There's a lot going on in marine aquaculture in the United States! NOAA, with its partners, plays a major role in developing environmentally and economically sustainable marine aquaculture practices, technologies and industry in the U.S. Marine aquaculture creates jobs, supports working waterfronts and coastal communities, provides new international trade opportunities, and provides a domestic source of sustainable seafood to complement our wild fisheries. Use this map to check out just some of the recent developments in the domestic marine aquaculture industry in your region, and how NOAA is involved. Click on the individual images to get project details, materials and links.

The Story Map Basic application is a simple map viewer with a minimalist user interface. Apart from the title bar, an optional legend, and a configurable search box the map fills the screen. Use this app to let your map speak for itself. Your users can click features on the map to get more information in pop-ups. The Story Map Basic application puts all the emphasis on your map, so it works best when your map has great cartography and tells a clear story.You can create a Basic story map by sharing a web map as an application from the map viewer. You can also click the 'Create a Web App' button on this page to create a story map with this application. Optionally, the application source code can be downloaded for further customization and hosted on your own web server.For more information about the Story Map Basic application, a step-by-step tutorial, and a gallery of examples, please see this page on the Esri Story Maps website.

To create this app:

In 2012 we started collaborating with commercial river guides (http://www.gcrg.org/) and Grand Canyon Youth (http://www.gcyouth.org/) to quantify insect emergence throughout the 240 mile long segment of the Colorado River in Marble and Grand Canyon. Each night in camp, guides put out a simple light trap to collect flying insects. After one hour, the light was turned off, the sample poured into a collection bottle, and some notes were recorded in a field book. After the conclusion of the river trip, guides dropped off samples and field notes at our office and we processed the samples in the laboratory. This project is ongoing and will be conducted annually. This web application shows data collected as part of this Citizen Science initiative for the years 2012 to 2014.

The Story Maps, developed by the Joint Research Centre, the Commission's science and knowledge service, inform in an easily accessible way about several initiatives across Europe linked to cultural heritage. These include actions like the European Heritage Days, the EU Prize for Cultural Heritage or the European Heritage Label, funded by Creative Europe, the EU programme that supports the cultural and creative sectors. The website also contains links to the digital collections of Europeana – the EU digital platform for cultural heritage. This platform allows users to explore more than 50 million artworks, artefacts, books, videos and sounds from more than 3500 museums, galleries, libraries and archives across Europe. These maps will be updated and developed, for example taking into account tips from young people exploring Europe's cultural heritage through the new DiscoverEU initiative.

Preserving and enhancing the discoverability of scientific information about geologic cores and samples.

This template is in Mature Support. Esri offers several other crowdsourcing and data collection apps. Story Map Crowdsource is a configurable application that lets you set up a Story Map that anyone can contribute to. Use it to engage a specific or general audience and collect their pictures and captions on any topic that interests you. Participants can log in with their social media account or ArcGIS account. When you configure a Crowdsource story, an interactive builder makes it easy to create your story and optionally review and approve contributions before they appear on the map.Use CasesStory Map Crowdsource can be used to create a crowdsourced map of photos related to any topic, event, or cause. The submissions can be all from a single neighborhood or from all over the world. Here are some examples:National Park MemoriesEsri 2016 User ConferenceGIS DayHonoring our VeteransUrban Food MovementConfigurable OptionsThe following aspects of a Story Map Crowdsource app can be configured using the Builder:Title, cover image, cover message, header logo and click-through link, button labels, social sharing options, and home map viewAuthentication services participants can use to sign inWhether new contributions are being acceptedWhether new contributions appear on the map immediately or only after the author approves themSupported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsStory Map Crowdsource does not require you to provide any geographic content, but a web map and feature service are created for your story in your account when the Builder is launched. An ArcGIS account with Publisher permissions is required to create a Crowdsource story.Get Started This application can be created in the following ways:Click the Create a Web App button on this page (sign in required)Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.For more information, see this FAQ and these blog posts..

This is an Expansion and Subset of the Internal Knowledge Map dataset that focuses on Story Writing and Role Playing. I was curious to see if I could adapt my IKM structure and approach to improve Story Telling, Role Playing/Character/Discourse in an LLM. Here are 2,071 highly-detailed and unique examples that allow an LLM to exhibit more depth, diverse perspectives and novel interactions. Side benefit is the LLM also writes in well-formed, aesthetically pleasing formatting and is an… See the full description on the dataset page: https://huggingface.co/datasets/Severian/Internal-Knowledge-Map-StoryWriter-RolePlaying.

The map displays examples from across the country of different organizations using MarineCadastre.gov data and products to meet their specific needs. A broad range of uses are covered, including evaluating impacts of offshore energy on navigation safety, researching how noise from large commercial vessels may affect marine mammals, and creating maps of proposed wave energy projects. Access to these data is provided by MarineCadastre.gov, a joint Bureau of Ocean Energy Management and National Oceanic and Atmospheric Administration initiative providing authoritative data to meet the needs of the offshore energy and marine planning communities.

Open the Data Resource: https://gis.chesapeakebay.net/viz/coastal/ This story map explains how 3-D landscape basecamps can be built, using an example that assesses the impacts of sea level rise on Norfolk, Virginia, within the context of global sea level rise.

This story map explains how to use heat mapping within smart mapping to show density within your maps in ArcGIS Online. You can easily select the heat map style to show where your data is spatially clustered. Go beyond the defaults to show density for an attribute, telling the story of an area that is statistically significant. Add the points layer back into the map with transparency as a reference to the heat map. This story map walks you through examples, which can help get you started with smart mapping heat maps. For more information, visit the Help Pages.

MapViewer is a graphical tool for viewing and comparing Gossypium spp. genetic maps. It includes dynamically scrollable maps, correspondence matrices, dot plots, links to details about map features, and exporting functionality. It was developed by the MainLab at Washington State University and is available for download for use in other Tripal databases. The query interface allows the user to select Species, Map, and Linkage Group options. Help information includes a video tutorial, user manual, and sample map, correspondence matrix, dot plot, and exported figures. Resources in this dataset:Resource Title: Website Pointer for CottonGen Map Viewer. File Name: Web Page, url: https://www.cottongen.org/MapViewer MapViewer is a graphical tool for viewing and comparing Gossypium spp. genetic maps. It includes dynamically scrollable maps, correspondence matrices, dot plots, links to details about map features, and exporting functionality. It was developed by the MainLab at Washington State University and is available for download for use in other Tripal databases. The query interface allows the user to select Species, Map, and Linkage Group options. Help information includes a video tutorial, user manual, and sample map, correspondence matrix, dot plot, and exported figures.

The District of Columbia shares story maps that combine impacting narratives and multimedia with data and analytics. These examples support agency programs and help educate how DC is using its data.

The Division of Forestry has been managing forest resources for many years in the Tanana Valley area. The purpose of this GIS layer, is to create a spatial coverage of vegetation on state lands to aid in forest management.

This story map describes and demonstrates how OpenStreetMap (OSM) data is accessible in ArcGIS, and how ArcGIS users can help to improve OSM with their GIS data. Learn the various ways in which you can access OSM data for your work, and how you can share data to be used in OSM.OpenStreetMap is a free, editable map of the world built by a community of mappers that contribute and maintain geospatial data about our world. It includes a worldwide database that is maintained by over 8 million registered users, with millions of map changes each day. Esri provides access to OSM data to ArcGIS users in multiple ways, including hosted vector tiles, feature layers, and scene layers.This story map shows several examples of how you can access OSM data in your work, and how ArcGIS organizations (e.g. cities, counties, states, nations) can share data they maintain (e.g. buildings, addresses, roads) to be used in OSM. The story illustrates the open data pipeline between ArcGIS and OSM, where open data created and published with ArcGIS can flow to OpenStreetMap and then OSM data flows back again to ArcGIS.

The data set includes data on the territorial distribution, species composition, nesting and behaviors of birds during the breeding season in permanent sample plots (areas for the bird census). All bird observations are marked on a map in WGS 84 coordinates determined using a mobile device with OS Android. Surveys were conducted from 2018 to 2020 in season of the birds nesting between end of April and the middle of July. The permanent sample plots are located in typical forest habitats of the strict nature reserve (category IUCN Ia) – Core area Prioiksko-Terrasnyi Biosphere Reserve. There are 3 sample plots in different type of forest: pine forest (sample plot 35, square 45 ha), oakwood (sample plot 41, square 25 ha), and mixed forest (sample plot 18, square 40 ha). During survey, 66 species of birds were recorded in total. Each census is a map of the distribution of birds on the site on the day of visit. The collected data make it possible to create a map of the distribution of the individual territories of birds on permanent areas in different years.

Набор данных включает данные о территориальном распределении, видовом составе, гнездовании и поведении птиц в период размножения на постоянных пробных площадках (площадки для учета птиц). Все наблюдения за птицами отмечены на карте в координатах WGS 84, определенных при помощи мобильного устройства c OC Андроид. Исследования проводились с 2018 по 2020 год в сезон гнездования птиц с конца апреля по середину июля. Постоянные пробные участки расположены в типичных лесных местообитаниях природного заповедника (категория IUCN Ia) – ядра Приокско-Террасного биосферного резервата. Обследование проведено на 3-х пробных площадях в различных типах спелого леса (возраст 70-120 лет): сосновый лес (plot 35, площадь 35 га), дубовый лес (plot 41, площадь 25 га) и смешанный лес (plot 18, площадь 40 га). Всего в ходе обследований было зафиксировано 66 видов птиц. Каждый учет представляет собой карту распределения птиц на площадке в день учета. Собранные данные позволяют составить карту распределения мест гнездования птиц на постоянных площадях в разные годы.

This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B03002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows poverty status by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Poverty status is based on income in past 12 months of survey. This layer is symbolized to show the percentage of the population whose income falls below the Federal poverty line. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Data CitationPlease cite this dataset as follows:Vasquez, V., Cushman, K., Ramos, P., Williamson, C., Villareal, P., Gomez Correa, L. F., & Muller-Landau, H. (2023). Barro Colorado Island 50-ha plot crown maps: manually segmented and instance segmented. (Version 2). Smithsonian Tropical Research Institute. https://doi.org/10.25573/data.24784053This data is licensed as CC BY 4.0 and is thus freely available for reuse with proper citation. We ask that data users share any resulting publications, preprints, associated analysis code, and derived data products with us by emailing mullerh@si.edu. We are open to contributing our expert knowledge of the study site and datasets to projects that use these data; please direct queries regarding potential collaboration to Vicente Vasquez, vasquezv@si.edu, and Helene Muller-Landau, mullerh@si.edu.Note that this dataset is part of a collection of Panama UAV data on Smithsonian Figshare, which can be viewed at https://smithsonian.figshare.com/projects/Panama_Forest_Landscapes_UAV/115572Additional information about this research can be found at the Muller-Landau lab web site at https://hmullerlandau.com/All required code is freely available at https://github.com/P-polycephalum/ForestLandscapes/blob/main/LandscapeScripts/segmentation.py and it can be cited as:Vicente Vasquez. (2023). P-polycephalum/ForestLandscapes: segmentwise (v0.0.2-beta). Zenodo. https://doi.org/10.5281/zenodo.10380517Data DescriptionThis dataset is part of a larger initiative monitoring forests in Panama using drones (unoccupied aerial vehicles), an initiative led by Dr. Helene Muller-Landau at the Smithsonian Tropical Research Institute. As part of this initiative, we have been collecting repeat imagery of the 50-ha forest dynamics plot on Barro Colorado Island (BCI), Panama, since October 2014 (see Garcia et al. 2021a, b for data products for 2014-2019).Contained within this dataset are two sets of field-derived crown maps, presented in both their raw and improved versions. The 2021 crown mapping campaign was overseen by KC Cushman, accompanied by field technician Pablo Ramos and Paulino Villarreal. Additionally, Cecilia Williamson and KC Cushman reviewed polygon quality and made necessary corrections. Image data occurred on August 1, 2020, utilizing a DJI Phantom 4 Pro at a resolution of 4cm per pixel. A total of 2454 polygons were manually delineated, encompassing insightful metrics like crown completeness and liana load.The 2023 crown mapping campaign, led by Vicente Vasquez and field technicians Pablo Ramos, Paulino Villarreal, involved quality revisions and corrections performed by Luisa Fernanda Gomez Correa and Vicente Vasquez. Image data collection occurred on September 29, 2022, utilizing a DJI Phantom 4 Pro drone at a 4cm per pixel resolution. The 2023 campaign integrated model 230103_randresize_full of the detectree2 model garden (Ball, 2023). Tree crown polygons were generated pre-field visit, with those attaining a field validation score of 7 or higher retained as true tree crowns.The data collection forms are prepared using ArcGIS field maps. The creator of the data forms uses the spatial points from the trees in the ForestGeo 50-ha censuses to facilitate finding the tree tags in the field (Condit et al., 2019). The field technicians confirm that the tree crown is visible from the drone imagery, they proceed to collect variables of interest and delineate the tree crown manually. In the case of the 2023 field campaign, the field technicians were able to skip manual delineation when the polygons generated by 230103_randresize_full were evaluated as true detection.The improved version of the 2023 and 2021 crown map data collection takes as input the raw crown maps and the globally aligned orthomosaics to refine the edges of the crown. We use the model SAM from segment-anything module developed my Meta AI (Krillov, 2023). We adapted the use of their instance segmentation algorithm to take geospatial imagery in the form of tiles. We inputted multiple bounding boxes in the form of CPU torch tensors for each of the files. Furthermore, we perform several tasks to clean the crowns and remove the polygons overlaps to avoid ambiguity. This results in a very well delineated crown map with no overlapping between tree crowns. Despite our diligent efforts in detecting, delineating, and evaluating all visible tree crowns from drone imagery, this dataset exhibits certain limitations. These include missing tags denoted as -9999, erroneous manual delineations or instance segmentation of tree crown polygons, duplicated tags, and undetected tree crowns. These limitations are primarily attributed to human error, logistical constraints, and the challenge of confirming individual tree crown emergence above the canopy. In numerous instances, particularly within densely vegetated areas, delineating polygons and assigning tags to numerous small trees posed significant challenges.MetadataThe dataset comprises four sets of crown maps bundled within .zip files, adhering to the naming convention MacroSite_plot_year_month_day_crownmap_type. As an illustration, a sample file name follows the structure: BCI_50ha_2020_08_01_improved.For a comprehensive understanding of variable nomenclature within each shapefile, exhaustive details are provided in the file named variables_description.csv. Additionally, our dataset incorporates visualization figures corresponding to both raw and refined crown maps.The raw crown maps contain:A GeoTiff-formatted raster image reflecting the image acquisition date during field data collection.The tiles folder housing all tiles utilized for instance segmentation.The most recent version of the raw crown map manually revised and retaining its original naming scheme.A reformatted iteration of the raw crown map, involving column renaming and the reprojection of its coordinate reference system.The improved crown maps contain:"_crownmap_segmented.shp" version: This subproduct has all polygons segmented via the SAM model from the segment-anything process."_crownmap_cleaned.shp" version: This subproduct features one polygon allocated per GlobalID, specifically the one with the highest segment-anything score."_crownmap_avoidance.shp" version: This subproduct is devoid of any overlapping polygons."_crownmap_improved.shp" version: The outcome of the instance crown segmentation workflow, incorporating all original crown map fields.Author contributionsVV wrote the code for standardized workflow for processing, alignment, and segmentation of the tree crowns. MG and MH led the drone imagery collection. HCM conceived the study, wrote the grant proposals to obtain funding, and supervised the research.AcknowledgmentsVicente Vasquez and KC Cushman created the field map forms and coordinated the 2023 and 2021 crown map field campaign. Milton Solano assistance with the ArcGIS platform. Field technicians Pablo Ramos, Paulino Villareal, and Melvin Hernandez delineated and evaluated tree crown polygons. Luisa Gomez-Correa and Cecilia Williamson assisted with quality assurance and quality control after field data collection. Milton Garcia and additional interns in the Muller-Landau lab assisted with drone data collection. Funding and/or in-kind support was provided by the Smithsonian Institution Scholarly Studies grant program (HCM), the Smithsonian Institution Equipment fund (HCM), Smithsonian ForestGEO, the Smithsonian Tropical Research Institute.ReferencesBall, J.G.C., Hickman, S.H.M., Jackson, T.D., Koay, X.J., Hirst, J., Jay, W., Archer, M., Aubry-Kientz, M., Vincent, G. and Coomes, D.A. (2023), Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN. Remote Sens Ecol Conserv. 9(5):641-655. https://doi.org/10.1002/rse2.332Condit, Richard et al. (2019). Complete data from the Barro Colorado 50-ha plot: 423617 trees, 35 years [Dataset]. Dryad. https://doi.org/10.15146/5xcp-0d46Garcia, M., J. P. Dandois, R. F. Araujo, S. Grubinger, and H. C. Muller-Landau. 2021b. Surface elevation models and associated canopy height change models for the 50-ha plot on Barro Colorado Island, Panama, for 2014-2019. . In Smithsonian Figshare, edited by S. T. R. Institute. https://doi.org/10.25573/data.14417933Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., & Girshick, R. (2023). Segment Anything. arXiv preprint arXiv:2304.02643.Scheffler D, Hollstein A, Diedrich H, Segl K, Hostert P. AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. Remote Sensing. 2017; 9(7):676.

The 'Data and Resources' box to the right includes a link to an FTP site where data, and a data dictionary, can be downloaded that provide access to compiled data from the primary ground-sampling programs managed by the Forest Analysis and Inventory Branch (FAIB) as well as a link to and Interactive Mapping App. FAIB ground-sampling programs include the Permanent Sample Plots (PSPs) that provide long term growth and yield information to support development and testing of growth-and-yield models. Active PSPs are the only plot type protected from harvesting. The Provincial Change Monitoring Inventory (CMI), Provincial Young Stand Monitoring (YSM) and National Forest Inventory (NFI) programs monitor the changes in growth, mortality, and forest health from statistically valid populations. Vegetation Resource Inventory (VRI) plots are used to audit and verify key spatial inventory attributes estimated during photo interpretation. The 'Ground Plot Data FTP' link contains tree- and plot-level compiled mensurational attributes for each ground plot across a series of repeated measurements. Both the PSP and non-PSP compilation outputs include a Data Dictionary that describes all the tables and attributes found in the downloadable files. The 'psp' dataset includes both inactive and active Permanent Sample Plot (PSP) data. The 'non-psp' dataset includes CMI, YSM, NFI, and VRI plots. The CMI, YSM and NFI plots are all located on a grid and only GENERALIZED COORDINATES are provided for these plot types. All PSP and VRI plots include REAL COORDINATES. The Interactive Mapping App provides a spatial view of FAIB ground plots with custom filters to enable selection of areas, BEC zones, species, TSA or plot types of interest. Once plots of interest are selected or filtered, an ‘export data’ button is available to download a plot summary file with limited attributes.