Geology topics

Rachel Sprague

Publications and source records attributed to Rachel Sprague.

2 recordsLinked to original sources

USGS2024

Using systematic conservation planning to recover climate resilient habitat for threatened and endangered species while retaining areas of cultural importance

The effective management of at-risk species often requires fine-scale actions by natural resource managers. However, balancing these actions with concurrent land uses is challenging, particularly when compounded by the interplay of climate shifts, and escalating wildland–urban interface conflicts. We used spatial prioritization tools designed for biodiversity conservation to help resource managers on the Island of Lānaʻi prioritize mutually exclusive land use objectives: endangered species recovery and subsistence and recreational hunting. We weighed the current and anticipated future distributions of threatened and endangered plant species against the distribution of non-native game mammals to plan for species recovery more effectively. Prioritization results identified multiple footprints that could support recovery of all endangered species targets in climate resilient areas while retaining the majority of existing hunting areas. However, very little native vegetation was retained in conservation footprints without deliberate inclusion, which increased footprint area by 268%. Scenarios which prioritized contiguous conservation areas also dramatically increased conservation footprint area, although these scenarios may reduce associated fencing costs. This work demonstrates how spatial prioritization may guide localized species recovery efforts by supporting long-term conservation planning that addresses anticipated climate-driven increases in conflict between conservation and other land uses, with clear applicability beyond Lānaʻi.

Conservation↗

USGS2024

Fine-resolution land cover mapping over large and mountainous areas for Lāna‘i, Hawaii using posterior probabilities, and expert knowledge

The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna‘i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna‘i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna‘i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.

Hawaii↗