干旱区生态环境知识资源中心(Arid): Digital Mapping of Vegetative Great Groups to Inform Management Strategies

Arid

DOI	10.1016/j.rama.2024.01.006
	Digital Mapping of Vegetative Great Groups to Inform Management Strategies
	Phipps, Lucas; Stringham, Tamzen K.
通讯作者	Phipps, L
来源期刊	RANGELAND ECOLOGY & MANAGEMENT
ISSN	1550-7424
EISSN	1551-5028
出版年	2024
卷号	94 页码:7-19
英文摘要	Ecological site descriptions have become a prominent way of describing plant communities across rangelands. Disturbance response groups (DRGs) stratify landscapes by grouping ecological sites on the basis of their responses to natural or anthropogenic disturbances. DRGs allow managers to organize, scale, and evaluate information collected on the ground, thus creating expectations of how sites with similar characteristics will respond to disturbance and management. While the importance and utility of these concepts are well understood, the location and spatial extent of DRGs are not. Uncertainty of DRG location and extent make it challenging to evaluate trends or degradation risks of a given area and difficult to define and organize adaptive management concerns and opportunities on a landscape scale. DRGs are organized by major land resource areas (MLRAs), which can make real-life applications across MLRA boundaries for natural phenomena (e.g., wildfire boundaries) repetitive for specific management objectives. Vegetative great groups have been used to overcome this challenge while retaining the state-and-transition model importance of ecological sites. Presented here is a gridded process for vegetative great group mapping across MLRA boundaries, as well as an assessment of the ecological implications of the information gained about the plant communities through the mapping effort s. The scale and output are designed to fit the Landsat library grid and its derived information. Computer machine learning was used to generate spatial maps of vegetative great groups that were compared with Natural Resources Conservation Services soil survey maps, which are currently used by public land management agencies. Machine learning enhanced accuracy by 14% versus conventional soil mapping, providing a more accurate way to conceptualize and manage plant communities at the landscape scale. Further, predictor variables used in machine learning can supplement our knowledge of ecological process information on sites and aid land managers in understanding the various plant community responses to disturbance. (c) 2024 The Author(s). Published by Elsevier Inc. on behalf of The Society for Range Management. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
英文关键词	adaptive management arid land ecological sites gridded soil mapping monitoring random forest rangeland remote sensing restoration sagebrush biome soil soil mapping vegetation mapping
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:001197147500001
WOS关键词	SAGE-GROUSE ; CLIMATE ; PRECIPITATION ; TEMPERATURE ; RESOURCES ; RADIATION ; POLARIS ; MODEL ; FIRE
WOS类目	Ecology ; Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405257
推荐引用方式 GB/T 7714	Phipps, Lucas,Stringham, Tamzen K.. Digital Mapping of Vegetative Great Groups to Inform Management Strategies[J],2024,94:7-19.
APA	Phipps, Lucas,&Stringham, Tamzen K..(2024).Digital Mapping of Vegetative Great Groups to Inform Management Strategies.RANGELAND ECOLOGY & MANAGEMENT,94,7-19.
MLA	Phipps, Lucas,et al."Digital Mapping of Vegetative Great Groups to Inform Management Strategies".RANGELAND ECOLOGY & MANAGEMENT 94(2024):7-19.