This is a vector tile service with labels for the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. Labels appear at scales greater than 1:10,000 and characterize stand height, stand canopy cover, stand map class, and stand impervious cover. This service is mean to be used in conjunction with the vector tile services of the polygon themselves (either the solid symbology service or the hollow symbology service). The key to the labels appears in the graphic below; the key to map class abbreviations can be found here. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

The map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap focuses on bathymetry. It also includes inland waters and roads, overlaid on land cover and shaded relief imagery.

These data sets include yearly maps of land cover classification for the state of Mato Grosso, Brazil, from 2001 (2000-09-01 to 2001-08-31) to 2017 (2016-09-01 to 2017-08-31), based on MODIS image time series (collection 6) at 250-meter spatial resolution (product MOD13Q1). Ground samples consisting of 1,892 time series with known labels are used as training data for a support vector machine classifier. We used the radial basis function kernel, with cost C=1 and gamma = 0.01086957. The classes include natural and human-transformed land areas, discriminating among different agricultural crops in state of land cover change maps for Mato Grosso State in Brazil. The results provide spatially explicit estimates of productivity increases in agriculture as well as the trade-offs between crop and pasture expansion. --- The correlation coefficients between the agricultural areas classified by our method and the estimates by IBGE (Brazil's Census Bureau) for the harvests from 2001 to 2017, were equal to 0.98. At the state level the soybean, cotton, corn and sunflower areas had a correlation equal 0.97, 0.85, 0.98 and 0.80. --- The areas classified as forest were compared with the Hansen et al. (2013, doi:10.1126/science.1244693) mapping for the year 2000. In order to separate the forest areas, we examined the areas with more than 25% tree cover on the Hansen et al. (2013, doi:10.1126/science.1244693) map. We found that 98% of the pixels classified as forest match the pixels indicated by Hansen et al. (2013) as having more than 25% tree cover. When we joined the cerrado and forest classes, 83% of the pixels match the pixels by Hansen et al. (2013) as having more than 25% tree cover. --- The pixels labeled as pasture were compared to the pasture mapping done by Parente et al. (2017, doi:10.1016/j.jag.2017.06.003). We found that 80% of the pixels classified as forest match the pixels indicated by Parente et al. (2017, doi:10.1016/j.jag.2017.06.003) for the state of Mato Grosso. --- In the land cover change maps for Mato Grosso State in Brazil version 3, we applied a methodology to deal with trajectories in classified maps. This methodology for reasoning about land-use change trajectories, called LUC Calculus, has been discussed in previous work (Maciel et al., 2018, doi:10.1080/13658816.2018.1520235). For reducing the temporal variability, we use the entire history of the study area considered as a set of land-use trajectories (from 2001 to 2017). For reasoning about this, we adopt the reference date 2001 and we used two-step post-processing, first applying masks and rules on the initial classified map (2001) and then land-use rules using LUC Calculus for the all years (2001-2017). The first-step post-processing was performed on the initial classified map (2001). We applied the forest mask to the classified map of the year 2001. This forest mask comes from the PRODES Project (http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes). In the non-forest, the appearance of secondary vegetation is not mapped. An additional set of rules was applied on the initial map using two sets of maps: PRODES map of the year 2001 and Cerrado map of 2000 (http://www.obt.inpe.br/cerrado). This mask of the Cerrado biome depicts the Cerrado within two classes: Anthropized Cerrado and Non-Anthropized Cerrado. The second-step post-processing was carried on the entire years from the classified map (2001-2017) using the LUC Calculus method. First, we elaborate a set of rules defined by experts in Amazon and Cerrado biomes. These rules express information about different trajectories of land-use change in MT that represent an irregular transition between classes. The rules used was: Forest (F), Cerrado (C), Pasture (P) and Soybean (S) 1. C -> F to C -> C 2. C -> C -> P -> C to C -> C -> C -> C 3. C -> C -> S -> C to C -> C -> C -> C 4. P -> P -> C -> C -> P to P -> P -> P -> P -> P 5. F -> C -> F -> F to F -> F -> F -> F 6. F -> F -> C -> F to F -> F -> F -> F 7. F -> C -> F to F -> F -> F 8. F -> C to F -> F 9. F -> F -> P -> F to F -> F -> P -> SV 10. P -> P -> F -> P to P -> P -> SV -> P The sequential application of the rules is able to ensure the temporal consistency among classes over the years. The class changed is highlighted with "*". From rule 1 to 8 we assume the reference date, 2001, as the starting point to find the class to will be changed. Rules 9 and 10 exemplify scenery where new class secondary vegetation (SV) occurs. The trajectory methodology enables us to include a new class called 'secondary vegetation'. This class represents a significant portion of the deforestation areas that have fallen into disuse or abandoned and have regrown as secondary forest. --- The following data sets are provided: (a) The classified maps in compressed TIFF format (one per year) at MODIS resolution. (b) A QGIS style file for displaying the data in the QGIS software (c) An csv file with the training data set (1,892 ground samples). --- The software used to produce the analysis is available as open source on https://github.com/e-sensing. --- Note: The TIFF raster files use the Sinusoidal Projection, which is the same cartographical projection used by the input MODIS images. When opening the TIFF raster maps in QGIS, to ensure correct navigation please use the Sinusoidal Projection, by selecting in QGIS projection menu, the following option: "Generated CRS (+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs)"

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. A draft hard copy vegetation map at the 1:12,000 scale was printed and checked against the interpreted aerial photographs. As a final internal accuracy check, we applied photointerpretative observations and classification relevés over the vegetation map to determine if the polygon labels matched the field data. Finally, map validation occurred prior to the accuracy assessment. Staff from RSGIG conducted a field trip in conjunction with other meetings in Flagstaff, AZ in January 2001 to refine and assess the initial mapping effort. On this trip we collected additional photointerpretative observations and ground-truthed aerial photograph signatures using landmarks and GPS waypoints. Map classes were lumped or split to account for inadequacies in the final photointerpretation. Metadata are required for all spatial data produced by the federal government. RSGIG used SIMMS™ software and CPRS used ArcCatalogue software to create the FGDC-compliant metadata files attached to the spatial databases and to this report (see Appendix A). The metadata files explain the vegetation coverage and ancillary coverages created by RSGIG, the classification relevé data coverage created by CPRS, and the accuracy assessment observation data created by CPRS.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

This data set maps and describes the geology of the San Bernardino Wash 7.5 minute quadrangle, Riverside County, southern California. The quadrangle, situated in Joshua Tree National Park in the eastern Transverse Ranges physiographic and structural province, encompasses parts of the northwestern Eagle Mountains, east-central Pinto Basin, and eastern Pinto Mountains. The quadrangle is underlain by a basement terrane comprising metamorphosed Proterozoic strata, Mesozoic plutonic rocks, and Jurassic and Mesozoic and (or) Cenozoic hypabyssal dikes. The basement terrane is capped by a widespread Tertiary erosion surface preserved in remnants in the Pinto and Eagle Mountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, a cover of Miocene sedimentary deposits and basalt overlie the erosion surface. A sequence of at least three Quaternary pediments is planed into the north piedmont of the Eagle Mountains, each in turn overlain by successively younger residual and alluvial, surficial deposits. The Tertiary erosion surface is deformed and broken by north-northwest-trending, high-angle, dip-slip faults in the Pinto and Eagle Mountains and an east-west trending system of high-angle dip- and left-slip faults along the range fronts facing Pinto Basin. In and around the San Bernardino Wash quadrangle, faults of the north-northwest-trending set displace Miocene sedimentary rocks and basalt deposited on the Tertiary erosion surface and some of the faults may offset Pliocene and (or) Pleistocene deposits that accumulated on the oldest pediment. Faults of this system appear to be overlain by Pleistocene deposits that accumulated on younger pediments. East-west trending faults are younger than and perhaps in part coeval with faults of the northwest-trending set. The San Bernardino Wash database was created using ARCVIEW and ARC/INFO, which are geographical information system (GIS) software products of Envronmental Systems Research Institute (ESRI). The database comprises five coverages: (1) a geologic layer showing the distribution of geologic contacts and units; (2) a structural layer showing the distribution of faults (arcs) and fault ornamentation data (points); (3) a layer showing the distribution of dikes (arcs); a structural point data layer showing (4) bedding and metamorphic foliation attitudes, and (5) cartographic map elements, including unit label leaders and geologic unit annotation. The dataset also includes a scanned topographic base at a scale of 1:24,000. Within the database coverages, geologic contacts , faults, and dikes are represented as lines (arcs and routes), geologic units as areas (polygons and regions), and site-specific data as points. Polygon, region, arc, route, and point attribute tables uniquely identify each geologic datum and link it to descriptive tables that provide more detailed geologic information. The digital database is accompanied by two derivative maps: (1) A portable document file (.pdf) containing a navigable graphic of the geologic map on a 1:24,000 topographic base and (2) a PostScript graphic-file containing the geologic map on a 1:24,000 topographic base. Each of these map products is accompanied by a marginal explanation consisting of a Description of Map Units (DMU), a Correlation of Map Units (CMU), and a key to point and line symbols. The database is further accompanied by three document files: (1) a readme that lists the contents of the database and describes how to access it, (2) a pamphlet file that describes the geology of the quadrangle and (3) this metadata file.

This is a vector tile service with labels for the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. Labels appear at scales greater than 1:5,000 and show the full Latin name or vegetation group name. At scales smaller than 1:5,000 the abbreviated vegetation class name is displayed. This service is mean to be used in conjunction with the vector tile services of the veg map polygons (either the solid symbology service or the hollow symbology service). The key to map class abbreviations can be found here. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8) The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

Chr: chromosome; bp: base pairSeven SNPs in ATXN2

Authority In the 1963 general session, the Utah State Legislature charged the Division of Water Resources with the responsibility of developing a State Water Plan. This plan is to coordinate and direct the activities of state and federal agencies concerned with Utah’s water resources. As a part of this objective, the Division of Water Resources collects water-related land use data for the entire state. This data includes the types and extent of irrigated crops as well as information concerning phreatophytes, wet/open water areas, dry land agriculture and urban areas. The data produced by the water-related land use program are used for various planning purposes. Some of these include: determining cropland water use, evaluating irrigated land losses and conversion to urban uses, planning for new water development, estimating irrigated acreages for any area, and developing water budgets. Additionally, the data are used by many other state and federal agencies. Previous Methods The land use inventory methods used by the division in conducting water-related land use studies have varied with regard to the procedures used and the precision obtained. During the 1960s and 70s, inventories were prepared using large format vertical-aerial photographs supplemented with field surveys to label boundaries, vegetation types, and other water use information. After identifying crops and labeling photographs, the information was transferred onto a base map and then planimetered or "dot-counted" to determine the acreage. Tables for individual townships and ranges were prepared showing the amount of land in each land use category within each section. Data were then available for use in preparing water budgets. In the early 1980s, the division began updating its methodology for collecting water-related land use data to take advantage of the rapidly growing fields of Remote Sensing and computerized Geographic Information Systems (GIS). For several years during the early 1980’s, the division contracted with the University of Utah Research Institute, Center for Remote Sensing and Cartography (CRSC), to prepare water-related land use inventories. During this period, water-related land use data was obtained by using high altitude color infrared photography and laboratory interpretation, with field checking. In March 1984, several division staff members visited the California Department of Water Resources to observe its methodology for collecting water-related land use data for state water planning purposes. Based on its review of the California methodology and its own experience, the division developed a water-related land use inventory program. This program included the use of 35mm slides, United States Geological Survey (USGS) 7-1/2 minute quadrangle maps, field-mapping using base maps produced from the 35mm photography and a computerized GIS to process, store and retrieve land use data. Areas for survey were first identified from previous land use studies and any other available information. The identified areas were then photographed using an aircraft carrying a high quality 35mm single lens reflex camera mounted to focus along a vertical axis to the earth. Photos were taken between 6,000 and 6,500 feet above the ground using a 24mm lens. This procedure allowed each slide to cover a little more than one square mile with approximately 30 percent overlap on the wide side of the slide and 5 percent on the slide's narrow side. The slides were then indexed according to a flight-line number, slide number, latitude and longitude. All 35mm slides were stored in files at the division offices and cataloged according to township, range and section, and quadrangle map location. Water-related land use areas were then transferred from the slide to USGS 7-1/2 minute quadrangle maps using a standard slide projector with a 100-200mm zoom lens. This step allowed the technician to project the slide onto the back of a quadrangle map. The image showing through the map was adjusted to the map scale with the zoom lens. Field boundaries and other water-use boundaries were then traced on the 7-1/2 minute quadrangle map. Next, a team was sent to use the map in the field to check the boundaries and current year land use field data on the 7-1/2 minute quadrangles. The final step was to digitize and process the field data using ARC/INFO software developed by Environmental Systems Research Institute (ESRI). Starting in 2000 with the land use survey of the Uintah Basin, the division further improved its land use program by using digital data for the purposes of outlining agricultural and other land cover boundaries. The division used satellite data, USGS Digital Orthophoto Quadrangles (DOQs), National Agricultural Imagery Program (NAIP), and other digital images in a heads-up digitizing mode for this process. This allowed the division to use multiple technicians for the digitizing process. Digitizing was done as line and polygon files using ArcView 3.2 with a satellite image, DOQ or NAIP image as a background with other layers added for reference. Boundary files were created in logical groups so that the process of edge-matching along quad lines was eliminated and precision increased. Subsequent inventories were digitized in the ArcMap 9.x software versions. Present Methodology Using the latest statewide NAIP Imagery and ArcGIS 10, all boundaries of individual agricultural fields, urban areas, and significant riparian areas are precisely digitized. Once the process of boundary digitizing is done, the polygons are loaded onto tablet PCs. Field crews are then sent to field check the crop and irrigation type for each agricultural polygon and label the shapefiles accordingly. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. This improved process has saved the division much time and money and even greater savings will be realized as the new statewide field boundaries are completed. Once processed and quality checked, the data is filed in the State Geographic Information Database (SGID) maintained by the State Automated Geographic Reference Center (AGRC). Once in the SGID, the data becomes available to the public. At this point, the data is also ready for use in preparing various planning studies. In conducting water-related land use inventories, the division attempts to inventory all lands or areas that consume or evaporate water other than natural precipitation. Areas not inventoried are mainly desert, rangeland and forested areas. Wet/open water areas and dry land agriculture areas are mapped if they are within or border irrigated lands. As a result, the numbers of acres of wet/open water areas and dry land agriculture reported by the division may not represent all such areas in a basin or county. During land use inventories, the division uses 11 hydrologic basins as the basic collection units. County data is obtained from the basin data. The water-related land use data collected statewide covers more than 4.3 million acres of dry and irrigated agricultural land. This represents about 8 percent of the total land area in the state. Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made in each succeeding year of the Water-Related Land Use Inventory. While this improves the data we report, it also makes comparisons to past years difficult. Making comparisons between datasets is still useful; however, increases or decreases in acres reported should not be construed to represent definite trends or total amounts of change up or down. To estimate such trends or change, more analysis is required.

description: The map shows the tracklines for bathymetric data collected between 1995 and 1999 for Florida Bay. The areas on the map are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the RMS computed from Ashtech PNAV software. The data set is labeled 1990 for easy comparison. The project duration was a decade.; abstract: The map shows the tracklines for bathymetric data collected between 1995 and 1999 for Florida Bay. The areas on the map are linked to the corresponding data sets which contain values for X (easting), Y (northing), Z (elevation), and the RMS computed from Ashtech PNAV software. The data set is labeled 1990 for easy comparison. The project duration was a decade.

This resource is a Vector based Minnesota bedrock geology map, 2011 available as an Interactive web application map and as an ArcGIS map layers. This web map application is made available by the Minnesota Geological Survey; see http://www.mngs.umn.edu/service.htm for more information. The available layers contain bedrock, contacts, and faults labeled by geologic age. This resource was provided by the Minnesota Geological Survey and made available for distribution through the National Geothermal Data System.

. A PDF document of mapsnp manual. (PDF)

Context and Aim

Deep learning in Earth Observation requires large image archives with highly reliable labels for model training and testing. However, a preferable quality standard for forest applications in Europe has not yet been determined. The TreeSatAI consortium investigated numerous sources for annotated datasets as an alternative to manually labeled training datasets.

We found the federal forest inventory of Lower Saxony, Germany represents an unseen treasure of annotated samples for training data generation. The respective 20-cm Color-infrared (CIR) imagery, which is used for forestry management through visual interpretation, constitutes an excellent baseline for deep learning tasks such as image segmentation and classification.

Description

The data archive is highly suitable for benchmarking as it represents the real-world data situation of many German forest management services. One the one hand, it has a high number of samples which are supported by the high-resolution aerial imagery. On the other hand, this data archive presents challenges, including class label imbalances between the different forest stand types.

The TreeSatAI Benchmark Archive contains:

• 50,381 image triplets (aerial, Sentinel-1, Sentinel-2)

• synchronized time steps and locations

• all original spectral bands/polarizations from the sensors

• 20 species classes (single labels)

• 12 age classes (single labels)

• 15 genus classes (multi labels)

• 60 m and 200 m patches

• fixed split for train (90%) and test (10%) data

• additional single labels such as English species name, genus, forest stand type, foliage type, land cover

The geoTIFF and GeoJSON files are readable in any GIS software, such as QGIS. For further information, we refer to the PDF document in the archive and publications in the reference section.

Version history

v1.0.0 - First release

Citation

Ahlswede et al. (in prep.)

GitHub

Full code examples and pre-trained models from the dataset article (Ahlswede et al. 2022) using the TreeSatAI Benchmark Archive are published on the GitHub repositories of the Remote Sensing Image Analysis (RSiM) Group (https://git.tu-berlin.de/rsim/treesat_benchmark). Code examples for the sampling strategy can be made available by Christian Schulz via email request.

Folder structure

We refer to the proposed folder structure in the PDF file.

• Folder “aerial” contains the aerial imagery patches derived from summertime orthophotos of the years 2011 to 2020. Patches are available in 60 x 60 m (304 x 304 pixels). Band order is near-infrared, red, green, and blue. Spatial resolution is 20 cm.

• Folder “s1” contains the Sentinel-1 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is VV, VH, and VV/VH ratio. Spatial resolution is 10 m.

• Folder “s2” contains the Sentinel-2 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12, B01, and B09. Spatial resolution is 10 m.

• The folder “labels” contains a JSON string which was used for multi-labeling of the training patches. Code example of an image sample with respective proportions of 94% for Abies and 6% for Larix is: "Abies_alba_3_834_WEFL_NLF.tif": [["Abies", 0.93771], ["Larix", 0.06229]]

• The two files “test_filesnames.lst” and “train_filenames.lst” define the filenames used for train (90%) and test (10%) split. We refer to this fixed split for better reproducibility and comparability.

• The folder “geojson” contains geoJSON files with all the samples chosen for the derivation of training patch generation (point, 60 m bounding box, 200 m bounding box).

CAUTION: As we could not upload the aerial patches as a single zip file on Zenodo, you need to download the 20 single species files (aerial_60m_…zip) separately. Then, unzip them into a folder named “aerial” with a subfolder named “60m”. This structure is recommended for better reproducibility and comparability to the experimental results of Ahlswede et al. (2022),

Join the archive

Model training, benchmarking, algorithm development… many applications are possible! Feel free to add samples from other regions in Europe or even worldwide. Additional remote sensing data from Lidar, UAVs or aerial imagery from different time steps are very welcome. This helps the research community in development of better deep learning and machine learning models for forest applications. You might have questions or want to share code/results/publications using that archive? Feel free to contact the authors.

Project description

This work was part of the project TreeSatAI (Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees at Infrastructures, Nature Conservation Sites and Forests). Its overall aim is the development of AI methods for the monitoring of forests and woody features on a local, regional and global scale. Based on freely available geodata from different sources (e.g., remote sensing, administration maps, and social media), prototypes will be developed for the deep learning-based extraction and classification of tree- and tree stand features. These prototypes deal with real cases from the monitoring of managed forests, nature conservation and infrastructures. The development of the resulting services by three enterprises (liveEO, Vision Impulse and LUP Potsdam) will be supported by three research institutes (German Research Center for Artificial Intelligence, TU Remote Sensing Image Analysis Group, TUB Geoinformation in Environmental Planning Lab).

Publications

Ahlswede et al. (2022, in prep.): TreeSatAI Dataset Publication

Ahlswede S., Nimisha, T.M., and Demir, B. (2022, in revision): Embedded Self-Enhancement Maps for Weakly Supervised Tree Species Mapping in Remote Sensing Images. IEEE Trans Geosci Remote Sens

Schulz et al. (2022, in prep.): Phenoprofiling

Conference contributions

S. Ahlswede, N. T. Madam, C. Schulz, B. Kleinschmit and B. Demіr, "Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods", IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.

C. Schulz, M. Förster, S. Vulova, T. Gränzig and B. Kleinschmit, “Exploring the temporal fingerprints of mid-European forest types from Sentinel-1 RVI and Sentinel-2 NDVI time series”, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.

C. Schulz, M. Förster, S. Vulova and B. Kleinschmit, “The temporal fingerprints of common European forest types from SAR and optical remote sensing data”, AGU Fall Meeting, New Orleans, USA, 2021.

B. Kleinschmit, M. Förster, C. Schulz, F. Arias, B. Demir, S. Ahlswede, A. K. Aksoy, T. Ha Minh, J. Hees, C. Gava, P. Helber, B. Bischke, P. Habelitz, A. Frick, R. Klinke, S. Gey, D. Seidel, S. Przywarra, R. Zondag and B. Odermatt, “Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees and Forests”, Living Planet Symposium, Bonn, Germany, 2022.

C. Schulz, M. Förster, S. Vulova, T. Gränzig and B. Kleinschmit, (2022, submitted): “Exploring the temporal fingerprints of sixteen mid-European forest types from Sentinel-1 and Sentinel-2 time series”, ForestSAT, Berlin, Germany, 2022.

description: This data set maps and describes the geology of the Cucamonga Peak 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the database consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing site-specific structural data, (3) a coverage containing geologic-unit label leaders and their associated attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix) and the graphic produced by the PostScript plot file. The Cucamonga Peak quadrangle includes part of the boundary between two major physiographic provinces of California, the Transverse Ranges Province to the north and the Peninsular Ranges Province to the south. The north part of the quadrangle is in the eastern San Gabriel Mountains, and the southern part includes an extensive Quaternary alluvial-fan complex flanking the upper Santa Ana River valley, the northernmost part of the Peninsular Ranges Province. Thrust faults of the active Cucamonga Fault zone along the the south margin of the San Gabriel Mountains are the rejuvenated eastern terminus of a major old fault zone that bounds the south side of the western and central Transverse Ranges (Morton and Matti, 1993). Rejuvenation of this old fault zone, including the Cucamonga Fault zone, is apparently in response to compression in the eastern San Gabriel Mountains resulting from initiation of right-lateral slip on the San Jacinto Fault zone in the Peninsular Ranges. Within the northern part of the quadrangle are several arcuate-in-plan faults that are part of an antiformal, schuppen-like fault complex of the eastern San Gabriel Mountains. Most of these arcuate faults are reactivated and deformed older faults that probably include the eastern part of the San Gabriel Fault. The structural grain within the San Gabriel Mountains, as defined by basement rocks, is generally east striking. Within the Cucamonga Peak quadrangle, these basement rocks include a Paleozoic schist and gneiss sequence which occurs as large, continuous and discontinuous bodies intruded by Cretaceous granitic rocks. Most of the granitic rocks are of tonalitic composition, and many are mylonitic. South of the granitic rocks is a comple assemblage of Proterozoic(?) metamorphic rocks, at least part of which is metasedimentary. This assemblage is intruded by Cretaceous tonalite on its north side, and by charnockitic rocks near the center of the mass. The charnockitic rocks are in contact with no other Cretaceous granitic rocks. Consequently, their relative position in the intrusive sequence is unknown. The Proterozoic(?) assemblage was metamorphosed to upper amphibolite and lower granulite grade, and subsequently to a lower metamorphic grade. It is also intensely deformed by mylonitization characterized by an east-striking, north-dipping foliation, and by a pronounced subhorizontal lineation that plunges shallowly east and west. The southern half of the quadrangle is dominated by extensive, symmetrical alluvial-fan complexes, particularly two emanating from Day and Deer Canyons. Other Quaternary units ranging from early Pleistocene to recent are mapped, and represent alluvial-fan, landslide, talus, and wash environments. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map supercedes an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Gabriel Mountains. The digital map was compiled on a base-stable cronoflex copy of the Cucamonga Peak 7.5' topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, from which lines and point were hand digitized. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.; abstract: This data set maps and describes the geology of the Cucamonga Peak 7.5' quadrangle, San Bernardino County, California. Created using Environmental Systems Research Institute's ARC/INFO software, the database consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing site-specific structural data, (3) a coverage containing geologic-unit label leaders and their associated attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) A PostScript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), an index map, a regional geologic and structure map, and a key for point and line symbols; (2) PDF files of this Readme (including the metadata file as an appendix) and the graphic produced by the PostScript plot file. The Cucamonga Peak quadrangle includes part of the boundary between two major physiographic provinces of California, the Transverse Ranges Province to the north and the Peninsular Ranges Province to the south. The north part of the quadrangle is in the eastern San Gabriel Mountains, and the southern part includes an extensive Quaternary alluvial-fan complex flanking the upper Santa Ana River valley, the northernmost part of the Peninsular Ranges Province. Thrust faults of the active Cucamonga Fault zone along the the south margin of the San Gabriel Mountains are the rejuvenated eastern terminus of a major old fault zone that bounds the south side of the western and central Transverse Ranges (Morton and Matti, 1993). Rejuvenation of this old fault zone, including the Cucamonga Fault zone, is apparently in response to compression in the eastern San Gabriel Mountains resulting from initiation of right-lateral slip on the San Jacinto Fault zone in the Peninsular Ranges. Within the northern part of the quadrangle are several arcuate-in-plan faults that are part of an antiformal, schuppen-like fault complex of the eastern San Gabriel Mountains. Most of these arcuate faults are reactivated and deformed older faults that probably include the eastern part of the San Gabriel Fault. The structural grain within the San Gabriel Mountains, as defined by basement rocks, is generally east striking. Within the Cucamonga Peak quadrangle, these basement rocks include a Paleozoic schist and gneiss sequence which occurs as large, continuous and discontinuous bodies intruded by Cretaceous granitic rocks. Most of the granitic rocks are of tonalitic composition, and many are mylonitic. South of the granitic rocks is a comple assemblage of Proterozoic(?) metamorphic rocks, at least part of which is metasedimentary. This assemblage is intruded by Cretaceous tonalite on its north side, and by charnockitic rocks near the center of the mass. The charnockitic rocks are in contact with no other Cretaceous granitic rocks. Consequently, their relative position in the intrusive sequence is unknown. The Proterozoic(?) assemblage was metamorphosed to upper amphibolite and lower granulite grade, and subsequently to a lower metamorphic grade. It is also intensely deformed by mylonitization characterized by an east-striking, north-dipping foliation, and by a pronounced subhorizontal lineation that plunges shallowly east and west. The southern half of the quadrangle is dominated by extensive, symmetrical alluvial-fan complexes, particularly two emanating from Day and Deer Canyons. Other Quaternary units ranging from early Pleistocene to recent are mapped, and represent alluvial-fan, landslide, talus, and wash environments. The geologic map database contains original U.S. Geological Survey data generated by detailed field observation and by interpretation of aerial photographs. This digital Open-File map supercedes an older analog Open-File map of the quadrangle, and includes extensive new data on the Quaternary deposits, and revises some fault and bedrock distribution within the San Gabriel Mountains. The digital map was compiled on a base-stable cronoflex copy of the Cucamonga Peak 7.5' topographic base and then scribed. This scribe guide was used to make a 0.007 mil blackline clear-film, from which lines and point were hand digitized. Lines, points, and polygons were subsequently edited at the USGS using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units as polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.

The following files are designed to be run using the Path Landscape Model software, version 3.0.4. Later versions of the software cannot run these files. To get a copy of this software, please contact Apex RMS at path@apexrms.com. 1) Path models MUST be run with the provided .MCM and .trd mulitplier files to apply the required transition probability adjustments for procesess such as insect outbreaks, wildfire, and climate change trends. Each Path database is set up with three folders: - The 'Common' folder contains a single Path scenario (also named 'Common'). The Transitions tab within the Common scenario contains the climate-smart STM. - The 'Multipliers' folder contains multipliers specific to each ownership-allocation to activate or deactivate transitions (both climate change and management). Actual treatments are input in the Treatments tab for each stratum in the 'Runs' folder. - The 'Runs' folder contains one Path scenario per modeling stratum, with initial conditions specific to each stratum (combination of watershed and ownership-allocation). The models are stored as a dependency from the 'Common' folder and the multipliers as a dependency from the 'Multipliers' scenario. For the scenarios that have management activities (current and restoration management), a specified number of acres for each treatment is shown in the Treatments tab for treated stata. There are 6 databases, one for each combintation of management and climate scenario run in northwest Washington. Climate scenarios include: - No climate change - continuing current climate (NoCC) - Hadley global circulation model, A2 Emissions Scenario (Hadley) - RegCM3 regional circulation model, A2 Emissions Scenario (RegCM3) Management scenarios include: - No management - no restoration treatments (NoMgt) - Current management - current treatment rates compiled from managers in the region (CurMgt) 2) The lookup tables folder contains files necessary for providing definitions and context for the information located in other folders. 3) This folder contains the NWW region stratum map, called Modeling_Strata.tif. This map can be joined to output from the climate-informed state-and-transition models to map projected future condition or northern spotted owl habitat, on the "Strata" field. Modeling strata consist of the intersection of watershed (Hydrologic unit code [HUC]) and ownership-allocation map. Watersheds are three digit codes starting at 101. Ownership-allocation categories are a two-character label, and are described in the attribute table through the fields "Ownership", and "Allocation". The field ScenarioID indicates the internal ID number used by the Path software to link results for modeling strata to its Scenario names. 4) The "Results" folder contains summarized modeling results that can be viewed on their own, or displayed across mapped modeling strata. The .tif file within the 3Spatial folder shows the spatial distribution of modeling strata. There are 3 subfolders: ClassesSummary, TransitionSummary, and HabitatSummary. The csv files within ClassesSummary contain summaries of state class area (in Acres) for each timestep, over monte carlo repetitions, within each modeling stratum. Those within TransitionSummary are summarized in the same manner, but contain summaries of area affected by each Transition Type. The csv files within HabitatSummary are designed to be joined ot the grid mentioned above, to allow for a spatial depiction of habitat projections. There is one column per modeled year, containing a summary that indicates the proportion of each modeling stratum that is comprised of potential northern spotted owl habitat (averaged over monte carlo repetitions). Sample queries outlining how to build new summaries of output data for mapping from the ClassesSummary files and lookup tables are included in the database: NWW_Summaries.accdb.

This data set maps and describes the geology of the Pinto Mountain 7.5 minute quadrangle, Riverside County, southern California. The quadrangle, situated in Joshua Tree National Park in the eastern Transverse Ranges physiographic and structural province, encompasses parts of the northeastern Hexie Mountains, central Pinto Mountains, and central Pinto Basin. The quadrangle is underlain by a basement terrane comprising Proterozoic metamorphic rocks, Mesozoic plutonic rocks, and Mesozoic and Mesozoic and (or) Cenozoic hypabyssal dikes. The basement terrane is capped by a widespread Tertiary erosion surface preserved in remnants in the Hexie and Pinto Mountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, a cover of Miocene sedimentary deposits and basalt overlie the erosion surface. Quaternary and (or) Tertiary lacustrine deposits crop out in the center of Pinto Basin and interfinger laterally with sandstone, conglomerate, and debris flows originating in the Pinto and Hexie Mountains. A sequence of at least three Quaternary pediments is planed into the north piedmonts of the Hexie and Eagle Mountains, each in turn overlain by successively younger residual and alluvial, surficial deposits. The Tertiary erosion surface is deformed and broken by north-northwest-trending, high-angle, dip-slip faults in the Pinto and Eagle Mountains and an east-west trending system of high-angle dip- and left-slip faults along the range fronts facing Pinto Basin. In and around the Pinto Mountain quadrangle, faults of the north-northwest-trending set displace Miocene sedimentary rocks and basalt deposited on the Tertiary erosion surface and some of the faults may offset Pliocene and (or) Pleistocene deposits that accumulated on the oldest pediment. Faults of this system appear to be overlain by Pleistocene deposits that accumulated on younger pediments. East-west trending faults are younger than and perhaps in part coeval with faults of the northwest-trending set. The Pinto Mountain database was created using ARCVIEW and ARC/INFO, which are geographical information system (GIS) software products of Envronmental Systems Research Institute (ESRI). The database comprises eight coverages: (1) a geologic layer showing the distribution of geologic contacts and units; (2) a structural layer showing the distribution of faults (arcs) and fault ornamentation data (points); (3) a layer showing the distribution of dikes (arcs); structural point data layers showing (4) bedding attitudes, (5) foliation attitudes, (6) lineations, (7) minor fold axes; and (8) cartographic map elements, including unit label leaders and geologic unit annotation. The dataset also includes a scanned topographic base at a scale of 1:24,000. Within the database coverages, geologic contacts , faults, and dikes are represented as lines (arcs and routes), geologic units as areas (polygons and regions), and site-specific data as points. Polygon, region, arc, route, and point attribute tables uniquely identify each geologic datum and link it to descriptive tables that provide more detailed geologic information. The digital database is accompanied by two derivative maps: (1) A portable document file (.pdf) containing a navigable graphic of the geologic map on a 1:24,000 topographic base and (2) a PostScript graphic-file containing the geologic map on a 1:24,000 topographic base. Each of these map products is accompanied by a marginal explanation consisting of a Description of Map Units (DMU), a Correlation of Map Units (CMU), and a key to point and line symbols. The database is further accompanied by three document files: (1) a readme that lists the contents of the database and describes how to access it, (2) a pamphlet file that describes the geology of the quadrangle, and (3) this metadata file.

The following files are designed to be run using the Path Landscape Model software, version 3.0.4. Later versions of the software cannot run these files. To get a copy of this software, please contact Apex RMS at path@apexrms.com. 1) "Path Landscape Mode" folder contains files to be run in the PLM softwarel, version 3.0.4 or later. Path models MUST be run with the provided MCM mulitplier files to apply the required transition probability adjustments for procesess such as insect outbreaks, wildfire, and climate change trends. Each Path database is set up with three folders: - The 'Common' folder contains a single Path scenario (also named 'Common'). The Transitions tab within the Common scenario contains the climate-smart STM. - The 'Multipliers' folder contains multipliers specific to each ownership-allocation to activate or deactivate transitions (both climate change and management). Actual treatments are input in the Treatments tab for each stratum in the 'Runs' folder. - The 'Runs' folder contains one Path scenario per modeling stratum, with initial conditions specific to each stratum (combination of watershed and ownership-allocation). The models are stored as a dependency from the 'Common' folder and the multipliers as a dependency from the 'Multipliers' scenario. For the scenarios that have management activities (current and restoration management), a specified number of acres for each treatment is shown in the Treatments tab for treated stata. There are 12 databases, one for each combintation of management and climate scenario run in southeast Oregon. Climate scenarios include: - No climate change - continuing current climate (NoCC) - HadGEM global circulation model, representative concentration pathway 8.5 (HadGEM) - NorESM global circulation model, representative concentration pathway 8.5 (NorESM) - MRI global circulation model, representative concentration pathway 8.5 (MRI) Management scenarios include: - No management - no restoration treatments (NoMgt) - Current management - current treatment rates compiled from managers in the region (CurMgt) - Restoration management - a restoration scenario to restore sage-grouse habitat (RestMgt) 2) The "lookupTables" folder contains files necessary for providing definitions and context for the information located in other folders. 3) The 'Spatial' folder contains the SEO region stratum map, called SEO_Modeling_Strata.tif. This map can be joined to output from the climate-informed state-and-transition models to map projected future condition or sage-grouse habitat on the "Strata" field. Modeling strata consist of the intersection of watershed (Hydrologic unit code [HUC]) and ownership-allocation map. Watersheds are three digit codes starting at 101. Ownership-allocation categories are a two-character label, and are described in the attribute table through the fields "Ownership", and "Allocation". The field ScenarioID indicates the internal ID number used by the Path software to link results for modeling strata to its Scenario names. 4) This folder contains summarized modeling results that can be viewed as nonspatial trends across the whole landscape or displayed across mapped modeling strata. The .tif file in the 3Spatial folder shows the spatial distribution of modeling strata. There are 3 subfolders: ClassesSummary, TransitionSummary, and HabitatSummary. The csv files within ClassesSummary contain summaries of state class area (in Acres) for each timestep, over monte carlo repetitions, within each modeling stratum. Those within TransitionSummary are summarized in the same manner, but contain summaries of area affected by each Transition Type. The csv files within HabitatSummary are designed to be joined ot the grid mentioned above, to allow for a spatial depiction of habitat projections. There is one column per modeled year, containing a summary that indicates the proportion of each modeling stratum that is comprised of potential greater sage grouse habitat (the general summary, not the high-quality summary, averaged over monte carlo repetitions). Sample queries outlining how to build new summaries of output data for mapping from the ClassesSummary files and lookup tables are included in the database: SEO_Summaries.accdb. Due to a minor error in starting conditions, summaries of sage-grouse habitat may be underestimated in the early years, especially within the northwestern quadrant of the map. Because of this, we have removed the summaries of the first four years within the Habitat summary files, and constrain the sample queries to only show projections for after 2011. If the early years of this summary data are needed for viewing other parts of the map, they can be constructed by removing a constraint within query1 of the sample queries shown above.

Images were acquired from approximately 80 m above ground surface on the 12th of February 2021, using a Phantom 4 Advanced drone with an FC330 camera. The images are in file input_images.zip.

The mission planning software DJI GS Pro was used to automatically acquire images at suitable locations across the survey area to enable the reconstruction of a three dimensional model.

Images 422 to 531 were imported to the photogrammetry software Pix4D (version 4.6.4). The created Pix4D project is Station12Feb2021_limited.p4d, and the processing report is Station12Feb2021_limited_report.pdf.

Four three-dimensional ground control points were used to improve the positioning of the model. No two dimensional control points or check points were used.

These points were in ITRF 2000@2000 datum (UTM Zone 49S), with co-ordinates as per the table below:

Label, Type, X(m), Y(m), Z(m), Accuracy Horz(m), Accuracy Vert(M) BM05, 3D GCP, 478814.460, 2648561.910, 38.558, 0.050, 0.100 EW-05, 3D GCP, 478635.540, 2648617.260, 27.260, 0.050, 0.100 FuelFlange, 3D GCP, 478970.810, 2648642.250, 21.920, 0.050, 0.100 MeltbellFootingA, 3D GCP, 478680.270, 2648466.547, 35.850, 0.050, 0.100

BM-05 is a survey benchmark near the Casey flagpoles, see https://data.aad.gov.au/aadc/survey/display_station.cfm?station_id=600 EW-05 is a 44 gallon drum used as a groundwater extraction well by the remediation project Fuel Flange is the last fuel flange located on the elevated fuel line prior to the fuel line “dipping” under the wharf road. Meltbell footing A is a concrete footing for the Casey melt bell (surveyed in 2019/20).

No point cloud processing (e.g. removal of errant points) was done prior to orthomosaic and model generation.

The resulting orthomosaic (Station12Feb2021_limited_transparent_mosaic_group1.tif) has an average ground sampling distance of 2.9 cm, and covers an area of approximately 15.8 hectares, encompassing the majority of buildings along “main street” at Casey. The quarry, biopiles, helipad, and upper fuel farm area are all visible.

Contour lines were generated in Pix4D at 0.5 m intervals.

Due to the limited number of ground control points, and their imprecision, the estimated residual mean squared error across three dimensions is 0.17 m (17cm), and will be worse on the periphery of the imaged area.

The orthomosaic was exported from ArcGIS to a Google Earth file (CaseyStation Orthomosaic Feb 12 2021.kmz) using XTools Pro Version 17.2.

A map was created in ArcGIS showing the orthomosaic with a background showing contour lines obtained from the AADC data product windmill_is.mdb.

The map was exported in .jpg and .pdf format at 250 dpi. Casey Station Orthomosaic Feb 12 2021.pdf Casey Station Orthomosaic Feb 12 2021.jpg

The Pix4D folder structure has been copied across (with the exception of the temp folder) and is included in this dataset.

Pix4D Folder Structure:

Station12Feb2021_limited.zip 1_intitial • Contains Pix4D files created during the project • Contains the final processing report (as .pdf) 2_densification • Contains the 3D mesh as an .obj file • Contains the point cloud as a .LAS and .PLY file • Contains the point cloud as a .p4b file 3_dsm_ortho • Contains the digital surface model as a georeferenced .tif file • Contains the orthomosaic as a georeferenced .tif file

A text readable log file from the project processing is in the file Station12Feb2021_limited.log

The arrival of ArcGIS Pro has brought a challenge to ArcMap users. The new software is sufficiently different in architecture and layout that switching from the old to the new is not a simple process. In some ways, Pro is harder to learn for ArcMap users than for new GIS users, because some workflows have to be unlearned, or at least heavily modified. Current ArcMap users are pressed for time, trying to learn the new software while still completing their daily tasks, so a book that teaches Pro from the start is not an efficient method.Switching to ArcGIS Pro from ArcMap aims to quickly transition ArcMap users to ArcGIS Pro. Rather than teaching Pro from the start, as for a novice user, this book focuses on how Pro is different from ArcMap. Covering the most common and important workflows required for most GIS work, it leverages the user’s prior experience to enable a more rapid adjustment to Pro.AUDIENCEProfessional and scholarly; College/higher education; General/trade.AUTHOR BIOMaribeth H. Price, PhD, South Dakota School of Mines and Technology, has been using Esri products since 1991, teaching college GIS since 1995 and writing textbooks utilizing Esri’s software since 2001. She has extensive familiarity with both ArcMap/ArcCatalog and Pro, both as a user and in the classroom, as well as long experience writing about GIS concepts and developing software tutorials. She teaches GIS workshops, having offered more than 100 workshops to over 1,200 participants since 2000.Pub Date: Print: 2/14/2019 Digital: 1/28/2019 Format: PaperbackISBN: Print: 9781589485440 Digital: 9781589485457 Trim: 8 x 10 in.Price: Print: $49.99 USD Digital: $49.99 USD Pages: 172Table of ContentsPreface1 Contemplating the switch to ArcGIS ProBackgroundSystem requirementsLicensingCapabilities of ArcGIS ProWhen should I switch?Time to exploreObjective 1.1: Downloading the data for these exercisesObjective 1.2: Starting ArcGIS Pro, signing in, creating a project, and exploring the interfaceObjective 1.3: Accessing maps and data from ArcGIS OnlineObjective 1.4: Arranging the windows and panesObjective 1.5: Accessing the helpObjective 1.6: Importing a map document2 Unpacking the GUIBackgroundThe ribbon and tabsPanesViewsTime to exploreObjective 2.1: Getting familiar with the Contents paneObjective 2.2: Learning to work with objects and tabsObjective 2.3: Exploring the Catalog pane3 The projectBackgroundWhat is a project?Items stored in a projectPaths in projectsRenaming projectsTime to exploreObjective 3.1: Exploring different elements of a projectObjective 3.2: Accessing properties of projects, maps, and other items4 Navigating and exploring mapsBackgroundExploring maps2D and 3D navigationTime to exploreObjective 4.1: Learning to use the Map toolsObjective 4.2: Exploring 3D scenes and linking views5 Symbolizing mapsBackgroundAccessing the symbol settings for layersAccessing the labeling propertiesSymbolizing rastersTime to exploreObjective 5.1: Modifying single symbolsObjective 5.2: Creating maps from attributesObjective 5.3: Creating labelsObjective 5.4: Managing labelsObjective 5.5: Symbolizing rasters6 GeoprocessingBackgroundWhat’s differentAnalysis buttons and toolsTool licensingTime to exploreObjective 6.1: Getting familiar with the geoprocessing interfaceObjective 6.2: Performing interactive selectionsObjective 6.3: Performing selections based on attributesObjective 6.4: Performing selections based on locationObjective 6.5: Practicing geoprocessing7 TablesBackgroundGeneral table characteristicsJoining and relating tablesMaking chartsTime to exploreObjective 7.1: Managing table viewsObjective 7.2: Creating and managing properties of a chartObjective 7.3: Calculating statistics for tablesObjective 7.4: Calculating and editing in tables8 LayoutsBackgroundLayouts and map framesLayout editing proceduresImporting map documents and templatesTime to exploreObjective 8.1: Creating the maps for the layoutObjective 8.2: Setting up a layout page with map framesObjective 8.3: Setting map frame extent and scaleObjective 8.4: Formatting the map frameObjective 8.5: Creating and formatting map elementsObjective 8.6: Fine-tuning the legendObjective 8.7: Accessing and copying layouts9 Managing dataBackgroundData modelsManaging the geodatabase schemaCreating domainsManaging data from diverse sourcesProject longevityManaging shared data for work groupsTime to exploreObjective 9.1: Creating a project and exporting data to itObjective 9.2: Creating feature classesObjective 9.3: Creating and managing metadataObjective 9.4: Creating fields and domainsObjective 9.5: Modifying the table schemaObjective 9.6: Sharing data using ArcGIS Online10 EditingBackgroundBasic editing functionsCreating featuresModifying existing featuresCreating and editing annotationTime to exploreObjective 10.1: Understanding the editing tools in ArcGIS ProObjective 10.2: Creating pointsObjective 10.3: Creating linesObjective 10.4: Creating polygonsObjective 10.5: Modifying existing featuresObjective 10.6: Creating an annotation feature classObjective 10.7: Editing annotationObjective 10.8: Creating annotation features11 Moving forwardData sourcesIndex

Distribution of Decevania speciesOriginal source: loan material. Software: Google Earth and Google Maps. ReadMe file contains labels of specimens inserted in map.Decevania distribution.kml