The Woodlands Township boundary and area including streets, fire station locations, parks and parcels feature data.

Web map feature service of The Woodlands Parks & Rec. Dept. George Mitchell Nature Preserve (GMNP) boundary

Displays current township boundary

Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, boundaries, and other selected map features. This map depicts geographic features on the surface of the earth. One intended purpose is to support emergency response at all levels of government. The geospatial data in this map are from selected National Map data holdings and other government sources.

The Woodlands land and property features web map including commercial & residential properties, area & boundary, open space reserves (OSR's), streets, neighborhoods & subdivision plats, MUD's (Municipal Utility Districts), RUD (Right-of-Way Utility District), associations, villages, county commissioner precincts, GreenUp (Earth Day) sites & routes, parks, aquatic pools, trails & trail features, lakes & ponds, and branches, creeks & streams.Users may explore the map using 'Search' box tool to find a property by address or business by name, and toggle on additional layers from layers list to display, interact with and query even more feature data on the map.

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 2019 and 2020 or colour aerial orthophotographic imagery available at the time of the assessment;• New planting information for the financial year 2019/2020, from grant schemes and the sub-compartment database covering the estate of 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 onwww.forestresearch.gov.uk

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region. Acknowledgements Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.

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The layer Forests greater than 4 hectares (ROP15_Forest4ha) is a component of Map 4 (The Countryside) of the Regional Official Plan (ROP) as adopted by Council on June 16, 2009, and approved by the Ontario Municipal Board on June 18, 2015. This layer represents one component of the provincial-level environmental constraints, as outlined in the Provincial Policy Statement. It may overlap with other environmental features such as wetlands or ESPAs. It is a subset of all forest cover in the Region, as captured by the For_RMW layer. Elements such as fence rows and regeneration, or recently reforested areas were excluded, based on data in the source layer. The remaining polygons were amalgamated where contiguous. Areas less than four hectares were then removed. As part of the land budget exercise for the new ROP, the boundaries of this layer were closely reviewed for any woodlands located within the Designated Greenfield Areas. Regional Planning staff will review the balance of the woodlands boundaries in 2016. Any changes to these boundaries would be implemented through a future amendment to the ROP.

The 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, including areas of new planting, clearfell, windblow and restocked areas.

The woodland map excludes all 'tarmac' roads, rivers and powerlines where the gap in the woodland is greater than 20 meters wide.

The woodland map is continually updated on an annual basis. The changes in the canopy cover have been identified on remote sensing imagery taken during spring/summer 2017 or colour aerial orthophotographic imagery available at the time of the assessment. Additionally, new planting information from grant schemes and the FE sub-compartment database for the financial year 2017/2018 have been added to the woodland map.

The changes in the woodland boundaries use the Ordnance Survey MasterMap® (OSMM) as a reference where appropriated. OSMM is the most up to date large-scale digital map of GB providing a seamless database for 1:1250, 1:2500 and 1:10000 survey data.

All woodland (both urban and rural, regardless of ownership) which 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 extend. Woodland less than 0.5 hectare in extent will not be described within the dataset but will be included in a separate sample survey of small woodland and tree features.

Any maps produced using this data should contain the following Forestry Commission

acknowledgement: "Contains, or is based on, information supplied by the Forestry Commission. © Crown copyright and database right 2019 Ordnance Survey [100021242]".

The 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, including areas of new planting, clearfell, windblow and restocked areas.

The woodland map excludes all 'tarmac' roads, rivers and powerlines where the gap in the woodland is greater than 20 meters wide.

The woodland map is continually updated on an annual basis. The changes in the canopy cover have been identified on remote sensing imagery taken during spring/summer 2016 or colour aerial orthophotographic imagery available at the time of the assessment. Additionally, new planting information from grant schemes and the FE sub-compartment database for the financial year 2017/2018 have been added to the woodland map.

The changes in the woodland boundaries use the Ordnance Survey MasterMap® (OSMM) as a reference where appropriated. OSMM is the most up to date large-scale digital map of GB providing a seamless database for 1:1250, 1:2500 and 1:10000 survey data.

All woodland (both urban and rural, regardless of ownership) which 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 extend. Woodland less than 0.5 hectare in extent will not be described within the dataset but will be included in a separate sample survey of small woodland and tree features.

Any maps produced using this data should contain the following Forestry Commission

acknowledgement: "Contains, or is based on, information supplied by the Forestry Commission. © Crown copyright and database right 2018 Ordnance Survey [100021242]".

Joshua tree is a visually distinctive plant found in California''s Mojave Desert and adjacent areas. The unique silhouette and tall stature of Joshua tree relative to typical surrounding vegetation make it one of the most recognizable native plants of California deserts. There are two species of Joshua tree in California, western Joshua Tree (Yucca brevifolia) and eastern Joshua tree (Yucca jaegeriana). Eastern Joshua tree (Yucca brevifolia ssp. jaegeriana) distribution is represented in the data incidentally, but the primary purpose of this dataset is to illustrate the distribution of western Joshua tree. Western Joshua tree is distributed in discontinuous populations in the Mojave Desert and in a portion of the Great Basin Desert. Western Joshua tree is often noted as being abundant near the borders of the Mojave Desert in transition zones. No attempt was made to map Joshua tree distribution outside of California, and therefore the data are limited to geographic areas within California. CDFW possesses vegetation maps that cover a large portion of the California deserts where Joshua tree generally occurs. CDFWs Vegetation Classification and Mapping Program (VegCAMP) uses a combination of aerial imagery and fieldwork to delineate polygons with similar vegetation and to categorize the polygons into vegetation types. In 2013, an effort was made to create a vegetation map that covers a large portion of the California deserts. The vegetation data from this project includes percent absolute cover of Joshua tree and in some instances only Joshua tree presence and absence data. Western Joshua tree and eastern Joshua tree were lumped together as one species in these vegetation maps. A rigorous accuracy assessment of Joshua tree woodland vegetation alliance was performed using field collected data and it was determined to be mapped with approximately 95 percent accuracy. This means that approximately 95 percent of field-verified, polygons mapped as Joshua tree woodland alliance were mapped correctly. While Joshua tree woodland alliance requires even cover of Joshua tree at greater than or equal to 1 percent, the vegetation dataset has polygons recorded with less than 1 percent cover of Joshua tree as well as simple presence and absence data. The CDFW used Joshua tree polygons from vegetation mapping combined with additional point data from other sources including herbarium records, Calflora, and iNaturalist to create the western Joshua tree range boundary used in the March 2022 Status Review of Western Joshua Tree. CDFW reviewed publicly available point observations that appeared to be geographic outliers to ensure that incorrectly mapped and erroneous observations did not substantially expand the presumed range of the species. In a limited region, hand digitized points were used where obvious Joshua tree occurrences that had not been mapped elsewhere were present on aerial photographs. Creating a range map with incomplete presence data can sometimes be misleading because the absence of data does not necessarily mean the absence of the species. Some of the observations used to produce the range map may also be old, particularly if they are based on herbarium records, and trees may no longer be present in some locations. Additionally, different buffer distances around data points can yield wildly different results for occupied areas. To create the the western Joshua tree range boundary used in the March 2022 Status Review of Western Joshua Tree, CDFW buffered presence locations, but did not use a specific buffer value, and instead used the data described above in a geographic information system exercise to extend the range polygons to closely follow known occurrence boundaries while eliminating small gaps between them.

The Woodlands land and property features interactive GIS web map app including open space, commercial & residential properties, area & boundary, streets, MUD's (Municipal Utility Districts), RUD (Right-of-Way Utility District), associations, neighborhoods, villages, county commissioner precincts, TWFD fire stations, parks, aquatic pools, trails & trail features, lakes & ponds, and branches, creeks & streams feature data layers.Users can explore the map and features using different functions and tools such as 'Near Me Buffer' to find and list properties or businesses within a specified search distance from a location, and 'Directions' to find driving or walking distance and time from one location to another including display of current traffic flows along thoroughfares (with 'Show traffic' option enabled).Turn on additional layers from layers list to display, interact with and query more feature data layers on the map.

The habitat network maps seek to apply the best evidence and principles and to use the best available nationally consistent spatial data. The habitat network maps are developed around 4 distinct habitat components sets and include 4 distinct network zones where action may be undertaken to build greater ecological resilience. The different elements of the maps are described below: Habitat Components; • The location of existing patches of a specific habitat for which the network is developed. This is termed the ‘Primary habitat’ e.g. lowland heathland. The main baseline data used for this is the Priority Habitat Inventories (PHIv2.2). • The location of additional habitat that naturally form mosaics with the primary habitat e.g. habitats that are most likely to form ecological mosaics possibly used by species associated with the primary habitat. This is termed the ‘Associated habitat’. The main baseline data used for this is the Priority Habitat Inventories (PHIv2.2). • The locations where habitat creation or restoration is known to occur, this is primarily sites under relevant agri-environment options. This is termed the ‘Habitat creation’ • Sites where data suggests small fragments of the primary habitat or degraded habitat exists and where restoration may be possible, this is primarily developed from information held within the current PHI v2.2. This is termed the ‘Restorable habitat’.
Network Zones; • Land within close proximity to the existing habitat components that are more likely to be suitable for habitat re-creation for the particular habitat. These areas are primarily based on soils but in many cases has been refined by also using other data such as hydrology, altitude and proximity to the coast. This is termed the ‘Network Enhancement Zone 1’. • Land within close proximity to the existing habitat components that are unlikely to be suitable for habitat re-creation but where other types of habitat may be created or land management may be enhanced including delivery of suitable Green Infrastructure. This is termed the ‘Network Enhancement Zone 2’. • Land immediately adjoining existing habitat patches that are small or have excessive edge to area ratio where habitat creation is likely to help reduce the effects of habitat fragmentation. This is termed the ‘Fragmentation Action Zone’. • Land within relatively close proximity to the Network Enhancement Zones 1 & 2 that are more likely to be suitable for habitat creation for the particular habitat and identifying possible locations for connecting and linking up networks across a landscape. This is termed the ‘Network Expansion Zone’.
Note: For some habitat networks not all of the habitat components or all the action zones are identified either because the data does not exist or the habitat does not lend itself to identifying particular types of action. Further details are outlined in the Habitat Network Mapping Guidance document. The Network boundary is drawn around the 4 habitat components using a variable buffering process with a generalised distance of 500m although 1km was used for Blanket Bog. As the boundary for each habitat network is tightly drawn around the existing patches of habitat this means that at a national scale the habitat network is composed of a series of smaller ‘networks’ that encapsulates one or more clusters of existing habitat patches. These may be considered as ‘network segments’. The Network Expansion Zone has been drawn around these segments to identify areas where additional action may be undertaken to build greater ecological resilience across the wider landscape.

Analysis of ‘Vegetation - Canada de los Osos Ecological Preserve [ds2943]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/eeaef31d-b48f-428b-9756-91aad2417004 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The vegetation map was created in ARCGIS using an orthophoto base layer from the National Agricultural Imagery Program (NAIP) with the Canada de los Osos Ecological Preserve boundary as an overlay. The original database derived for the purpose of creating a vegetation map for CDLO contained an appreciable level of detail and consequent large number of classifications that were not suitable for the mapping process. Therefore, all subsets of a classification were grouped into a single classification while retaining the vegetation labels defined by the Wildlife and Habitat Branch. For example, while there may have initially been four or five different types of Umbellularia californica '' Quercus agrifolia habitat based on percent cover, height and DBH classes etc., there is now only one type of Umbellularia californica '' Quercus agrifolia habitat based on dominant species indicated on the map.Furthermore, all of the features used to delineate a given subset of the original classification scheme do not apply to the modified classifications used to create the map. As a result, the original database was retained and a copy of that was modified and saved. The modified database was used as an attributes table for the vegetation shape files in the map.Mapping consisted of creating vegetation polygons on the NAIP layer within ARCGIS. Vegetation at the Alliance level is generally more easily mapped by aerial photo than that described at the Association level. Therefore, it was necessary to ground-truth for purposes of quality control.Certain “mapping rules” were created to maintain consistency when creating polygons.Trees separated from a larger, woodland stand by greater than two times their canopy width were not included within the larger stand.Similarly, grassland polygons can contain trees that do not appear associated with an adjacent woodland polygon.Trees within grassland forming an apparent savannah community were mapped as a community separate from the extenuating grassland. If members of that community appeared to merge with adjacent woodland, they were retained in the savannah polygon.Areas of grassland or scrubland surrounded by woodland and less than five meters in width were incorporated into the woodland polygon.The map was prepared for publication in BIOS by the California Department of Fish and Wildlife Vegetation Classification and Mapping Program.

--- Original source retains full ownership of the source dataset ---

The primary objective is to create a new digital map of all woodland in Great Britain using O.S.MasterMap features as boundaries where appropriate.

The vegetation map was created in ARCGIS using an orthophoto base layer from the National Agricultural Imagery Program (NAIP) with the Canada de los Osos Ecological Preserve boundary as an overlay. The original database derived for the purpose of creating a vegetation map for CDLO contained an appreciable level of detail and consequent large number of classifications that were not suitable for the mapping process. Therefore, all subsets of a classification were grouped into a single classification while retaining the vegetation labels defined by the Wildlife and Habitat Branch. For example, while there may have initially been four or five different types of Umbellularia californica '' Quercus agrifolia habitat based on percent cover, height and DBH classes etc., there is now only one type of Umbellularia californica '' Quercus agrifolia habitat based on dominant species indicated on the map.Furthermore, all of the features used to delineate a given subset of the original classification scheme do not apply to the modified classifications used to create the map. As a result, the original database was retained and a copy of that was modified and saved. The modified database was used as an attributes table for the vegetation shape files in the map.Mapping consisted of creating vegetation polygons on the NAIP layer within ARCGIS. Vegetation at the Alliance level is generally more easily mapped by aerial photo than that described at the Association level. Therefore, it was necessary to ground-truth for purposes of quality control.Certain “mapping rules” were created to maintain consistency when creating polygons.Trees separated from a larger, woodland stand by greater than two times their canopy width were not included within the larger stand.Similarly, grassland polygons can contain trees that do not appear associated with an adjacent woodland polygon.Trees within grassland forming an apparent savannah community were mapped as a community separate from the extenuating grassland. If members of that community appeared to merge with adjacent woodland, they were retained in the savannah polygon.Areas of grassland or scrubland surrounded by woodland and less than five meters in width were incorporated into the woodland polygon.The map was prepared for publication in BIOS by the California Department of Fish and Wildlife Vegetation Classification and Mapping Program.

Indicative map for Lowland Grassy Woodland:\r \r The indicative map for Lowland Grassy Woodland was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the eastern Regional Forest Agreements. The determination of Lowland Grassy Woodland was reviewed by the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel), and a set of diagnostic parameters for the identifying the Lowland Grassy Woodland TEC was agreed upon.\r Using these diagnostic parameters, we sampled candidate areas from existing vegetation maps to identify potential areas of Lowland Grassy Woodland occurrence in 296 000 hectares of State Forest and undertook additional mapping work using two independent mapping methods. Random Forest models (predictive habitat models) were generated using plot data and a selection of environmental variables. Aerial photo interpretation targeted stands of forests dominated by Eucalyptus tereticornis to refine the potential boundaries of Lowland Grassy Woodland.\r We tested whether Lowland Grassy Woodland was present in State Forest by completing systematic plot surveys within mapped areas indicating potential presence. We compared our collected data to a large regional pool of plot data that contained a subset of plots assigned to vegetation map units cited in the determination for the Lowland Grassy Woodland TEC (see Gellie 2005, Tozer et al 2006, and Keith and Bedward 1999). Our analysis of data confidently assigned only a few plots in State Forest to Lowland Grassy Woodland (2/43).\r From these results, we were unable to construct an operational map for Lowland Grassy Woodland. The relationship between the existing mapping cited in the determination and the plot data on State Forest was not strong enough to be a reliable basis for mapping the TEC. We also found that Eucalyptus tereticornis could not reliably be used as an indicator of Lowland Grassy Woodland in State forests. As a result, we were unable to map this TEC from the few confirmed sampling points without including a significant area of forest that was highly unlikely to be Lowland Grassy Woodland. However, we created indicative maps of Lowland Grassy Woodland by merging our predictive and API maps to provide an indication of the likely extent of Lowland Grassy Woodland in State Forests.\r \r Operational map for Brogo Wet Vine Forest:\r \r The operational map for Brogo Wet Wine Forest (BWVF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the eastern Regional Forest Agreements. We assessed whether BWVF was likely to be present in more than 296 000 hectares of State Forest in the South-east Corner Bioregion.\r The project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for BWVF and reaching an agreed interpretation of floristic, environmental and distributional characteristics. The Panel found that BWVF is primarily defined by a source vegetation community derived from quantitative floristic plot data (Keith and Bedward, 1999), with additional defining characteristics relating to bioregion and elevation.\r The Panel’s interpretation resulted in the identification of all State Forests located below an elevation threshold of 550 metres within the South East Corner Bioregion as potentially containing BWVF. We identified other potential areas of BWVF by overlaying the cited vegetation maps and any State Forest mapping where vegetation was dominated by or includes Eucalyptus tereticornis (a defining species of BWVF).\r Within these state forests, we used aerial photo interpretation (API) to identify and delineate potential areas of BWVF based on structural characteristics and overstorey and understorey attributes, namely dominance or inclusion of Eucalyptus tereticornis. \r We then compiled floristic plot data for all State Forest areas within our study area. The floristic plot data was sourced from both existing flora surveys held in the OEH VIS database and from targeted flora surveys conducted specifically for this project. We used multivariate analysis to compare plots assigned to vegetation communities identified as BWVF in the determination to all other plots in the study area. We used explicit membership thresholds to identify whether plots in State forests and elsewhere belonged to one or more of the communities listed in the BWVF determination.\r We used the plot assignments to candidate BWVF to develop a predictive presence and absence Random Forest statistical model. The model generates a probability of occurrence of BWVF for each grid cell using plot data and a selection of environmental and remote-sensing variables. \r We constructed our operational map using the API line work in combination with the floristic plot data and our predictive habitat models to identify and map the locations and extent of BWVF. Our mapping identified six small areas of Brogo Wet Vine Forest totalling 17.5 hectares. All areas were within Bodalla State Forest and were located on the exposed lower slopes of Mount Dromedary.\r \r Operational map for Dry Rainforest of the South East Forests:\r \r The operational map for Dry Rainforest of the South East Forests (Dry Rainforest) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the eastern Regional Forest Agreements. The determination of Dry Rainforest was reviewed by the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel), and a set of diagnostic parameters for the identifying the Dry Rainforest TEC was agreed upon.\r Using these diagnostic parameters, we sampled candidate areas from existing vegetation maps to identify potential areas of Dry Rainforest occurrence in 296 000 hectares of State Forest and undertook additional mapping work using two independent mapping methods. Random Forest models (predictive habitat models) were generated using plot data and a selection of environmental variables. Aerial photo interpretation targeted stands of forests dominated by Ficus rubiginosa to refine the potential boundaries of Dry Rainforest.\r We tested whether Dry Rainforest was present in State Forest by completing systematic plot surveys within mapped areas indicating potential presence. We compared our collected data to a large regional pool of plot data that contained a subset of plots assigned to vegetation map units cited in the determination for the Dry Rainforests TEC (see Keith and Bedward 1999). Our analysis of data confidently assigned only a few plots in State Forest to Dry Rainforest (2/21).\r From these results, we were able to construct an operational map for Dry Rainforest. We identified six small patches of Dry Rainforest but only one patch was located within the study area. This patch was located in Towamba State Forest and was 0.53 hectares.\r \r Operational TEC Mapping have been derived by API at a viewing scale between 1-4000 using ADS40 50 cm pixel imagery and 1 m derived LIDAR DEM grids for floodplain EECs.\r \r Indicative TEC Mapping have been generated from best available composite environmental data layers - standardised to 30 m pixels.\r

This feature layer displays the fire district boundaries that represent the coverage areas of individual fire stations within Montgomery County. Unlike the broader Emergency Service Districts (ESD) layer, which defines the general operational boundaries, this layer provides a more detailed view of each fire department's service area. For example, within ESD 14, which covers South Montgomery County including Timberlakes and The Woodlands Township, different fire departments (Timberlakes Fire Department and The Woodlands Fire Department) have jurisdiction over distinct areas.The dataset includes the following information:Fire District Boundaries: Polygons representing each fire station’s jurisdiction.Station Details: Includes the name, address, and website information for each fire station within the district.The data is sourced directly from the fire stations, either through the ESD, fire station leadership, or maps available on their websites.Data Includes:Fire district boundaries for each fire stationAddress and contact information for each fire stationWebsite information for further referenceData Source:Data collected from individual fire stations, either via the ESD, fire station leadership, or published maps available on fire station websites.Access:This layer is shared publicly for use by departments involved in emergency management and fire response planning.Update Frequency:The data is updated as needed, as new fire district information is received or updated by fire station leadership.

The NFI definition of woodland is a minimum area of 0.5 hectares under stands of trees with, or with the potential to achieve, tree crown cover of more than 20% of the ground. Areas of young trees, which have the potential to achieve a canopy cover of more than 20%, will also be interpreted as woodland and mapped. The minimum width for woodland is 20 m, although where woodlands are connected by a narrow neck of woodland less than 20 m wide, the break may be disregarded if less than 20 m in extent. Intervening land classes such as Roads - all 'tarmac' roads should be excluded from the woodland area, but internal forest tracks, farmers tracks, rides etc. will be included as part of the woodland if< 20m wide. Rivers - where the gap in woodland is 20m then rivers will be excluded from the woodland area.Power lines etc. - where the gap in woodland is 20m then power lines will be excluded from the woodland area.Railways - all normal gauge railways should be excluded from woodland Scrubby vegetation" is included within this survey where low woody growth seems to dominate a likely woodland site. The definition of an open area is any open area that is 20m wide and 0.5 ha in extent and is completely surrounded by woodland.The woodland boundaries have been interpreted from colour aerial orthophotographic imagery. For the base map, photographic images aimed to be no older than 3 years at the time of mapping (i.e. areas mapped in 2007 would be based on photographs that were ideally taken no earlier than 2004). As the map is be the basis for a longer rolling programme of sample field surveys it has been necessary to develop procedures to update the map to the date of the field survey, currently 2011, for the purpose of reporting on the current phase.The map is continually updated on an annual basis. These updates will are achieved by a combination of remote sensing and updated aerial imagery analysis for changes in the woodland structure and with reference to available new planting information from grant schemes and the FE sub-compartment database. Ordnance Survey MasterMap® (OSMM) features have been used as a reference for capturing the woodland boundaries. OSMM is the most up to date large-scale digital map of GB providing a seamless database for 1:1250, 1:2500 and 1:10000 survey data. All woodland (both urban and rural, regardless of ownership) which is 0.5ha or greater in extent, with the exception of Assumed woodland or Low density areas that can be 0.1ha or greater in extend, as been mapped Woodland that is less than 0.5ha in extent will not be described within the dataset but will be included in a separate sample survey of small woodland and tree features. The primary objective is to create a new digital map of all woodland in Great Britain using O.S.MasterMap features as boundaries where appropriate. The map shows the extent of all woodland of 0.5 ha.Woodland categories are defined by IFT (Interpreted Forest Type) values. Detailed Woodland categories are: BroadleavedConiferFelledGround Prepared for New PlantingMixed - predominantly BroadleavedMixed - predominantly ConiferYoung TreesCoppiceCoppice with StandardsShrub LandUncertainCloud or ShadowLow DensityAssumed woodlandFailedWindthrow/WindblowNon woodland categories are defined by the IOA (Interpreted Open Area) values. Detailed Non woodland categories are:Agriculture landBare areaGrassOpen waterOther vegetationPower lineQuarryRiverRoadUrbanWindfarm