Export 187 results:
Author Title Type [ Year(Asc)]
Fetene A, Hilker T, Yeshitela R, Prasse R, Cohen WB, Yang Z.  2016.  Detecting trends in landuse and landcover change of Nech Sar National Park, Ethiopia. Environmental Management . 57:137-147.
Cohen WB, Yang Z, Stehman SV, Schroeder TA, Bell DM, Masek JG, Huang C, Meigs GW.  2016.  Forest disturbance across the conterminous United States from 1985–2012: The emerging dominance of forest decline. Forest Ecology and Management. 360:242-252.
Mildrexler D, Yang Z, Cohen WB, Bell DM.  2016.  A forest vulnerability index based on drought and high temperatures. Remote Sensing of Environment . 173:314-325.
Wulder MA, White JC, Loveland TR, Woodcock CE, Belward AS, Cohen WB, Fosnight G, Shaw J, Masek JG, Roy DP.  2016.  The global Landsat archive: Status, consolidation, and direction.. Remote Sensing of Environment . 185:271-283.
Hais M, Wild J, Berec L, Brůna J, Kennedy RE, Braaten J, Brož Z.  2016.  Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance. Remote Sensing. 8(8)
Fickas KC, Cohen WB, Yang Z.  2016.  Landsat-based monitoring of annual wetland change in the Willamette Valley of Oregon, USA from 1972 to 2012. Wetlands Ecology and Management . 24:73-92.
Vogeler JC, Yang Z, Cohen WB.  2016.  Mapping post-fire habitat characteristics through the fusion of remote sensing tools. Remote Sensing of Environment . (173):294-303.
Vogeler JC, Cohen WB.  2016.  A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models. Revista de Teledetección (Spanish Journal of Remote Sensing) . 45:1-14.
Kennedy RE, Yang Z, Braaten J, Copass C, Antonova N, Jordan C, Nelson P.  2015.  Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA. Remote Sensing of Environment. 166:271-285.
Braaten J, Cohen WB, Yang Z.  2015.  Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems. Remote Sensing of Environment . 169:128-138.
Montagnoli A, Fusco S, Terzaghi M, Kirschbaum AA, Pflugmacher D, Cohen WB, Scippa GS, Chiatante D.  2015.  Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps. Forest Ecosystems . 2(10)
Zhu Z, Woodcock CE, Holden C, Yang Z.  2015.  Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time. Remote Sensing of Environment. 162:67-83.
Wing BM, Ritchie M, Boston K, Cohen WB, Olsen M.  2015.  Individual snag detection using neighborhood attribute filtered airborne lidar data. Remote Sensing of the Environment. 163:164-179.
Davis RJ, Ohmann JL, Kennedy RE, Cohen WB, Gregory MJ, Yang Z, Roberts HM, Gray AN, Spies TA.  2015.  Northwest Forest Plan–The First 20 Years (1994-2013): Status and Trends of Late-successional and Old-growth Forests. Gen. Tech. Rep. :112p.
Meigs GW, Kennedy RE, Gray AN, Gregory MJ.  2015.  Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region, USA. Forest Ecology and Management. 339:71-86.
Woodall CW, Coulston JW, Domke GM, Walters BF, Wear DN, Smith JE, Anderson HE, Clough BJ, Cohen WB, Griffiths DM et al..  2015.  The US Forest Carbon Accounting Framework: Stocks and Stock Change, 1990-2016. General Technical Report. :49p.
Kennedy RE, Andréfouët S, Cohen WB, Gómez C, Griffiths P, Hais M, Healey SP, Helmer E, Hostert P, Lyons MB et al..  2014.  Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology and the Environment. 12(6):339-346.
CEOS.  2014.  CEOS Strategy for Carbon Observations from Space, The Committee on Earth Observation Satellites (CEOS) Response to the Group on Earth Observations (GEO) Carbon Strategy.
Torresan C, Strunk J, Zald HS, Yang Z, Cohen WB.  2014.  Comparing statistical techniques to classify the structure of mountain forest stands using CHM-derived metrics in Trento province (Italy). European Journal of Remote Sensing. 47:75-94.
Sulla-Menashe D, Kennedy RE, Yang Z, Braaten J, Krankina ON, Friedl MA.  2014.  Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation. Remote Sensing of Environment. 151:114-123.
Vierling KT, Swift CE, Hudak AT, Vogeler JC, Vierling LA.  2014.  How much does the time lag between wildlife field-data collection and LiDAR-data acquisition matter for studies of animal distributions? A case study using bird communities Remote Sensing Letters. 5(2):185-193.
Schroeder TA, Healey SP, Moisen GG, Frescino TS, Cohen WB, Huang C, Kennedy RE, Yang Z.  2014.  Improving estimates of forest disturbance by combining observations from Landsat time series with US Forest Service Forest Inventory and Analysis data. Remote Sensing of Environment. 154:61-73.
Zald HS, Ohmann JL, Roberts HM, Gregory MJ, Henderson EB, McGaughey RJ, Braaten J.  2014.  Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure. Remote Sensing of Environment. 143:26–38.