas.1412999111.
77. Mascaro, J., C. M. Litton, R. F. Hughes, A. Uowolo, and S. A. Schnitzer (2011a), Minimizing bias in biomass allometry: Model selection and log-transformation of data, Biotropica, 43(6), 649–653, doi:10.1111/j.1744-7429.2011.00798.x.
78. Mascaro, J., M. Detto, G. P. Asner, and H. C. Muller-Landau (2011b), Evaluating uncertainty in mapping forest carbon with airborne LiDAR, Remote Sens. Environ., 115(12), 3770–3774, doi:10.1016/j.rse.2011.07.019.
79. Mascaro, J., G. P. Asner, D. E. Knapp, T. Kennedy-Bowdoin, R. E. Martin, C. Anderson, M. Higgins, and K. D. Chadwick (2014), A tale of two “forests”: Random forest machine learning aids tropical forest carbon mapping, PLoS One, 9(1), E85993, doi:10.1371/journal.pone.0085993.
80. Mason, D. M., and J. H. Schuenemeyer (1983), A modified Kolmogorov-Smirnov test sensitive to tail alternatives, Ann. Stat., 11(3), 933–946, doi:10.1214/aos/1176346259.
81. Mauya, E., E. Hansen, T. Gobakken, O. Bollandsas, R. Malimbwi, and E. Næsset (2015), Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania, Carbon Balance Manage., 10(10), 1–14, doi:10.1186/s13021-015-0021-x.
82. McGaughey, R. J. (2014), FUSION/LDV: Software for LIDAR Data Analysis and Visualization, Manual, USFS Pacific Northwest Research Station, Seattle, Wash.
83. Meyer, V., S. S. Saatchi, J. Chave, J. W. Dalling, S. Bohlman, G. A. Fricker, C. Robinson, M. Neumann, and S. Hubbell (2013), Detecting tropical forest biomass dynamics from repeated airborne lidar measurements, Biogeosciences, 10(8), 5421–5438, doi:10.5194/bg-10-5421-2013.
84. Miller, A. J. (1984), Selection of subsets of regression variables, J. R. Stat. Soc., 147(3), 389–425, doi:10.2307/2981576.
85. Molina, P. X., G. P. Asner, M. Farjas Abadía, J. C. Ojeda Manrique, L. A. Sánchez Diez, and R. Valencia (2016), Spatially-explicit testing of a general aboveground carbon density estimation model in a Western Amazonian forest using airborne LiDAR, Remote Sens., 8(1), 9, doi:10.3390/rs8010009.
86. Morton, D. C. (2016), Forest carbon fluxes: A satellite perspective, Nat. Clim. Change, 6(4), 346–348, doi:10.1038/nclimate2978.
87. Morton, D. C., Y. Le Page, R. S. DeFries, G. J. Collatz, and G. C. Hurtt (2013), Understorey fire frequency and the fate of burned forests in southern Amazonia, Phil. Trans. R. Soc. B, 368, 20120163, doi:10.1098/rstb.2012.0163.
88. Næsset, E. (2004a), Accuracy of forest inventory using airborne laser scanning: Evaluating the first nordic full-scale operational project, Scand. J. For. Res., 19(6), 554–557, doi:10.1080/02827580410019544.
89. Næsset, E. (2004b), Effects of different flying altitudes on biophysical stand properties estimated from canopy height and density measured with a small-footprint airborne scanning laser, Remote Sens. Environ., 91(2), 243–255, doi:10.1016/j.rse.2004.03.009.
90. Nepstad, D., et al. (2014), Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains, Science, 344(6188), 1118–1123, doi:10.1126/science.1248525.
91. Nogueira, E. M., P. M. Fearnside, B. W. Nelson, R. I. Barbosa, and E. W. H. Keizer (2008), Estimates of forest biomass in the Brazilian Amazon: New allometric equations and adjustments to biomass from wood-volume inventories, Forest Ecol. Manag., 256(11), 1853–1867, doi:10.1016/j.foreco.2008.07.022.
92. Nogueira, E. M., A. M. Yanai, F. O. R. Fonseca, and P. M. Fearnside (2015), Carbon stock loss from deforestation through 2013 in Brazilian Amazonia, Glob. Change Biol., 21(3), 1271–1292, doi:10.1111/gcb.12798.
93. Ometto, J. P., A. P. Aguiar, T. Assis, L. Soler, P. Valle, G. Tejada, D. M. Lapola, and P. Meir(2014), Amazon forest biomass density maps: Tackling the uncertainty in carbon emission estimates, Clim. Change, 124(3), 545–560, doi:10.1007/s10584-014-1058-7.
94. Palace, M., M. Keller, G. P. Asner, J. N. M. Silva, and C. Passos (2007), Necromass in undisturbed and logged forests in the Brazilian Amazon, Forest Ecol. Manage., 238(1–3), 309–318, doi:10.1016/j.foreco.2006.10.026.
95. Phillips, S. J., R. P. Anderson, and R. E. Schapire (2006), Maximum entropy modeling of species geographic distributions, Ecol. Model., 190(3–4), 231–259, doi:10.1016/j.ecolmodel.2005.03.026.
96. Pinto, A., P. Amaral, C. M. Souza Jr., A. Veríssimo, R. Salomão, G. Gomes, and C. Balieiro(2009), Diagnóstico socioeconômico e florestal do município de Paragominas, Tech. Rep., Instituto do Homem e Meio Ambiente da Amazônia (Imazon), Belém, PA, Brazil.
97. Pütz, S., J. Groeneveld, K. Henle, C. Knogge, A. C. Martensen, M. Metz, J. P. Metzger, M. C. Ribeiro, M. D. de Paula, and A. Huth (2014), Long-term carbon loss in fragmented Neotropical forests, Nat. Commun., 5, 5037, doi:10.1038/ncomms6037.
98. Pyle, E. H., et al. (2008), Dynamics of carbon, biomass, and structure in two Amazonian forests, J. Geophys. Res., 113(G1), G00B08, doi:10.1029/2007JG000592.
99. R Core Team (2015), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
100. Réjou-Méchain, M., B. Tymen, L. Blanc, S. Fauset, T. R. Feldpausch, A. Monteagudo, O. L. Phillips, H. Richard, and J. Chave (2015), Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest, Remote Sens. Environ., 169, 93–101, doi:10.1016/j.rse.2015.08.001.
101. Rice, A., E. Pyle, S. Saleska, L. Hutyra, M. Palace, M. Keller, P. de Camargo, K. Portilho, D. Marques, and S. Wofsy (2004), Carbon balance and vegetation dynamics in an old-growth Amazonian forest, Ecol. Appl., 14(S4), S55—S71, doi:10.1890/02-6006.
102. Ridgeway, G. (2015), GBM: Generalized Boosted Regression Models, R package version 2.1.1. With contributions from others. [Available at https://github.com/gbm-developers/gbm.]
103. Rutishauser, E., et al. (2015), Rapid tree carbon stock recovery in managed Amazonian forests, Curr. Biol., 25(18), R787—R788, doi:10.1016/j.cub.2015.07.034.
104. Rutishauser, E., B. Hérault, P. Petronelli, and P. Sist (2016), Tree height reduction after selective logging in a tropical forest, Biotropica, 48(3), 285–289, doi:10.1111/btp.12326.
105. Saatchi, S., M. Marlier, R. L. Chazdon, D. B. Clark, and A. E. Russell (2011), Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass, Remote Sens. Environ., 115(11), 2836–2849, doi:10.1016/j.rse.2010.07.015.
106. Saatchi, S. S., R. A. Houghton, R. C. D. S. Alvalá, J. V. Soares, and Y. Yu (2007), Distribution of aboveground live biomass in the Amazon basin, Glob. Change Biol., 13(4), 816–837, doi:10.1111/j.1365-2486.2007.01323.x.
107. Saatchi, S. S., et al. (2011), Benchmark map of forest carbon stocks in tropical regions across three continents, Proc. Natl. Acad. Sci. U.S.A., 108(24), 9899–9904, doi:10.1073/pnas.1019576108.
108. Sabine, C. L., et al. (2004), Current status and past trends of the global carbon cycle, in The Global Carbon Cycle: Integrating Humans, Climate, and the Natural World, Scientific Committee on Problems of the Environment, vol. 62, edited by C. B. Field and M. R. Raupach, pp. 17–44, chap. 2, Island Press, Washington, D. C.
109. Schnitzer, S. A., J. W. Dalling, and W. P. Carson (2000), The impact of lianas on tree regeneration in tropical forest canopy gaps: Evidence for an alternative pathway of gap-phase regeneration, J. Ecol., 88(4), 655–666, doi:10.1046/j.1365-2745.2000.00489.x.
110. Schnitzer, S. A., S. J. DeWalt, and J. Chave (2006), Censusing and measuring lianas: A quantitative comparison of the common methods, Biotropica, 38(5), 581–591, doi:10.1111/j.1744-7429.2006.00187.x.
111. Schwarz, G. (1978), Estimating the dimension of a model, Ann. Stat., 6(2), 461–464, doi:10.1214/aos/1176344136.
112. Silvério, D. (2015), Alterações na estrutura e funcionamento de florestas transicionais na Amazônia associadas à degradação florestal e transições no uso da terra, PhD thesis, Univ. of Brasília, Brasília, Brazil.
113. Silvério, D. V., P. M. Brando, J. K. Balch, F. E. Putz, D. C. Nepstad, C. Oliveira-Santos, and M. M. C. Bustamante (2013), Testing the Amazon savannization hypothesis: Fire effects on invasion of a neotropical forest by native cerrado and exotic pasture grasses, Philos. Trans. R. Soc. B, 368, 20120427, doi:10.1098/rstb.2012.0427.
114. Sist, P., and F. N. Ferreira (2007), Sustainability of reduced-impact logging in the Eastern Amazon, Forest Ecol. Manage., 243(2–3), 199–209, doi:10.1016/j.foreco.2007.02.014.
115. Sist, P., L. Mazzei, L. Blanc, and E. Rutishauser (2014), Large trees as key elements of carbon storage and dynamics after selective logging in the Eastern Amazon, Forest Ecol. Manage., 318, 103–109, doi:10.1016/j.foreco.2014.01.005.
116. Slik, J. W. F., et al. (2013), Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics, Global Ecol. Biogeogr., 22(12), 1261–1271, doi:10.1111/geb.12092.
117. Smith, P., et al. (2014), Agriculture, forestry and other land use (AFOLU), in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, vol. 11, edited by O. Edenhofer et al., pp. 811–922, Cambridge Univ. Press, Cambridge U. K. and New York.
118. Townshend, J. R. G., M. Carroll, C. Dimiceli, R. Sohlberg, M. C. Hansen, and R. S. DeFries(2001), Vegetation Continuous Fields MOD44B, 2012 and 2013 percent tree cover, Collection 5. [Available at http://reverb.echo.nasa.gov.]
119. UN-FCCC (2016), Report of the conference of the parties on its twenty-first session, held in Paris from 30 November to 13 December 2015. Part two: Action taken by the conference of the parties at its twenty-first session, Report FCCC/CP/2015/10/Add.1, United Nations Framework Convention on Climate Change, Paris.
120. van der Laan-Luijkx, I. T., et al. (2015), Response of the Amazon carbon balance to the 2010 drought derived with CarbonTracker South America, Global Biogeochem. Cycles, 29(7), 1092–1108, doi:10.1002/2014GB005082.
121. 2 emissions from forest loss, Nature Geosci., 2(11), 737–738, doi:10.1038/ngeo671.
122. Veríssimo, A., P. Barreto, M. Mattos, R. Tarifa, and C. Uhl (1992), Logging impacts and prospects for sustainable forest management in an old Amazonian frontier: The case of Paragominas, Forest Ecol. Manage., 55(1–4), 169–199, doi:10.1016/0378-1127(92)90099-U.
123. Vincent, G., D. Sabatier, and E. Rutishauser (2014), Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: Scaling-up from tree to stand to landscape, Oecologia, 175(2), 439–443, doi:10.1007/s00442-014-2913-y.
124. West, T. A. P., E. Vidal, and F. E. Putz (2014), Forest biomass recovery after conventional and reduced-impact logging in Amazonian Brazil, Forest Ecol. Manage., 314, 59–63, doi:10.1016/j.foreco.2013.11.022.
125. White, J. C., M. A. Wulder, A. Varhola, M. Vastaranta, N. C. Coops, B. D. Cook, D. Pitt, and M. Woods (2013), A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach (version 2.0), Information Rep., Canadian Wood Fibre Centre, Victoria, British Columbia, Canada.
126. Zanne, A. E., G. Lopez-Gonzalez, D. A. Coomes, J. Ilic, S. Jansen, S. L. Lewis, R. B. Miller, N. G. Swenson, M. C. Wiemann, and J. Chave (2009), Data from: Towards a worldwide wood economics spectrum, Dryad Data Repository, doi:10.5061/dryad.234.