research article

Fine-scale habitat factors linked to density but not distribution of an invasive estuarine predator

Jerry D Davis — Thu, 08 May 2025 20:52:09 +0000

Authors:

Blumenthal JG, Chang AL, Cheng BS, Hines EM, Nanus L, Zabin CJ

Department Author(s)

Ellen Hines

Leora Nanus

Published

2025

Journal Title or Book Publisher

Aquatic Invasions

Publication type

research article

Citation

Blumenthal JG, Chang AL, Cheng BS, Hines EM, Nanus L, Zabin CJ (2025). Fine-scale habitat factors linked to density but not distribution of an invasive estuarine predator, Aquatic Invasions, 20 (1): 69-87, doi:10.3391/ ai.2025.20.1.143501.

Link

https://doi.org/10.3391/ai.2025.20.1.143501

An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management

Jerry D Davis — Tue, 01 Apr 2025 15:51:36 +0000

Authors:

Von Nonn J, Villarreal ML, Blesius L, Davis JD, Corbett S

Department Author(s)

Josh von Nonn

Leonhard Blesius

Jerry Davis

Published

2024

Journal Title or Book Publisher

Environmental Modelling & Software

Publication type

research article

Citation

Von Nonn J, Villarreal ML, Blesius L, Davis JD, Corbett S (2024). An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management. Environmental Modelling & Software, Volume 172, 2024, 105903, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2023.105903 .

Link

https://doi.org/10.1016/j.envsoft.2023.105903

Abstract

Wildfires are increasing in size and severity across much of the western United States, exposing vulnerable wildland-urban interfaces to post-fire hazards. The Mediterranean chaparral region of Northern California contains many high sloping watersheds prone to hazardous post-fire flood events and identifying watersheds at high risk of soil loss and debris flows is a priority for post-fire response and management. Uncrewed Aerial Systems (UAS; aka drones) offer post-fire management teams the ability to quickly mobilize and survey burned areas with very high-resolution imagery (∼1 cm), facilitating emergency management and post-fire hazard assessment. However, adoption of this technology by hazard response teams may be hindered by complicated workflows for UAS data acquisition, image processing and analysis. We present an open-source workflow using mature Geographic Information Systems (GIS) software and Python packages in a Jupyter Notebook environment that guides users through classification of true-color UAS imagery to generate high resolution burn severity maps which can then be scaled across larger watersheds using Sentinel-2 normalized burn ratio (NBR) images. Soil burn severity classifications using a weighted brightness (WB) image and Char Index (CI) generated from UAS imagery were validated with in-situ data and random stratified points, resulting in the CI having the highest overall accuracy of 87.5%. CI also displayed a marginally stronger relationship over the WB with the post-fire Sentinel-2 NBR, R² = 0.79 and R² = 0.78 respectively. Our methods offer the unique opportunity to standardize GIS workflows, promoting replication through transparency, while improving the user's understanding of scientific GIS functionality.

Combining potential and realized distribution modeling of telemetry data for a bycatch risk assessment

Jerry D Davis — Tue, 01 Apr 2025 15:39:33 +0000

Authors:

Frantz BH, Sepúlveda M, García-Reyes M, Vega R, Palacios DM, Bedriñana-Romano L, Hückstädt LA, Santos-Carvallo M, Davis JD, Hines E

Department Author(s)

Bethany Frantz

Ellen Hines

Jerry Davis

Published

2024

Journal Title or Book Publisher

Ecology and Evolution

Publication type

research article

Citation

Frantz BH, Sepúlveda M, García-Reyes M, Vega R, Palacios DM, Bedriñana-Romano L, Hückstädt LA, Santos-Carvallo M, Davis JD, Hines E (2024). Combining potential and realized distribution modeling of telemetry data for a bycatch risk assessment. Ecology and Evolution 14 (6). https://doi.org/10.1002/ece3.11541

Link

https://doi.org/10.1002/ece3.11541

Abstract

Establishing marine species distributions is essential for guiding management and can be estimated by identifying potential favorable habitat at a population level and incorporating individual-level information (e.g., movement constraints) to inform realized space use. In this research, we applied a combined modeling approach to tracking data of adult female and juvenile South American sea lions (Otaria flavescens; n = 9) from July to November 2011 to make habitat predictions for populations in northern Chile. We incorporated topographic and oceanographic predictors with sea lion locations and environmentally based pseudo-absences in a generalized linear model for estimating population-level distribution. For the individual approach, we used a generalized linear mixed-effects model with a negative exponential kernel variable to quantify distance-dependent movement from the colony. Spatial predictions from both approaches were combined in a bivariate color map to identify areas of agreement. We then used a GIS-based risk model to characterize bycatch risk in industrial and artisanal purse-seine fisheries based on fishing set data from scientific observers and artisanal fleet logs (2010–2015), the bivariate sea lion distribution map, and criteria ratings of interaction characteristics. Our results indicate population-level associations with productive, shallow, low slope waters, near to river-mouths, and with high eddy activity. Individual distribution was restricted to shallow slopes and cool waters. Variation between approaches may reflect intrinsic factors restricting use of otherwise favorable habitat; however, sample size was limited, and additional data are needed to establish the full range of individual-level distributions. Our bycatch risk outputs identified highest risk from industrial fisheries operating nearshore (within 5 NM) and risk was lower, overall, for the artisanal fleet. This research demonstrates the potential for integrating potential and realized distribution models within a spatial risk assessment and fills a gap in knowledge on this species' distribution, providing a basis for targeting bycatch mitigation outreach and interventions.

Using Unoccupied Aerial Systems (UAS) and Structure-from-Motion (SfM) to measure forest canopy cover and individual tree height metrics in northern California forests

Jerry D Davis — Mon, 24 Mar 2025 19:16:23 +0000

Authors:

Kelly AC, Blesius L, Davis JD, Bentley LP

Department Author(s)

Allison Kelly

Leonhard Blesius

Jerry Davis

Published

2025

Journal Title or Book Publisher

Forests

Publication type

research article

Citation

Kelly, Allison, Leonhard Blesius, Jerry D. Davis, and Lisa Patrick Bentley. 2025. "Using Unoccupied Aerial Systems (UAS) and Structure-from-Motion (SfM) to Measure Forest Canopy Cover and Individual Tree Height Metrics in Northern California Forests" Forests 16, no. 4: 564. https://doi.org/10.3390/f16040564

Link

https://www.mdpi.com/1999-4907/16/4/564

Abstract

Quantifying forest structure to assess changing wildfire risk factors is critical as vulnerable areas require mitigation, management, and resource allocation strategies. Remote sensing offers the opportunity to accurately measure forest attributes without time intensive field inventory campaigns. Here, we quantified forest canopy cover and individual tree metrics across 44 plots (20 m × 20 m) in oak woodlands and mixed-conifer forests in Northern California using structure-from-motion (SfM) 3D point clouds derived from unoccupied aerial systems (UAS) multispectral imagery. In addition, we compared UAS–SfM estimates with those derived using similar methods applied to Airborne Laser Scanning (ALS) 3D point clouds as well as traditional ground-based measurements. Canopy cover estimates were similar across remote sensing (ALS, UAS-SfM) and groundbased approaches (r2 = 0.79, RMSE = 16.49%). Compared to ground-based approaches, UAS-SfM point clouds allowed for correct detection of 68% of trees and estimated tree heights were significantly correlated (r2 = 0.69, RMSE = 5.1 m). UAS-SfM was not able to estimate canopy base height due to its inability to penetrate dense canopies in these forests. Since canopy cover and individual tree heights were accurately estimated at the plot scale in this unique bioregion with diverse topography and complex species composition, we recommend UAS-SfM as a viable approach and affordable solution to estimate these critical forest parameters for predictive wildfire modeling.