利用Sentinel-2数据和环境因子绘制云杉山林死亡率时空格局

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Marcin Kluczek, Bogdan Zagajewski
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引用次数: 0

摘要

气候变化正在增加极端事件的频率,包括森林事件。欧洲森林变化的主要驱动力之一是树皮甲虫,它导致云杉林地区发生大规模的年度变化。山林尤其脆弱,因为变化迅速,需要对正在发生的生态变化进行长期监测。为此目的,将Sentinel-2光学卫星数据与Sentinel-1雷达和地形衍生物融合的10年时间序列应用于中欧塔特拉山脉的天然林。基于机器学习算法和迭代方法,实现了0.94-0.96的总体分类准确率,f1评分为0.81-0.98。2018年观察到的云杉死亡率最高,大面积的障碍区一直持续到2024年。该研究表明,较小的侵染斑块(<;0.1公顷)一直主导着景观,在2018年达到顶峰,而更大的斑块(>;0.5 ha)呈下降趋势,特别是在2020年之后。变量重要度分析表明,地形因子对森林扰动模式的预测具有重要意义。海拔是最显著的预测因子,平均精度下降范围从95到150,其次是坡度和坡向。海拔高度对断峰发生的影响较大,平均海拔在700 ~ 1700 m之间,中位海拔从2015年的1150 m增加到2024年的1400 m。坡度也发挥了重要作用,断峰发生的中位数坡度在15°至25°之间,表明在中等坡度上有死亡率的趋势,尽管偶尔会观察到更陡峭的斜坡(高达50°)的死亡率,特别是在2017年和2023年。在坡向方面,东南和东部的云杉死亡率始终较高(特别是在2017年至2021年之间)。云杉死亡率与温度相关变量之间存在很强的相关性,特别是在关键月份(4月、6月和7月)高于5°C和8.3°C的天数。年平均气温呈负相关,而与降水相关的变量(包括标准化降水蒸散指数SPEI)呈负相关,尤其是SPEI 01的中位数。这些发现提高了对干扰引起的森林长期变化的认识,并为气候变化中数据驱动的森林保护管理提供了关键见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping spatiotemporal mortality patterns in spruce mountain forests using Sentinel-2 data and environmental factors
Climate change is increasing the frequency of extreme events, including those in forests. One of the major drivers of forest change in Europe is the bark beetle, which causes large-scale annual changes in spruce forest areas. Mountain forests are particularly vulnerable as changes occur rapidly and require long-term monitoring of ongoing ecological changes. For this purpose, a 10-year time series of Sentinel-2 optical satellite data fused with Sentinel-1 radar and topographic derivatives was applied to the natural forests of the Tatra Mountains in Central Europe. Based on machine learning algorithms and iterative methods, overall classification accuracies of 0.94–0.96 and snags with an F1-score of 0.81–0.98 were achieved. The highest spruce mortality rate was observed in 2018, with extensive snag areas persisting until 2024. This study revealed that smaller infestation patches (< 0.1 ha) consistently dominated the landscape, peaking in 2018, whereas larger patches (> 0.5 ha) showed a declining trend, particularly after 2020. The variable importance analysis revealed that topographic factors are critical for predicting forest disturbance patterns. Elevation emerged as the most significant predictor with a Mean Decrease in Accuracy ranging from 95 to 150, followed by slope and aspect. Snag occurrence was strongly influenced by elevation, ranging from 700 to 1700 m a.s.l., with the median elevation increasing from 1150 m in 2015 to 1400 m in 2024. The slope also played an important role, with the median slopes for snag occurrences ranging from 15° to 25°, indicating a tendency for mortality on moderate inclines, although mortality on steeper slopes (up to 50°) was occasionally observed, particularly in 2017 and 2023. Regarding the slope orientation, the southeastern and eastern aspects consistently experienced higher proportions of spruce mortality (particularly between 2017 and 2021). A strong correlation between spruce mortality and temperature-related variables was identified, particularly degree days above 5 °C and 8.3 °C during key months (April, June, and July). Median yearly air temperature showed a correlation, whereas precipitation-related variables, including the Standardised Precipitation Evapotranspiration Index (SPEI), exhibited negative correlations, particularly the SPEI 01 median. These findings improve the understanding of long-term forest changes caused by disturbances and provide key insights for the data-driven management of protected forests in a changing climate.
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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