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Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve

Author

Listed:
  • Fabio Recanatesi

    (Department of Agricultural and Forestry Sciences (D.A.F.N.E.), Tuscia University, 01100 Viterbo, Italy)

  • Chiara Giuliani

    (Department Landscape and Planning, La Sapienza University, 00185 Rome, Italy)

  • Maria Nicolina Ripa

    (Department of Agricultural and Forestry Sciences (D.A.F.N.E.), Tuscia University, 01100 Viterbo, Italy)

Abstract

Climate change and human activities in particular are important causes of the possible variations in Mediterranean basin forest health conditions. Over the last decades, deciduous oak-forest mortality has been a recurrent problem in central and southern Italy. Despite the perception of increasingly visible damage in oak forests in drought sites, the role of various environmental factors in their decline is not completely clear. Among the modern methods of monitoring terrestrial ecosystems, remote sensing is of prime importance thanks to its ability to provide synoptic information on large areas with a high frequency of acquisition. This paper reports the preliminary results regarding a replicable and low cost monitoring tool planned to quantify forest health conditions based on the application of the Normalized Difference Vegetation Index (NDVI), using the diachronic images provided by the Sentinel-2 satellite. The study area is represented by a peri-urban forest of natural Mediterranean deciduous oaks, characterized by a high variability in the composition of the species and in the silvicultural structures. In order to monitor the health conditions of a specific forest canopy cover with remote sensing data, it is necessary to classify the forest canopy cover in advance to separate it from other species and from the Mediterranean scrub. This is due to the spatial distribution of vegetation and the high rate of biodiversity in the Mediterranean natural environment. To achieve this, Light Detection and Ranging (LiDAR) data, forest management data and field sampling data were analyzed. The main results of this research show a widespread decline in oak health conditions over the observed period (2015–2017). Specifically, for the studied area, thanks to the specific localization of the oak canopy cover, we detected a high potential concerning the Sentinel-2 data application in monitoring forest health conditions by NDVI application.

Suggested Citation

  • Fabio Recanatesi & Chiara Giuliani & Maria Nicolina Ripa, 2018. "Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve," Sustainability, MDPI, vol. 10(9), pages 1-10, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3308-:d:170143
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    Citations

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    Cited by:

    1. Lijun Mao & Mingshi Li & Wenjuan Shen, 2020. "Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade," Sustainability, MDPI, vol. 12(12), pages 1-27, June.
    2. Yves Hategekimana & Mona Allam & Qingyan Meng & Yueping Nie & Elhag Mohamed, 2020. "Quantification of Soil Losses along the Coastal Protected Areas in Kenya," Land, MDPI, vol. 9(5), pages 1-16, May.
    3. Jinsun Kim & Jimin Lee & Minji Park & Joong-Hyuk Min & Jong Mun Lee & Heeseon Jang & Eun Hye Na, 2024. "The Impact of Non-Point Source (NPS) Management on Non-Point Source Reduction and Water Cycle Improvement in an Urban Area," Sustainability, MDPI, vol. 16(3), pages 1-26, February.
    4. Giuseppe Cillis & Dina Statuto & Pietro Picuno, 2021. "Historical GIS as a Tool for Monitoring, Preserving and Planning Forest Landscape: A Case Study in a Mediterranean Region," Land, MDPI, vol. 10(8), pages 1-20, August.
    5. Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Motirh Al-Mutiry, 2022. "GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria," Sustainability, MDPI, vol. 14(8), pages 1-20, April.

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