Mapping above-ground biomass in a tropical forest in Cambodia using canopy textures derived from Google Earth

Remote Sensing

Volumn 7, Issue 5, 2015, Pages 5057-5076

  Singh, Minerva ^a   Evans, Damian ^b   Friess, Daniel A ^c   Tan, Boun Suy ^d   Nin, Chan Samean ^d  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b Seoul Center   (France)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^d APSARA National Authority   (Cambodia)

Author keywords

Above ground biomass; Angkor Thom; Fourier based textural ordination; Google Earth; LiDAR; Machine learning; Support vector regression

Indexed keywords

AERIAL PHOTOGRAPHY; ARTIFICIAL INTELLIGENCE; BIOMASS; DECISION TREES; LEARNING SYSTEMS; OPTICAL RADAR; REGRESSION ANALYSIS;

ABOVE GROUND BIOMASS; ANGKOR THOM; FOURIER; GOOGLE EARTHS; SUPPORT VECTOR REGRESSION (SVR);

TEXTURES;

EID: 84930034363     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70505057     Document Type: Article

References (44)

1. Pan, Y.; Birdsey, R.; Fang, J.; Houghton, R.; Kauppi, P.; Kurz, W.; Phillips, O. A large and persistent carbon sink in the world's forests. Science 2011, 333, 988-993.
2. Sodhi, N.; Koh, L.; Brook, B.; Ng, P. Southeast Asian biodiversity: An impending disaster. Trends Ecol. Evol. 2004, 19, 654-660.
3. Hansen, M.; Potapov, P.; Moore, R.; Hancher, M.; Turubanova, S.; Tyukavina, A.; Thau, D.; Stehman, S.; Goetz, S.; Loveland, T.; et al. High-resolution global maps of 21st-century forest cover change. Science 2013, 342, 850-853.
4. Southworth, J.; Nagendra, H.; Cassidy, L. Forest transition pathways in Asia-Studies from Nepal, India, Thailand and Cambodia. J. Land Use Sci. 2012, 7, 51-65.
5. Zsombor, P. Loss of Forest in Cambodia among Worst in the World. The Cambodia Daily, 19 November 2013.
6. Jakubowski, M.; Li, W.; Guo, Q.; Kelly, M. Delineating individual trees from LiDAR data: A comparison of vector-and raster-based segmentation approaches. Remote Sens. 2013, 5, 4163-4186.
7. Mumby, P.; Green, E.; Edwards, A.; Clark, C. The cost-effectiveness of remote sensing for tropical coastal resources assessment and management. J. Environ. Manag. 1999, 55, 157-166.
8. Friess, D.; Kudavidanage, E.; Webb, E. The digital globe is our oyster. Front. Ecol. Environ. 2011, 9, 542-552.
9. Singh, M.; Malhi, Y.; Bhagwat, S. Evaluating land use and aboveground biomass dynamics in an oil palm-dominated landscape in Borneo using optical remote sensing. J. Appl. Remote Sens. 2014, 8, 83695-83695.
10. Bastin, J.; Barbier, N.; Couteron, P.; Adams, B.; Shapiro, A.; Bogaert, J.; de Cannière, C. Aboveground biomass mapping of African forest mosaics using canopy texture analysis: Towards a regional approach. Ecol. Appl. 2014, 24, 1984-2001.
11. Drake, J.; Knox, R.; Dubayah, R.; Clark, D.; Condit, R.; Blair, J.; Hofton, M. Above-ground biomass estimation in closed canopy neotropical forests using LiDAR remote sensing: Factors affecting the generality of relationships. Glob. Ecol. Biogeogr. 2003, 12, 147-159.
12. Wijaya, A.; Kusnadi, S.; Gloaguen, R.; Heilmeier, H. Improved strategy for estimating stem volume and forest biomass using moderate resolution remote sensing data and GIS. J. For. Res. 2010, 21, 1-12.
13. Lu, D. The potential and challenges of remote sensing based biomass estimates. Int. J. Remote Sens. 2006, 27, 1297-1328.
14. Singh, M.; Malhi, Y.; Bhagwat, S. Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery. Int. J. Remote Sens. 2014, 35, 3331-3349.
15. Proisy, C.; Couteron, P.; Fromard, F. Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images. Remote Sens. Environ. 2007, 109, 379-392.
16. Kennel, P.; Tramon, M.; Barbier, N.; Vincent, G. Canopy height model characteristics derived from airborne laser scanning and its effectiveness in discriminating various tropical moist forest types. Int. J. Remote Sens. 2013, 34, 8917-8935.
17. Wang, Z.; Boesch, R.; Ginzler, C. Forest delineation of aerial images with Gabor wavelets. Int. J. Remote Sens. 2012, 33, 2196-2213.
18. Goetz, S.; Baccini, A.; Laporte, N.; Johns, T.; Walker, W.; Kellndorfer, J.; Houghton, R.; Sun, M. Mapping and monitoring carbon stocks with satellite observations: A comparison of methods. Carbon Balance Manag. 2009, 4, 2-8.
19. Zhang, J.; Huang, S.; Hogg, E.; Lieffers, V.; Qin, Y.; He, F. Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data. Biogeosciences 2014, 11, 2793-2808.
20. Maxwell, J. Vegetation and vascular flora of the Ban Saneh Pawng area, Lai Wo subdistrict, Sangklaburi district, Kanchanaburi Province, Thailand. Nat. Hist. Bull. Siam Soc. 1995, 43, 131-170.
21. Wales, N. Combining Remote Sensing Change Detection and Qualitative Data to Examine Landscape Change in the Context of World Heritage. Ph.D. Thesis, University of Sydney, Sydney, Australia, October 2012.
22. Marthews, T.; Metcalfe, D.; Malhi, Y.; Phillips, O.; Huasco, W.; Riutta, T.; Jaén, M.; Girardin, C.; Urrutia, R.; Butt, N. Measuring Tropical Forest Carbon Allocation and Cycling: A RAINFOR-GEM Field Manual for Intensive Census Plots (v2.2); University of Oxford: Oxford, UK, 2012.
23. Asner, G.; Mascaro, J. Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric. Remote Sens. Environ. 2014, 140, 614-624.
24. Meyer, V.; Saatchi, S.; Chave, J.; Dalling, J.; Bohlman, S.; Fricker, G.; Robinson, C.; Neumann, M.; Hubbell, S. Detecting tropical forest biomass dynamics from repeated airborne LiDAR measurements. Biogeosciences 2013, 10, 1957-1992.
25. Chave, J.; Andalo, C.; Brown, S.; Cairns, M.; Chambers, J.; Eamus, D.; Folster, H.; Fromard, F.; Higuchi, N.; Kira, T.; et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 2005, 145, 87-99.
26. Brown, S.; Iverson, L.; Prasad, A.; Liu, D. Geographical distributions of carbon in biomass and soils of tropical Asian forests. Geocarto Int. 1993, 8, 45-59.
27. Google Satellite Maps Downloader. Available online: http://www.allallsoft.com/gsmd/(accessed on (20 December 2012).
28. McGaughey, R. Fusion/ldv: Software for LiDAR Data Analysis and Visualization [Computer Program]; U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: Seattle, WA, USA, 2010.
29. St-Louis, V.; Pidgeon, A.; Radeloff, V.; Hawbaker, T.; Clayton, M. High-resolution image texture as a predictor of bird species richness. Remote Sens. Environ. 2006, 105, 299-312.
30. Couteron, P.; Pélissier, R.; Nicolini, E.; Paget, D. Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images. J. Appl. Ecol. 2005, 42, 1121-1128.
31. Yu, P.; Chen, S.; Chang, I. Support vector regression for real-time flood stage forecasting. J. Hydrol. 2006, 328, 704-716.
32. Yu, X.; Wang, X.; Chen, J. Support vector machine regression for reactivity parameters of vinyl monomers. J. Chil. Chem. Soc. 2011, 56, 746-751.
33. Paneque-Gálvez, J.; Mas, J.-F.; More, G.; Cristobal, J.; Orta-Martinez, M.; Luz, A.C.; Gueze, M.; Macia, M.J.; Reyes-Garcia, V. Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity. Int. J. Appl. Earth Obs. Geoinf. 2013, 23, 372-383.
34. Zeng, Z.; Hsieh, W.W.; Shabbar, A.; Burrows, W.R. Seasonal prediction of winter extreme precipitation over Canada by support vector regression. Hydrol. Earth Syst. Sci. 2011, 15, 65-74.
35. Kuhn, M. Caret: Classification and Regression Training; R package version 4.58 2014. Available online: http://CRAN.R-project.org/package=caret (accessed on 21 April 2015).
36. Lantz, B. Machine Learning with R; Packt Publishing Ltd.: Birmingham, UK, 2013.
37. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
38. Mascaro, J.; Detto, M.; Asner, G.; Muller-Landau, H. Evaluating uncertainty in mapping forest carbon with airborne LiDAR. Remote Sens. Environ. 2011, 115, 3770-3774.
39. Townend, J. Practical Statistics for Environmental and Biological Scientists; John Wiley & Sons, Hoboken, NJ, USA, 2013.
40. Chen, G.; Hay, G.; St-Onge, B. A GEOBIA framework to estimate forest parameters from LiDAR transects, QuickBird imagery and machine learning: A case study in Quebec, Canada. Int. J. Appl. Earth Obs. Geoinf. 2012, 15, 28-37.
41. Shataee, J. Forest attributes estimation using aerial laser scanner and TM data. For. Syst. 2013, 22, 484-496.
42. Ploton, P.; Pélissier, R.; Proisy, C.; Flavenot, T.; Barbier, N.; Rai, S.N.; Couteron, P. Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecol. Appl. 2012, 22, 993-1003.
43. Gupta, A.; Lövbrand, E.; Turnhout, E.; Vijge, M. In pursuit of carbon accountability: The politics of REDD+ measuring, reporting and verification systems. Curr. Opin. Environ. Sustain. 2012, 4, 726-731.
44. Friess, D.; Phelps, J.; Garmendia, E.; Gómez-Baggethun, E. Payments for ecosystem services (PES) in the face of external biophysical stressors. Glob. Environ. Chang. 2015, 30, 31-42.