Flood has long been known as one of the most catastrophic natural hazards worldwide. Mapping flood-prone areas is an important part of flood disaster management. In this study, a flood susceptibility mapping framework was developed based on a novel integration of nature-inspired algorithms into support vector regression (SVR). To this end, various remote sensing (RS) and geographic information system (GIS) datasets were applied to the hybridized SVR models to map flood susceptibility in Ahwaz township, Iran. The proposed framework has two main steps: 1) updating the flood inventory (historical flooded locations) using the proposed RS-based flood detection method developed within the google earth engine (GEE) platform. The mosaicked images of multi-temporal Sentinel-1 synthetic aperture radar (SAR) data have been used in this step; 2) producing flood susceptibility map using the standalone SVR and hybridized model of SVR. The hybridized methods were derived from a novel integration of SVR with meta-heuristic algorithms, hence forming the SVR-bat algorithm (SVR-BA), SVR-invasive weed optimization (SVR-IWO), and SVR-firefly algorithm (SVR-FA). A spatial database of flood locations and 11 conditioning factors (altitude, slope angle, aspect, topographic wetness index, stream power index, normalized difference vegetation index (NDVI), distance to stream, curvature, rainfall, soil type, and land use/cover) were built for the susceptibility modelling. The accuracy of the proposed model was evaluated using the statistical and sensitivity indices, such as root mean square error (RMSE), receiver operating characteristic (ROC) and area under the ROC curve (AUROC) index. The results indicated that all hybridized models outperformed the standalone SVR. According to AUROC values, the predictive power of the SVR-FA was the highest with the value of 0.81, followed by SVR-IWO, SVR-BA, and SVR with values of 0.80, 0.79, and 0.77, respectively.

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Progress toward habitat protection goals can effectively be performed using satellite imagery and machine-learning (ML) models at various spatial and temporal scales. In this regard, habitat types and landscape structures can be discriminated against using remote-sensing (RS) datasets. However, most existing research in three-dimensional (3D) habitat mapping primarily relies on same/cross-sensor features like features derived from multibeam Light Detection And Ranging (LiDAR), hydrographic LiDAR, and aerial images, often overlooking the potential benefits of considering multi-sensor data integration. To address this gap, this study introduced a novel approach to creating 3D habitat maps by using high-resolution multispectral images and a LiDAR-derived Digital Surface Model (DSM) coupled with an object-based Random Forest (RF) algorithm. LiDAR-derived products were also used to improve the accuracy of the habitat classification, especially for the habitat classes with similar spectral characteristics but different heights. Two study areas in the United Kingdom (UK) were chosen to explore the accuracy of the developed models. The overall accuracies for the two mentioned study areas were high (91% and 82%), which is indicative of the high potential of the developed RS method for 3D habitat mapping. Overall, it was observed that a combination of high-resolution multispectral imagery and LiDAR data could help the separation of different habitat types and provide reliable 3D information.@en

Remote sensing datasets are great resources to map habitat types. In this study, 3D habitat maps were generated using high-resolution multispectral imagery and a LiDAR-derived digital surface model (DSM). Two study areas in the United Kingdom (UK) were selected to investigate the potential of the developed models in habitat classification. The overall classification accuracies for the two study areas were high (91% and 82%), indicating the satisfactory performance of the developed approach for habitat mapping in the study areas. Overall, it was observed that a synergy of high-resolution multi-spectral imagery and LiDAR data could provide reliable 3D information on habitat types.@en

Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km² ) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided.

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