This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat-5 and Landsat-8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook’s distance, the effect of missing values in the analysis of the time series were examined. Results from the cross-correlation and cross-variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline.

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.

This study aimed to evaluate the impact of a wildfire on vegetation recovery and hydrological processes in a Mediterranean peri-urban system, using remote sensing and hydrological modeling. NDVI and MSAVI₂ time series extracted from burned areas, control plots, and VAR-modeled plots were used to analyze vegetation regeneration. The SWAT model, calibrated for pre-fire conditions due to data limitations, was used to evaluate subbasin-scale hydrological impacts. Results showed limited recovery in the first post-fire year, with vegetation indices remaining lower in burned areas compared to control plots. High- and moderate-burn-severity areas presented the most significant NDVI and MSAVI₂ increases. The SWAT model showed increased water yield, percolation, and surface runoff with reduced evapotranspiration in post-fire conditions. Peak discharges were notably higher during wet periods. Modified land use and soil properties affected the catchment’s hydrological balance, emphasizing the complexities of post-fire catchment dynamics.

Forest fires release gases, ashes and particulate matter into the atmosphere, affecting the atmospheric radiation budget. The increment of aerosols by particles emanating from biomass burning affects solar radiation, mainly in the troposphere, by multiple scattering and interactions between molecule and aerosol scattering for several days after the fire. Among the topographic normalization methods, the Minnaert method assumes the anisotropic reflectance of surfaces by introducing the Minnaert constant, the value of which is affected by the atmospheric diffusion of light. In this paper, a methodology is developed in order to study atmospheric effects on this value. This is based on the optimum value of the Minnaert constant, which is the value that leads to the higher classification accuracy. The methodology has been tested on four Landsat TM and three SPOT XS images respectively. The optimum Minnaert value presented a high decrement (0.1–0.2 instead of 0.8–0.9) for two images. Both of them had been captured shortly after fires, without being visibly affected by smoke and atmospheric perturbation effects. The proposed methodology has successfully revealed fires through the Minnaert constant. This methodology has also revealed the quality of the image. Sahara dust events are also to be investigated through the Minnaert constant.