Licensed agricultural tile drainage contractors create plans for numerous agricultural tile drainage systems and install thousands of feet of agricultural drainage tile each year. As a requirement of their license, each contractor must report to the Ontario Ministry of Agriculture, Food, and Rural Affairs (OMAFRA) the location of the area where they installed drainage tile. These areas are represented as polygon features.Legislated or Legal Authority for Collection: Agricultural Tile Drainage Installation Act (Regulation 18)Additional Time Period Information: The legislation was official as of 1983 but some the data holding may contain data that was installed prior to 1983. The legislation is still in effect therefore the data holding is still currently receiving information.Additional Metadata Location: Ontario Ministry of Agriculture and Food, Ontario Ministry of Rural Affairs website This class has related tables. Tile Drainage Area related tables Additional DocumentationTile Drainage Area - Data Description (PDF)Tile Drainage Area - Documentation (Word) Status On going: data is being continually updated Maintenance and Update Frequency Irregular: data is updated in intervals that are uneven in duration Contact Ontario Ministry of Agriculture, Food and Rural Affairs, omafra.gis@ontario.ca

This is a vector tile service of the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. It is mean to be used in conjunction with the vector tile service that provides labels for each polygon. There is an additional vector tile service that provides solid colored polygons for the vegetation map if hollow outlines are not desired. 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 Class 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 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).

Calumet County LiDAR tile index polygon file created as a product of the 2018 LiDAR project.

The aerial imagery used to create this tile layer is available for download in TIFF format on PASDA.The 2020 Aerial Photography Tile Index is also available for download on PASDA.View metadata for key information about this dataset.For questions about this dataset or technical assistance, email maps@phila.gov.

(Mature Support) This item is in mature support and is no longer updated. Available for historical reference only. Please visit njgin.nj.gov/edata/elevation for the latest information on elevation products available for download. This is a GIS polygon layer defining the geographic extents for all the LiDAR projects and DEM products in New Jersey. This layer was derived from the original LiDAR extents layer generated by NJDEP 20161230. Features were created from tile extents, project-specific boundaries provided in the deliverables, and county boundaries. Attributes were populated from LiDAR project metadata and fact sheets.

This web map provides a detailed vector basemap for the world symbolized with a classic Esri topographic map style including a shaded relief layer for added context. The web map is very similar in content and style to the popular World Topographic Map, which is delivered as a tile layer with raster fused map cache. This map includes a vector tile layer that provides unique capabilities for customization and high-resolution display. This comprehensive topographic map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries, designed for use with shaded relief for added context. The layers in this map are built using the same data sources used for the World Topographic Map and other Esri basemaps. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority. Updated Map DesignThis style is an update from our raster Topographic style. The land fill and land use opacity was decreased to better emphasize the relief. Land fill polygon changes from white at a small scale to gray tone at larger scales. Labels of a number of feature classes were improved in color, size, and/or spacing. Open water bathymetric colors were improved to allow a smooth transition to scales without the water depth polygons. Road color, line width and effects were adjusted. Overall, additional feature class specifications were changed in conjunction with the land fill opacity change. Use this Map This map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map. Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. See the Vector Basemap group for other vector web maps. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog.

Product: Hydroflattened breaklines covering the Yosemite National Park USGS 3DEP Lidar project area. Geographic Extent: This dataset and derived products encompass an area covering approximately 803,364 acres of the western Sierra Nevada mountain range of Central California. Dataset Description: Dataset Description: Lidar flight line swaths were processed to create 3,437 classified LAS 1.4 files delineated in 1,000m x 1,000m tiles tiles. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Breaklines were developed using an algorithm which weights lidar-derived slopes, intensities, and return densities to detect the water's edge. The water's edge was then manually reviewed and edited as necessary. Lakes were assigned a consistent elevation for an entire polygon while rivers were assigned consistent elevations on opposing banks and smoothed to ensure downstream flow through the entire river channel. Additional products include classified LAS files, intensity images, tiled 0.5 meter contours and tiled bare earth surface models. Ground Conditions: Acquisition occurred free of smoke, fog and cloud during a time frame absent of unusual flooding or inundation and during leaf off conditions when possible. However, due to its high elevation, there are a few patches of snow throughout the dataset which were classed accordingly and delineated by a snow polygon.IRMA Data Store Reference

Source DataThe National Agriculture Imagery Program (NAIP) Color Infrared Imagery, captured in 2018 Processing Methodsdownloaded NAIP imagery tiles for all Southern Appalachian sky islands with spruce forest type present. Mosaiced individual imagery tiles by sky island. This step resulted in a single, seamless imagery raster dataset for each sky island.Changed the raster band combination of the mosaiced sky island imagery to visually enhance the spruce forest type from the other forest types. Typically, the band combination was Band 2 for Red, Band 3 for Green, and Band 1 for Blue. Utilizing the ArcGIS Pro Image Analyst extension, performed an image segmentation of the mosaiced sky island imagery. Segmentation is a process in which adjacent pixels with similar multispectral or spatial characteristics are grouped together. These objects represent partial or complete features on the landscape. In this case, it simplified the imagery to be more uniform by forest type present in the imagery, especially for the spruce forest type.Utilizing the segmented mosaiced sky island imagery, training samples were digitized. Training samples are areas in the imagery that contain representative sites of a classification type that are used to train the imagery classification. Adequate training samples were digitized for every classification type required for the imagery classification. The spruce forest type was included for every sky island. Classified the segmented mosaiced sky island imagery utilizing a Support Vector Machine (SVM) classifier. The SVM provides a powerful, supervised classification method that is less susceptible to noise, correlated bands, and an unbalanced number or size of training sites within each class and is widely used among researchers. This step took the segmented mosaiced sky island imagery and created a classified raster dataset based on the training sample classification scheme. Reclassified the classified dataset only retaining the spruce forest type and shadows class.Converted the spruce and shadows raster dataset to polygon.