Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data

Remote Sensing of Environment

Volumn 182, Issue , 2016, Pages 1-12

  Babcock, Chad ^a   Finley, Andrew O ^b   Cook, Bruce D ^c   Weiskittel, Aaron ^d   Woodall, Christopher W ^e  

^a UNIVERSITY OF WASHINGTON   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

^c NASA GODDARD SPACE FLIGHT CENTER   (United States)

^d UNIVERSITY OF MAINE   (United States)

^e USDA FOREST SERVICE   (United States)

Author keywords

Bayesian hierarchical models; Biomass growth; Forest biomass; Gaussian process; Geospatial; LiDAR; Long term forest inventory; Markov Chain Monte Carlo; Temporal misalignment

Indexed keywords

ALIGNMENT; BAYESIAN NETWORKS; BIOMASS; ECOLOGY; FORECASTING; FORESTRY; HIERARCHICAL SYSTEMS; MARKOV PROCESSES; MEAN SQUARE ERROR; NASA; RANDOM PROCESSES; REMOTE SENSING; UNCERTAINTY ANALYSIS;

BAYESIAN HIERARCHICAL MODEL; BIOMASS GROWTH; FOREST BIOMASS; FOREST INVENTORY; GAUSSIAN PROCESSES; GEO-SPATIAL; MARKOV CHAIN MONTE-CARLO; TEMPORAL MISALIGNMENT;

OPTICAL RADAR;

ABOVEGROUND BIOMASS; ANALYTICAL HIERARCHY PROCESS; BAYESIAN ANALYSIS; DATA PROCESSING; DATA SET; ECOSYSTEM MODELING; EOS; ERROR ANALYSIS; FOREST ECOSYSTEM; FOREST INVENTORY; GAUSSIAN METHOD; GEOLOGICAL MAPPING; GROWTH; LIDAR; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; REMOTE SENSING; UNCERTAINTY ANALYSIS;

MAINE; UNITED STATES;

EID: 84965136456     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.04.014     Document Type: Article

References (54)

1. Abdalati W., Zwally H., Bindschadler R., Csatho B., Farrell S., Fricker H., Webb C. The icesat-2 laser altimetry mission. Proceedings of the IEEE 2010, 98:735-751.
2. Albercht A., Hanewinkel M., Bauhus J. How does silviculture affect storm damage in forests of south-western Germany? Results from empirical modeling based on long-term observations. European Journal of Forest Resources 2012, 131:229-247.
3. Anderson J.E., Plourde L.C., Martin M.E., Braswell B.H., Smith M.L., Dubayah R.O., Blair J.B. Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest. Remote Sensing of Environment 2008, 112:1856-1870.
4. Asner G., Hughes R., Varga T., Knapp D., Kennedy-Bowdoin T. Environmental and biotic controls over aboveground biomass throughout a tropical rain forest. Ecosystems 2009, 12:261-278.
5. Awadallah A., Abbott V., Wynne R., Nelson R. Estimating forest canopy height and biophysical parameters using photon-counting laser altimetry. Proc. 13th international conference on LiDAR applications for assessing forest ecosystems (SilviLaser 2013) 2013, 129-136.
6. Babcock C., Finley A.O., Bradford J.B., Kolka R., Birdsey R., Ryan M.G. Lidar based prediction of forest biomass using hierarchical models with spatially varying coefficients. Remote Sensing of Environment 2015, 169:113-127.
7. Babcock C., Matney J., Finley A., Weiskittel A., Cook B. Multivariate spatial regression models for predicting individual tree structure variables using lidar data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2013, 6:6-14.
8. Banerjee S., Carlin C., Gelfand A. Hierarchical modeling and analysis for spatial data. Chapman & Hall/CRC monographs on statistics & applied probability 2014, Chapman & Hall/CRC. 2nd ed.
9. Banerjee S., Finley A. Bayesian multi-resolution modeling for spatially replicated data sets with application to forest biomass data. Journal of Statistical Planning and Inference 2007, 137:3193-3205.
10. Banerjee S., Johnson G. Coregionalized single- and multi-resolution spatially-varying growth curve modelling with application to weed growth. Biometrics 2006, 61:617-625.
11. Bechtold W.A., Patterson P.L. The enhanced forest inventory and analysis program: National sampling design and estimation procedures. US Department of Agriculture Forest Service 2005, Southern Research Station Asheville, North Carolina.
12. Blackard J., Finco M., Helmer E., Holden G., Hoppus M., Jacobs D., Lister A., Moisen G., Nelson M., Riemann R., et al. Mapping us forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment 2008, 112:1658-1677.
13. Blair J., Hofton M. Modeling laser altimeter return waveforms over complex vegetation using high-resolution elevation data. Geophysical Research Letters 1999, 26:2509-2512.
14. Brissette J., Kenefic Study plan: Silvicultural effects on composition. Structure and growth of northern conifers in the Acadian forest region: Revision of the compartment management study on the Penobscot Experimental Forest 2008, USDA Forest Service, Northern Research Station, Newtown Square, PA.
15. Brissette J., Kenefic L., Russel M., Puhlick J. Overstory tree and regeneration data from the "silvicultural effects on composition, structure, and growth" study at Penobscot Experimental Forest 2012, USDA Forest Service, Northern Research Station, Newtown Square, PA.
16. Carlin C., Louis T. Bayesian methods for data analysis. Chapman & Hall/CRC texts in statistical science 2008, Chapman & Hall/CRC. 2nd ed.
17. Chen Q., Vaglio Laurin G., Battles J.J., Saah D. Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass. Remote Sensing of Environment 2012, 121:108-117.
18. CMS NASA carbon monitoring system (http://carbon.nasa.gov. Accessed: 5-22-2014).
19. Cook B., Corp L., Nelson R., Middleton E., Morton D., McCorkel J., Montesano P. NASA goddards lidar, hyperspectral and thermal (g-liht) airborne imager. Remote Sensing 2013, 5:4045-4066.
20. Cowles M., Carlin B. Markov Chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association 1996, 91:883-904. (URL: http://www.jstor.org/stable/2291683).
21. Datta A., Banerjee S., Finley A., Gelfand A. Hierarchical nearest neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association 2015, (in press). 10.1080/01621459.2015.1044091.
22. Finley A., Banerjee S., MacFarlane D. A hierarchical model for quantifying forest variables over large heterogeneous landscapes with uncertain forest areas. Journal of the American Statistical Association 2011, 106:31-48.
23. Finley A.O., Banerjee S., Gelfand A.E. spbayes for large univariate and multivariate point-referenced spatio-temporal data models. Journal of Statstical Software 2015, 63.
24. García M., Riaño D., Chuvieco E., Danson F.M. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using lidar height and intensity data. Remote Sensing of Environment 2010, 114:816-830.
25. GEDI Global ecosystem dynamics investigation lidar (http://science.nasa.gov/missions/gedi/. Accessed: 1-5-2015).
26. Gelfand A.E., Kim H.J., Sirmans C., Banerjee S. Spatial modeling with spatially varying coefficient processes. Journal of the American Statistical Association 2003, 98:387-396.
27. Gelman A., Carlin J., Stern H., Rubin D. Bayesian data analysis 2013, Chapman & Hall/CRC Texts in Statistical Science, Chapman & Hall/CRC. 3rd ed.
28. Gelman A., Rubin D.B. Inference from iterative simulation using multiple sequences. Statistical Science 1992, 457-472.
29. Gregoire T.G., Ståhl G., Næsset E., Gobakken T., Nelson R., Holm S. Model-assisted estimation of biomass in a lidar sample survey in Hedmark County, Norway. Canadian Journal of Forest Research 2011, 41:83-95.
30. Hayashi R., Weiskittel A., Sader S. Assessing the feasibility of low-density lidar for stand inventory attribute predictions in complex and managed forests of northern Maine, USA. Forests 2014, 5:363-383.
31. Healey S., Patterson P., Saatchi S., Lefsky M., Lister A., Freeman E. A sample design for globally consistent biomass estimation using lidar data from the geoscience laser altimeter system (glas). Carbon Balance and Management 2012, 7:10.
32. ICESat-2 Ice, cloud, and land elevation satellite-2 (http://icesat.gsfc.nasa.gov/. Accessed: 1-5-2015).
33. Iqbal I., Dash J., Ullah S., Ahmad G. A novel approach to estimate canopy height using icesat/glas data: A case study in the New Forest National Park, UK. International Jorunal of Applied Earth Observation and Geoinformation 2013, 23:109-118.
34. Jenkins J., Chojnacky D., Heath L., Birdsey R. National-scale biomass estimators for United States tree species. Forest Science 2003, 49:12-35.
35. Kampe T., Johnson B., Kuester M., Keller M. Neon: The first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure. Journal of Applied Remote Sensing 2010, 4.
36. Kao R.H., Gibson C.M., Gallery R.E., Meier C.L., Barnett D.T., Docherty K.M., Schimel D. NEON terrestrial field observations: Designing continental-scale, standardized sampling. Ecosphere 2012, 3. (art115. URL: http://www.esajournals.org/doi/abs/10.1890/ES12-00196.1). 10.1890/ES12-00196.1.
37. Le Toan T., Quegan S., Davidson M., Balzter H., Paillou P., Papathanassiou K., Plummer S., Rocca F., Saatchi S., Shugart H., et al. The biomass mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sensing of Environment 2011, 115:2850-2860.
38. Michener W., Porter J., Servilla M., Vanderbilt K. Long term ecological research and information management. Ecological Informatics 2011, 6:13-24.
39. Muss J., Mladenoff D., Townsend P. A pseudo-waveform technique to assess forest structure using discrete lidar data. Remote Sensing of Environment 2011, 115:824-835.
40. Næsset E. Estimating above-ground biomass in young forests with airborne laser scanning. International Journal of Remote Sensing 2011, 32:473-501.
41. Nathoo F.S. Space-time regression modeling of tree growth using the skew-t distribution. Environmetrics 2010, 21:817-833. (URL: http://dx.doi.org/10.1002/env.1057). 10.1002/env.1057.
42. Neigh C., Nelson R., Ranson K., Margolis H., Montesano P., Sun G., Andersen H. Taking stock of circumboreal forest carbon with ground measurements, airborne and spaceborne lidar. Remote Sensing of Environment 2013, 137:274-287.
43. Nyland R. Silviculture: Concepts and applications. McGraw-Hill series in forest resources 2007, Waveland PressInc.
44. Oliver C., Larson B. Forest stand dynamics. Biological resource management 1996, Wiley.
45. Ometto J.P., Aguiar A.P., Assis T., Soler L., Valle P., Tejada G., Meir P. Amazon forest biomass density maps: Tackling the uncertainty in carbon emission estimates. Climatic Change 2014, 1-16.
46. Phillips O.L., Malhi Y., Higuchi N., Laurance W.F., Nez P.V., Vsquez R.M., Grace J. Changes in the carbon balance of tropical forests: Evidence from long-term plots. Science 1998, 282:439-442.
47. Saatchi S.S., Harris N.L., Brown S., Lefsky M., Mitchard E.T., Salas W., Zutta B.R., Buermann W., Lewis S.L., Hagen S., et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences 2011, 108:9899-9904.
48. Sendak P.E., Brissette J.C., Frank R.M. Silviculture affects composition, growth, and yield in mixed northern conifers: 40-year results from the Penobscot Experimental Forest. Canadian Journal of Forest Research 2003, 33:2116-2128.
49. Smith W. Forest inventory and analysis: A national inventory and monitoring program. Environmental Pollution 2002, 116(Supplement 1):S233-S242.
50. Stow C.A., Reckhow K.H., Qian S.S. A bayesian approach to retransformation bias in transformed regression. Ecology 2006, 87:1472-1477.
51. Talbot J., Lewis S.L., Lopez-Gonzalez G., Brienen R.J., Monteagudo A., Baker T.R., Phillips O.L. Methods to estimate aboveground wood productivity from long-term forest inventory plots. Forest Ecology and Management 2014, 320:30-38.
52. UN-REDD The UN-REDD Programme (http://www.un-redd.org/. Accessed: 5-22-2014).
53. Weiskittel A.R., Hann D.W., Kershaw J.A., Vanclay J.K. Forest growth and yield modeling 2011, John Wiley & Sons.
54. Zhang G., Ganguly S., Nemani R.R., White M.A., Milesi C., Hashimoto H., Myneni R.B. Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sensing of Environment 2014, 151:44-56.