ABSTRACT:The ground resolved distance (GRD) of an imaging sensor, i.e. the size of the smallest element distinguishable on acquired imagery, is one of the most important sensor quality assessment factors, as it is directly linked to the amount of information that can be derived from the end product. The paper is a review of a wide variety of calibration targets used for determining the spatial resolution of remote sensing sensors. The author provides a description of calibration targets used historically and then moves on to high-frequency targets used for high-resolution remote sensing imaging equipment. As analysis is made which of these types of targets are best suited for UAV sensors, taking into account parameters very specific to UAVs: frame size, small GSD values and low flight stability.
Indicator 11.3. 1 of the 2030 sustainable development goals (SDG) 11, i.e., the ratio of the land use to the population growth rate, is currently classified by the United Nations as a Tier II indicator, as there is a globally-accepted methodology for its calculation, but the data are not available, nor are not regularly updated. Recently, the increased availability of remotely sensed data and products allows not only for the calculation of the SDG 11.3. 1, but also for its monitoring at different levels of detail. That is why this study aims to address the interrelationships between population development and land use changes in Poland and Lithuania, two neighboring countries in Central and Eastern Europe, using the publicly available remotely sensed products, CORINE land cover and GHS-POP. The paper introduces a map modelling process that starts with data transformation through GIS analyses and results in the geovisualisation of the LCRPGR (land use efficiency), the PGR (population growth rate), and the LCR (land use rate). We investigated the spatial patterns of the index values by utilizing hotspot analyses, autocorrelations, and outlier analyses. The results show how the indicators’ values were concentrated in both countries; the average value of SDG 11.3. 1, from 2000 to 2018 in Poland amounted to 0.115 and, in Lithuania, to −0.054. The average population growth ratio (PGR) in Poland equaled 0.0132, and in Lithuania, it was −0.0067, while the average land consumption ratios (LCRs) were 0.0462 and 0.0067, respectively. Areas with an increase in built-up areas were concentrated mainly on the outskirts of large cities, whereas outliers of the LCRPGR index were mainly caused by the uncertainty of the source data and the way the indicator is interpreted.
ABSTRACT:Nowadays remote sensing plays a very important role in many different study fields, i.e. environmental studies, hydrology, mineralogy, ecosystem studies, etc. One of the key areas of remote sensing applications is water quality monitoring. Understanding and monitoring of the water quality parameters and detecting different water contaminants is an important issue in water management and protection of whole environment and especially the water ecosystem. There are many remote sensing methods to monitor water quality and detect water pollutants. One of the most widely used method for substance detection with remote sensing techniques is based on usage of spectral reflectance coefficients. They are usually acquired using discrete methods such as spectrometric measurements. These however can be very time consuming, therefore image-based methods are used more and more often. In order to work out the proper methodology of obtaining spectral reflectance coefficients from hyperspectral and multispectral images, it is necessary to verify the impact of cameras radiometric resolution on the accuracy of determination of them. This paper presents laboratory experiments that were conducted using two monochromatic XEVA video sensors (400-1700nm spectral data registration) with two different radiometric resolutions (12 and 14 bits). In view of determining spectral characteristics from images, the research team used set of interferometric filters. All data collected with multispectral digital video cameras were compared with spectral reflectance coefficients obtained with spectroradiometer. The objective of this research is to find the impact of cameras radiometric resolution on reflectance values in chosen wavelength. The main topic of this study is the analysis of accuracy of spectral coefficients from sensors with different radiometric resolution. By comparing values collected from images acquired with XEVA sensors and with the curves obtained with spectroradiometer it's possible to determine accuracy of imagebased spectral reflectance coefficients and decide which sensor will be more accurate to determine them for protection of water aquatic environment purpose.
The rapid and accurate detection and identification of water pollutants play an enormous role is preserving water ecosystems and protecting the surrounding environment. Such detection and identification are usually conducted in situ, using traditional measurement methods. These methods however are very time consuming and costly especially when conducting them on a large scale. A team of specialists from the Department of Remote Sensing and Photogrammetry from the Military University of Technology in Warsaw have been taking part in a project entitled "IRAMSWater-Innovative remote sensing system for the monitoring of pollutants in rivers, offshore waters and flooded areas" (PBS1/B9/8/2012) financed by the polish National Centre for Research and Development. Its main aim is the creation of a remote sensing system based on hyperspectral sensors which will enable the evaluation, detection and distribution of biological, physical and chemical pollutants in the examined waters in real time. These analyses will be conducted based on spectral characteristics of a wide selection of pollutants. In order to acquire these spectral characteristics in a precise manner from hyperspectral data, the research team was required to establish a precise methodology for obtaining these data. The most common approach is to acquire the imagery first, ensuring that each scene contains at least one reference panel with a well know spectral characteristic, and then transforming the imagery and calculating the spectral response curves during post-processing. The IRAMSWater team had proposed a method of extracting precise reflectance coefficients in quasi real time without the need for using a reference panel on the scene. This is done by precisely determining the camera exposure parameters in laboratory conditions. The article presents laboratory experiments conducted using two XEVA video sensors (400-1700 nm spectral data registration), which allowed the authors to form a methodology for acquiring spectral reflectance coefficients of objects within a scene without the need for a reference panel.
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