Despite recent progress in landslide susceptibility mapping, a holistic method is still needed to integrate and customize influential factors with the focus on forest regions. This study was accomplished to test the performance of geographic object-based random forest in modeling the susceptibility of protected and non-protected forests to landslides in northeast Iran. Moreover, it investigated the influential conditioning and triggering factors that control the susceptibility of these two forest areas to landslides. After surveying the landslide events, segment objects were generated from the Landsat 8 multispectral images and digital elevation model (DEM) data. The features of conditioning factors were derived from the DEM and available thematic layers. Natural triggering factors were derived from the historical events of rainfall, floods, and earthquake. The object-based image analysis was used for deriving anthropogenic-induced forest loss and fragmentation. The layers of logging and mining were obtained from available historical data. Landslide samples were extracted from field observations, satellite images, and available database. A single database was generated including all conditioning and triggering object features, and landslide samples for modeling the susceptibility of two forest areas to landslides using the random forest algorithm. The optimal performance of random forest was obtained after building 500 trees with the area under the receiver operating characteristics (AUROC) values of 86.3 and 81.8% for the protected and non-protected forests, respectively. The top influential factors were the topographic and hydrologic features for mapping landslide susceptibility in the protected forest. However, the scores were loaded evenly among the topographic, hydrologic, natural, and anthropogenic triggers in the non-protected forest. The topographic features obtained about 60% of the importance values with the domination of the topographic ruggedness index and slope in the protected forest. Although the importance of topographic features was reduced to 36% in the non-protected forest, anthropogenic and natural triggering factors remarkably gained 33.4% of the importance values in this area. This study confirms that some anthropogenic activities such as forest fragmentation and logging significantly intensified the susceptibility of the non-protected forest to landslides.

Southern Africa experiences a great number of wildfires, but the dependence on low-resolution products to detect and quantify fires means both that there is a time lag and that many small fire events are never identified. This is particularly relevant in miombo woodlands, where fires are frequent and predominantly small. We developed a cutting-edge deep-learning-based approach that uses freely available Sentinel-2 data for near-real-time, high-resolution fire detection in Mozambique. The importance of Sentinel-2 main bands and their derivatives was evaluated using TreeNet, and the top five variables were selected to create three training datasets. We designed a UNet architecture, including contraction and expansion paths and a bridge between them with several layers and functions. We then added attention gate units (AUNet) and residual blocks and attention gate units (RAUNet) to the UNet architecture. We trained the three models with the three datasets. The efficiency of all three models was high (intersection over union (IoU) > 0.85) and increased with more variables. This is the first time an RAUNet architecture has been used to detect fire events, and it performed better than the UNet and AUNet models—especially for detecting small fires. The RAUNet model with five variables had IoU = 0.9238 and overall accuracy = 0.985. We suggest that others test the RAUNet model with large datasets from different regions and other satellites so that it may be applied more broadly to improve the detection of wildfires.

Intermittent fires in Northeast Iran in the autumn of 2010 resulted in the burning of some valuable forest habitats. The objective of this study was to apply geographic information systems (GIS) to determine to what degree three key factors (environmental, climatic, and anthropogenic) influence the severity rating of fires in these forests. The forest fire sites were surveyed and imported into GIS. The severity of burnt areas was considered in relation to the three factors. Statistical functions were used to calculate the effect of the factors at each fire site. Logistic and stepwise regressions were used to determine the fire severity rating related to each factor. The results indicate that as the number of cumulative days after the onset of fire increased, the burnt areas also increased at a rate of 303.5 ha/day (R² = 0.95). Consequently, forest density, daily mean wind speed, daily mean temperature and distance to roads were highly correlated with the daily severity rating of forest fires, and only daily temperature and forest density affected the size of the burnt areas. Prediction maps show that about 24% of the forests have high fire durability, amounting to 7% of the fire-sensitive area. The findings from this case indicate that GIS can be effectively employed in fire management to assess damage, and possibly to prevent future fires, thus assisting in the preservation of valuable forest resources.

Despite facilitating transport by low‐volume roads for multiple purposes, these roads also open corridors to the remote pristine forests and accelerate forest dynamics with deleterious consequences to the forest functionalities and indigenous inhabitants. We assessed the spatial variations of Hyrcanian forest loss, fragmentation, and degradation resulting from the expansion of rural, logging, and mine roads between 1966 and 2016 in northeast Iran. Various data were employed to generate a precise road network; the density of road segments was weighted on the basis of their carrying capacity during 1966–1986, 1986–2000, and 2000–2016. Three dimensions of forest changes were retrieved using the Landsat time‐series and object‐based image analysis. The spatial patterns of high rates of forest changes were clustered using spatial autocorrelation indicators. The spatial regression models were carried out to explore relationships between forest change and road expansion. The results showed that rural roads were upgraded but forest and mine roads remarkably expanded in recent decades. The spatial variations of forest‐dynamic patterns have been changing from forest loss (1966–2000) to forest fragmentation and degradation (1986–2016). The high density of rural roads was significant on the high rates of forest loss and fragmentation during 1966–2000, and the expansion of forest and mine roads significantly intensified the rates of fragmentation and degradation during 1986–2016. Our findings suggest for mitigating destructive schemes over Hyrcanian forests, developing either protected areas or joining unprotected forests to the reserved areas should be prioritized.

Despite increasing efforts in the mapping of landslides using Sentinel-1 and -2, research on their combination for discerning historical landslides in forest areas is still lacking, particularly using object-oriented machine learning approaches. This study was accomplished to test the efficiency of Sentinel-derived features and digital elevation model (DEM) derivatives for mapping old and new landslides, using object-oriented random forest. Two forest subsets were selected including a protected and non-protected forest in northeast Iran. Landslide samples were obtained from CORONA images and aerial photos (old landslides), and also field mensuration and high-resolution images (new landslides). Segment objects were generated from a set combination of Sentinel-1A, Sentinel-2A, and some topographic-derived indices using multiresolution segmentation algorithm. Various object features were derived from the main channels of Sentinel images and DEM derivatives in the seven main groups, including spectral layers, spectral indices, geometric, contextual, textural, topographic, and hydrologic features. A single database was created, including landslide samples and Sentinel- and DEM-derived object features. Roughly 20% of landslide-affected objects and non-landslide-affected objects were randomly selected as an input for training the random forest classifier. Two-thirds of the selected objects were assigned as learning samples for classification, and the remainder were used for testing the accuracy of landslide and non-landslide classification. Results indicated that: (1) The sensitivity of mapping historical landslides was 86.6% and 80.3% in the protected and non-protected forests, respectively; (2) the object features of Sentinel-2A and DEM obtained the highest importance with the total scores of 55.6% and 32%, respectively in the protected forests, and 65.4% and 21% respectively in the non-protected forests; (3) the features derived from the combination of Sentinel-1 and -2A demonstrated a total importance of 10% for mapping new landslides; and (4) textural features were obtained in approximately two-thirds of the total scores for mapping new landslides, however a combination of topographic, spectral, textural, and contextual features were the effective predictors for mapping old landslides. This research proposes applying a synergetic analysis of Sentinel- and DEM-derived features for mapping historical landslides; however, there are no uniformly pre-defined influential variables for mapping historical landslides in different forest areas.