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Quantifying the contribution of sediment delivered to rivers by landslides is needed to assess a river's sediment load in regions prone to mass wasting. Monitoring such events, however, remains difficult. This study utilised six years of remotely sensed imagery (PlanetScope and RapidEye, Imagery courtesy of Planet Labs, Inc., San Francisco, CA, USA), topographic surveys, and field observation to examine a hydro-geologically controlled, retrogressive landslide near a tributary to the Peace River, British Columbia. The slide has been active since 2014, delivering large amounts of sediment to the Peace River, visible in a persistent plume. Here, we quantify the landslide's sediment contribution to the Peace River, assess the hydro-meteorological drivers of plume variability, and test whether plume activity can be directly linked to landslide activity for monitoring purposes. Our results show that the landslide on average delivered 165,000 tonnes of sediment per year, a seven-fold increase of the tributary's regular load and near half of the Peace River's load at this location. Due to continuous erosion of landslide material, sediment supply is steady and fuelled by repeated failures. Using thresholding, the identification of 'high' plume activity was possible, which positively correlated with the water level in a nearby reservoir, a proxy for the state of groundwater in this region. We reason that 'high' plume activity is linked to increased groundwater pressure because landslide activity is groundwater-controlled and failures fuel sediment delivery to the Peace River. Using readily available imagery, it is thus possible to monitor the activity of this recurrent landslide when field data are difficult to obtain. [ABSTRACT FROM AUTHOR]

Abstract: In early July 2002, a large rockslide–debris avalanche occurred on the west slope of the Pink Mountain anticline in northeastern British Columbia (57°04′N; 122°52′W). The slopes have been subjected to previous deformation and landsliding. Pre- and post-slide digital elevation models (DEMs) were created from aerial photography. Approximately 1.04 M m3 of sandstone and shale travelled nearly 2 km from an elevation of 1460 m to 1010 m. The landslide has a fahrböschung of 11.6° and covered 43 ha. The rock debris entrained a certain amount of fine-textured till and colluvium increasing its volume by 15%. This entrainment caused partial liquefaction of the underlying soil thereby enhancing the mobility of the landslide. We used a Bingham rheological model to dynamically model the landslide with excellent correspondence between the analysis of the actual observed behaviour of the landslide. [Copyright &y& Elsevier]

With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers. [ABSTRACT FROM AUTHOR]

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas. [ABSTRACT FROM AUTHOR]