干旱区生态环境知识资源中心(Arid): Multisensor models for assessing recurrent fire compatibility with habitat recovery for a critically endangered species

Arid

DOI	10.1016/j.rse.2021.112824
	Multisensor models for assessing recurrent fire compatibility with habitat recovery for a critically endangered species
	Sesnie, Steven E.; Johnson, Lacrecia; Yurcich, Emily; Sisk, Thomas D.; Goodwin, John; Chester, Rebecca
通讯作者	Sesnie, SE
来源期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
EISSN	1879-0704
出版年	2022
卷号	269
英文摘要	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, R-2 = 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, R-2 = 0.41) and positive relationship with time since last burn (F = 41.3, p <= 0.001, R-2 = 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.
英文关键词	Multisensor fusion LiDAR Worldview3 Machine learning Habitat suitability Masked bobwhite quail Structural equation models
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000759695200005
WOS关键词	GRASSLAND ; LIDAR ; DYNAMICS ; GRASSES ; CYCLE ; RARE
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394259
推荐引用方式 GB/T 7714	Sesnie, Steven E.,Johnson, Lacrecia,Yurcich, Emily,et al. Multisensor models for assessing recurrent fire compatibility with habitat recovery for a critically endangered species[J],2022,269.
APA	Sesnie, Steven E.,Johnson, Lacrecia,Yurcich, Emily,Sisk, Thomas D.,Goodwin, John,&Chester, Rebecca.(2022).Multisensor models for assessing recurrent fire compatibility with habitat recovery for a critically endangered species.REMOTE SENSING OF ENVIRONMENT,269.
MLA	Sesnie, Steven E.,et al."Multisensor models for assessing recurrent fire compatibility with habitat recovery for a critically endangered species".REMOTE SENSING OF ENVIRONMENT 269(2022).