Mapping Drought Incidents in the Mediterranean Region with Remote Sensing: A Step Toward Climate Adaptation
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Wildfire Events Overview
2.3. Data Collection and Processing
2.4. Seasonal Compositing
2.5. Drought Level Determination
2.6. Visualization and Export
3. Results
3.1. Greece—Evros Region
3.1.1. Pre-Fire Period (Spring 2020–Spring 2023)
3.1.2. Fire Season (Summer 2023)
3.1.3. Post-Fire Period (Autumn 2023–Winter 2024)
3.2. France—Gironde Region
3.2.1. Pre-Fire Period (Spring 2020–Spring 2022)
3.2.2. Fire Season (Summer 2022)
3.2.3. Post-Fire Period (Autumn 2022–Winter 2024)
3.3. Italy—Montiferru Region
3.3.1. Pre-Fire Period (Spring 2019–Spring 2021)
3.3.2. Fire Season (Summer 2021)
3.3.3. Post-Fire Period (Autumn 2021–Winter 2023)
3.4. Spain—Benahavis Region
3.4.1. Pre-Fire Period (Spring 2020–Spring 2022)
3.4.2. Fire Season (Summer 2022)
3.4.3. Post-Fire Period (Autumn 2022–Winter 2024)
3.5. Statistical Analysis of Drought Metrics
3.5.1. Pearson Correlation
3.5.2. One-Way Analysis of Variance
3.5.3. Regression Analysis
4. Discussion
4.1. Limitations
4.2. Contribution to the Field
5. Conclusions
Actionable Future Directions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Cos, J.; Doblas-Reyes, F.; Jury, M.; Marcos, R.; Bretonnière, P.-A.; Samsó, M. The Mediterranean Climate Change Hotspot in the CMIP5 and CMIP6 Projections. Earth Syst. Dyn. 2022, 13, 321–340. [Google Scholar] [CrossRef]
- Bleu, P.; Antipolis, S.; Plan, B. The Blue Plan’s Sustainable Development Outlook for the Mediterranean; United Nations Environment Programme, Mediterranean Action Plan, Blue Plan-Regional Activity Centre: Sophia Antipolis and Marseille, France, 2008; English Translation from an Original Document in French. [Google Scholar]
- Mishra, D.; Goswami, S.; Matin, S.; Sarup, J. Analyzing the Extent of Drought in the Rajasthan State of India Using Vegetation Condition Index and Standardized Precipitation Index. Model. Earth Syst. Environ. 2022, 8, 601–610. [Google Scholar] [CrossRef]
- Pozzi, W.; Sheffield, J.; Stefanski, R.; Cripe, D.; Pulwarty, R.; Vogt, J.V.; Heim, R.R.; Brewer, M.J.; Svoboda, M.; Westerhoff, R.; et al. Toward Global Drought Early Warning Capability: Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting. Bull. Am. Meteorol. Soc. 2013, 94, 776–785. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M.; Domínguez-Castro, F.; Reig, F.; Beguería, S.; Tomas-Burguera, M.; Latorre, B.; Peña-Angulo, D.; Noguera, I.; Rabanaque, I.; Luna, Y.; et al. A near Real-Time Drought Monitoring System for Spain Using Automatic Weather Station Network. Atmos. Res. 2022, 271, 106095. [Google Scholar] [CrossRef]
- Cook, B.I.; Mankin, J.S.; Anchukaitis, K.J. Climate Change and Drought: From Past to Future. Curr. Clim. Chang. Rep. 2018, 4, 164–179. [Google Scholar] [CrossRef]
- Lanet, M.; Li, L.; Ehret, A.; Turquety, S.; Le Treut, H. Attribution of Summer 2022 Extreme Wildfire Season in Southwest France to Anthropogenic Climate Change. NPJ Clim. Atmos. Sci. 2024, 7, 267. [Google Scholar] [CrossRef]
- Tramblay, Y.; Koutroulis, A.; Samaniego, L.; Vicente-Serrano, S.M.; Volaire, F.; Boone, A.; Le Page, M.; Llasat, M.C.; Albergel, C.; Burak, S.; et al. Challenges for Drought Assessment in the Mediterranean Region under Future Climate Scenarios. Earth-Sci. Rev. 2020, 210, 103348. [Google Scholar] [CrossRef]
- Hao, Z.; Yuan, X.; Xia, Y.; Hao, F.; Singh, V.P. An Overview of Drought Monitoring and Prediction Systems at Regional and Global Scales. Bull. Am. Meteorol. Soc. 2017, 98, 1879–1896. [Google Scholar] [CrossRef]
- Kumar, L.; Mutanga, O. Google Earth Engine Applications since Inception: Usage, Trends, and Potential. Remote Sens. 2018, 10, 1509. [Google Scholar] [CrossRef]
- Gao, F.; Anderson, M.; Houborg, R. Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection. Remote Sens. 2024, 16, 4145. [Google Scholar] [CrossRef]
- Yan, K.; Gao, S.; Yan, G.; Ma, X.; Chen, X.; Zhu, P.; Li, J.; Gao, S.; Gastellu-Etchegorry, J.-P.; Myneni, R.B.; et al. A Global Systematic Review of the Remote Sensing Vegetation Indices. Int. J. Appl. Earth Obs. Geoinf. 2025, 139, 104560. [Google Scholar] [CrossRef]
- Khikmah, F.; Sebald, C.; Metzner, M.; Schwieger, V. Modelling Vegetation Health and Its Relation to Climate Conditions Using Copernicus Data in the City of Constance. Remote Sens. 2024, 16, 40691. [Google Scholar] [CrossRef]
- Matyukira, C.; Mhangara, P. Advances in Vegetation Mapping through Remote Sensing and Machine Learning Techniques: A Scientometric Review. Eur. J. Remote Sens. 2024, 57, 2422330. [Google Scholar] [CrossRef]
- Gao, S.; Yan, K.; Liu, J.; Pu, J.; Zou, D.; Qi, J.; Mu, X.; Yan, G. Assessment of Remote-Sensed Vegetation Indices for Estimating Forest Chlorophyll Concentration. Ecol. Indic. 2024, 162, 112001. [Google Scholar] [CrossRef]
- Yan, K.; Gao, S.; Chi, H.; Qi, J.; Song, W.; Tong, Y.; Mu, X.; Yan, G. Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation. IEEE Trans. Geosci. Remote Sens. 2022, 60, 4400514. [Google Scholar] [CrossRef]
- Jackson, T.J.; Chen, D.; Cosh, M.; Li, F.; Anderson, M.; Walthall, C.; Doriaswamy, P.; Hunt, E.R. Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans. Remote Sens. Environ. 2003, 92, 475–482. [Google Scholar] [CrossRef]
- Carlson, T.N.; Ripley, D.A. On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sens. Environ. 1997, 62, 241–252. [Google Scholar] [CrossRef]
- Pettorelli, N.; Vik, J.O.; Mysterud, A.; Gaillard, J.-M.; Tucker, C.J.; Stenseth, N.C. Using the Satellite-Derived NDVI to Assess Ecological Responses to Environmental Change. Trends Ecol. Evol. 2005, 20, 503–510. [Google Scholar] [CrossRef]
- Xie, F.; Fan, H. Deriving Drought Indices from MODIS Vegetation Indices (NDVI/EVI) and Land Surface Temperature (LST): Is Data Reconstruction Necessary? Int. J. Appl. Earth Obs. Geoinf. 2021, 101, 102352. [Google Scholar] [CrossRef]
- Gao, B. NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Mcfeeters, S.K. Remote Sensing Using the Normalized Difference Water Index (NDWI) within a Geographic Information System to Detect Swimming Pools for Mosquito Abatement: A Practical Approach. Remote Sens. 2013, 5, 3544–3561. [Google Scholar] [CrossRef]
- Xu, H. Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [Google Scholar] [CrossRef]
- Patil, P.P.; Jagtap, M.P.; Khatri, N.; Madan, H.; Vadduri, A.A.; Patodia, T. Exploration and Advancement of NDDI Leveraging NDVI and NDWI in Indian Semi-Arid Regions: A Remote Sensing-Based Study. Case Stud. Chem. Environ. Eng. 2024, 9, 100573. [Google Scholar] [CrossRef]
- Ejaz, N.; Bahrawi, J.; Alghamdi, K.M.; Rahman, K.U.; Shang, S. Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia. Remote Sens. 2023, 15, 984. [Google Scholar] [CrossRef]
- Chou, C.-B.; Weng, M.-C.; Huang, H.-P.; Chang, Y.-C.; Chang, H.-C.; Yeh, T.-Y. Monitoring the Spring 2021 Drought Event in Taiwan Using Multiple Satellite-Based Vegetation and Water Indices. Atmosphere 2022, 13, 1374. [Google Scholar] [CrossRef]
- Bhaga, T.D.; Dube, T.; Shekede, M.D.; Shoko, C. Investigating the Effectiveness of Landsat-8 OLI and Sentinel-2 MSI Satellite Data in Monitoring the Effects of Drought on Surface Water RESOURCES in the Western Cape Province, South Africa. Remote Sens. Appl. Soc. Environ. 2023, 32, 101037. [Google Scholar] [CrossRef]
- Chávez, R.O.; Castillo-Soto, M.E.; Traipe, K.; Olea, M.; Lastra, J.A.; Quiñones, T. A Probabilistic Multi-Source Remote Sensing Approach to Evaluate Extreme Precursory Drought Conditions of a Wildfire Event in Central Chile. Front. Environ. Sci. 2022, 10, 865406. [Google Scholar] [CrossRef]
- Filipponi, F. Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires. Remote Sens. 2019, 11, 622. [Google Scholar] [CrossRef]
- Ma, B.; Liu, X.; Tong, Z.; Zhang, J.; Wang, X. Coupled Effects of High Temperatures and Droughts on Forest Fires in Northeast China. Remote Sens. 2024, 16, 3784. [Google Scholar] [CrossRef]
- Farrokhi, A.; Farzin, S.; Mousavi, S.-F. Meteorological Drought Analysis in Response to Climate Change Conditions, Based on Combined Four-Dimensional Vine Copulas and Data Mining (VC-DM). J. Hydrol. 2021, 603, 127135. [Google Scholar] [CrossRef]
- Leblon, B.; San-Miguel-Ayanz, J.; Bourgeau-Chavez, L.; Kong, M. Remote Sensing of Wildfires. In Land Surface Remote Sensing; Baghdadi, N., Zribi, M., Eds.; Elsevier: Amsterdam, The Netherlands, 2016; pp. 55–95. ISBN 978-1-78548-105-5. [Google Scholar]
- Wu, Q.; Li, H.-Q.; Wang, R.-S.; Paulussen, J.; He, Y.; Wang, M.; Wang, B.-H.; Wang, Z. Monitoring and Predicting Land Use Change in BEIJING Using Remote Sensing and GIS. Landsc. Urban Plan. 2006, 78, 322–333. [Google Scholar] [CrossRef]
- UNESCO World Heritage Centre. National Park of Dadia-Lefkimi-Souflion. Available online: https://whc.unesco.org/en/tentativelists/5856/ (accessed on 3 June 2025).
- Wildfires: 2023 among the Worst in the EU in This Century—European Commission. Available online: https://joint-research-centre.ec.europa.eu/jrc-news-and-updates/wildfires-2023-among-worst-eu-century-2024-04-10_en (accessed on 3 June 2025).
- Michailidis, K.; Garane, K.; Karagkiozidis, D.; Peletidou, G.; Voudouri, K.-A.; Balis, D.; Bais, A. Extreme Wildfires over Northern Greece during Summer 2023—Part A: Effects on Aerosol Optical Properties and Solar UV Radiation. Atmos. Res. 2024, 311, 107700. [Google Scholar] [CrossRef]
- Menut, L.; Cholakian, A.; Siour, G.; Lapere, R.; Pennel, R.; Mailler, S.; Bessagnet, B. Impact of Landes Forest Fires on Air Quality in France during the 2022 Summer. Atmos. Chem. Phys. 2023, 23, 7281–7296. [Google Scholar] [CrossRef]
- Pinna, M.T.; Cuccu, M.G.; Giannasi, M.P.; Casula, A.; Cabiddu, S. The 2021 Montiferru Wildfire, Sardinia (Italy): Analysis of a Large Wildfire. Environ. Sci. Proc. 2022, 17, 108. [Google Scholar] [CrossRef]
- Copernicus EMS Pujerra, Spain Wildfire. Available online: https://mapping.emergency.copernicus.eu/activations/EMSN130/#activation-details (accessed on 11 June 2025).
- EMSN212—CEMS Risk and Recovery Mapping. Available online: https://riskandrecovery.emergency.copernicus.eu/EMSN212/?embed=true (accessed on 11 June 2025).
- Copernicus EMS. Available online: https://mapping.emergency.copernicus.eu/ (accessed on 11 June 2025).
- Statista. Available online: https://www.statista.com/statistics/1322203/area-burned-by-wildfire-in-france/ (accessed on 11 June 2025).
- Sentinel Online. Available online: https://sentinels.copernicus.eu/web/success-stories/-/aftermath-of-the-montiferru-fires (accessed on 11 June 2025).
- Gascon, F.; Bouzinac, C.; Thépaut, O.; Jung, M.; Francesconi, B.; Louis, J.; Lonjou, V.; Lafrance, B.; Massera, S.; Gaudel-Vacaresse, A.; et al. Copernicus Sentinel-2A Calibration and Products Validation Status. Remote Sens. 2017, 9, 584. [Google Scholar] [CrossRef]
- Landsat Normalized Difference Vegetation Index|U.S. Geological Survey. Available online: https://www.usgs.gov/landsat-missions/landsat-normalized-difference-vegetation-index (accessed on 2 June 2025).
- Tucker, C.J. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef]
- Artikanur, S.D.; Widiatmaka; Setiawan, Y.; Marimin. Normalized Difference Drought Index (NDDI) Computation for Mapping Drought Severity in Bojonegoro Regency, East Java, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 2022, 1109, 012027. [Google Scholar] [CrossRef]
- Ruffault, J.; Moron, V.; Trigo, R.M.; Curt, T. Objective Identification of Multiple Large Fire Climatologies: An Application to a Mediterranean Ecosystem. Environ. Res. Lett. 2016, 11, 075006. [Google Scholar] [CrossRef]
- Jolly, W.M.; Cochrane, M.A.; Freeborn, P.H.; Holden, Z.A.; Brown, T.J.; Williamson, G.J.; Bowman, D.M.J.S. Climate-Induced Variations in Global Wildfire Danger from 1979 to 2013. Nat. Commun. 2015, 6, 7537. [Google Scholar] [CrossRef]
- Bowman, D.M.J.S.; Balch, J.K.; Artaxo, P.; Bond, W.J.; Carlson, J.M.; Cochrane, M.A.; D’Antonio, C.M.; DeFries, R.S.; Doyle, J.C.; Harrison, S.P.; et al. Fire in the Earth System. Science 2009, 324, 481–484. [Google Scholar] [CrossRef]
- Pausas, J.G.; Keeley, J.E. Wildfires and Global Change. Front. Ecol. Environ. 2021, 19, 387–395. [Google Scholar] [CrossRef]
NDVI Value | Interpretation |
---|---|
<0.2 | Very sparse vegetation |
0.2–0.4 | Sparse vegetation |
0.4–0.6 | Moderate vegetation |
0.6–0.9 | Dense vegetation |
0.9–1 | Healthy vegetation |
NDWI Value | Interpretation |
---|---|
<−0.3 | Extensive dryness |
−0.3–0.0 | Moderate drought |
0.0–0.2 | High moisture levels |
0.2–1.0 | Water surfaces |
No. | NDDI Value | Drought Severity Class |
---|---|---|
1 | <−2 | Very low |
2 | −2–0.7 | Low |
3 | 0.7–1.25 | Moderate |
4 | 1.25–3 | High |
5 | >3 | Very high |
Drought Level | NDDI | NDVI | NDWI | Season | Year | Drought Level Explained |
---|---|---|---|---|---|---|
4.13 | 7.14 | 0.41 | −0.36 | Spring | 2020 | Very high |
3.55 | 4.07 | 0.44 | −0.40 | Summer | 2020 | Very high |
3.29 | 2.98 | 0.35 | −0.33 | Autumn | 2020 | High |
3.91 | 5.73 | 0.27 | −0.23 | Winter | 2020 | Very high |
4.09 | 6.80 | 0.38 | −0.35 | Spring | 2021 | Very high |
3.68 | 4.88 | 0.46 | −0.42 | Summer | 2021 | Very high |
3.39 | 2.49 | 0.34 | −0.31 | Autumn | 2021 | High |
3.74 | 4.85 | 0.28 | −0.25 | Winter | 2021 | Very high |
3.84 | 5.39 | 0.39 | −0.36 | Spring | 2022 | Very high |
3.75 | 4.88 | 0.45 | −0.41 | Summer | 2022 | Very high |
3.24 | 1.13 | 0.34 | −0.32 | Autumn | 2022 | Moderate |
3.81 | 4.60 | 0.26 | −0.23 | Winter | 2022 | Very high |
3.94 | 5.50 | 0.41 | −0.36 | Spring | 2023 | Very high |
3.39 | 3.71 | 0.41 | −0.39 | Summer | 2023 | Very high |
2.63 | 0.08 | 0.27 | −0.28 | Autumn | 2023 | Moderate |
3.66 | 3.50 | 0.22 | −0.19 | Winter | 2023 | Very high |
4.17 | 8.73 | 0.39 | −0.35 | Spring | 2024 | Very high |
2.80 | 1.19 | 0.36 | −0.37 | Summer | 2024 | Moderate |
2.71 | 0.16 | 0.28 | −0.28 | Autumn | 2024 | Moderate |
3.69 | 4.40 | 0.23 | −0.20 | Winter | 2024 | Very high |
Drought Level | NDDI | NDVI | NDWI | Season | Year | Drought Level Explained |
---|---|---|---|---|---|---|
4.59 | 8.07 | 0.47 | −0.41 | Spring | 2020 | High |
4.77 | 8.25 | 0.51 | −0.43 | Summer | 2020 | High |
4.85 | 8.61 | 0.48 | −0.39 | Autumn | 2020 | High |
4.63 | 8.37 | 0.42 | −0.34 | Winter | 2020 | High |
4.16 | 8.62 | 0.43 | −0.38 | Spring | 2021 | High |
4.89 | 9.12 | 0.55 | −0.45 | Summer | 2021 | High |
4.84 | 8.75 | 0.53 | −0.43 | Autumn | 2021 | High |
3.61 | 5.36 | 0.36 | −0.29 | Winter | 2021 | High |
4.93 | 10.58 | 0.44 | −0.39 | Spring | 2022 | High |
4.63 | 8.77 | 0.51 | −0.44 | Summer | 2022 | High |
3.68 | 6.48 | 0.39 | −0.32 | Autumn | 2022 | High |
3.49 | 6.53 | 0.36 | −0.29 | Winter | 2022 | High |
4.63 | 8.03 | 0.41 | −0.35 | Spring | 2023 | High |
4.85 | 9.14 | 0.53 | −0.44 | Summer | 2023 | High |
4.78 | 9.57 | 0.51 | −0.41 | Autumn | 2023 | High |
4.80 | 6.67 | 0.40 | −0.31 | Winter | 2023 | High |
5.00 | 10.84 | 0.52 | −0.45 | Spring | 2024 | High |
4.84 | 9.27 | 0.56 | −0.46 | Summer | 2024 | High |
4.86 | 7.75 | 0.49 | −0.39 | Autumn | 2024 | High |
3.26 | 5.71 | 0.37 | −0.29 | Winter | 2024 | High |
Drought Level | NDDI | NDVI | NDWI | Season | Year | Drought Level Explained |
---|---|---|---|---|---|---|
4.90 | 12.22 | 0.56 | −0.48 | Spring | 2019 | High |
2.78 | 2.18 | 0.43 | −0.43 | Summer | 2019 | Low |
3.49 | 2.43 | 0.34 | −0.32 | Autumn | 2019 | Moderate |
4.92 | 9.40 | 0.51 | −0.41 | Winter | 2019 | High |
4.92 | 10.13 | 0.58 | −0.49 | Spring | 2020 | High |
2.63 | 0.43 | 0.39 | −0.40 | Summer | 2020 | Low |
4.88 | 6.68 | 0.47 | −0.40 | Autumn | 2020 | High |
4.94 | 10.16 | 0.56 | −0.46 | Winter | 2020 | High |
4.88 | 11.62 | 0.63 | −0.53 | Spring | 2021 | High |
2.53 | −0.12 | 0.35 | −0.37 | Summer | 2021 | Low |
2.91 | 0.50 | 0.30 | −0.29 | Autumn | 2021 | Low |
4.80 | 8.43 | 0.44 | −0.35 | Winter | 2021 | High |
4.89 | 11.05 | 0.59 | −0.50 | Spring | 2022 | High |
2.15 | −1.66 | 0.35 | −0.39 | Summer | 2022 | Low |
4.68 | 5.06 | 0.39 | −0.34 | Autumn | 2022 | High |
4.92 | 9.89 | 0.52 | −0.42 | Winter | 2022 | High |
4.91 | 10.52 | 0.54 | −0.46 | Spring | 2023 | High |
2.75 | 1.68 | 0.40 | −0.40 | Summer | 2023 | Low |
2.97 | 0.54 | 0.35 | −0.34 | Autumn | 2023 | Low |
4.92 | 9.32 | 0.54 | −0.43 | Winter | 2023 | High |
Drought Level | NDDI | NDVI | NDWI | Season | Year | Drought Level Explained |
---|---|---|---|---|---|---|
4.93 | 10.00 | 0.48 | −0.39 | Spring | 2020 | High |
4.79 | 11.64 | 0.42 | −0.36 | Summer | 2020 | High |
4.89 | 8.86 | 0.42 | −0.33 | Autumn | 2020 | High |
4.83 | 8.51 | 0.51 | −0.38 | Winter | 2020 | High |
4.93 | 8.55 | 0.45 | −0.37 | Spring | 2021 | High |
4.69 | 12.50 | 0.41 | −0.36 | Summer | 2021 | High |
4.86 | 8.39 | 0.41 | −0.32 | Autumn | 2021 | High |
4.84 | 6.83 | 0.41 | −0.31 | Winter | 2021 | High |
4.94 | 10.67 | 0.50 | −0.41 | Spring | 2022 | High |
2.75 | 1.49 | 0.11 | −0.11 | Summer | 2022 | Moderate |
2.27 | 3.11 | 0.11 | −0.12 | Autumn | 2022 | Moderate |
2.66 | −1.95 | 0.10 | −0.08 | Winter | 2022 | Moderate |
2.01 | −5.83 | 0.20 | −0.22 | Spring | 2023 | Moderate |
1.46 | 9.30 | 0.20 | −0.23 | Summer | 2023 | Moderate |
2.56 | −3.15 | 0.21 | −0.21 | Autumn | 2023 | Moderate |
3.28 | 0.58 | 0.27 | −0.23 | Winter | 2023 | High |
3.22 | 1.73 | 0.34 | −0.33 | Spring | 2024 | High |
1.49 | 11.00 | 0.26 | −0.29 | Summer | 2024 | Moderate |
2.65 | 3.68 | 0.26 | −0.27 | Autumn | 2024 | Moderate |
4.24 | 8.02 | 0.37 | −0.31 | Winter | 2024 | High |
Pearson Correlation | Drought Level | NDDI | NDVI | NDWI |
---|---|---|---|---|
Greece—Evros region | ||||
Drought level | 1.000 | 0.955 | 0.267 | −0.058 |
NDDI | 0.955 | 1.000 | 0.359 | −0.179 |
NDVI | 0.267 | 0.359 | 1.000 | −0.974 |
NDWI | −0.058 | −0.179 | −0.974 | 1.000 |
France—Gironde region | ||||
Drought level | 1.000 | 0.798 | 0.775 | −0.781 |
NDDI | 0.798 | 1.000 | 0.748 | −0.826 |
NDVI | 0.775 | 0.748 | 1.000 | −0.972 |
NDWI | −0.781 | −0.826 | −0.972 | 1.000 |
Italy—Montiferru region | ||||
Drought level | 1.000 | 0.948 | 0.816 | −0.523 |
NDDI | 0.948 | 1.000 | 0.931 | −0.720 |
NDVI | 0.816 | 0.931 | 1.000 | −0.911 |
NDWI | −0.523 | −0.720 | −0.911 | 1.000 |
Spain—Benahavis region | ||||
Drought level | 1.000 | 0.550 | 0.872 | −0.717 |
NDDI | 0.550 | 1.000 | 0.691 | −0.705 |
NDVI | 0.872 | 0.691 | 1.000 | −0.957 |
NDWI | −0.717 | −0.705 | −0.957 | 1.000 |
ANOVA Results of Greece—Evros Region | |||||
Drought Level | |||||
Sum of Squares | df | Mean Square | F | Sig. | |
Between Groups | 2.695 | 3 | 0.898 | 12.011 | 0.000 |
Within Groups | 1.197 | 16 | 0.075 | ||
Total | 3.891 | 19 | |||
ANOVA results of France—Gironde region | |||||
Drought Level | |||||
Sum of Squares | df | Mean Square | F | Sig. | |
Between Groups | 2.673 | 3 | 0.874 | 11.950 | 0.000 |
Within Groups | 1.024 | 16 | 0.069 | ||
Total | 3.697 | 19 | |||
ANOVA results of Italy—Montiferru region | |||||
Drought Level | |||||
Sum of Squares | df | Mean Square | F | Sig. | |
Between Groups | 18.552 | 3 | 6.184 | 26.116 | 0.000 |
Within Groups | 3.789 | 16 | 0.237 | ||
Total | 22.341 | 19 | |||
ANOVA results of Spain—Benahavis region | |||||
Drought Level | |||||
Sum of Squares | df | Mean Square | F | Sig. | |
Between Groups | 3.214 | 3 | 1.071 | 0.599 | 0.625 |
Within Groups | 28.597 | 16 | 1.787 | ||
Total | 31.811 | 19 |
Regression Analysis Results of Greece—Evros Region Model Summary | ||||||
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | ||
1 | 0.986 a | 0.972 | 0.967 | 0.08280 | ||
a: Predictors: (Constant), NDWI, NDDI, NDVI | ||||||
Coefficients a | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | 2.996 | 0.090 | 33.209 | 0.000 | |
NDDI | 0.133 | 0.014 | 0.673 | 9.271 | 0.000 | |
NDVI | 9.830 | 1.913 | 1.632 | 5.137 | 0.000 | |
NDWI | 10.588 | 1.940 | 1.648 | 5.456 | 0.000 | |
a: Dependent Variable: Drought Level. | ||||||
Regression Analysis results of France—Gironde region Model Summary | ||||||
Model | R | R Square | Adjusted RSquare | Std. Error of the Estimate | ||
1 | 0.857 a | 0.734 | 0.684 | 0.30602 | ||
a: Predictors: (Constant), NDWI, NDDI, NDVI | ||||||
Coefficients a | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | 2.996 | 0.090 | 33.209 | 0.000 | |
NDDI | 0.133 | 0.014 | 0.673 | 9.271 | 0.000 | |
NDVI | 9.830 | 1.913 | 1.632 | 5.137 | 0.000 | |
NDWI | 10.588 | 1.940 | 1.648 | 5.456 | 0.000 | |
a: Dependent Variable: Drought Level. | ||||||
Regression Analysis results of Italy—Montiferru region Model Summary | ||||||
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | ||
1 | 0.980 a | 0.961 | 0.954 | 0.23264 | ||
a: Predictors: (Constant), NDWI, NDDI, NDVI | ||||||
Coefficients a | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | 4.025 | 0.535 | 7.522 | 0.000 | |
NDDI | 0.151 | 0.060 | 0.661 | 2.508 | 0.023 | |
NDVI | 9.922 | 4.851 | 0.915 | 2.045 | 0.058 | |
NDWI | 13.348 | 4.010 | 0.788 | 3.329 | 0.004 | |
a: Dependent Variable: Drought Level. | ||||||
Regression Analysis results of Spain—Benahavis region Model Summary | ||||||
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | ||
1 | 0.956 a | 0.914 | 0.898 | 0.41426 | ||
a: Predictors: (Constant), NDWI, NDDI, NDVI | ||||||
Coefficients a | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | 2.130 | 0.352 | 6.056 | 0.000 | |
NDDI | 0.007 | 0.025 | 0.029 | 0.282 | 0.782 | |
NDVI | 21.630 | 2.567 | 2.254 | 8.425 | 0.000 | |
NDWI | 19.612 | 3.687 | 1.461 | 5.320 | 0.000 | |
a: Dependent Variable: Drought Level. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Stamou, A.; Bakousi, A.; Dosiou, A.; Tsifodimou, Z.-E.; Karachaliou, E.; Tavantzis, I.; Stylianidis, E. Mapping Drought Incidents in the Mediterranean Region with Remote Sensing: A Step Toward Climate Adaptation. Land 2025, 14, 1564. https://doi.org/10.3390/land14081564
Stamou A, Bakousi A, Dosiou A, Tsifodimou Z-E, Karachaliou E, Tavantzis I, Stylianidis E. Mapping Drought Incidents in the Mediterranean Region with Remote Sensing: A Step Toward Climate Adaptation. Land. 2025; 14(8):1564. https://doi.org/10.3390/land14081564
Chicago/Turabian StyleStamou, Aikaterini, Aikaterini Bakousi, Anna Dosiou, Zoi-Eirini Tsifodimou, Eleni Karachaliou, Ioannis Tavantzis, and Efstratios Stylianidis. 2025. "Mapping Drought Incidents in the Mediterranean Region with Remote Sensing: A Step Toward Climate Adaptation" Land 14, no. 8: 1564. https://doi.org/10.3390/land14081564
APA StyleStamou, A., Bakousi, A., Dosiou, A., Tsifodimou, Z.-E., Karachaliou, E., Tavantzis, I., & Stylianidis, E. (2025). Mapping Drought Incidents in the Mediterranean Region with Remote Sensing: A Step Toward Climate Adaptation. Land, 14(8), 1564. https://doi.org/10.3390/land14081564