Multisensor models for assessing recurrent fire compatibility with habitat recovery for a critically endangered species

Steven E. Sesnie, Lacrecia Johnson, Emily Yurcich, Thomas D. Sisk, John Goodwin, Rebecca Chester

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The increased variety and availability of remotely sensed data from satellite and airborne platforms are expected to enhance data fusion approaches aimed at characterizing wildlife habitat. We investigated multisensor machine learning (ML) models to estimate desert grassland habitat suitability for a critically endangered species, the mask bobwhite quail (Colinus virginianus ridgewayi). Contemporary habitat conditions are marked by a history of intensive livestock grazing, periodic drought, and changes in arid land hydrology. Historical land use and vegetation changes were accompanied by altered disturbance regimes that once supported recurrent fire important to maintaining grassland composition and structure. Habitat assessment plots were combined with multidate and multispectral Worldview-3 (WV3) satellite imagery and airborne light detection and ranging (LiDAR) metrics to model habitat suitability on the Buenos Aires National Wildlife Refuge (BANWR). We used regression and structural equation models (SEM) to assess fire history, climate, and site biophysical effects on habitat suitability from 239 vegetation plots stratified by topography and fire frequency. We found that combined WV3/LiDAR models improved suitability predictions and consistency over WV3-only models comparing the root mean squared and mean absolute error from training data. Hyperparameter tuning for ML habitat models further improved performance. Gradient boosted regression trees and WV3/LiDAR habitat suitability models showed superior performance and correspondence to independent field validation data (F = 92.7, p ≤ 0.001, R2 = 0.57). Comparisons between predicted habitat suitability and fire history variables from 84 management units showed a significant negative relationship with increased fire frequency (F = 27.6, p ≤0.001, R2 = 0.41) and positive relationship with time since last burn (F = 41.3, p ≤0.001, R2 = 0.34). SEMs established that masked bobwhite habitat occurrence was strongly associated with site biophysical conditions that supported greater woody plant cover important for nesting, thermal, and predator protection. Our results suggest that desert grasslands associated with low primary productivity may require recovery periods in excess of 20 years to develop a desired mix of herbaceous and woody plant cover for masked bobwhite, when ≥2 fires have occurred over the last 30 years. Multiple sensor types provided a unique set of variables describing horizontal and vertical habitat structure and composition, essential to understanding fire effects on masked bobwhite habitat suitability. Combined information from active and passive remote sensing systems can likely enhance mapping and monitoring applications necessary for assessing recovery needs for numerous other wildlife species with diverse habitat requirements.

Original languageEnglish (US)
Article number112824
JournalRemote Sensing of Environment
StatePublished - Feb 2022


  • Habitat suitability
  • LiDAR
  • Machine learning
  • Masked bobwhite quail
  • Multisensor fusion
  • Structural equation models
  • Worldview3

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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