Southern Alabama Prescribed Burn AOI

Here’s the general aoi (a bounding box that’s a big larger) of a prescribed burn that happened on Feb 19, 2023 as part of a longleaf pine restoration initiative. This burn is located in Conecuh National Forest, close to the town of Wing, AL. This particular burn was a broadcast burn carried out with a drip torch, and intended as a fuels reduction treatment. Approximately 137 acres were estimated to have been treated.

https://www.planet.com/stories/prescribed-burn-in-southern-al-1a_Qly-NR Planet time series of burned site

Here’s what the burn perimeter looks like (the lighter color blue shape) with reference to the aoi I sent (the black dotted line box) approximately:

The polygon shown above can also be viewed using this GUI:
https://data-usfs.hub.arcgis.com/datasets/usfs::hazardous-fuel-treatment-reduction-polygon-feature-layer/explore?location=31.009123%2C-86.641976%2C12.86

Relationship between GEDI Measurements and Rx Burning

Importance of each ecological variable in practicing prescribed burning in long leaf pine forests:

• ground elevation (within the forest)
  • Reason this data is valuable for prescribed burn management: Accurate and full coverage elevation and slope data for under forest canopies is not frequently available at high resolution or incompletely available. Elevation can correlate to vegetation densities, seasonalities/phenology, and impact the likelihood of fire direction of spread and spread rate (if on a sloped terrain, such as a mountainous region). For example, on steep slopes, fire is more likely to spread up a hill/mountain. However, the aoi provided does not have steep slopes/terrain and is not high elevation, so it is unlikely that elevation would impact fire spread in this case.
  • Potential advantages to having these metrics: Improved understanding of habitat zones, fire direction of spread likelihood, and difficulty of access to planned burn areas can improve field campaign team building, equipment planning, and size of the burn area to maintain and keep under control and where managers will need to be stationed in case of uncontrolled spread.
  • Potential applications now possible with this data: improved/more detailed elevation and slope maps can help foresters prioritize field campaigns schedules, equipment selection, and emergency fire prevention management due to understanding of potential spread rates and directional patterns that are improved by elevation data.
• canopy top height
  • Reason this data is valuable for prescribed burn management: For a LLP forest, this would again indicate forest composition. Since the return value would be an average value over a pixel that covers multiple trees, a lower value would probably indicate lower tree density for LLP-dominated stands, or in mixed stands, the signal meaning may be more complex. However, it can contribute to characterizing the forest. Lower values of canopy top height within an area with known LLP presence may either indicate more widely spaced trees, or the prevalence of more mid- to understory species.
  • Potential advantages to having these metrics: This metric might be difficult to apply by itself to questions related to fuels or succession in LLP forests, but can still give information that would be difficult to collect in the field.
  • Potential applications now possible with this data: Could be an indicator of fuels, but there are many factors that could be involved in this metric’s value.
• relative height metrics (vertical profile of a sample)
  • Reason this data is valuable for prescribed burn management: this could give an idea of where most of the biomass is concentrated within a vertical profile, which could be a measure of how open the forest is and the proportion of canopy biomass to ground cover or understory. Burn managers would likely want to know how much ground cover or understory is present compared to the tree canopy.
  • Potential advantages to having these metrics: This info would give land managers or foresters an idea of how crowded or open a forested area is, and whether they may want to consider fuel reduction.
  • Potential applications now possible with this data: Decision makers could use this data to help monitor succession after a burn, or decide when to perform fuels reduction treatments (prescribed burns).
• total canopy cover (cover)
  • Reason this data is valuable for prescribed burn management: Canopy cover may give an idea of tree density. Generally, longleaf pine ecosystems have more space in between trees, resulting in a forest that is more open and park-like. However, longleaf can exist within a mixed forest as well, resulting in denser trees. I think canopy cover could hint at forest species composition, with lower canopy cover indicating more ‘pure’ stands of longleaf, and greater canopy cover indicating mixed stands. Tracking total canopy cover overtime in a known longleaf pine stand could also give an idea of successional dynamics (longleaf is prone to convert to mixed or other forest types over time if regular fire is not present on the landscape).
  • Potential advantages to having these metrics: Better understanding of forest composition, ecosystem type, and succession dynamics
  • Potential applications now possible with this data: Forest managers could use this data to track forest succession over longer time periods, or to possibly characterize longleaf pine stands.
• vertical canopy cover profile (cover_z)
  • Reason this data is valuable for prescribed burn management: Although tree spacing in LLP forests may be relatively large, understory or ground cover may look different. Getting a vertical profile of canopy cover would likely be more useful than a total (or top-level?) canopy cover, since it would also provide information on fuel density.
  • Potential advantages to having these metrics:
  • Potential applications now possible with this data: This would help managers decide when to burn, likely in tandem with field visits. It could potentially decrease the amount of time spent doing field work if it is calibrated well and has greater spatial coverage.
• total plant area index (PAI)
  • Reason this data is valuable for prescribed burn management: PAI would provide info on fuels coverage, or how much of the ground is covered by fuels.
  • Potential advantages to having these metrics:
  • Potential applications now possible with this data: Similar to canopy cover and vertical profile, it could inform whether fuels treatments are needed in a particular area. This measurement would probably be more useful than canopy measurements, as usually only understory and ground cover (not canopy trees) are intended to be burned when doing a fuels reduction treatment in a LLP ecosystem (trees are not meant to be killed or removed, but might be charred during the burn.)
• vertical plant area index profile (PAI_z)
  • Reason this data is valuable for prescribed burn management: See the above response. The only difference here is that more information is provided in this measurement.
  • Potential advantages to having these metrics:
  • Potential applications now possible with this data:
• vertical plant area volume density profile (PAVD_z)
  • Reason this data is valuable for prescribed burn management: plant area volume density versus plant area along the vertical axis of a sample typically offers an improved understanding of fuel density over other measures. This probably wouldn’t offer enough information to identify plants on a species level, but it could give an idea of ecosystem type and basic composition (how much understory biomass vs canopy? Is it more likely a mixed forest stand or heavily LLP?)
  • Potential advantages to having these metrics: Quantifying regrowth rates, vegetation density
  • Potential applications now possible with this data: Decide on burn zones for highest density plant areas or decide to burn only understory or topstory at certain dates.
• foliage height diversity index (FHD)
  • Reason this data is valuable for prescribed burn management: This could be an indicator for forest health. It’s not clear to me if a healthy LLP ecosystem would have higher or lower FDH (more likely something in between), but since pure LLP stands tend to have lots of low vegetation and high vegetation and not as much in between, this could indicate the health, maintenance, or composition of a LLP forest.
  • Potential advantages to having these metrics:
  • Potential applications now possible with this data: Similar to other variables (detecting fuels buildup or forest composition.)
• above ground biomass
  • Reason this data is valuable for prescribed burn management: Biomass would be helpful in assessing the fuel load / buildup of the forest. Fuel loads for LLP shouldn’t be too high, or else they are more prone to destructive wildfire, or to transition into other forest types.
  • Potential advantages to having these metrics: AGB is difficult to measure for a large area using field measurements. Using a remotely sensed product would be extremely helpful for estimating biomass.
  • Potential applications now possible with this data: LLP stands with high biomass density could be prioritized for treatment.
• structural complexity index WSCI
  • Reason this data is valuable for prescribed burn management: It would seem that LLP stands in need of fuels reduction treatments or burning would have a greater structural complexity, so this variable could be related to
  • Potential advantages to having these metrics:
  • Potential applications now possible with this data: This could be used to assess the need for burning and prioritize accordingly.

Formal Site Description

Prompts:

1. who (partners, SCO pitch, impacted people or business, etc. in SE USA and alabama),
2. what (what is prescribed burning, how it works, how they are planned and managed in this area, what role they play and why they are important to improve),
3. where (where prescribed burns occur, changing patterns of occurrence, more details on the provided AOI for this tutorial,
4. when (what is the observation for this provided AOI and why this selected time period),
5. why (what is the current challenge, what don’t we understand and why? who is acting to solve these issues, why explore the capabilities of GEDI in this tutorial for burns, and
6. how (how can remote sensing help address this issue? insert previous description of GEDI data products relevance to this application.)

Answers:

1. Who: Early forestry in the Southeast US, as well as indigenous communities, led the way in adopting prescribed (Rx) fire practices in the 20th century, while much of the west was still suppressing all fire. Various entities, including indigenous communities, recognized the benefits of using fire to preserve and encourage generation of longleaf pine ecosystems in the south. In Alabama today, various agencies employ Rx fire or support the use of Rx fire to manage forests, including the US Forest Service (USFS), The Nature Conservancy (TNC), the Alabama Forestry Commission, and the Alabama Forestry Foundation. Other agencies aim to conduct field conservation activities and connect with landowners on longleaf pine conservation and restoration, including the US Department of Agriculture’s Longleaf Pine Initiative and The Longleaf Alliance. Earthrise is currently partnering with TNC and the USFS to study post-burn vegetation dynamics and impacts of prescribed burning in Alabama.
2. What: Wildland fires, referring to all fires occurring in the natural environment, include both prescribed (Rx) fires and wildfires. Rx fires are planned and controlled fires used as a landscape or forest management tool. It can be used as a ‘treatment’ to manage natural resources and wildlife, encourage forest regeneration, maintain native ecosystems, and to prevent large wildfires. In Alabama, burns must be supervised by a certified prescribed burn manager, have a written prescription that is witnessed or notarized, and obtain a burning permit from the Alabama Forestry Commission (AFC). Comprehensive planning is done before undertaking a prescribed burn, including an evaluation of weather conditions and plans for the methods that will be used for carrying out and controlling the burn, as well as planned fire breaks. It is often carried out by trained professionals or landowners using drip torches or chemical ignition using ‘ping pong’ balls. Entities such as TNC and the Forest Service submit detailed plans for prescribed burns ahead of time, and will conduct burns at frequencies depending on the needs of the particular ecosystem.
3. Where: Rx burns are conducted on USFS managed lands and on National Park Service managed lands, wherever it is deemed appropriate for land management. The AOI represents an area where Rx burning was conducted by the USFS as part of a hazardous fuel reduction treatment, and as part of the Boggy Hollow Longleaf Pine Management Project (according to National Environmental Policy Act (NEPA) documentation). This AOI was also burned in 2012, 2014, 2016, 2021, 2024, and partly burned in 2019, as part of the Conecuh National Forest Prescribed Burning Program and the Longleaf Ecosystem Restoration II Project.
4. When: The AOI burn date was February 19, 2023 (this burn was clearly visible using high-resolution Planet imagery). However, the AOI was also burned in 2012, 2014, 2016, 2021, 2024, and partly burned in 2019, as part of the Conecuh National Forest Prescribed Burning Program and the Longleaf Ecosystem Restoration II Project. Longleaf ecosystems benefit from frequent burning every 2 to 3 years (according to our conversations with the USFS and TNC), and the management history shows this.
5. Why: Although the AOI chosen is in Conecuh National Forest, which has several longleaf pine (LLP) management and restoration initiatives undertaking frequent Rx burns, management of LLP in other national forests in Alabama may not be resourced in the same way. My project team has heard from USFS and TNC contacts that there are currently not enough staff to conduct Rx burns, and so the frequency of burning may be limited by this factor. On private land, there are not enough landowners trained and qualified to burn their own land, and so they may rely on Forest Service or contracted professionals. People conducting prescribed burns do site visits to assess site conditions for burning, and may also revisit the site after a burn, which can be time-consuming, especially with a backup of sites to burn. Monitoring vegetation dynamics with remote sensing, such as Lidar, may provide supplemental information and a wider spatial coverage of information to forest managers ahead of or after a burn, reducing strain on staff. Using remote sensing can also help assess the impacts and ‘success’ of Rx burns multiple points in time. Since the impacts or severity of a burn may vary spatially over the area, especially for large burns, it can be especially important to have a broader view, which remote sensing can provide. Similarly, Lidar can also be used to assess the same things related to wildfire.
6. How: (summary of previous description of GEDI data products relevance)
   1. GEDI variables related to fuels and topography, like ground elevation, biomass, and total plant area index, could be used to give information on fire behavior, such as spread rate or direction (important for planning risks and fire breaks ahead of a burn).
   2. GEDI variables related to forest/understory structure, like relative height metrics, vertical plant area index profile, vertical plant area volume density profile, structural complexity index, and foliage height diversity index could also give a better idea of the fuel load in the forest, and could potentially differentiate between the effects of burning on these fuels versus on trees. It could also help determine whether a fuel treatment is needed. It may be possible to study post-burn effects over time using this data.
   3. GEDI variables related to canopy height, such as canopy top height and canopy cover, may give an idea on the density, maturity, or prevalence of LLP trees in a given area (a higher average value may indicate a more mature forest, or higher density of trees compared to other vegetation).

   Overall, GEDI (or other Lidar) could help to assess the need or frequency of burning, and to monitor post-burn impacts.

Paragraph format:

Prescribed fire (Rx fire) has long been used in the Southeast, first by Indigenous communities and later by foresters in the 20th century, even as much of the western U.S. suppressed fire. Fire was recognized as essential for sustaining longleaf pine ecosystems, and today agencies such as the U.S. Forest Service (USFS), The Nature Conservancy (TNC), the Alabama Forestry Commission, and the Alabama Forestry Foundation continue to employ or support prescribed fire. Conservation initiatives—including the USDA Longleaf Pine Initiative and The Longleaf Alliance—work with landowners to restore longleaf ecosystems. Earthrise is currently partnering with TNC and USFS to study post-burn vegetation dynamics and the ecological impacts of Rx fire in Alabama.

Wildland fire encompasses both wildfires and prescribed burns. Rx fires are carefully planned, permitted, and supervised by certified managers to achieve multiple goals: reducing hazardous fuels, regenerating forests, maintaining ecosystems, supporting wildlife, and preventing severe wildfires. Burns require written prescriptions, weather assessments, fire breaks, and approved ignition methods such as drip torches or chemical ignition. Entities like TNC and USFS develop detailed plans and adjust burn frequency based on ecosystem needs—longleaf pine systems typically require fire every two to three years.

In Conecuh National Forest, Rx fire is a central part of longleaf management, with burns documented in 2012, 2014, 2016, 2019 (partial), 2021, 2023, and 2024 under the Conecuh Prescribed Burning Program and the Longleaf Ecosystem Restoration II Project. The most recent burn in the AOI occurred on February 19, 2023, observed in high-resolution satellite imagery. However, while Conecuh benefits from frequent burns, other forests in Alabama face staffing and resource shortages, limiting burn frequency. Many private landowners also lack the training or certification to conduct burns, relying instead on agencies or contractors. This creates backlogs in site visits, implementation, and post-burn monitoring.

Remote sensing tools, such as GEDI lidar, can help address these challenges. GEDI data products provide information on fuels, forest structure, and canopy conditions. Metrics related to elevation, biomass, plant area index, and understory structure can improve planning, indicating fuel loads, fire behavior, and treatment needs. Post-burn, GEDI can help track vegetation recovery, assess effectiveness, and compare prescribed fire impacts with those of wildfires. By expanding monitoring capacity beyond field visits, these data can support more effective and frequent fire management in longleaf ecosystems.

Overall, GEDI (or other Lidar) could help to assess the need or frequency of burning, and to monitor post-burn impacts.

Potential condensed:

Prescribed fire (Rx fire) has deep roots in the Southeast, first practiced by Indigenous communities and later by foresters in the 20th century to sustain longleaf pine ecosystems, while much of the West still suppressed fire. Today, agencies such as the U.S. Forest Service (USFS), The Nature Conservancy (TNC), the Alabama Forestry Commission, and the Alabama Forestry Foundation continue to use and promote Rx fire, with support from initiatives like the USDA Longleaf Pine Initiative and The Longleaf Alliance. In Alabama, burns must follow strict protocols that are supervised by certified managers and require permits. The burns are guided by detailed decision making processes that account for weather, fire breaks, and ignition methods. Longleaf ecosystems typically require fire every 2-3 years, and in Conecuh National Forest, Rx burns have been carried out regularly as part of longleaf restoration projects, including the most recent on February 19, 2023.

Despite Conecuh’s success, staffing shortages and limited resources constrain the frequency of Rx fire in other forests and on private lands, where many landowners rely on agencies or contractors. Monitoring also demands significant time for site visits and follow-up surveys. Satellite remote sensing tools like GEDI lidar can help close these gaps by providing data on fuels, structure, and canopy conditions. Metrics such as biomass, plant area index, canopy height, and structural complexity can improve data-driven burn planning, assess fuel loads, and track post-burn recovery, offering broader coverage than field monitoring alone. By supplementing limited staff capacity, GEDI data can strengthen prescribed fire programs and support the long-term health of Alabama’s longleaf pine ecosystems.