Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

"© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).Many applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral-hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset (R-2 = 0.62, p 0.1). Maximal R-2 values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.This work was supported in part by the Belgian Science Policy Office in the frame of the Stereo II program (Hypermix project-SR/00/141), in part by the project Chameleon of the Flemish Agency for Innovation by Science and Technology (IWT), and in part by the Spanish Ministry of Science and Education (MEC) for the projects AGL2012-40053-C03-01 and CONSOLIDER RIDECO (CSD2006-67). The European Facility for Airborne Research EUFAR (www.eufar.net) funded the flight campaign (Transnational Access Project 'Hyper-Stress'). The work of D. S. Intrigliolo was supported by the Spanish Ministry of Economy and Competitiveness program "Ramon y Cajal."Delalieux, S.; Zarco-Tejada, PJ.; Tits, L.; Jiménez Bello, MÁ.; Intrigliolo Molina, DS.; Somers, B. (2014). Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6):2571-2582. https://doi.org/10.1109/JSTARS.2014.2330352S257125827

Robótica y fenotipado de alta capacidad con relevamiento de datos en campo : Aplicaciones en agricultura de precisión

En la UNNOBA se está trabajando en la aplicación combinada de diferentes desarrollos tecnológicos y Agricultura de Precisión. Ambas áreas de estudio constituyen una herramienta fundamental para lograr un manejo adecuado y preciso del suelo y sus cultivos en base a su variabilidad dentro de un lote, permitiendo adaptarse a las exigencias de la agricultura moderna en el manejo óptimo de grandes extensiones. El uso de herramientas tecnológicas orientadas al uso de imágenes y sensores (GPS, sensores, UAVs) en la agricultura de precisión, permite diferenciar variabilidad y características particulares de diferentes coberturas terrestres para mejorar la toma de decisiones en pos de obtener mayores rendimientos. En la actualidad se encuentran muchos trabajos de investigación que utilizan imágenes de sensado remoto (satélites, áreas). La presente línea de investigación pretende aportar desde otra perspectiva mediante el uso plataformas robóticas de sensado a campo y el uso de imágenes digitales capturadas con cámaras de luz visible, multi o hiper espectrales, térmicas, más la utilización de técnicas de procesamiento digital, con el fin de aportar un valor agregado a las tecnologías ya existentes en esta área. Esto permitirá mejorar el estudio de aspectos cualitativos y cuantitativos de diferentes tipos de cultivos en relación a su variabilidad fenológica, morfológica, fisiológica, temporal y espacial.Eje: Procesamiento de Señales y Sistemas de Tiempo RealRed de Universidades con Carreras en Informática (RedUNCI

Robótica y fenotipado de alta capacidad con relevamiento de datos en campo : Aplicaciones en agricultura de precisión

En la UNNOBA se está trabajando en la aplicación combinada de diferentes desarrollos tecnológicos y Agricultura de Precisión. Ambas áreas de estudio constituyen una herramienta fundamental para lograr un manejo adecuado y preciso del suelo y sus cultivos en base a su variabilidad dentro de un lote, permitiendo adaptarse a las exigencias de la agricultura moderna en el manejo óptimo de grandes extensiones. El uso de herramientas tecnológicas orientadas al uso de imágenes y sensores (GPS, sensores, UAVs) en la agricultura de precisión, permite diferenciar variabilidad y características particulares de diferentes coberturas terrestres para mejorar la toma de decisiones en pos de obtener mayores rendimientos. En la actualidad se encuentran muchos trabajos de investigación que utilizan imágenes de sensado remoto (satélites, áreas). La presente línea de investigación pretende aportar desde otra perspectiva mediante el uso plataformas robóticas de sensado a campo y el uso de imágenes digitales capturadas con cámaras de luz visible, multi o hiper espectrales, térmicas, más la utilización de técnicas de procesamiento digital, con el fin de aportar un valor agregado a las tecnologías ya existentes en esta área. Esto permitirá mejorar el estudio de aspectos cualitativos y cuantitativos de diferentes tipos de cultivos en relación a su variabilidad fenológica, morfológica, fisiológica, temporal y espacial.Eje: Procesamiento de Señales y Sistemas de Tiempo RealRed de Universidades con Carreras en Informática (RedUNCI

Robótica y fenotipado de alta capacidad con relevamiento de datos en campo : Aplicaciones en agricultura de precisión

En la UNNOBA se está trabajando en la aplicación combinada de diferentes desarrollos tecnológicos y Agricultura de Precisión. Ambas áreas de estudio constituyen una herramienta fundamental para lograr un manejo adecuado y preciso del suelo y sus cultivos en base a su variabilidad dentro de un lote, permitiendo adaptarse a las exigencias de la agricultura moderna en el manejo óptimo de grandes extensiones. El uso de herramientas tecnológicas orientadas al uso de imágenes y sensores (GPS, sensores, UAVs) en la agricultura de precisión, permite diferenciar variabilidad y características particulares de diferentes coberturas terrestres para mejorar la toma de decisiones en pos de obtener mayores rendimientos. En la actualidad se encuentran muchos trabajos de investigación que utilizan imágenes de sensado remoto (satélites, áreas). La presente línea de investigación pretende aportar desde otra perspectiva mediante el uso plataformas robóticas de sensado a campo y el uso de imágenes digitales capturadas con cámaras de luz visible, multi o hiper espectrales, térmicas, más la utilización de técnicas de procesamiento digital, con el fin de aportar un valor agregado a las tecnologías ya existentes en esta área. Esto permitirá mejorar el estudio de aspectos cualitativos y cuantitativos de diferentes tipos de cultivos en relación a su variabilidad fenológica, morfológica, fisiológica, temporal y espacial.Eje: Procesamiento de Señales y Sistemas de Tiempo RealRed de Universidades con Carreras en Informática (RedUNCI

Ontology and Weighted D-S Evidence Theory-Based Vulnerability Data Fusion Method

With the rapid development of high-speed and large-scale complex network, network vulnerability data presents the characteristics of massive, multi-source and heterogeneous, which makes data fusion become more complex. Although existing data fusion methods can fuse multi-source data, they do not consider that the multisource data may affect the accuracy of fusion result. To solve this problem, we propose an ontology and weighted D-S evidence theory-based vulnerability data fusion method. In our method, we utilize ontology to describe the network vulnerability semantically and construct the network vulnerability ontology hierarchically. Then we use weighted D-S evidence theory to perform the operation of probability distribution and fusion processing. Besides, we simulate our method on MapReduce parallel computing platform. The experiment results show that our method is more effective and accurate compared with existing fusion approaches using single detection tool and traditional D-S evidence theory

Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion

Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing SU methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, the determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this article, a geographically weighted SU (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatiotemporal fusion. SU-GW is a general model suitable for any SU method. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for SU. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatiotemporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatiotemporal fusion, which can be used to update existing methods and future potential versions

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

WICC 2016 : XVIII Workshop de Investigadores en Ciencias de la Computación

Actas del XVIII Workshop de Investigadores en Ciencias de la Computación (WICC 2016), realizado en la Universidad Nacional de Entre Ríos, el 14 y 15 de abril de 2016.Red de Universidades con Carreras en Informática (RedUNCI