In the context of climate change, the increasing demand for energy-efficient buildings and sustainable urban development has become a pressing issue due to the significant proportion of global energy consumption and carbon dioxide (CO2) emissions attributable to the building sector. This requires a concerted effort to reduce its environmental impact, and Geographic Information System (GIS) applications are vital tools for achieving this by optimizing heat supply, calculating costs, analyzing profitability, and balancing CO2 emissions. This study aims to address the challenge of achieving energy efficiency and reducing CO2 emissions in the building sector, specifically at the district level. To this end, the research objective is to develop a QGIS plugin that can simulate urban energy demand at the district level by integrating 2D data with CityGML files and connecting QGIS to SimStadt software via API to visualize the simulated urban energy results in 3D on the Web Globe. The proposed plugin leverages the open-source QGIS tool QField to capture building conditions and connect 2D and 3D data on urban energy simulation. Supplementary to this, this plugin provides up-to-date information on energy demand, consumption, CO2 emissions, building component conditions via updating related tables in the database. Decision-makers can use this comprehensive and user-friendly tool to understand and act on the results, ultimately leading to a CO2-neutral district by 2045. The development of the QGIS plugin represents a significant step towards sustainable urban development and climate change mitigation by utilizing GIS applications for optimizing energy demand and reducing CO2 emissions in the built environment.

Keywords: Energy demand estimation, reducing carbon dioxide, ALKIS and CityGML data, QGIS plugin, 3D visualization

The coastal regions in the Persian Gulf are renowned for having the largest oil reserves. Pipelines, as a means of oil transportation, are one of the most common options for transporting petroleum products in these regions. Hence, the risk of oil spill pollution has become a vital challenge for local authorities, which has gradually increased since the last decade. Therefore, it is essential to use a method for timely detection of oil spills to prevent environmental damage. In this study, Sentinel-1 SAR data and Sentinel-2 image have been used for timely detection of oil slicks in the Persian Gulf. The area of oil pollution that extracted from Sentinel-2 (optical data), Sentinel-1 (SAR data), and field data is 114.7, 98.5, and 124.7 square kilometres, respectively. The results indicated that the object-based image analysis (OBIA) method using optical data had better results than using SAR data. In addition, the results indicated that the OBIA could be used as a method to produce ground truth map. Therefore, this research demonstrates the applicability of remote sensing data to recognize oil spill pollution on the surface of the water with the applicability of Sentinel-1 and Sentinel-2 data.

Keywords: Oil spill detection; Object-based image analysis; Sentinel-1; Sentinel-2; Remote sensing

Crops monitoring is a challengeable subject that radar images can help it. The applicability of Sentinel-1 SAR data with dual polarization provided a splendid opportunity to develop a method for estimating rice parameters. Heights of cereal and biomass are two significant characteristics of rice that can be estimated with assessing satellite data and field measurements by classical regression methods [multiple linear regression (MLR), relevance vector regression (RVR), and support vector regression (SVR)]. In this study, Sentinel-1 SAR data from April 2018 to September 2018 in Astaneh-ye Ashrafiyeh region in the north of Iran were used. To evaluate and analyze validation of regression methods, field measurements (gathered from 15 plots) were utilized. The efficiency of nonparametric methods (SVR and RVR) is much better than that of the parametric regression (MLR) for rice parameter estimations. Among nonparametric approaches, RVR method has better results than SVR, because of the highest correlation coefficient (R2) and lowest root mean square error (RMSE). R2= 0.92, RMSE = 162.1, and MAE = 971.9 and R2 = 0.92, RMSE = 10.9, and MAE = 70.71 are the results of height and biomass, respectively

Keywords: Sentinel-1; Rice; Biomass estimation; Height estimation; Remote sensing

Time series analysis of satellite data is a very powerful tool for change detection applications especially for monitoring urban expansion. Urban expansion results in an accelerating proportion of the population living in cities and caused considerable effects on green space structures and areas. Many methods and models have been tested to determine urban expansion. In this paper, after computing the Tehran area and its green space coverage, the relationship between the population with urban expansion and green space coverage is estimated by regression analysis. As a consequence of Tehran texture and dataset for processing, raster to vector conversion was the most effective method for computing urban area, and also green space coverage was determined with normalized difference vegetation index. Machine learning algorithms such as support vector regression (SVR) and random forest (nonparametric method) were utilized to estimate green space coverage and urban area, respectively. Although RF model improves the accuracy of estimated population (R2 = 0.97, RMSE = 38.18, and MAE = 83.44), the use of SVR model considerably improves the estimation of green space coverage (R2 = 0.93, RMSE = 47.12, and MAE = 100.18) as well.

Keywords: Time series data analysis; Remote sensing; Urban expansion; Green space coverage; Nonparametric; regression mode