Uncovering the Past Landscape of Central New Hampshire: Accuracy Assessment for Identifying Stonewalls Using LiDAR-derived Products

Revealing a landscape’s manmade history can be difficult if only satellite or aerial imagery is available. With the advent of LiDAR, changes along the bare-earth can easily be seen and further teased apart. LiDAR-derived products can be utilized to successfully identify historical resources found along the landscape, such as stonewalls. Common approaches include creating various terrain products from LiDAR-derived digital elevation models (DEMs), such as hillshade and slope, that are used as visualization tools. It is important to evaluate the accuracy of digitizing historical resources through field sampling. The objectives of this study are to evaluate the effectiveness of the most common LiDAR-derived visualization products in accurately identifying stonewalls within a study area located in the White Mountain National Forest, NH. Line-transect sampling was used to develop a field accuracy assessment of both the presence and absence of said digitized stonewalls. This field approach will be essential for standardizing statewide and regional digitizing efforts of historical resources with the newly available New Hampshire LiDAR data

Observational Verification of the Cumulative Resilience Screening Index (CRSI) Using Hurricanes, Inland Floods, and Wildfires From 2016 to 2019

Users can apply three processes to develop confidence in decision-making tools like models and indices—validation, verification, and observation. The utility of the Cumulative Resilience Screening Index (CRSI) was demonstrated by combining the processes of verification and observation using real-world natural hazard events (i.e., hurricanes, inland flooding, and wildfires). The ability of CRSI to determine the counties most vulnerable to hazards and least likely to recover quickly from natural hazards is demonstrated using these natural hazard events from outside the original index construction data set. Using Hurricane Harvey and Hurricane Michael, the counties in Texas and Florida/Georgia, respectively, experiencing the most damage and the most extended recovery intervals were determined accurately. Similarly, the most vulnerable and least recoverable counties were correctly identified as those associated with the Great Louisiana Flood of 2016. Finally, three different types of wildfires in California were examined to determine the likelihood of recovery and the strength of pre-event planning. All models and indices developed for use by decision-makers should consider undertaking this verification or a similar validation operation to enhance user confidence

Gulf of Mexico Coastal County Resilience to Natural Hazards

Using a Cumulative Resilience Screening Index (CRSI) that was developed to represent resilience to natural hazards at multiple scales for the United States, the U.S. coastal counties of the Gulf of Mexico (GOM) region of the United States are compared for resilience for these types of natural hazards. The assessment compares the domains, indicators and metrics of CRSI, addressing environmental, economic and societal aspects of resilience to natural hazards at county scales. The index was applied at the county scale and aggregated to represent states and two regions of the U.S. GOM coastline. Assessments showed county—level resilience in all GOM counties was low, generally below the U.S. average. Comparisons showed higher levels of resilience in the western GOM region while select counties in Louisiana, Mississippi and Alabama exhibited the lowest resilience