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.

The Division of Forestry Geographic Information Systems home page provides information on GIS information, Spatial Data, GIS Web Applications depicting current wild land fire information and forest resource information for the entire state of Alaska.

Polygon boundaries differentiating forest cover types located on State Forest lands.Service is updated as needed.For additional information see https://www.dec.ny.gov/lands/4972.html

The FS National Forests Dataset (US Forest Service Proclaimed Forests) is a depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.Metadata and Downloads - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Original+Proclaimed+National+Forests

October 2009

The National Forest Inventory (NFI) woodland map covers all forest and woodland area over 0.5 hectare with a minimum of 20% canopy cover, or the potential to achieve it, and a minimum width of 20 metres. This includes areas of new planting, clearfell, windblow and restock. The woodland map excludes all 'tarmac' roads and active railways, and forest roads, rivers and powerlines where the gap in the woodland is greater than 20 meters wide.All woodland (both urban and rural), regardless of ownership, is 0.5 hectare or greater in extent, with the exception of Assumed woodland or Low density areas that can be 0.1 hectare or greater in extent. Also, in the case of woodland areas that cross the countries borders, the minimum size restriction does not apply if the overall area complies with the minimum size.Woodland less than 0.5 hectare in extent, with the expectation of the areas above, will not be described within the dataset but will be included in a separate sample survey of small woodland and tree features.The woodland map is updated on an annual basis and the changes in the woodland boundaries use the Ordnance Survey MasterMap® (OSMM) as a reference where appropriated.The changes in the canopy cover have been identified on:Sentinel 2 imagery taken during spring/summer 2022 or colour aerial orthophotographic imagery available at the time of the assessment;New planting information for the financial year 2021/2022, from grant schemes and the sub-compartment database covering the estate of Forestry England, Forestry and Land Scotland and Natural Resources Wales;Woodland areas, greater than 0.5 hectares, are classified as an interpreted forest type (IFT) from aerial photography and satellite imagery. Non-woodland areas, open areas greater than 0.5 hectare completely surrounded by woodland are described according to open area types.IFT categories are Conifer, Broadleaved, Mixed mainly conifer, Mixed mainly broadleaved, Coppice, Coppice with standards, Shrub, Young trees, Felled, Ground prep, Cloud \ shadow, Uncertain, Low density, Assumed woodland, Failed, Windblow.IOA categories are Open water, Grassland, Agricultural land, Urban, Road, River, Powerline, Quarry, Bare area, Windfarm, Other vegetation.For further information regarding the interpreted forest types (IFT) and the interpreted open areas (IOA) please see NFI description of attributes available on www.forestresearch.gov.uk

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

• indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

• NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.

GIS data is available on the Forest’s FTP site in the form of “shape files” or layers and is available free for downloading. To utilize these data layers you will need a program that uses the Geographic Information System (GIS) such as ESRI’s ArcMap, ArcView or the free map reading program ArcGIS Explorer. ArcGIS Explorer has tools that let you zoom in/out, print the map, and query data. It also has map tips to identify features, and a help menu. ArcGIS Explorer is available as a free download from the ESRI website. Included is a list of GIS data files available for the Shawnee National Forest. These GIS data files are updated on a continuing basis. It should be noted that this data may have been developed from sources of differing accuracy, accurate only at certain scales, based on modeling or interpretation, or incomplete while being created or revised. Overall accuracy, completeness and timeliness may vary. The following geospatial information/data was prepared by the Shawnee National Forests (US Forest Service). The Forest Service reserves the right to correct, update, modify or replace GIS data without notification. Resources in this dataset:Resource Title: Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/main/shawnee/landmanagement/gis Information about the geospatial data and a ftp link to download Forest GIS Data Shapefiles.

The global Forestry Management Services market is booming, projected to reach $85 billion by 2033 at a 7% CAGR. Driven by sustainability, regulations, and technological advancements, this report analyzes market trends, key players (CREAF, Amata, Coillte, etc.), and regional growth across North America, Europe, Asia-Pacific, and more. Discover insights into forestry resource, protection, and economic management.

Selected GIS data that encompass Coconino National Forest are available for download from this page. A link to the FGDC compliant metadata is provided for each dataset. All data are in zipped shapefile format, in the following projection: Universal Transverse Mercator Zone: 12 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Resources in this dataset:Resource Title: Coconino National Forest GIS Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=stelprdb5209303

This data support the paper "A systematic review on the integration of remote sensing and GIS to forest and grassland ecosystem health attributes, indicators, and measures " by Irini Soubry, Thuy Doan, Thuan Chu and Xulin Guo 2021 in the journal of "Remote Sensing" by MDPI. It includes the "Search_Effort.csv" list with the keywords and number of studies selected for further examination, the "Potential_Studies.csv" with the post-filtering of suitability and notes related to each study, the "Metadata.csv" with the information collected for each metadata variable per study, and the "ExtractedData.csv" with the information collected for each extracted dta variable per study. More information about the data collection and procedures can be found in the respective manuscript.

Matrix sites are large contiguous areas whose size and natural condition allow for the maintenance of ecological processes, viable occurrences of matrix forest communities, embedded large and small patch communities, and embedded species populations. The goal of the matrix forest selection was to identify viable examples of the dominant forest types that, if protected and allowed to regain their natural condition, would serve as critical source areas for all species requiring interior forest conditions or associated with the dominant forest types.

Preparation of GIS Maps for land use and Forest cover assessment of PFI Information Year 2021

ABSTRACT This paper presents a GIS methodological approach for mapping forest landscape multifunctionality. The aims of the present study were: (1) to integrate and prioritize production and protection functions by multicriteria spatial analysis using the Analytic Hierarchy Process (AHP); and (2) to produce a multifunctionality map (e.g., production, protection, conservation and recreation) for a forest management unit. For this, a study area in inner Portugal occupied by forest and with an important protection area was selected. Based on maps for functions identified in the study area, it was possible to improve the scenic value and the biodiversity of the landscape to mitigate fire hazard and to diversify goods and services. The developed methodology is a key tool for producing maps for decision making support in integrated landscape planning and forest management.

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html

Selected GIS data that encompass Carson National Forest are available for download from this page. A link to the FGDC compliant metadata is provided for each dataset. All data are in zipped shapefile format, in the following projection: Universal Transverse Mercator Zone: 13 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Resources in this dataset:Resource Title: Carson National Forest GIS Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=stelprdb5202766

Trails within the Forest Preserve District of Cook County. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS, is required.

This polygon feature class contains the boundaries of 86 of 87 experimental forests, ranges and watersheds, including cooperating experimental areas. Experimental Forest and Range Areas Metadata

This dataset contains forest features in Frederick County. Existng forest outlines that have been changed by cutting, burning, or other means of removal were reflected in the deliverable. Any new forest features were captured at the outer edges of the tree trunks. Forest is defined as the edge of a tree mass of 10,000 square feet or greater, not less than 35' in length and follows along the outer edge of the tree trunks.

DefinitionThis indicator identifies forest condition regarding the availability and risk of mortality of forested stands within the Midwest Landscape. It prioritizes areas based on locations of forested stands and risks from major forest insects and diseases. Pixels can take the following values: 0 – Not a forest pixel1 – Forest pixels at risk for mortality loss 2 – Previously considered at risk but likely no longer at risk3 – Forest pixels not modeled as at riskSelectionThis indicator was chosen as a targetable, important feature of the MLI goals that will be used to track conditions over time and prioritize areas for conservation. Indicators were defined through elicitation and prioritization exercises with federal and state participants. Criteria for the indicators includes 1) actionable, 2) measurable, 3) relevant to multiple groups across the region, and/or 4) representative of other social and/or environmental values.Input Data & Mapping StepsThis indicator originates from the U.S. Forest Service National Insect and Disease Risk Map and the National Land Cover Database (NLCD). To create this layer, MLI partners, members, and staff completed the following mapping steps: projected all input data to NAD83 (2011) UTM Zone 15N, resampled the Forest layer to a 30m raster, and reclassed the raster into the following 3 classes: 1 – Pixels at hazard for mortality losses exceeding 25% of their 2012 basal area over the 2013-2027 time frame, 2 – Pixels considered at risk in the NIDRM_2012 assessment, but that are likely no longer at risk, 3 – Treed areas, but not modeled as “At Risk”. Due to the low resolution of the Forest data, many non-forest pixels are incorrectly classified as treed stands. To correct the issue, pixels classified by NLCD as non-forest were removed from the Forest data. Finally, we removed highly altered areas from this layer using our Highly Altered Areas mask. For full mapping details, please refer to the Midwest Conservation Blueprint 2024 Development Process. For a complete download of all Blueprint input and output data, visit the Midwest Conservation Blueprint 2024 Data Download.