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.

来自卫星和机载平台的遥感数据的多样性和可用性的增加有望增强旨在表征野生动物栖息地的数据融合方法。我们研究了多传感器机器学习 (ML) 模型，以估计沙漠草原栖息地对极度濒危物种的适宜性，即面具短尾鹌鹑 ( Colinus virginianus ridgewayi）。当代栖息地条件的标志是密集的牲畜放牧、周期性干旱和干旱土地水文变化的历史。历史上的土地利用和植被变化伴随着改变的干扰机制，这些干扰机制曾经支持对维持草原组成和结构很重要的经常性火灾。栖息地评估图与多日期和多光谱 Worldview-3 (WV3) 卫星图像和机载光探测和测距 (LiDAR) 指标相结合，以模拟布宜诺斯艾利斯国家野生动物保护区 (BANWR) 的栖息地适宜性。我们使用回归和结构方程模型 (SEM) 从 239 个按地形和火灾频率分层的植被地块中评估火灾历史、气候和场地生物物理对栖息地适宜性的影响。我们发现组合 WV3/LiDAR 模型比仅 WV3 模型改进了适用性预测和一致性，比较了训练数据的均方根和平均绝对误差。ML 栖息地模型的超参数调整进一步提高了性能。梯度增强回归树和 WV3/LiDAR 栖息地适宜性模型显示出卓越的性能和与独立现场验证数据的对应关系 (F = 92.7,p  ≤ 0.001，R ²  = 0.57)。来自 84 个管理单位的预测栖息地适宜性和火灾历史变量之间的比较显示，与火灾频率增加呈显着负相关（F = 27.6，p  ≤0.001，R ²  = 0.41），与自上次燃烧以来的时间呈正相关（F = 41.3，p  ≤0.001，R ² = 0.34）。SEM 确定，被遮蔽的短白栖息地的发生与场地生物物理条件密切相关，这些条件支持更大的木本植物覆盖，对于筑巢、热和捕食者保护很重要。我们的研究结果表明，与低初级生产力相关的荒漠草原可能需要超过 20 年的恢复期，才能在过去 30 年发生≥2 次火灾的情况下为蒙面山毛榉形成理想的草本和木本植物覆盖组合。多种传感器类型提供了一组独特的变量，用于描述水平和垂直栖息地结构和组成，这对于了解火灾对蒙面白毛栖息地适宜性的影响至关重要。