Assessing Southern Pine Beetle Epidemics in Alabama Using NASA Earth Observations

EarthzineDEVELOP Spring 2017 Article Session, DEVELOP Virtual Poster Session, In-Depth

Image A. Historical Southern Pine Beetle Density Maps showing the coverage of SPB recorded outbreaks from 2010 to 2015. Image Credit: NASA DEVELOP

This article is a part of the NASA DEVELOP’s Spring 2017 Article Session. For more articles like these, click here

Southern pine beetles are known to have caused several hundreds of thousands of dollars in damages in Alabama due to their widespread destruction of pine trees. This project aimed to model areas in Alabama that are susceptible to outbreaks using NASA Earth observations.

Authors:

Maggi Klug

Leigh Sinclair

Ryan Schick

Kelsey Herndon

The Southern pine beetle (SPB) (Dendroctonus frontalis) is an opportunistic species that attacks stressed trees weakened by drought, storm damage, or fire. SPB is one of the most destructive pine pests in the Southeastern United States causing hundreds of thousands of dollars in damage to pine trees each year (1). Damages incurred from the SPB may result in limited diversity in surrounding plants and harm other animals‰Ûª habitats, such as the near-threatened red-cockaded woodpecker. In 2000, about 18,600 acres of pine forest were damaged throughout the Bankhead National Forest due to SPB [2]. Our team partnered with Dace Casey, Dr. John Nowak, and Dr. Chris Asaro of the U.S. Forest Service (USFS), to assist in monitoring these destructive species.

During the time of this research, our team visited the Forest Service in Bankhead National Forest to ask about mitigation efforts and see the damages the SPB can create firsthand. Currently, the Forest Service uses expensive manned aerial surveys, such as aerial photographs and Light Detection and Ranging (LiDAR), as well as Moderate Resolution Imaging Spectroradiometer (MODIS) ForWarn and Forest Disturbance Monitor data. With the aerial data, field surveys must also be used to positively identify which beetle species is present before species-specific mitigation efforts can be implemented. For each species of beetle, specific management control and recommendations are needed as the beetles attack different pine trees at varying health and stress levels. Suppression techniques include the removal and salvage of infested trees, cut-and-leave, piling and burning, or chemical control.

To help our project partners in this effort, we aimed to create Historic Southern Pine Beetle Distribution Density Maps, a Southern Pine Beetle Prediction Map, and a Near Real-Time Southern Pine Beetle Susceptibility Model. The Historic Southern Pine Beetle Coverage Maps illustrate previous SPB infestations throughout Alabama, including the Bankhead National Forest, to determine which environmental thresholds are good indicators of SPB presence. The Southern Pine Beetle Future Prediction Map was created using the information gathered from the Historic Southern Pine Beetle Density Maps using the Maximum Entropy Distribution Model (MaxEnt) developed at Princeton University. The Near Real-Time Southern Pine Beetle Susceptibility Model determines which areas are susceptible to a SPB outbreak in near real-time by automatically downloading and analyzing the most recent Landsat 8 Operational Land Imager (OLI) imagery. Project partners used these methodologies and end products to help with SPB mitigation efforts.

The study area included Alabama, with a focus on the Bankhead National Forest (Forest Service). Land cover in Alabama is estimated to be 69 percent forest, consisting of mostly hardwoods and pine trees, with Bankhead National Forest being covered in mostly loblolly pine (Pinus taeda) and longleaf pine (Pinus palustris), which is the preferred host of the SPB [3]. The study period was 2010 to 2015, in addition to forecasting SPB susceptibility to 2050.

To create the Historic Southern Pine Beetle Density Maps, the team used in situ data of known SPB infestations gathered from the Alabama Forestry Commission. The time series of the coverage maps for 2010 to 2015 were created by using the ‰ÛÏPoint Density‰Û tool in ArcMap 10.3 with the in situ data. These maps (Image A) show how the outbreaks change from year-to-year with some years more intense than others.

The team forecasted future conditions for SPB suitability by projecting to 2050 using WorldClim climate data. Future climatic variables for 2050, including minimum temperature, maximum temperature, mean temperature, and 19 bioclimatic variables were downloaded from the WorldClim website. In addition, the team designated a baseline for determining which conditions are suitable to SPB, such as land cover type, elevation, and vegetation health. Land cover type was determined using Landscape Fire and Resources Management Planning Tools (LANDFIRE) data to identify tree species within Bankhead National Forest. Elevation was derived using Shuttle Radar Topography Mission (SRTM) version 2. Vegetation health was quantified by calculating the Normalized Difference Vegetation Index (NDVI) and the Green Red Vegetation Index (GRVI) over the study area with 30-meter Surface Reflectance data from Landsat 5 Thematic Mapper (TM) and Landsat 8 OLI [4] [5]. NDVI and GRVI are calculated by the following equation:

Where Near Infrared (NIR) is band 5 (0.85-0.88 ë_m) and Red is band 4 (0.64-0.67 ë_m), and Green is band 3 (0.53-0.59 ë_m) on Landsat 8 OLI.

Image B. MaxEnt model results that predict SPB susceptibility at 2050. Image Credit: NASA DEVELOP

All data were projected, clipped to the study area, and converted to ASCII files. These files, along with the WorldClim bioclimatic variables and in situ data (with 25 percent of the points being withheld for an accuracy assessment) were added into MaxEnt in order to model SPB habitat suitability. Understanding suitable SPB habitat informs the team which areas will be more susceptible to outbreaks. The model itself determines which tree species, elevation, NDVI, and GRVI values are suitable for the beetles based off of the in situ point data. After the model was run, the Southern Pine Beetle Prediction Map (Image B) was produced as output. From this, we determined that the southwestern portion of Alabama was the most at risk as it is the area with the most suitable environmental conditions for SPB. One main contribution to this is the fact that most of the trees in southwest Alabama are trees that the SPB prefers.

The bulk of this project consisted of the formation of a Python Script that produced a near real-time susceptibility model of SPB infestations. Tree species and elevation data were pulled in as variables, Landsat data were coded to download automatically once new satellite data became available, NDVI and GRVI were calculated, and all were used in the Fuzzy Logic Model in ArcMap. This model assigns Fuzzy Memberships for each variable and then creates a Fuzzy Overlay, to overlay areas that reveal to be the most suitable, based on the Fuzzy Memberships, giving us the Near Real-Time Southern Pine Beetle Susceptibility Map (Image C). The script produced a map for end users to determine which areas are most susceptible to a SPB infestation.

Image C. Output from the Near Real-Time Southern Pine Beetle Susceptibility Model created entirely by Python scripting. Image Credit: NASA DEVELOP

The Historic Southern Pine Beetle Density Maps showed a cyclical pattern of SPB infestations, supporting our project partners‰Ûª theory that the beetles are inactive for certain years, but return with ferocity. Following along the same spatial coverage as the historical maps, the Southern Pine Beetle Prediction maps illustrated that future outbreaks will occur in southwest portion of Alabama. This model had an accuracy of 86 percent. Finally, the Near Real-Time Southern Pine Beetle Susceptibility Model proved to be a very accurate tool, with an 88 percent accuracy, and provides the susceptibility in Alabama as new Landsat tiles become available. These end products were handed off to our partners, which they will use to manage several thousand acres of land upon the project‰Ûªs completion.

References

[1] J. R. Meeker, W.N. Dixon, J. L. Foltz, and T. R. Fasulo. ‰ÛÏFeatured Creatures: Southern Pine Beetle‰Û University of Florida, Institute of Food and Agricultural Sciences. November 2000.

[2] M. L. Addor and J. Birkhoff. ‰ÛÏBankhead National Forest Health & Restoration Initiative: Final Report‰Û. Natural Resources Leadership Institute, NC State University and RESOLVE, Inc. April 2016.

[3] ‰ÛÏAlabama Forest Facts‰Û Alabama Forestry Commission. 1999.

[4] Tucker, Compton J. ‰ÛÏRed and photographic infrared linear combinations for monitoring vegetation.‰Û Remote Sensing of Environment. 1979: 127-150.

[5] Motohka, Takeshi, Kenlo Nishida Nasahara, Hiroyuki Oguma, Satoshi Tsuchida. ‰ÛÏApplicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology‰Û Remote Sensing. 2010: 2369-2387

Author Biographies

Maggi Klug is a student from the University of Alabama in Huntsville working with DEVELOP at NASA Marshall Space Flight Center as the center lead and was an independent research consultant on the Alabama Ecological Forecasting project.

Leigh Sinclair is a recent graduate from the University of Alabama in Huntsville working with DEVELOP at NASA Marshall Space Flight Center as a mentor and was the center lead and an independent research consultant on the Alabama Ecological Forecasting project.

Ryan Schick is a recent graduate from the University of South Alabama who worked with DEVELOP at NASA Marshall Space Flight Center as an independent research consultant on the Alabama Ecological Forecasting project.

Kelsey Herndon is a student at the University of Alabama in Huntsville who worked with DEVELOP at NASA Marshall Space Flight Center as an independent research consultant on the Alabama Ecological Forecasting project.