Jenkins JC, Birdsey RA, Pan Y (2001) Biomass and NPP estimation for the mid-Atlantic region
(USA) using plot-level forest inventory data. Ecol Appl 11:1174–1193
Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for
United States tree species. For Sci 49(1):12–35
Katila M, Tomppo E (2001) Selecting estimation parameters for the Finnish multisource National
Forest Inventory. Remote Sens Environ 76:16–32
Kaufman YJ, Tanré D (1992) Atmospherically resistant vegetation index (ARVI) for EOSMODIS. IEEE Trans Geosci Remote Sens 30:261–270
Kellndorfer J, Walker W, Pierce L, Dobson C, Fites JA, Hunsaker C, Vona J, Clutter M (2004)
Vegetation height estimation from shuttle radar topography mission and national elevation
datasets. J Remote Sens 93(5):339-358
Kellndorfer J, Walker W, LaPoint E, Kirsch K (2010) 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. Geophys Res Lett
115:G00E08
Kellndorfer J, Walker W, LaPoint E, Cormier T, Bishop J, Fiske G, Kirsch K (2013) Vegetation
height, biomass, and carbon stock for the conterminous United States: a high-resolution
dataset from Landsat ETM+, SRTM-InSAR, National Land Cover Database, and Forest
Inventory and Analysis data fusion (in review)
Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated
characterization of forest disturbance dynamics. Remote Sens Environ 110:370–386
Kimes DS, Ranson KJ, Sun G, Blair JB (2006) Predicting lidar measured forest vertical structure
from multi-angle spectral data. Remote Sens Environ 100:503–511
Knyazikhin Y, Martonchik JV, Myneni RB, Diner DJ, Running SW (1998) Synergistic algorithm
for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically
active radiation from MODIS and MISR data. J Geophys Res 103:32257–32274
Koukal T, Suppan F, Schneider W (2005) The impact of radiometric calibration on kNN
predictions of forest attributes. In: Olsson H (ed) Proceedings of ForestSat 2005. Borås,
Sweden, 31May–3 June 2005, pp 17–21. http://www.skogsstyrelsen.se
Kraenzel M, Castillo A, Moore T, Potvin C (2003) Carbon storage of harvest-age teak (Tectona
grandis) plantations, Panama. For Ecol Manage 173:213–225
Krankina ON, Harmon ME, Cohen WB, Oetter DR, Zyrina O, Duane MV (2004) Carbon stores,
sinks, and sources in forests of Northwestern Russia: can we reconcile forest inventories with
remote sensing results? Clim Chang 67:257–272
Labrecque S, Fournier RA, Luther JE, Piercey D (2006) A comparison of four methods to map
biomass from Landsat-TM and inventory data in western Newfoundland. For Ecol Manag
226:129–144
Laclau P (2003) Biomass and carbon sequestration of ponderosa pine plantations and native
cypress forests in northwest Patagonia. For Ecol Manag 180:317–333
Larsson H (1993) Linear regression for canopy cover estimation in Acacia woodlands using
Landsat-TM, -MSS, and SPOT HRV XS data. Int J Remote Sens 14:2129–2136
Laurent VCE, Verhoef W, Clevers JGPW, Schaepman M E (2011) Estimating forest variables
from top-of-atmosphere radiance satellite measurements using coupled radiative transfer
models. Remote Sens Environ 115:1043–1052
Le Toan T, Beaudoin A, Riom J, Guyon D (1992) Relating forest biomass to SAR data. IEEE
Trans Geosci Remote Sens 30:403–411
Le Toan T, Quegan S, Davidson MWJ, Balzter H, Paillou P, Papathanassiou K, Plummer S,
Rocca F, Saatchi S, Shugart H, Ulander L (2011) The BIOMASS mission: mapping global
forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ
115:2850–2860
Leboeuf A, Beaudoin A, Fournier RA, Guindon L, Luther JE, Lambert M-C (2007) A shadow
fraction method for mapping biomass of northern boreal black spruce forests using QuickBird
imagery. Remote Sens Environ 110:488–500
3 Remote Sensing of Forest Biomass
93
(USA) using plot-level forest inventory data. Ecol Appl 11:1174–1193
Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for
United States tree species. For Sci 49(1):12–35
Katila M, Tomppo E (2001) Selecting estimation parameters for the Finnish multisource National
Forest Inventory. Remote Sens Environ 76:16–32
Kaufman YJ, Tanré D (1992) Atmospherically resistant vegetation index (ARVI) for EOSMODIS. IEEE Trans Geosci Remote Sens 30:261–270
Kellndorfer J, Walker W, Pierce L, Dobson C, Fites JA, Hunsaker C, Vona J, Clutter M (2004)
Vegetation height estimation from shuttle radar topography mission and national elevation
datasets. J Remote Sens 93(5):339-358
Kellndorfer J, Walker W, LaPoint E, Kirsch K (2010) 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. Geophys Res Lett
115:G00E08
Kellndorfer J, Walker W, LaPoint E, Cormier T, Bishop J, Fiske G, Kirsch K (2013) Vegetation
height, biomass, and carbon stock for the conterminous United States: a high-resolution
dataset from Landsat ETM+, SRTM-InSAR, National Land Cover Database, and Forest
Inventory and Analysis data fusion (in review)
Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated
characterization of forest disturbance dynamics. Remote Sens Environ 110:370–386
Kimes DS, Ranson KJ, Sun G, Blair JB (2006) Predicting lidar measured forest vertical structure
from multi-angle spectral data. Remote Sens Environ 100:503–511
Knyazikhin Y, Martonchik JV, Myneni RB, Diner DJ, Running SW (1998) Synergistic algorithm
for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically
active radiation from MODIS and MISR data. J Geophys Res 103:32257–32274
Koukal T, Suppan F, Schneider W (2005) The impact of radiometric calibration on kNN
predictions of forest attributes. In: Olsson H (ed) Proceedings of ForestSat 2005. Borås,
Sweden, 31May–3 June 2005, pp 17–21. http://www.skogsstyrelsen.se
Kraenzel M, Castillo A, Moore T, Potvin C (2003) Carbon storage of harvest-age teak (Tectona
grandis) plantations, Panama. For Ecol Manage 173:213–225
Krankina ON, Harmon ME, Cohen WB, Oetter DR, Zyrina O, Duane MV (2004) Carbon stores,
sinks, and sources in forests of Northwestern Russia: can we reconcile forest inventories with
remote sensing results? Clim Chang 67:257–272
Labrecque S, Fournier RA, Luther JE, Piercey D (2006) A comparison of four methods to map
biomass from Landsat-TM and inventory data in western Newfoundland. For Ecol Manag
226:129–144
Laclau P (2003) Biomass and carbon sequestration of ponderosa pine plantations and native
cypress forests in northwest Patagonia. For Ecol Manag 180:317–333
Larsson H (1993) Linear regression for canopy cover estimation in Acacia woodlands using
Landsat-TM, -MSS, and SPOT HRV XS data. Int J Remote Sens 14:2129–2136
Laurent VCE, Verhoef W, Clevers JGPW, Schaepman M E (2011) Estimating forest variables
from top-of-atmosphere radiance satellite measurements using coupled radiative transfer
models. Remote Sens Environ 115:1043–1052
Le Toan T, Beaudoin A, Riom J, Guyon D (1992) Relating forest biomass to SAR data. IEEE
Trans Geosci Remote Sens 30:403–411
Le Toan T, Quegan S, Davidson MWJ, Balzter H, Paillou P, Papathanassiou K, Plummer S,
Rocca F, Saatchi S, Shugart H, Ulander L (2011) The BIOMASS mission: mapping global
forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ
115:2850–2860
Leboeuf A, Beaudoin A, Fournier RA, Guindon L, Luther JE, Lambert M-C (2007) A shadow
fraction method for mapping biomass of northern boreal black spruce forests using QuickBird
imagery. Remote Sens Environ 110:488–500
3 Remote Sensing of Forest Biomass
93