This session showcases fresh, original perspectives that tackle unique challenges in geospatial technology. Explore innovative approaches to gaining access to historical and archival imagery and discover new ways to modernize GIS for today’s needs.
4:30 PM – 4:45 PM – Satellite-Based Crop Classification for Navajo Nation: Integrating High-Confidence Cropland Data Layer Pixels with Sentinel-2 Data
The agricultural industry is the primary source of food, goods, and commodity services. Despite advancements in technology, predicting crop yield and productivity remains a significant challenge. Accurate predictions largely depend on effective crop monitoring and optimization of resources such as fertilizers, water, and pesticides. In the United States, crop classification primarily relies on open-source data from the United States Department of Agriculture (USDA), specifically the Crop Data Layer (CDL). This study focuses on the Navajo Nation, where agriculture is deeply tied to both livelihoods and cultural preservation, and the region remains highly vulnerable to drought impacts. To support sustainable agricultural practices, this research demonstrates crop classification using high-confidence pixels, which have high certainty based on strong agreement with reference data. These reliable pixels are derived from the USDA Cropland Data Layer (CDL) for the years 2017 and 2022. These years are selected to align with the Census of Agriculture statistics and supplemented with Sentinel-2 satellite data. The Random Forest machine learning algorithm is used to label crop data and spectral features from Sentinel-2 imagery and classify land cover types. To ensure robust model performance, the dataset is split into 80% for training and 20% for validation. This approach allows the Random Forest model to accurately distinguish between crop types and assess classification reliability, reinforcing the effectiveness of high-confidence pixels in regional crop mapping. Spectral indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Green Chlorophyll Vegetation Index (GCVI), and Land Surface Water Index (LSWI) are used to improve the classification accuracy of crops derived from Random Forest model. The presentation will discuss the classification accuracy of the data with and without the use of high-confidence pixels for 2017 and 2022 respectively and their significance in decision making, A more accurate crop classification will likely enhance the effectiveness of crop monitoring and resource optimization by facilitating timely data sharing and knowledge exchange among the Navajo Nation farming community. This will also support coordinated responses to threats such as weeds, diseases, insects, pests, and weather, ultimately ensuring farm profitability, with potential for scaling up to larger geographic areas.
Varatharajaperumal Thangavel, Florida Atlantic University
4:45 PM – 5:00 PM – Mapping Urban Expansion of Santiago De Queretaro (Mexico) Using Multi-Temporal Landsat Imagery
Mexico has experienced steady development since the early 2000s, with some regions growing more rapidly than others, particularly major urban centers such as Mexico City. Urbanization across the country has been driven largely by the expansion of commercial industries and increased trade, contributing to economic growth and improved access to goods and services for the middle class. One city strongly shaped by these dynamics is Santiago de Querétaro, an emerging urban hub for Mexico’s aerospace industry. Querétaro has developed one of the fastest-growing aerospace clusters in the country, and this economic growth has been accompanied by rapid urban expansion.
Since the 1990s, Santiago de Querétaro has experienced increasing migration and consistent population growth. The city is situated within a semi-arid valley, where the surrounding topography creates a bowl-like landscape that has constrained development to lower elevations. As these lower-elevation areas have become increasingly developed, urban expansion has begun to shift upslope into higher-elevation terrain. This study analyzes the spatial patterns of urban growth in Santiago de Querétaro using multispectral satellite and aerial imagery. An unsupervised classification approach, combined with change matrix analysis, is applied to track urban expansion over time and to examine the transition of development from lower to higher elevations.
Rachelle Lavariega, University of Wyoming
5:00 PM – 5:15 PM – A Deep Learning-Powered Super-Resolution Algorithm for Sentinel-2 Images to Bridge the Resolution Gap in Flood Mapping
Timely and accurate geospatial data is necessary for flood mapping, yet publicly accessible Sentinel-2 imagery with a resolution of 10 m frequently falls short in capturing small-scale flood dynamics. This study introduces STCN-SR, a novel hybrid deep learning architecture that improves Sentinel-2 imagery to a resolution of 5 m by merging transformers and convolutional neural networks. The model is trained and assessed on the DeepFlood dataset, and it produces visually clear, spatially aligned outputs that are on par with high-resolution UAV imagery. It also obtains superior PSNR and SSIM scores. STCN-SR produces georeferenced, super-resolved tiles that can be used directly in GIS workflows, in contrast to conventional techniques. This study shows that super-resolution can be used to improve flood boundary delineation by aligning outputs with UAV ground truth. This is particularly useful in situations where data is scarce or emergency response is required.
Jagrati Talreja, North Carolina A&T State University
Leila Hashemi Beni, North Carolina A&T State University
5:15 PM – 5:30 PM – Validating UAS-Based NDVI Data with Satellite Landsat Imagery for Bald Eagle Habitat Prediction in the Del Rio Springs Ecosystem
Vegetation health can be assessed using the Normalized Difference Vegetation Index (NDVI), which can be collected from a variety of multispectral sensor types, each offering different spatial resolutions. Validating the datasets of these sensors is crucial to determine how accurately the platforms portray information, especially when using them for environmental monitoring. Two multispectral datasets collected over the same area were used for this analysis. Landsat, a joint satellite mission between USGS and NASA, provided moderate-resolution imagery suitable for vegetation assessment. The MicaSense sensor, developed by EagleNXT for mounting on Unmanned Aircraft Systems (UAS), supplied high-resolution imagery that enabled finer-scale vegetation detail.
NDVI values derived from both datasets were compared to examine spatial correspondence between the satellite and UAS-based vegetation indices. We applied the coefficient of determination (R2) to assess the percentage of spatial variability in the Landsat data explained by the UAS-based data. This comparison evaluates the reliability of the platforms and helps determine how effectively they can be used to monitor vegetation across differing spatial scales. We applied a hotspot analysis, using the Getis-Ord Gi* statistic, to the high-resolution MicaSense NDVI data to detect statistically significant MicaSense vegetation clusters to support the detection of bald eagle nesting and foraging, demonstrating its value for habitat prediction.
Noah Morales, Embry-Riddle Aeronautical University
