Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Carbon Balance and Management

Volumn 12, Issue 1, 2017, Pages

  Garcia, Mariano ^a,b   Saatchi, Sassan ^a   Ferraz, Antonio ^a   Silva, Carlos Alberto ^a,c,d   Ustin, Susan ^e   Koltunov, Alexander ^e   Balzter, Heiko ^b  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

^c ROCKY MOUNTAIN RESEARCH STATION   (United States)

^d UNIVERSITY OF IDAHO   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Aboveground biomass; Airborne LiDAR data; Canopy height model; Data thinning; Echo based; Point density

Indexed keywords

ABOVEGROUND BIOMASS; AIRBORNE SENSOR; BIOME; CARBON CYCLE; CLIMATE CHANGE; LIDAR; MOUNTAIN REGION; NUMERICAL MODEL; POLICY IMPLEMENTATION; SATELLITE DATA; TEMPERATE FOREST; THREE-DIMENSIONAL MODELING; TROPICAL FOREST;

BARRO COLORADO ISLAND; BRAZIL; CALIFORNIA; COLORADO; PANAMA OESTE; PANAMA [CENTRAL AMERICA]; SIERRA NEVADA [CALIFORNIA]; UNITED STATES;

EID: 85013354732     PISSN: None     EISSN: 17500680     Source Type: Journal    
DOI: 10.1186/s13021-017-0073-1     Document Type: Article

References (31)

1. FAO. Global forest resources assessment 2010: main report. Rome: Food and Agriculture Organization of the United Nations; 2010. p. 2010.
2. García M, Riaño D, Chuvieco E, et al. Estimating biomass carbon stocks for a Mediterranean forest in Spain using height and intensity LiDAR data. Remote Sens Environ. 2010;114(4):816-30.
3. Hudak AT, Strand EK, Vierling LA, et al. Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys. Remote Sens Environ. 2012;123:25-40.
4. Meyer V, Saatchi SS, Chave J, et al. Detecting tropical forest biomass dynamics from repeated airborne lidar measurements. Biogeosciences. 2013;10(8):5421-38.
5. Saatchi SS, Harris NL, Brown S, et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc Natl Acad Sci. 2011;108:9899-904.
6. Clark DB, Kellner JR. Tropical forest biomass estimation and the fallacy of misplaced concreteness. J Veg Sci. 2012;23(6):1191-6.
7. Morsdorf F, Frey O, Meier E, et al. Assessment of the influence of flying altitude and scan angle on biophysical vegetation products derived from airborne laser scanning. Int J Remote Sens. 2008;29:1387-406.
8. Næsset E. Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data. Remote Sens Environ. 2009;113(1):148-59.
9. Hopkinson C. The influence of flying altitude, beam divergence, and pulse repetition frequency on laser pulse return intensity and canopy frequency distribution. Can J Remote Sens. 2007;33(4):312-24.
10. Disney MI, Kalogirou V, Lewis P, et al. Simulating the impact of discretereturn lidar system and survey characteristics over young conifer and broadleaf forests. Remote Sens Environ. 2010;114(7):1546-60.
11. Gaulton R, Malthus TJ. LiDAR mapping of canopy gaps in continuous cover forests: a comparison of canopy height model and point cloud based techniques. Int J Remote Sens. 2010;31:1193-211.
12. Chirici G, McRoberts RE, Fattorini L, et al. Comparing echo-based and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a model-assisted framework. Remote Sens Environ. 2016;174:1-9.
13. Leitold V, Keller M, Morton DC, et al. Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+. Carbon Balance Manag. 2015;10(1):3.
14. Chasmer L, Hopkinson C, Smith B, et al. Examining the influence of changing laser pulse repetition frequencies on conifer forest canopy returns. Photogramm Eng Remote Sens. 2006;72:1359-67.
15. Frazer GW, Magnussen S, Wulder MA, et al. Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sens Environ. 2011;115:636-49.
16. Mascaro J, Asner GP, Muller-Landau HC, et al. Controls over aboveground forest carbon density on Barro Colorado Island, Panama. Biogeosciences. 2011;8(6):1615-29.
17. Hall SA, Burke IC, Box DO, et al. Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. For Ecol Manag. 2005;208(1-3):189-209. doi:10.1016/j.foreco.2004.12.001.
18. Næsset E, Gobakken T. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens Environ. 2008;112:3079-90.
19. Casas Á, García M, Siegel RB, et al. Burned forest characterization at singletree level with airborne laser scanning for assessing wildlife habitat. Remote Sens Environ. 2016;175:231-41.
20. Condit R, Engelbrecht BMJ, Pino D, et al. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proc Natl Acad Sci. 2013;110:5064-8.
21. Fricker GA, Saatchi SS, Meyer V, et al. Application of semi-automated filter to improve waveform lidar sub-canopy elevation model. Remote Sens. 2012;4(6):1494.
22. Holdridge LR. Determination of world plant formations from simple climatic data. Science. 1947;105(2727):367-8.
23. Alves LF, Vieira SA, Scaranello MA, et al. Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil). For Ecol Manag. 2010;260:679-91.
24. Vieira SA, Alves LF, Duarte-Neto PJ, et al. Stocks of carbon and nitrogen and partitioning between above- and belowground pools in the Brazilian coastal Atlantic Forest elevation range. Ecol Evol. 2011;1:421-34.
25. Garcia M, Saatchi S, Casas A, et al. Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data. J Geophys Res Biogeosci. 2016.
26. Ferraz A, Saatchi S, Mallet C, et al. Lidar detection of individual tree size in tropical forests. Remote Sens Environ. 2016;183:318-33.
27. Hubbell SP, Foster RB. Tropical rain forest: ecology and management. Oxford: British Ecological Society; 1983. p. 25-41.
28. Bohlman S, O'Brien S. Allometry, adult stature and regeneration requirement of 65 tree species on Barro Colorado Island, Panama. J Trop Ecol. 2006;22(02):123-36.
29. Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure JP. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia. 2005;145(1):87-99.
30. Hughes RF. Effects of deforestation and land use on biomass, carbon and nutrient polls in the Los Tuxtlas region, Mexico. Corvallis: Oregon State University; 1997.
31. Goodwin NR, Coops NC, Culvenor DS. Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sens Environ. 2006;103(2):140-52.