Agriculture is the defining cover in the supplied 2025 Worth County map. It accounts for 42.58% of the county, well ahead of forest at 25.00% and wetlands at 18.40%. A compact developed concentration stands out near the middle of the county, while farms, wooded blocks, and wetland bands dominate most of the surrounding area.
This page pairs four views of the same county: current land cover, forest and farmland, impervious surface and developed land, and a 1985–2025 class-difference map. The WebP versions are shown with the discussion below, and the four original JPG maps are available together in one ZIP download for county profiles, lessons, reports, and presentation graphics.
Table of Contents
Large farm blocks shape the county, but they are broken by forest and wet ground
The yellow agricultural class covers much of the southern half and large parts of the eastern side. Broad farm blocks also appear in the north, although they are more frequently interrupted there by forest and wetlands. The eastern extension of the county is especially agricultural, while the western side contains larger dark-green forest pieces. This uneven distribution matters because the 42.58% countywide agricultural share does not mean that every part of Worth County has the same farm-dominant appearance.
Forest is the second-largest class at 25.00%. Some of the strongest wooded areas occur along the western side and through parts of the central and southern interior. Wetlands cover 18.40% and appear as teal ribbons and larger patches that repeatedly separate farmland from forest. Developed land is 6.94%, grassland 3.00%, shrubland 2.76%, and water 1.28%. Those smaller categories add important local detail even though agriculture, forest, and wetlands account for most of the map.
The south and east are more open, while the west carries more woodland
Worth County does not have a simple north-south split, but several directional differences are clear. The southern half contains many large agricultural areas, and the eastern side also has long stretches of open farm cover. The west is more heavily interrupted by forest, especially where larger wooded blocks remain between the farm areas. In the north, agriculture, forest, wetland, and other open classes form a tighter patchwork, so no single color stays continuous for as long.
Wetlands help explain why the agricultural areas appear divided rather than continuous. Narrow teal bands wind through the northern, central, and southern portions of the county, while a few broader wet areas occupy larger spaces. They often sit directly beside fields and forest. For a classroom or county profile, showing the percentage and the location together is more informative than simply listing the 18.40% wetland share. The map makes clear that wet ground is part of the countywide pattern rather than a feature confined to one corner.
A vegetation-focused view makes the farm-and-forest contrast easier to follow
Removing most of the developed detail makes the agricultural pattern easier to trace. Cropland remains widespread through the south and east, while forest is more continuous on the western side and in several interior blocks. Wetlands cut across both covers rather than forming a separate zone. The northern part is the most mixed: smaller field units, wooded patches, and wetland strips repeat over short distances, producing a much more fragmented appearance than the broad agricultural ground farther south.
The legend also prevents a common reading error. Not every light or open-looking area is cropland. Grassland represents 3.00% and shrubland 2.76%, and those classes can sit beside or within larger agricultural areas. When the map is used in a lesson, report, or presentation, keeping cropland, pasture-like cover, shrub or grass, and forest separate gives a more accurate picture of the surface. The map describes cover, not the legal use of a parcel or a farm-management decision.
The built footprint is concentrated around one strong central cluster
Developed cover represents 6.94% of the 2025 summary, but its location makes it much more noticeable than that percentage might suggest. The largest red concentration sits near the middle of the county. Thin developed lines extend away from it in several directions, while smaller spots occur elsewhere. Away from that center, most of Worth County quickly returns to agriculture, forest, wetlands, and other open land. The contrast between one strong developed area and a broad rural landscape is one of the clearest features in the map set.
The supplied maps do not label municipal boundaries or road names, so the red cluster should be described as a mapped central developed concentration rather than assigned to a particular boundary without another verified layer. This caution is important for every class. Land cover describes what appears to cover the surface, such as crops, trees, water, or developed ground. It does not determine zoning, ownership, parcel lines, development rights, or current site conditions.
Impervious cover averages only 1.24%, with the highest values packed into the same center
Impervious surface means hard ground such as roofs and pavement where water does not readily soak into the soil. Worth County has a mean impervious value of 1.24%, and just 0.26% of the county is mapped at 50% or more impervious. Most of the image is therefore very pale. The exception is the central built cluster, where orange and red values are much denser. Fine lines radiate outward and smaller marks appear elsewhere, but none approach the concentration in the middle.
The difference between 6.94% developed cover and a 1.24% mean impervious value is expected. Developed land-cover classes can include lawns, trees, soil, and other permeable surfaces around buildings and roads. Impervious percentage focuses more directly on rooftops, paved roads, parking areas, and similar hard materials. One map therefore answers how much area is classified as developed, while the other helps show how intensely hard surfaces occupy those developed places.
The 1985–2025 comparison is more dispersed than the current development pattern
The endpoint comparison reports a total class difference of 21.47%. Forest loss accounts for 7.77%, agricultural loss for 4.50%, land classified as developed for 1.53%, and other class difference for 5.17%. Orange forest-loss patches are especially frequent through the northern half, but smaller pieces continue into the south and east. Red developed-change cells are less extensive and stand out more around the current central developed concentration.
That 21.47% figure should not be read as the share of the county that was built over, cleared, or permanently damaged. It is the share of cells assigned a different class at the 1985 and 2025 endpoints, and the map notes that classification variation can contribute to the difference. A location may have changed more than once between those years, and the two-date image cannot identify the exact timing or cause. Intermediate aerial imagery and local records are needed for that level of explanation.
Choose the map that matches the question instead of shrinking all four into one view
The current land-cover overview is the best starting point for a general county description because it shows the complete mix of agriculture, forest, wetlands, developed land, grassland, shrubland, and water. The forest-and-farmland view is better when the main topic is the relationship between working land and natural cover. For development intensity, the impervious map removes much of the other information and makes the central hard-surface concentration easy to see.
The change map works best after the present-day pattern has been explained. Its colors represent differences between two dates rather than current land-cover classes, so showing it first can lead viewers to mistake orange or purple areas for present forest or farmland. In a presentation, use the 2025 overview as the base image and add one supporting view for the specific point being discussed. That keeps the legend readable and makes comparisons easier for people who do not work with GIS data.
Raster cells, mixed surfaces, and the observation year limit fine-scale interpretation
Annual NLCD is a raster dataset, meaning the landscape is divided into small grid cells and each cell receives one predominant land-cover class. Real places are more complicated. A single area may contain crops, grass, a drainage ditch, trees, a road, and a building close together, yet the map must summarize that space with one main class. Narrow roads, small ponds, thin wetland strips, and field edges can therefore appear wider, narrower, or less continuous than they are on the ground.
The current maps represent the supplied 2025 classification year. Construction, harvest, planting, vegetation recovery, water conditions, or other changes after that observation are not guaranteed to appear. Enlarging the JPG can make the legend and pattern easier to see, but it cannot recover parcel-level detail that is not in the underlying data. Property boundaries, wetland jurisdiction, permits, zoning, ownership, and current field conditions require newer and more specialized sources.
What is included in the original JPG package
The download contains one original JPG for each of the four views used in this article: 2025 land cover, forest and farmland, impervious surface and developed land, and 1985–2025 land-cover change. Each image measures 2480 × 1754 pixels. The supplied package does not identify these files as meeting an A3 high-resolution reference, so test a proof before using them for a large poster. Their original dimensions are well suited to screens, ordinary reports, presentations, and many standard print layouts.
For a one-image county profile, start with the general land-cover file. Add the vegetation map when the discussion centers on farms, forest, or wetlands; use the impervious image when roads, rooftops, and concentrated hard surfaces are the subject; and use the change map only when a two-date comparison is needed. Keeping the four files separate avoids a crowded composite and lets each legend remain readable.
Frequently Asked Questions
What is the largest land-cover class in Worth County?
Agriculture is the largest class in the supplied 2025 summary at 42.58%. Forest follows at 25.00%, wetlands at 18.40%, and developed land at 6.94%. The map shows the broadest agricultural areas in the south and east, with forest and wetland bands breaking them into smaller units.
Why is developed cover 6.94% while mean impervious cover is only 1.24%?
Developed land can include lawns, trees, soil, and other permeable surfaces around buildings and roads. Impervious cover focuses on hard materials such as rooftops and pavement. The measures are related but do not represent the same thing, so a lower countywide impervious average is expected.
Does the 21.47% class difference mean 21.47% of Worth County was developed?
No. It is the share of cells classified differently in the 1985 and 2025 endpoint comparison. The total includes 7.77% forest loss, 4.50% agricultural loss, 1.53% classified as developed, 5.17% other difference, and additional mapped differences. Classification variation may also contribute, so timing and cause require additional evidence.
Sources and Reference Data
- MRLC Annual NLCD Data – official access point for the land-cover and annual comparison products used for county-scale mapping
- USGS Annual NLCD Land Cover Classification – definitions for forest, agriculture, wetlands, developed land, and other mapped classes
- U.S. Census Bureau TIGER/Line Shapefiles – official geographic boundary source for county mapping
Map File Information
The ZIP contains the four original Worth County JPG maps used to compare 2025 land cover, forest and farmland, impervious surfaces, and 1985–2025 class differences.
- Included Files: Use only the versions confirmed in the article and supplied images
- File Type: Use the confirmed download contents
- Intended Use: Printing, education, presentations, and map-based projects
Related Maps
- Appling County Georgia Land Cover Map
- Atkinson County Georgia Land Cover Map
- Bacon County Georgia Land Cover Map
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.
