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.

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.

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

October 2009

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.

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.

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

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.

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

The forestry consulting services market is experiencing robust growth, driven by increasing demand for sustainable forest management practices and the rising need for efficient resource utilization. The market size in 2025 is estimated at $2.5 billion, reflecting a Compound Annual Growth Rate (CAGR) of approximately 7% from 2019 to 2024. This growth is fueled by several key factors: the expanding global population leading to increased timber demand, stricter government regulations promoting environmental conservation, and the growing adoption of advanced technologies like GIS and remote sensing in forestry operations. Furthermore, the increasing awareness of climate change and its impact on forests is driving demand for expert advice on carbon sequestration, forest health, and mitigation strategies. Key players like Atlas Information Management, Forest Resource Consultants, Inc., and others are actively shaping the market through innovative solutions and expanding service offerings. The market is segmented based on service type (e.g., forest inventory, sustainable forest management planning, environmental impact assessments), client type (e.g., government agencies, private landowners, timber companies), and geographic region. The forecast period from 2025 to 2033 projects continued expansion, with the market expected to reach approximately $4.2 billion by 2033. However, challenges remain, including fluctuations in timber prices, economic downturns impacting investment in forestry, and the scarcity of skilled professionals in the field. Despite these restraints, the long-term outlook remains positive, driven by the ongoing need for responsible forest management and the increasing recognition of forests' crucial role in mitigating climate change and biodiversity loss. The market will likely see consolidation among consulting firms, partnerships with technology providers, and a greater focus on data-driven solutions to optimize forest management practices. This will drive further innovation and specialization within the sector, enhancing the overall quality and effectiveness of forestry consulting services globally.

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.

The Forestry Software Market is booming, projected to reach $2344.22 million by 2033 with a 6.51% CAGR. Discover key trends, leading companies, and regional insights in this comprehensive market analysis. Explore cloud-based solutions, GIS integration, and sustainable forestry software driving this growth.

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

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

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.

Discover the booming market for satellite forest monitoring! Learn about the latest trends, technological advancements, and key players shaping this $2.5 billion industry projected to reach $7 billion by 2033. Explore regional insights and the impact of L-band and X-band radar satellites.

This is the final export from the Forest Inventory Module (FIM) system, retired on 6/29/2022.

This layer is a digital inventory of individual forest stands. The data is collected by MNDNR Foresters in each MNDNR Forestry Administrative Area, and is updated on a continuous basis, as needed. Most stands are field checked and their characteristics described. Follows internal MNDNR classification schema. This data originates from the MNDNR's "Forest Inventory Management" system (also referred to as FIM).

This resource was replaced by MNDNR Forest Inventory: https://gisdata.mn.gov/dataset/biota-dnr-forest-inventory

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

This layer file consists of three related datasets:
- Statutory boundary polygons of State Forests
- Lands managed by the Division of Forestry within the statutory boundaries, known as Management Units
- Lands managed by the Division of Forestry outside of the statutory boundaries, known as Other Forestry Lands

State Forests - Statutory Boundaries:
This theme shows the boundaries of those areas of Minnesota that have been legislatively designated as State Forests ( http://www.dnr.state.mn.us/state_forests/index.html )

Minnesota's 58 state forests were established to produce timber and other forest crops, provide outdoor recreation, protect watersheds, and perpetuate rare and distinctive species of native flora and fauna. The mapped boundaries are based on legislative/statutory language and are described in broad terms based on legal descriptions. Private or other ownerships included inside a State Forest boundary are typically NOT identified in legislative language and subsequently are NOT mapped in this layer. It is important to note that these data do not represent public ownership. State Forest boundaries often include private land and should not be used to determine ownership. Ownership information can be found in State Surface Interests Administered by MNDNR or by Counties ( https://gisdata.mn.gov/dataset/plan-stateland-dnrcounty ) and the GAP Stewardship 2008 layer ( http://gisdata.mn.gov/dataset/plan-gap-stewardship-2008 ).

Data has been updated during 2009 by the MNDNR Forest Resource Assessment office.

State Forests - Management Units
This theme shows the land owned and managed by the Division of Forestry within the Statutory Boundaries. The shapes were derived mostly from county parcel data, where available, and from plat maps and other ownership resources. This data presents an approximate location of the land ownership and is intended for cartographic purposes only. It is not survey quality and should never be used to resolve land ownership disputes.

State Forests - Other Forest Lands
This theme shows State Forest lands outside of the State Forest Statutory Boundaries. It was derived from MNDNR's Land Records System PLS40 data layer. Sub-40 shapes are not represented. Partial PLS40 ownership is represented as a whole PLS40. This data is not survey quality and should never be used to resolve land ownership disputes.