Geology topics

Wade T. Tinkham

Publications and source records attributed to Wade T. Tinkham.

3 recordsLinked to original sources

USGS2026

Drought on the fly: UAS mapping of conifer drought stress

Foliar moisture content (FMC) plays a crucial role in arid, frequent-fire forests by influencing fire behavior, tree survival, and serving as a proxy for tree health. Management actions such as mechanical thinning, flammable fuels reduction, and prescribed fires can act to mitigate wildfire risk and improve forest health by reducing fuel connectivity, diversifying forest structure, and creating canopy gaps. These management actions could be improved by the targeted removal of moisture stressed and potentially more flammable trees during treatments. However, maps of FMC are not widely available at the tree-level, with no existing products to map FMC of western conifers at the stand-level (< 0.25 m). This project focuses on the scalability of laboratory-developed models predicting sapling FMC to develop and assess tree-level FMC in natural forest conditions. Specifically, we tested whether existing FMC models accurately predict individual tree FMC using uncrewed aerial system (UAS) data. While laboratory-developed models did not translate well to field sites, a field-developed model achieved a mean absolute error of 6.5% FMC and 81% of predictions were within 10% FMC of the observed value. In ranking the observed and predicted FMC values, the field model successfully classified trees into FMC categories with up to 85.9% accuracy. This successful classification demonstrates the utility of UAS-based FMC mapping to inform management prescriptions. Integrating UAS-derived FMC monitoring into forest management could enhance forest resilience and adaptive capacity.

Colorado↗

USGS2024

Snow-cover remote sensing of conifer tree recovery in high-severity burn patches

The number of large, high-severity wildfires has been increasing across the western United States over the last several decades. It is not fully understood how changes in the frequency of large, severe wildfires may impact the resilience of conifer forests, due to alterations in regeneration success or failure. Our research investigates 30 years of conifer recovery patterns within 34 high-severity wildfire complexes (1988–1991) of the Northern Rocky Mountains. We evaluate the capability of snow-cover Landsat to characterize conifer tree recolonization of high-severity burn patches. Snow-cover images isolate conifer-specific vegetation signals by diminishing spectral contributions from soil and deciduous vegetation. The presence of conifer regeneration was successfully classified by snow-cover Landsat at >10% canopy cover at 98% accuracy and modeled at 3-year intervals post-fire. Spectral detectability of regenerating conifer vegetation began 11–19 years post-fire, varying across forest types. Thirty years post-fire, 65% of the total high-severity burn area had been recolonized by conifer trees, with differences observed between forest types: 72% of lodgepole pine, 77% of Douglas-fir, and 44% of fir-spruce severely burned areas containing conifer regeneration. Projected recovery timelines to pre-fire conifer vegetation also differed between lodgepole pine (29.5 years), Douglas-fir (36.9 years), and fir-spruce forests (48.7 years), as estimated from snow-cover NDVI trends. Although we generally documented patterns of conifer resilience, we also identified reduced likelihoods of recovery within high-severity burn patches exhibiting greater area-to-perimeter ratios, aridity, south-facing aspects, slopes, and elevation. Snow-cover Landsat imagery was shown to improve the characterization of post-fire forest recovery and may be applied to support forest restoration decision-making following high-severity wildfire.

Remote Sensing of Environment↗

USGS2016

High resolution mapping of development in the wildland-urban interface using object based image extraction

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m 2 ) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

Colorado↗