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Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time-series analysis based on Sentinel-1 and Sentinel-2
Remote Sensing in Ecology and Conservation ( IF 3.9 ) Pub Date : 2024-04-03 , DOI: 10.1002/rse2.386 Patrick Kacic 1 , Ursula Gessner 2 , Stefanie Holzwarth 2 , Frank Thonfeld 2 , Claudia Kuenzer 1, 2
Remote Sensing in Ecology and Conservation ( IF 3.9 ) Pub Date : 2024-04-03 , DOI: 10.1002/rse2.386 Patrick Kacic 1 , Ursula Gessner 2 , Stefanie Holzwarth 2 , Frank Thonfeld 2 , Claudia Kuenzer 1, 2
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Assessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch-network in managed broad-leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time-series from high spatial resolution imagery of Sentinel-1 (Synthetic-Aperture Radar, SAR) and Sentinel-2 (multispectral) are analyzed in time-series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel-1 (n = 84) and Sentinel-2 (n = 903) are calculated at patch-level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel-1 VH and Sentinel-2 NMDI (Normalized Multi-band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time-series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time.
中文翻译：
评估增强中欧森林结构复杂性的实验性造林处理——基于 Sentinel-1 和 Sentinel-2 的 BEAST 时间序列分析
在全球变暖、生物多样性丧失和干扰增加的时期，评估森林结构复杂性的动态是一项关键任务，以确保森林的恢复力。最近对森林生物多样性和森林结构的研究强调了枯木积累和光照条件多样化对于增强结构复杂性的重要作用。在德国管理的阔叶林中实施实验性斑块网络，可以对以不同枯木和轻质结构为特征的各种聚合和分布式处理进行标准化分析。为了监测森林结构复杂性作为季节和趋势成分的动态变化，在时间序列分解中对 Sentinel-1（合成孔径雷达，SAR）和 Sentinel-2（多光谱）的高空间分辨率图像的密集时间序列进行了分析模型（BEAST、突变贝叶斯估计器、季节变化和趋势）。基于多项空间统计数据和光谱指数综合目录，Sentinel-1 ( n = 84) 和 Sentinel-2 ( n = 903) 的指标在补丁级别进行计算。通过变化点日期和概率得分来评估最能识别治疗实施事件的指标。 Sentinel-1 VH 和 Sentinel-2 NMDI（标准化多波段干旱指数）的异质性指标最准确地捕获了治疗实施事件，对于识别聚合治疗具有明显的优势。此外，还可以表征倒下或无枯木的聚集结构，以及更复杂的直立结构，例如障碍物或栖息地树木。总之，互补的高空间分辨率传感器的密集时间序列有可能评估各种聚合森林结构的复杂性，从而支持对森林栖息地和功能的持续监测。
更新日期：2024-04-08
中文翻译：
评估增强中欧森林结构复杂性的实验性造林处理——基于 Sentinel-1 和 Sentinel-2 的 BEAST 时间序列分析
在全球变暖、生物多样性丧失和干扰增加的时期，评估森林结构复杂性的动态是一项关键任务，以确保森林的恢复力。最近对森林生物多样性和森林结构的研究强调了枯木积累和光照条件多样化对于增强结构复杂性的重要作用。在德国管理的阔叶林中实施实验性斑块网络，可以对以不同枯木和轻质结构为特征的各种聚合和分布式处理进行标准化分析。为了监测森林结构复杂性作为季节和趋势成分的动态变化，在时间序列分解中对 Sentinel-1（合成孔径雷达，SAR）和 Sentinel-2（多光谱）的高空间分辨率图像的密集时间序列进行了分析模型（BEAST、突变贝叶斯估计器、季节变化和趋势）。基于多项空间统计数据和光谱指数综合目录，Sentinel-1 ( n = 84) 和 Sentinel-2 ( n = 903) 的指标在补丁级别进行计算。通过变化点日期和概率得分来评估最能识别治疗实施事件的指标。 Sentinel-1 VH 和 Sentinel-2 NMDI（标准化多波段干旱指数）的异质性指标最准确地捕获了治疗实施事件，对于识别聚合治疗具有明显的优势。此外，还可以表征倒下或无枯木的聚集结构，以及更复杂的直立结构，例如障碍物或栖息地树木。总之，互补的高空间分辨率传感器的密集时间序列有可能评估各种聚合森林结构的复杂性，从而支持对森林栖息地和功能的持续监测。
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