The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition. NAIP is administered by the USDA's Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.

What is NAIP?The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the contiguous U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.NAIP is administered by the USDA's Farm Production and Conservation Business Center through the Aerial Photography Field Office in Salt Lake City. The APFO as of August 16, 2020 has transitioned to the USDA FPAC-BC's Geospatial Enterprise Operations Branch (GEO). This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.How can I Access NAIP?On the web GEO (APFO) public image services can be accessed through the REST endpoint here. Compressed County Mosaics (CCMs) are available to the general public through the USDA Geospatial Data Gateway. All years of available imagery may be downloaded as 1/2, 1, or 2 meter CCMs depending on the original spatial resolution. CCMs with a file size larger than 8 GB are not able to be downloaded from the Gateway. Full resolution 4 band quarter quads (DOQQs) are available for purchase from FPAC GEO. Contact the GEO Customer Service Section for information on pricing for DOQQs and how to obtain CCMs larger than 8 GB. A NAIP image service is also available on ArcGIS Online through an organizational subscription.How can NAIP be used?NAIP is used by many non-FSA public and private sector customers for a wide variety of projects. A detailed study is available in the Qualitative and Quantitative Synopsis on NAIP Usage from 2004 -2008: Click here for a list of NAIP Information and Distribution Nodes.When is NAIP acquired?NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, a three-year cycle began in 2009, NAIP was on a two-year cycle until 2016, currently NAIP is on a 3 year refresh cycle. Click here >> for an interactive PDF status map of NAIP acquisitions from 2002 - 2018. 2021 acquisition status dashboard is available here.What are NAIP Specifications?NAIP imagery is currently acquired at 60cm ground sample distance (GSD) with a horizontal accuracy that matches within four meters of photo-identifiable ground control points.The default spectral resolution beginning in 2010 is four bands: Red, Green, Blue and Near Infrared.Contractually, every attempt will be made to comply with the specification of no more than 10% cloud cover per quarter quad tile, weather conditions permitting.All imagery is inspected for horizontal accuracy and tonal quality. Make Comments/Observations about current NAIP imagery.If you use NAIP imagery and have comments or find a problem with the imagery please use the NAIP Imagery Feedback Map to let us know what you find or how you are using NAIP imagery. Click here to access the map.**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: National Agriculture Imagery Program (NAIP) History 2002-2021Item Type: Web Mapping Application URL Summary: Story map depicting the highlights and changes throughout the National Agriculture Imagery Program (NAIP) from 2002-2021.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: URL referencing this original map product: https://nmcdc.maps.arcgis.com/home/item.html?id=445e3dfd16c4401f95f78ad5905a4cceFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=8eb6c5e7adc54ec889dd6fc9cc2c14c4UID: 26Data Requested: Ag CensusMethod of Acquisition: Living AtlasDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery.

This is an ArcGIS Server Image Service of the 4-band 2021 National Agricultural Imagery Program (NAIP) orthorectified digital aerial photos of Montana. Imagery defaults to natural color. To view the imagery as false-color infrared (CIR), select band 4 as the red image, band 1 as the green, and band 2 as the blue. This data set contains imagery from the National Agriculture Imagery Program (NAIP). These data are digital aerial photos, at 60 centimeter resolution, of most of the state of Montana, taken in 2019. Due to snow cover the imagery acquisition was not completed in 2019 and some areas were acquired in 2020. https://docs.msl.mt.gov/News/20191115_News_2019NAIPGaps.pdf The data are available from the State Library in two different formats. The most accessible format is a downloadable collection of compressed county mosaic (CCM) natural color MrSID images. These data are in UTM coordinates. The FTP folder containing these images is https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2019_NAIP/UTM_County_Mosaics The data are available from the State Library as a collection 11,775 4-band (near infrared, red, green and blue) TIF images in UTM coordinates. Each image is about 400 megabytes. The tiling format of the TIFF imagery is based on 3.75 x 3.75 minute quarter-quadrangles with a 300 pixel buffer on all four sides. An ESRI shapefile index showing the extent and acquisition dates of the TIF images is available at https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2019_NAIP/ NAIP_2019_MT_tileindex_CurrentlyAvailable.zip To order TIFF images from the State Library, select the quadrangles you want from the tiff index shapefile and send them to the Library, along with a storage device of sufficient size to hold them and return postage for the device.

2020 National Agriculture Imagery Program (NAIP) natural color .6-meter pixel resolution. The imagery was collected statewide from June 23, 2020 through September 10, 2020. This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the creation of DOQQs hosted in FSA image services. These seam polygons can be used as a tool in determining the image source and date of each portion of the imagery. The NAIP acquires 4 band digital ortho imagery from airborne and/or space based platforms during the agricultural growing seasons in the U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within sixty days of acquisition. The NAIP provides 60 centimeter ground sampel distance ortho imagery rectified within +/- 4 meters to true ground at a 95% confidence level. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 (plus or minus 30) pixel buffer on all four sides. The NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83).

2020 NAIP imagery for Wisconsin has a .6-meter (60cm) spatial resolution. There are DOQQ tiles as well as county mosaics available. [From USDA: This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery. The NAIP imagery is typically available for distribution within 60 days of the end of a flying season and is intended to provide current information of agricultural conditions in support of USDA farm programs. For USDA Farm Service Agency, the 1 meter and 1/2 meter GSD product provides an ortho image base for Common Land Unit boundaries and other data sets. The NAIP imagery is generally acquired in projects covering full states in cooperation with state government and other federal agencies who use the imagery for a variety of purposes including land use planning and natural resource assessment. The NAIP is also used for disaster response often providing the most current pre-event imagery.] This dataset is also available as a natural color mosaic referenced to the Wisconsin Transverse Mercator (WTM) coordinate system, and as a color infrared mosaic referenced to the Universal Transverse Mercator (UTM) coordinate system.

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery.

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery.

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery.

The US Fish and Wildlife Service (USFWS) has an interest in completing the fine-scale vegetation mapping of the remainder of the Simi Valley-Santa Susana Mountains Ecological Subsection of the Southern California Coast Ecological Section (total Project size approximately 155,519 acres). Aerial Information Systems, Inc. (AIS), under a separate contract with the National Park Service (NPS), had completed a large portion of the subsection as part of Santa Monica Mountains National Recreation Area (SAMO) vegetation mapping effort. However, USFWS has had limited funding available so the Project was divided into multiple parts based on funding allotments. Phase 1 - previously allocated funding to develop the floristic classification and mapping of 28,900 acres, conducted in 2019, Phase 2 - current allocation of funds to map an additional 48,700 acres, conducted in 2021-23, and “Future Funding” Phases that are required to map the remainder of the area and expand the classification development to include the Northeast Extension Area when funding becomes available. Protocols comply with state and national standards as defined by the Survey of California Vegetation (SCV) and the US National Vegetation Classification (USNVC).The data collection task for Phase 1 was conducted by California Native Plant Society (CNPS). CNPS collected 80 surveys in the data collection process. For Phase 2, AIS in collaboration with USFWS determined the study area for mapping. The study area was divided into three working modules. All pertinent materials and data files were acquired. The base for mapping was the 2020 NAIP natural color and color infrared imagery. The 2018 NAIP imagery (used for Phase 1), as well as Google Earth, was also available for reference. Fire perimeter files, the Phase 1 classification survey data, land access files, road files, and geology were also obtained. For the Phase 1 and 2 mapping, established statewide standard guidelines are used. Statewide standard for minimum mapping unit (MMU) size is 1 acre for upland types and ¼ acre for wetland and riparian types. The Phase 1 and 2 data completed thus far is not considered a “final” product since ground-truth reconnaissance of Phase 1, accuracy assessment (AA), final data processing and the overall Project final report and metadata creation have not been funded. However, users may find the interim data useful for habitat modeling and/or other land management planning purposes. Additional information can be found in the Phase 1 Final Performance Report here: https://www.cnps.org/wp-content/uploads/2023/12/SantaSusanaMappingReport_Phase-I_Dec2019.pdf. More information can be found in the 2023 project report, which is bundled with the vegetation map published for BIOS here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/3100_3199/ds3131.zip

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

The four adjacent Outer Cape communities of Eastham, Truro, Provincetown, and Wellfleet have built an intermunicipal partnership to pursue a regional approach to shoreline management. This partnership promotes short- and long-term science-based decisions that will maximize the effectiveness and efficiency of community responses to the increased threat of coastal hazards. This map set is a product of that partnership, the Intermunicipal Shoreline Management Project, a project first initiated in 2019 with funding from CZM's Coastal Resilience Grant Program.Contemporary salt marsh extents were delineated based on photointerpretation and image classification of salt marsh vegetation from 60 cm resolution, 4-band, digital georectified images acquired by the National Agriculture Imagery Program (NAIP) in August of 2018. Visual comparison of the classified raster and NAIP imagery, displayed as a color-infrared (CIR) image, was performed to extract vegetated classes over known salt marsh areas. Then parcels with the future potential to accommodate salt marsh under a sea level rise scenario of 1 meter were identified. Parcel selection and scoring was based on the following: 1. Parcel and suitable space contiguity 2. Ownership 3. Salt marsh adjacency 4. Total suitable area 5. Percentage of the parcel’s total area suitable for salt marsh migration In general parcels were scored relative to each other based on the percentage of the parcel’s total area suitable for salt marsh migration, a higher percentage resulted in a higher score. However, a few characteristics were considered to be highly desirable, resulting in the highest possible score regardless of relative percentage. All town, state, federal and conservation organization owned parcels were also removed, as this work primarily focuses on the identification of parcels for further review by municipal open space committees and local land trusts for future acquisitions planning. In total 229 parcels were identified: 93 in Eastham, 113 in Wellfleet, 23 in Truro and 0 in Provincetown. All suitable migration space in Provincetown was located within 3 parcels (federal and state owned), the majority in Cape Cod National Seashore. To locate possible parcels of interest a suitability base map was created to identify areas within the ISM planning area with the potential to accommodate salt marsh under a sea level rise scenario of 1 meter. The following criteria were considered: elevation, slope, connectivity and proximity to salt marsh and land cover.ElevationAreas with the future potential to accommodate salt marsh under a sea level rise scenario of 1 meter from current levels were identified and delineated based on an estimated suitable elevation range determined from the following generalized relationships between dominant salt marsh vegetation and tidal stage (Ayers, 1959; Redfield, 1972; Teal, 1986; Bertness, 1987; Bertness, 1991): Inland salt marsh boundary = MHHW + 2.5 ft Seaward salt marsh boundary = MHW – 2/3 MNThe current suitable elevation range for salt marsh within the ISM planning area was estimated to be -0.75 m (-2.46 ft) to 2.25 m (7.38 ft) NAVD88 (based on tidal profiles from Provincetown Harbor, Pamet Harbor, Wellfleet Harbor, Rock Harbor and Sesuit Harbor). To simulate 1 meter of sea level rise, both the upper and lower limits were adjusted by 1 meter. All areas with elevation values of 0.25 to 3.25 m were evaluated.SlopeSuitable slopes were determined based on Smith, 2020 and Kirwan et al., 2016, where the potential for marsh expansion generally decreases with increasing slope. Gentler slopes were most suitable (<1%), moderate slopes likely suitable (1-5%) and steeper slopes (>5%) less suitable. Severe slopes (>20%) were treated as unsuitable migration space. Connectivity and Proximity to Existing Salt MarshAreas were classified based on physical relationship and proximity to existing salt marsh and the presence of anthropogenic barriers (roads, parking lots, shoreline armoring, culverts) influencing salt marsh migration and then ranked accordingly. Areas with no hydrologic connection to existing salt marsh were treated as fragmented accommodation space and were designated as unsuitable.Land CoverWith no clear methodology for classifying land cover suitability for salt marsh migration (as demonstrated in Smith, 2020) general assumptions were made. The primary assumption reflects the concept that areas most suitable now (e.g., emergent wetlands) are more likely to be suitable in the future while the most uncertain transitions would be those dependent on forest retreat. Impervious area was classified as least suitable. Parcels The suitability base map was used to extract parcels intersecting the analysis area, and a series of operations were carried out to remove parcels selected due to noise in the data, parcels with minimal suitable space and parcels completely separated from other extracted parcels by topographic or anthropogenic barriers. Tax Parcel and assessor information was obtained from MassGIS Data: Property Tax Parcels (M086TaxPar last updated 4/2020, M242TaxPar last updated 6/2020, M300TaxPar last updated 11/2020, M318TaxPar last updated 2/2019). Please note a select number of parcels were designated as restoration parcels. These parcels currently contain large areas of mudflat and with increased deposition and/or human intervention could become more suitable in the future. Parcels designated as restoration parcels were not scored.

Tree canopy cover is a commonly used metric in the U.S. Department of Agriculture, Forest Service that provides data on tree distribution on the landscape and informs resource management activities such as forest thinning and habitat mapping. Canopy cover and the trees that create it are critical components of urban ecosystems, which provide important ecosystem services that range from reducing temperatures and energy costs to providing habitat for wildlife and improving human health.

Furthermore, canopy cover data is an important component of equity and environmental justice efforts, as studies have found that trees and the ecosystem services they provide are often unequally and inequitably distributed and/or managed in many cities (McDonald et al., 2021; Nyelele and Kroll, 2020). These findings highlight the necessity of understanding the extent and distribution of canopy in urban areas. One of the main challenges to managing and expanding urban tree canopy cover, however, is inventorying the location and extent of urban trees. High-resolution canopy cover maps are useful tools for identifying urban tree disparity and planning urban tree plantings and management in underserved communities to strategically increase ecosystem services, such as reducing temperatures, managing stormwater runoff, and improving wildlife habitat and human health (Nyelele and Kroll, 2020).

Several tree canopy cover datasets are currently available for the U.S. Caribbean islands of Puerto Rico and the U.S. Virgin Islands, including the 2016 National Land Cover Database (NLCD) and the Coastal Change Analysis Program (C-CAP) land cover dataset. The 2016 NLCD is derived from medium resolution (30 meter) Landsat data and includes a tree canopy cover component (Dewitz et al., 2018; Yang et al., 2019). Although this tree canopy cover dataset is extremely useful for a variety of applications, it lacks a fine enough resolution to support analysis of tree canopy distributions in complex urban landscapes, where it is common to have individual trees in backyards and surrounded by impermeable surface alongside streets. When aggregated, these smaller intraurban green spaces make up an important portion of permeable and vegetated land within a city, highlighting the importance of very high spatial resolution data for urban forestry and planning (Ramos, 2014).

C-CAP data, by contrast, is produced at a higher resolution (2 meter) and includes a forest class that provides a more detailed depiction of tree canopy cover in urban areas. However, C-CAP was last produced for Puerto Rico in 2010 and the U.S. Virgin Islands in 2012, meaning that there is a need for more recent high-resolution tree canopy cover data. The lack of regularly collected, high-resolution aerial imagery (e.g., the National Agriculture Imagery Program (NAIP)) means that there are few readily available resources to create canopy cover maps that can capture the fine-grained detail of urban systems.

In 2017, Hurricanes Irma and María devastated Puerto Rico and the U.S. Virgin Islands, furthering the need for up-to-date, high-resolution land cover and tree canopy cover maps that capture spatial information on infrastructure and vegetation across the islands. Although the U.S. Geological Survey (USGS) acquired lidar data over all of Puerto Rico in 2016-17, the data depicts conditions pre-storm and does not capture any of the significant disturbances and loss of tree canopy cover caused by Hurricane María. To support disaster recovery efforts, the USGS helped fund another lidar acquisition in 2018-19 that captures post-hurricane conditions in Puerto Rico and the U.S. Virgin Islands.

Recent urban canopy cover mapping efforts have demonstrated the effectiveness of using lidar data to map urban tree canopy cover at a fine scale over large cities in the U.S. (O’Neil-Dunne et al.,2013; MacFaden et al.,2012; EarthDefine et al.,2018). In addition, the strategic goals of the Forest Service Urban and Community Forestry Program highlight the need to continue developing strategies and protocols for mapping urban tree canopy cover, as well as continuing research that has demonstrated the value of lidar data for depicting the location and extent of tree canopy cover (Wolf 2015).

To address the need for urban tree canopy cover data in Puerto Rico and the U.S. Virgin Islands, the Forest Service Geospatial Technology and Applications Center (GTAC) and the International Institute of Tropical Forestry partnered to develop a workflow that produces high-resolution tree canopy cover data using available lidar data and Forest Service enterprise software. Once developed, remote sensing specialists at GTAC implemented this workflow across the 2016 and 2018 lidar data acquisitions, resulting in 1 meter spatial resolution tree canopy cover data for 2016 and 2018. These data display the presence of tree canopy and were used to calculate percent canopy cover within administrative boundaries in Puerto Rico and the U.S. Virgin Islands.

The 2016 NLCD, was derived from medium resolution (30 meter) Landsat data and includes percent tree canopy cover estimates.1,2 Although this dataset is extremely useful for a variety of applications, it lacks the resolution needed to support analysis of tree canopy distributions in complex urban landscapes, where it is common to have individual backyard trees and trees surrounded by impermeable surfaces, such as streets. When aggregated, these smaller intraurban green spaces make up an important portion of permeable and vegetated land within a city, highlighting the increasing need of high-resolution spatial data for urban forestry and planning.3

C-CAP data, by contrast, is produced at a higher resolution (2-meter) and includes a forest class that provides a more detailed depiction of tree canopy cover in urban areas. However, C-CAP was last produced for Puerto Rico in 2010 and the U.S. Virgin Islands in 2012, meaning that there is a need for more recent high-resolution tree canopy cover data. The lack of regularly collected, high-resolution aerial imagery in the U.S. Carribean (e.g., the National Agriculture Imagery Program (NAIP)) means that there are few readily available resources to create canopy cover maps that can capture the fine-grained detail of urban systems.

The invasive shrub, Russian olive (Elaeagnus augustifolia), is widely established within riparian areas across the western United States (U.S.). Limited information on its distribution and invasion dynamics in northern regions has hampered understanding and management efforts. Given this lack of spatial and ecological information we worked with local stakeholders and developed two main objectives: 1) map the distribution of Russian olive along the Powder River (Montana and Wyoming, U.S.) with field data and remote sensing; and 2) relate that distribution to environmental variables to understand its habitat suitability and community/invasion dynamics. In the study watershed, field data showed Russian olive has reached near equal canopy cover (18.3%) to native plains cottonwood (Populus deltoides; 19.1%), with higher cover closer to the channel and over a broader range of elevations. At the basin scale, we modeled Russian olive distribution using field surveys, ocular sampling of aerial i..., Model Training Data To predict Russian olive percent cover across the Powder River Basin, we created a spectral detection model for the year 2020. The model was trained using two different data collection methods: (1) field data and (2) ocular samples from NAIP 2019 aerial imagery. Field data were collected in June 2021 (Figure 1A). Ten meter radius plots were placed on transects (25 on the east bank and 17 on the west bank) perpendicular to the river and about 50 m apart, for a total of 276 plots (Figure 1A). Within each plot, vegetation cover was estimated for each woody species, including Russian olive, plains cottonwood (Populus deltoides), and tamarisk (Tamarix ramosissima), and height of the tallest woody plant was measured using a survey rod or clinometer. Of the 276 field data plots, 185 contained Russian olive. To increase the dataset size and spatial representation, we conducted randomized ocular image sampling using NAIP 2019 true and false color imagery following a similar..., , # Modeling the distribution of Russian olive and invasion dynamics: a case study from the Powder River, USA

https://doi.org/10.5061/dryad.p8cz8w9z8

Four datasets are included here. The first is field sampled plot data from the Powder River, used as training data in the Russian olive detection model. Second, is the ocular sampling data also included as training data in the detection model. Third, is the raster output of the final detection model for Russian olive in the study area. And finally, there is a shapefile of the field plot cover data with physiographic and grouping analysis in the attribute table (NAD_1983_UTM_Zone_13N).

Description of the data and file structure

The field data has its own metadata sheet that has a table describing each species name abbreviation. Plots were placed on transects that ran perpendicular to the river to capture topographic gradient and temporal shifts of river over time. Plots were 10 m radius; cover...

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery.

Aim Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions.

Location White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA.

Taxon High-elevation trees – Abies balsamea, Picea mariana, and Betula cordata.

Methods We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional potential drivers of treeline locations. We used multiple linear regression models to examine the importance of both topographic and climatic variables on treeline advance.

Results Regional treelines have significantly shifted upslope over the past several decades (on average by 3 m/decade). Diffuse treelines (low tree densities and temperature limited) experienced significantly greater upslope shifts (5 m/decade) compared to other treeline forms, suggesting that both climate warming and treeline demography are important drivers of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R2 = 0.32).

Main conclusions The observed advance of regional treelines suggests that climate warming induces upslope treeline shifts particularly at higher elevations where greater upslope shifts occurred in areas with lower AGDD. Overall, our findings suggest that diffuse treelines at high-elevations are more a of a result of climate warming than other alpine treeline ecotones and thus they can serve as key indicators of ongoing climatic changes. Methods Remote sensing analysis Physical copies of true color high resolution historical aerial imagery (sub-meter resolution) were acquired from the Appalachian Mountain Club (AMC) and the USFS White Mountain National Forest Headquarters. Imagery for the Presidential Range was taken in 1978 and Katahdin imagery was taken in 1991. Hard copy images were scanned and converted to TIFF format at 300 dpi (resulting in 0.5 m resolution images). Spatial analyses of change in treeline positions over time were enabled by acquiring high resolution 2018 false-color near-infrared imagery from the National Agriculture Inventory Program (NAIP 2021). Both sets of imagery were taken during summer months (1:40,000 scale). Using ArcGIS 10.8 (ESRI 2011, Redlands, CA, USA), historic imagery was ortho- and georectified to newer imagery via a spline function along 60 ground control points, and then converted into one orthomosaic image (RMSE < 1m). Exact error was always below 5 m for each individual image.

All areas above treeline were manually digitized based on observed tree cover for both sets of images, and the resulting polygons were converted to raster format at 2 m resolution (all raster pixels within each polygon had a value of 1). We identified forest cover only as areas with overlapping crowns and seen as green reflectance in historic imagery and red reflectance in contemporary false-color near-infrared imagery (no visible bare earth or easily identified alpine vegetation). Isolated tree island edges were also digitized and included as treeline if they were >20 m in diameter in any direction (determined in ArcGIS) and included an individual >2 m in height as validated in the field. Alpine rasters were aligned to and multiplied by Lidar-derived digital elevation models (DEMs; 2 m resolution) acquired from New Hampshire and Maine state GIS repositories in order to determine treeline elevations. A total of 400 random sample points (200 for each range, using the ArcGIS random sample point tool) were placed along the outer boundary of the alpine rasters derived from our contemporary imagery, and for each of them we established a paired point at the nearest location along the alpine raster boundary derived from our historic imagery.

Field surveys Field sampling was carried out in the summer of 2021 to characterize tree demography and demographic variation among different treeline forms identified from the current imagery. A subset of contemporary points from our GIS-based sample point pairs (n = 54, 33 in the Presidential Range, 21 in the Katahdin Range, see above) were selected using a random number generator to serve as sites for establishing belt transects. Each belt transect was 100 m in length and 4 m wide (2 m on either side of transect for a total area of 400 m2) and perpendicular to elevation contours, spanning the ecotone between closed forest interior and open alpine habitat. The start of each transect (the lowest elevation on the transect, set as 0 m) was located 50 m downslope (straight-line distance) of contemporary sample points. The start and end of each belt transect were recorded using a Garmin GPSMAP 64 (Garmin, Olathe, Kansas, USA). Each tree > 0.1 m in height with a stem rooted within the transect was recorded noting species, basal diameter (10 cm from the ground), height, horizontal distance from the transect, and distance along the transect (to estimate stem density of trees). Slope, aspect, elevation, and soil depth to bedrock (using a metal soil probe) were recorded at 20 m intervals along the belt transect centerline (0 m, 20 m, 40 m, 60 m, 80 m, 100 m).

For all belt transects, treeline form was assigned based on visual assessments (based on changes in tree height and density across the ecotone). Additionally, we visited a majority of our other accessible contemporary random sample points (~80%) in order to assign treeline form and ground-truth remote sensed treeline classifications. For all visited sample points we took a new GPS point at the field-verified treeline location (continuous canopy cover and at least one individual >2 m in height) nearest to our random sample points (assigned from our treeline delineation procedure). The new points were compared to the original sample point locations and assessed for accuracy (measuring linear distance between points). Eye-level photos of treelines were taken at all sample points to keep a permanent record of treeline appearance. We stress that because tree height could not be extracted or field validated from our historic imagery, some krummholz individuals (<2 m) may have been present above our treeline delineation using our classification scheme. Out of all 400 sample point pairs across both the Presidentials and Katahdin, 88 were classified as abrupt (22%), 70 as diffuse (17.5%), 84 as island (21%), and 162 as krummholz (40.5%).

Spatial data processing To examine the factors potentially influencing the spatial dynamics of treeline advance, both climatological and topographical variables were extracted for the Presidential Range. We could not conduct a similar analysis for Katahdin given the lack of fine-scale climatological data in that area. Elevation was extracted from 2 m state produced DEMs. Using the Spatial Analyst toolbox in ArcGIS, topographical variables such as slope, aspect, and curvature (measure of convex or concave shape of the terrain ranging between -4 and 4) were extracted from our DEMs. Circular aspect data (measured in degrees, 0-360⁰) were converted to radians and linearized (east and west = 1, north and south = 0).

Before linearization, aspect values were used to calculate degree difference from prevailing wind (DDPW - 290˚) and degree difference from south (DDS - 180˚) variables. DDPW is a proxy for exposure to strong winds that can cause both direct physical damage and damage from icing, as well as a proxy for the potential for snow accumulation. The prevailing wind direction for the Presidential range (290˚) was based on wind measurements from the Mount Washington Observatory. DDS is a proxy for the amount of direct solar radiation (in the northern hemisphere). Average monthly mean, maximum, and minimum temperatures as well as annual accumulated growing degree days (AGDD) were calculated from an array of 34 HOBO dataloggers (Onset Computer Corporation, Bourne, MA, USA) placed at various elevations and adjacent to Appalachian Mountain Club buildings in the White Mountains of New Hampshire. HOBO loggers have recorded hourly air temperature at ground level (0 m height) continuously since 2007. Air temperature means and AGDD were calculated from HOBO logger data; for AGDD calculations we used a base temperature of 4˚C, consistent with other studies examining growth patterns of balsam fir, the dominant species within studied treelines. AGDD was calculated as the accumulated maximum value of growing degree days (GDD) in a year.

Gridded maps (90 m spatial resolution) of mean annual temperature (Tmean, between 2007 and 2020) and AGDD for the Presidential Range region were produced using a cokriging interpolation method. To do this, temperatures and AGDD response variables were first checked for normality using qq-plots. Next, correlation between response variables and potential covariates was assessed; both elevation and aspect were highly correlated with HOBO derived temperature and AGDD. We used normal-score simple cokriging with a stable semi-variogram model to interpolate (prediction map) climate variables over the entire spatial extent of the Presidential Range (RMSE ~ 1 for both Tmean and AGDD). Mean annual precipitation was estimated from 30-year normal PRISM climate data (1991-2020; PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu).

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery.

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery.

This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides 60 centimeter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 4 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP). The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. The target value for the compression ratio is 40:1 for imagery.