{"title":"Mapping spatiotemporal mortality patterns in spruce mountain forests using Sentinel-2 data and environmental factors","authors":"Marcin Kluczek, Bogdan Zagajewski","doi":"10.1016/j.ecoinf.2025.103074","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103074"},"PeriodicalIF":5.8000,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574954125000834","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.