This dataset contains data used to test the protocol for high-resolution mapping and monitoring of recreational impacts in protected natural areas (PNAs) using unmanned aerial vehicle (UAV) surveys, Structure-from-Motion (SfM) data processing and geographic information systems (GIS) analysis to derive spatially coherent information about trail conditions (Tomczyk et al., 2023). Dataset includes the following folders:

Cocora_raster_data (~3GB) and Vinicunca_raster_data (~32GB) - a very high-resolution (cm-scale) dataset derived from UAV-generated images. Data covers selected recreational trails in Colombia (Valle de Cocora) and Peru (Vinicunca). UAV-captured images were processed using the structure-from-motion approach in Agisoft Metashape software. Data are available as GeoTIFF files in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru). Individual files are named as follows [location]_[year]_[product]_[raster cell size].tif, where:

[location] is the place of data collection (e.g., Cocora, Vinicucna)

[year] is the year of data collection (e.g., 2023)

[product] is the tape of files: DEM = digital elevation model; ortho = orthomosaic; hs = hillshade

[raster cell size] is the dimension of individual raster cell in mm (e.g., 15mm)

Cocora_vector_data. and Vinicunca_vector_data – mapping of trail tread and conditions in GIS environment (ArcPro). Data are available as shp files. Data are in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru).

Structure-from-motio n processing was performed in Agisoft Metashape (https://www.agisoft.com/, Agisoft, 2023). Mapping was performed in ArcGIS Pro (https://www.esri.com/en-us/arcgis/about-arcgis/overview, Esri, 2022). Data can be used in any GIS software, including commercial (e.g. ArcGIS) or open source (e.g. QGIS).

Tomczyk, A. M., Ewertowski, M. W., Creany, N., Monz, C. A., & Ancin-Murguzur, F. J. (2023). The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions. International Journal of Applied Earth Observations and Geoinformation, 103474. doi: https://doi.org/10.1016/j.jag.2023.103474

The aerial mapping camera system market is experiencing robust growth, driven by increasing demand across various sectors. Advancements in sensor technology, particularly in high-resolution imagery and improved processing capabilities, are fueling market expansion. The integration of these systems with UAVs (Unmanned Aerial Vehicles) for cost-effective and efficient data acquisition is a significant trend. Furthermore, the rising adoption of GIS (Geographic Information Systems) and 3D modeling applications across construction, agriculture, and environmental monitoring further boosts market demand. While challenges like high initial investment costs and regulatory hurdles exist, the overall market outlook remains positive. Let's assume, for illustrative purposes, a 2025 market size of $500 million with a CAGR of 12% for the forecast period (2025-2033). This implies a substantial market expansion, reaching an estimated $1.6 billion by 2033. The market is segmented by scanner type (Linear Array, Area Array) and application (manned and unmanned aircraft). The linear array scanners currently hold a larger market share due to their established technology and widespread adoption, though area array systems are expected to gain traction due to their higher speed and efficiency, particularly in applications involving larger areas. The unmanned aircraft segment demonstrates the fastest growth rate, driven by cost efficiency and accessibility of drone technology. Key players like Vexcel Imaging, Leica Geosystems, and Teledyne Optech are strategically investing in R&D and acquisitions to strengthen their market positions. Geographic regions like North America and Europe currently dominate the market, with Asia-Pacific projected to experience the fastest growth due to increasing infrastructure development and urbanization. This report provides an in-depth analysis of the global aerial mapping camera system market, valued at approximately $2.5 billion in 2023, projecting a Compound Annual Growth Rate (CAGR) of 7% to reach $3.8 billion by 2028. It covers market segmentation, key trends, leading players, and future growth opportunities. This report is essential for businesses involved in surveying, mapping, agriculture, construction, and infrastructure development, as well as investors seeking opportunities in this rapidly evolving technology sector.

The Southeast Texas Urban Integrated field lab’s Co-design team captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through autonomous flight, and models were processed through the DroneDeploy engine. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point Cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset will support researchers' decision-making processes under uncertainties.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including: I. Beaumont Soccer Club II. Corps’ Port Arthur Resident Office III. Halbouty Pump Station comprises its vicinity IV. Lamar University (Including Exxon Power Plants close to Lamar Univ.) V. MLK Boulevard for aerial images of the industry and the ship channel VI. Salt Water Barrier (include some aerial images about the Big Thicket) Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location. The processed data package including 3D models, geospatial data, mappings, point clouds, and the animation video of Halbouty Pump Station has various file types: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well. In October 2023, we had our follow-up data collection, including: I. Beaumont Soccer Club II. Shipping and Receiving Center at Lamar University After the aerial collection, we obtained aerial photos of those two locations mentioned above, as well as processed data (such as point clouds and models).

This collection contains 2 2022 1-centimeter RGB (red, green, blue) orthorectified image of two study site within the 2015-Soda wildfire boundary along the Oregon/Idaho border. These data were acquired on June 30, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products (dense point cloud, orthophoto, DSM) collected 2022-06-30 from two sites within the 2015-SODA wildfire boundary (FireCode: ID4311811696020150810, Welty and Jeffries 2020) spanning the Oregon/Idaho border adjacent to US Highway-95. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern (20 degree offset) at 44m above ground level; the resulting imagery have a ground resolution of 1.0cm/pixel. The multispectral imagery was collected at 38m above ground level (no crossgrid pattern); the resulting imagery have a ground resolution of 2.0cm/pixel. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N. Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from the 2015-SODA wildfire boundary near the Oregon/Idaho border USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/59M8-5S68Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTAdditional SODA1 Aerial Imagery Data (2019, 2020 & 2021)Roser, A., Marie, V., Olsoy, P., Delparte, D., & Caughlin, T. T. (2022). Unoccupied aerial systems imagery from the Soda Fire Natural Area Idaho (Version 1.0) [Data set]. University of Idaho. https://doi.org/10.7923/VCAP-4128Funding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/59M8-5S68

TThis collection contains 1 2022 1-centimeter RGB (red, green, blue) orthorectified image of a study site within the 1988-Stewart and 1996-Eighth Street wildfire boundaries near Boise, Idaho. These data were acquired on June 29, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products collected 2022-06-29 within the lower Dry Creek watershed within the 1988-STEWART and 1996-EIGHTH STREET wildfire boundaries (FireCode: ID4368311615219880802 and ID4366611613519960826, respectively, Welty and Jeffries 2020) near Boise Idaho, approximately 20 minutes from Boise off Bogus Basin Road. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern (20 degree offset) at 44m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The multispectral imagery was collected at 38m above ground level (no crossgrid pattern); the resulting imagery have ground resolution of 2cm/pixel. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from within the lower Dry Creek watershed near Boise Idaho USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/ZS2V-7B04Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTFunding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/ZS2V-7B04

This collection contains 2 2022 1-centimeter RGB (red, green, blue) orthorectified image of two study site within and near the 2013-Pony Complex wildfire boundary Near Mountain Home, Idaho. These data were acquired on June 15, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products (dense point cloud, orthophoto, DSM) collected 2022-06-15 from two sites within the 2013-PONY COMPLEX wildfire boundary (FireCode: ID4329411554820130809, Welty and Jeffries 2020) near Mountain Home Idaho. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern (20 degree offset) at 44m above ground level; the resulting imagery have a ground resolution of 1.0cm/pixel. The multispectral imagery was collected at 38m above ground level (no crossgrid pattern); the resulting imagery have a ground resolution of 2.0cm/pixel. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from the 2013-PONY COMPLEX wildfire boundary near Mountain Home Idaho USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/AEZG-KD35Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTFunding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/AEZG-KD35

The global Aerial Mapping System market is experiencing robust growth, driven by increasing demand across various sectors. Technological advancements in sensor technology, particularly in LiDAR and hyperspectral imaging, are fueling higher resolution data acquisition and improved analytical capabilities. This, combined with the decreasing cost of drone technology and the rise of cloud-based data processing platforms, is making aerial mapping more accessible and cost-effective for a wider range of applications. The market is segmented by system type (Vertical Aerial Photogrammetry System, Lidar Mapping System, Spectral Remote Sensing Mapping System) and application (Civil, Military). While precise market size figures for 2025 are unavailable, based on industry reports indicating substantial growth and considering a plausible CAGR of 15% from a reasonably estimated 2019 market size of $3 Billion, the market value in 2025 is projected to be approximately $5 Billion. This growth trajectory is expected to continue, with the market projected to reach approximately $11 Billion by 2033, driven by consistent technological innovation and expanding application across diverse sectors including precision agriculture, infrastructure monitoring, urban planning, and environmental management. The market’s growth, however, is subject to certain restraints. These include the high initial investment costs associated with advanced aerial mapping systems, regulatory hurdles regarding airspace access and data privacy, and the need for skilled professionals to operate and interpret the complex datasets generated. Nevertheless, the substantial benefits offered by aerial mapping in terms of improved efficiency, accuracy, and cost-effectiveness across multiple industries are expected to outweigh these challenges, ensuring continued market expansion. Key players like TOPCOM, Teledyne Geospatial, Riegl, and others are actively shaping this landscape through continuous product innovation and strategic partnerships, further driving market growth and competition. The North American and European markets currently hold significant market share, but the Asia-Pacific region is expected to exhibit the highest growth rate in the coming years due to rapid infrastructure development and increasing adoption of advanced technologies. This report provides a detailed analysis of the global aerial mapping system market, projected to reach a valuation exceeding $15 billion by 2030. It offers invaluable insights into market dynamics, key players, and future growth prospects, utilizing data-driven analysis and industry expert projections. This report is essential for businesses seeking to understand and navigate the complexities of this rapidly evolving sector. Keywords: Aerial Mapping, Drone Mapping, Lidar, Photogrammetry, Remote Sensing, GIS, Geospatial, Surveying, Mapping Technology, UAV Mapping, Orthophotography, 3D Modeling.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

The global Digital Aerial Photography System market is experiencing robust growth, driven by increasing demand across various sectors like surveying, mapping, agriculture, and infrastructure development. The market's expansion is fueled by technological advancements in sensor technology, drone integration, and data processing capabilities, leading to higher resolution imagery and faster data analysis. Furthermore, the rising adoption of cloud-based solutions for data storage and processing is streamlining workflows and reducing operational costs. Based on industry analysis and considering typical growth trajectories for similar technology markets, we estimate the 2025 market size to be approximately $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 8% projected from 2025 to 2033. This signifies a considerable market opportunity for established players and new entrants. This growth is anticipated to continue due to the increasing need for precise geospatial data in urban planning, environmental monitoring, and disaster management. However, challenges remain, including high initial investment costs for advanced systems, regulatory hurdles surrounding drone usage, and the need for skilled professionals to operate and interpret the data. Nevertheless, ongoing technological innovations, particularly in AI-powered image analysis and automation, are poised to mitigate these challenges and further propel market expansion. The segmentation of the market, encompassing various sensor types, software solutions, and service offerings, creates diverse avenues for specialized businesses to thrive in this dynamic sector.

Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy-forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource-focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp, and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management. Methods Priority survey site selection: We selected sites for UAV emergent kelp canopy surveys using a prioritization framework for kelp recovery efforts based on data from OAV surveys, subtidal surveys, areas of cultural significance, areas of economic significance, accessibility, and proximity to marine protected areas (MPAs) (1). A total of 37 sites were identified in Mendocino and Sonoma Counties (i.e., the “North Coast”), hereafter referred to as ‘priority sites’. Ten of the sites are in actively managed state MPAs and 27 are in the Greater Farallones National Marine Sanctuary (NMS). Thirty-six of the 37 sites were surveyed with UAVs between 2019 and 2020, with 21 sites surveyed in both 2019 and 2020. The average priority site area was 1 km2 (range 0.2-1.7 km2).
UAV flights, timing and environmental sources of variation and error Due to the 90 km stretch of coastline within which the noncontiguous priority sites are located, numerous pilots participated in data collection and we developed a repeatable workflow building upon the efforts of Katherine C. Cavanaugh et al. 2021 to ensure data consistency. We obtained state and federal permits to allow UAV use in restricted areas and we established criteria for UAV launch sites (i.e., public coastal access, no large obstacles, flat area with minimal ecological impact potential, and located mid-way in the survey area to maintain telemetry link between the UAV and controller). We used small UAV platforms from the same manufacturer and each pilot selected their own flight software. Pilots flew at an altitude of 120 m above mean sea level with a minimum front and side overlap of 75%, nadir angle of the sensor, auto white balance, and UAV speeds between 10 to 12 m/s. The image processing softwares used included Agisoft Metashape, DroneDeploy, and Pix4D; all orthomosaics were reviewed by expert annotators and when output orthomosaics were incomplete or contained significant defects, the imagery was reprocessed using at least one of the two other software options.

All UAV pilots acquired imagery using the built-in Red-Green-Blue (RGB) sensor. We coordinated flights to coincide with the annual peak biomass of bull kelp, which typically occurs in late summer/early fall on the North Coast. Our team surveyed during the lowest tide series of the month and aimed to survey at the lowest tide of the day, as tidal height and surface currents have been shown to impact the amount of kelp canopy exposed on the water surface (2) and these impacts can vary regionally (3). Because sun angle, wind, and weather conditions varied significantly throughout the data collection process, surveys were not restricted to a specific daily tidal height or current speed; data were collected when field conditions allowed for stable UAV launch and landing and this structure resulted in random sampling throughout the tidal range within and between years, addressing sampling bias in our data. Kelp Detection, Classification, and Quantification We identified kelp pixels in each UAV image using a band combination between the red and blue bands (Red - Blue), which has been shown to best distinguish kelp from water in RGB-UAV imagery relative to other RGB vegetation indices (3). Before applying a threshold to our image, we manually masked all terrestrial objects (e.g., land and intertidal rocks). Due to radiometric and spectral variability present in the imagery, we manually selected thresholds to distinguish kelp from seawater. For individual sites with high levels of spectral variability due to turbidity, sun glint, or other artifacts, a single threshold could not be used for kelp identification because the threshold varied throughout the image within a site (3). For these sites, we gridded images into subsets (ranging from 1000 x 1000 m areas to 5000 x 5000 m areas, depending on the level of variability), and each grid was assigned a unique threshold. As a result, multiple thresholds were used for classification for these sites. We mosaicked the classified grids back to their original extent and manually reviewed all classified mosaics for quality assurance. We used binary classification values (i.e., “Kelp” or “Not Kelp”) except for mixed-species marine algal beds and the occasionally blurred image, which were assigned “No Data” values. We worked in a GIS environment to determine the area of kelp at a given site by multiplying the number of kelp pixels by the area of the pixels (ArcGIS Pro 2.7).

Comparison to multi-decadal Landsat data:

To give multi-decadal temporal context to the UAV surveys, we examined long-term trends in kelp canopy dynamics along the North Coast using Landsat satellite imagery. (n=36). To control for differences in available reef habitat between priority sites, we selected the maximum area of kelp canopy (m2) that occurred within a site in each year and normalized that amount by the historical maximum extent of emergent kelp canopy (i.e., the cumulative area within a site where kelp was ever observed between 1984-2020) to produce a time series of annual, proportional coverage values. We also used Landsat emergent kelp canopy data to produce maps of canopy persistence at our case-study sites, where relative persistence was defined as the number of years from 1984-2020 in which a pixel contained kelp canopy (4). Maps of emergent kelp canopy for case-study sites during a given year used the maximum canopy area observed. (1) Hohman, R., Hutto, S., Catton, C. and F. Koe. 2019. Sonoma-Mendocino Bull Kelp Recovery Plan. Plan for the Greater Farallones National Marine Sanctuary and the California Department of Fish and Wildlife. Greater Farallones Association. San Francisco, CA. 166 pp. https://farallones.org/wp-content/uploads/2019/06/Bull-Kelp-Recovery-Plan-2019.pdf. (2) Britton-Simmons, K., Eckman, J. E. & Duggins, D. O. Effect of tidal currents and tidal stage on estimates of bed size in the kelp Nereocystis luetkeana. Marine Ecology Progress Series vol. 355 95–105 (2008). (3) Cavanaugh, K. C., Cavanaugh, K. C., Bell, T. W. & Hockridge, E. G. An Automated Method for Mapping Giant Kelp Canopy Dynamics from UAV. Front. Environ. Sci. Eng. China 0, (2021). (4) Bell, T. W., Allen, J. G., Cavanaugh, K. C., & Siegel, D. A. (2020). Three decades of variability in California’s giant kelp forests from the Landsat satellites. In Remote Sensing of Environment (Vol. 238, p. 110811). https://doi.org/10.1016/j.rse.2018.06.039

This collection contains 1 2022 1-centimeter RGB (red, green, blue) orthorectified image of a study site along the 2007-Cold wildfire boundary north of Glenns Ferry, Idaho. These data were acquired on June 10, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products collected 2022-06-10 at the 2007-COLD wildfire boundary (FireCode: ID4311811531420070801, Welty and Jeffries 2020) due east of Mountain Home Idaho and due north of Glenns Ferry Idaho, approximately one hour southeast of Boise Idaho off Interstate-84. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern (20 degree offset) at 44m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The multispectral imagery was collected at 38m above ground level (no crossgrid pattern); the resulting imagery have a ground resolution of 2cm/pixel. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from the 2007-COLD wildfire boundary near Glenns Ferry Idaho USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/RAGG-TV25Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTFunding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/RAGG-TV25

This collection contains 1 2022 1-centimeter RGB (red, green, blue) orthorectified image of a study site near Corrals Trailhead along the 1991 BF3 wildfire boundary north of Boise, Idaho. These data were acquired on May 20, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products collected 2022-05-20 at the 1991-BF3 wildfire boundary (FireCode: ID4367211616519910731, Welty and Jeffries 2020) near Boise Idaho, approximately 0.25 miles up from the Corrals Trailhead off Bogus Basin Road. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern at 41m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The multispectral imagery was collected at 66m above ground level (no crossgrid pattern); the resulting imagery have a ground resolution of 3.5cm/pixel. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from the 1991-BF3 wildfire boundary near Corrals Trailhead Boise Idaho USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/NFW8-6V95Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTFunding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/NFW8-6V95

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.

TThis collection contains 1 2022 1-centimeter RGB (red, green, blue) orthorectified image of a study site within the 1988-Stewart and 1996-Eighth Street wildfire boundaries near Boise, Idaho. These data were acquired on June 29, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products collected 2022-06-29 within the lower Dry Creek watershed within the 1988-STEWART and 1996-EIGHTH STREET wildfire boundaries (FireCode: ID4368311615219880802 and ID4366611613519960826, respectively, Welty and Jeffries 2020) near Boise Idaho, approximately 20 minutes from Boise off Bogus Basin Road. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern (20 degree offset) at 44m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The multispectral imagery was collected at 38m above ground level (no crossgrid pattern); the resulting imagery have ground resolution of 2cm/pixel. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from within the lower Dry Creek watershed near Boise Idaho USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/ZS2V-7B04Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTFunding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/ZS2V-7B04

This collection contains 1 2022 1-centimeter RGB (red, green, blue) orthorectified image of a study site along the 2005-North Ham wildfire boundary north of Hammett, Idaho. These data were acquired on May 24, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products (dense point cloud, orthophoto, DSM) collected 2022-05-24 within the 2005-NORTH HAM wildfire boundary (FireCode: ID4299311546620050621, Welty and Jeffries 2020) near Hammett Idaho. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern at 41m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The multispectral imagery was collected at 66m above ground level (no crossgrid pattern); the resulting imagery have a ground resolution of 3.5cm/pixel. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from the 2005-NORTH HAM wildfire boundary near Hammett Idaho USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/2Q8W-SN16Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTFunding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/2Q8W-SN16

This collection contains 1 2022 1-centimeter RGB (red, green, blue) orthorectified image of a study site along the 1996 Coyote Butte wildfire boundary near Initial Point south of Kuna, Idaho. These data were acquired on June 3, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products (dense point cloud, orthophoto, DSM) collected 2022-06-03 at the 1996-COYOTE BUTTE wildfire boundary (FireCode: ID4336711639119960730, Welty and Jeffries 2020) near Initial Point Kuna Idaho. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern (20 degree offset) at 41m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The multispectral imagery was collected at 38m above ground level (no crossgrid pattern); the resulting imagery have a ground resolution of 2.0cm/pixel. Additionally, we completed a test flight with the multispectral drone to assess if slickspot peppergrass can be identified. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from the 1996-COYOTE BUTTE wildfire boundary near Initial Point Kuna Idaho USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/QXAV-S561Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTFunding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/QXAV-S561

This collection contains 1 2022 1-centimeter RGB (red, green, blue) orthorectified image of study site along the 2010-South Trail wildfire boundary north of Hammett, Idaho. These data were acquired on May 25, 2022. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products which includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products collected 2022-05-25 at the 2010-SOUTH TRAIL wildfire boundary (FireCode: ID4301711528820100724, Welty and Jeffries 2020) near Hammett Idaho, approximately one hour southeast of Boise Idaho off Interstate-84. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern at 41m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The multispectral imagery was collected at two altitudes: 37m and 66m above ground level (no crossgrid pattern); the resulting imagery have ground resolution of 2cm/pixel and 3.5cm/pixel, respectively. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.Recommended Citation: Marie, V., Zaiats, A., Roser, A., Olsoy, P., Delparte, D., Wickersham, R., & Caughlin, T. T. (2023). Digital aerial imagery (RGB and multispectral) from the 2010-SOUTH TRAIL wildfire boundary near Hammett Idaho USA-2022 [Data set]. University of Idaho. https://doi.org/10.7923/5JCE-YE15Ancillary ODM Workflow: Marie, V., Zaiats, A., Wickersham, R., & Caughlin, T. T. (2023). Open Drone Map: Structure-from-Motion Workflow (Version 1.0). University of Idaho. https://doi.org/10.7923/92HF-GP09Ancillary Fire Dataset: Welty, J.L., and Jeffries, M.I., 2020, Combined wildfire datasets for the United States and certain territories, 1878-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z2VVRTFunding:US National Science Foundation Idaho EPSCoR, Award: OIA-1757324US National Science Foundation, Award: BIO-2207158National Aeronautics and Space Administration, Award: 80NSSC21K1638Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/5JCE-YE15