www.alachua-wild.app
Near-real-time mapping of forest-cover change and oak-forest conservation priorities in Alachua County, Florida. |
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Geospatial analyses use data and/or input from the Alachua Conservation Trust (ACT), the Florida Natural Areas Inventory (FNAI), and Alachua County.
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Web-app launch instructions: Launch links are below in orange. Use a desktop browser with a good connection; the app may take up to ~5 minutes to fully load.
(1) The Alachua-Wild web app: live deforestation maps, oak-forest priority areas, and full statistics.
(2) High-resolution web viewer — inspect detected deforestation against high-resolution airborne/satellite imagery (2005–2023, or most recent) and Dynamic World tree-% (2016–present). Uses deforestation previously computed by the main app.
(3) Land-cover time-series web viewer — pixel-resolution land-cover % and time-series, as show in the three examples below.
(1) The Alachua-Wild web app: live deforestation maps, oak-forest priority areas, and full statistics.
(2) High-resolution web viewer — inspect detected deforestation against high-resolution airborne/satellite imagery (2005–2023, or most recent) and Dynamic World tree-% (2016–present). Uses deforestation previously computed by the main app.
(3) Land-cover time-series web viewer — pixel-resolution land-cover % and time-series, as show in the three examples below.
Tree % time-series: Oak forest -> Deforestation -> Development
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Tree % time-series: Oak forest -> Deforestation -> Pine plantation
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Tree % time-series: Pine forest -> intermittent prescribed fire
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Downloads: Static KMLs (oak-forest and all-forest priority conservation areas) and GeoTIFF rasters (deforestation year and binary) are posted intermittently to the download folder; dynamic, live files are available through the web app itself. Annual mean tree-% cover at 10 m for Alachua County is available for each full year from 2016 to present. Link here
Motivation: Alachua County's oak-dominated forests — upland hardwood forests and hammocks in which live oak, laurel oak and other oaks (Quercus spp.) are characteristic — are the focus of this project. They provide wildlife habitat and are mapped as distinct natural communities by the Florida Natural Areas Inventory (FNAI). The county lies within the North American Coastal Plain, a region identified as a global biodiversity hotspot on the basis of exceptional plant endemism and extensive historical habitat loss (Noss et al. 2014). The Alachua-Wild web app provides near-real-time, county-wide, objective information on forest-cover change — live deforestation maps, automated identification of priority conservation areas, and extensive spatial and statistical outputs — so residents, agencies, and partners can monitor change and draw their own conclusions.
Church Grove Forest (before)
Objectives: Our objective is to map and visualize recent forest-cover change across Alachua County using 10 m satellite data, and to identify and delineate key oak-forest ecosystems. The product is designed for rapid situational awareness, field planning, and clear communication with partners. By providing near-real-time, county-wide, objective information, we aim to support informed decision-making about resource use — for current residents and for future generations.
Results: In January 2017, mixed-species forest outside protected and urban areas covered approximately 114,300 acres in Alachua County, of which about 101,500 acres were oak-dominated. Between January 2017 and October 2025 the analysis mapped roughly 2,860 acres of oak forest as cleared — a mean of about 325 acres per year, or close to 2.8% of the oak baseline. Among the 100 highest-ranked oak conservation areas identified for the January 2017 baseline, about 3.9% had been cleared by October 2025. Within those priority oak areas, loss was higher in some municipalities — about 15% in Gainesville and 9% in Archer. The highest-ranked site, the "Kanapaha Forest" adjacent to West Paynes Prairie, lost roughly 20% of its area, much of it in a single 2019 clearing, and has continued to fragment. A simple linear extrapolation of the mean 2017-to-present loss rate — remaining oak area divided by average annual loss — projects county-wide oak forest reaching zero on the order of three centuries out. This is an illustrative extrapolation of a recent-average rate, not a prediction; actual trajectories depend on land-use decisions. Per-municipality and per-priority-area breakdowns are in the app's statistics panel and the downloadable tables.
Table 1 — Oak forest loss and linear "zero-year" projection, by region. Zero-year = the year oak forest would reach zero if the mean 2017-to-present loss rate continued unchanged. These are illustrative extrapolations of a recent-average rate, not predictions.
Church Grove Forest (1-week later)
Priority areas: The highest-ranked conservation area identified by the Alachua-Wild algorithm is the Kanapaha Forest core, adjacent to Paynes Prairie (outlined in red on the map). It is among the last large, contiguous stands of oak forest outside existing protected lands and contains exceptionally large oaks. Its position next to Paynes Prairie supports water quality, biodiversity, and habitat connectivity. Additional high-priority zones nearby — including stands adjacent to Kanapaha Prairie — extend this contiguous oak-forest network. Priority-area polygons can be viewed in the Alachua-Wild GIS static web map and are computed by the automated prioritization algorithm in the app. Caption: "Kanapaha Forest core — the highest-ranked oak-forest conservation priority in Alachua."
Please see a link here to view priority area polygons in the Alachua-Wild GIS static web map, and as are calculated using our automated prioritization algorithms in the Alachua-Wild web app.
Please see a link here to view priority area polygons in the Alachua-Wild GIS static web map, and as are calculated using our automated prioritization algorithms in the Alachua-Wild web app.
Kanapaha Forest core, the leading priority conservation area for Oak forest in Alachua
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Oak Forest conservation priority identification and mapping:
Alachua-Wild runs an automated, near-real-time algorithm that identifies the largest, most intact remaining oak-forest patches. It maps all oak forest in the county (FNAI oak classes), then uses a multi-scale connected-components procedure to delineate contiguous patches, integrating the near-real-time deforestation data. Each patch is scored by a weighted sum of patch area (weight 2.0) and compactness — mean distance-to-edge, favoring dense, interior-rich stands (weight 1.5). (Earlier versions also weighted mean tree height; that term was removed because the available 2020 canopy-height layer cannot represent the 2017 baseline.) The top-ranked area is the Kanapaha Forest core; the full ranked list is in the app. The top-ranked area is the Kanapaha Forest core, with a priority score of 3.50 — the maximum on this scale, because it ranks first on both terms (area 363 ha and mean distance-to-edge 197 m are each the highest in the set, giving 1.0 × 2.0 + 1.0 × 1.5). The next-ranked area scores 2.51. The full ranked list is in the app and in Table 2 below. |
Top 20 Oak forest priorities as identified (Oak Priority v27 algorithm)
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Deforestation priority events: Alachua-Wild also identifies the highest-impact deforestation events (2015–present) using the same multi-scale scoring as the conservation priorities. Event impact is scored by patch area and interior intactness (mean distance-to-edge) — the same two weighted terms used for conservation priorities. Rank #1 marks the most impactful loss patch mapped in Alachua since 2015.
#1 Oak Forest Conservation Priority (Kanapaha Forest; Red outline); with #2 most impactful deforestation event (pink)
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#3 Oak Forest Conservation Priority (Jonesville Forest; Red outline); with #5 most impactful deforestation event (pink)
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Oak Forest Conservation Priority (Red outlines); with most impactful deforestation events (pink) in Alachua County
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Significant deforestation events: (1) In mid-2019, the central portion of "Kanapaha Forest – North," a leading oak-forest conservation priority before the event, was cleared to bare soil and converted to a pine plantation — among the larger single oak-forest losses recorded in the region since monitoring began in 2015. (2) Rapid deforestation surrounded the remaining core of the Jonesville Forest, adjacent to the soccer fields (Google Earth location linked). Link to location on Google Earth here.
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2018 pre-clearing
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2019 post-clearing
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Methods: Forest cover is from Google Dynamic World (DW, v1) "trees" probability at 10 m over the Alachua County boundary (US Census TIGER/2018). Two fixed 12-month windows are used: an early window (the first 12 months of DW data for the county) and a recent window (the most recent 12 months). Pixels are analyzed only where both windows have at least one valid observation. A pixel is flagged as deforested when it was forest in the early window (trees ≥ 60%) and fell below 20% trees in the recent window; the earliest qualifying period sets the deforestation year. Single-period dips are shown separately as "forest disturbance." Analyses exclude aquatic and plantation classes (FNAI), and candidate/priority masks additionally exclude protected and urban areas. Priority areas are scored by patch area and compactness (mean distance-to-edge), as described above. Sentinel-2 true-color mosaics (with cloud/shadow masking) support visual validation. A 10 m canopy-height layer (ETH Global Canopy Height 2020) is provided as structural context on the map but is not used in the priority score. An optional AlphaEarth spectral-textural clustering (K = 30) summarizes morphological variability over pixels with ≥ 40% tree cover in the early or recent window. The deforestation map is produced at 10 m; area statistics are summarized at 50 m. For performance, some statistics and layers are precomputed and refreshed periodically; the load date reflects the current data.
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Deforestation (red), which are areas of permanent natural forest cover loss, and forest disturbances (orange), which are temporary and include areas of prescribed fire management and non-pine tree plantations (typically pecans in this area).
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Validation: Field and image-based validation is being added progressively: sites are selected to span location, intensity, and type of canopy loss, and assessed with high-resolution satellite imagery (0.5 to 3 m).
Future steps: Planned work includes deforestation area and rate within smaller areas of interest (city limits, water catchments, riparian zones), breakdowns by forest type and key tree species, time-series analysis of deforestation fate (e.g., low-density urban) and analysis of where the tallest trees, largest-crowns and highest-biomass stands occur — to help identify groves at elevated risk of conversion - for conservation prioritization.
References:
Noss, R.F., Platt, W.J., Sorrie, B.A., Weakley, A.S., Means, D.B., Costanza, J. & Peet, R.K. (2014) How global biodiversity hotspots may go unrecognized: lessons from the North American Coastal Plain. Diversity and Distributions 20: 236–244. https://onlinelibrary.wiley.com/doi/10.1111/ddi.12278
Lindenmayer, D.B. & Laurance, W.F. (2016) The ecology, distribution, conservation and management of large old trees. Biological Reviews 91: 723–743. https://onlinelibrary.wiley.com/doi/10.1111/brv.12290
Noss, R.F., Platt, W.J., Sorrie, B.A., Weakley, A.S., Means, D.B., Costanza, J. & Peet, R.K. (2014) How global biodiversity hotspots may go unrecognized: lessons from the North American Coastal Plain. Diversity and Distributions 20: 236–244. https://onlinelibrary.wiley.com/doi/10.1111/ddi.12278
Lindenmayer, D.B. & Laurance, W.F. (2016) The ecology, distribution, conservation and management of large old trees. Biological Reviews 91: 723–743. https://onlinelibrary.wiley.com/doi/10.1111/brv.12290
Disclaimer: This is a beta system. The results provided are intended for preliminary and informational purposes only. We make no representations or warranties regarding accuracy or completeness and disclaim liability for any damages arising from use. Users are solely responsible for interpretation and application. Portions of the software development and text revisions were completed with assistance from AI tools. System powered by Google Earth Engine, utilizing low- to zero-carbon energy sources.
