Satellite Image Processing Systems: An Architectural Perspective

Satellite image processing systems include satellite image classification, long ranged data processing, yield prediction systems, etc. All of these systems require a large quantity of images for effective processing, and thus they are directed towards big-data applications. All these applications require a series of highly complex image processing and signal processing steps, which include but are not limited to image acquisition, image pre-processing, segmentation, feature extraction & selection, classification and post processing. Numerous researchers globally have proposed a large variety of algorithms, protocols and techniques in order to effectively process satellite images. This makes it very difficult for any satellite image system designer to develop a highly effective and application-oriented processing system. In this paper, we aim to categorize these large number of researches w.r.t. their effectiveness and further perform statistical analysis on the same. This study will assist researchers in selecting the best and most optimally performing algorithmic combinations in order to design a highly accurate satellite image processing system.

Big Data for Satellite Image Processing

This chapter presents an introduction to the basics in big data including architecture, modeling, and the tools used. Big data is a term that is used for serving the high volume of data that can be used as an alternative to RDBMS and the other analytical technologies such as OLAP. For every application there exist databases that contain the essential information. But the sizes of the databases vary in different applications and we need to store, extract, and modify these databases. In order to make it useful, we have to deal with it efficiently. This is the place that big data plays an important role. Big data exceeds the processing and the overall capacity of other traditional databases. In this chapter, the basic architecture, tools, modeling, and challenges are presented in each section.

Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas

In recent years, satellite imagery has shown its potential to support the sustainable management of land, water, and natural resources. In particular, it can provide key information about the properties and behavior of sandy beaches and the surrounding vegetation, improving the ecomorphological understanding and modeling of coastal dynamics. Although satellite image processing usually demands high memory and computational resources, free online platforms such as Google Earth Engine (GEE) have recently enabled their users to leverage cloud-based tools and handle big satellite data. In this technical note, we describe an algorithm to classify the coastal land cover and retrieve relevant information from Sentinel-2 and Landsat image collections at specific times or in a multitemporal way: the extent of the beach and vegetation strips, the statistics of the grass cover, and the position of the shoreline and the vegetation–sand interface. Furthermore, we validate the algorithm through both quantitative and qualitative methods, demonstrating the goodness of the derived classification (accuracy of approximately 90%) and showing some examples about the use of the algorithm’s output to study coastal physical and ecological dynamics. Finally, we discuss the algorithm’s limitations and potentialities in light of its scaling for global analyses.

Stabilization of the Lower Jamuna River in Bangladesh—Hydraulic and Morphological Assessment

This paper presents a hydraulic and morphological analysis of the Lower Jamuna in Bangladesh with a focus on two key bifurcations that are important for stabilization of the Lower Jamuna reach. We used ground measurements, historical data, multispectral satellite images from various sources as well as numerical models. We carried out hydraulic analyses of the changes and their peculiarities, such as flow distributions at the bifurcation and hysteresis of the stage–discharge relationships. We supplemented our analysis by using numerical models to simulate discharge distribution at the bifurcations under various flow and riverbed conditions. We developed an advanced and automated satellite image processing application for the Lower Jamuna, referred to as Morphology Monitor (MoMo), using the Google Earth Engine. MoMo was found to be an effective tool for a rapid assessment and analysis of the changes in deep-channel and sandbar areas. It is also useful for monitoring and assessing riverbank and char erosion and accretion, which is important not only for morphological but also ecological impact assessment. The application can be adapted as an operational tool as well. Furthermore, we assessed the evolution of deep channels at the bifurcations based on regularly and extensively measured bathymetry data. The analysis was carried out in complement with morphological modeling, particularly for short-term prediction. In this paper we present the major findings of the analysis and discuss their implications for adaptive river management.

Hydrogeological characterization and water use of a sector of sands, sandstones and gravel on the coast of Baixo Alentejo

<p>This reasearch discusses the necessary tasks to carry out the hydrogeological characterization of the sands, sandstones, and gravels of the Baixo Alentejo coast. Currently, this characterization has done in detail only in the areas where these formations constitute hydro-stratigraphic units of the aquifer systems of Sines and the Alvalade Basin. In addition to system hydrogeological characterization of the system, the volume of water used for irrigation in the study area was estimated, with the aim of characterizing its inter-annual evolution between 2000 and 2018 and intra-annual for the year 2018. To do so, remote sensing and satellite image processing methods were used (LANDSAT 5 and 8 and MODIS). A synthesis of the hydrogeological characterization is presented in an area of 195.8 km<sup>2</sup>, divided into two aquifer sectors, one located north of the Mira River with 94.12 km<sup>2</sup> and the other south with 101.75 km<sup>2</sup>. The first stage of the work consisted of the analysis of the studied aquifers recharge based on precipitation and the analysis of piezometry data in order to define the conceptual model of hydraulic functioning of the system. The available data were obtained from fieldwork and from the LIFE-Charcos Project (LIFE12NAT / PT / 997). In parallel, an analysis of land use and occupation performed, with emphasis on the identification of irrigation areas. Finally, the volume of water used in agriculture irrigation was determined using the method of estimating the consumptive use of water in irrigation at a local scale, based on the determination of evapotranspiration values, using the algorithm SEBAL, precipitation, and  irrigation efficiency. The results obtained were validated, with high precision, through the comparison with the irrigation volumes known during 2018, and the calibration of the monthly sequential water balance model at ground level.</p><p>Key words: aquifer system of sands, sandstones and gravels of the Baixo Alentejo coast; hydrogeology; Irrigation; Remote Sensing.</p>