Merge pull request #6 from paraskevioik/main

Modified profile, added publication

Author: KarasanteIl

content/authors/paraskevi-oikonomou/_index.md

@@ -19,7 +19,7 @@ last_name: Oikonomou
 superuser: true
 
 # Role/position
-role: Research Associate
+role: Research Assistant
 
 ## Organizations/Affiliations
 organizations:

content/authors/paraskevi-oikonomou/avatar.jpeg

@@ -0,0 +1,17 @@
+
+@Article{rs17050897,
+AUTHOR = {Oikonomou, Paraskevi and Karathanassi, Vassilia and Andronis, Vassilis and Papoutsis, Ioannis},
+TITLE = {Assessing and Forecasting Natural Regeneration in Mediterranean Landscapes After Wildfires},
+JOURNAL = {Remote Sensing},
+VOLUME = {17},
+YEAR = {2025},
+NUMBER = {5},
+ARTICLE-NUMBER = {897},
+URL = {https://www.mdpi.com/2072-4292/17/5/897},
+ISSN = {2072-4292},
+ABSTRACT = {Forest ecosystems in the Mediterranean basin are significantly affected by summer wildfires. Drought, extreme temperatures, and strong winds increase the fire risk in Greece. This study explores the potential of NDVI for assessing and forecasting post-fire regeneration in burnt areas of the Peloponnese (2007) and Evros (2011). NDVI data from Landsat 7 and 9 were analyzed to identify the stages of the regeneration process and the dominant vegetation species at each stage. Comparing pre-fire and post-fire values highlighted the recovery rate, while the trendline slope indicated the regeneration rate. This combined analysis forms a methodology that allows drawing conclusions about the vegetation type that prevails after the fire. Validation was conducted using photointerpretation techniques and CORINE land cover data. The findings suggest that sclerophyllous species regenerate faster, while fir forests recover slowly and may be replaced by sclerophylls. To predict vegetation regrowth, two time series models (ARMA, VARIMA) and two machine learning-based ones (random forest, XGBoost) were tested. Their performance was evaluated by comparing the predicted and actual numerical values, calculating error metrics (RMSE, MAPE), and analyzing how the predicted patterns align with the observed ones. The results showed the overperformance of multivariate models and the need to introduce additional variables, such as soil characteristics and the effect of climate change on weather parameters, to improve predictions.},
+DOI = {10.3390/rs17050897}
+}
+
+
+

content/authors/paraskevi-oikonomou/avatar.jpg

@@ -0,0 +1,80 @@
+---
+title: 'Assessing and Forecasting Natural Regeneration in Mediterranean Landscapes After Wildfires'
+
+# Authors
+# A YAML list of author names
+# If you created a profile for a user (e.g. the default `admin` user at `content/authors/admin/`), 
+# write the username (folder name) here, and it will be replaced with their full name and linked to their profile.
+authors:
+- Paraskevi Oikonomou
+- Vassilia Karathanassi 
+- Vassilis Andronis
+- Ioannis Papoutsis
+
+
+# Author notes (such as 'Equal Contribution')
+# A YAML list of notes for each author in the above `authors` list
+author_notes: []
+
+date: '2025-03-04'
+
+# Date to publish webpage (NOT necessarily Bibtex publication's date).
+publishDate: '2025-03-04T00:00:00.000000Z'
+
+# Publication type.
+# A single CSL publication type but formatted as a YAML list (for Hugo requirements).
+publication_types:
+- manuscript
+
+# Publication name and optional abbreviated publication name.
+publication: '*MDPI*'
+publication_short: ''
+
+doi: ''
+
+abstract: "Forest ecosystems in the Mediterranean basin are significantly affected by summer wildfires. Drought, extreme temperatures, and strong winds increase the fire risk in Greece. This study explores the potential of NDVI for assessing and forecasting post-fire regeneration in burnt areas of the Peloponnese (2007) and Evros (2011). NDVI data from Landsat 7 and 9 were analyzed to identify the stages of the regeneration process and the dominant vegetation species at each stage. Comparing pre-fire and post-fire values highlighted the recovery rate, while the trendline slope indicated the regeneration rate. This combined analysis forms a methodology that allows drawing conclusions about the vegetation type that prevails after the fire. Validation was conducted using photointerpretation techniques and CORINE land cover data. The findings suggest that sclerophyllous species regenerate faster, while fir forests recover slowly and may be replaced by sclerophylls. To predict vegetation regrowth, two time series models (ARMA, VARIMA) and two machine learning-based ones (random forest, XGBoost) were tested. Their performance was evaluated by comparing the predicted and actual numerical values, calculating error metrics (RMSE, MAPE), and analyzing how the predicted patterns align with the observed ones. The results showed the overperformance of multivariate models and the need to introduce additional variables, such as soil characteristics and the effect of climate change on weather parameters, to improve predictions."
+
+# Summary. An optional shortened abstract.
+summary: ''
+
+tags: ['regeneration', 'wildfires', 'vegetation', 'Landsat', 'NDVI', 'meteorological', 'time series', 'machine learning', 'prediction']
+
+#categories: []
+
+# Display this page in a list of Featured pages?
+#featured: false
+
+# Links
+url_pdf: ''
+url_code: ''
+url_dataset: ''
+url_poster: ''
+url_project: ''
+url_slides: ''
+url_source: ''
+url_video: ''
+
+# Custom links (uncomment lines below)
+# links:
+# - name: Custom Link
+#   url: http://example.org
+
+# Publication image
+# Add an image named `featured.jpg/png` to your page's folder then add a caption below.
+image:
+  caption: ''
+  focal_point: ''
+  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `projects: ['internal-project']` links to `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+projects: []
+links:
+- name: URL
+  url: https://www.mdpi.com/2072-4292/17/5/897
+---
+
+Add the **full text** or **supplementary notes** for the publication here using Markdown formatting.