Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error

Remote Sensing of Environment

Volumn 130, Issue , 2013, Pages 153-170

  Montesano, P M ^a,b,c   Cook, B D ^b   Sun, G ^c   Simard, M ^d   Nelson, R F ^b   Ranson, K J ^b   Zhang, Z ^c   Luthcke, S ^b  

^a SIGMA SPACE CORPORATION   (United States)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

^c UNIVERSITY OF MARYLAND   (United States)

^d CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Biomass; Data fusion; LiDAR; Sampling error; SAR

Indexed keywords

3D INFORMATION; AIRBORNE DATA; CLOUD COVER; CONTIGUOUS AREA; FIELD CAMPAIGN; FOREST BIOMASS; FOREST PATCHES; FOREST STRUCTURE; FORESTED AREA; GLOBAL SCALE; GRID CELLS; GRID LEVELS; GRID SCALE; IMAGE DATA; IMAGING SENSORS; LIGHT DETECTION AND RANGING; MACHINE LEARNING METHODS; MAPPING MISSIONS; MULTI-SPECTRAL IMAGERY; OPTICAL IMAGE; PASSIVE REMOTE SENSING; PREDICTION METHODS; SAMPLING ERRORS; SAR; SPACE-BORNE; SPACE-BORNE LIDAR; SPACEBORNE DATA; SUB-GRIDS; VEGETATION STRUCTURE;

BIOMASS; DATA FUSION; ERRORS; ESTIMATION; GEOMETRICAL OPTICS; LEARNING SYSTEMS; MAPPING; NASA; OPTICAL RADAR; REMOTE SENSING; SYNTHETIC APERTURE RADAR; VEGETATION;

FORESTRY;

ACCURACY ASSESSMENT; BIOMASS; DATA SET; ESTIMATION METHOD; FOREST ECOSYSTEM; HOMOGENEITY; LANDSCAPE; LIDAR; MAPPING METHOD; NUMERICAL MODEL; PREDICTION; REMOTE SENSING; SYNTHETIC APERTURE RADAR; VEGETATION STRUCTURE;

EID: 84871395709     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2012.11.016     Document Type: Article

References (64)

1. Abdalati W., Zwally H.J., Bindschadler R., Csatho B., Farrell S.L., Fricker H.A., et al. The ICESat-2 laser altimetry mission. Proceedings of the IEEE 2010, 98(5):735-751. 10.1109/JPROC.2009.2034765.
2. Anon. Definiens eCognition Developer 8. User guide 2009, Definiens AG, Munich, Germany.
3. Baatz M., Schäpe A. Multiresolution segmentation - An optimization approach for high quality multi-scale segmentation. Angewandte Geographische Informationsverarbeitung, Salzburg 2000, 12-23.
4. Baccini A., Laporte N., Goetz S.J., Sun M., Don H. A first map of Tropical Africa's above-ground biomass derived from satellite imagery. Environmental Research Letters 2008, 3:1-9.
5. Benz U.C., Hofmann P., Willhauck G., Lingenfelder I., Heynen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 2004, 58:239-258. 10.1016/j.isprsjprs.2003.10.002.
6. Bergen K., Goetz S.J., Dubayah R.O., Henebry G.M., Hunsaker C.T., Imhoff M.L., et al. Remote sensing of vegetation 3D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. Journal of Geophysical Research 2009, 114(G00E06). 10.1029/2008JG000883.
7. Blair J.B., Rabine D.L., Hofton M.A. The laser vegetation imaging sensor: A medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS Journal of Photogrammetry and Remote Sensing 1999, 54:115-122.
8. Blaschke T., Lang S., Lorup E., Strobl J., Zeil P. Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environmental Information for Planning, Politics and the Public 2000, 2:555-570.
9. Breiman L. Random forests. Machine Learning 2001, 45(1):5-32.
10. Cohen W.B., Maiersperger T.K., Gower S.T., Turner D.P. An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sensing of Environment 2003, 84:561-571.
11. Crist E.P., Cicone R.C. A physically-based transformation of Thematic Mapper data - The TM tasseled cap. IEEE Transactions on Geoscience and Remote Sensing 1984, 3:256-263.
12. Curran P.J., Hay A.M. The importance of measurement error for certain procedures in remote sensing at optical wavelengths. Photogrammetric Engineering and Remote Sensing 1986, 52:229-241.
13. Dietze M.C., Wolosin M.S., Clark J.S. Capturing diversity and interspecific variability in allometries: A hierarchical approach. Forest Ecology and Management 2008, 256:1939-1948.
14. Drake J.B., Dubayah R.O., Clark D.B., Knox R.G., Blair J.B., Hofton M.A., et al. Estimation of tropical forest structural characteristics using large footprint lidar. Remote Sensing of Environment 2002, 79:305-319.
15. Drake J.B., Knox R.G., Dubayah R.O., Clark D.B., Condit R., Blair J.B., et al. Above-ground biomass estimation in closed canopy neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Global Ecology and Biogeography 2003, 12(2):147-159.
16. Dubayah R.O., Sheldon S.L., Clark D.B., Hofton M.A., Blair J.B., Hurtt G.C., et al. Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. Journal of Geophysical Research 2010, 115(G00E09):1-17. 10.1029/2009JG000933.
17. Esch T., Thiel M., Bock M., Roth A., Dech S. Improvement of image segmentation accuracy based on multiscale optimization procedure. IEEE Geoscience and Remote Sensing Letters 2008, 5(3):463-467.
18. Gao Q., Yu M., Yang X. A simulation analysis of the relationship between regional primary production and vegetation structure under climatic change scenarios. Ecological Modelling 2000, 131(1):33-45.
19. Goetz S.J., Baccini A., Laporte N.T., Johns T., Walker W., Kellndorfer J., et al. Mapping and monitoring carbon stocks with satellite observations: A comparison of methods. Carbon Balance and Management 2009, 4(2). (Available at http://www.cbmjournal.com/content/4/1/2). 10.1186/1750-0680-1184-1182.
20. Goetz S., Dubayah R. Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change. Carbon Management 2011, 2(3):231-244.
21. Griffith, G. E., Omernik, J. M., Bryce, S. A., Royte, J., Hoar, W. D., Homer, J. W., et al., 2009. Ecoregions of New England (color poster with map, descriptive text, summary tables, and photographs): Reston, Virginia, U.S. Geological Survey (map scale 1:1,325,000).
22. Hall F.G., Bergen K., Blair J.B., Dubayah R., Houghton R., Hurtt G., et al. Characterizing 3D vegetation structure from space: mission requirements. Remote Sensing of Environment 2011, 115(11):2753-2775. 10.1016/j.rse.2011.01.024.
23. Heiskanen J. Estimating aboveground tree biomass and leaf area Index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing 2006, 27(6):1135-1158. 10.1080/01431160500353858.
24. Hollinger D.Y., Goltz S.M., Davidson E.A., Lee J.T., Tu K., Valentine H.T. Seasonal patterns and environmental control of carbon dioxide and water vapor exchange in an ecotonal boreal forest. Global Change Biology 1999, 5:891-902.
25. Houghton R.A. Aboveground forest biomass and the global carbon balance. Global Change Biology 2005, 11:945-958. 10.1111/j.1365-2486.2005.00955.x.
26. Houghton R.A. Importance of biomass in global carbon cycle. Journal of Geophysical Research 2009, 114:G00E03. 10.1029/2009JG000935.
27. Hudak A.T., Lefsky M.A., Cohen W.B., Berterretche M. Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height. Remote Sensing of Environment 2002, 82:397-416.
28. Hyde P., Dubayah R., Walker W., Blair J.B., Hofton M., Hunsaker C. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment 2006, 102:63-73.
29. Jenkins J., Chojnacky D., Heath L., Birdsey R. National-scale biomass estimators for United States tree species. Forest Science 2003, 49(1):12-35.
30. Kellndorfer J.M., Walker W.S., LaPoint E., Kirsch K., Bishop J., Fiske G. Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets. Journal of Geophysical Research 2010, 115(G00E08):1-10.
31. Kimes D., Gastellu-Etchegorry J., Esteve P. Recovery of forest canopy characteristics through inversion of a complex 3D model. Remote Sensing of Environment 2002, 79:320-328.
32. Kimes D.S., Knyazikhin Y., Privette J.L., Abuelgasim A.A., Gao F. Inversion methods for physically-based models. Remote Sensing Reviews 2000, September, 18(2-4):381-439. 10.1080/02757250009532396.
33. Kimes D.S., Nelson R.F., Manry M.T., Fung A.K. Review article: Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. International Journal of Remote Sensing 1998, 19(14):2639-2663.
34. Kimes D.S., Ranson K.J., Sun G., Blair J.B. Predicting lidar measured forest vertical structure from multi-angle spectral data. Remote Sensing of Environment 2006, 100:503-511.
35. Lefsky M.A. A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophysical Research Letters 2010, 37:L15401. 10.1029/2010GL043622.
36. Lefsky M.A., Ramond T., Weimer C.S. Alternate spatial sampling approaches for ecosystem structure inventory using spaceborne lidar. Remote Sensing of Environment 2011, 115:1361-1368.
37. Liaw A., Wierner M. Classification and regression by randomForest. R News 2002, 2(3):18-22.
38. Lillesand T.M., Kiefer R.W., Chipman J.W. Remote sensing and image interpretation 2008, John Wiley & Sons, New York. 6th ed.
39. Masek J.G., Vermote E.F., Saleous N., Wolfe R., Hall F.G., Huemmrich F., et al. A Landsat surface reflectance data set for North America, 1990-2000. IEEE Geoscience and Remote Sensing Letters 2006, 3:68-72.
40. Mette T., Papathanassiou K., Hajnsek I. Biomass estimation from polarimetric SAR interferometry over heterogeneous forest terrain. Geoscience and Remote Sensing Symposium, 2004. IGARSS'04. Proceedings. 2004 IEEE International 2004, 1:511-514.
41. Nelson R.F., Ranson K.J., Sun G., Kimes D.S., Kharuk V., Montesano P. Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sensing of Environment 2009, 113:691-701.
42. Olson D.M., Dinerstein E., Wikramanayake E.D., Burgess N.D., Powell G.V.N., Underwood E.C., et al. Terrestrial ecoregions of the world: A new map of life on Earth. Bioscience 2001, 51:933-938.
43. Pang Y., Lefsky M.A., Sun G., Ranson J. Impact of footprint diameter and off-nadir pointing on the precision of canopy height estimates from spaceborne lidar. Remote Sensing of Environment 2011, 115:2798-2809. 10.1016/j.rse.2010.08.025.
44. Popescu S.C., Zhao K., Neuenschwander A., Lin C. Satellite lidar vs. small footprint airborne lidar: comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sensing of Environment 2011, 115:2786-2797. 10.1016/j.rse.2011.01.026.
45. Powell S.L., Cohen W.B., Healey S.P., Kennedy R.E., Moisen G.G., Pierce K.B., et al. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sensing of Environment 2010, 114:1053-1068. 10.1016/j.rse.2009.12.018.
46. Price C.A., Ogle K., White E.P., Weitz J.S. Evaluating scaling models in biology using hierarchical Bayesian approaches. Ecology Letters 2009, 12:641-651.
47. R Development Core Team R: A language and environment for statistical computing 2011, R Foundation for Statistical Computing, Vienna, Austria, (http://www.R-project.org).
48. Ranson K.J., Montesano P.M., Nelson R.F. Object-based mapping of the circumpolar taiga-tundra ecotone with MODIS tree cover. Remote Sensing of Environment 2011, 115(12):3670-3680.
49. Ranson K.J., Sun G. Northern forest classification using temporal multi-frequency and multi-polarimetric SAR images. Remote Sensing of Environment 1994, 47:142-153.
50. Rosen P.A., Hensley S., Wheeler K., Sadowy G., Miller T., Shaffer S., et al. UAVSAR: A new NASA Airborne SAR system for science and technology research. Proceedings of the IEEE Conference on Radar, 24-27 April 2006 2006.
51. Saatchi S.S., Harris N.L., Brown S., Lefsky M., Mitchard E.T.A., Sala W., et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences 2011, 108(24):9899-9990.
52. Safford L.O., Frank R.M., Little E.L. Trees and shrubs of the Penobscot experimental forest, Penobscot County, Maine. Research Paper NE-128 1969, USDA, Forest Service, Northeastern Forest Experiment Station, Upper Darby, PA, (32 pp.).
53. 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(11):2116-2128.
54. Shugart H.H., Saatchi S., Hall F.G. Importance of structure and its measurement in quantifying function of forest ecosystems. Journal of Geophysical Research 2010, 115:G00E13. 10.1029/2009JG000993.
55. Simard M., Pinto N., Fisher J., Baccini A. Mapping forest canopy height globally with spaceborne lidar. Journal of Geophysical Research 2011, 116:1-12. 10.1029/2011JG001708.
56. Simard M., Zhang K., Rivera-Monroy V.H., Ross M., Ruiz P., Castaneda-Moya E., et al. Mapping mangrove height and estimate biomass in the Everglades using SRTM elevation data. Photogrammetric Engineering and Remote Sensing 2006, 72(3):299-311.
57. Small D. Flattening gamma: Radiometric terrain correction for SAR imagery. IEEE Transactions on Geoscience and Remote Sensing 2011, 49(8):3081-3093.
58. Stojanova D., Panov P., Gjorgjioski V., Kobler A., Džeroski S. Estimating vegetation height and canopy cover from remotely sensed data with machine learning. Ecological Informatics 2010, 5(4):256-266.
59. Sun G., Ranson K.J., Guo Z., Zhang Z., Montesano P., Kimes D. Forest biomass mapping from lidar and radar synergies. Remote Sensing of Environment 2011, 115(11):2906-2916. 10.1016/j.rse.2011.03.021.
60. Treuhaft R.N., Asner G.P., Law B.E. Structure-based forest biomass from fusion of radar and hyperspectral observations. Geophysical Research Letters 2003, 30(9):1472.
61. Treuhaft R.N., Law B.E., Asner G.P. Forest attributes from radar interferometric structure and its fusion with optical remote sensing. Bioscience 2004, 54(6):561-571.
62. Walker W.S., Kellndorfer J.M., LaPoint E., Hoppus M., Westfall J. An empirical InSAR-optical fusion approach to mapping vegetation canopy height. Remote Sensing of Environment 2007, 109:482-499. 10.1016/j.rse.2007.02.001.
63. Wulder M.A., Han T., White J.C., Sweda T., Tsuzuki H. Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization. Remote Sensing of Environment 2007, 110:123-137.
64. Wulder M.A., Seemann D. Forest inventory height update through the integration of lidar data with segmented Landsat imagery. Canadian Journal of Remote Sensing 2003, 29(5):536-543.