This dataset contains the White Mountain National Forest Boundary. The boundary was extracted from the National Forest boundaries coverage for the lower 48 states, including Puerto Rico developed by the USDA Forest Service - Geospatial Service and Technology Center. The coverage was projected from decimal degrees to UTM zone 19. This dataset includes administrative unit boundaries, derived primarily from the GSTC SOC data system, comprised of Cartographic Feature Files (CFFs), using ESRI Spatial Data Engine (SDE) and an Oracle database. The data that was available in SOC was extracted on November 10, 1999. Some of the data that had been entered into SOC was outdated, and some national forest boundaries had never been entered for a variety of reasons. The USDA Forest Service, Geospatial Service and Technology Center has edited this data in places where it was questionable or missing, to match the National Forest Inventoried Roadless Area data submitted for the President's Roadless Area Initiative. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.

The present study produced a new reference forest map for China from three land cover products for 2020. These datasets included the World Cover 2020 (ESA-2020), ESRI 2020 Land Cover (ESRI-2020), and the GlobeLand30 version of V2020 (GLC-2020). Within the production of the reference forest map for China, a pixel was assumed to represent forest when the same pixel among multiple land cover products showed forest properties, thereby decreasing the uncertainties of classification of forests at a large scale.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. The contour lines were manually digitized from the map. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N

This data package contains 3 GIS layers showing generalized forest types across New England as delineated in older forestry publications. These were digitized so that they can be used to illustrate broad vegetation patterns across the region in modern publications. These GIS layers include maps drawn by Hawley and Hawes (1912), RT Fisher (1933), and Westveld and the Committee on Silviculture, New England Section, Society of American Foresters (1956).

This dataset contains boundaries of the New Mexico Forestry Districts, plus the names of the district offices. It is in a vector digital structure digitized from a U.S. Geological Survey (USGS) 1:500,000 scale mylar of the state of New Mexico. The source software used was ARC/INFO 6.1.1 and the conversion software was ARC/INFO 7.0.3. The size of the file is 0.03, compressed.

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.

Description

This map represents a classification of forest types based on the influence of the edge effect (distance to the forest edge) and elevation effect (temperature and area) on tree community richness.

The edge effect influences tree diversity through an environmental aridity filter. In New Caledonia, the maximum temperature recorded at the forest edge is 41°C in February, while it never exceeds 24°C beyond 100 meters from the edge. This temperature difference induces a selection for species that tolerate the most arid conditions, leading to a reduction in the biological richness of tree communities (Ibanez et al., 2017; Birnbaum et al., 2022; Blanchard et al., 2023).

Altitude also affects tree diversity due to temperature variation and available area (Ibanez et al., 2014; Birnbaum et al., 2015; Pouteau et al., 2015; Ibanez et al., 2016; Ibanez et al., 2018). In New Caledonia, observed tree community richness ranges from 35 to 121 species per hectare within the NC-PIPPN network, peaking at mid-altitude ranges (refer to figure 'amap_elevation_richness.png'). Potential richness was assessed using the S-SDM model, with the 80th percentile used as a threshold to distinguish low and high potential richness across three elevation classes: [0 - 400m[, [400 - 900m[, and [900 - 1628m[.

The classification of forest types combines distance from the forest edge and potential richness by elevation into three major categories, as illustrated in the figure 'amap_forest_types_nc.png':

Edge Forest: Parts of the forest located less than 100 meters from the forest edge.

Mature Forest: Parts of the forest located beyond 100 meters from the edge with a lower potential richness of tree communities.

Core Forest: Parts of the forest located more than 300 meters from the edge with a higher potential richness of tree communities.

Content

The map is computed from the Forest Map of New Caledonia (v2024) and the Potential Tree Species Richness in the Forests of New Caledonia (v2024). This dataset was produced, analyzed, and verified using a combination of open-source software, including QGIS, PostgreSQL, PostGIS, Python, R, and the GDAL library, all running on Linux.

amap_forest_types_nc.png is a picture illustrating the forest type classification

amap_forest_types_nc.zip is a compressed file contains the six essential files for an ESRI-format GIS system, using the WGS84 international coordinate system, and can be uploaded to a spatial database such as PostgreSQL/PostGIS. Each row of the attribute table represents a forest type (a multi-polygon) with associated fields :

Field Type Description

type TEXT One of the three forest types ("Edge Forest", "Mature Forest", "Core forest")

area_ha NUMERIC (2 DECIMALS) Area of the multi-polygon in hectares

description TEXT Description of the three forest types

geom GEOMETRY (MULTIPOLYGON, 4326)) Geometry with datum EPSG: 4326 (WGS 84 – World Geodetic System 1984)

Limitations

We caution users that the distinction between the three classes is based on an ecological interpretation and does not reflect directly perceptible breaks in the forest. The ecological transition from the edge to the core of the forest follows multiple gradient modulated by environmental conditions.

Moreover, this classification is based on local observations and measurements, which are complex to generalize and extrapolate across a territory as environmentally diverse as New Caledonia. Nevertheless, it allows us to address the impact of fragmentation at the scale of New Caledonia.

This map displays forest treatments conducted by New Mexico Forestry Division and its partners including New Mexico Game & Fish. Forest treatments improve forest health, improve wildlife habitatsand reduce catastrophic fire risk in the wildland-urban interface (WUI)by increasing the defensible space around homes. Forest treatments are presented by state fiscal year (July 1 -June 30) and span from 2009 -2018.Data is compiled from a variety of sources, including N.M.State Forestry Division, N.M.Game & Fish, the N.M.Resource Geographic Information System Program Data Clearinghouse (RGIS), the U.S.GeologicalSurvey, and Bureau of Land Management.This mapping application is compatible on Chrome, Firefox,Internet Explorer(v11)and Safari, as well as mobile devices.

This document is the result of the cadastral consolidation during the fourth forest resource survey. It is mainly based on the second revised forest management area map completed in 2009 and the cadastral data applied to the Ministry of the Interior in 2014. The cadastral boundaries managed by this authority were reorganized to align with the forest management area boundaries. This mapping production task was carried out by the various forest management offices within their respective jurisdictions to adjust and revise the forest management area boundaries. The task was completed from early 2014 to early 2015 and then handed over to the Agricultural Aviation Engineering Division for map verification and consolidation, with the forest compartment boundaries and coding based on the second round revised forest compartment map. As a result, the new map's forest compartment boundaries are consistent with those of the second round revised version, with only some adjustments made to areas adjoining forest land outside the area to align with the cadastral and forest management area boundaries.

Background 1996-2000: The PNG Forestry Authority (PNGFA) with support from CSIRO developed the Forest Inventory Mapping (FIM) System to specifically map forest and vegetation types using forest mapping units or boundaries (or FMU) derived from aerial photography in 1973-4 at 1:100,000 scale and other relevant map overlays.

🇫🇷 프랑스

This web map shows the Experimental Forests and Ranges of the Northern Research Station, with a focus on the Bartlett Experimental Forest. This interactive web map was created to accompany the Bartlett Experimental Forest Storymap - celebrating 90 years of research and management. This storymap uses a timeline format to highlight the rich history of the research and management conducted on the Bartlett Experimental Forest in Bartlett, New Hampshire.

The bedrock geology of the Hubbard Brook Experimental Forest, Grafton County, New Hampshire is described in this report of new field investigation. The database includes contacts of bedrock geologic units, faults, folds, and other structural geologic information, as well as the base maps on which the mapped geological features are registered. This report supersedes Barton (1997).

1.1.1 Forest Service Mapping Series 6 (FSMS6)

The 23 completed maps provide the distribution of indigenous forest vegetation for all of the North Island and the bulk of the South Island at a scale of 1:250,000. These maps were primarily compiled by Mr John Nicholls with some of the South Island maps compiled by Mr Dudley Franklin. Black and white aerial photographs, dating from 1948 to 1955 and at a scale of 15 chains per inch, supplemented by extensive ground truthing and some 16,000 National Forest Survey and Ecosurvey plots, were used to determine forest class boundaries. These were transferred to 1:63360 topographic maps. The maps were field checked and then copied for production by FRI graphics staff (Herbert 1997, pers. comm.).

Most maps were completed by the NZ Forest Service, with a small number being finished by the Ministry of Forestry and then by Landcare Research Ltd. Appendix 1 gives the list of maps digitised. The date of the photographs that were used to compile each map is not known exactly.

1.1.2 Forest Service Mapping Series 15 (FSMS15)

There are two FSMS15 comprising 1:1,000,000 maps of the North Island, and South Island (including Stewart Island). These were compiled by NZFS Conservancy and Head Office staff for the 1974 Forestry Development Conference. Forest boundaries for the 1:1,000,000 FSMS15 maps are significantly less accurate than those for the 1:250,000 FSMS6 maps (Herbert and Nicholls, 1997, pers. comm.). Data sources included existing FSMS6 maps (with 18 classes coalesced into eight super classes), local published and unpublished maps and local knowledge for areas not cover by the FSMS6. The Te Anau, Hauroko and Mataura FSMS6 series maps were substituted for by the South Island FSMS15 map.

1.1.3 Forest Service Type Map Series No. 2 (FSTM2)

These are a collection of detailed forest class maps at 1:63360 scale. Coverage is confined to parts of the central North Island.

### 1.1.4 Vegetation of Stewart Island

Mr Hugh Wilson (Wilson, 1987) developed a detailed map of the vegetation of Steward Island. Wilson’s Podocarp/hardwood forest, and rata-kamahi hardwood forest polygons (Types A 1-2, B3) were digitised.

1.2 Forest Class Description

There are eighteen forest classes described in the FSMS6 map series. These are described in Table 1. The source is Nicholls and Herbert (1995). FSMS15 has eight super classes and these are defined in Table 2.

*Table 1: Forest classes, codes and IPCC class

        (Dbase)

*Class Code IPCC Class

*Kauri A C

*Kauri -Softwoods-Hardwoods B M

*Kauri -Softwoods-Hardwoods-Beeches C M

*Softwoods L C

*Rimu-Matai-Hardwoods M M

*Rimu-Taraire - Tawa E M

*Rimu-Tawa D M

*Rimu-General Hardwoods F M

*Lowland Steepland and Highland Softwoods - Hardwoods G M

*Rimu-Tawa-Beeches H M

*Rimu - General Hardwoods - Beeches I M

*Highland Softwoods-Beeches J M

*Taraire-Tawa S B

*Tawa N B

*General Hardwoods P B

*Tawa Beeches O B

*General Hardwoods - Beeches T B

*Beeches K B

IPCC Class Definitions: C: Conifer, B: Broadleaf, M: Mixed.

Table 2: FSMS15 forest classes

      Dbase

Class code / FSMS6Classes Description IPCC Class

Kauri - Podocarp - Hardwood /A, B, C All forest containing kauri, including minor area of pure kauri and local occurrence of beech M

Podocarp L/ L Forest of abundant podocarps C

Lowland Podocarp - Hardwood 1/ D, E, F, M, pt. G Virgin or lightly logged podocarp - hardwood forest below the altitudinal limit of rimu M

Lowland Hardwood 2/ N, S, pt. P Residual and second growth forest below the altitudinal limit of rimu and minor areas of natural pure hardwood forest. B

Upland Podocarp - Hardwood 3/ Pts G, P Virgin or lightly logged podocarp - hardwood above the altitudinal limit of rimu and minor areas of natural pure hardwood forest.
M

Podocarp - Hardwood - Beech 4/ H, I Virgin or lightly logged forest of mixed podocarp - hardwood and beech below the altitudinal limit of rimu M

Hardwood - Beech 5/ O, T Residual or second growth forest and minor areas of natural pure hardwood - beech. B

Beech 6/ J, K Virgin and lightly logged or second-growth forests predominantly composed of beech B

Wilson Stewart Island 7/ Podocarp/hardwood forest, and rata-kamahi hardwood forest. M

2. METHODS

2.1 Digitising and Topology Generation

The maps were digitised by staff at the Forest Research Institute under standards listed in Appendix 2, using the Terrasoft Geographic Information System. The linear features that made up each forest class polygon are shared between two feature classes one, called NZFS6 which contains the national coverage, and the other based on the respective map sheet number. This allows themes to be developed for a national view and also for the individual map sheets.

The line work is topologically correct with no over-, or under- shoots.

Each polygon has a nationally unique identifier and which is linked to a dbase table containing a code letter which describes the forest vegetation class.

These maps were digitised for the purpose of providing indigenous forest vegetation cover for usage at a national scale. There has been no formal checking of the accuracy of the digitised linework. Any errors are considered to be insignificant for determining a 1990 indigenous forest vegetation baseline database. Each polygon was checked to confirm correct tagging. During that process any significant linear differences were noted and corrected.

2.2 Problems

2.2.1 Incorrect map details

In several places errors on the maps were found. Either the FSTM2 maps were consulted for greater detail where coverage existed or Mr John Nicholls was, personally, consulted and the error corrected.

2.2.2 Map source quality

Most FSMS6 maps where unused, unfolded sheets with only sheet 12 being an unused folded map. The FSMS15 South Island map was a well used map with significant fold lines. This map also had other printed information which made precise measurement of some forest class boundaries difficult.

Standards

This document defines the standards used for digitising the forest class maps (NZFS Map Series 6, FSMS15 and Wilson, 1987).

Source

The source of the FSMS6 data is the 1:125,000 flat map sheets, the FSMS15 maps and the Vegetation map contained in Wilson (1987).

Digitising

The following digitising standards were used.

A minimum of five points for registration should be selected from a rectangular range encapsulating the immediate digitising area. These points then should he entered into Convert and both the input and the resultant NZMG coordinates checked before the map is registered. The registration error should be (in Terrasoft) 0.00%. The media should be anchored firmly to the digitiser. The RMU laboratory should be used with the air conditioning turn on. Registration should occur at least twice a day, but occur more frequently if the humidity changes. All lines and polygon which represent a forest type needs to be captured irrespective of size. All intersections should have a node digitised. The two feature classes are NZFS6 and NZFS6_

Output

Shape must be identical

Theme creation

A Theme will be created for each map sheet. The national NZFS6 theme will be created by including the previously digitised map sheets and the FSMS15 and Wilson’s map. Polygon tags are to be corrected between the map sheets to make them all unique. All dangles and overlaps, and bad polygons are to be corrected.

Tagging

All polygons are to be tagged with a code representing the forest type. All sliver polygons are to be removed.

Checking

A plot should be created at the original scale and overlayed over the original map. Each polygon is checked to confirm correct tagging.

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.

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Process Description:A number of data layers are used to develop a ground dataset of habitat reference data, which are then used to inform a machine-learning model and spatial analyses to generate a map of the likely locations and distributions of habitats across England. The main source data layers underpinning the spatial framework and models are Sentinel-2 and Sentinel-1 satellite data from the ESA Copernicus programme, Lidar from the EA's national Lidar Programme and collected data through the project's national survey programme. Additional datasets informing the approach as detailed below and outlined in the accompanying technical user guide.Datasets used:OS MasterMap® Topography Layer; Geology aka BGS Bedrock Mapping 1:50k; Long Term Monitoring Network; Uplands Inventory; Coastal Dune Geomatics Mapping Ground Truthing; Crop Map of England (RPA) CROME; Lowland Heathland Survey; National Grassland Survey; National Plant Monitoring Scheme; NE field Unit Surveys; Northumberland Border Mires Survey; Sentinel-2 multispectral imagery; Sentinel-1 backscatter imagery; Sentinel-1 single look complex (SLC) imagery; National forest inventory (NFI); Cranfield NATMAP; Agri-Environment HLS Monitoring; Living England desktop validation; Priority Habitat Inventory; Space2 Eye Lens: Ainsdale NNR, State of the Bog Bowland Survey, State of the Bog Dark Peak Condition Survey, State of the Bog Manchester Metropolitan University (MMU) Mountain Hare Habitat Survey Dark Peak, State of the Bog; Moors for the Future Dark Peak Survey; West Pennines Designation NVC Survey; Wetland Annex 1 inventory; Soils-BGS Soil Parent Material; Met Office HadUK gridded climate product; Saltmarsh Extent and Zonation; EA LiDAR DSM & DTM; New Forest Mires Wetland Survey; New Forest Mires Wetland Survey; West Cumbria Mires Survey; England Peat Map Vegetation Surveys; NE protected sites monitoring; ERA5; OS Open Built-up Areas; OS Boundaries dataset; EA IHM (Integrated height model) DTM; OS VectorMap District; EA Coastal Flood Boundary: Extreme Sea Levels; AIMS Spatial Sea Defences; LIDAR Sand Dunes 2022; EA Coastal saltmarsh species surveys; Aerial Photography GB (APGB); NASA SRT (Shuttle Radar Topography Mission) M30; Provisional Agricultural Land Classification; Renewable Energy Planning Database (REPD); Open Street Map 2024.Attribute descriptions: Column Heading Full Name Format Description

SegID SegID Character (100) Unique Living England segment identifier. Format is LEZZZZ_BGZXX_YYYYYYY where Z = release year (2223 for this version), X = BGZ and Y = Unique 7-digit number

Prmry_H Primary_Habitat Date Primary Living England Habitat

Relblty Reliability
Character (12) Reliability Metric Score

Mdl_Hbs Model_Habs Interger List of likely habitats output by the Random Forest model.

Mdl_Prb Model_Probs Double (6,2) List of probabilities for habitats listed in ‘Model_Habs’, calculated by the Random Forest model.

Mixd_Sg Mixed_Segment Character (50) Indication of the likelihood a segment contains a mixture of dominant habitats. Either Unlikely or Probable.

Source Source

Description of how the habitat classification was derived. Options are: Random Forest; Vector OSMM Urban; Vector Classified OS Water; Vector EA saltmarsh; LE saltmarsh & QA; Vector RPA Crome, ALC grades 1-4; Vector LE Bare Ground Analysis; LE QA Adjusted

SorcRsn Source_Reason

Reasoning for habitat class adjustment if ‘Source’ equals ‘LE QA Adjusted’

Shap_Ar Shape_Area

Segment area (m2) Full metadata can be viewed on data.gov.uk.

Light detection and ranging (LiDAR) has become a common tool for generating remotely sensed forest inventories. However, regional modeling of forest attributes using LiDAR has remained challenging due to varying parameters between LiDAR datasets, such as pulse density. Here we develop a regional model using a three dimensional convolutional neural network (CNN). We then apply our model to publicly available data over New England, generating maps of fourteen forest attributes at a 10 m resolution over 85 % of the region. Attributes include aboveground biomass (kg), total biomass (kg), tree count (#), percent conifer (%), basal area (m^2), mean height (m), quadratic mean diameter (cm), percent spruce/fir (%), percent white pine (%), inner bark volume (m^3), merchantable volume (m^3), and spruce/fir volume (m^3. All values correspond to the amount per pixel cell (I.E. kg of biomass found within that pixel). Map/model performance was assessed using the USFS’s FIA inventory, which constituted an independent dataset free from spatial autocorrelation. More data can be found in the following pre-print: Ayrey, E., Hayes, D. J., Kilbride, J. B., Fraver, S., Kershaw, J. A., Cook, B. D., & Weiskittel, A. R. (2019). Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Forest Inventories. bioRxiv , 580514.

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.

The base data set used in this forest fragmentation analysis is the 2010 C-CAP Land Cover Analysis (http://http://coast.noaa.gov/ccapftp/). Land cover categories that were considered 'forest' for this analysis include Deciduous Forest, Evergreen Forest, Mixed Forest, Estuarine Forested Wetland, and Palustrine Forested Wetland. Two buffered roads layers were erased from the forest polygons, in order to approximate the fragmenting effect of roads on the landscape. Because the area of interest crosses the boundaries of multiple states, the ESRI North America Detailed Streets layer (http://www.arcgis.com/home/item.html?id=f38b87cc295541fb88513d1ed7cec9fd) was used. Two selections of the roads data were extracted and buffered: Interstate roads were buffered 150 feet from the center line in both directions, while US, State, and County roads were buffered 33 feet from the center line. The final data set is limited to forest patches falling within a 5 mile radius of either the Hudson River Estuary watershed boundary or the 10 counties of New York's Hudson Valley.The accompanying symbology layer divides forests into four size classes following the Orange County Open Space Plan (Orange County Planning Department 2004): Globally important (greater than 15,000 acres). These large and intact forest ecosystems support characteristic, wide-ranging, and area-sensitive species, especially those that depend on interior forest. Globally important forests are large enough so over time they will express a range of forest successional stages including areas that have been subjected to recent large-scale disturbance such as blowdowns and fire, areas under recovery, and mature areas. These forests also provide sufficient area to support enough individuals of most species to maintain genetic diversity over several generations. Regionally important: (6,000 - 14,999 acres). Patches 6,000 acres and greater provide habitat to more area-sensitive species and can accommodate large-scale disturbances that maintain forest health over time. Smaller patches are often less able to maintain the entire range of needed habitats and successional stages after large-scale disturbances. Locally important: 2,000 – 5,999 acres). These smaller but locally important forest ecosystems often represent the lower limit of intact, viable forest size for forest-dependent birds. Such bird species often require 2,500 to 7,500 acres of intact interior habitat. These forests, like the larger regionally important forests, can also provide important corridors and connectivity among forest ecosystems. Stepping stone forests: (200 – 1,999 acres) These examples of smaller forest ecosystems provide valuable, relatively broad corridors (not just a narrow strip) and links to larger patches of habitat such as local, regional, and global forests. These smaller forests, therefore, enable a large array of species, including wide-ranging and area-sensitive species, to move from one habitat to another across an otherwise hostile and fragmented landscape. They also provide important habitat at key times during many animals’ life cycles. These forests should be considered the absolute minimum size for intact forest ecosystems. Forests as small as 200 acres will support some forest interior bird species, but several may be missing, and species that prefer “edge” habitats will dominate. Forest patches less than 200 acres have lesser ecological significance at the landscape scale and were excluded from the symbology layer, However, smaller forests may have local importance, and can be viewed by changing the symbology settings.

© Cornell University Department of Natural Resources 2014. This Project was funded by the New York State Environmental Protection Fund through the Hudson River Estuary Program of the New York State Department of Environmental Conservation. This layer is sourced from giswww.westchestergov.com.