Primary and secondary remote sensing products predict post-fire Lewis's woodpecker nesting habitat. Northwest Science.. In Press.
Using Landsat time series and lidar to inform aboveground biomass baselines in northern Minnesota. Canadian Journal of Remote Sensing.. In Press.
Detecting trends in landuse and landcover change of Nech Sar National Park, Ethiopia. Environmental Management . 57:137-147.. 2016.
Forest disturbance across the conterminous United States from 1985–2012: The emerging dominance of forest decline. Forest Ecology and Management. 360:242-252.. 2016.
A forest vulnerability index based on drought and high temperatures. Remote Sensing of Environment . 173:314-325.. 2016.
The global Landsat archive: Status, consolidation, and direction.. Remote Sensing of Environment . 185:271-283.. 2016.
Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance. Remote Sensing. 8(8). 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.. 2016.
Mapping post-fire habitat characteristics through the fusion of remote sensing tools. Remote Sensing of Environment . (173):294-303.. 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.. 2016.
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.. 2015.
Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems. Remote Sensing of Environment . 169:128-138.. 2015.
Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps. Forest Ecosystems . 2(10). 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.. 2015.
Individual snag detection using neighborhood attribute filtered airborne lidar data. Remote Sensing of the Environment. 163:164-179.. 2015.
Northwest Forest Plan–The First 20 Years (1994-2013): Status and Trends of Late-successional and Old-growth Forests. Gen. Tech. Rep. :112p.. 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.. 2015.
The US Forest Carbon Accounting Framework: Stocks and Stock Change, 1990-2016. General Technical Report. :49p.. 2015.
Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology and the Environment. 12(6):339-346.. 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.. 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.. 2014.
Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation. Remote Sensing of Environment. 151:114-123.. 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.. 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.. 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.. 2014.