This Esri supported add-in is supported in ArcMap Desktop 10.4 and higher, and used to:1. Update the position of fabric points, using the geometry of features in a reference layer that you configure.2. Merge multiple close fabric points to a specific location that you define.The Fabric Point Move to Feature add-in provides methods to update the positions of parcel points based on feature geometry locations. Feature layers are used as a target reference, and contain the features that are used to update the fabric points.Fabric points can be updated using either a line layer or a point layer.For a demonstration of how to use this tool, please see the Help video available from the toolbar, or directly from here.The source code is available on GitHub.Installing a different version of an add-in.If you are installing the add-in directly on your client machine, as opposed to placing the add-in file at a network share location, then follow these steps:First un-install the version currently on the client machine. 1. In ArcMap go to Customize -> Add-in Manager2. On the Add-ins tab click to select the add-in you want to un-install, and then click the Delete button.3. Click Yes on the dialog that asks for confirmation on the delete.4. Click Close.5. Close ArcMap.6. Start ArcMap and use Add-in Manager to confirm the add-in is not listed under the My Add-ins section of the left pane.7. Close ArcMap.8. Double-click the add-in file for the version of the add-in that you want to install.9. Click the Install Add-in button.10. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under My Add-ins.Troubleshooting Notes: A. if problems are encountered when attemping to run the add-in, check to make sure you have privileges on the well-known folder. You should be able to browse to the file add-in location on disk, in the well-known folder: C:\Users<username>\Documents\ArcGIS\AddIns\Desktop10.<0-1>\B. Alternatively, consider using a network share for your add-in, and follow the steps below.If you use a network share to load the add-in, then follow these steps:1. In ArcMap go to Customize -> Add-in Manager.2. In the left pane on the Add-ins tab, scroll down to the Shared Add-ins.3. Under Shared Add-ins, click on the add-in name that you want to change and confirm the add-in version in the right pane is the one you want to change from.4. Click the Options tab on the Add-in Manager and get the share location for the add-in you want to change from.4. Click Close on the Add-in Manager and close ArcMap.5. Using the required privileges, browse to the share location and replace the add-in file with the version of the add-in file that you want to change to.6. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under Shared Add-ins.General notes and resources:A. See the Administrator Settings heading under the help section here: https://bit.ly/2XD5mb8B. Additional uninstall and re-install steps: https://bit.ly/2xN8dPy

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf. This dataset represents heat zones, or the mean number of days over 30 C, in 4 time periods (1980-2009, 2010-2039, 2040-2069, and 2070-2099), using two emissions scenarios (RCP 4.5 and 8.5, the medium and high scenarios, respectively).

This Esri supported add-in is supported in ArcMap 10.1 and higher, and is used to:1. Visualize quality indicators on parcels.2. Re-calculate directions or distances based on filters that you define.3. Get a combined scale factor for distance calculations, by specifying an elevation source.4. Add or update elevations on parcels and control points, by interpolating from a surface or manually entering a value.5. Re-calculate the stated area from direction and distance attributes. The source code is available on GitHub.Installing a different version of an add-in.If you are installing the add-in directly on your client machine, as opposed to placing the add-in file at a network share location, then follow these steps:First un-install the version currently on the client machine. 1. In ArcMap go to Customize -> Add-in Manager2. On the Add-ins tab click to select the add-in you want to un-install, and then click the Delete button.3. Click Yes on the dialog that asks for confirmation on the delete.4. Click Close.5. Close ArcMap.6. Start ArcMap and use Add-in Manager to confirm the add-in is not listed under the My Add-ins section of the left pane.7. Close ArcMap.8. Double-click the add-in file for the version of the add-in that you want to install.9. Click the Install Add-in button.10. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under My Add-ins.Troubleshooting Notes: A. if problems are encountered when attemping to run the add-in, check to make sure you have privileges on the well-known folder. You should be able to browse to the file add-in location on disk, in the well-known folder: C:\Users<username>\Documents\ArcGIS\AddIns\Desktop10.<0-1>\B. Alternatively, consider using a network share for your add-in, and follow the steps below.If you use a network share to load the add-in, then follow these steps:1. In ArcMap go to Customize -> Add-in Manager.2. In the left pane on the Add-ins tab, scroll down to the Shared Add-ins.3. Under Shared Add-ins, click on the add-in name that you want to change and confirm the add-in version in the right pane is the one you want to change from.4. Click the Options tab on the Add-in Manager and get the share location for the add-in you want to change from.4. Click Close on the Add-in Manager and close ArcMap.5. Using the required privileges, browse to the share location and replace the add-in file with the version of the add-in file that you want to change to.6. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under Shared Add-ins.General notes and resources:A. See the Administrator Settings heading under the help section here: https://bit.ly/2XD5mb8B. Additional uninstall and re-install steps:https://bit.ly/2xN8dPy

This Esri supported add-in is useful for the following:Deleting a large number of selected control points, parcels, connection lines, or line points from versioned or un-versioned fabrics.Finding and optionally deleting inconsistent fabric records such as points that are not attached to lines, lines with the same from and to points, lines that are not attached to parcels, parcels with no lines, and line-points with incorrect point references, in one of two waysIn batch for the whole fabric, orBy dragging a small rectangle over fabric lines and points in the map.Finding and optionally deleting empty plans.Truncating fabric tables on an un-versioned fabric (removes ALL rows from the chosen fabric tables)For a full description of how to use this tool, please read the Add-in documentation:Delete Fabric Records Add-in.The source code is available on GitHub.Installing a different version of an add-in.If you are installing the add-in directly on your client machine, as opposed to placing the add-in file at a network share location, then follow these steps:First un-install the version currently on the client machine. 1. In ArcMap go to Customize -> Add-in Manager2. On the Add-ins tab click to select the add-in you want to un-install, and then click the Delete button.3. Click Yes on the dialog that asks for confirmation on the delete.4. Click Close.5. Close ArcMap.6. Start ArcMap and use Add-in Manager to confirm the add-in is not listed under the My Add-ins section of the left pane.7. Close ArcMap.8. Double-click the add-in file for the version of the add-in that you want to install.9. Click the Install Add-in button.10. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under My Add-ins.Troubleshooting Notes: A. if problems are encountered when attempting to run the add-in, check to make sure you have privileges on the well-known folder. You should be able to browse to the file add-in location on disk, in the well-known folder: C:\Users<username>\Documents\ArcGIS\AddIns\Desktop10.<0-1>\B. Alternatively, consider using a network share for your add-in, and follow the steps below.C. Make sure that the add-in is being loaded from ONLY ONE location, by confirming that you do not have the add-in file on both a network share as well as on your well-known folder location. This can cause conflicts and may result in the add-in not loading.If you use a network share to load the add-in, then follow these steps:1. In ArcMap go to Customize -> Add-in Manager.2. In the left pane on the Add-ins tab, scroll down to the Shared Add-ins.3. Under Shared Add-ins, click on the add-in name that you want to change and confirm the add-in version in the right pane is the one you want to change from.4. Click the Options tab on the Add-in Manager and get the share location for the add-in you want to change from.4. Click Close on the Add-in Manager and close ArcMap.5. Using the required privileges, browse to the share location and replace the add-in file with the version of the add-in file that you want to change to.6. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under Shared Add-ins.General notes and resources:A. See the Administrator Settings heading under the help section here: https://bit.ly/2XD5mb8B. Additional uninstall and re-install steps:https://bit.ly/2xN8dPy

This Esri supported add-in is used to:1. Split multi-segment lines at inflection points; for example, at locations where one curve transitions into another, or at sharp bends or corners between two straight-line segments.2. Convert densified lines into one or more separate circular arcs by fitting circular arcs to the straight-line segment sequences.3. Simplify lines by testing segment tangency and removing unneeded vertices along a straight line or along a circular arc.4. Select multi-segment lines based on the properties of the segments.5. Select features that have multi-part geometries.For a full description of how to use this tool, please read the Add-in documentation:Curves And Lines Add-inAlso see the Help video available from the toolbar, or directly from here.Archived older versions of this add-in available from here.Installing a different version of an add-in.If you are installing the add-in directly on your client machine, as opposed to placing the add-in file at a network share location, then follow these steps:First un-install the version currently on the client machine. 1. In ArcMap go to Customize -> Add-in Manager2. On the Add-ins tab click to select the add-in you want to un-install, and then click the Delete button.3. Click Yes on the dialog that asks for confirmation on the delete.4. Click Close.5. Close ArcMap.6. Start ArcMap and use Add-in Manager to confirm the add-in is not listed under the My Add-ins section of the left pane.7. Close ArcMap.8. Double-click the add-in file for the version of the add-in that you want to install.9. Click the Install Add-in button.10. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under My Add-ins.Troubleshooting Notes: A. if problems are encountered when attempting to run the add-in, check to make sure you have privileges on the well-known folder. You should be able to browse to the file add-in location on disk, in the well-known folder: C:\Users<username>\Documents\ArcGIS\AddIns\Desktop10.<0-1>\B. Alternatively, consider using a network share for your add-in, and follow the steps below.C. Make sure that the add-in is being loaded from ONLY ONE location, by confirming that you do not have the add-in file on both a network share as well as on your well-known folder location. This can cause conflicts and may result in the add-in not loading.If you use a network share to load the add-in, then follow these steps:1. In ArcMap go to Customize -> Add-in Manager.2. In the left pane on the Add-ins tab, scroll down to the Shared Add-ins.3. Under Shared Add-ins, click on the add-in name that you want to change and confirm the add-in version in the right pane is the one you want to change from.4. Click the Options tab on the Add-in Manager and get the share location for the add-in you want to change from.4. Click Close on the Add-in Manager and close ArcMap.5. Using the required privileges, browse to the share location and replace the add-in file with the version of the add-in file that you want to change to.6. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under Shared Add-ins.General notes and resources:A. See the Administrator Settings heading under the help section here: https://bit.ly/2XD5mb8NOTE:ArcGIS Pro 2.1 introduced a geoprocessing tool that will convert densified lines into one or more circular arc segments by fitting circular arcs to the straight-line segment sequences. This new gp tool can be found in the Editing toolbox and is called Simplify By Straight Lines And Circular Arcs.

Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 °C (86 °F). Daily maximum temperature values >= 30 °C during the period 1980 - 2009 were tallied annually and are reported as the average annual number of days during a 30-year period.

Projected change in average number of days of precipitation (>0.1 inch) between 1985-2005 and 2071-2090 (RCP 8.5) time periods

Learn more about the project and how to use the canopy assessment data by visiting the StoryMap!

Data Dictionary

Note: This LCMS CONUS Cause of Change image service has been deprecated. It has been replaced by the LCMS CONUS Annual Change image service, which provides updated and consolidated change data.Please refer to the new service here: https://usfs.maps.arcgis.com/home/item.html?id=085626ec50324e5e9ad6323c050ac84dThis product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. https://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data, downloaded from here: http://www.scrimhub.org/resources/topowx/

Populations of many cold-water species are likely to decline this century with global warming, but declines will vary spatially and some populations will persist even under extreme climate change scenarios. Especially cold habitats could provide important refugia from both future environmental change and invasions by non-native species that prefer warmer waters. The Climate Shield website hosts geospatial data and related information that describes specific locations of cold-water refuge streams for native Cutthroat Trout (Oncorhynchus clarkii) and Bull Trout (Salvelinus confluentus) across the American West. Forecasts about the locations of refugia could enable the protection of key watersheds, inform support among multiple stakeholders, and provide a foundation for planning climate-smart conservation networks that improve the odds of preserving native trout populations through the 21st century. The Northern Rockies Adaptation Partnership provided a valuable forum that accelerated this work. The Great Northern and North Pacific Landscape Conservation Cooperatives generously funded the NorWeST project, which serves as the foundation for Climate Shield. The Climate Shield Cutthroat Trout and Bull Trout models were developed from fish surveys conducted at more than 4,500 locations in over 500 streams, as described in the cited peer-reviewed studies and agency reports. Resources in this dataset:Resource Title: Digital Maps and ArcGIS Shapefiles. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects/ClimateShield/maps.html Information is available here to download as easy-to-use digital maps (.pdf files) and ArcGIS shapefiles for all streams within the historical ranges of native trout across the northwestern U.S. The geographic areas match the NorWeST production units because those stream temperature scenarios are integral to Climate Shield.

Learn more about the project and how to use the canopy assessment data by visiting the StoryMap!

Data Dictionary

CDFW BIOS GIS Dataset, Contact: Charles Steinback, Description: This data set is a part of Ecotrust's project entitled: Establishing a Baseline and Assessing Spatial and Socioeconomic Change in the California Central Coast Commercial and CPFV Fisheries. This project is a component of the California Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the Central Coast Region since MPA implementation.

A tree crowns layer was derived from 2018 NAIP and 2019 LiDAR, and then each tree crown polygon was populated with the 95th percentile nDSM (height above ground) values from LiDAR collected in 2014 and in 2019. Object-based image analysis techniques (OBIA) were employed to extract potential tree crowns including the area of the crown and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2000 and all observable errors were corrected.

This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.

The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole.

Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model".

The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals - Please note that this dataset has been moved to a separate catalog post (https://doi.org/10.5878/t8s9-6y15) and unpublished due to licensing restrictions on its source dataset. • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals

In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city.

The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’.

The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives.

1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press.
2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge

• 12 GIS-layers of the street network in Gothenburg, from 1960 to 2015, in 5-year intervals. File format: shapefile (.shp), MapinfoTAB (.TAB). The coordinate system used is SWEREF 99TM, EPSG:3006.

• 12 GIS-layers of plots in Gothenburg, from 1960 to 2015, in 5-year intervals. Only built upon plots (plots with buildings) are included. File format: shapefile (.shp), MapinfoTAB (.TAB). The coordinate system used is SWEREF 99TM, EPSG:3006.

See the attached Technical Documentation for the description and further details on the production of the datasets. See the attached Report for the description of the related research project.

These data were compiled to perform analyses of hydrologic change, changes in sediment transport, and channel change within Moenkopi Wash, Arizona. Objective(s) of our study were to quantify the magnitude and timing of changes in hydrology, sediment transport, and channel form within Moenkopi Wash and to determine the downstream effects of those changes on sediment delivery downstream to the Little Colorado River, and the Colorado River. These data represent instantaneous discharge records, suspended-sediment sample records, topographic survey data, historical aerial imagery, and channel polygons and centerlines mapped on the historical imagery. Instantaneous discharge records in this study began in 1926 and extend to 2022 and were collected at 5 different stream gages within Moenkopi Wash. Suspended-sediment samples were collected between 1948 and 2022 at four stream gage locations. Topographic datasets were collected by field surveys between 1940 and 2016 at five stream gage locations. Aerial imagery datasets were collected in the 1930s, 1952, 1968, 1979, 1992, 1997, 2007, 2013, and 2019. The 1968 and 1979 aerial imagery was collected by the U. S. Geological Survey. The 1952 imagery was collected by the U.S. Army Map Service. The 1992 and 1997 imagery were collected by the National Aerial Imagery Program. The 2007, 2013 and 2019 aerial images were collected by the National Agricultural Program. These data can be used to analyze changes in hydrology, sediment transport, and channel change within Moenkopi Wash.

The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf.

CDFW BIOS GIS Dataset, Contact: Charles Steinback, Description: This data set is a part of Ecotrust's project entitled: Establishing a Baseline and Assessing Spatial and Socioeconomic Change in the California Central Coast Commercial and CPFV Fisheries. This project is a component of the California Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the Central Coast Region since MPA implementation.