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Scalable mapping and monitoring of Mediterranean-climate oak landscapes with temporal mixture models
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.rse.2020.111937
Daniel Sousa , Frank W. Davis

Abstract 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.

中文翻译:

使用时间混合模型对地中海气候橡树景观进行可扩展的制图和监测

摘要 地中海气候橡树林地因其生物多样性、美学和生态系统服务而备受推崇。这些景观的保护和维护需要对能够跨越数百万公顷的现状和历史条件进行准确观察。十米光学卫星图像时间序列的观测覆盖范围可以满足这一需求,仅 Landsat 计划就有近 40 年的相互校准的全球观测。然而,利用这些观察结果进行橡树生态系统监测的最佳方法仍然难以捉摸。时间混合模型 (TMM) 可以提供解决方案。TMM 使用基于时间端元 (tEM) 的线性逆模型,以通过 1) 具有明确的生物物理意义来优化简约性和信息内容,和 2) 在由低阶主成分组成的时间特征空间 (TFS) 中准确表示观测值的方差结构。我们将这种方法应用于加利福尼亚内华达山脉山麓的橡树林地。≈ 1200 km2 研究区域的低阶 TFS 结构始终受到 4 种 tEM 物候的限制:一年生草本、多年生常绿植物、多年生落叶植物 + 阴影和无植被区域。基于卫星的 tEM 物候对应于基于地面的 PhenoCam 时间序列(相关性 0.8 到 0.9)。进行系统时间抽取以模拟具有不同数量的无云测量的年份。观察到时间端元的分数覆盖每年使用少至 6 个图像线性缩放,并且保留粗特征空间拓扑,每年使用少至 4 个适时的图像。在比较 10 m 与 30 m 像素分辨率时,观察到线性缩放,相关系数为 0.78–0.95。San Joaquin 实验范围内 10 m Sentinel-2 和 LiDAR 得出的树木覆盖估计值的比较显示相关性为 0.74。视觉正射影像验证显示一年生、落叶和常绿覆盖率的准确度为 74-88% (n = 102)。对红杉国家公园 8 月图像的多年分析,以研究与 2012-2016 年干旱相关的动态,揭示了 5 个 tEM,对应于:稳定增长、稳定下降、早期衰退然后再生、持久植被和无植被。验证图像很少,但在可用的情况下,减少、增长和持续植被多年的最终成员 (n = 102) 的准确性在 88% 到 91% 的范围内。最近的实地研究记录了橡木死亡率降低的地区。总体而言,我们的结果表明 TMM 方法有望成为一种准确、可解释且线性可扩展的方法,用于地中海气候橡树景观的回顾性分析和前瞻性监测。
更新日期:2020-09-01
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