Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model based on the Cascade Forest Model (CFM) was developed for FSM. Satellite imagery, historical reports, and field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. The performance of the proposed CFM was evaluated over two study areas, and the results were compared with those of other six machine learning methods, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Deep Neural Network (DNN), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The result showed CFM produced the highest accuracy compared to other models over both study areas. The Overall Accuracy (AC), Kappa Coefficient (KC), and Area Under the Receiver Operating Characteristic Curve (AUC) of the proposed model were more than 95%, 0.8, 0.95, respectively. Most of these models recognized the southwestern part of the Karun basin, northern and northwestern regions of the Gorganrud basin as susceptible areas.

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.

The Earth’s surface is constantly changing due to variations originating from the increasing human population. In the last decade, numerous methods were presented in the literature for change detection using multispectral image data. Owing to the increasing availability of hyperspectral images, these methods are now being applied to hyperspectral images. The main objective of this study is to present different change detection methods in hyperspectral imagery. Numerous algorithms (more than 43 algorithms) have been proposed for change detection in hyperspectral imagery over the last decade. In this work, we provide a comparative review of these algorithms through experimental results. We place the algorithms in five major groups: (1) match-based, (2) transformation-based, (3) direct classification-based, (4) post-classification-based, and (5) hybrid-based. We evaluate and compare the performances of all five groups using two real-world data sets of multi-temporal hyperspectral imagery. This comparative study investigates the advantages and disadvantages of the effects of preprocessing steps in the efficiency of the hyperspectral change detection (HSCD) methods. These preprocessing steps are considered in four scenarios, including: (1) considering only spatial or geometric correction without noise reduction and spectral correction; (2) spatial, atmospheric, and radiometric corrections without noise reduction; (3) spatial correction and noise reduction without atmospheric and radiometric corrections; and (4) spatial, atmospheric, and radiometric correction with noise reduction. The empirical results, followed by a summary of the pros and cons of each algorithm, aim to help researchers select the procedures with the best characteristics for HSCD applications.

The diversity of change detection (CD) methods and the limitations in generalizing these techniques using different types of remote sensing datasets over various study areas have been a challenge for CD applications. Additionally, most CD methods have been implemented in two intensive and time-consuming steps: (a) predicting change areas, and (b) decision on predicted areas. In this study, a novel CD framework based on the convolutional neural network (CNN) is proposed to not only address the aforementioned problems but also to considerably improve the level of accuracy. The proposed CNN-based CD network contains three parallel channels: the first and second channels, respectively, extract deep features on the original first- and second-time imagery and the third channel focuses on the extraction of change deep features based on differencing and staking deep features. Additionally, each channel includes three types of convolution kernels: 1D-, 2D-, and 3D-dilated-convolution. The effectiveness and reliability of the proposed CD method are evaluated using three different types of remote sensing benchmark datasets (i.e., multispectral, hyperspectral, and Polarimetric Synthetic Aperture RADAR (PolSAR)). The results of the CD maps are also evaluated both visually and statistically by calculating nine different accuracy indices. Moreover, the results of the CD using the proposed method are compared to those of several state-of-the-art CD algorithms. All the results prove that the proposed method outperforms the other remote sensing CD techniques. For instance, considering different scenarios, the Overall Accuracies (OAs) and Kappa Coefficients (KCs) of the proposed CD method are better than 95.89% and 0.805, respectively, and the Miss Detection (MD) and the False Alarm (FA) rates are lower than 12% and 3%, respectively.

Accurate and timely mapping of crop types and having reliable information about the cultivation pattern/area play a key role in various applications, including food security and sustainable agriculture management. Remote sensing (RS) has extensively been employed for crop type classification. However, accurate mapping of crop types and extents is still a challenge, especially using traditional machine learning methods. Therefore, in this study, a novel framework based on a deep convolutional neural network (CNN) and a dual attention module (DAM) and using Sentinel-2 time-series datasets was proposed to classify crops. A new DAM was implemented to extract informative deep features by taking advantage of both spectral and spatial characteristics of Sentinel-2 datasets. The spectral and spatial attention modules (AMs) were respectively applied to investigate the behavior of crops during the growing season and their neighborhood properties (e.g., textural characteristics and spatial relation to surrounding crops). The proposed network contained two streams: (1) convolution blocks for deep feature extraction and (2) several DAMs, which were employed after each convolution block. The first stream included three multi-scale residual convolution blocks, where the spectral attention blocks were mainly applied to extract deep spectral features. The second stream was built using four multi-scale convolution blocks with a spatial AM. In this study, over 200,000 samples from six different crop types (i.e., alfalfa, broad bean, wheat, barley, canola, and garden) and three non-crop classes (i.e., built-up, barren, and water) were collected to train and validate the proposed framework. The results demonstrated that the proposed method achieved high overall accuracy and a Kappa coefficient of 98.54% and 0.981, respectively. It also outperformed other state-of-the-art classification methods, including RF, XGBOOST, R-CNN, 2D-CNN, 3D-CNN, and CBAM, indicating its high potential to discriminate different crop types.

Although half of Earth’s population resides in the wildland-urban interface, human exposure to wildland fires remains unquantified. We show that the population directly exposed to wildland fires increased 40% globally from 2002 to 2021 despite a 26% decline in burned area. Increased exposure was mainly driven by enhanced colocation of wildland fires and human settlements, doubling the exposure per unit burned area. We show that population dynamics accounted for 25% of the 440 million human exposures to wildland fires. Although wildfire disasters in North America, Europe, and Oceania have garnered the most attention, 85% of global exposures occurred in Africa. The top 0.01% of fires by intensity accounted for 0.6 and 5% of global exposures and burned area, respectively, warranting enhanced efforts to increase fire resilience in disaster-prone regions.