Extension of the Representative Elementary Watershed approach by incorporating energy balance equations

International audienceThe paper extends the Representative Elementary Watershed (REW) theory for cold regions by extending the energy balance equations to include associated processes and descriptions. A new definition of REW is presented which separates the REW into six surface sub-regions and two subsurface sub-regions. Soil ice, vegetation, vapor, snow and glacier ice are included in the system so that such phenomena as evaporation, transpiration, freezing and thawing can be modeled in a physically reasonable way. The final system of 24 ordinary differential equations (ODEs) can meet the requirement for most hydrological modeling applications, and the formulation procedure is re-arranged so that further inclusion of sub-regions and substances could be done more easily. The number of unknowns is more than the number of equations, which leads to the indeterminate system. Complementary equations are provided based on geometric relationships and constitutive relationships that represent geomorphological and hydrological characteristics of a watershed. Reggiani et al. (1999, 2000, 2001) and Lee et al. (2005b) have previously proposed sets of closure relationships for unknown mass and momentum exchange fluxes. The additional geometric and constitutive relationships required to close the new set of balance equations will be pursued in a subsequent paper

Classification of Broadleaf and Grass Weeds Using Gabor Wavelets and an Artificial Neural Network

A texture–based weed classification method was developed. The method consisted of a low–level Gabor wavelets–based feature extraction algorithm and a high–level neural network–based pattern recognition algorithm. This classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass categories for spatially selective weed control. In this research, three species of broadleaf weeds (common cocklebur, velvetleaf, and ivyleaf morning glory) and two grasses (giant foxtail and crabgrass) that are common in Illinois were studied. After processing 40 sample images with 20 samples from each class, the results showed that the method was capable of classifying all the samples correctly with high computational efficiency, demonstrating its potential for practical implementation under real–time constraints

Technical Note: Field-Scale Surface Soil Moisture Patterns and Their Relationship to Topographic Indices

Understanding variability patterns in soil moisture is critical for determining an optimal sampling scheme both in space and in time, as well as for determining optimal management zones for agricultural applications that involve moisture status. In this study, distributed near-surface gravimetric soil moisture samples were collected across a 3.3 ha field in central Illinois for ten dates in the summer of 2002, along with dense elevation data. Temporal stability and consistency of the moisture patterns were analyzed in order to determine a suitable grid size for mapping and management, as well as to investigate relationships between moisture patterns and topographic and soil property influences. Variogram analysis of surface moisture data revealed that the geospatial characteristics of the soil moisture patterns are similar from one date to another, which may allow for a single, rather than temporally variable, variogram to describe the spatial structure. For this field, a maximum cell size of 10 m was found to be appropriate for soil moisture studies on most of the sampling occasions. This could indicate an appropriate scale for precision farming operations or for intensive ground sampling. While some areas had consistent behavior with respect to field mean moisture content, no conclusive relationships between the overall patterns in the moisture data and the topographic and soil indices were identified. There were, however, some small but significant correlations between these two sets of data, particularly plan and tangential curvature, and also slopes. In areas of convergent flow, moisture content exhibited a slight tendency to be wetter than average. There also seemed to be a small influence of scale on the relationship between moisture patterns and topographic curvatures

Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory

This study proposed a deep learning-based tracking method for ultrasound (US) image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from a US image to a suspected area of landmark motion in order to reduce the search region. The mask R-CNN then produces multiple region-of-interest (ROI) proposals in the reduced region and identifies the proposed landmark via three network heads: bounding box regression, proposal classification, and landmark segmentation. The LSTM network models the temporal relationship among the successive image frames for bounding box regression and proposal classification. To consolidate the final proposal, a selection method is designed according to the similarities between sequential frames. The proposed method was tested on the liver US tracking datasets used in the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 challenges, where the landmarks were annotated by three experienced observers to obtain their mean positions. Five-fold cross-validation on the 24 given US sequences with ground truths shows that the mean tracking error for all landmarks is 0.65+/-0.56 mm, and the errors of all landmarks are within 2 mm. We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0.94+/-0.83 mm. Our experimental results have demonstrated the feasibility and accuracy of our proposed method in tracking liver anatomic landmarks using US images, providing a potential solution for real-time liver tracking for active motion management during radiation therapy

Multi-contrast imaging and digital refocusing on a mobile microscope with a domed LED array

We demonstrate the design and application of an add-on device for improving the diagnostic and research capabilities of CellScope--a low-cost, smartphone-based point-of-care microscope. We replace the single LED illumination of the original CellScope with a programmable domed LED array. By leveraging recent advances in computational illumination, this new device enables simultaneous multi-contrast imaging with brightfield, darkfield, and phase imaging modes. Further, we scan through illumination angles to capture lightfield datasets, which can be used to recover 3D intensity and phase images without any hardware changes. This digital refocusing procedure can be used for either 3D imaging or software-only focus correction, reducing the need for precise mechanical focusing during field experiments. All acquisition and processing is performed on the mobile phone and controlled through a smartphone application, making the computational microscope compact and portable. Using multiple samples and different objective magnifications, we demonstrate that the performance of our device is comparable to that of a commercial microscope. This unique device platform extends the field imaging capabilities of CellScope, opening up new clinical and research possibilities