Tropical landscapes are, in general, a mosaic of pasture, agriculture, and forest undergoing various stages of succession. Forest succession is comprised of continuous structural changes over time and results in increases in aboveground biomass (AGB). New remote sensing methods, including sensors, image processing, statistical methods, and uncertainty evaluations, are constantly being developed to estimate biophysical forest changes. We review 318 peer-reviewed studies related to the use of remotely sensed AGB estimations in tropical forest succession studies and summarize their geographic distribution, sensors and methods used, and their most frequent ecological inferences. Remotely sensed AGB is broadly used in forest management studies, conservation status evaluations, carbon source and sink investigations, and for studies of the relationships between environmental conditions and forest structure. Uncertainties in AGB estimations were found to be heterogeneous with biases related to sensor type, processing methodology, ground truthing availability, and forest characteristics. Remotely sensed AGB of successional forests is more reliable for the study of spatial patterns of forest succession and over large time scales than that of individual stands. Remote sensing of temporal patterns in biomass requires further study, in particular, as it is critical for understanding forest regrowth at scales useful for regional or global analyses. 1. Introduction Secondary and disturbed forests comprise roughly 30–50% of the area covered by tropical forests [1–4]. These forests play important roles as habitats for animal and plant species [5–7] and store significant amount of carbon per unit area [8]. Secondary forests emerge following natural or human disturbances, such as clearing, selective logging, introduction of invasive species, storms, or wild fires. Forest succession is a natural response to these disturbances and occurs at varying rates and in different directions, as indicated through species composition and forest structure, depending on environment conditions [9]. Changes in structure and species composition during forest succession typically result in substantial increases in aboveground biomass [10, 11]. Thereby spatial and temporal changes in aboveground biomass (AGB) can be useful indicator of the velocity and direction of the forest succession and patterns of AGB distribution and change through time can help to understand how forest structure is related to the natural environmental conditions [12, 13] and global carbon cycle [14]. The use of
References
[1]
F. Achard, H. D. Eva, H.-J. Stibig et al., “Determination of deforestation rates of the world's humid tropical forests,” Science, vol. 297, no. 5583, pp. 999–1002, 2002.
[2]
A. Grainger, “Difficulties in tracking the long-term global trend in tropical forest area,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 2, pp. 818–823, 2008.
[3]
G. P. Asner, T. K. Rudel, T. M. Aide, R. Defries, and R. Emerson, “A contemporary assessment of change in humid tropical forests,” Conservation Biology, vol. 23, no. 6, pp. 1386–1395, 2009.
[4]
J. Blaser, A. Sarre, D. Poore, and S. Johnson, Status of Tropical Forest Management, Technical Series No. 38, International Tropical Timber Organization, Yokohama, Japan, 2011, Edited by ITTO.
[5]
S. J. Wright, “Tropical forests in a changing environment,” Trends in Ecology & Evolution, vol. 20, no. 10, pp. 553–560, 2005.
[6]
R. L. Chazdon, C. A. Peres, D. Dent et al., “The potential for species conservation in tropical secondary forests,” Conservation Biology, vol. 23, no. 6, pp. 1406–1417, 2009.
[7]
D. H. Dent and S. J. Wright, “The future of tropical species in secondary forests: a quantitative review,” Biological Conservation, vol. 142, no. 12, pp. 2833–2843, 2009.
[8]
A. R. Martin and S. C. Thomas, “A reassessment of carbon content in tropical trees,” PLoS ONE, vol. 6, no. 8, Article ID e23533, 2011.
[9]
A. E. Lugo, “The emerging era of novel tropical forests,” Biotropica, vol. 41, no. 5, pp. 589–591, 2009.
[10]
S. Brown and A. E. Lugo, “Tropical secondary forests,” Journal of Tropical Ecology, vol. 6, no. 1, pp. 1–32, 1990.
[11]
I. C. G. Vieira, A. S. de Almeida, E. A. Davidson, T. A. Stone, C. J. Reis de Carvalho, and J. B. Guerrero, “Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amaz？nia,” Remote Sensing of Environment, vol. 87, no. 4, pp. 470–481, 2003.
[12]
S. S. Saatchi, R. A. Houghton, R. C. Dos Santos Alvalá, J. V. Soares, and Y. Yu, “Distribution of aboveground live biomass in the Amazon basin,” Global Change Biology, vol. 13, no. 4, pp. 816–837, 2007.
[13]
G. P. Asner, R. F. Hughes, T. A. Varga, D. E. Knapp, and T. Kennedy-Bowdoin, “Environmental and biotic controls over aboveground biomass throughout a tropical rain forest,” Ecosystems, vol. 12, no. 2, pp. 261–278, 2009.
[14]
J. Grace, “Understanding and managing the global carbon cycle,” Journal of Ecology, vol. 92, no. 2, pp. 189–202, 2004.
[15]
M. Kalacska, G. A. Sanchez-Azofeifa, B. Rivard, T. Caelli, H. P. White, and J. C. Calvo-Alvarado, “Ecological fingerprinting of ecosystem succession: estimating secondary tropical dry forest structure and diversity using imaging spectroscopy,” Remote Sensing of Environment, vol. 108, no. 1, pp. 82–96, 2007.
[16]
J. Q. Chambers, G. P. Asner, D. C. Morton et al., “Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests,” Trends in Ecology & Evolution, vol. 22, no. 8, pp. 414–423, 2007.
[17]
G. M. Foody, D. S. Boyd, and M. E. J. Cutler, “Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions,” Remote Sensing of Environment, vol. 85, no. 4, pp. 463–474, 2003.
[18]
C. Wang and J. Qi, “Biophysical estimation in tropical forests using JERS-1 SAR and VNIR imagery. II. Aboveground woody biomass,” International Journal of Remote Sensing, vol. 29, no. 23, pp. 6827–6849, 2008.
[19]
F. G. Hall, K. Bergen, J. B. Blair et al., “Characterizing 3D vegetation structure from space: mission requirements,” Remote Sensing of Environment, vol. 115, no. 11, pp. 2753–2775, 2011.
[20]
D. Lu, “The potential and challenge of remote sensing-based biomass estimation,” International Journal of Remote Sensing, vol. 27, no. 7, pp. 1297–1328, 2006.
[21]
C. Eisfelder, C. Kuenzer, and S. Dech, “Derivation of biomass information for semi-arid areas using remote-sensing data,” International Journal of Remote Sensing, vol. 33, no. 9, pp. 2937–2984, 2012.
[22]
R. L. Chazdon, S. G. Letcher, M. van Breugel, M. Martínez-Ramos, F. Bongers, and B. Finegan, “Rates of change in tree communities of secondary Neotropical forests following major disturbances,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 362, no. 1478, pp. 273–289, 2007.
[23]
J. Groeneveld, L. F. Alves, L. C. Bernacci et al., “The impact of fragmentation and density regulation on forest succession in the Atlantic rain forest,” Ecological Modelling, vol. 220, no. 19, pp. 2450–2459, 2009.
[24]
B. A. Santos, C. A. Peres, M. A. Oliveira, A. Grillo, C. P. Alves-Costa, and M. Tabarelli, “Drastic erosion in functional attributes of tree assemblages in Atlantic forest fragments of northeastern Brazil,” Biological Conservation, vol. 141, no. 1, pp. 249–260, 2008.
[25]
M. Tabarelli, A. V. Lopes, and C. A. Peres, “Edge-effects drive tropical forest fragments towards an early-successional system,” Biotropica, vol. 40, no. 6, pp. 657–661, 2008.
[26]
G. P. Asner, R. E. Martin, D. E. Knapp, and T. Kennedy-Bowdoin, “Effects of Morella faya tree invasion on aboveground carbon storage in Hawaii,” Biological Invasions, vol. 12, no. 3, pp. 477–494, 2010.
[27]
C. Uhl, R. Buschbacher, and E. A. S. Serrao, “Abandoned pastures in eastern Amazonia. I. Patterns of plant succession,” Journal of Ecology, vol. 76, no. 3, pp. 663–681, 1988.
[28]
D. B. Clark, J. M. Read, M. L. Clark, A. M. Cruz, M. F. Dotti, and D. A. Clark, “Application of 1-m and 4-m resolution satellite data to ecological studies of tropical rain forests,” Ecological Applications, vol. 14, no. 1, pp. 61–74, 2004.
[29]
L. Naughton-Treves, “Deforestation and carbon emissions at tropical frontiers: a case study from the Peruvian Amazon,” World Development, vol. 32, no. 1, pp. 173–190, 2004.
[30]
E. H. Helmer, M. A. Lefsky, and D. A. Roberts, “Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System,” Journal of Applied Remote Sensing, vol. 3, no. 1, Article ID 033505, pp. 1–31, 2009.
[31]
S. G. Rolim, R. M. Jesus, H. E. M. Nascimento, H. T. Z. do Couto, and J. Q. Chambers, “Biomass change in an Atlantic tropical moist forest: the ENSO effect in permanent sample plots over a 22-year period,” Oecologia, vol. 142, no. 2, pp. 238–246, 2005.
[32]
M. E. Swanson, J. F. Franklin, R. L. Beschta et al., “The forgotten stage of forest succession: early-successional ecosystems on forest sites,” Frontiers in Ecology and the Environment, vol. 9, no. 2, pp. 117–125, 2011.
[33]
M. R. Guariguata and R. Ostertag, “Neotropical secondary forest succession: changes in structural and functional characteristics,” Forest Ecology and Management, vol. 148, no. 1–3, pp. 185–206, 2001.
[34]
D. Liebsch, M. C. M. Marques, and R. Goldenberg, “How long does the Atlantic Rain Forest take to recover after a disturbance? Changes in species composition and ecological features during secondary succession,” Biological Conservation, vol. 141, no. 6, pp. 1717–1725, 2008.
[35]
M. E. Bowen, C. A. McAlpine, A. P. N. House, and G. C. Smith, “Regrowth forests on abandoned agricultural land: a review of their habitat values for recovering forest fauna,” Biological Conservation, vol. 140, no. 3-4, pp. 273–296, 2007.
[36]
A. M. A. Zambrano, E. N. Broadbent, M. Schmink, S. G. Perz, and G. P. Asner, “Deforestation drivers in Southwest Amazonia: comparing smallholder farmers in I？apari, Peru, and Assis Brasil, Brazil,” Conservation & Society, vol. 8, no. 3, pp. 157–170, 2010.
[37]
A. Wijaya, V. Liesenberg, and R. Gloaguen, “Retrieval of forest attributes in complex successional forests of Central Indonesia: modeling and estimation of bitemporal data,” Forest Ecology and Management, vol. 259, no. 12, pp. 2315–2326, 2010.
[38]
J. R. Kellner, D. B. Clark, and S. P. Hubbell, “Pervasive canopy dynamics produce short-term stability in a tropical rain forest landscape,” Ecology Letters, vol. 12, no. 2, pp. 155–164, 2009.
[39]
A. T. Hudak, E. K. Strand, L. A. Vierling et al., “Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys,” Remote Sensing of Environment, vol. 123, pp. 25–40, 2012.
[40]
C. M. Ryan, T. Hill, E. Woollen et al., “Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery,” Global Change Biology, vol. 18, no. 1, pp. 243–257, 2012.
[41]
A. S. Antonarakis, S. S. Saatchi, R. L. Chazdon, and P. R. Moorcroft, “Using LiDAR and Radar measurements to constrain predictions of forest ecosystem structure and function,” Ecological Applications, vol. 21, no. 4, pp. 1120–1137, 2011.
[42]
S. R. Baptista and T. K. Rudel, “A re-emerging Atlantic forest? Urbanization, industrialization and the forest transition in Santa Catarina, southern Brazil,” Environmental Conservation, vol. 33, no. 3, pp. 195–202, 2006.
[43]
S. R. Baptista, “Metropolization and forest recovery in southern Brazil: a multiscale analysis of the Florianópolis city-region, Santa Catarina state, 1970 to 2005,” Ecology and Society, vol. 13, no. 2, article 5, 2008.
[44]
R. A. Houghton, “Aboveground forest biomass and the global carbon balance,” Global Change Biology, vol. 11, no. 6, pp. 945–958, 2005.
[45]
W. Liu, C. Song, T. A. Schroeder, and W. B. Cohen, “Predicting forest successional stages using multitemporal Landsat imagery with forest inventory and analysis data,” International Journal of Remote Sensing, vol. 29, no. 13, pp. 3855–3872, 2008.
[46]
T. Neeff, R. M. Lucas, J. R. D. Santos, E. S. Brondizio, and C. C. Freitas, “Area and age of secondary forests in Brazilian Amazonia 1978–2002: an empirical estimate,” Ecosystems, vol. 9, no. 4, pp. 609–623, 2006.
[47]
A. H. Sirén and E. S. Brondizio, “Detecting subtle land use change in tropical forests,” Applied Geography, vol. 29, no. 2, pp. 201–211, 2009.
[48]
K. A. Dolan, G. C. Hurtt, J. Q. Chambers, R. O. Dubayah, S. Frolking, and J. G. Masek, “Using ICESat's Geoscience Laser Altimeter System (GLAS) to assess large-scale forest disturbance caused by hurricane Katrina,” Remote Sensing of Environment, vol. 115, no. 1, pp. 86–96, 2011.
[49]
H. Tangki and N. A. Chappell, “Biomass variation across selectively logged forest within a 225-km2 region of Borneo and its prediction by Landsat TM,” Forest Ecology and Management, vol. 256, no. 11, pp. 1960–1970, 2008.
[50]
T. Okuda, M. Suzuki, S. Numata et al., “Estimation of aboveground biomass in logged and primary lowland rainforests using 3-D photogrammetric analysis,” Forest Ecology and Management, vol. 203, no. 1–3, pp. 63–75, 2004.
[51]
P. E. Busby, P. Vitousek, and R. Dirzo, “Prevalence of tree regeneration by sprouting and seeding along a rainfall gradient in Hawai'i,” Biotropica, vol. 42, no. 1, pp. 80–86, 2010.
[52]
D. A. Clark, S. Brown, D. W. Kicklighter et al., “Net primary production in tropical forests: an evaluation and synthesis of existing field data,” Ecological Applications, vol. 11, no. 2, pp. 371–384, 2001.
[53]
J. B. Drake, R. G. Knox, R. O. Dubayah et al., “Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships,” Global Ecology & Biogeography, vol. 12, no. 2, pp. 147–159, 2003.
[54]
L. F. Alves, S. A. Vieira, M. A. Scaranello et al., “Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil),” Forest Ecology and Management, vol. 260, no. 5, pp. 679–691, 2010.
[55]
G. P. Asner, G. V. N. Powell, J. Mascaro et al., “High-resolution forest carbon stocks and emissions in the Amazon,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 38, pp. 16738–16742, 2010.
[56]
S. Brown and A. E. Lugo, “The storage and production of organic matter in tropical forests and their role in the global carbon cycle,” Biotropica, vol. 14, no. 3, pp. 161–187, 1982.
[57]
IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, National Greenhouse Gas Inventories Programme, Kanagawa, Japan, 2006, Edited by H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara and K. Tanabe.
[58]
IPCC, “Expert meeting on national forest GHC inventories,” in National Forest GHC Inventories—A Stock Taking, H. S. Eggleston, N. Srivastava, K. Tanabe, and J. Baasansuren, Eds., IGES, Hayama, Japan, 2010.
[59]
S. S. Saatchi, N. L. Harris, S. Brown et al., “Benchmark map of forest carbon stocks in tropical regions across three continents,” Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 24, pp. 9899–9904, 2011.
[60]
FAO, State of the World’s Forests, Food and Agriculture Organization, Rome, Italy, 2011.
[61]
B. Freedman, G. Stinson, and P. Lacoul, “Carbon credits and the conservation of natural areas,” Environmental Reviews, vol. 17, pp. 1–19, 2009.
[62]
M. Heindl and H. Winkler, “Vertical lek placement of forest-dwelling manakin species (Aves, Pipridae) is associated with vertical gradients of ambient light,” Biological Journal of the Linnean Society, vol. 80, no. 4, pp. 647–658, 2003.
[63]
J. Tews, U. Brose, V. Grimm et al., “Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures,” Journal of Biogeography, vol. 31, no. 1, pp. 79–92, 2004.
[64]
J. Fischer and D. L. Lindenmayer, “Beyond fragmentation: the continuum model for fauna research and conservation in human-modified landscapes,” Oikos, vol. 112, no. 2, pp. 473–480, 2006.
[65]
T. M. Kuplich, “Classifying regenerating forest stages in Amaz？nia using remotely sensed images and a neural network,” Forest Ecology and Management, vol. 234, no. 1–3, pp. 1–9, 2006.
[66]
S. G. Zolkos, S. J. Goetz, and R. Dubayah, “A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing,” Remote Sensing of Environment, vol. 128, pp. 289–298, 2013.
[67]
J. Pearlman, S. Carman, C. Segal, P. Jarecke, P. Clancy, and W. Browne, “Overview of the hyperion imaging spectrometer for the NASA EO-1 mission,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS '01), vol. 7, pp. 3036–3038, Sydney, Australia, July 2001.
[68]
C. J. Legleiter, W. A. Marcus, and R. L. Lawrence, “Effects of sensor resolution on mapping in-stream habitats,” Photogrammetric Engineering & Remote Sensing, vol. 68, no. 8, pp. 801–807, 2002.
[69]
P. S. Thenkabail, E. A. Enclona, M. S. Ashton, C. Legg, and M. J. de Dieu, “Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests,” Remote Sensing of Environment, vol. 90, no. 1, pp. 23–43, 2004.
[70]
G. P. Asner, D. E. Knapp, J. Boardman et al., “Carnegie Airborne Observatory-2: increasing science data dimensionality via high-fidelity multi-sensor fusion,” Remote Sensing of Environment, vol. 124, pp. 454–465, 2012.
[71]
J. B. Féret and G. P. Asner, “Semi-supervised methods to identify individual crowns of lowland tropical canopy species using imaging spectroscopy and LiDAR,” Remote Sensing, vol. 4, no. 8, pp. 2457–2476, 2012.
[72]
M. L. Imhoff, “Theoretical analysis of the effect of forest structure on synthetic aperture radar backscatter and the remote sensing of biomass,” IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 2, pp. 341–352, 1995.
[73]
J. A. Anaya, E. Chuvieco, and A. Palacios-Orueta, “Aboveground biomass assessment in Colombia: a remote sensing approach,” Forest Ecology and Management, vol. 257, no. 4, pp. 1237–1246, 2009.
[74]
E. F. Vermote, D. Tanré, J. L. Deuzé, M. Herman, and J.-J. Morcrette, “Second simulation of the satellite signal in the solar spectrum, 6s: an overview,” IEEE Transactions on Geoscience and Remote Sensing, vol. 35, no. 3, pp. 675–686, 1997.
[75]
M. Latorre, O. A. C. Júnior, A. P. F. Carvalho, and Y. E. Shimabukuro, “Corre？？o atmosférica: conceito e fundamentos,” Espa？o & Geografia, vol. 5, pp. 153–178, 2002.
[76]
C. Song and C. E. Woodcock, “Monitoring forest succession with multitemporal landsat images: factors of uncertainty,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 11, pp. 2557–2567, 2003.
[77]
S. Brown and A. E. Lugo, “Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon,” Interciência, vol. 17, pp. 8–18, 1992.
[78]
G. P. Asner, J. Mascaro, H. C. Muller-Landau et al., “A universal airborne LiDAR approach for tropical forest carbon mapping,” Oecologia, vol. 168, no. 4, pp. 1147–1160, 2012.
[79]
A. Luckman, J. Baker, T. M. Kuplich, F. Y. Corina da Costa, and C. F. Alejandro, “A study of the relationship between radar backscatter and regenerating tropical forest biomass for spaceborne SAR instruments,” Remote Sensing of Environment, vol. 60, no. 1, pp. 1–13, 1997.
[80]
R. A. Houghton, K. T. Lawrence, J. L. Hackler, and S. Brown, “The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates,” Global Change Biology, vol. 7, no. 7, pp. 731–746, 2001.
[81]
E. Tomppo, H. Olsson, G. St？hl, M. Nilsson, O. Hagner, and M. Katila, “Combining national forest inventory field plots and remote sensing data for forest databases,” Remote Sensing of Environment, vol. 112, no. 5, pp. 1982–1999, 2008.
[82]
S. P. Hubbell, “Tree dispersion, abundance, and diversity in a tropical dry forest,” Science, vol. 203, no. 4387, pp. 1299–1309, 1979.
[83]
S. Saatchi, M. Marlier, R. L. Chazdon, D. B. Clark, and A. E. Russell, “Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass,” Remote Sensing of Environment, vol. 115, no. 11, pp. 2836–2849, 2011.
[84]
G. W. Frazer, S. Magnussen, M. A. Wulder, and K. O. Niemann, “Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass,” Remote Sensing of Environment, vol. 115, no. 2, pp. 636–649, 2011.
[85]
A. A. Lindsey, J. D. Barton Jr., and S. R. Miles, “Field efficiencies of forest sampling methods,” Ecology, vol. 39, no. 3, pp. 428–444, 1958.
[86]
M. D. Bitencourt, H. N. de Mesquita Jr., G. Kuntschik, H. R. da Rocha, and P. A. Furley, “Cerrado vegetation study using optical and radar remote sensing: two Brazilian case studies,” Canadian Journal of Remote Sensing, vol. 33, no. 6, pp. 468–480, 2007.
[87]
J. M. Barbosa, I. Melendez-Pastor, J. Navarro-Pedre？o, and M. D. Bitencourt, “Remotely sensed biomass over steep slopes: an evaluation among successional stands of the Atlantic Forest, Brazil,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 88, pp. 91–100, 2014.
[88]
H. Li, P. Mausel, E. Brondizio, and D. Deardorff, “A framework for creating and validating a non-linear spectrum-biomass model to estimate the secondary succession biomass in moist tropical forests,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, no. 2, pp. 241–254, 2010.
[89]
A. M. Wilson, J. A. Silander, A. Gelfand, and J. H. Glenn, “Scaling up: linking field data and remote sensing with a hierarchical model,” International Journal of Geographical Information Science, vol. 25, no. 3, pp. 509–521, 2011.
[90]
D. A. King, S. J. Davies, S. Tan, and N. S. M. Noor, “The role of wood density and stem support costs in the growth and mortality of tropical trees,” Journal of Ecology, vol. 94, no. 3, pp. 670–680, 2006.
[91]
D. M. D. Burger and W. B. C. Delitti, “Allometric models for estimating the phytomass of a secondary Atlantic Forest area of southeastern Brazil,” Biota Neotropica, vol. 8, no. 4, pp. 131–136, 2008.
[92]
S. A. Vieira, L. F. Alves, M. Aidar et al., “Estimation of biomass and carbon stocks: the case of the Atlantic Forest,” Biota Neotropica, vol. 8, no. 2, pp. 21–29, 2008.
[93]
B. W. Nelson, R. Mesquita, J. L. G. Pereira, S. G. A. de Souza, G. T. Batista, and L. B. Couto, “Allometric regressions for improved estimate of secondary forest biomass in the central Amazon,” Forest Ecology and Management, vol. 117, no. 1–3, pp. 149–167, 1999.
[94]
E. N. Broadbent, G. P. Asner, M. Pe？a-Claros, M. Palace, and M. Soriano, “Spatial partitioning of biomass and diversity in a lowland Bolivian forest: linking field and remote sensing measurements,” Forest Ecology and Management, vol. 255, no. 7, pp. 2602–2616, 2008.
[95]
T. Kajisa, T. Murakami, N. Mizoue, N. Top, and S. Yoshida, “Object-based forest biomass estimation using Landsat ETM+ in Kampong Thom Province, Cambodia,” Journal of Forest Research, vol. 14, no. 4, pp. 203–211, 2009.
[96]
B. M. Bolker, Ecological models and data in R, Princeton University Press, Princeton, NJ, USA, 2008.
[97]
J. C. Ingram, T. P. Dawson, and R. J. Whittaker, “Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks,” Remote Sensing of Environment, vol. 94, no. 4, pp. 491–507, 2005.
[98]
G. M. Foody, M. E. Cutler, J. McMorrow et al., “Mapping the biomass of Bornean tropical rain forest from remotely sensed data,” Global Ecology & Biogeography, vol. 10, no. 4, pp. 379–387, 2001.
[99]
D. Ria？o, E. Chuvieco, J. Salas, and I. Aguado, “Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003),” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 5, pp. 1056–1061, 2003.
[100]
Y. Gao and W. Zhang, “A simple empirical topographic correction method for ETM+ imagery,” International Journal of Remote Sensing, vol. 30, no. 9, pp. 2259–2275, 2009.
[101]
M. A. Lefsky, W. B. Cohen, S. A. Acker, G. G. Parker, T. A. Spies, and D. Harding, “Lidar remote sensing of the canopy structure and biophysical properties of Douglas-Fir Western Hemlock Forests,” Remote Sensing of Environment, vol. 70, no. 3, pp. 339–361, 1999.
[102]
P. K？hler and A. Huth, “Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests,” Biogeosciences, vol. 7, no. 8, pp. 2531–2543, 2010.
[103]
J. Chave, C. Andalo, S. Brown et al., “Tree allometry and improved estimation of carbon stocks and balance in tropical forests,” Oecologia, vol. 145, no. 1, pp. 87–99, 2005.
[104]
J. R. Santos, C. C. Freitas, L. S. Araujo et al., “Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest,” Remote Sensing of Environment, vol. 87, no. 4, pp. 482–493, 2003.
[105]
D. H. Hoekman and M. J. Qui？ones, “Land cover type and biomass classification using AirSAR data for evaluation of monitoring scenarios in the Colombian Amazon,” IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 2 I, pp. 685–696, 2000.
[106]
J. P. Arroyo-Mora, G. A. Sánchez-Azofeifa, M. E. R. Kalacska, B. Rivard, J. C. Calvo-Alvarado, and D. H. Janzen, “Secondary forest detection in a Neotropical dry forest landscape using Landsat 7 ETM+ and IKONOS imagery,” Biotropica, vol. 37, no. 4, pp. 497–507, 2005.
[107]
D. A. Coomes, R. B. Allen, N. A. Scott, C. Goulding, and P. Beets, “Designing systems to monitor carbon stocks in forests and shrublands,” Forest Ecology and Management, vol. 164, no. 1–3, pp. 89–108, 2002.
[108]
G. C. Hurtt, R. Dubayah, J. Drake et al., “Beyond potential vegetation: combining lidar data and a height-structured model for carbon studies,” Ecological Applications, vol. 14, no. 3, pp. 873–883, 2004.
[109]
A. C. Morel, S. S. Saatchi, Y. Malhi et al., “Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data,” Forest Ecology and Management, vol. 262, no. 9, pp. 1786–1798, 2011.
[110]
S. Descloux, V. Chanudet, H. Poilvé, and A. Grégoire, “Co-assessment of biomass and soil organic carbon stocks in a future reservoir area located in Southeast Asia,” Environmental Monitoring and Assessment, vol. 173, no. 1–4, pp. 723–741, 2011.
[111]
J. B. Drake, R. O. Dubayah, D. B. Clark et al., “Estimation of tropical forest structural characteristics, using large-footprint lidar,” Remote Sensing of Environment, vol. 79, no. 2-3, pp. 305–319, 2002.
[112]
S. Eckert, H. R. Ratsimba, L. O. Rakotondrasoa, L. G. Rajoelison, and A. Ehrensperger, “Deforestation and forest degradation monitoring and assessment of biomass and carbon stock of lowland rainforest in the Analanjirofo region, Madagascar,” Forest Ecology and Management, vol. 262, no. 11, pp. 1996–2007, 2011.
[113]
D. Lu, P. Mausel, E. Brondízio, and E. Moran, “Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin,” Forest Ecology and Management, vol. 198, no. 1–3, pp. 149–167, 2004.
[114]
C. Proisy, P. Couteron, and F. Fromard, “Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images,” Remote Sensing of Environment, vol. 109, no. 3, pp. 379–392, 2007.
[115]
I. da Silva Narvaes, A. de Queiroz da Silva, and J. R. dos Santos, “Evaluation of the interaction between SAR L-band signal and structural parameters of forest cover,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS '07), pp. 1607–1610, Barcelona, Spain, June 2007.
[116]
M. Simard, V. H. Rivera-Monroy, J. E. Mancera-Pineda, E. Casta？eda-Moya, and R. R. Twilley, “A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: application to Ciénaga Grande de Santa Marta, Colombia,” Remote Sensing of Environment, vol. 112, no. 5, pp. 2131–2144, 2008.
[117]
F. J. Ponzoni, L. S. Galvao, V. Liesenberg, and J. R. Santos, “Impact of multi-angular CHRIS/PROBA data on their empirical relationships with tropical forest biomass,” International Journal of Remote Sensing, vol. 31, no. 19, pp. 5257–5273, 2010.
[118]
J. E. Nichol and M. L. R. Sarker, “Improved biomass estimation using the texture parameters of two high-resolution optical sensors,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 3, pp. 930–948, 2011.
[119]
L. R. Sarker and J. E. Nichol, “Improved forest biomass estimates using ALOS AVNIR-2 texture indices,” Remote Sensing of Environment, vol. 115, no. 4, pp. 968–977, 2011.
[120]
A. Luckman, J. Baker, T. M. Kuplich, F. Y. Corina da Costa, and C. F. Alejandro, “A study of the relationship between radar backscatter and regenerating tropical forest biomass for spaceborne SAR instruments,” Remote Sensing of Environment, vol. 60, no. 1, pp. 1–13, 1997.
[121]
D. H. T. Minh, T. le Toan, F. Rocca, S. Tebaldini, M. M. D'Alessandro, and L. Villard, “Relating P-band synthetic aperture radar tomography to tropical forest biomass,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 2, pp. 967–979, 2014.
[122]
P. Ploton, R. Pélissier, C. Proisy et al., “Assessing aboveground tropical forest biomass using Google Earth canopy images,” Ecological Applications, vol. 22, no. 3, pp. 993–1003, 2012.
[123]
G. M. Foody and P. J. Curran, “Estimation of tropical forest extent and regenerative stage using remotely sensed data,” Journal of Biogeography, vol. 21, no. 3, pp. 223–244, 1994.
[124]
G. M. Foody, R. M. Green, R. M. Lucas, P. J. Curran, M. Honzak, and I. do Amaral, “Observations on the relationship between SIR-C radar backscatter and the biomass of regenerating tropical forests,” International Journal of Remote Sensing, vol. 18, no. 3, pp. 687–694, 1997.
[125]
D. S. Boyd, G. M. Foody, and P. J. Curran, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0–5.0？μm),” International Journal of Remote Sensing, vol. 20, no. 5, pp. 1017–1023, 1999.
[126]
W. A. Salas, M. J. Ducey, E. Rignot, and D. Skole, “Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: I. Spatial and temporal variability in backscatter across a chrono-sequence of secondary vegetation stands in Rondonia,” International Journal of Remote Sensing, vol. 23, no. 7, pp. 1357–1379, 2002.
[127]
T. Neeff, L. V. Dutra, J. R. dos Santos, C. da Costa Freitas, and L. S. Araujo, “Tropical forest measurement by interferometric height modeling and P-band radar backscatter,” Forest Science, vol. 51, no. 6, pp. 585–594, 2005.
[128]
M. A. Castillo-Santiago, M. Ricker, and B. H. J. de Jong, “Estimation of tropical forest structure from SPOT-5 satellite images,” International Journal of Remote Sensing, vol. 31, no. 10, pp. 2767–2782, 2010.
[129]
M. L. Clark, D. A. Roberts, J. J. Ewel, and D. B. Clark, “Estimation of tropical rain forest aboveground biomass with small-footprint LiDAR and hyperspectral sensors,” Remote Sensing of Environment, vol. 115, no. 11, pp. 2931–2942, 2011.
[130]
C. Baraloto, S. Rabaud, Q. Molto et al., “Disentangling stand and environmental correlates of aboveground biomass in Amazonian forests,” Global Change Biology, vol. 17, no. 8, pp. 2677–2688, 2011.
[131]
G. P. Asner and J. Mascaro, “Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric,” Remote Sensing of Environment, vol. 140, pp. 614–624, 2014.
[132]
M. L. R. Sarker, J. Nichol, B. Ahmad, I. Busu, and A. A. Rahman, “Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 69, pp. 146–166, 2012.
