Abstract
Clonal poplar plantations under conditions of water stress are more susceptible to pests and diseases, in addition to having lower growth rates than in optimal conditions of water availability. Likewise, water management is essential to guarantee responsible and sustainable wood production, with a minimum water footprint. The aim of this paper was to develop a user-friendly online system based on satellite imagery to detect and monitor damages caused by water stress in poplar plantations, so it could be used by the poplar owners/managers without previous knowledge of remote sensing. PoplarAlert is a free online web application which was developed using Sentinel-2 MSI imagery, Google Earth Engine, Python. It allows the user to obtain, through the application, clear and immediate information on the probability that damage due to water stress has occurred (in- formation in the form of an image, graph, vector or table). In addition, this tool allows the temporary reconstruction of a damage that is detected (to go back in time and try to identify the trigger). The results of testing it in two different plantations confirmed the capability of PoplarAlert to detect water stress once there was some leaf loss and/or drier/yellower leaves still on the tree (previously or during the leaf loss).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gómez, C., Alejandro, P., Hermosilla, T., Montes, F., Pascual, C., Ruiz, L., Álvarez- Taboada, F., Tanase, M., Valbuena, R.: Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities. Forest Syst. 28(1), eR001 (2019). https://doi.org/10.5424/fs/2019281-14221
Álvarez-Taboada, F., Sanz-Ablanedo, E., Rodríguez Pérez, J.R., Castedo-Dorado, F., Lombardero, M.J.: Multi-sensor and multi-scale system for monitoring forest health in Pinus radiata stands defoliated by Lymantria dispar in NW Spain. In: Proceedings of the ForestSAT Open Conference System, Riva del Garda, Italia (2014)
Giannetti, F., Chianucci, F., Tattoni, C., Puletti, N., Giorcelli, A., Bisaglia, C., Romano1, E., et al.: PRECISIONPOP: a multi-scale monitoring system for poplar plantations integrating field, aerial and satellite remote sensing. In: Proceedings of the ForestSAT Open Conference System. Berlin, Germany (2022). https://www.conftool.org/forestsat2022/index.php?page=browseSessions&abstracts=show&form_session=23&presentations=show. Accessed 2022/09/15
Mazzia, V., Khaliq, A., Chiaberge, M.: Improvement in land cover and crop classification based on temporal features learning from Sentinel-2 data using recurrent-convolutional neural network (R-CNN). Appl. Sci. 10, 1–23 (2022)
Yu, X., Wu, X., Luo, C., Ren, P.: Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GIScience Remote Sens. 54, 741–758 (2017)
Hu, Y., Zhang, Q., Zhang, Y., Yan, H.: A deep convolution neural network method for land cover mapping: a case study of Qinhuangdao, China. IEEE Trans. Geosci. Remote Sens. 10, 1–17 (2018)
D’Amico, G., Francini, S., Giannetti, F., Vangi, E., Travaglini, D., Chianucci, F., Mattioli, W., Grotti, M., Puletti, N., Corona, P., Chirici, G.: A deep learning approach for automatic mapping of poplar plantations using Sentinel-2 imagery. GIScience Remote Sens. 58(8), 1352–1368 (2021)
Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N.: Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58, 289–298 (1996)
https://developers.google.com/earth-engine/datasets/cata-log/COPERNICUS_S2_SR#bands. Accessed 2022/09/15
Löw, M., Koukal, T.: Phenology modelling and forest disturbance mapping with Sentinel-2 time series in Austria. Remote Sens. (12), 4191 (2020). https://doi.org/10.3390/rs12244191
Pacheco-Pascagaza, A.M., Gou, Y., Louis, V., Roberts, J.F., Rodríguez-Veiga, P., da Conceição Bispo, P., Espírito-Santo, F.D.B., Robb, C., Upton, C., Galindo, G., Cabrera, E., Pachón Cendales, I.P., Castillo Santiago, M.A., Carrillo Negrete, O., Meneses, C., Iñiguez, M., Balzter, H.: Near Real-time change detection system using Sentinel-2 and machine learning: a test for Mexican and Colombian forests. Remote Sens. (14), 707 (2022). https://doi.org/10.3390/rs14030707
https://developers.google.com/earth-engine/datasets/cata-log/COPERNICUS_S2_CLOUD_PROBABILITY. Accessed 2022/09/15
Wu, Q.: Geemap: a Python package for interactive mapping with Google Earth Engine. J. Open Source Softw 5(51), 2305 (2020). https://doi.org/10.21105/joss.02305
Verbesselt, J., Zeileis, A., Herold, M.: Near real-time disturbance detection using satellite image time series. Remote Sens. Environ. 123, 98–108 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Arhrib, Y.J., Francini, S., D’Amico, G., Castedo-Dorado, F., Garnica-López, J., Álvarez-Taboada, M.F. (2023). Web Application Based on Sentinel-2 Satellite Imagery for Water Stress Detection and Monitoring in Poplar Plantations. In: Benítez-Andrades, J.A., García-Llamas, P., Taboada, Á., Estévez-Mauriz, L., Baelo, R. (eds) Global Challenges for a Sustainable Society . EURECA-PRO 2022. Springer Proceedings in Earth and Environmental Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-25840-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-25840-4_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25839-8
Online ISBN: 978-3-031-25840-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)