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.

Explore the mean age of women at the birth of their first child in 2012. Find insights on first child birth age trends for females in multiple countries. Click here for more information. First child, age, female, birth Portugal, Belgium, Spain, Bosnia and Herzegovina, France, Denmark, Italy, Uzbekistan, Bulgaria, United Kingdom, Slovenia, Czechia, Poland, Ukraine, Latvia, Sweden, Iceland, Armenia, Georgia, Canada, Montenegro, Hungary, United States, Andorra, Republic of Moldova, Croatia, Malta, San Marino, Turkmenistan, Azerbaijan, Kyrgyzstan, North Macedonia, Russian Federation, Greece, Luxembourg, Slovakia, Norway, Tajikistan, Albania, Liechtenstein, Serbia, Switzerland, Lithuania, Estonia, Turkiye, Cyprus, Germany, Finland, Ireland, Israel, Kazakhstan, Austria, Belarus, Netherlands, RomaniaFollow data.kapsarc.org for timely data to advance energy economics research..Source: UNECE Statistical Database, compiled from national and international (Eurostat and UNICEF TransMONEE) official sources.Definition: The mean age of women at birth of first child is the weighted average of the different childbearing ages, using as weights the age-specific fertility rates of first-order births.General note: Data come from registers, unless otherwise specified. Data for 2010 come from the European Demographic Data Sheet (Wittgenstein Centre) for the following countries: Albania, Cyprus, Malta, Montenegro, Sweden and Turkey.

Transport for NSW provides projections of population and dwellings at the small area (Travel Zone or TZ) level for NSW. The latest version is Travel Zone Projections 2022 (TZP22), released November 2022. The projections are developed to support a strategic view of NSW and are aligned with the [NSW Government Common Planning Assumptions](https://www.treasury.nsw.gov.au/information-public-entities/common-planning-assumptions). This new version TZP22 is an update on the previously published [TZP19](https://opendata.transport.nsw.gov.au/dataset/population-projections/resource/08dffce8-081b-4117-91b1-9075566618ac) **The TZP22 Population & Dwellings Projections dataset covers the following variables:** * Estimated Resident Population * Occupied Private Dwellings * Population in Occupied Private Dwellings, by 5-year Age categories & by Sex * Population in Non-Private Dwellings The projections in this release, TZP22, are presented annually 2016 to 2026 and five-yearly from 2026 to 2066, and are in TZ16 geography. Please note, TZP22 is based on best available data as at early to mid 2022. It includes the impacts from the Covid-19 pandemic and does not include results from the ABS 2021 Census as the relevant data had not been released at the time of TZP22 production. **Key Data Inputs used in TZP22:** * [2022 NSW Population projections data](https://www.planning.nsw.gov.au/Research-and-Demography/Population-projections) – NSW Department of Planning, Industry & Environment * [2022 NSW Household and Dwelling projections data](https://www.planning.nsw.gov.au/Research-and-Demography/Population-projections) – NSW Department of Planning, Industry & Environment * [2016 Census data](https://www.abs.gov.au/) - Australian Bureau of Statistics (including dwellings by occupancy, total dwellings by Mesh Block, historical household sizes, private dwellings by occupancy, population age and gender, persons by place of usual residence) * The 2022 NSW Population, Household and Dwelling projections do not include 2021 Census data as the relevant data has not been released at the time of TZP22 production. For a summary of the TZP22 Projections method please refer to the [TZP22 Factsheet](https://opendata.transport.nsw.gov.au/dataset/population-projections/resource/cadd7bb9-da0f-4409-80ea-db0eb4603b8e) For more detail on the projection process please refer to the [TZP22 Technical Guide](https://opendata.transport.nsw.gov.au/dataset/population-projections/resource/cb7f1454-dad7-49f1-97b6-679780a1ffa2) Additional land use information for [workforce](https://opendata.transport.nsw.gov.au/dataset/workforce-projections) and [employment](https://opendata.transport.nsw.gov.au/dataset/employment-projections) as well as [Travel Zone boundaries](https://opendata.transport.nsw.gov.au/dataset/travel-zones-2016) and concordance files are also available for download on the Open Data Hub. A visualisation of the population projections is available on the Transport for NSW Website under [Reference Information](https://www.transport.nsw.gov.au/data-and-research/reference-information/travel-zone-explorer-visualisation). **Cautions** The TZP22 dataset represents one view of the future aligned with the NSW Government Common Planning Assumptions and population and economic projections. The projections are not based on specific assumptions about future new transport infrastructure, but do take into account known land-use developments underway or planned, and strategic plans. * TZP22 is a strategic state-wide dataset and caution should be exercised when considering results at detailed breakdowns. * The TZP22 outputs represent a point in time set of projections (as at early to mid 2022). * The projections are not government targets. * Travel Zone (TZ) level outputs are projections only and should be used as a guide. As with all small area data, aggregating of travel zone projections to higher geographies leads to more robust results. * As a general rule, TZ-level projections are illustrative of a possible future only. * More specific advice about data reliability for the specific variables projected is provided in the “Read Me” page of the Excel format summary spreadsheets on the TfNSW Open Data Hub. * Caution is advised when comparing TZP22 with the previous set of projections (TZP19) due to addition of new data sources for the most recent years, and adjustments to methodology. **Further cautions and notes can be found in the TZP22 Technical Guide**

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.

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

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.

Population age structure from the UK Climate Resilience Programme UK-SSPs project. This dataset contains only SSP2, the 'Middle of the Road' scenario.This data contains a field for each year (on a decadal basis). A separate field for 'Age Class' allow the data to be filtered e.g. by age class '10-14'.Boundaries use ONS LAD boundaries and have been simplified to 10m resolution. Indicator Demography Metric Age Structure Unit Thousands per age class Spatial Resolution LAD Temporal Resolution Yearly Sectoral Categories 19 age classes Baseline Data Source ONS 2019 Projection Trend Source IIASA What are Shared Socioeconomic Pathways (SSPs)?The global SSPs, used in IPCC assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.Until recently, UK-specific versions of the global SSPs were not available combined with the RCP-based climate projections. The aim of the project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience. More details can be found on the UK SSP project site and in this storymap. This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/