Erasmi Stefan, Klinge Michael, Dulamsuren Choimaa, Schneider Florian, Hauck Markus
Thuenen Institute of Farm Economics, Bundesallee 63, 38116, Braunschweig, Germany.
Department of Physical Geography, Institute of Geography, University of Goettingen, Goldschmidtstraße 5, 37077, Goettingen, Germany.
Environ Monit Assess. 2021 Mar 18;193(4):200. doi: 10.1007/s10661-021-08996-1.
The monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape's forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.
在陆地生态系统从大气中进行碳固存的背景下，监测植被生产力的时空动态具有重要意义。长达数十年的中等分辨率地球观测数据完整档案的可获取性，极大地提升了遥感技术在支持全球气候变化和陆地碳循环研究方面的潜力。我们研究了蒙古森林草原北方森林南缘多传感器陆地卫星归一化植被指数（NDVI）的密集时间序列数据，以评估其捕捉径向树干生长增量年际变化进而反映森林生产力的能力。通过对不同年龄和林分规模的森林斑块中15个样地的西伯利亚落叶松（Larix sibirica）进行树木年代学序列分析，评估森林生产力。结果显示，在20年的观测期内，森林站点生长季最大NDVI与树轮宽度之间存在很强的相关性。这种关系与林分规模和景观中的森林与草原比例无关。从我们案例研究的一致结果中，我们得出结论，生长季最大NDVI可用于对更大区域的森林生产力进行回顾性建模。在半干旱地区，用草地NDVI作为森林NDVI的替代指标来监测森林生产力的有效性只能得到部分证实。森林和草地NDVI在时空上的不一致是不同植被生理和生态特性的结果。由于现有卫星数据空间分辨率粗糙，以往研究无法考虑像森林草原中破碎森林这样的小尺度土地覆盖斑块。陆地卫星时间序列能够分离这些影响，从而有助于更好地理解全球气候变化对自然生态系统的影响。
