Abstract
Advances in geospatial sensing technologies have led to an increase in the use of satellite and airborne imagery for remote monitoring of large, heterogeneous burned areas. The rapid proliferation of high-resolution imagery has proven useful for mapping and modeling the post-fire environment across various spatial, temporal, and spectral scales. The post-fire ecosystem is highly dynamic and disturbance-driven, components such as vegetation response and ground cover can change rapidly and non-linearly which makes recovery predictions complex. The scale and degree of the soil and vegetation disturbance can affect a secondary response in terms of hydrologic and ecological processes that can persist for months to decades and may be so extreme as to create permanent successional changes. Because of the potential consequence of long-term post-fire effects, timely and accurate information detailing the condition of the burned area is needed for natural resource managers to make effective decisions. This dissertation is focused on developing novel, yet operational and implementable, techniques and decision-making tools for natural resources managers in the post-fire environment. In Chapter 1 I provide background and rationale for a ‘cost-benefit’ type of analysis on choosing the appropriate scale of data for the question being asked. In the current surge of data availability, data science, and cloud computing there is extensive opportunity for the creation and accumulation of a plethora of data. Data which requires processing time, storage and computing availability, and potential financial cost. Attaining the right balance of adequate, quality data with the time-sensitive nature of post-wildfire emergency response can be a challenge. In the following chapters, I evaluated three post-fire management concerns and presented Earth Observation (EO) data solutions appropriate for each situation. In Chapter 2, field measurements were coupled with EO data to monitor the persistence of ash cover and depth for up to 90 days post-fire after two wildfires in Idaho and Washington, USA. The imagery spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and UAS). It was found that the Blue Normalized Difference Vegetation Index (BNDVI) calculated with monthly Sentinel-2 imagery was especially well-suited (r = 0.6–0.85) for monitoring the change in ash cover during its ephemeral period. Maps of ash cover can be related to ash depth or load, which can be incorporated into watershed models for predictions of hydrologic transport. In Chapter 3, I evaluated detrimental soil disturbance (DSD) due to post-fire salvage logging after three large wildfires in western Montana, USA. Seven logging units were burned at low, moderate, and high soil burn severity between 2016–2017 and were salvage logged 2017–2019. Field measurements in 2022 indicated disturbance had persisted as exposed soil (5–25%) and DSD ranging from 3–20% remained. Exposed soil and DSD were significantly correlated at r = 0.88, thus soil was a suitable proxy for mapping DSD. Very-high-resolution WorldView-2 imagery that coincided with the field campaign was used to calculate the Normalized Difference Vegetation Index (NDVI) across the salvaged areas and NDVI values correlated with DSD at r = 0.87. A decision-making tool which estimates the contributions of equipment type, seasonality, topography, burn severity, and classified NDVI values to DSD is presented to support land managers in planning, evaluating, and monitoring disturbance from post-fire salvage logging. In Chapter 4, I evaluated fuels, precipitation, soil moisture, and subsequent wildfire activity after the 2011 Wallow Fire, Arizona, USA. The Wallow Fire was considered a mega-fire as it burned more than 200,000 ha. About 25% of the area was classified as moderate or high soil burn severity, conditions which are generally thought to preclude reburns for 10–12 years. However, in the 12 years following the Wallow Fire, 30,000 ha burned via wildfire ignition or prescribed fires due to the accumulation and availability of fuels and multiple seasons of below average precipitation. Data from NASA’s Soil Moisture Active Passive (SMAP) mission was used to map surface soil moisture (SSM) and root-zone soil moisture (RZSM) from 2015 to 2023 at a weekly timestep and 9 km spatial resolution. In seasons where precipitation was lower than average, soil moisture was also below average as mapped by SMAP, and with the exception of one year which had higher than average precipitation, all wildfire initiations began during a season which had significantly lower than seasonal average SSM. The novel use of SMAP data to correlate wildfire occurrence and soil moisture at a landscape scale provides information that can potentially be useful for wildfire occurrence and severity forecasting, which is particularly timely in this changing climate.
The final chapter is a synthesis of how spatial and temporal scale were evaluated in each chapter of this dissertation in an attempt to match the scale of a post-wildfire process with the appropriate data, while still meeting emergency management needs. Data limitations and future work relating EO data to the post-wildfire environment are summarized.