Monitoring the effects of extreme drought events on forest health by Sentinel-2 imagery

被引:33
|
作者
Puletti, Nicola [1 ]
Mattioli, Walter [1 ]
Bussotti, Filippo [2 ]
Pollastrini, Martina [2 ]
机构
[1] CREA, Res Ctr Forestry & Wood, Arezzo, Italy
[2] Univ Florence, Dept Agrifood Prod & Environm Sci, Florence, Italy
关键词
forest health; dry spell; crown dieback; Mediterranean forest stands; Copernicus program;
D O I
10.1117/1.JRS.13.020501
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Global climate change is expected to result in more frequent and intense drought events, especially during the warm season. In such perspective, it is crucial to assess the forest stands vulnerability to extreme climatic events, such as drought, even for Mediterranean forest tree species, commonly considered resistant to dry spell. To test the capability of multitemporal imagery derived by Sentinel-2 (S2) in detecting the impacts of extreme drought events on forest health assessed as crown dieback, some forest stands in Tuscany (central Italy) were analyzed. Vegetation indices (VIs) and ancillary digital terrain model-derived data have been collected in 118 observational samples distributed along an ecological gradient. VIs detected a reduction of trees of photosynthetic activity in August 2017. S2 data have allowed the observation of the different response strategies of the tree species considered in this study to the extreme climatic event that occurred. The case study presented shows that S2 can be applied for monitoring climate-related processes providing a synthetic overview of forest conditions at regional scale. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:8
相关论文
共 50 条
  • [31] INTERPRETABLE SCENICNESS FROM SENTINEL-2 IMAGERY
    Levering, Alex
    Marcos, Diego
    Lobry, Sylvain
    Tuia, Devis
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 3983 - 3986
  • [32] Automated Mosaicking of Sentinel-2 Satellite Imagery
    Shepherd, James D.
    Schindler, Jan
    Dymond, John R.
    REMOTE SENSING, 2020, 12 (22) : 1 - 14
  • [33] Reviewing the Potential of Sentinel-2 in Assessing the Drought
    Varghese, Dani
    Radulovic, Mirjana
    Stojkovic, Stefanija
    Crnojevic, Vladimir
    REMOTE SENSING, 2021, 13 (17)
  • [34] Combined Sentinel-1 and Sentinel-2 Imagery for Destroyed Building Classification in Gaza Strip With Random Forest
    Li, Xinchen
    Guo, Liang
    Chan, Jonathan Cheung-Wai
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 3827 - 3839
  • [35] Cotton aphid infestation monitoring using Sentinel-2 MSI imagery coupled with derivative of ratio spectroscopy and random forest algorithm
    Fu, Hancong
    Zhao, Hengqian
    Song, Rui
    Yang, Yifeng
    Li, Zihan
    Zhang, Shijia
    FRONTIERS IN PLANT SCIENCE, 2022, 13
  • [36] Assessing Vegetation Response to Soil Moisture Fluctuation under Extreme Drought Using Sentinel-2
    West, Harry
    Quinn, Nevil
    Horswell, Michael
    White, Paul
    WATER, 2018, 10 (07)
  • [37] Monitoring intertidal golden tides dominated by Ectocarpus siliculosus using Sentinel-2 imagery
    Haro, Sara
    Bermejo, Ricardo
    Wilkes, Robert
    Bull, Lorraine
    Morrison, Liam
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 122
  • [38] Forest mapping and monitoring in Africa using Sentinel-2 data and deep learning
    Waldeland, Anders U.
    Trier, oivind Due
    Salberg, Arnt-Borre
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2022, 111
  • [39] Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest
    Astola, Heikki
    Seitsonen, Lauri
    Halme, Eelis
    Molinier, Matthieu
    Lonnqvist, Anne
    REMOTE SENSING, 2021, 13 (12)
  • [40] Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery
    Zhang, Xiaoli
    Shen, Hanwen
    Huang, Tianbao
    Wu, Yong
    Guo, Binbing
    Liu, Zhi
    Luo, Hongbin
    Tang, Jing
    Zhou, Hang
    Wang, Leiguang
    Xu, Weiheng
    Ou, Guanglong
    ECOLOGICAL INDICATORS, 2024, 159