Samereh Falahatkar

TL;DR: In this paper, a study was conducted to determine the changes in the soil carbon stocks as influenced by land use in a humid zone of Deylaman district (10,876 ha), a mountainous region of northern Iran.

TL;DR: In this paper, artificial neural networks (ANNs) were employed to develop models to predict soil organic carbon density (SOCD) at different depths of soil layers, where selected environmental variables such as vegetation indices, soil particle size distribution, land use type, besides primary and secondary terrain attributes were considered as the input variables.

TL;DR: In this article, the authors used remote sensing imagery for detecting changes occurred in residential areas of Isfahan during 1956-2006; this is the maximum period for measuring changes in this region using aerial photos and satellite images.

TL;DR: In this paper, the relationship between soil organic carbon (SOC) and remotely sensed and easily accessible variables have been rarely reported, and the main objective of the present study is to estimate SOC using the remote sensing of satellite images as well as some field variables for the Shazand Watershed, Iran.

TL;DR: In this article, the authors quantify the relationship between CO2 emission and urban form, using panel data analysis for Iran's 15 cities from 2001 to 2015, and find that increased urban complexity represented a positive relationship with CO2 emissions and conversely, increased urban compactness led to reductions in carbon dioxide emissions.