Intern, Geospatial Analysis

American Forests

1d•$25•Remote

About The Position

As wildfires reshape our forests at an unprecedented scale, understanding how to best support our forests following high severity wildfire in a changing climate is key to maintaining forest cover and the ecosystem services they provide. American Forests’ is working to better understand post-fire forest dynamics and translate that understanding into improved climate-informed planting prescriptions. As a Geospatial Analysis Intern, your primary project will focus on assessing spatial patterns of natural regeneration in post-fire landscapes and comparing those patterns to various planting orientations (e.g., grid, clustered, random) as progressive mortality occurs over time. By analyzing how natural spatial structure emerges and evolves, and how different planted configurations converge toward or diverge from those natural patterns, you will help inform reforestation prescriptions that produce more resilient, ecologically appropriate forest structures. In addition to this core research, you may support related work such as assessing monitoring data, identifying ideal spatial orientations and planting densities, and other analytical duties as needed. Working closely with the Reforestation Field Managers and the regional Senior Manager, you will develop spatial analyses, simulations, and data products that directly inform on-the-ground reforestation decisions. This internship is ideal for a graduate student or recent graduate eager to apply spatial statistics and geospatial analysis skills to real-world forest restoration challenges.

Requirements

Currently enrolled in or recently completed a Master’s degree in Geospatial Analysis, GIS, Remote Sensing, Forestry, Environmental Science, or a related field.
Proficiency with ArcGIS Pro and/or ArcGIS Online.
Programming experience in Python and/or R for geospatial data processing and analysis.
Working knowledge of PostgreSQL/PostGIS for spatial database management.
Foundational knowledge of spatial statistics and remote sensing techniques.
Self-starter with attention to detail, willingness to learn, and strong communication skills.

Responsibilities

Characterize the spatial structure of natural regeneration in post-fire landscapes using point pattern analysis (e.g., Ripley’s K/L functions, pair correlation functions, nearest-neighbor distributions) to quantify clustering, regularity, and randomness at multiple scales.
Simulate and compare various planting orientations (grid, clustered, staggered, random) and model how progressive mortality reshapes planted spatial patterns over time toward or away from naturally regenerated structures.
Analyze how site conditions (slope, aspect, soil type, burn severity, distance to seed source) influence the spatial patterns of natural regeneration and seedling survival.
Develop metrics and indices to quantify the degree of convergence between planted configurations and natural spatial patterns as stands mature and experience mortality.
Process and analyze remote sensing data (e.g., Landsat, Sentinel-2, LiDAR) and field survey data to map regeneration patterns, stem densities, and canopy structure across project areas.
Build reproducible analytical workflows in Python and/or R to automate spatial pattern analyses and mortality simulations, ensuring methods are well-documented and transferable.
Create maps, figures, and data visualizations that communicate spatial analysis findings to field managers, partners, and USDA Forest Service staff.
Assess reforestation monitoring data (stocking surveys, survival surveys, growth measurements) to evaluate planting outcomes and identify trends across project sites and planting configurations.
Analyze planting density and spatial orientation data to identify configurations that optimize seedling survival, growth, and long-term stand resilience under varying site conditions.
Support the development of evidence-based planting density and orientation recommendations by synthesizing spatial analysis results with field monitoring data and published literature.
Develop and maintain geospatial databases in PostgreSQL/PostGIS to store, query, and manage regeneration survey data, planting records, and spatial analysis outputs.
Integrate field-collected data (GPS points, ESRI Field Maps data, survey results) into centralized databases and ensure data quality through validation and documentation where needed.
Support additional geospatial analysis tasks as needed, including burn severity mapping, seed source proximity analysis, site suitability modeling, and other duties as assigned.
Collaborate with Reforestation Field Managers to translate spatial analysis findings into actionable planting prescriptions and field guidance.
Prepare technical summaries, maps, and presentations communicating research results to internal teams and external partners including USDA Forest Service staff.
Coordinate with research scientists whose studies are being implemented as part of post-recovery project areas.
Document workflows, methodologies, and standard operating procedures to support institutional knowledge transfer and reproducibility.