Estimation of Leaf Area Index with a Multi-Channel Spectral Micro-Sensor for Wireless Sensing Networks

The leaf area index (LAI) is a key parameter in the context of monitoring the development of tree crowns and plants in general. As parameters such as carbon assimilation, environmental stress on carbon, and the water fluxes within tree canopies are correlated to the leaves surface, this parameter is essential for understanding and modeling ecological processes. However, its continuous monitoring using manual state-of-the-art measurement instruments is still challenging. To address this challenge, we present an innovative sensor concept to obtain the LAI based on the cheap and easy to integrate multi-channel spectral sensor AS7341. Additionally, we present a method for processing and filtering the gathered data, which enables very high accuracy measurements with an nRMSE of only 0.098, compared to the manually-operated state-of-the-art instrument LAI-2200C (LiCor). The sensor that is embedded on a sensor node has been tested in long-term experiments, proving its suitability for continuous deployment over an entire season. It permits the estimation of both the plant area index (PAI) and leaf area index (LAI) and provides the first wireless system that obtains the LAI solely powered by solar cells. Its energy autonomy and wireless connectivity make it suitable for a massive deployment over large areas and at different levels of the tree crown. It may be upgraded to allow the parallel measurement of photosynthetic active radiation (PAR) and light quality, relevant parameters for monitoring processes within tree canopies

Texture feature ranking with relevance learning to classify interstitial lung disease patterns

Objective: The generalized matrix learning vector quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography images. Methodology: After a stochastic gradient descent, the GMLVQ algorithm provides a discriminative distance measure of relevance factors, which can account for pairwise correlations between different texture features and their importance for the classification of healthy and diseased patterns. 65 texture features were extracted from gray-level co-occurrence matrices (GLCMs). These features were ranked and selected according to their relevance obtained by GMLVQ and, for comparison, to a mutual information (MI) criteria. The classification performance for different feature subsets was calculated for a k-nearest-neighbor (kNN) and a random forests classifier (RanForest), and support vector machines with a linear and a radial basis function kernel (SVMlin and SVMrbf). Results: For all classifiers, feature sets selected by the relevance ranking assessed by GMLVQ had a significantly better classification performance (p <0.05) for many texture feature sets compared to the MI approach. For kNN, RanForest, and SVMrbf, some of these feature subsets had a significantly better classification performance when compared to the set consisting of all features (p <0.05). Conclusion: While this approach estimates the relevance of single features, future considerations of GMLVQ should include the pairwise correlation for the feature ranking, e.g. to reduce the redundancy of two equally relevant features. (C) 2012 Elsevier B.V. All rights reserved

Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023-2032

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) scientific decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: “Tipping points and thresholds in hydrology”, “Intensification of the water cycle”, and “Water services under pressure”. Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other Early-Career Scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement

Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: “Tipping points and thresholds in hydrology,” “Intensification of the water cycle,” and “Water services under pressure.” Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement