Assessing and mapping distribution, area, and density of riparian forests in southern Iran using Sentinel-2A, Google earth, and field data.

Riparian forests in Iran are valuable ecosystems which have many ecological values. Because of destruction of these forests in recent years, providing spatio-temporal information from area and distribution of these ecosystems has been receiving much attention. This study was performed for mapping distribution, area and density of riparian forests in southern Iran using Sentinel-2A, Google Earth, and field data.

Shade moderates the drought stress on saplings of Beneh (Pistacia atlantica Desf. subsp. mutica) in semiarid areas of Iran.

Pistacia atlantica Desf. (Beneh) is an important woody species that has been facing significant challenges to its natural regeneration and reforestation in Iran. This study investigates the interaction of soil moisture and shade on growth, chemical contents, and morphological and physiological characteristics of Beneh saplings.

Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models.

Fires have increased in northeastern Iran as its semi-arid climate landscape is desiccated by human activities. To combat fire outbreaks in any region, fire susceptibility must be mapped using accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of Golestan Province in northeastern Iran using new data-mining models.

A machine learning framework for multi-hazards modeling and mapping in a mountainous area.

This study sought to produce an accurate multi-hazard risk map for a mountainous region of Iran. The study area is in southwestern Iran. The region has experienced numerous extreme natural events in recent decades.

Assessing, mapping, and optimizing the locations of sediment control check dams construction.

Check dams are considered to be one of the most effective measures for conservation of the soil and water resources. However, identifying the most suitable sites for the installation of check dams remain quite demanding. This research investigates and compares five machine learning algorithms (MLAs) - boosted regression trees (BRT), multivariate adaptive regression spline (MARS), mixture discriminant analysis (MDA), random forest (RF), and support vector machine (SVM) - for generating check-dam site-suitability maps (CDSSMs) and assessing them in Firuzkuh County, Iran.