23 Apr 2026

Applications of Snow-Covered Areas from Unoccupied Aerial Systems (UAS) Visible Imagery: A Demonstration in Southeastern New Hampshire

Abstract: Remote sensing observations of snow-covered areas (SCA) are important for monitoring and modeling energy balances, hydrologic processes, and climate change. For an agricultural field, we produced 12 snow cover maps from UAS imagery during an approximately 3-week-long spring snowmelt period. SCA maps were used to characterize snow cover patterns, validate satellite snow cover products, translate satellite Normalized Difference Snow Index (NDSI) to fractional SCA (fSCA), and downscale satellite SCA observations. Compared to manually delineated SCA, the UAS SCA accuracy was 85%, with misclassifications due to shadows, ice, and patchy snow conditions. During snowmelt, UAS-derived maps of bare earth patches exhibited self-similarity, behaving as fractal objects over scales from 0.01 to 100 m2. As a validation tool, the UAS-derived SCA showed that satellite snow cover observations accurately captured the fSCA evolution during snowmelt (R2 = 0.93−0.98). A random forest satellite downscaling model, trained using 20 m Sentinel-2 NDSI observations and 20 cm vegetation and terrain features, produced realistic (>90%accuracy), high-resolution SCA maps. While similar to traditional Sentinel-2 SCA in most conditions, downscaling snow cover significantly improved performance during periods of patchy snow cover and produced more realistic bare patches. UAS optical sensing demonstrates the potential uses for high-resolution snow cover mapping and recommends future research avenues for using UAS SCA maps.

7 Aug 2024

A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a

Abstract: Harmful algal blooms degrade water quality and can adversely impact human and wildlife health. Monitoring these at scale is difficult due to the lack of coincident data. Additionally, traditional field collection methods are labor- and cost-prohibitive, resulting in disparate data collection in capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to alleviate this by leveraging large, public, water quality databases and open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usability of four common chlorophyll a algorithms as a proxy for detecting and mapping algal blooms nationwide. Chlorophyll a data were aggregated from spatially diverse sites across the continental US between 2019 and 2022. The 2BDA and the NDCI algorithms were the most viable for broadscale mapping of chlorophyll a, which performed moderately well, encompassing highly diverse spatial, temporal, and physical conditions. The most compatible field data acquisition method was the chlorophyll a, water, trichromatic method, uncorrected. Resulting data indicate the feasibility of utilizing band ratio algorithms for broadscale detection and mapping of chlorophyll a as a proxy for HABs, which is valuable when coincident data are unavailable or limited.

20 Sep 2021

A Multi-biome Study of Tree Cover Detection Using the Forest Cover Index

Abstract: Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.