Baker County’s 2025 land-cover pattern is dominated by two classes: forest at 42.14% and wetlands at 34.97%. Together they account for roughly 77% of the county, which is easy to see in the broad green and teal areas covering much of the west and north. Developed land is only 7.65% countywide, yet a distinct concentration appears around the Macclenny–Glen St. Mary area in the southeastern part of the map. That contrast between extensive natural cover and a compact development core is the clearest feature of this map set.
This page includes four views: 2025 land cover, forest and farmland, impervious/developed land, and a 1985–2025 land-cover change comparison. The WebP images are meant for quick viewing, while the four original JPG maps are available together in one ZIP download. Land cover describes the physical surface seen by the classification system. It does not define zoning, ownership, buildability, conservation status, or a legally surveyed wetland boundary.
Table of Contents
A compact southeastern development area stands out against a wet, forested county
The countywide pattern is easiest to understand by separating the broad natural background from the smaller developed center. Forest and wetlands dominate the western half, the north, and large parts of the interior. Red developed classes become much more visible toward the southeast, while agriculture, grassland, and shrubland appear in smaller patches around the same general portion of the county. Baker County therefore does not look like a landscape where development is spread evenly from edge to edge. Most of the mapped surface remains forested or wet.
Baker County’s 2040 Comprehensive Plan provides useful local context because it addresses future land use together with wetlands, floodplains, surface-water quality, and other natural resources. The plan gives particular attention to the St. Mary’s River and its tributaries, including development standards, buffers, and floodplain protection. Those planning policies help explain why river and wetland geography matters in this county, but they are not the same thing as the NLCD classes shown here. A teal raster cell should never be substituted for an official wetland or regulatory boundary.
The county’s planning material also identifies Macclenny and Glen St. Mary as the municipalities providing centralized water and sewer services. It discusses the possibility of directing more efficient growth near those service areas rather than extending urban-style development uniformly across the county. The impervious map’s strongest cluster falls in the same general southeastern area, making the planning information useful context for reading the map. Exact municipal or utility boundaries, however, require the county’s official GIS and planning maps.
Forest is the largest 2025 class at 42.14%
Forest is the largest single class at 42.14%. It covers broad parts of the west, north, and interior, but it does not form one uninterrupted block. Wetlands, grassland, agriculture, roads, and developed areas break the forest into a varied pattern. That matters when using the map for habitat or conservation discussions because a countywide percentage cannot describe connectivity by itself. The image is more useful when the percentage is read together with the shape and location of the forest patches.
Wetlands are nearly as important at 34.97%, only 7.17 percentage points below forest. Large teal areas occupy much of the western and northern county, with additional wetland cover extending through the east and south. Baker County emergency-management material describes flood-prone areas associated with the St. Mary’s River, its tributaries, and large swamp systems such as Pinhook Swamp, Big Gum Swamp, and New River Swamp. Those named features provide county-specific background, but the supplied land-cover image does not label them individually.
Developed land is 7.65%, grassland 5.19%, agriculture 4.51%, and shrubland 4.16%. These smaller classes are much more common in the southeastern half than in the wettest western areas. Water is only 0.55% in the county summary, although several visible water bodies and river corridors still matter geographically. A small open-water percentage should not be interpreted as a lack of hydrologic importance, especially in a county where wetlands occupy more than one-third of the mapped area.
The forest-and-farmland view reveals how wetlands divide the natural landscape
The second map reduces developed and miscellaneous classes to a pale background, allowing forest, wetlands, agriculture, grassland, and shrubland to carry the visual weight. Forest at 42.14% and wetlands at 34.97% clearly dominate. Agricultural cover is only 4.51%, while grassland and shrubland contribute another 5.19% and 4.16%. The west and northwest appear especially wet, with large teal areas bordered by dark-green forest. This makes the map useful for environmental education and broad habitat comparisons.
Farther southeast, pale developed/other areas become more common and agriculture, grassland, and shrubland appear in a more mixed pattern. The contrast shows that the county’s settled landscape occupies a relatively small portion of a much larger forest-and-wetland system. The agricultural colors should not be read as parcel boundaries or as proof that every colored cell is currently part of an active farm. NLCD is a surface classification, not a cadastral or agricultural-operation database.
U.S. Forest Service information for the Osceola area in Baker County describes pine flatwoods, cypress swamp, and mixed swamp forest communities. That local ecological context is consistent with a county where forest and wetland classes repeatedly meet on the map. The supplied image does not separate tree species or forest types, though, so dark green should remain labeled simply as forest unless a more detailed vegetation dataset is added.
Mean impervious cover is just 1.63%, with the strongest concentration near Macclenny
Impervious surface represents hard cover such as pavement, parking areas, and rooftops where rainfall does not readily soak into the ground. Baker County’s mean impervious cover is only 1.63%, and just 0.32% of the county lies in cells with at least 50% impervious cover. These numbers are much lower than the 7.65% developed-land share because developed classes may include lawns, trees, bare soil, and other permeable surfaces. Developed land and impervious surface measure related but different conditions.
The darkest impervious colors form a compact southeastern cluster around the Macclenny area, with smaller concentrations scattered nearby and along thin linear corridors. Most of the west and north remain very pale. That visual pattern explains why the countywide mean is so low even though an urban center is clearly present. The map is useful for identifying development intensity, but it cannot provide a reliable street-level or municipal percentage without the original raster and an exact boundary.
Baker County’s planning documents discuss how centralized water and sewer service in Macclenny and Glen St. Mary can influence where more intensive development is practical. Reading that information beside the impervious layer provides a clearer picture of why a localized development core exists. Still, a low countywide impervious percentage does not mean flood risk is low. Wetlands, rivers, drainage conditions, soil, topography, and rainfall must be considered separately.
The 1985–2025 comparison shows 21.97% class difference and 9.71% forest loss
The change map reports that 21.97% of cells have a different land-cover class in 2025 than in 1985. The summary separates 9.71% forest loss, 2.34% changed to a class mapped as developed in 2025, 0.43% agricultural loss, and 9.32% other class difference. Orange forest-loss cells are widespread across much of the southern and eastern county, while red developed-change cells cluster more strongly in and around the southeastern settlement area.
The 21.97% value is not a development rate. It combines every mapped class difference between the two dates. Some differences may represent real landscape change, while imagery, classification methods, mixed pixels, and small boundary shifts can also affect the comparison. Readers interested specifically in development should focus on the 2.34% “to developed” category, and even that number should be checked against aerial photography or local development records before assigning a cause.
Forest loss at 9.71% is the largest named change category, but the map does not identify a single cause or a precise year of change. Forestry activity, development, natural disturbance, hydrologic change, conversion to grass or shrub cover, and classification variation can all contribute to different pixel classes. The safest use is to identify clusters where follow-up research is worthwhile rather than treating every orange cell as evidence of the same process.
The St. Mary’s River and large wetland systems provide essential geographic context
Baker County’s Comprehensive Plan contains specific policies for development, floodplain protection, water quality, and vegetated buffers along the St. Mary’s River and its significant tributaries. This emphasis reflects the importance of river and wetland systems to the county’s northern landscape. St. Mary’s Shoals Park, a county facility along the Florida–Georgia border, also provides public access to nearly two miles of the river and preserves a large natural area.
Those official sources make it easier to understand why wetland cover is so extensive, but they should remain separate from the land-cover classification. A 30-meter wetland pixel is not a surveyed jurisdictional wetland line, and a forest pixel does not identify ownership or conservation status. Projects involving development permits, floodplain review, wetlands, or property boundaries need more precise county, state, and federal data.
Baker County’s public Map Library is useful for that next step because it provides official county maps and municipal-boundary information. A practical workflow is to use the land-cover maps first to identify the broad pattern, then compare the area of interest with official administrative or planning maps. This keeps the strengths of the NLCD image—consistent countywide comparison—without asking it to answer questions it was not designed to resolve.
Use each map for a different question and keep the county summary visible
For a general county profile, start with the full land-cover map because it shows the dominant forest-and-wetland pattern and the smaller developed core in one image. The forest-and-farmland view is better for habitat, conservation, and rural-land discussions. The impervious map is the clearest choice for development intensity and stormwater context. When explaining change through time, show the 2025 pattern first and place the change map afterward so readers have a current reference before interpreting the 1985–2025 colors.
The asset manifest records each original JPG at 2480 by 1754 pixels. The package does not mark those dimensions as meeting its A3 high-resolution reference, so large-format printing should be tested before final use. In reports and presentations, preserve the original aspect ratio and keep the legend and County summary visible. Cropping those elements can remove the exact percentages and definitions required to interpret the map correctly.
Thirty-meter cells and a 2025 observation date set important limits
Annual NLCD is built for consistent land-cover comparison over large areas and commonly uses a Landsat-based 30-meter grid. A single cell can contain trees, a road edge, grass, standing water, or wetland vegetation at the same time. The land-cover product assigns a representative class, while the impervious product estimates hard-surface percentage. Enlarging the image cannot create parcel-level detail that was absent from the original dataset.
The land-cover, forest-and-farmland, and impervious maps describe 2025 conditions, while the change layer compares 1985 with 2025. Construction, forestry, wetland change, or other land-cover transitions after the observation period may not appear. Work that needs current conditions should pair these maps with newer aerial imagery and Baker County planning or GIS information.
All percentages on these maps are countywide. The 42.14% forest share, 34.97% wetland share, and 1.63% mean impervious value do not represent Macclenny, Glen St. Mary, Sanderson, a watershed, a park, or an individual property. Smaller-area statistics require a new calculation using the appropriate boundary and the original raster data.
Frequently Asked Questions
What are the largest land-cover classes in Baker County in 2025?
Forest is the largest at 42.14%, followed closely by wetlands at 34.97%. Together they account for roughly 77% of the county.
Why is developed land 7.65% while mean impervious cover is only 1.63%?
Developed classes can include lawns, trees, soil, and other permeable surfaces. Impervious cover measures hard surfaces such as pavement and rooftops separately, so the countywide mean is lower.
Does the 21.97% 1985–2025 class difference mean 21.97% of Baker County was developed?
No. It is the share of cells with a different mapped land-cover class between the two dates. The map identifies 2.34% as changes classified as developed in 2025, and some differences may include classification variation.
Sources and reference material
- MRLC Annual NLCD Data – land-cover and impervious source data
- USGS Annual NLCD Land Cover Classification – class definitions and interpretation
- U.S. Census Bureau TIGER/Line Shapefiles – county-boundary reference
- Baker County 2040 Comprehensive Plan – natural resources, wetlands, the St. Mary’s River, and future land-use context
- Baker County Map Library – official county GIS and municipal-boundary maps
- St. Mary’s Shoals Park – county information on the St. Mary’s River and northern natural landscape
Map File Information
Download the four original JPG maps in one ZIP: 2025 land cover, forest and farmland, impervious/developed land, and the 1985–2025 land-cover change comparison.
- Included Files: 4 JPG maps
Related Maps
Green Map creates custom-edited map images using open geographic data sources such as geoBoundaries, Natural Earth, OpenStreetMap, and government open data.
These maps are edited visual materials, not raw data files, and are provided for education, documents, presentations, and graphic reference.
