Geology topics

Scott J. Goetz

Publications and source records attributed to Scott J. Goetz.

5 recordsLinked to original sources

USGS2025

Canopy and surface fuels measurement using terrestrial lidar single-scan approach in the Mogollon highlands of Arizona

Background Fuel monitoring data are essential to evaluate wildfire risk, plan management activities and evaluate fuel treatment effects. Terrestrial light detection and ranging (lidar) is a field-based 3D scanning technology with great potential to reduce labor-intensive field measurements and provide new depths of vegetation structure data. Aims To facilitate the integration of terrestrial lidar into fuel monitoring programs, we developed a model, training process, and Python program that produces canopy fuel, surface fuel and terrain metrics commonly used in fire behavior and fire risk modeling. Methods We estimated canopy and surface fuel metrics from terrestrial lidar using a semi-empirical model incorporating physically based modeling of leaf area density and occlusion and a non-destructive model calibration process leveraging Bayesian regression. We compared lidar-derived fuel estimates with conventional fuel estimates across diverse conditions in semi-arid shrubland, woodland and forest in Arizona. We also compared estimates using single- and multiple-scan modes. Key results In single-scan mode, our lidar-derived fuel estimates were significantly related to conventional estimates of total canopy fuel load, maximum canopy bulk density, downed surface fuel load and standing surface fuel load. Implications Our methods provide opportunities to increase the scalability of fuel monitoring to better understand wildfire risk and treatment effectiveness.

Arizona↗

USGS2019

UAV-derived estimates of forest structure to inform ponderosa pine forest restoration

Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in density (e.g., basal area). Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon field‐based measurements, manned airborne or satellite remote sensing datasets. We used unmanned aerial vehicle (UAV ) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine‐ (<4 ha) and mid‐scales (4–400 ha) across a forest density gradient. We then used these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter were most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low‐ to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV ‐derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows.

Western United States↗

USGS2018

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio‐environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high‐latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time‐series analysis of moderate—and high‐resolution imagery was used to characterize land‐ and water‐surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land‐surface greening, browning, and wetness/moisture trend parameters derived from peak‐growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km 2 ) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface‐water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high‐latitude regions.

Alaska↗

USGS2012

The footprint of Alaskan tundra fires during the past half-century: implications for surface properties and radiative forcing

Recent large and frequent fires above the Alaskan arctic circle have forced a reassessment of the ecological and climatological importance of fire in arctic tundra ecosystems. Here we provide a general overview of the occurrence, distribution, and ecological and climate implications of Alaskan tundra fires over the past half-century using spatially explicit climate, fire, vegetation and remote sensing datasets for Alaska. Our analyses highlight the importance of vegetation biomass and environmental conditions in regulating tundra burning, and demonstrate that most tundra ecosystems are susceptible to burn, providing the environmental conditions are right. Over the past two decades, fire perimeters above the arctic circle have increased in size and importance, especially on the North Slope, indicating that future wildfire projections should account for fire regime changes in these regions. Remote sensing data and a literature review of thaw depths indicate that tundra fires have both positive and negative implications for climatic feedbacks including a decadal increase in albedo radiative forcing immediately after a fire, a stimulation of surface greenness and a persistent long-term (>10 year) increase in thaw depth. In order to address the future impact of tundra fires on climate, a better understanding of the control of tundra fire occurrence as well as the long-term impacts on ecosystem carbon cycling will be required.

Alaska↗

USGS2010

Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model

This paper presents a fine-scale (30 meter resolution) regional land cover modeling system, based on the SLEUTH cellular automata model, that was developed for a 257000 km 2 area comprising the Chesapeake Bay drainage basin in the eastern United States. As part of this effort, we developed a new version of the SLEUTH model (SLEUTH-3r), which introduces new functionality and fit metrics that substantially increase the performance and applicability of the model. In addition, we developed methods that expand the capability of SLEUTH to incorporate economic, cultural and policy information, opening up new avenues for the integration of SLEUTH with other land-change models. SLEUTH-3r is also more computationally efficient (by a factor of 5) and uses less memory (reduced 65%) than the original software. With the new version of SLEUTH, we were able to achieve high accuracies at both the aggregate level of 15 sub-regional modeling units and at finer scales. We present forecasts to 2030 of urban development under a current trends scenario across the entire Chesapeake Bay drainage basin, and three alternative scenarios for a sub-region within the Chesapeake Bay watershed to illustrate the new ability of SLEUTH-3r to generate forecasts across a broad range of conditions.

Computers, Environment and Urban Systems↗