This session will feature a range of remote sensing and GIS projects primarily from our student attendees.
10:30 AM – 10:45 AM – Generating High-Resolution Crop Canopy Height Information Using SkySat Stereopairs
Large-scale phenotyping of crops is pivotal in advancing precision agriculture (PA). It is fundamental to plant breeding that aims to develop improved crop varieties with desirable traits and enhanced nutritional or industrial value. While Uncrewed Aircraft Systems (UAS) have emerged as a popular tool for phenotyping, UAS are limited in area coverage, require consistent calibration and validation, and may suffer from inconsistencies due to variations in flight conditions, sensor performance, and environmental factors. This study addresses these challenges by presenting a farm-scale crop canopy height estimation framework that utilizes high-resolution SkySat tri-stereo satellite imagery, tailored specifically for low-relief agricultural landscapes. The methodology centers on a customized stereophotogrammetric workflow that integrates dual-season image acquisitions and Rational Polynomial Coefficient (RPC) refinement using high-precision Ground Control Points (GCPs). Early-season imagery is used to generate a Digital Terrain Model (DTM), capturing bare-earth conditions, while late-season acquisitions produce Digital Surface Models (DSMs) that reflect the full canopy structure. By differencing these temporally paired surfaces, absolute canopy height models are derived at 1-meter spatial resolution. Initial results over selected farm fields in Driscoll, Texas show the workflow estimates canopy height to 2 m vertical accuracy when compared to UAS lidar derived canopy height. This result demonstrates the feasibility of accurate crop height estimation using commercial satellite imagery and provides a transferable methodology for broader agricultural monitoring applications.
Benjamin Ghansah, Texas A&M University-Corpus Christi
10:45 AM – 11:00 AM – Utilizing GeoAI for Rapid and Accurate Damage Assessment
Geographic Information System (GIS) Artificial Intelligence [GeoAI] is a tool for analyzing and interpreting large spatial data with many applications. GeoAI enhances interpretation, analytics, and scalability of spatial data processing. GeoAI can extract data that may be hidden that normally would be looked over. GeoAI can aid a geospatial analyst in various needs and expedite the process of analyzing datasets and complex functions, thus increasing productivity. Subsets of GeoAI such as machine learning [ML] and deep learning [DL], allow for the automation of complex tasks such as data classification, object detection, and pattern recognition. AI/ML/DL models re[1]quire a substantial amount of data to train a model. To reduce the amount of data needed to train a model, Pre-trained deep learning models were used. Pre-trained AI models are ML models that are trained on large datasets that can be implemented in a GIS environment to address problems such as hurricane damage assessment in south Louisiana. The objective of this presentation is to showcase the significance of GeoAI in damage assessment classification and detection. Using pre-trained DL AI models as well as developing our own DL models, post-hurricane damage assessment and other use case in South Louisiana were attempted. The results from this ongoing project are presented.
Samuel Landry, Nicholls State University
11:00 AM – 11:15 AM – Mapping Seagrass-Coral Reef Mosaics Using Satellite Imagery to Quantify Seascape Structure in the Florida Keys
Seagrass meadows are vital benthic habitats that support biodiversity, facilitate biogeochemical cycling, and provide nursery and foraging grounds for a range of ecologically and economically important species. In the Florida Keys, seagrasses form dynamic mosaics with coral reef habitats, shaping the structure of benthic ecosystems across the seascape. Understanding the spatial structure of these mosaics (i.e., how seagrass and reef patches are distributed, shaped, and arranged) is critical for informing management decisions. However, these structural patterns remain under-characterized, especially at fine spatial resolutions. This study aims to quantify the seascape structure of seagrass–coral reef systems across three areas of interest along the Florida Keys Reef Tract: the Upper, Middle, and Lower Keys. These regions span a gradient of environmental conditions and disturbance regimes and were selected to reflect spatial heterogeneity within the broader reef tract. By integrating high-resolution PlanetScope satellite imagery (3-meter spatial resolution) with in situ ground-truth data, we developed benthic habitat maps tailored to the ecological characteristics of each area. The mapping approach combined supervised classification with a machine learning Random Forest algorithm, trained using field-based observations of benthic cover collected by the project team. The resulting maps varied in thematic resolution across the three regions, depending on local complexity and spectral properties of both habitats and data. Habitat classes include seagrass meadows, coral reef structures, bare sand, and hardbottom features. Classification schemes were customized per region to ensure ecological coherence and maximize classification accuracy. From the classified maps, we derived a suite of spatial pattern metrics to characterize the structural properties of seagrass beds as well as their spatial relationship to adjacent coral reef features. These metrics provide quantitative insight into habitat configuration, heterogeneity, and connectivity, which are critical drivers of ecological interactions such as species movement, trophic linkages, and resilience to disturbance. By integrating satellite remote sensing with field-based validation and landscape ecology techniques, this study advances our capacity to assess benthic habitat structure in complex seascapes. The resulting spatial data products and metrics offer valuable tools for resource managers aiming to protect and sustain the ecological integrity of the Florida Keys coastal seascapes.
Marianna Coppola, Florida International University
11:15 AM – 11:30 AM – Mapping Tomorrow: Igniting Geospatial Futures
A discussion for college students and folks that are early career geospatial. Focuses on talking about what skillsets are the most important for early career geospatial folks to succeed and advance in the industry. The discussion concludes with what ideas do early careers have to improve and revolutionize the industry, and how can we all get involved to support these.
Kimberly Mantey, U.S. Geological Survey: National Geospatial Technical Operations Center
11:30 AM – 11:45 AM – Integrated Geospatial Analysis of Burn Severity and Vegetation Recovery in the August Complex Fire (2020)
The August Complex Fire of 2020 was the largest wildfire in California history, burning over one million acres across the Mendocino National Forest. This study evaluates vegetation recovery over a 10-year period (2015–2025) integrating Landsat-derived Normalized Burn Ratio (NBR), differenced NBR (dNBR), hotspot analysis, and lidar-derived elevation and vegetation structure metrics. NBR change was assessed for pre-fire, fire-year, and post-fire conditions to quantify burn severity and recovery percentage. Hotspot (Gi*) analysis revealed spatial clusters of high-severity burn patterns, while lidar data provided insights into canopy loss and early regrowth patterns. Results indicate that vegetation has recovered to roughly 80–85% of pre-fire conditions by 2025, with the strongest regrowth occurring in low- and moderate-severity areas. High-severity zones show slower recovery, supported by both dNBR and lidar-derived canopy height. This project demonstrates how combining advanced GIS, remote sensing, and lidar techniques can provide a comprehensive assessment of post-fire landscape dynamics.
Dharm Barot, Embry-Riddle Aeronautical University
