DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

UPDATE: Data no longer available from this page. All non-working links have been removed (19/7/21) Users must follow instructions below from NASA to access data: SRTM data are also available globally at 1 arc second resolution (SRTMGL1.003) through the Data Pool (https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/) or from EarthExplorer where it is listed as NASA SRTM3 SRTMGL1. Please sign in with NASA Earthdata Login Credentials to download data from the NASA LP DAAC Collections. These datasets require login on both NASA Earthdata and USGS EarthExplorer systems to access data. After you create your account, you will also need to “authorize” the LP DAAC Data Pool application. On the Profile page in your Earthdata account you will need to select My Applications. On that page make sure the LP DAAC Data Pool is listed. If it isn't then select Authorize More Applications. In the dialog box type in LP DAAC Data Pool and click Search For Applications. Select Approve when presented with the lpdaac_datapool. Keep everything checked but you can uncheck the Yes, I would like to be notified box. Select Authorize and the LP DAAC Data Pool should be added to your Approved Applications. You might benefit from using the AppEEARS tool. · o AppEEARS landing page: https://lpdaacsvc.cr.usgs.gov/appeears/ · o The users will need and https://urs.earthdata.nasa.gov/?_ga=2.148606453.334533939.1615325167-1213876668.1613754504. Click or tap if you trust this link.">Earthdata Login · o Getting started instructions can be found here: https://lpdaacsvc.cr.usgs.gov/appeears/help Previously available here: Digital Elevation Model of Ireland, from NASA's Shuttle Radar Topography Mission (SRTM), sampled at 3 arc second intervals in latitude & longitude (about every 90m) in heightmap (.HGT) format.''Latitudes & longitudes are referenced to WGS84, heights are in meters referenced to the WGS84/EGM96 geoid.'' Please see the linked pdf files for further documentation.''A QGIS project for the hgt files is also attached.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites. See readme .txt files and final report for additional metadata. A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.

Zipped file contains raster data (TIF) files and thumbnail image (PNG) files associated with modeling known and predicting structural complexity (SC) for the state of Oklahoma, two SIMPA simulation files (.SIJN) and two README (.txt) files to run both the known SC and proxy SC models using the SIMPA tool, and a "Source data catalog.xlsx" file that provides information on the source data and how it was used to represent each raster Note: Metadata including processing steps can be viewed in ESRI's ArcCatalog software

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

DOWNLOAD RASTER IMAGERYThese layers show current Resource Inventory Units (RIUs) symbolized using attributes populated from remote-sensing predictionsRS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value. RS-FRIS 4.0 was constructed using remote-sensing data collected in 2019 and 2020. Origin year and age are periodically updated to reflect harvest activities; all other attributes continue to report conditions as shown in the remote sensing data.Layers include: AGE, BA, BA_4, BA_4_CONIFER, BA_4_HWD, BA_6, BA_T100, BAP_HWD, BFVOL_GROSS, BFVOL_NET, BIOMASS_ALL, BIOMASS_LIVE, CANOPY_LAYERS, CARBON_ALL, CARBON_LIVE, CFVOL_DDWM, CFVOL_TOTAL, CLOSURE, COVER, HT_LOREY, HT_T40, HT_T100, HTMAX, QMD, QMD_6, QMD_T100, RD, RD_6, RD_SUM, SDI_SUM, SDI_SUM_4, SNAG_ACRE_15, SNAG_ACRE_20, SNAG_ACRE_21, SNAG_ACRE_30, TREE_ACRE, TREE_ACRE_4, TREE_ACRE_4_CONIFER, TREE_ACRE_6, TREE_ACRE_8, TREE_ACRE_11, TREE_ACRE_20, TREE_ACRE_21, TREE_ACRE_30 and TREE_ACRE_31.

The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri, brady_som_output.grd, brady_som_output.* - desert_som_output.gri, desert_som_output.grd, desert_som_output.* The data corresponds to two sites: Brady Hot Springs and Desert Peak, both located near Fallon, NV. Input layers include: - Geothermal: Labeled data (0: Non-geothermal; 1: Geothermal) - Minerals: Hydrothermal mineral alterations, as a result of spectral analysis using Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite - Temperature: Land surface temperature (% of times a pixel was classified as "Hot" by K-Means) - Faults: Fault density with a 300mradius - Subsidence: PSInSAR results showing subsidence displacement of more than 5mm - Uplift: PSInSAR results showing subsidence displacement of more than 5mm Also, the results of the classification using Brady and Desert Peak to build 2 Convolutional Neural Networks. These were applied to the training site as well as the other site, the results are in GeoTiff format. - brady_classification: Results of classification of the Brady-trained model - desert_classification: Results of classification of the Desert Peak-trained model - b2d_classification: Results of classification of Desert Peak using the Brady-trained model - d2b_classification: Results of classification of Brady using the Desert Peak-trained model