Aboveground biomass estimates of sagebrush using terrestrial and airborne LiDAR data in a dryland ecosystem

Agricultural and Forest Meteorology

Volumn 213, Issue , 2015, Pages 138-147

  Li, Aihua ^a   Glenn, Nancy F ^a   Olsoy, Peter J ^a   Mitchell, Jessica J ^b   Shrestha, Rupesh ^a  

^a BOISE STATE UNIVERSITY   (United States)

^b APPALACHIAN STATE UNIVERSITY   (United States)

Author keywords

Aboveground biomass; Aboveground carbon; Airborne laser scanning; Artemisia tridentata; Reynolds Creek Experimental Watershed; Terrestrial laser scanning

Indexed keywords

ABOVEGROUND BIOMASS; AIRBORNE SENSING; ARID REGION; ECOLOGICAL MODELING; ESTIMATION METHOD; FLUX MEASUREMENT; HERB; LIDAR; PLANT COMMUNITY; PREDICTION; REYNOLDS NUMBER; SATELLITE DATA; SHRUB; SHRUBLAND; TERRESTRIAL ENVIRONMENT; VEGETATION STRUCTURE;

GREAT BASIN; UNITED STATES;

ARTEMISIA TRIDENTATA;

EID: 84936862853     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2015.06.005     Document Type: Article

References (66)

1. Abdalati W., Zwally H.J., Bindschadler R., Csathó B., Farrell S.L., Fricker H.A., Harding D., Kwok R., Lefsky M.A., Markus T., Marshak A., Neumann T., Palm S.P., Schutz B.E., Smith B., Spinhirne J.D., Webb C.E. The ICESat-2 Laser Altimetry Mission. Proc. IEEE 2010, 98:735-751.
2. Barber C.B., Dobkin D.P., Huhdanpaa H.T. The Quickhull algorithm for convex hulls. ACM Trans. Math. Software 1996, 22:469-483.
3. Béland M., Widlowski J.L., Fournier R.A. A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR. Environ. Model. Software 2014, 51:184-189.
4. Benz U., Hofmann P., Willhauck G., Lingenfelder I., Heynen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 2004, 58:239-258.
5. Berterretche M., Hudak A.T., Cohen W.B., Maiersperger T.K., Gower S.T., Dungan J. Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest. Remote Sens. Environ. 2005, 96:49-61.
6. Breshears D.D. The grassland-forest continuum: trends in ecosystem properties for woody plant mosaics?. Front. Ecol. Environ. 2006, 42:96-104.
7. Chambers J.C., Eldredge E.P., Snyder K.A., Board D.I., Forbis T., Hubbard V. Restoring abandoned agricultural lands in cold desert shrublands: tradeoffs between water availability and invasive species. Invasive Plant Sci. Manage. 2014, 7:176-189.
8. Chazelle B. An optimal convex hull algorithm in any fixed dimension. Discrete Comput. Geom. 1993, 10:377-409.
9. Côté J.F., Fournier R.A., Egli R. An architectural model of trees to estimate forest structural attributes using terrestrial LiDAR. Environ. Model. Software 2011, 26:761-777.
10. Davies K.W., Bates J.D., Miller R.F. Short-term effects of burning Wyoming Big Sagebrush Steppe in southeast Oregon. Rangeland Ecol. Manage. 2007, 60:515-522.
11. Doiron M., Legagneux P., Gauthier G., Lévesque E. Broad-scale satellite normalized difference vegetation index data predict plant biomass and peak date of nitrogen concentration in Arctic tundra vegetation. Appl. Veg. Sci 2013, 16:343-351.
12. Estornell J., Ruiz L.A., Velázquez-Martí B., Fernández-Sarría A. Estimation of shrub biomass by airborne LiDAR data in small forest stands. For. Ecol. Manage. 2011, 262:1697-1703.
13. Estornell J., Ruiz L.A., Velázquez-Martí B., Hermosilla T. Estimation of biomass and volume of shrub vegetation using LiDAR and spectral data in a Mediterranean environment. Biomass Bioenergy 2012, 46:710-721.
14. Frandsen W.H. Modeling big sagebrush as a fuel. J. Range Manage. 1983, 36:596-600.
15. Gasparri N.I., Parmuchi M.G., Bono J., Karszenbaum H., Montenegro C.L. Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. J. Arid Environ. 2010, 74:1262-1270.
16. Glenn N.F., Spaete L.P., Sankey T.T., Derryberry D.R., Hardegree S.P., Mitchell J.J. Errors in LiDAR-derived shrub height and crown area on sloped terrain. J. Arid Environ. 2011, 75:377-382.
17. Hancock S., Essery R., Reid T., Carle J., Baxter R., Rutter N., Huntley B. Characterising forest gap fraction with terrestrial lidar and photography: an examination of relative limitations. Agric. For. Meteorol. 2014, 189-190:105-114.
18. Harte J., Saleska S., Shih T. Shifts in plant dominance control carbon-cycle response to experimental warming and widespread drought. Environ. Res. Lett. 2006, 1:1-4.
19. Hese S., Lucht W., Schmullius C., Barnsley M., Dubayah R., Knorr D., Neumann K., Riedel T., Schröter K. Global biomass mapping for an improved understanding of the CO2 balance - the Earth observation mission carbon-3D. Remote Sens. Environ. 2005, 94:94-104.
20. Hilker T., Leeuwen M., Coops N.C., Wulder M.A., Newnham G.J., Jupp D.L.B., Culvenor D.S. Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand. Trees 2010, 24:819-832.
21. Houghton R.A. Aboveground forest biomass and the global carbon balance. Global Change Biol. 2005, 11:945-958.
22. Huang S., Crabtree R.L., Swanson A.K., Halligan K.Q., Harmsen J.A. Error analysis and correction for extracting the forest height from airborne C-band interferometric SAR and national elevation datasets. Int. J. Remote Sens. 2010, 31:2493-2512.
23. Jupp D.L.B., Culvenor D.S., Lovell J.L., Newnham G.J., Strahler A.H., Woodcock C.E. Estimating forest LAI profiles and structural parameters using a ground-based laser called 'Echidna®'. Tree Physiol. 2008, 29:171-181.
24. Ku N.W., Popescu S.C., Ansley R.J., Perotto-Baldivieso H.L. Assessment of available rangeland woody plant biomass with a terrestrial LiDAR system. Photogramm. Eng. Remote Sens. 2012, 78:349-361.
25. Le Toan T., Quegan S., Davidson M.W.J., Balzter H., Paillou P., Papathanassiou K., Plummer S., Rocca F., Saatchi S., Shugart H., Ulander L. The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens. Environ. 2011, 115:2850-2860.
26. Lefsky M.A. A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophys. Res. Lett. 2010, 37:1-5.
27. Lefsky M.A., Harding D.J., Keller M., Cohen W.B., Carabajal C.C., Del Bom Espirito-Santo F., Hunter M.O., de Oliveira R., de Camargo P.B. Estimates of forest canopy height and aboveground biomass using ICESat. Geophys. Res Lett. 2005, 32:1-4.
28. Levia D.F. A generalized allometric equation to predict foliar dry weight on the basis of trunk diameter for eastern white pine (Pinus strobus L.). For. Ecol. Manage. 2008, 255:1789-1792.
29. Lin Y., Hyyppä J., Kukko A., Jaakkola A., Kaartinen H. Tree height growth measurement with single-scan airborne, static terrestrial and mobile laser scanning. Sensors 2012, 12:12798-12813.
30. Lovell J.L., Jupp D.L.B., Culvenor D.S., Coops N.C. Using airborne and ground-based ranging LiDAR to measure canopy structure in Australian forests. Can. J. Remote Sens. 2003, 29:607-622.
31. Lu D., Chen Q., Wang G., Moran E., Batistella M., Zhang M., Laurin G.V., Saah D. Aboveground forest biomass estimation with Landsat and LiDAR Data and uncertainty analysis of the estimates. Int. J. For. Res. 2012, 2012:1-16.
32. Marks D.G., Seyfried M.S., Flerchinger G.N., Winstral A.H. Research data collection at the Reynolds Creek Experimental Watershed. J. Service Climatol. 2007, 1:1-12.
33. Maselli F., Chiesi M. Evaluation of statistical methods to estimate forest volume in a Mediterranean region. IEEE Trans. Geosci. Remote Sens. 2006, 44:2239-2250.
34. Mitchard E.T.A., Saatchi S.S., White L.J.T., Abernethy K.A., Jeffery K.J., Lewis S.L., Collins M., Lefsky M.A., Leal M.E., Woodhouse I.H., Meir P. Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: overcoming problems of high biomass and persistent cloud. Biogeosciences 2012, 9:179-191.
35. Mitchell J.J., Glenn N.F., Sankey T.T., Derryberry D.R., Anderson M.O., Hruska R.C. Small-footprint LiDAR estimations of sagebrush canopy characteristics. Photogramm. Eng. Remote Sens. 2011, 77:521-530.
36. Mitchell J.J., Shrestha R., Spaete L.P., Glenn N.F. Combining airborne hyperspectral and LiDAR data across local sites for upscaling shrubland structural information: lessons for HyspIRI. Remote Sens. Environ. 2015, in press. 10.1016/j.rse.2015.04.015.
37. Moorthy I., Miller J.R., Berni J.A.J., Zarco-Tejada P., Hu B., Chen J. Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agric. For. Meteorol. 2011, 151:204-214.
38. Moskal L.M., Zheng G. Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest. Remote Sens. 2012, 4:1-20.
39. Nanos N., Calama R., Montero G., Gil L. Geostatistical prediction of height/diameter models. For. Ecol. Manage. 2004, 195:221-235.
40. Nelson R. Model effects on GLAS-based regional estimates of forest biomass and carbon. Int. J. Remote Sens. 2010, 31:1359-1372.
41. Ni-Meister W., Lee S., Strahler A.H., Woodcock C.E., Schaaf C., Yao T., Blair J.B. Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from lidar remote sensing. J. Geophys. Res. Biogeosci. 2010, 115(G00E11). 10.1029/2009JG000936.
42. Okin G.S., Roberts D.A., Murray B., Okin W.J. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sens. Environ. 2001, 77:212-215.
43. Olsoy P.J., Glenn N.F., Clark P.E. Estimating sagebrush biomass using terrestrial laser scanning (TLS). Rangeland Ecol. Manage. 2014, 67:224-228.
44. Olsoy P.J., Glenn N.F., Clark P.E., Derryberry D.R. Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning. ISPRS J. Photogramm. Remote Sens. 2014, 88:166-173.
45. Pascual C., Garcia-Abril A., Cohen W.B., Martin-Fernandez S. Relationship between LiDAR-derived forest canopy height and Landsat images. Int. J. Remote Sens. 2010, 31:1261-1280.
46. Petrie G., Toth C.K. Introduction to laser ranging, profiling, and scanning. Topographic laser ranging and scanning principles and processing 2008, 1-27. CRC Press, London, UK. J. Shan, C.K. Toth (Eds.).
47. Popescu S.C., Zhao K. A voxel-based LiDAR method for assessing crown base height. Remote Sens. Environ. 2008, 112:767-781.
48. Rossi R.E., Dungan J.L., Beck L.R. Kriging in the shadows: geostatistical interpolation for remote sensing. Remote Sens. Environ. 1994, 491:32-40.
49. Salas C., Ene L., Gregoire T.G., Næsset E., Gobakken T. Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models. Remote Sens. Environ. 2010, 114:1277-1285.
50. Sales M.H., Souza C.M., Kyriakidis P.C., Roberts D.A., Vida E. Improving spatial distribution estimation of forest biomass with geostatistics: a case study for Rondonia, Brazil. Ecol. Modell. 2007, 205:221-230.
51. Seyfried, M.S., Harris, R.C., Marks, D., Jacob, B., 2000. A geographic database for watershed research: Reynolds Creek Experimental Watershed, Idaho, USA. Northwest Watershed Research Center, USDA-Agricultural Research Service, Boise, Idaho. Technical Bulletin NWRC-2000-3, 26 pp.
52. Seyfried M.S., Harris R.C., Marks D.G., Jacob B. Geographic database Reynolds Creek Experimental Watershed, Idaho, United States. Water Resour. Res. 2001, 37:2825-2829.
53. Streutker D., Glenn N.F. LiDAR measurement of sagebrush steppe vegetation heights. Remote Sens. Environ. 2006, 102:135-145.
54. Swatantran A., Dubayah R., Roberts D., Hofton M., Blair J.B. Mapping biomass and stress in the Sierra Nevada using LiDAR and hyperspectral data fusion. Remote Sens. Environ. 2011, 115:2917-2930.
55. Viana H., Aranha J., Lopes D., Cohen W.B. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecol Model. 2012, 226:22-35.
56. Vierling L.A., Xu Y., Eitel J.U.H., Oldow J.S. Shrub characterization using terrestrial laser scanning and implications for airborne LiDAR assessment. Can. J. Remote Sens. 2013, 38:709-722.
57. Wang, M., Tseng, Y.H., 2004. LIDAR data segmentation and classification based on octree structure. In XXth International Society for Photogrammetry and Remote Sensing (ISPRS) Congress, 12-23 July 2004, Istanbul, Turkey. Edited by O. Altan. ISPRS Archives - Volume XXXV Part B3. pp. 308-313.
58. Watts M.J., Wambolt C.L. Long-term recovery of Wyoming big sagebrush after four treatments. J. Environ. Manage. 1996, 46:95-102.
59. Winstral A.H., Marks D.G., Gurney R. Assessing the sensitivities of distributed snow models to forcing data resolution. J. Hydrometeorol. 2014, 15:1366-1383.
60. Yang J., Weisberg P.J., Bristow N.A. Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: comparison of vegetation indices and spectral mixture analysis. Remote Sens. Environ. 2012, 119:62-71.
61. Yao T., Yang X.Y., Zhao F., Wang Z.S., Zhang Q.L., Jupp D., Lovell J., Culvenor D., Newnham G., Ni-Meister W., Schaaf C., Woodcock C., Wang J.D., Li X.W., Strahler A. Measuring forest structure and biomass in New England forest stands using Echidna ground-based LiDAR. Remote Sens. Environ. 2011, 115:2965-2974.
62. Zandler H., Brenning A., Samimi C. Quantifying dwarf shrub biomass in an arid environment: comparing empirical methods in a high dimensional setting. Remote Sens. Environ. 2015, 158:140-155.
63. Zeng X., Zeng X., Barlage M. Growing temperate shrubs over arid and semiarid regions in the Community Land Model-Dynamic Global Vegetation Model. Global Biogeochem. Cycles. 2008, 22:GB3003.
64. Zhao K., Popescu S.C., Nelson R.F. LiDAR remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sens. Environ. 2009, 113:182-196.
65. Zheng G., Moskal L.M., Kim Soo-Hyung. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning. IEEE Trans. Geosci. Remote Sens. 2013, 51:777-786.
66. Zolkos S.G., Goetz S.J., Dubayah R. A meta-analysis of terrestrial aboveground biomass estimation using LiDAR remote sensing. Remote Sens. Environ. 2013, 128:289-298.