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Instream large wood (i.e., downed trees, branches and roots larger than 1 m in length and 10 cm diameter) has essential geopmorphological and ecological functions supporting the health of river ecosystems. Still, even though its transport during floods may pose a risk, it is rarely observed and, therefore, poorly understood. This paper presents a novel approach to detect pieces of instream wood from video. The approach uses a Convolutional Neural Network to detect wood automatically. We sampled data to represent different wood transport conditions, combining 20 datasets to yield thousands of instream wood images. We designed multiple scenarios using different data subsets with and without data augmentation and analyzed the contribution of each one to the effectiveness of the model using k-fold cross-validation. The mean average precision of the model varies between 35 and 93 percent, and is highly influenced by the quality of the data which it detects. When the image resolution is low, the identified components in the labeled pieces, rather than exhibiting distinct characteristics such as bark or branches, appear more akin to amorphous masses or 'blobs'. We found that the model detects wood with a mean average precision of 67 percent when using a 418 pixels input image resolution. Also, improvements of up to 23 percent could be achieved in some instances and increasing the input resolution raised the weighted mean average precision to 74 percent. We show that the detection performance on a specific dataset is not solely determined by the complexity of the network or the training data. Therefore, the findings of this paper can be used when designing a custom wood detection network. With the growing availability of flood-related videos featuring wood uploaded to the internet, this methodology facilitates the quantification of wood transport across a wide variety of data sources. [ABSTRACT FROM AUTHOR]

Sediment grains are progressively rounded during their transport down a river. For more than a century, Earth scientists have used the roundness of pebbles within modern sediment, and of clasts within conglomerates, as a key metric to constrain the sediment's transport history and source area(s). However, the current practices of assessment of pebble roundness are mainly qualitative and based on time-consuming manual measurement methods. This qualitative judgement provides the transport history only in a broad sense, such as classifying distance as "near" or "far". In this study, we propose a new model that quantifies the relationship between roundness and the transport distance. We demonstrate that this model can be applied to the clasts of multiple lithologies including modern sediment, as well as conglomerates, deposited by ancient river systems. We present field data from two Himalayan catchments in Nepal. We use the normalized isoperimetric ratio (IRn), which relates a pebble's area (A) to its perimeter (P), to quantify roundness. The maximum analytical value for IRn is 1, and IRn is expected to increase with transport distance. We propose a non-linear roundness model based on our field data, whereby the difference between a grain's IRn and the maximum value of 1 decays exponentially with transport distance, mirroring Sternberg's model of mass loss or size reduction by abrasion. This roundness model predicts an asymptotic behaviour for IRn , and the distance over which IRn approaches the asymptote is controlled by a rounding coefficient. Our field data suggest that the roundness coefficient for granite pebbles is 9 times that of quartzite pebbles. Using this model, we reconstruct the transport history of a Pliocene paleo-river deposit preserved at the base of the Kathmandu intermontane basin. These results, along with other sedimentary evidence, imply that the paleo-river was much longer than the length of the Kathmandu Basin and that it must have lost its headwaters through drainage capture. We further explore the extreme rounding of clasts from Miocene conglomerate of the Siwalik zone and find evidence of sediment recycling. [ABSTRACT FROM AUTHOR]

Sediment grains are progressively rounded during their transport down a river. For more than a century, Earth scientists have used the roundness of pebbles within modern sediment, and of clasts within conglomerates, as a key metric to constrain the sediment's transport history and source area(s). However, the current practices of assessment of pebble roundness are mainly qualitative and based on time consuming manual measurement methods. This qualitative judgement provides the transport history only in a broad sense, such as classifying distance as 'near' or 'far'. In this study, we propose a new model that quantifies the relationship between roundness and the transport distance. We demonstrate that this model can be applied to the clasts of multiple lithologies including modern sediment as well as conglomerates deposited by ancient river systems. We present field data from two Himalayan catchments in Nepal. We use the Normalized Isoperimetric Ratio (IRn) which relates a pebble's area (A) to its perimeter (P), to quantify roundness. The maximum analytical value for IRn is 1, and IRn is expected to increase with transport distance. We propose a non-linear roundness model based on our field data, whereby the difference between a grain's IRn and the maximum value of 1 decays exponentially with transport distance, mirroring Sternberg's model of mass loss or size reduction by abrasion. This roundness model predicts an asymptotic behaviour for IRn , and the distance over which IRn approaches the asymptote is controlled by a rounding coefficient. Our field data suggest that the roundness coefficient for granite pebbles is eight times that of quartzite pebbles. Using this model, we reconstruct the transport history of a Pliocene paleo-river deposit preserved at the base of the Kathmandu intermontane Basin. These results, along with other sedimentary evidence, imply that the paleo-river was much longer than the length of the Kathmandu Basin, and that it must have lost its headwaters through drainage capture. We further explore the extreme rounding of clasts from Miocene conglomerate of the Siwaliks Zone and find evidence of sediment recycling. [ABSTRACT FROM AUTHOR]

Automatic photogrammetric processing of historical (or archival) aerial photos is still a challenging task, particularly in cases of missing ancillary information, low radiometric and image quality, limited stereo coverage or large temporal span. However, with recent advances in photogrammetry and Artificial Intelligence (AI) algorithms for image processing and interpretation, an increasing number of applications are now feasible. The article presents the TIME (hisTorical aerIal iMagEs) benchmark (https://time.fbk.eu/), promoted by EuroSDR to explore the potential of historical aerial images. Realized in collaboration with various European NMCAs, the benchmark has garnered aerial image blocks and time series imagery captured since the 1950s. To support the photogrammetric processing of the digitized photos, ancillary data are supplied with available information about flight missions, taking cameras, and ground control points (GCPs). Several diverse investigations have been undertaken with the benchmark datasets, all captured over historical urban areas or landscapes. The paper describes the benchmark datasets and some potential research topics, presenting several tests and analyses realized with the collated and shared data. [ABSTRACT FROM AUTHOR]

Wood is an essential component of rivers and plays a significant role in ecology and morphology. It can be also considered a risk factor in rivers due to its influence on erosion and flooding. Quantifying and characterizing wood fluxes in rivers during floods would improve our understanding of the key processes but are hindered by technical challenges. Among various techniques for monitoring wood in rivers, streamside videography is a powerful approach to quantify different characteristics of wood in rivers, but past research has employed a manual approach that has many limitations. In this work, we introduce new software for the automatic detection of wood pieces in rivers. We apply different image analysis techniques such as static and dynamic masks, object tracking, and object characterization to minimize false positive and missed detections. To assess the software performance, results are compared with manual detections of wood from the same videos, which was a time-consuming process. Key parameters that affect detection are assessed, including surface reflections, lighting conditions, flow discharge, wood position relative to the camera, and the length of wood pieces. Preliminary results had a 36 % rate of false positive detection, primarily due to light reflection and water waves, but post-processing reduced this rate to 15 %. The missed detection rate was 71 % of piece numbers in the preliminary result, but post-processing reduced this error to only 6.5 % of piece numbers and 13.5 % of volume. The high precision of the software shows that it can be used to massively increase the quantity of wood flux data in rivers around the world, potentially in real time. The significant impact of post-processing indicates that it is necessary to train the software in various situations (location, time span, weather conditions) to ensure reliable results. Manual wood detections and annotations for this work took over 150 labor hours. In comparison, the presented software coupled with an appropriate post-processing step performed the same task in real time (55 h) on a standard desktop computer. [ABSTRACT FROM AUTHOR]

Remote sensing applied to river monitoring adds complementary information useful for understanding the system behaviour. In this paper, we present a method for visual stage gauging and water surface width measurement using a ground-based time-lapse camera and a fully automatic image analysis algorithm for flow monitoring at a river cross section of a steep, bouldery channel. The remote stage measurement was coupled with a water level logger (pressure transducer) on site and shows that the image-based method gives a reliable estimate of the water height variation and daily flow record when validated against the pressure transducer (R = 0:91). From the remotely sensed pictures, we also extracted the water width and show that it is possible to correlate water surface width and stage. The images also provide valuable ancillary information for interpreting and understanding flow hydraulics and site weather conditions. This image-based gauging method is a reliable, informative and inexpensive alternative or adjunct to conventional stage measurement especially for remote sites. [ABSTRACT FROM AUTHOR]

UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field. [ABSTRACT FROM AUTHOR]