Dual-satellite cloud product generation using temporally updated canonical coordinate features

Includes bibliographical references.State-of-the-art cloud products are typically generated using scientific polar orbiting satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, they do not allow for observation of the same region at a regular temporal frequency, rendering them ineffectual for nowcasting problems. Operational satellites such as Meteosat-8 SEVIRI, in contrast, are geostationary and provide continual data at a regular temporal frequency over a much larger region. MODIS-like cloud products cannot be directly generated from operational satellites as they typically have a smaller number of spectral bands and different wavelengths and spatial resolution. This paper applies the canonical coordinate decomposition method to estimate scientific cloud products using imagery from operational satellites. Using the proposed method features of the Meteosat-8 imagery data that are maximally coherent with the data from the MODIS are generated. These features are temporally updated at times and locations where MODIS data are unavailable using the alternating block power method. A subset of the canonical coordinates of Meteosat-8 SEVIRI is then used to create MODIS-like cloud products using several neural networks. The quality of the generated cloud products and their temporal consistency have been demonstrated on several data sets from July 2004. A benchmarking with an independent Meteosat-8-based algorithm is also provided, which shows the promise of our approach in generating MODIS-like cloud products.This work was supported by the Department of Defense Center for Geosciences/Atmospheric Research, Colorado State University, via Cooperative Agreement DAAD19-02-2-0005 with the Army Research Laboratory

Surface soil moisture estimate from Sentinel-1 and Sentinel-2 data in agricultural fields in areas of high vulnerability to climate variations: the Marche region (Italy) case study

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model’s accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4)

Informal settlement segmentation using VHR RGB and height information from UAV imagery: a case study of Nepal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesInformal settlement in developing countries are complex. They are contextually and radiometrically very similar to formal settlement. Resolution offered by Remote sensing is not sufficient to capture high variations and feature size in informal settlements in these situations. UAV imageries offers solution with higher resolution. Incorporating UAV image and normalized DSM obtained from UAV provides an opportunity of including information on 3D space. This can be a crucial factor for informal settlement extraction in countries like Nepal. While formal and informal settlements have similar texture, they differ significantly in height. In this regard, we propose segmentation of informal settlement of Nepal using UAV and normalized DSM, against traditional approach of orthophoto only or orthophoto and DSM. Absolute height, normalized DSM(nDSM) and vegetation index from visual band added to 8 bit RGB channels are used to locate informal settlements. Segmentation including nDSM resulted in 6 % increment in Intersection over Union for informal settlements. IoU of 85% for informal settlement is obtained using nDSM trained end to end on Resnet18 based Unet. Use of threshold value had same effect as using absolute height, meaning use of threshold does not alter result from using absolute nDSM. Integration of height as additional band showed better performance over model that trained height separately. Interestingly, benefits of vegetation index is limited to settlements with small huts partly covered with vegetation, which has no or negative effect elsewhere

An Architecture for distributed multimedia database systems

In the past few years considerable demand for user oriented multimedia information systems has developed. These systems must provide a rich set of functionality so that new, complex, and interesting applications can be addressed. This places considerable importance on the management of diverse data types including text, images, audio and video. These requirements generate the need for a new generation of distributed heterogeneous multimedia database systems. In this paper we identify a set of functional requirements for a multimedia server considering database management, object synchronization and integration, and multimedia query processing. A generalization of the requirements to a distributed system is presented, and some of our current research and developing activities are discussed

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Ionospheric Multi-Spacecraft Analysis Tools

This open access book provides a comprehensive toolbox of analysis techniques for ionospheric multi-satellite missions. The immediate need for this volume was motivated by the ongoing ESA Swarm satellite mission, but the tools that are described are general and can be used for any future ionospheric multi-satellite mission with comparable instrumentation. In addition to researching the immediate plasma environment and its coupling to other regions, such a mission aims to study the Earth’s main magnetic field and its anomalies caused by core, mantle, or crustal sources. The parameters for carrying out this kind of work are examined in these chapters. Besides currents, electric fields, and plasma convection, these parameters include ionospheric conductance, Joule heating, neutral gas densities, and neutral winds.

Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in each task are analyzed with regard to what governing physical processes are modeled, formulated and how they are incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency and generalization in increasingly realistic applications

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Assesment of biomass and carbon dynamics in pine forests of the Spanish central range: A remote sensing approach

Forests play a dynamic role in the terrestrial carbon (C) budget, by means of the biomass stock and C fluxes involved in photosynthesis and respiration. Remote sensing in combination with data analysis constitute a practical means for evaluation of forest implications in the carbon cycle, providing spatially explicit estimations of the amount, quality, and spatio-temporal dynamics of biomass and C stocks. Medium and high spatial resolution optical data from satellite-borne sensors were employed, supported by field measures, to investigate the carbon role of Mediterranean pines in the Central Range of Spain during a 25 year period (1984-2009). The location, extent, and distribution of pine forests were characterized, and spatial changes occurred in three sub-periods were evaluated. Capitalizing on temporal series of spectral data from Landsat sensors, novel techniques for processing and data analysis were developed to identify successional processes at the landscape level, and to characterize carbon stocking condition locally, enabling simultaneous characterization of trends and patterns of change. High spatial resolution data captured by the commercial satellite QuickBird-2 were employed to model structural attributes at the stand level, and to explore forest structural diversity