Quantifying leaf phenology of individual trees and species in a tropical forest using unmanned aerial vehicle (UAV) images

Remote Sensing

Volumn 11, Issue 13, 2019, Pages

  Park, John Y ^a   Muller Landau, Helene C ^b   Lichstein, Jeremy W ^a   Rifai, Sami W ^a,c   Dandois, Jonathan P ^b,d   Bohlman, Stephanie A ^a,b  

^a UNIVERSITY OF FLORIDA   (United States)

^b SMITHSONIAN TROPICAL RESEARCH INSTITUTE   (Panama)

^c UNIVERSITY OF OXFORD   (United Kingdom)

^d JMT Technology Group   (United States)

Author keywords

Drones; Machine learning; Near surface remote sensing; Phenology; Seasonality; Species diversity; Tropical forest; Unmanned Aerial Vehicles (UAV), texture features

Indexed keywords

ANTENNAS; BIODIVERSITY; BIOLOGY; DRONES; LARGE DATASET; LEARNING ALGORITHMS; LEARNING SYSTEMS; MACHINE LEARNING; REMOTE SENSING; TESTING; TEXTURES; TROPICS; UNMANNED AERIAL VEHICLES (UAV);

NEAR SURFACES; PHENOLOGY; SEASONALITY; TEXTURE FEATURES; TROPICAL FOREST;

FORESTRY;

EID: 85068505177     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs11131534     Document Type: Article

References (51)

1. Pachauri, R.K.; Allen, M.R.; Barros, V.R.; Broome, J.; Cramer, W.; Christ, R.; Church, J.A.; Clarke, L.; Dahe, Q.; Dasgupta, P.; et al. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Pachauri, R.K., Meyer, L., Eds.; IPCC: Geneva, Switzerland, 2014
2. Richardson, A.D.; Keenan, T.F.; Migliavacca, M.; Ryu, Y.; Sonnentag, O.; Toomey, M. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 2013, 169, 156-173
3. Wolkovich, E.M.; Cook, B.I.; Davies, T.J. Progress towards an interdisciplinary science of plant phenology: Building predictions across space, time and species diversity. New Phytol. 2014, 201, 1156-1162
4. Croat, T.B. Flora of Barro Colorado Island; Stanford University Press: Palo Alto, CA, USA, 1978; ISBN 978-0-8047-0950-7
5. Leigh, E.G. Tropical Forest Ecology: A View from Barro Colorado Island; Oxford University Press: Oxford, UK, 1999; ISBN 978-0-19-509603-3
6. Condit, R.; Watts, K.; Bohlman, S.A.; Pérez, R.; Foster, R.B.; Hubbell, S.P. Quantifying the deciduousness of tropical forest canopies under varying climates. J. Veg. Sci. 2000, 11, 649-658
7. Reich, P.B.; Uhl, C.; Walters, M.B.; Prugh, L.; Ellsworth, D.S. Leaf demography and phenology in Amazonian rain forest: A census of 40,000 leaves of 23 tree species. Ecol. Monogr. 2004, 74, 3-23
8. Elliott, S.; Baker, P.J.; Borchert, R. Leaf flushing during the dry season: The paradox of Asian monsoon forests. Glob. Ecol. Biogeogr. 2006, 15, 248-257
9. Williams, L.J.; Bunyavejchewin, S.; Baker, P.J. Deciduousness in a seasonal tropical forest in western Thailand: Interannual and intraspecific variation in timing, duration and environmental cues. Oecologia 2008, 155, 571-582
10. Wright, S.J.; Cornejo, F.H. Seasonal drought and leaf fall in a tropical forest. Ecology 1990, 71, 1165-1175
11. Detto, M.; Wright, S.J.; Calderón, O.; Muller-Landau, H.C. Resource acquisition and reproductive strategies of tropical forest in response to the El Niño-Southern Oscillation. Nat. Commun. 2018, 9, 913
12. Samanta, A.; Ganguly, S.; Vermote, E.; Nemani, R.R.; Myneni, R.B. Why is remote sensing of Amazon forest greenness so challenging? Earth Interact. 2012, 16, 1-14
13. Morton, D.C.; Nagol, J.; Carabajal, C.C.; Rosette, J.; Palace, M.; Cook, B.D.; Vermote, E.F.; Harding, D.J.; North, P.R.J. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 2014, 506, 221-224
14. Wu, J.; Albert, L.P.; Lopes, A.P.; Restrepo-Coupe, N.; Hayek, M.; Wiedemann, K.T.; Guan, K.; Stark, S.C.; Christoffersen, B.; Prohaska, N.; et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science 2016, 351, 972-976
15. Lopes,A.P.;Nelson, B.W.;Wu, J.; deAlencastroGraça, P.M.L.; Tavares, J.V.; Prohaska,N.;Martins,G.A.; Saleska, S.R. Leaf flush drives dry season green-up of the Central Amazon. Remote Sens. Environ. 2016, 182, 90-98
16. Saleska, S.R.;Wu, J.; Guan, K.; Araujo, A.C.; Huete, A.; Nobre, A.D.; Restrepo-Coupe, N. Dry-season greening of Amazon forests. Nature 2016, 531, E4-E5
17. Sánchez-Azofeifa, A.; Rivard, B.; Wright, J.; Feng, J.-L.; Li, P.; Chong, M.M.; Bohlman, S.A. Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery. Sensors 2011, 11, 3831-3851
18. Kellner, J.R.; Hubbell, S.P. Adult mortality in a low-density tree population using high-resolution remote sensing. Ecology 2017, 98, 1700-1709
19. Alberton, B.; Almeida, J.; Helm, R.; Torres, R.D.S.; Menzel, A.; Morellato, L.P.C. Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation. Ecol. Inform. 2014, 19, 62-70
20. Klosterman, S.; Richardson, A.D. Observing spring and fall phenology in a deciduous forest with aerial drone imagery. Sensors 2017, 17, 2852
21. Klosterman, S. Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography. Agric. For. Meteorol. 2018, 248, 397-407
22. Srinivasan, G.; Shobha, G. Statistical texture analysis. Proc. World Acad. Sci. Eng. Technol. 2008, 36, 1264-1269
23. Culbert, P.D.; Radeloff, V.C.; St-Louis, V.; Flather, C.H.; Rittenhouse, C.D.; Albright, T.P.; Pidgeon, A.M. Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture. Remote Sens. Environ. 2012, 118, 140-150
24. Tuanmu, M.-N.; Jetz, W. A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 2015, 24, 1329-1339
25. Hofmann, S.; Everaars, J.; Schweiger, O.; Frenzel, M.; Bannehr, L.; Cord, A.F. Modelling patterns of pollinator species richness and diversity using satellite image texture. PLoS ONE 2017, 12, e0185591
26. Freeman, E.A.; Moisen, G.G.; Coulston, J.W.;Wilson, B.T. Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance. Can. J. For. Res. 2015, 46, 323-339
27. Ahmed, O.S.; Franklin, S.E.;Wulder, M.A.; White, J.C. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm. ISPRS J. Photogramm. Remote Sens. 2015, 101, 89-101
28. Hubbell, S.P.; Foster, R.B.; O'Brien, S.T.; Harms, K.E.; Condit, R.;Wechsler, B.;Wright, S.J.; De Lao, S.L. Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science 1999, 283, 554-557
29. Paton, S. Meteorological and Hydrological Summary for Barro Colorado Island; Smithsonian Tropical Research Institute: Balboa, Panama, 2017
30. Dandois, J.P.; Olano, M.; Ellis, E.C. Optimal Altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sens. 2015, 7, 13895-13920
31. Zahawi, R.A.; Dandois, J.P.; Holl, K.D.; Nadwodny, D.; Reid, J.L.; Ellis, E.C. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biol. Conserv. 2015, 186, 287-295
32. Lobo, E.; Dalling, J.W. Effects of topography, soil type and forest age on the frequency and size distribution of canopy gap disturbances in a tropical forest. Biogeosciences 2013, 10, 6769-6781
33. Bohlman, S.; Pacala, S. A forest structure model that determines crown layers and partitions growth and mortality rates for landscape-scale applications of tropical forests. J. Ecol. 2012, 100, 508-518
34. Graves, S.; Gearhart, J.; Caughlin, T.T.; Bohlman, S. A digital mapping method for linking high-resolution remote sensing images to individual tree crowns. PeerJ Prepr. 2018, 6, e27182v1
35. Richardson, A.D.; Braswell, B.H.; Hollinger, D.Y.; Jenkins, J.P.; Ollinger, S.V. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 2009, 19, 1417-1428
36. Adams, J.B.; Sabol, D.E.; Kapos, V.; Almeida Filho, R.; Roberts, D.A.; Smith, M.O.; Gillespie, A.R. Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote Sens. Environ. 1995, 52, 137-154
37. Haralick, R.M.; Shanmugam, K. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, 6, 610-621
38. Friedman, J.H. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002, 38, 367-378
39. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.; Springer Series in Statistics; Springer: New York, NY, USA, 2009; ISBN 978-0-387-84857-0
40. Frankie, G.W.; Baker, H.G.; Opler, P.A. Comparative phenological studies of trees in tropical wet and dry forests in the lowlands of Costa Rica. J. Ecol. 1974, 62, 881-919
41. Asner, G.P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 1998, 64, 234-253
42. Wu, J.; Chavana-Bryant, C.; Prohaska, N.; Serbin, S.P.; Guan, K.; Albert, L.P.; Yang, X.; van Leeuwen,W.J.D.; Garnello, A.J.; Martins, G.; et al. Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests. New Phytol. 2017, 214, 1033-1048
43. Wieder, R.K.; Wright, S.J. Tropical forest litter dynamics and dry season irrigation on Barro Colorado Island, Panama. Ecology 1995, 76, 1971-1979
44. Anderson-Teixeira, K.J.; Davies, S.J.; Bennett, A.C.; Gonzalez-Akre, E.B.; Muller-Landau, H.C.;Wright, S.J.; Abu Salim, K.; Almeyda Zambrano, A.M.; Alonso, A.; Baltzer, J.L.; et al. CTFS-ForestGEO: A worldwide network monitoring forests in an era of global change. Glob. Chang. Biol. 2015, 21, 528-549
45. Xu, X.; Medvigy, D.; Powers, J.S.; Becknell, J.M.; Guan, K. Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests. New Phytol. 2016, 212, 80-95
46. Du, Y.; Teillet, P.M.; Cihlar, J. Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sens. Environ. 2002, 82, 123-134
47. Adams, J.B.; Gillespie, A.R. Remote Sensing of Landscapes with Spectral Images; Cambridge University Press: Cambridge, UK, 2006; ISBN 978-0-521-66221-5
48. Grizonnet, M.; Michel, J.; Poughon, V.; Inglada, J.; Savinaud, M.; Cresson, R. Orfeo ToolBox: Open source processing of remote sensing images. Open Geospat. Data Softw. Stand. 2017, 2, 15
49. Jordahl, K. GeoPandas: Python Tools for Geographic Data. Available online: https://github.com/geopandas/geopandas (accessed on 15 May 2019)
50. Ridgeway, G. GBM: Generalized boosted regression models. R Package Version 2006, 1, 55
51. Ridgeway, G. Generalized Boosted Models: A guide to the gbm package. Update 2007, 1, 2007