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The increasing wildfire occurrence due to global climate changes urged the improvement of present wildfire growth prediction and evaluation methods. This study aimed to propose novel solutions to their two primary limitations, including the lack of robust fuel classification method and the low spatial resolution of wildfire growth accuracy assessment while ensuring wide applicability using open data satellite missions and software. The first objective was to create a robust two-step fuel model classification method consisted of the supervised machine learning classification of generalized land cover classes in the 1st level and their individual unsupervised classification to vegetation subtypes in the 2nd level. The second objective was creating a wildfire prediction accuracy assessment method using MODIS 250 m images, which overcome the limitations of low spatial resolution while preserving sub-daily temporal resolution. The wildfire on the Korčula island in Croatia was analyzed in the study, being specific for its long duration from 18 to 24 July 2015. The wildfire ignition occurred in the isolated area, which prolonged the response time from emergency agencies. Random Forest (RF) with input Landsat 8 spectral bands and indices resulted in the highest classification accuracy in the 1st classification level with an overall agreement of 83.6%. The vegetation subclasses from the 2nd classification level were matched to the 13 standard fuel models for the input in FARSITE software. The predicted wildfire evaluation showed the highest mean accuracy of 0.906 for the first two days, which decreased to 0.722 in the latter stages of the active wildfire caused by overprediction. The proposed two-step fuel model classification presented a cost-efficient solution to the fuel map creation in any part of the world, with a disadvantage of no in-situ ground truth identification and accuracy assessment for 2nd classification level. The evaluation of wildfire growth prediction with 250 m images enabled high spatial and temporal resolution of the assessment, while its limitations of wildfire overprediction and the negative effects of wildfire smoke in MODIS images should be addressed in future research. [Display omitted] • Wildfire growth was predicted and evaluated using Landsat and MODIS images. • The fuel model was classified using a robust two-step classification method. • Wildfire perimeters were evaluated using 250 m daily NDVI time series. • FARSITE prediction mean accuracy was 0.906 in the first two days of active wildfire. • Proposed cost-efficient methods support the decision-making in web services. [ABSTRACT FROM AUTHOR]

Globally, some vegetation has grown significantly over the past decades, but the climate benefits remain unclear, especially in the temperate regions. Understanding the biophysical effects and identifying the potential of vegetation will help to mitigate climate change. Here, we propose a vegetation-adjusted temperature index to understand how terrestrial vegetation growth in China affects the air temperature for 2001–2013, based on satellite-derived normalized difference vegetation index, near-surface air temperature (T a) and the land surface temperature. Grassland growth and cropland growth are found to cool the T a by −0.08 ± 0.32 °C (mean ± one standard deviation) and −0.06 ± 0.28 °C, respectively. Forest growth results in net climate warming by 0.05 ± 0.29 °C. Biophysical effects, elevation and background climate are used to explain the climate impacts of vegetation. Results show that the biophysical effects dominate the climate impact. More specifically, evapotranspiration (ET) controls the daytime climate impact, and at night, an indirect effect of albedo (the release of daytime heat storage) dominates it. Lower precipitation, temperature and elevation reinforce the warming potential. Moreover, the effects of albedo and ET on climate are nonlinear. During the day, although lower albedo absorbs more incoming radiation, it releases more extra heat per unit ET that can compensate for the increased incoming radiation. At night, the warming effect reflects the release of daytime heat storage. Overall, tropical vegetation growth (−0.04 ± 0.10 °C) and warm temperate vegetation growth (−0.08 ± 0.15 °C) achieve the climatic benefits. Overall, the method proposed contributes to quantitatively evaluate the role of afforestation plan on regional climate cooling, and provides some policy/practical implications for future afforestation projects. Future afforestation projects should carefully consider the biophysical process and background climate to mitigate climate change. • A vegetation-adjusted temperature index is proposed based on satellite data. • Different terrestrial vegetation types have different biophysical effects on climate. • Geographical location affects the biophysical effects of vegetation on climate. • Evapotranspiration controls the daytime climate impact. • An indirect effect of albedo (heat storage) controls the nighttime climate impact. [ABSTRACT FROM AUTHOR]