Twenty-first century remote sensing technologies are revolutionizing the study of tropical forests

Biotropica

Volumn 49, Issue 5, 2017, Pages 604-619

  Sanchez Azofeifa, Arturo ^a   Antonio Guzmán, Jose ^a   Campos, Carlos A ^a   Castro, Saulo ^a   Garcia Millan, Virginia ^a   Nightingale, Joanne ^b   Rankine, Cassidy ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b NATIONAL PHYSICAL LABORATORY   (United Kingdom)

Author keywords

hyperspectral remote sensing; LiDAR; multispectral remote sensing; near surface remote sensing; wireless sensor networks

Indexed keywords

CONSERVATION; LIDAR; MULTISPECTRAL IMAGE; REMOTE SENSING; SENSOR; SPATIAL RESOLUTION; TECHNOLOGICAL DEVELOPMENT; TROPICAL FOREST; TWENTY FIRST CENTURY;

EID: 85020751821     PISSN: 00063606     EISSN: 17447429     Source Type: Journal    
DOI: 10.1111/btp.12454     Document Type: Review

References (208)

1. Aggarwal, S. 2004. Principles of Remote Sensing. In M. V. K. Sivakumar, P. S. Roy, K. Harsen, and S. K. Saha (Eds.). Satellite remote sensing and GIS applications in agricultural meteorology, pp. 1–23. World Meteorological Organisation, Geneva, Switzerland.
2. Anderson, J., M. E. Martin, M.-L. Smith, R. O. Dubayah, M. A. Hofton, P. Hyde, B. E. Peterson, J. B. Blair, and R. G. Knox. 2006. The use of waveform LiDAR to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire. Remote Sens. Environ. 105: 248–261.
3. Angelstam, P. 1997. Landscape analysis as a tool for the scientific management of biodiversity. Ecol. Bull. 140–170.
4. Armston, J., M. Disney, P. Lewis, P. Scarth, S. Phinn, R. Lucas, P. Bunting, and N. Goodwin. 2013. Direct retrieval of canopy gap probability using airborne waveform lidar. Remote Sens. Environ. 134: 24–38.
5. Arroyo-Mora, J. P., G. A. Sánchez-Azofeifa, M. E. R. Kalacska, B. Rivard, J. C. Calvo-Alvarado, and D. H. Janzen. 2005. Secondary forest detection in a neotropical dry lorest landscape using Landsat 7 ETM+ and IKONOS imagery. Biotropica 37: 497–507.
6. Asner, G. P. 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 64: 234–253.
7. Asner, G. P., and K. B. Heidebrecht. 2005. Desertification alters regional ecosystem–climate interactions. Glob. Chang. Biol. 11: 182–194.
8. 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: 480–482.
9. Asner, G. P., and R. E. Martin. 2016. Spectranomics: emerging science and conservation opportunities at the interface of biodiversity and remote sensing. Glob. Ecol. Conserv. 8: 212–219.
10. Asner, G. P., J. Mascaro, H. C. Muller-Landau, G. Vieilledent, R. Vaudry, M. Rasamoelina, J. S. Hall, and M. van Breugel. 2012. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168: 1147–1160.
11. Asner, G. P., and A. S. Warner. 2003. Canopy shadow in IKONOS satellite observations of tropical forests and savannas. Remote Sens. Environ. 87: 521–533.
12. Bachmann, C. M., T. F. Donato, K. Dubois, R. A. Fusina, M. Bettenhausen, J. H. Porter, and B. R. Truitt. 2001. Automatic detection of an invasive plant species on a barrier island in the Virginia. IEEE Proc. IGARSS. 2172–2174.
13. Balzarolo, M. K., C. Anderson, M. Nichol, L. Rossini, N. Vescovo, G. Arriga, J. C. Wohlfahrt, A. Calvet, S. Carrara, S. Cerasoli, F. Cogliati, L. Daumard, J. A. Eklundh, F. Elbers, R. N. Evrendilek, J. Handcock, K. Kaduk, B. Klumpp, G. Longdoz, M. Matteucci, L. Meroni, J. M. Montagnani, E. P. Ourcival, J. Y. Sánchez-Cañete, R. Pontailler, B. Scholes Juszczak, and M. P. Martín. 2011. Ground-based optical measurements at european flux sites: a review of methods, instruments and current controversies. Sensors 11: 7954–7981.
14. Barker Schaaf, C., and A. H. Strahlerl. 1994. Validation of bidirectional and hemispherical reflectances from a geometric-optical model using ASAS imagery and pyranometer measurements of a spruce forest. Remote Sens. Environ. 49: 138–144.
15. Barnsley, M. J., J. J. Settle, M. A. Cutter, D. R. Lobb, and F. Teston. 2004. The PROBA/CHRIS mission: a low-cost smallsat for hyperspectral multiangle observations of the Earth surface and atmosphere. IEEE Trans. Geosci. Remote Sens. 42: 1512–1520.
16. Berni, J. A. J., P. J. Zarco-Tejada, L. Suarez, and E. Fereres. 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an umanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 47: 722–738.
17. Björkman, O., and B. Demmig-Adams. 1995. Regulation of Photosynthetic Light Energy Capture, Conversion, and Dissipation in Leaves of Higher Plants. In E.-D. Schulze, and M. M. Caldwell (Eds.). Ecophysiology of photosynthesis, pp. 17–47. Springer, Berlin Heidelberg, Berlin, Heidelberg.
18. Blackburn, G. A., and C. M. Steele. 1999. Towards the remote sensing of matorral vegetation physiology: relationships between spectral reflectance, pigment, and biophysical characteristics of semiarid bushland canopies. Remote Sens. Environ. 70: 278–292.
19. Blair, J. B., and M. A. Hofton. 1999. Modeling laser altimeter return waveforms over complex vegetation using high-resolution elevation data. Geophys. Res. Lett. 26: 2509–2512.
20. Blair, J. B., D. L. Rabine, and M. A. Hofton. 1999. The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS J. Photogramm. Remote Sens. 54: 115–122.
21. Boudreau, J., R. F. Nelson, H. A. Margolis, A. Beaudoin, L. Guindon, and D. S. Kimes. 2008. Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. Remote Sens. Environ. 112: 3876–3890.
22. Braswell, B. H., S. C. Hagen, S. E. Frolking, and W. A. Salas. 2003. A multivariable approach for mapping sub-pixel land cover distributions using MISR and MODIS: application in the Brazilian Amazon region. Remote Sens. Environ. 87: 243–256.
23. Carey, A. B. 1996. Interactions of Northwest forest canopies and arboreal mammals. Northwest Sci. 70: 72–78.
24. Carlson, K. M., L. M. Curran, G. P. Asner, A. M. Pittman, S. N. Trigg, and J. Marion Adeney. 2013. Carbon emissions from forest conversion by Kalimantan oil palm plantations. Nat. Clim. Chang. 3: 283–287.
25. Castillo, M., B. Rivard, A. Sánchez-Azofeifa, J. Calvo-Alvarado, and R. Dubayah. 2012. LIDAR remote sensing for secondary Tropical Dry Forest identification. Remote Sens. Environ. 121: 132–143.
26. Castro-Esau, K. L., G. A. Sánchez-Azofeifa, and T. Caelli. 2004. Discrimination of lianas and trees with leaf-level hyperspectral data. Remote Sens. Environ. 90: 353–372.
27. Chambers, J. Q., G. P. Asner, D. C. Morton, L. O. Anderson, S. S. Saatchi, F. D. B. Espírito-Santo, M. Palace, and C. Souza, Jr. 2007. Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends Ecol. Evol. 22: 414–423.
28. Chen, J. M., C. H. Menges, and S. G. Leblanc. 2005. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sens. Environ. 97: 447–457.
29. Chi, H., Z. Guo, and G. Sun. 2011. Research on tropical forest aboveground biomass estimating using ICESat/GLAS data in Xishuangbanna, Southwest China. In IEEE International Geoscience and Remote Sensing Symposium. pp. 744–747.
30. Chi, H., G. Sun, J. Huang, Z. Guo, W. Ni, and A. Fu. 2015. National forest aboveground biomass mapping from ICESat/GLAS data and MODIS imagery in china. Remote Sens. 7: 5534–5564.
31. Chisholm, R. A., J. Cui, S. K. Y. Lum, and B. M. Chen. 2013. UAV LiDAR for below-canopy forest surveys. J. Unmanned Veh. Syst. 1: 61–68.
32. Cho, M. A., A. Skidmore, F. Corsi, S. E. van Wieren, and I. Sobhan. 2007. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int. J. Appl. Earth Obs. Geoinf. 9: 414–424.
33. Chopping, M., G. G. Moisen, L. Su, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters. 2008. Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer. Remote Sens. Environ. 112: 2051–2063.
34. Chopping, M., A. Nolin, G. G. Moisen, J. V. Martonchik, and M. Bull. 2009. Forest canopy height from the Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar. Remote Sens. Environ. 113: 2172–2185.
35. Chopping, M., L. Su, A. Laliberte, A. Rango, D. P. C. Peters, and N. Kollikkathara. 2006. Mapping shrub abundance in desert grasslands using geometric-optical modeling and multi-angle remote sensing with CHRIS/Proba. Remote Sens. Environ. 104: 62–73.
36. Churkina, G., D. Schimel, B. H. Braswell, and X. Xiao. 2005. Spatial analysis of growing season length control over net ecosystem exchange. Glob. Chang. Biol. 11: 1777–1787.
37. Clark, M. L., T. M. Aide, and G. Riner. 2012. Land change for all municipalities in Latin America and the Caribbean assessed from 250-m MODIS imagery (2001–2010). Remote Sens. Environ. 126: 84–103.
38. Clark, M. L., D. B. Clark, and D. A. Roberts. 2004. Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sens. Environ. 91: 68–89.
39. Clark, M. L., D. A. Roberts, and D. B. Clark. 2005. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens. Environ. 96: 375–398.
40. Cochrane, M. A. 2000. Using vegetation reflectance variability for species level classification of hyperspectral data. Int. J. Remote Sens. 21: 2075–2087.
41. Cook, D. B., A. L. Corp, F. R. Nelson, M. E. Middleton, C. D. Morton, T. J. McCorkel, G. J. Masek, J. K. Ranson, V. Ly, and M. P. Montesano. 2013. NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager. Remote Sens. 5: 4045–4066.
42. Corrigan, C. E., G. C. Roberts, M. V. Ramana, D. Kim, and V. Ramanathan. 2007. Capturing vertical profiles of aerosols and black carbon over the Indian Ocean using autonomous unmanned aerial vehicles. Atmos. Chem. Phys. Discuss. 7: 11429–11463.
43. Curran, P. J. 1994. Imaging spectrometry. Prog. Phys. Geogr. 18: 247–266.
44. Cutler, T. L., and D. E. Swann. 1999. Using remote photography in wildlife ecology: a review. Wildl. Soc. Bull. 27: 571–581.
45. DeFries, R. S., M. Hansen, J. R. G. Townshend, and R. Sohlberg. 1998. Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. Int. J. Remote Sens. 19: 3141–3168.
46. DeFries, R. S., and J. R. G. Townshend. 1994. NDVI-derived land cover classifications at a global scale. Int. J. Remote Sens. 15: 3567–3586.
47. DeFries, R. S., and J. R. G. Townshend. 1999. Global land cover characterization from satellite data: from research to operational implementation? Glob. Ecol. Biogeogr. 8: 367–379.
48. Diner, D. J., G. P. Asner, R. Davies, Y. Knyazikhin, J.-P. Muller, A. W. Nolin, B. Pinty, C. B. Schaaf, and J. Stroeve. 1999. New directions in Earth observing: scientific applications of multiangle remote sensing. Bull. Am. Meteorol. Soc. 80: 2209–2228.
49. Dixon, R. K., S. Brown, R. M. Houghton, A. M. Solomon, M. C. Trexler, and J. Wisneiwski. 1994. Carbon pools and flux of global forest ecosystems. Science (80-.). 263: 185–190.
50. Drake, J. B., R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince. 2002. Estimation of tropical forest structural characteristics using large-footprint LiDAR. Remote Sens. Environ. 79: 305–319.
51. Drake, J. B., R. G. Knox, R. O. Dubayah, D. B. Clark, R. Condit, J. B. Blair, and M. Hofton. 2003. Above-ground biomass estimation in closed canopy Neotropical forests using LiDAR remote sensing: factors affecting the generality of relationships. Glob. Ecol. Biogeogr. 12: 147–159.
52. Duan, S.-B., Z.-L. Li, H. Wu, B.-H. Tang, L. Ma, E. Zhao, and C. Li. 2014. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 26: 12–20.
53. Dubayah, R. O., and J. B. Drake. 2000. Lidar remote sensing for forestry. J. For. 98: 44–46.
54. Dunford, R., K. Michel, M. Gagnage, H. Piégay, and M.-L. Trémelo. 2009. Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest. Int. J. Remote Sens. 30: 4915–4935.
55. Eklundh, L., H. Jin, P. Schubert, R. Guzinski, and M. Heliasz. 2011. An optical sensor network for vegetation phenology monitoring and satellite data calibration. Sensors 11: 7678–7709.
56. Faiçal, B. S., F. G. Costa, G. Pessin, J. Ueyama, H. Freitas, A. Colombo, P. H. Fini, L. Villas, F. S. Osório, P. A. Vargas, and T. Braun. 2014. The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides. J. Syst. Archit. 60: 393–404.
57. Fayad, I., N. Baghdadi, V. Gond, J. S. Bailly, N. Barbier, M. El Hajj, and F. Fabre. 2014. Coupling potential of ICESat/GLAS and SRTM for the discrimination of forest landscape types in French Guiana. Int. J. Appl. Earth Obs. Geoinf. 33: 21–31.
58. Fuentes, D. A., J. A. Gamon, Y. Cheng, H. C. Claudio, H. Qiu, Z. Mao, D. A. Sims, A. F. Rahman, W. Oechel, and H. Luo. 2006. Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS. Remote Sens. Environ. 103: 312–323.
59. Fukushima, Y., T. Hiura, and S. Tanabe. 1998. Accuracy of the MacArthur-Horn method for estimating a foliage profile. Agric. For. Meteorol. 92: 203–210.
60. Galvão, L. S., F. J. Ponzoni, V. Liesenberg, and J. R. dos Santos. 2009. Possibilities of discriminating tropical secondary succession in Amazônia using hyperspectral and multiangular CHRIS/PROBA data. Int. J. Appl. Earth Obs. Geoinf. 11: 8–14.
61. Gao, J. 1999. A comparative study on spatial and spectral resolutions of satellite data in mapping mangrove forests. Int. J. Remote Sens. 20: 2823–2833.
62. Gao, B.-C., and A. F. H. Goetz. 1994. Extraction of dry leaf spectral features from reflectance spectra of green vegetation. Remote Sens. Environ. 47: 369–374.
63. García Millán, V. E., A. Sánchez-Azofeifa, G. C. Málvarez García, and B. Rivard. 2014. Quantifying tropical dry forest succession in the Americas using CHRIS/PROBA. Remote Sens. Environ. 144: 120–136.
64. Garrity, S. R., L. A. Vierling, and K. Bickford. 2010. A simple filtered photodiode instrument for continuous measurement of narrowband NDVI and PRI over vegetated canopies. Agric. For. Meteorol. 150: 489–496.
65. Gastellu-Etchegorry, J. P., F. Zagolski, E. Mougtn, G. Marty, and G. Giordano. 1995. An assessment of canopy chemistry with AVIRIS—a case study in the Landes Forest, South-west France. Int. J. Remote Sens. 16: 487–501.
66. Gerard, F. F., and P. R. J. North. 1997. Analyzing the effect of structural variability and canopy gaps on forest BRDF using a geometric-optical model. Remote Sens. Environ. 62: 46–62.
67. Gibbs, H. K., S. Brown, J. O. Niles, and J. A. Foley. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett. 2: 45023.
68. Gillespie, T. W., J. Brock, and C. W. Wright. 2004. Prospects for quantifying structure, floristic composition and species richness of tropical forests. Int. J. Remote Sens. 25: 707–715.
69. Giri, C., E. Ochieng, L. L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek, and N. Duke. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20: 154–159.
70. Gonzalez-Dugo, V., P. Zarco-Tejada, E. Nicolás, P. A. Nortes, J. J. Alarcón, D. S. Intrigliolo, and E. Fereres. 2013. Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precis. Agric. 14: 660–678.
71. Goulden, M. L., J. W. Munger, S.-M. Fan, B. C. Daube, and S. C. Wofsy. 1996. Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy. Glob. Chang. Biol. 2: 169–182.
72. Gregg, W. W., and N. W. Casey. 2004. Global and regional evaluation of the SeaWiFS chlorophyll data set. Remote Sens. Environ. 93: 463–479.
73. Grenzdörffer, G. J., A. Engel, and B. Teichert. 2008. The photogrammetric potential of low-cost UAVs in forestry and agriculture. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 31: 1207–1214.
74. Guzmán, J., and H. Vega. 2015. Is forest cover conserved and restored by protected areas?: the case of two wild protected areas in the Central Pacific of Costa Rica. Int. J. Trop. Biol. Conserv. 63: 579–590.
75. Haarpaintner, J., K. Einzmann, D. Pedrazzani, M. T. M. S. Juan, M. G. Giménez, J. Heinzel, le Enß F., and L. Mane. 2012. Tropical forest remote sensing services for the Democratic Republic of Congo case inside the EU FP7 “ReCover” project (1st iteration). In IEEE International Geoscience and Remote Sensing Symposium. pp. 6392–6395.
76. Hall, F. G., K. Bergen, J. B. Blair, R. Dubayah, R. Houghton, G. Hurtt, J. Kellndorfer, M. Lefsky, J. Ranson, S. Saatchi, H. H. Shugart, and D. Wickland. 2011. Characterizing 3D vegetation structure from space: Mission requirements. Remote Sens. Environ. 115: 2753–2775.
77. Hamilton, M. P., E. A. Graham, P. W. Rundel, M. F. Allen, W. Kaiser, M. H. Hansen, and D. L. Estrin. 2007. New approaches in embedded networked sensing for terrestrial ecological observatories. Environ. Eng. Sci. 24: 192–204.
78. Hansen, M. C., S. V. Stehman, and P. V. Potapov. 2010. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107: 8650–8655.
79. Harding, D. J., J. B. Blair, J. B. Garvin, and W. T. Lawrence. 1994. Laser altimetry waveform measurement of vegetation canopy structure. In International Geoscience and Remote Sensing Symposium. pp. 1251–1253.
80. Harding, D. J., M. A. Lefsky, G. G. Parker, and J. B. Blair. 2001. Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests. Remote Sens. Environ. 76: 283–297.
81. Heiskanen, J. 2006. Tree cover and height estimation in the Fennoscandian tundra–taiga transition zone using multiangular MISR data. Remote Sens. Environ. 103: 97–114.
82. Helmer, E. H., M. A. Lefsky, and D. A. Roberts. 2009. Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System. J. Appl. Remote Sens. 3: 33505–33531.
83. Hilker, T., N. C. Coops, M. A. Wulder, T. A. Black, and R. D. Guy. 2008. The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements. Sci. Total Environ. 404: 411–423.
84. Hinsley, S. A., R. A. Hill, D. L. A. Gaveau, and P. E. Bellamy. 2002. Quantifying woodland structure and habitat quality for birds using airborne laser scanning. Funct. Ecol. 16: 851–857.
85. Hofton, M. A., L. E. Rocchio, J. B. Blair, and R. Dubayah. 2002. Validation of vegetation canopy Lidar sub-canopy topography measurements for a dense tropical forest. J. Geodyn. 34: 491–502.
86. Hovi, A., and I. Korpela. 2014. Real and simulated waveform-recording LiDAR data in juvenile boreal forest vegetation. Remote Sens. Environ. 140: 665–678.
87. Huang, C., N. Thomas, S. N. Goward, J. G. Masek, Z. Zhu, J. R. G. Townshend, and J. E. Vogelmann. 2010. Automated masking of cloud and cloud shadow for forest change analysis using Landsat images. Int. J. Remote Sens. 31: 5449–5464.
88. Huemmrich, K. F., T. A. Black, P. G. Jarvis, J. H. McCaughey, and F. G. Hall. 1999. High temporal resolution NDVI phenology from micrometeorological radiation sensors. J. Geophys. Res. Atmos. 104: 27935–27944.
89. Jaakkola, A., J. Hyyppä, A. Kukko, X. Yu, H. Kaartinen, M. Lehtomäki, and Y. Lin. 2010. A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS J. Photogramm. Remote Sens. 65: 514–522.
90. Jenkins, J. P., A. D. Richardson, B. H. Braswell, S. V. Ollinger, D. Y. Hollinger, and M.-L. Smith. 2007. Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Agric. For. Meteorol. 143: 64–79.
91. Jensen, J. R. 2005. Introductory digital image processing: a remote sensing perspective. (3rd Edition). Prentice Hall Inc, Upper Saddle River, New Jersey.
92. Johansen, K., N. C. Coops, S. E. Gergel, and Y. Stange. 2007. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sens. Environ. 110: 29–44.
93. Justice, C. O., et al. 1998. The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens. 36: 1228–1249.
94. Kalacska, M., S. Bohlman, G. A. Sánchez-Azofeifa, K. Castro-Esau, and T. Caelli. 2007a. Hyperspectral discrimination of tropical dry forest lianas and trees: comparative data reduction approaches at the leaf and canopy levels. Remote Sens. Environ. 109: 406–415.
95. Kalacska, M., G. A. Sánchez-Azofeifa, T. Caelli, B. Rivard, and B. Boerlage. 2005b. Estimating leaf area index from satellite imagery using Bayesian networks. IEEE Trans. Geosci. Remote Sens. 43: 1866–1873.
96. Kalacska, M. E. R., G. A. Sánchez-Azofeifa, J. C. Calvo-Alvarado, B. Rivard, and M. Quesada. 2005a. Effects of season and successional stage on leaf area index and spectral vegetation indices in three Mesoamerican tropical dry forests. Biotropica 37: 486–496.
97. Kalacska, M., G. A. Sánchez-Azofeifa, B. Rivard, T. Caelli, H. P. White, and J. C. Calvo-Alvarado. 2007b. Ecological fingerprinting of ecosystem succession: estimating secondary tropical dry forest structure and diversity using imaging spectroscopy. Remote Sens. Environ. 108: 82–96.
98. Kampe, T. U., B. R. Johnson, M. Kuester, and M. Keller. 2010. NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure. J. Appl. Remote Sens. 4: 43510–43524.
99. Kasischke, E. S., J. M. Melack, and M. Craig Dobson. 1997. The use of imaging radars for ecological applications—a review. Remote Sens. Environ. 59: 141–156.
100. Kayitakire, F., C. Hamel, and P. Defourny. 2006. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens. Environ. 102: 390–401.
101. Keenan, R. J., G. A. Reams, F. Achard, J. V. de Freitas, A. Grainger, and E. Lindquist. 2015. Dynamics of global forest area: results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manage. 352: 9–20.
102. Khorram, S., F. H. Koch, C. F. van der Wiele, and S. A. C. Nelson. 2012. Introduction. In Khorram, S., Koch, F.H., van der Wiele, C.F., Nelson, S.A.C. (Eds). Remote sensing, pp. 1–15. Springer, US, Boston, Massachusetts.
103. Khorram, S., C. F. van der Wiele, F. H. Koch, S. A. C. Nelson, and M. D. Potts. 2016. Data Acquisition. In Khorram, S., van der Wiele, C.F., Koch, F.H., Nelson, S.A.C., Potts, M.D. (Eds). Principles of applied remote sensing, pp. 21–67. Springer International Publishing, Cham.
104. Kimes, D. S., K. J. Ranson, G. Sun, and J. B. Blair. 2006. Predicting lidar measured forest vertical structure from multi-angle spectral data. Remote Sens. Environ. 100: 503–511.
105. Kneubühler, M., B. Koetz, S. Huber, N. E. Zimmermann, and M. E. Schaepman. 2008. Space-based spectrodirectional measurements for the improved estimation of ecosystem variables. Can. J. Remote. Sens. 34: 192–205.
106. Koo, V. C., Y. K. Chan, G. Vetharatnam, M. Y. Chua, C. H. Lim, C.-S. Lim, C. C. Thum, T. S. Lim, Z. bin Ahmad, K. M. Mahmood, M. H. Bin Shahid, C. Y. Ang, Q. W. Tan, P. M. Tan, K. S. Yee, G. W. Cheaw, H. S. Boey, A. L. Choo, and B. C. Sew. 2012. A new unmanned aerial vehicle synthetic aperture radar for environmental monitoring. Prog. Electromagn. Res. 122: 245–268.
107. Lass, L. W., D. C. Thill, B. Shafii, and T. S. Prather. 2002. Detecting spotted knapweed (Centaurea maculosa) with hyperspectral remote sensing technology. Weed Technol. 16: 426–432.
108. Leblanc, S. G., J. M. Chen, H. P. White, R. Latifovic, R. Lacaze, and J.-L. Roujean. 2005. Canada-wide foliage clumping index mapping from multiangular POLDER measurements. Can. J. Remote. Sens. 31: 364–376.
109. Lee, K.-S., W. B. Cohen, R. E. Kennedy, T. K. Maiersperger, and S. T. Gower. 2004. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sens. Environ. 91: 508–520.
110. Lee, S. K., T. Fatoyinbo, B. Osmanoglu, and G. Sun. 2014. Polarimetric SAR interferometry evaluation in mangroves. In IEEE Geoscience and Remote Sensing Symposium. pp. 4584–4587.
111. Lefsky, M. A., W. B. Cohen, S. A. Acker, G. G. Parker, T. A. Spies, and D. Harding. 1999. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-Fir Western Hemlock forests. Remote Sens. Environ. 70: 339–361.
112. Li, X., A. H. Strahler, and C. E. Woodcock. 1995. A hybrid geometric optical-radiative transfer approach for modeling albedo and directional reflectance of discontinuous canopies. IEEE Trans. Geosci. Remote Sens. 33: 466–480.
113. Liesenberg, V., H. D. V Boehm, and R. Gloaguen. 2009. The contribution of CHRIS/PROBA data for tropical peat swamp landscape discrimination purposes. In IEEE International Geoscience and Remote Sensing Symposium. p. II-57-II-60.
114. Lillesand, T. M., R. W. Kiefer, and J. W. Chipman. 2004. Remote sensing and image interpretation Seventh. John Wiley & Sons, Ltd., New York, New York.
115. Lin, Y., J. Hyyppa, and A. Jaakkola. 2011. Mini-UAV-Borne LIDAR for Fine-Scale Mapping. IEEE Geosci. Remote Sens. Lett. 8: 426–430.
116. Luckman, A., J. Baker, T. M. Kuplich, C. da Costa Freitas Yanasse, and A. C. Frery. 1997. A study of the relationship between radar backscatter and regenerating tropical forest biomass for spaceborne SAR instruments. Remote Sens. Environ. 60: 1–13.
117. MacArthur, R. H. 1958. Population ecology of some warblers of northeastern coniferous forests. Ecology 39: 599–619.
118. Malhi, Y., and J. Grace. 2000. Tropical forests and atmospheric carbon dioxide. Trends Ecol. Evol. 15: 332–337.
119. Martin, M. E., and J. D. Aber. 1997. High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecol. Appl. 7: 431–443.
120. Martin, M. E., S. D. Newman, J. D. Aber, and R. G. Congalton. 1998. Determining forest species composition using high spectral resolution remote sensing data. Remote Sens. Environ. 65: 249–254.
121. Means, J. E., S. A. Acker, D. J. Harding, J. B. Blair, M. A. Lefsky, W. B. Cohen, M. E. Harmon, and W. A. McKee. 1999. Use of large-footprint scanning airborne lidar to estimate forest stand Characteristics in the Western Cascades of Oregon. Remote Sens. Environ. 67: 298–308.
122. Meroni, M., M. Rossini, L. Guanter, L. Alonso, U. Rascher, R. Colombo, and J. Moreno. 2009. Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sens. Environ. 113: 2037–2051.
123. 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: 5421–5438.
124. Miles, L., A. C. Newton, R. S. DeFries, C. Ravilious, I. May, S. Blyth, V. Kapos, and J. E. Gordon. 2006. A global overview of the conservation status of tropical dry forests. J. Biogeogr. 33: 491–505.
125. Mitchard, E. T. A., S. S. Saatchi, L. J. T. White, K. A. Abernethy, K. J. Jeffery, S. L. Lewis, M. Collins, M. A. Lefsky, M. E. Leal, I. H. Woodhouse, and P. Meir. 2012. Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: overcoming problems of high biomass and persistent cloud. Biogeosciences 9: 179–191.
126. Mumby, P. J., E. P. Green, A. J. Edwards, and C. D. Clark. 1999. The cost-effectiveness of remote sensing for tropical coastal resources assessment and management. J. Environ. Manage. 55: 157–166.
127. Mutanga, O., and A. K. Skidmore. 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 25: 3999–4014.
128. Næsset, E. 1997. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J. Photogramm. Remote Sens. 52: 49–56.
129. Næsset, E. 2004. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand. J. For. Res. 19: 164–179.
130. Næsset, E., and T. Økland. 2002. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sens. Environ. 79: 105–115.
131. Nagendra, H. 2001. Using remote sensing to assess biodiversity. Int. J. Remote Sens. 22: 2377–2400.
132. Nelson, R. 1997. Modeling forest canopy heights: the effects of canopy shape. Remote Sens. Environ. 60: 327–334.
133. Nelson, R., R. Oderwald, and T. G. Gregoire. 1997. Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass. Remote Sens. Environ. 60: 311–326.
134. Nelson, R., K. J. Ranson, G. Sun, D. S. Kimes, V. Kharuk, and P. Montesano. 2009. Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sens. Environ. 113: 691–701.
135. Nicodemus, F. E., J. C. Richmond, and J. J. Hsia. 1977. Geometrical considerations and nomenclature for reflectance. National Bureau of Standards, US Department of Commerce, Washington, DC.
136. Nilsson, M. 1996. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens. Environ. 56: 1–7.
137. Nobre, C. A., and L. D. S. Borma. 2009. “Tipping points” for the Amazon forest. Curr. Opin. Environ. Sustain. 1: 28–36.
138. Numata, I., M. A. Cochrane, C. A. Souza, Jr, and M. H. Sales. 2011. Carbon emissions from deforestation and forest fragmentation in the Brazilian Amazon. Environ. Res. Lett. 6: 44003.
139. Oldeland, J., W. Dorigo, D. Wesuls, and N. Jürgens. 2010. Mapping bush encroaching species by seasonal differences in hyperspectral imagery. Remote Sens. 2: 1416–1438.
140. Palacz, A. P., and F. Chai. 2012. Spatial and temporal variability in nutrients and carbon uptake during 2004 and 2005 in the eastern equatorial Pacific Ocean. Biogeosciences 9: 4369–4383.
141. Parrish, C. E., and F. L. Scarpace. 2007. Investigating full-waveform LiDAR data for detection and recognition of vertical objects. In American Society for Photogrammetry and Remote Sensing (ASPRS). Society for Photogrammetry and Remote Sensing (ASPRS).
142. Pekin, B. K., J. Jung, L. J. Villanueva-Rivera, B. C. Pijanowski, and J. A. Ahumada. 2012. Modeling acoustic diversity using soundscape recordings and LIDAR-derived metrics of vertical forest structure in a neotropical rainforest. Landsc. Ecol. 27: 1513–1522.
143. Pijanowski, B. C., A. Farina, S. H. Gage, S. L. Dumyahn, and B. L. Krause. 2011. What is soundscape ecology? An introduction and overview of an emerging new science. Landsc. Ecol. 26: 1213–1232.
144. Pinty, B., J. L. Widlowski, N. Gobron, M. M. Verstraete, and D. J. Diner. 2002. Uniqueness of multiangular measurements. I. An indicator of subpixel surface heterogeneity from MISR. IEEE Trans. Geosci. Remote Sens. 40: 1560–1573.
145. Pocewicz, A., L. A. Vierling, L. B. Lentile, and R. Smith. 2007. View angle effects on relationships between MISR vegetation indices and leaf area index in a recently burned ponderosa pine forest. Remote Sens. Environ. 107: 322–333.
146. Ponzoni, F. J., L. S. Galvão, V. Liesenberg, and J. R. Santos. 2010. Impact of multi-angular CHRIS/PROBA data on their empirical relationships with tropical forest biomass. Int. J. Remote Sens. 31: 5257–5273.
147. Popescu, S. C., K. Zhao, A. Neuenschwander, and C. Lin. 2011. Satellite lidar vs. small footprint airborne LiDAR: comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sens. Environ. 115: 2786–2797.
148. Porter, J., P. Arzberger, H.-W. Braun, P. Bryant, S. Gage, T. Hansen, P. Hanson, C.-C. Lin, F.-P. Lin, T. Kratz, W. Michener, S. Shapiro, and T. Williams. 2005. Wireless sensor networks for ecology. Bioscience 55: 561–572.
149. Portillo-Quintero, C. A., and G. A. Sánchez-Azofeifa. 2010. Extent and conservation of tropical dry forests in the Americas. Biol. Conserv. 143: 144–155.
150. Post, W. M. 1993. Uncertainties in the Terrestrial Carbon Cycle BT. In A. M. Solomon, and H. H. Shugart (Eds.). Vegetation dynamics & global change, pp. 116–132. Springer, US, Boston, Massachusetts.
151. Ranson, K. J., G. Sun, J. F. Weishampel, and R. G. Knox. 1997. Forest biomass from combined ecosystem and radar backscatter modeling. Remote Sens. Environ. 59: 118–133.
152. Reiche, J., J. Verbesselt, D. Hoekman, and M. Herold. 2015. Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sens. Environ. 156: 276–293.
153. Ribeiro da Luz, B., and J. K. Crowley. 2010. Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0–13.5 μm) imagery. Remote Sens. Environ. 114: 404–413.
154. Richardson, A. D., B. H. Braswell, D. Y. Hollinger, J. P. Jenkins, and S. V. Ollinger. 2009. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 19: 1417–1428.
155. Roberts, D. A., J. B. Adams, and M. O. Smith. 1990. Predicted distribution of visible and near-infrared radiant flux above and below a transmittant leaf. Remote Sens. Environ. 34: 1–17.
156. Roberts, D. A., R. O. Grern, D. E. Sabol, and J. B. Adams. 1993. Temporal changes in endmember abundances, liquid water and water vapor over vegetation at Jasper Ridge. In Summaries of the Fourth Annual JPL Airborne Geoscience Workshop. pp. 153–156, Jet Propulsion Laboratory, Pasadena, CA, USA.
157. Rocchini, D. 2007. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sens. Environ. 111: 423–434.
158. Rogers, A., B. E. Medlyn, J. S. Dukes, G. Bonan, S. von Caemmerer, M. C. Dietze, J. Kattge, A. D. B. Leakey, L. M. Mercado, Ü. Niinemets, I. C. Prentice, S. P. Serbin, S. Sitch, D. A. Way, and S. Zaehle. 2017. A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol. 213: 22–42.
159. Roll, U., E. Geffen, and Y. Yom-Tov. 2015. Linking vertebrate species richness to tree canopy height on a global scale. Glob. Ecol. Biogeogr. 24: 814–825.
160. Rothermel, R. 1991. Predicting behavior and size of crown fires in the Northern Rocky mountains. Forest Service, United States Department of Agriculture, Intermountain Research Station, Ogden, UT, U.S.A.
161. Running, S. W., C. O. Justice, V. Salomonson, D. Hall, J. Barker, Y. J. Kaufmann, A. H. Strahler, A. R. Huete, J.-P. Muller, V. Vanderbilt, Z. M. Wan, P. Teillet, and D. Carneggie. 1994. Terrestrial remote sensing science and algorithms planned for EOS/MODIS. Int. J. Remote Sens. 15: 3587–3620.
162. Running, S. W., and R. R. Nemani. 1991. Regional hydrologic and carbon balance responses of forests resulting from potential climate change. Clim. Change. 19: 349–368.
163. Ryan, M. G., and B. J. Yoder. 1997. Hydraulic limits to tree height and tree growth. Bioscience 47: 235–242.
164. Saari, H., I. Pellikka, L. Pesonen, S. Tuominen, J. Heikkilä, C. Holmlund, J. Mäkynen, K. Ojala, and T. Antila. 2011. Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII. p. 81740H–81740H–15.
165. Sabol, Jr, D. E., A. R. Gillespie, J. B. Adams, M. O. Smith, and C. J. Tucker. 2002. Structural stage in Pacific Northwest forests estimated using simple mixing models of multispectral images. Remote Sens. Environ. 80: 1–16.
166. Sánchez-Azofeifa, G. A., K. L. Castro, W. A. Kurz, and M. A. Joyce. 2009. Monitoring carbon stocks in the tropics and the remote sensing operational limitations: from local to regional projects. Ecol. Appl. 19: 134–142.
167. Sánchez-Azofeifa, G. A., K. L. Castro, B. Rivard, M. R. Kalascka, and R. C. Harriss. 2005. Remote sensing reseach priorities in tropical dry forests environments. Biotropica 35: 134–142.
168. Sánchez-Azofeifa, G. A., J. A. Guzmán-Quesada, M. Vega-Araya, C. Campos-Vargas, S. M. Durán, N. D'Souza, T. Gianoli, C. Portillo-Quintero, and I. Sharp. 2017. Can terrestrial laser scanners (TLSs) and hemispherical photographs predict tropical dry forest succession with liana abundance? Biogeosciences 14: 977–988.
169. Sánchez-Azofeifa, G. A., C. Rankine, M. M. do. E. Santo, R. Fatland, and M. Garcia. 2011. Wireless sensing networks for environmental monitoring: two case studies from tropical forests. In IEEE Seventh International Conference on eScience. pp. 70–76.
170. Schaepman, M. E., S. L. Ustin, A. J. Plaza, T. H. Painter, J. Verrelst, and S. Liang. 2009. Earth system science related imaging spectroscopy—An assessment. Remote Sens. Environ. 113: S123–S137.
171. Schimel, D. S., et al. 2001. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature 414: 169–172.
172. Schowalter, T. D. 1995. Canopy arthropod communities in relation to forest age and alternative harvest practices in western Oregon. For. Ecol. Manage. 78: 115–125.
173. Schutz, B. E., H. J. Zwally, C. A. Shuman, D. Hancock, and J. P. DiMarzio. 2005. Overview of the ICESat Mission. Geophys. Res. Lett. 32: L21S01.
174. Simard, M., V. H. Rivera-Monroy, J. E. Mancera-Pineda, E. Castañeda-Moya, and R. R. Twilley. 2008. 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 Sens. Environ. 112: 2131–2144.
175. Skowronski, N. S., K. L. Clark, M. Gallagher, R. A. Birdsey, and J. L. Hom. 2014. Airborne laser scanner-assisted estimation of aboveground biomass change in a temperate oak–pine forest. Remote Sens. Environ. 151: 166–174.
176. Smith, M.-L., S. V. Ollinger, M. E. Martin, J. D. Aber, R. A. Hallett, and C. L. Goodale. 2002. Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecol. Appl. 12: 1286–1302.
177. Soudani, K., et al. 2012. Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sens. Environ. 123: 234–245.
178. Steininger, M. K. 2000. Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia. Int. J. Remote Sens. 21: 1139–1157.
179. Sullivan, F. B., M. Palace, and M. Ducey. 2014. Multivariate statistical analysis of asynchronous lidar data and vegetation models in a neotropical forest. Remote Sens. Environ. 154: 368–377.
180. Sun, G., K. J. Ranson, D. S. Kimes, J. B. Blair, and K. Kovacs. 2008. Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data. Remote Sens. Environ. 112: 107–117.
181. Tang, H., M. Brolly, F. Zhao, A. H. Strahler, C. L. Schaaf, S. Ganguly, G. Zhang, and R. Dubayah. 2014. Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through LiDAR remote sensing: A case study in Sierra National Forest, CA. Remote Sens. Environ. 143: 131–141.
182. Tang, H., R. Dubayah, A. Swatantran, M. Hofton, S. Sheldon, D. B. Clark, and B. Blair. 2012. Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica. Remote Sens. Environ. 124: 242–250.
183. Thenkabail, P. S. 2003. Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images. Int. J. Remote Sens. 24: 2879–2904.
184. Thenkabail, P. S., E. A. Enclona, M. S. Ashton, C. Legg, and M. J. De Dieu. 2004. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sens. Environ. 90: 23–43.
185. Townsend, A. R., G. P. Asner, and C. C. Cleveland. 2008. The biogeochemical heterogeneity of tropical forests. Trends Ecol. Evol. 23: 424–431.
186. Treuhaft, R. N., B. D. Chapman, J. R. dos Santos, F. G. Gonçalves, L. V. Dutra, P. M. L. A. Graça, and J. B. Drake. 2009. Vegetation profiles in tropical forests from multibaseline interferometric synthetic aperture radar, field, and lidar measurements. J. Geophys. Res. Atmos. 114: D23110.
187. Treuhaft, R. N., B. E. Law, and G. P. Asner. 2004. Forest attributes from radar interferometric structure and its fusion with optical remote sensing. Bioscience 54: 561–571.
188. Turner, W., S. Spector, N. Gardiner, M. Fladeland, E. Sterling, and M. Steininger. 2003. Remote sensing for biodiversity science and conservation. Trends Ecol. Evol. 18: 306–314.
189. Underwood, E., S. Ustin, and D. DiPietro. 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sens. Environ. 86: 150–161.
190. Ustin, S. L., D. A. Roberts, J. A. Gamon, G. P. Asner, and R. O. Green. 2004. Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54: 523–534.
191. Ustin, S. L., M. O. Smith, D. Roberts, J. A. Gamon, and C. B. Field. 1992. Using AVIRIS images to measure temporal trends, in abundance of non-photosynthetic canopy components. In Summaries of the Third Annual JPL Airborne Geoscience Workshop. pp. 5–7, Jet Propulsion Laboratory, Pasadena, CA, USA.
192. Vreugdenhil, D., J. Meerman, A. Meyrat, L. D. Gómez, and D. J. Graham. 2002. Map of the ecosystems of Central America: final report. Washington, DC., USA.
193. Wallace, L., A. Lucieer, C. Watson, and D. Turner. 2012. Development of a UAV-LiDAR system with application to forest inventory. Remote Sens. 4: 1519–1543.
194. Wang, L., W. P. Sousa, and P. Gong. 2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int. J. Remote Sens. 25: 5655–5668.
195. Weishampel, J. F., J. B. Blair, R. G. Knox, R. Dubayah, and D. B. Clark. 2000. Volumetric LiDAR return patterns from an old-growth tropical rainforest canopy. Int. J. Remote Sens. 21: 409–415.
196. Weishampel, J. F., K. J. Ranson, and D. J. Harding. 1996. Remote sensing of forest canopies. Selbyana 17: 6–14.
197. van der Werf, G. R., J. T. Randerson, L. Giglio, G. J. Collatz, P. S. Kasibhatla, and A. F. Arellano, Jr. 2006. Interannual variability in global biomass burning emissions from 1997 to 2004. Atmos. Chem. Phys. 6: 3423–3441.
198. Werth, D., and R. Avissar. 2004. The regional evapotranspiration of the Amazon. J. Hydrometeorol. 5: 100–109.
199. Wessels, K. J., R. S. De Fries, J. Dempewolf, L. O. Anderson, A. J. Hansen, S. L. Powell, and E. F. Moran. 2004. Mapping regional land cover with MODIS data for biological conservation: examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil. Remote Sens. Environ. 92: 67–83.
200. White, M. A., P. E. Thornton, and S. W. Running. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem. Cycles 11: 217–234.
201. Whitehead, K., C. H. Hugenholtz, S. Myshak, O. Brown, A. LeClair, A. Tamminga, T. E. Barchyn, B. Moorman, and B. Eaton. 2014. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: scientific and commercial applications. J. Unmanned Veh. Syst. 2: 86–102.
202. Widlowski, J.-L., B. Pinty, N. Gobron, M. M. Verstraete, and A. B. Davis. 2001. Characterization of surface heterogeneity detected at the MISR/TERRA subpixel scale. Geophys. Res. Lett. 28: 4639–4642.
203. Widlowski, J.-L., B. Pinty, N. Gobron, M. M. Verstraete, D. J. Diner, and A. B. Davis. 2004. Canopy structure parameters derived from multi-angular remote sensing data for terrestrial carbon studies. Clim. Change. 67: 403–415.
204. Wilson, T. B., and T. P. Meyers. 2007. Determining vegetation indices from solar and photosynthetically active radiation fluxes. Agric. For. Meteorol. 144: 160–179.
205. Yang, X., C. Schaaf, A. Strahler, T. Kunz, N. Fuller, M. Betke, Z. Wu, Z. Wang, D. Theriault, D. Culvenor, D. Jupp, G. Newnham, and J. Lovell. 2013. Study of bat flight behavior by combining thermal image analysis with a LiDAR forest reconstruction. Can. J. Remote. Sens. 39: S112–S125.
206. Yu, Y., X. Yang, and W. Fan. 2015. Estimates of forest structure parameters from GLAS data and multi-angle imaging spectrometer data. Int. J. Appl. Earth Obs. Geoinf. 38: 65–71.
207. Zarco-Tejada, P. J., V. González-Dugo, and J. A. J. Berni. 2012. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 117: 322–337.
208. Zhao, F., Q. Guo, and M. Kelly. 2012. Allometric equation choice impacts lidar-based forest biomass estimates: A case study from the Sierra National Forest, CA. Agric. For. Meteorol. 165: 64–72.