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Landsat-based detection of mast events in white spruce (Picea glauca) forests
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-01-05 , DOI: 10.1016/j.rse.2020.112278
Matthew Garcia , Benjamin Zuckerberg , Jalene M. LaMontagne , Philip A. Townsend

Mast seeding in conifers is characterized by the spatially synchronous and temporally variable production of seed cone crops. Large mast seeding events (known as “mast years”) can be a visually stunning and ecologically important phenomenon, supporting trophic interactions and survival of seed predators as well as forest regeneration. Documenting patterns in mast seeding is generally labor-intensive, requiring repeated visual cone counts at consistent and widespread locations over long periods to quantify the spatiotemporal variability of cone production. Our goal in this work was to evaluate the correspondence of multispectral vegetation indexes (VIs) from Landsat with ground-based observations of mast seeding in white spruce (Picea glauca) forests of the Kluane region, Yukon, Canada. Given the visual characteristics of mast seeding in white spruce, we tested: 1) whether photosynthesis- and color-oriented VIs can identify senescence of spruce cones in late summer and autumn during mast years, and 2) if moisture-oriented VIs can distinguish the significant drying of seed cones from the surrounding spruce canopy vegetation during that senescence and after seeds are released. We hypothesized that the slope of late season decline in VIs in spruce forests would be related to masting (i.e., greater decline in VI during mast years). Using generalized linear mixed-effects modeling (GLMM), we compared more than 100 site-year combinations of mast/non-mast observations to develop VI-based regressions. We found some success identifying mast years with moisture-oriented VIs, while models using the photosynthesis- and color-oriented VIs were not supported, given the data. However, we found that models containing multiple VIs from both categories were more successful than any single-VI model, accurately predicting four of sixteen mast events in site observations. We provide compelling evidence that mast-seeding patterns may be detectable using moisture-oriented Landsat observations over large coniferous forest areas. Additional work is warranted to distinguish the signal for mast events from confounding disturbance-related effects and to differentiate variation in VI signals attributable to masting productivity in contrast to effects of climatological variability on reflectance.



中文翻译:

基于Landsat的白云杉(Pice glauca)森林中桅杆事件的检测

针叶树上的桅杆播种的特征在于,种锥作物的空间同步生产和时变生产。大型桅杆播种事件(称为“肥大年”)可能是视觉上令人震惊的重要生态现象,支持营养相互作用和种子捕食者的生存以及森林更新。桅杆播种中的记录模式通常是劳动密集型的,需要长时间在一致且广泛的位置重复进行可视视锥计数,以量化视锥生产的时空变异性。我们这项工作的目的是评估Landsat的多光谱植被指数(VIs)与白云杉(Picea glauca)加拿大育空地区Kluane地区的森林。鉴于白色云杉中桅杆播种的视觉特征,我们进行了以下测试:1)光合作用和颜色导向的VI是否可以识别肥大年夏末和秋季的云杉球果衰老,以及2)水分导向的VI是否可以区分在衰老期间和种子释放后,周围的云杉冠层植被中的视锥显着干燥。我们假设云杉林中VI的后期季节下降的斜率与疏松有关(,在肥大年中VI的下降幅度更大)。使用广义线性混合效应模型(GLMM),我们比较了100多个站点/年度桅杆/非桅杆观测值的组合,以开发基于VI的回归。我们发现,在以水分为导向的VI识别肥大年限方面取得了一些成功,而根据数据,不支持使用光合作用和以颜色为导向的VI的模型。但是,我们发现包含两个类别的多个VI的模型比任何单个VI的模型都更为成功,可以在现场观测中准确预测十六个桅杆事件中的四个。我们提供了令人信服的证据,表明使用大面积针叶林地区的水分导向的Landsat观测结果可以检测到肥大苗模式。

更新日期:2021-01-05
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