气候变化领域集成服务门户(CCPortal): Scalable mapping and monitoring of Mediterranean-climate oak landscapes with temporal mixture models

CCPortal

DOI	10.1016/j.rse.2020.111937
	Scalable mapping and monitoring of Mediterranean-climate oak landscapes with temporal mixture models
	Sousa D.; Davis F.W.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Mediterranean-climate oak woodlands are prized for their biodiversity, aesthetics, and ecosystem services. Conservation and maintenance of these landscapes requires accurate observations of both present and historic conditions capable of spanning millions of hectares. Decameter optical satellite image time series have the observational coverage to meet this need, with almost 40 years of intercalibrated global observations from the Landsat program alone. However, the optimal approach to leverage these observations for oak ecosystem monitoring remains elusive. Temporal mixture models (TMMs) may offer a solution. TMMs use a linear inverse model based on temporal endmembers (tEMs) chosen to optimize both parsimony and information content by 1) possessing clear biophysical meaning, and 2) accurately representing the variance structure of the observations in the temporal feature space (TFS) composed of low-order Principal Components. We apply this approach to oak woodlands of the California Sierra Nevada foothills. Low-order TFS structure across the ≈1200 km2 study area is consistently bounded by 4 tEM phenologies: annual grasses, evergreen perennials, deciduous perennials + shadow, and unvegetated areas. Satellite-based tEM phenologies correspond to ground-based PhenoCam time series (correlations 0.8 to 0.9). Systematic temporal decimation is conducted to simulate years with varying numbers of cloud free measurements. Fractional cover of temporal endmembers is observed to scale linearly using as few as 6 images per year and coarse feature space topology is retained with as few as 4 well-timed images per year. In comparing 10 m versus 30 m pixel resolution, linear scaling is observed with correlations of 0.78–0.95. Comparison of 10 m Sentinel-2 and LiDAR-derived tree cover estimates at San Joaquin Experimental Range shows a correlation of 0.74. Visual orthophoto validation shows accuracies of annual, deciduous, and evergreen cover fractions of 74–88% (n = 102). Multi-year analysis of August imagery at Sequoia National Park to investigate dynamics associated with the 2012–2016 drought reveals 5 tEMs corresponding to: steady growth, steady decline, early decline then regrowth, persistent vegetation, and no vegetation. Validation images are sparse, but where available show accuracies in the 88 to 91% range for decrease, growth, and persistently vegetated multiyear endmembers (n = 102). Decreases are observed in areas with oak mortality documented in a recent field-based study. Overall, our results suggest the TMM approach has promise as an accurate, explainable, and linearly scalable method for retrospective analysis and prospective monitoring of Mediterranean-climate oak landscapes. © 2020 Elsevier Inc.
英文关键词	California; Landsat; Mediterranean ecosystems; Oak woodland; Phenology; Savanna; Sentinel
语种	英语
scopus关键词	Biodiversity; Climatology; Ecosystems; Inverse problems; Mixtures; Optical radar; Scalability; Time series; Vegetation; Conservation and maintenance; Ecosystem monitoring; Information contents; Linear inverse models; Mediterranean climates; Optical satellite images; Principal Components; Retrospective analysis; Climate models; deciduous tree; Landsat; landscape; mapping method; Mediterranean environment; perennial plant; phenology; satellite imagery; Sentinel; spatiotemporal analysis; woodland; California; Sequoia National Park; Sierra Nevada [California]; United States; Poaceae
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179217
作者单位	National Center for Ecological Analysis and Synthesis, 735 State Street #300, Santa Barbara, CA 93101, United States; Earth Research Institute, UC Santa Barbara, Santa Barbara, CA 93101, United States; La Kretz Research Center at Sedgwick Reserve, 3566 Brinkerhoff Ave, Santa Ynez, California, 93460, United States; Bren School of Environmental Science and Management, UC Santa Barbara, Santa Barbara, CA 93106, United States
推荐引用方式 GB/T 7714	Sousa D.,Davis F.W.. Scalable mapping and monitoring of Mediterranean-climate oak landscapes with temporal mixture models[J],2020,247.
APA	Sousa D.,&Davis F.W..(2020).Scalable mapping and monitoring of Mediterranean-climate oak landscapes with temporal mixture models.Remote Sensing of Environment,247.
MLA	Sousa D.,et al."Scalable mapping and monitoring of Mediterranean-climate oak landscapes with temporal mixture models".Remote Sensing of Environment 247(2020).