Multidecadal satellite-derived Portuguese Burn Severity Atlas (1984-2022) Dina Jahanianfard, Joana Parente, Oscar Gonzalez-Pelayo, Akli Benali Earth System Science Data, 2025 Long-term burn severity assessment can support better pre- and post-fire management plans. In this study, the Portuguese Burn Severity Atlas was created, containing historical fires in Portugal from 1984 to 2022. As prerequisites, fire data were gathered and delimited for all years. Due to the availability of satellite images, for different years, different imageries from Landsat sensors (30 m) were applied. Exploratory analysis showed that burn severity estimates are significantly affected by the time lag between the satellite imagery acquisition and the fire date. We explicitly incorporated the effect of time lag into the degradation of burn severity estimates in the selection of the most suitable pre- and post-fire satellite images for each fire. Using Google Earth Engine, burn severity estimates were calculated for fires that were equal to or larger than 100 ha and that occurred from 1984 to 2022 with known dates (valid fires). Different indices were calculated, such as the differenced normalized burn ratio (dNBR), the relative dNBR (RdNBR), the relativized burn ratio (RBR), and a burn severity index that combines the dNBR with the enhanced vegetation index (dNBR-EVI). Overall, in Portugal, 4.85×106 ha burned over the 38-year period (1984–2022), with the burned area covering 3.29×106 ha being caused by valid fires (68 %). Among these, a total area of 3.18×106 ha had burn severity estimates via the applied indices (97 % of valid and 66 % of all fires). Results show that Portugal has experienced, on average, “high” burn severity throughout this period, with large percentages of dNBR pixels between 0.419 and 0.66 (32 %) and >0.66 (21 %). Estimates from different burn severity indices provided a more complete representation of the burn severity impacts. This atlas can be accessed at https://doi.org/10.5281/zenodo.12773611 (Jahanianfard et al., 2025) and can be used by researchers to have a better understanding of historical fires and their corresponding impacts on vegetation cover, air, soil, and water quality, as well as to identify the most influential environmental and climatic drivers of burn severity.
A sustainable approach for site selection of underground hydrogen storage facilities using fuzzy-delphi methodology Dina JAHANIANFARD, Behrouz NEMATI, Mahsa MAPAR, Peyman DAVARAZAR, Sara ZANDI, et al. Journal of Settlements and Spatial Planning, 2020 Behrouz NEMATI1, Mahsa MAPAR2, Peyman DAVARAZAR3, Sara ZANDI1, Mahsa DAVARAZAR1, Dina JAHANIANFARD***1, Mehdi MOHAMMADI1 * Corresponding author ** The author has the same share as the first author 1 University of Aveiro, Department of Environment and Planning, Aveiro, PORTUGAL 2 NOVA University Lisbon, School of Science and Technology, Department of Environmental Sciences and Engineering, CENSE, Centre for Environmental and Sustainability Research, Caparica, PORTUGAL 3 University of Aveiro, Department of Civil Engineering, Aveiro, PORTUGAL E-mail: dinaj@ua.pt DOI: 10.24193/JSSPSI.2020.6.02 https://doi.org/10.24193/JSSPSI.2020.6.02
Building information modelling execution in administrative and commercial spaces in iran – a fuzzy-delphi criteria prioritization Behrouz NEMATI, Sara ZANDI, Babak AMINNEJAD, Mahsa DAVARAZAR, Yahya SHEIKHNEJAD, et al. Journal of Settlements and Spatial Planning, 2020 Behrouz NEMATI*1, Sara ZANDI**1, Babak AMINNEJAD2, Mahsa DAVARAZAR1, Yahya SHEIKHNEJAD3, Dina JAHANIANFARD1, Amid MOSTAFAIE4 * Corresponding author ** The author has the same share as the first author 1 University of Aveiro, Department of Environment and Planning, Aveiro, PORTUGAL 2 Islamic Azad University, Department of Civil Engineering, Roudehen Branch, Tehran, IRAN 3 University of Aveiro, Department of Mechanical Engineering, Centro de Tecnologia Mecânica e Automação (TEMA), Aveiro, PORTUGAL 4 University of Aveiro, Department of Biology, Aveiro, PORTUGAL E-mail: behrouz.nemati@ua.pt, sara.zandi@ua.pt, aminnejad@riau.ac.ir, m.davarazar@ua.pt, yahya@ua.pt, dinaj@ua.pt, amid.mostafaie@ua.pt DOI: 10.24193/JSSPSI.2020.6.03 https://doi.org/10.24193/JSSPSI.2020.6.03
An Auto Regressive Integrated Moving Average (ARIMA) Model for prediction of energy consumption by household sector in Euro area Akram Jahanshahi, Dina Jahanianfard, Amid Mostafaie, Mohammadreza Kamali, and Aims Energy, 2019 Accurate estimation of the energy need and consumption is considered as one of the most important basis of the economy worldwide. It is also of high importance to mitigate the adverse effects of the release of CO 2 (e.g., climate change) from conventional energy sources by using renewable energies, as recommended by European commission. Thus, in this study a forecast regarding the residential energy consumption of the household sector in countries belonging to the Euro area was executed. To proceed with this prediction, time related data from 1990 till 2015 along with Auto Regressive Integrated Moving Average (ARIMA) model were applied. ARIMA model was considered due to possessing the ability of providing accurate results while being able to receive stationary and non-stationary data. The obtained results from the analysis clarified that ARIMA (0,1,1) model is the most accurate model to undertake such prediction as the amount of RMSE achieved was 0.097. This comparison was accomplished by considering the ARIMA (0,1,0) and ARIMA (1,1,2) models as their amounts regarding RMSE were respectively 0.1068149 and 0.0975575. The results indicate that the amount of the energy predicted to be consumed in household sector in EU area is estimated to be 186244 toe (tonne of oil equivalent) which shows a drop in the energy consumption in Euro area probably due to the increase in the energy efficiency especially in recent years.