The link: * Access the data directory* is available in the section*Dataset description sheets; Additional information*. The forest maps from the first inventory are available at a scale of 1/20,000. They cover almost all of the territory south of the 52nd parallel. Each file covers an area of approximately 250 km2. These digital maps correspond to the black and white paper maps with a dimension of 125 cm X 75 cm that have been digitized and georeferenced. They illustrate forest stands. They were prepared from the photo-interpretation of aerial photos on a scale of 1/15,000. Main components: •outline of forest stands; • type of vegetation (forest species, density, height and stage of development, origin); • disturbances; • nature of the terrain (peatlands, gravel, etc.); • territorial subdivisions; • territorial subdivisions; • hydrography (lakes, rivers, streams, streams, swamps, etc.); • disturbances; • nature of the terrain (peatlands, gravel, etc.); • territorial subdivisions; • hydrography (lakes, rivers, streams, swamps, etc.); • topography (level curves). The units of measurement shown on the maps in the first inventory are those of the English imperial system of measurement.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Forest structure is strongly related to forest ecology and it is a key parameter to understand ecosystem processes and services. Airborne laser scanning (ALS) is becoming an important tool in environmental mapping. It is increasingly common to collect ALS data at high enough point density to recognize individual tree crowns (ITCs) allowing analyses to move beyond classical stand level approaches. In this paper an effective and simple method to map ITCs, and their stem diameter and above ground biomass is presented. ALS data were used to delineate ITCs and to extract ITCs’ height and crown diameter; then using newly developed allometries the ITCs’ DBH and AGB were predicted. Gini coefficient of DBHs was also predicted and mapped aggregating ITCs predictions. Two datasets from spruce dominated temperate forests were considered: one was used to develop the allometric models, while the second was used to validate the methodology. The proposed approach provides accurate predictions of indivi...

This is the evaluation data associated with the project: “Status and Trends of Deciduous Communities in the Bighorn Mountains”. The aim of the study is to assess the current trends of deciduous communities in the Bighorn National Forest in north-central Wyoming. The data here represents phase I of the project, completed in FY2017. The USGS created a synthesis map of coniferous and deciduous communities in the Bighorn Mountains of Wyoming using a species distribution modeling approach developed in the Wyoming Landscape Conservation Initiative (WLCI) (Assal et al. 2015). The modeling framework utilized a number of topographic covariates and temporal remote sensing data from the early, mid and late growing season to capitalize on phenological differences in vegetation types. We used the program RandomForest in the R statistical program to generate probability of occurrence models for deciduous and coniferous vegetation. The binary maps were combined into a synthesis map using the procedure from Assal et al. 2015. In Phase II of this project (to be completed in FY2018 and 2019), the USGS will conduct a preliminary assessment on the baseline condition of riparian deciduous communities. This will be a proof-of-concept study where the USGS will apply a framework used in prior research in upland aspen and sagebrush communities to detect trends in riparian vegetation condition from the mid-1980s to present. Literature Cited Assal et al. 2015: https://doi.org/10.1080/2150704X.2015.1072289

The link: * Access the data directory* is available in the section*Dataset description sheets; Additional information*. The forest maps in the second inventory are available at a scale of 1/20,000. They cover almost all of the territory south of the 52nd parallel. Each file covers an area of approximately 250 km2. These digital cards correspond to the black and white paper cards with a dimension of 125 cm X 75 cm that have been scanned. They illustrate forest stands. They were prepared from the photo-interpretation of aerial photos on a scale of 1/15,000. Main components • outline of forest stands; • sub-groupings of species in all stands; • type of vegetation (forest species, density, height and stage of development, origin); • age class. • disturbances; • nature of the terrain (peatlands, gravel, etc.); • nature of the terrain (peatlands, gravel, etc.); • land types (peatlands, gravels, etc.); • territorial subdivisions; • territorial subdivisions; • hydrography; • hydrography; • transport network and bridges; • topography (level curves). • topography (level curves). • slope classes; • gravel fields, etc.); • nature of the terrain (peatlands, gravel, etc.); • territorial subdivisions; • territorial subdivisions; • hydrography; • hydrography; • transport network and bridges; • topography (level curves). defoliation;**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.

Available water supply varies greatly across the United States depending on topography, climate, elevation and geology. Forested and mountainous locations, such as national forests, tend to receive more precipitation than adjacent non-forested or low-lying areas. However, contributions of national forest lands to regional streamflow volumes is largely unknown. Using outputs from the Variable Infiltration Capacity hydrologic model, we calculated mean annual and mean summer (July and August) streamflow metrics based on total flow and flow from national forest lands for each 1:100,000 scale National Hydrography Dataset stream reach in the contiguous United States. Specifically, this data publication contains twenty-one comma-delimited ASCII text files (for different drainage areas and processing units across the United States) containing 1915-2011 mean annual flow and mean summer flow.Data can be downloaded here: Geodatabase or ShapefileThese files also contain the mean annual and mean summer flows from National Forest System (NFS) lands as well as the portion of total mean annual and summer flow contributed by flow from NFS lands.These data provide insight into 1915-2011 hydrologic regimes and national forest contributions to total water yield. These non-spatial files were then merged and joined to the September 2012 snapshot of the National Hydrography Dataset (NHD), version 2.Note: 'Forest Service lands' are here defined as those lands within the Forest Service administrative boundaries; these include some inholdings and other non-USFS lands enclosed within these boundaries.

Forest resources in Washington and Oregon were surveyed in the 1930s by employees of the USDA Forest Service, Pacific Northwest Forest Experiment Station. As part of this process, forest cover maps were created on paper at an original scale of 1:253,440. Forest and land cover types recorded include classifications such as: agricultural, balsam fir mountain hemlock, cedar-redwood, deforested burns, Douglas-fir, hardwood, juniper, lodgepole pine, non-forest pine mix, ponderosa pine, recent cutover, spruce-hemlock, subalpine and non-commercial, water, etc. An additional subcategory classification is also provided which gives additional insight into tree size classes for conifers or species group for hardwoods. These forest and land cover types are provided as both a shapefile and geopackage for Washington and Oregon combined.The 1928 McSweeney-McNary Forestry Research Act (P.L. 70-466, 45 Stat. 699-702) directed the Secretary of Agriculture to make and keep current a comprehensive inventory and analysis of the nation's forest resources. The decision was made to begin the nationwide survey with the Douglas-fir region and shortly thereafter to expand to the other forested lands of Washington and Oregon. Surveys were conducted between 1930 and 1936. Results of these surveys were reported in many formats including quarter state maps (4 maps per state) as well as many printed reports.The history of this project and copies of some of the early results as well, were published in Harrington (2003) which included a CD with a digital map (an ArcView GIS shapefile) for all of Washington and Oregon.

These data were compiled for the creation of a continuous, transboundary land cover map of Bird Conservation Region 33, Sonoran and Mojave Deserts (BCR 33). Objective(s) of our study were to, 1) develop a machine learning (ML) algorithm trained to classify vegetation land cover using remote sensing spectral data and phenology metrics from 2013-2020, over a large subregion of the Sonoran and Mojave Deserts BCR, 2) Calibrate, validate, and refine the final ML-derived vegetation map using a collection of openly sourced remote sensing and ground-based ancillary data, images, and limited fieldwork, and 3) Harmonize a new transboundary classification system by expanding existing land cover mapping resources from the United States portion of BCR 33 into Mexico. These data represent the final land cover maps produced by the developed random forest model, with additional ancillary labels for urban and agriculture areas. These data were created within a subregion of the Sonoran and Mojave Deserts BCR which spans from Phoenix, Arizona, US to Hermosillo, Sonora, Mexico for the time of April 2013 to December 2020. These data were created by the University of Arizona Vegetation Index and Phenology Lab who collected, processed, and analyzed all of the data and developed the random forest model used to produce the final mapping results. These data can be used to guide land management and conservation decisions within the Sonoran and Mojave Deserts BCR.

This interactive web map shows the Experimental Forests and Ranges of the Northern Research Station. This particular map highlights the location of the Bartlett Experimental Forest on the White Mountain National Forest. This web map is part of a storymap, Bartlett Experimental Forest Through the Years: celebrating 90 years of forest management and research.

For animals with spatially complex behaviours at relatively small scales, the resolution of a global positioning system (GPS) receiver location is often below the resolution needed to correctly map animals’ spatial behaviour. Natural conditions such as canopy cover, canyons or clouds can further degrade GPS receiver reception. Here we present a detailed, high-resolution map of a 4.6 ha Neotropical river island and a 8.3 ha mainland plot with the location of every tree >5 cm DBH and all structures on the forest floor, which are relevant to our study species, the territorial frog Allobates femoralis (Dendrobatidae). The map was derived using distance- and compass-based survey techniques, rooted on dGPS reference points, and incorporates altitudinal information based on a LiDAR survey of the area.

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.

Web map that is used to distribute the Forest Series maps. Forest Series maps are designed as a general basemap for forest service and public use. The Forest maps are designed to show one full national forest on a single sheet of paper. This rule is adjusted for some forests as they would become unprintable by most users. In these cases, a forest was split into two or more maps as necessary with a forest name and cardinal direction to identify which area of the forest. The maps have transportation, land ownership, recreation, hydro, and other base layers for use by the Forest Service and public.

The WMS shows the location characteristics for forest sites in NRW in scale 1: 50,000 based on projection data according to climate scenario RCP8.5 for the period 2017-2100. The total water balance and the natural nutrient supply of the sites are presented. Derived from the total water balance, the drought sensitivity of the forest sites is shown. In further layers, the location properties are aggregated into site types according to the forest construction concept NRW as well as the location suitability of 16 important forest tree species according to the criteria of the forest construction concept NRW. The information page presents the properties for each area and provides links to the information of the forest construction concept NRW. This is an evaluation of the soil map of NRW 1: 50,000 in conjunction with climate projection data for NRW for the scenario RCP8.5 of the German Weather Service (2070-2100, DWD) and relief data (DGM10, Geobasis NRW). All soil areas are treated equally, regardless of their current use as forest sites or potential forest sites.

We summarized annual remote sensing land cover classifications from the U.S. Geological Survey Land Cover Monitoring, Assessment, and Projection (LCMAP) annual time series to characterize forest change across the conterminous United States (CONUS) for the years 1985-2020. The raster output includes a map where each pixel is given an integer value based on the number of years in which it was classified as forest across the annual LCMAP time series. Values of 36 indicate the pixel was classified as forest across all years while a value of 0 indicates forests (tree cover) was never detected during the time series.

Available water supply varies greatly across the United States depending on topography, climate, elevation and geology. Forested and mountainous locations, such as national forests, tend to receive more precipitation than adjacent non-forested or low-lying areas. However, contributions of national forest lands to regional streamflow volumes is largely unknown. Using outputs from the Variable Infiltration Capacity hydrologic model, we calculated mean annual and mean summer (July and August) streamflow metrics based on total flow and flow from national forest lands for each 1:100,000 scale National Hydrography Dataset stream reach in the contiguous United States. Specifically, this data publication contains twenty-one comma-delimited ASCII text files (for different drainage areas and processing units across the United States) containing 1915-2011 mean annual flow and mean summer flow.Data can be downloaded here: Geodatabase or ShapefileThese files also contain the mean annual and mean summer flows from National Forest System (NFS) lands as well as the portion of total mean annual and summer flow contributed by flow from NFS lands.These data provide insight into 1915-2011 hydrologic regimes and national forest contributions to total water yield. These non-spatial files were then merged and joined to the September 2012 snapshot of the National Hydrography Dataset (NHD), version 2.Note: 'Forest Service lands' are here defined as those lands within the Forest Service administrative boundaries; these include some inholdings and other non-USFS lands enclosed within these boundaries.

Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped contiguously over large areas. Nearest-neighbor imputation maps have high potential for use as the initial condition within FLMs, but the tendency for field plots to be imputed over large geographical distances results in species frequently mapped outside of their home ranges, which is problematic. We developed an approach for evaluating and selecting field plots for imputation based on their similarity in feature-space, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM. We applied this approach to map 13m ha of forest throughout the six New England states (Rhode Island, Connecticut, Massachusetts, New Hampshire, Vermont, and Maine). The map itself is a .img raster file of FIA plot CN numbers. To access FIA data from this map, one has to link the mapcodes in this map to FIA data supplied by USDA FIA database (https://apps.fs.usda.gov/fia/datamart/datamart.html). Due to plot confidentiality and integrity concerns, pixels containing FIA plots were always assigned to some other plot than the actual one found there.

The WMS shows the location characteristics for forest sites in NRW in scale 1: 5,000 based on projection data according to climate scenario RCP8.5 for the period 2071-2100. The total water balance and the natural nutrient supply of the sites are presented. Derived from the total water balance, the drought sensitivity of the forest sites is shown. In further layers, the location properties are aggregated into site types according to the forest construction concept NRW as well as the location suitability of 16 important forest tree species according to the criteria of the forest construction concept NRW. The information page presents the properties for each area and provides links to the information of the forest construction concept NRW. This is an evaluation of the soil map of NRW 1: 5,000 in conjunction with climate projection data for NRW for the scenario RCP8.5 of the German Weather Service (2071-2100, DWD) and relief data (DGM10, Geobasis NRW). All floor areas are presented, of which a digital soil map for forest site exploration — BK5 F — is available at the time of deployment.

This dataset provides annual 30-m forest cover maps and annual 30-m evergreen forest cover maps in the Contiguous United States (CONUS) from 2015 to 2017. This dataset was generated based on the PALSAR-2 and Landsat images. The forest pixels are valued as 1. In the evergreen forest maps, the value of 1 was used to present evergreen forest pixels. The maps are stored in a GeoTIFF formatted unsigned integer. Each map has one band using a spatial reference of GCS-WGS_1984.

Abstract Among the various characteristics of the Brazilian territory, one is foremost: the country has the second largest forest reserve on the planet, accounting for approximately 10% of the total recorded global forest formations. In this scenario, seasonally dry tropical forests (SDTF) are the second smallest forest type in Brazil, located predominantly in non-forested biomes, such as the Cerrado and Caatinga. Consequently, correct identification is fundamental to their conservation, which is hampered as SDTF areas are generally classified as other types of vegetation. Therefore, this research aimed to monitor the Land Use and Coverage in 2007 and 2016 in the continuous strip from the North of Minas Gerais to the South of Piauí, to diagnose the current situation of Brazilian deciduous forests and verify the chief agents that affect its deforestation and regeneration. Our findings were that the significant increase in cultivated areas and the spatial mobility of pastures contributed decisively to the changes presented by plant formations. However, these drivers played different roles in the losses/gains. In particular, it was concluded that the changes occurring to deciduous forests are particularly explained by pastured areas. The other vegetation types were equally impacted by this class, but with a more incisive participation of cultivation.