Scalable mapping and monitoring of Mediterranean-climate oak landscapes with temporal mixture models

REMOTE SENSING OF ENVIRONMENT

卷:247

Article

Sousa, Daniel^1,2  Davis, Frank W.^3,4 

[1] Natl Ctr Ecol Anal & Synth, 735 State St 300, Santa Barbara, CA 93101 USA

[2] UC Santa Barbara, Earth Res Inst, Santa Barbara, CA 93101 USA

[3] La Kretz Res Ctr Sedgwick Reserve, 3566 Brinkerhoff Ave, Santa Ynez, CA 93460 USA

[4] UC Santa Barbara, Bren Sch Environm Sci & Management, Santa Barbara, CA 93106 USA

关键词: Mediterranean ecosystems;    California;    Oak woodland;    Savanna;    Phenology;    Landsat;    Sentinel;   

DOI  :  10.1016/j.rse.2020.111937

来源: Elsevier

【 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 approximate to 1200 km(2) 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.

【 授权许可】

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