Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2

IF 3.9 2区环境科学与生态学 Q1 ECOLOGY

Remote Sensing in Ecology and Conservation Pub Date : 2024-04-03 DOI:10.1002/rse2.386

Patrick Kacic, Ursula Gessner, Stefanie Holzwarth, Frank Thonfeld, Claudia Kuenzer

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引用次数: 0

Abstract

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.

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评估提高中欧森林结构复杂性的试验性造林处理--基于哨兵-1 和哨兵-2 的 BEAST 时间序列分析

在全球变暖、生物多样性丧失和干扰不断增加的情况下，评估森林结构的动态复杂性是确保森林恢复力的一项关键任务。最近关于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提高结构复杂性的重要作用。通过在德国管理的阔叶林中实施试验性斑块网络，可以对以不同枯落物和光照结构为特征的各种聚集和分布处理进行标准化分析。为了监测作为季节和趋势成分的森林结构复杂性增强的动态，利用时间序列分解模型（BEAST，突变、季节变化和趋势贝叶斯估计模型）分析了来自哨兵-1（合成孔径雷达）和哨兵-2（多光谱）高空间分辨率图像的密集时间序列。根据若干空间统计数据和光谱指数综合目录，计算出来自哨兵-1（n = 84）和哨兵-2（n = 903）的斑块级指标。通过变化点日期和概率分数评估了最能确定治疗实施事件的指标。哨兵-1 VH 和哨兵-2 NMDI（归一化多波段干旱指数）的异质性指标能最准确地捕捉到处理实施事件，在识别聚集处理方面具有明显优势。此外，还可对倒伏或无枯木的聚集结构以及更复杂的立木结构（如树桠或栖息地树木）进行定性。总之，高空间分辨率互补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长期连续监测。

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来源期刊

Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences

CiteScore

9.80

自引率

5.50%

发文量

审稿时长

18 weeks

期刊介绍： emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.