In-Situ and Remote Sensing Platforms for Mapping Fine-Fuels and Fuel-Types in Sonoran Semi-Desert Grasslands

Abstract

Fire has historically played an important role in shaping the structure and composition of Sonoran semi-desert grassland vegetation. Yet, human use and land management activities have significantly altered arid grassland ecosystems over the last century, often producing novel fuel conditions. The variety of continuously updated satellite remote sensing systems provide opportunities for efficiently mapping combustible fine-fuels and fuel-types (e.g., grass, shrub, or tree cover) over large landscapes that are helpful for evaluating fire hazard and risk. For this study, we compared field ceptometer leaf area index (LAI) measurements to conventional means for estimating fine-fuel biomass on 20, 50 m × 20 m plots and 431, 0.5 m × 0.5 m quadrats on the Buenos Aires National Wildlife Refuge (BANWR) in southern Arizona. LAI explained 65% of the variance in fine-fuel biomass using simple linear regression. An additional 19% of variance was explained from Random Forest regression tree models that included herbaceous plant height and cover as predictors. Field biomass and vegetation measurements were used to map fine-fuel and vegetation cover (fuel-type) from plots on BANWR comparing outcomes from multi-date (peak green and dormant period) Worldview-3 (WV3) and Landsat Operational Land Imager (OLI) imagery. Fine-fuel biomass predicted from WV3 imagery combined with terrain information from a digital elevation model explained greater variance using regression tree models (65%) as compared to OLI models (58%). Vegetation indices developed using red-edge bands as well as modeled bare ground and herbaceous cover were important to improve WV3 biomass estimates. Land cover classification for 11 cover categories with high spatial resolution WV3 imagery showed 80% overall accuracy and highlighted areas dominated by non-native grasses with 87% user’s class accuracy. Mixed native and non-native grass and shrublands showed 59% accuracy and less common areas dominated by native grasses on plots showed low class accuracy (23%). Digital data layers from WV3 models showed a significantly positive relationship (r2 = 0.68, F = 119.2, p < 0.001) between non-native grass cover (e.g., Eragrostis lehmanniana) and average fine-fuel biomass within refuge fire management units. Overall, both WV3 and OLI produced similar fine-fuel biomass estimates although WV3 showed better model performance and helped characterized fine-scale changes in fuel-type and continuity across the study area.