Formation of Fused Images of the Land Surface from Radar and Optical Images in Spatially Distributed On-Board Operational Monitoring Systems

This paper considers the issues of image fusion in a spatially distributed small-size on-board location system for operational monitoring. The purpose of this research is to develop a new method for the formation of fused images of the land surface based on data obtained from optical and radar devices operated from two-position spatially distributed systems of small aircraft, including unmanned aerial vehicles. The advantages of the method for integrating information from radar and optical information-measuring systems are justified. The combined approach allows removing the limitations of each separate system. The practicality of choosing the integration of information from several widely used variants of heterogeneous sources is shown. An iterative approach is used in the method for combining multi-angle location images. This approach improves the quality of synthesis and increases the accuracy of integration, as well as improves the information content and reliability of the final fused image by using the pixel clustering algorithm, which produces many partitions into clusters. The search for reference points on isolated contours is carried out on a pair of left and right images of the docked image from the selected partition. For these reference points, a functional transformation is determined. Having applied it to the original multi-angle heterogeneous images, the degree of correlation of the fused image is assessed. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the quality assessment of the fusion becomes acceptable. The type of functional transformation is selected based on clustered images and then applied to the original multi-angle heterogeneous images. This process is repeated for clustered images with greater granularity in case if quality assessment of the fusion is considered to be poor. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are isolated with the use of two image segmentation methods. Experiments on the formation of fused images are presented. The result of the research is the proposed method for integrating information obtained from a two-position airborne small-sized radar system and an optical location system. The implemented method can improve the information content, quality, and reliability of the finally established fused image of the land surface.

Cloud-based software tools for the rapid development of web GIS

The article discusses methods for building geospatial web applications based on the ICM SB RAS geoportal resources. The sources of information for them are two types of data: web-service-based cartographic layers and data from the operational monitoring sub-system. The developed instrumental software allows you to select data on the geoportal, form, and configure the user interface. The modular structure and functional capabilities of the developed system are considered.

Brief communication: Detection of glacier surge activity using cloud computing of Sentinel-1 radar data

For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges and some other special events such as large rock and snow avalanches.

Sustainability Monitoring: Principles, Challenges, and Approaches

Because of the inherent complexity of urban brownfield regeneration projects, achieving sustainability objectives is not easy. It requires approaches adapted to the specificities of this type of operation, that allow for structured and regular follow-up, and that are integrated into the project dynamics. In this chapter, we argue that sustainability monitoring can help address this challenge. We start by defining the principles of sustainability evaluation and monitoring. Then, we look deeper at the challenges of an operational monitoring tool from the brownfield regeneration perspective. On this basis, we plead for the necessity of tailor-made operational monitoring tools for this type of operation and define, to this end, three general requirements for said tools. Finally, we make a critical analysis of existing certifications at the neighbourhood scale and different approaches developed for brownfield regeneration projects.

An Operational Monitoring Tool

The transition from an urban brownfield to a sustainable neighbourhood is a complex operation. To help decision-makers reach sustainability objectives through measurement, follow-up, and communication about performance indicators, we introduce in this chapter a tailor-made operational monitoring tool. Such a tool should satisfy three general requirements: a search for overall quality, adequacy with the specificities of urban brownfield regeneration projects, and integration into the project dynamics. Accordingly, the multi-criteria evaluation system SIPRIUS and the quality management monitoring software OKpilot are hybridized to create SIPRIUS+. In the first section, we explain the functioning of the two existing methodologies and the adaptions we made to help meet the general requirements and to create the hybrid tool. Then, we present the resulting monitoring tool, SIPRIUS+, and its functionalities.

Application to a Case Study

We present an application of the operational monitoring tool SIPRIUS+ to an ongoing case study representative of urban brownfield regeneration projects: the Pôle Viotte neighbourhood, located in Besançon (France). We start with a description of the brownfield site, followed by the regeneration project. Then, we evaluate 52 indicators linked to the context, project, and governance. Each monitoring result is illustrated by a graph showing the evolution of the performances. Then, we analyse the overall results, which allows us to assess the sustainability strategy of the regeneration project. Through this analysis, we identify four actions to improve sustainability objectives. Finally, we conclude that SIPRIUS+ has the potential to contribute to the integration of sustainability issues into the dynamics of neighbourhoods in transition. The operational monitoring tool is expected to contribute to decision-making in a multi-disciplinary manner, without giving ready-made solutions. Interactions with project stakeholders reveal that, while the use of such a tool would require a change in project management, the evolutions to adopt to include this practice appear not only feasible but realistic and desired.

Segmentation and quantification of solar PV losses using data-driven algorithms to help better design and operational monitoring

<div>In the current scenario of increasing demand for solar Photo-voltaic (PV) systems, the need to predict their feasibility and monitor performance is more than ever. Although PV systems are known for their reliability, they are not above the damaging effects of their surroundings. Various lossy phenomena affect overall plant performance. In this paper, several of such losses, namely thermal, soiling, module degradation and inverter clipping, are discussed. Algorithms to evaluate these losses are proposed which are data-driven and empirical in nature. This is done as an effort to leverage the analytical capabilities provided by the plant data. The paper also compares the estimated losses with those obtained using the PVsyst simulation. As the latter is an independent industrial standard, it helps in understanding the ground reality of PV performance and insights for better operational monitoring. These insights are of immense business value and are aimed at optimizing performance and thereby revenue. As part of our asset management, all the solar PV plant components have sensors whose measurements are sent to the servers on a real-time basis. This is incorporated into our analytics portal which is used for operations and monitoring. The data used for this study is time-series in nature with a temporal least count of 5 minutes (instantaneous values spaced every 5min throughout the period of data capture). The actual data and its list of parameters is dependent on solar plant capacity and design site. For the reference dataset, a grid-connected solar rooftop PV plant in India was studied and its loss parameters were estimated. The plant components are discussed in the prologue of the results section. Solar PV is such a technology which has been enjoying increasing demand and this market scenario is quite favourable for innovation in energy research. This paper hopes to not only introduce the context of PV losses but also tries to engage the motivation to adopt data-driven and empirical methodologies to understand modern systems. This approach is better in the sense that it only gets better at prediction as time goes by and there is more data. Industrial research such as the above work in critical analysis of PV systems not only helps identify possible limitations but also suggest room for improvement. Since energy generation and project cost are key towards maximizing revenue, these estimation models aimed at predicting PV losses are to be deemed indispensable. As with any estimation, there is no one unique way of hitting the bull’s eye that is to know the exact value. The algorithms proposed above are very much dependent on the quality and quantity of data. However, the comparison between losses estimated using plant data and standard simulation using energy modelling can act as feedback towards improving the design and maintenance of such PV systems.</div>

Segmentation and quantification of solar PV losses using data-driven algorithms to help better design and operational monitoring

<div>In the current scenario of increasing demand for solar Photo-voltaic (PV) systems, the need to predict their feasibility and monitor performance is more than ever. Although PV systems are known for their reliability, they are not above the damaging effects of their surroundings. Various lossy phenomena affect overall plant performance. In this paper, several of such losses, namely thermal, soiling, module degradation and inverter clipping, are discussed. Algorithms to evaluate these losses are proposed which are data-driven and empirical in nature. This is done as an effort to leverage the analytical capabilities provided by the plant data. The paper also compares the estimated losses with those obtained using the PVsyst simulation. As the latter is an independent industrial standard, it helps in understanding the ground reality of PV performance and insights for better operational monitoring. These insights are of immense business value and are aimed at optimizing performance and thereby revenue. As part of our asset management, all the solar PV plant components have sensors whose measurements are sent to the servers on a real-time basis. This is incorporated into our analytics portal which is used for operations and monitoring. The data used for this study is time-series in nature with a temporal least count of 5 minutes (instantaneous values spaced every 5min throughout the period of data capture). The actual data and its list of parameters is dependent on solar plant capacity and design site. For the reference dataset, a grid-connected solar rooftop PV plant in India was studied and its loss parameters were estimated. The plant components are discussed in the prologue of the results section. Solar PV is such a technology which has been enjoying increasing demand and this market scenario is quite favourable for innovation in energy research. This paper hopes to not only introduce the context of PV losses but also tries to engage the motivation to adopt data-driven and empirical methodologies to understand modern systems. This approach is better in the sense that it only gets better at prediction as time goes by and there is more data. Industrial research such as the above work in critical analysis of PV systems not only helps identify possible limitations but also suggest room for improvement. Since energy generation and project cost are key towards maximizing revenue, these estimation models aimed at predicting PV losses are to be deemed indispensable. As with any estimation, there is no one unique way of hitting the bull’s eye that is to know the exact value. The algorithms proposed above are very much dependent on the quality and quantity of data. However, the comparison between losses estimated using plant data and standard simulation using energy modelling can act as feedback towards improving the design and maintenance of such PV systems.</div>