Forests cover large areas of Canada but only some of these forests are actively managed. The Map of Forest Management in Canada provides a generalized classification of forest management in Canada, including: protected areas, Treaty/Settlement Lands (including Treaty Lands identified in Final Agreements, Land Claim Agreements and Settlements), Indian Reserves, other federal reserves (including military training areas), provincial and territorial reserves and restricted use areas, private lands, short- and long-term Crown forest tenure areas and areas with no current Crown timber dispositions. The Managed Forest Map of Canada dataset provides a wall-to-wall classification of lands in Canada. It does not differentiate areas of forest from non-forest. The Managed Forest Map of Canada differs from maps defining the area designated as “managed forest” for greenhouse gas inventory reporting purposes and does not replace those maps. Instead, the Managed Forest Map of Canada shows areas that are currently managed, as of June 2017, and provides generalized management type classification for those areas. Collaborating agencies plan to update the dataset periodically as needed, and remain open to receiving advice from experts concerning refinement priorities for future versions.

We present a forest management map for Europe. Forest is classified in 5 distinct forest management classes: unmanaged forest, close-to-nature forestry, combined objective forestry, intensive forestry and very intensive forestry. Data on disturbance area, disturbance frequency, forest age, forest age evenness, fast-growing tree species and primary forest is used to classify forest.

Region 5 Forest Health Treatment Priority MappingThe number of acres of forests burning at high severity in recent years, combined with the recent drought-induced tree mortality event of 2015-2016 have more than ever highlighted unsustainable forest health conditions in California. Urgency for implementing preventative landscape-level tree density and fuels reduction treatments to restore and maintain forest resiliency to wildfires and drought (bark beetles) has now become an emergency. To accomplish meaningful landscape level treatments, land managers must be able to prioritize areas of highest risk that are conducive to project implementation. Forest Health Protection has analyzed a variety of readily available corporate GIS data sets to identify areas that are considered most at risk to high levels of bark beetle-caused tree mortality, have a high likelihood of experiencing stand replacing wildfire and are accessible and appropriate for mechanical thinning. This product has been used on several R5 National Forests for 5-year planning, identifying cross collaboration, all lands opportunities, and guiding layout of new projects using the Farm Bill insect and disease treatment Categorical Exclusion authority under NEPA. This webmap illustrates areas deemed at high risk of tree mortality, due to bark beetles, on all lands throughout the state. These same areas should also be considered at a risk to high-severity wildfire due to overstocked conditions and generally high fuel loading from past tree mortality. The webmap is suitable for landscape-level planning, rather than stand-level planning, as the data used to identify priority treatment areas are not sufficiently detailed for use at the stand level. Ground verification of areas identified in the map as priorities for treatment is highly recommended. Areas mapped outside of USDA National Forest System lands may not reflect recent management activities. Basic consideration for classification as high priority for treatment required that areas:Have not suffered moderate or high severity wildfire since at least 1998;Have not been thinned by the USDA Forest Service since at least 2005;Have not experienced stand-replacing disturbance, owing to clear-cut or natural mortality, since at least 2005;Contain stands with 60% or higher relative stand density;Are dominated by trees with diameter at breast height (DBH) of 11” or more.Lands that met the basic conditions were then classified as high priority for treatment based on the species composition and density of the stands that they contain.Highest priority was assigned to locations with stands that contain:Pines principally, and have stand density index (SDI) of 220 or higher; OR Fir-dominated mixed conifer and white fir, have SDI 270 or higher, and historically contained mostly pines; OR Pine-dominated mixed conifers, and have SDI 270 or higher.Pine-dominated stands are typically associated with drier sites and often experience higher levels of tree mortality associated with high stand density, bark beetles, and drought.Second priority was assigned to locations with stands that:Contain fir-dominated mixed conifer and white fir, have SDI 330 or higher;Were not classified as highest priority.Fir-dominated stands found on more mesic sites can also experience elevated tree mortality associated with high stand density, bark beetles, and drought, though generally at a lower level than pine-dominated stands or fir-dominated stands growing on historically pine-dominated sites.Download the thinning priority layers displayed in this WebMap. In addition to what is displayed on this webmap, the download also includesThird priority including smaller DBH of 6" - 11" 50% relative stand density (dependent on dominant species)Regional Dominance Type for each priority pixel

The Forest Service National Maps experience page is designed to distribute and deliver maps to the Forest Service and public. Maps cover Forest Service lands. Map series include National; Regional; Admin; Forest; Ranger District and 24K or better known as FSTopo, and our historical product FSTopo Legacy.

We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m² for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.

For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40 European countries, forming a continuous coverage of the western part of the European continent.

A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to 73 % the South-Eastern area.

The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.

The author list is sorted by last name except for the first and last authors who also serve as corresponding authors.

Corresponding authors: Jukka.Miettinen@vtt.fi, Johannes.Breidenbach@nibio.no

Using the national GIS cadastral map of the Ministry of Agriculture as the reference, the land cadastral information corresponding to the national forest management area and marking the percentage based on the estimated area involved. During the use of the map data, please note: 1. Some plot numbers may overlap different forest management areas, and in order to separately indicate the percentage of the area within each forest management area, the features may be duplicated; plot numbers within the same forest management area are not duplicated. 2. The national GIS cadastral map was referenced and processed by the National Land Surveying and Mapping Center with the Taiwan general electronic map for alignment to facilitate reference with the topography and land features. If it involves land rights processing, it still needs to revert to the original cadastral data of each local land office. 3. The national forest management area mainly consists of land registered by the Forestry Bureau and the Conservation and Natural Resources Administration as the managing authority, including some unregistered land that was included in the forest management area earlier. Due to frequent changes in cadastral data, if the range map data is not updated, it can be judged based on the owner registration of the land as described above (the managing authority is the "Forestry Bureau and the Conservation and Natural Resources Administration" or the "Forest Bureau of the Council of Agriculture" before restructuring) to determine whether it still belongs to the national forest management area.

The data are designed for strategic analyses at a national or regional scale which require spatially explicit information regarding the extent, distribution, and prevalence of the ownership types represented. The data are not recommended for tactical analyses on a sub-regional scale, or for informing local management decisions. Furthermore, map accuracies vary considerably and thus the utility of these data can vary geographically under different ownership patterns.

The El Pilar Project has been conducting research at El Pilar, Belize and Guatemala since 1993, and was founded on a base of survey work that goes back to 1983. This unusual archaeological program recognizes the present environment as a part of the ancient Maya past. Our mission is the preservation and conservation of endangered resources through local and international education. Addressing tensions between culture and nature, we use the past as a reference to build a responsible future. Weaving together traditional knowledge and practice with scientific inquiry and interpretation, we promote a deeper awareness of heritage through local partnership.

The University of California Santa Barbara (UCSB) Maya Forest GIS is an essential tool to organize and use the numerous geographic resources involved in our studies, and provide reliable datasets for the project.

The map service (WMS Group) presents the digital geodata from the forestry sector of the Saarland.:Location mapping in the forest

The National Trees Outside Woodland (TOW) V1 map is a vector product funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme produced under Forest Research’s Earth Observation for Trees and Woodlands (EOTW) project. The TOW map identifies canopy cover over 3m tall and 5m2 area which exists outside the National Forest Inventory (National Forest Inventory - Forest Research). Canopy cover is categorised into the following woodland types - lone trees, groups of trees and small woodlands. The data set was derived from the Vegetation Object Model (VOM) (Environment Agency, EA), the National Lidar Survey (EA), and Sentinel-2 (European Space Agency) imagery using spatial algorithms. The method is fully automated with no manual manipulation or editing. The map and its production method has been quality assured by DEFRA science assurance protocols and assessed for accuracy using ground truth data. Because the process classifies objects based on proximity to features within OS mapping, there could be some misclassifications of those objects not included in the OS (specifically: static caravans, shipping containers, large tents, marquees, coastal cliffs and solar farms). This is a first release of this dataset, the quality of the production methods will be reviewed over the next year, and improvements will be made where possible. The TOW map is available under open government licence and free to download from the Forestry Commission open data download website (Forestry Commission) and view online on the NCEA ArcGIS Online web portal (Trees Outside Woodland). A full report containing details on methodology, accuracy and user guide is available. TOW map web portal link : Trees Outside Woodland Public Map FR TOW map web page : Trees Outside Woodland Map - Forest Research

This publication contains a raster maps at 250 m resolution of the merchantable volume (m3/ha) of the mature Canadian forest available for harvesting in the next 20 years (2011 to 2031). The maps were produced from remote sensing products at a spatial resolution of 250 m on the MODIS pixel grid and 30 m on the Landsat pixel grid. More specifically, we used forest attribute data at the 250 m pixel for the years 2001 and 2011 (Beaudoin et al 2014 and 2018) combined with forest cover changes for the years 1985 to 2015 at 30 m (Guindon et al. 2017 and 2018). The map of mature forests in Canada was prepared at the forest management unit (FMU) level and therefore exclude private lands. To be considered mature (i.e. available for cutting in the next 20 years), the forest pixels of Beaudoin et al. (2018) was to have a merchantable volume per ha equal to or greater than 80% of the average merchantable volume of the pixels that were harvested between 2001 and 2011 per forest management unit. A scientific article gives additional details on the methodology: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Can. J. For. Res. 48: http://www.nrcresearchpress.com/doi/10.1139/cjfr-2018-0080 Reference for this dataset: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/dd94871a-9a20-47f5-825b-768518140f35

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.

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. Formerly, forest maps were produced by the Land Survey Department of the Ministry of Lands and Forests. Some of these maps dating from 1924 to 1946 still exist and are treasured at the National Archives of Quebec. The information they contain makes it possible to locate and characterize forest areas in certain regions of Quebec. Color codes were then assigned for each of the following classes: young forests, old forests, burned, logged, rocky, savannas, and colonization. **These historical forest maps are available in two digital formats (PDF and TIFF) . ****This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This map depicts the boundary of Chipola Experimental Forest overlaying NAIP imagery, supplied by ESRI as a basemap layer, with roads labeled and other data layers shown. The purpose of this map is for inclusion in the EFR Story map to show the NAIP aerial imagery within the boundary, as well as additional GIS layers.

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 Forest Map of Spain (MFE) is the basic forest mapping at the state level, which includes the distribution of Spanish forest ecosystems. It is a project led by the General Directorate of Biodiversity, Forests and Desertification using a working methodology based on photointerpretation, with field verification. The MFE is a fundamental component of the Spanish Inventory of Natural Heritage and Biodiversity. The MFE provides detailed and homogeneous vector information for the entire Spanish territory of the structural type or main use of each tesela, the degree of coverage and the main mapped tree species, among others. It constitutes the cartographic basis of the National Forestry Inventory (IFN), and therefore, analogous to the IFN, has a continuous character and a periodicity of updating at least ten years. The most up-to-date Spanish Forest Map is prepared by documenting the provicies of the previous MFE50 with the new provinces that are being generated from the MFE25.

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.

We provide four data records:

1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes:

• “ID” is a unique location identifier

• “Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel.

• “Land_use_ID “is a land use class:

  • 11 - Naturally regenerating forest without any signs of human activities, e.g., primary forests.
  • 20 - Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc.
  • 31 - Planted forest.
  • 32 - Short rotation plantations for timber.
  • 40 - Oil palm plantations.
  • 53 - Agroforestry.

• “Flag” identifies a data origin: 1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach.

2. The 100 m forest management map in a geoTiff format with the classes presented - "FML_v3.2.tif ".

3. The predicted class probability from the Random Forest classification in a geoTiff format - "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management--layer-proba_EPSG-4326.tif"

4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes:

• “ID” is a unique location identifier

• “pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel in lat/lon projection

• “first_landuse_class “is a land use class, as in (1).

• “second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence.

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.

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).**