School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Department of Biological and Environmental Sciences, Concordia University of Edmonton, Edmonton, Alberta, Canada.
Glob Chang Biol. 2023 Nov;29(21):6120-6138. doi: 10.1111/gcb.16916. Epub 2023 Aug 17.
Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.
地处高纬度地区且季节性温度波动较大的北方森林对气候变化非常敏感，其生产力既有增加也有减少，具体情况取决于各种条件。基于光谱反射率的植被指数光学遥感为监测植被光合作用活动提供了一种手段，是观察北方森林如何响应不断变化的环境条件的有力工具。在高纬度或高海拔地区，基于反射率的遥感光学信号很容易受到积雪覆盖的影响，从而阻碍了基于卫星的植被指数在大范围内跟踪植被生产力的应用。从卫星数据中揭示积雪的影响可能具有挑战性，尤其是在缺乏验证数据的情况下。在这项研究中，我们在加拿大阿尔伯塔省建立了一个实验系统，包括六种北方树种，既有常绿树种也有落叶树种，以评估积雪对三种植被指数的混淆影响：归一化差异植被指数（NDVI）、光化学反射率指数（PRI）和叶绿素/类胡萝卜素指数（CCI），这些指数都用于跟踪北方森林的植被生产力。我们的结果表明，积雪对冠层反射率和植被指数有很大的影响，表现为反射率增加、NDVI 值降低、PRI 和 CCI 值增加。这些影响因物种和功能组（常绿和落叶）而异，不同的植被指数受到的影响也不同，这表明积雪对这些指数的影响是矛盾的、混淆的。除了积雪的影响外，我们还评估了落叶树种对常绿和落叶树种混交林中植被指数的贡献，这有助于解释为什么许多以常绿树种为主但含有落叶成分的森林的基于绿色度的指数与生态系统生产力之间存在观察到的关系。我们的研究结果表明，积雪和植被类型对植被指数存在混淆和相互作用的影响，并说明了在使用遥感植被指数进行任何全球尺度光合作用监测工作中明确考虑积雪影响的重要性。
