scholarly journals Assessment and Quantification of the Accuracy of Low- and High-Resolution Remote Sensing Data for Shoreline Monitoring

2020 ◽  
Vol 9 (6) ◽  
pp. 391
Author(s):  
Dionysios N. Apostolopoulos ◽  
Konstantinos G. Nikolakopoulos
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Sandy Beaches ◽  
Landsat Images ◽  
High Resolution Data ◽  
Sensing Data ◽  
Shoreline Evolution ◽  
Landsat Satellite

Τhe accuracy of low-resolution remote sensing data for monitoring shoreline evolution is the main issue that researchers have been trying to overcome in recent decades. The drawback of the Landsat satellite archive is its spatial resolution, which is appropriate only for low-scale mapping. The present study investigates the potentialities and limitations of remote sensing data and GIS techniques in shoreline evolution modeling, with a focus on two major aspects: (a) assessing and quantifying the accuracy of low- and high-resolution remote sensing data for shoreline mapping; and (b) calculating the divergence in the forecasting of coastline evolution based on low- and high-resolution datasets. Shorelines derived from diachronic Landsat images are compared with the corresponding shorelines derived from high-spatial-resolution airphotos or Worldview-2 images. The accuracy of each dataset is assessed, and the possibility of forecasting shoreline evolution is investigated. Two sandy beaches, named Kalamaki and Karnari, which are located in Northwestern Peloponnese, Greece, are used as test sites. It is proved that the shorelines derived from the Landsat data present a displacement error of between 6 and 11 m. The specific data are not suitable for the shoreline forecasting procedure and should not be used in related studies, as they yield less accurate results for the two study areas in comparison with the high-resolution data.

scholarly journals Performance evaluation of super resolution algorithms in generating high resolution images using MSE and PSNR

2021 ◽  
Vol 10 (02) ◽  
pp. 25284-25291
Author(s):  
Palani Murugan ◽  
Vivek Kumar Gautam ◽  
V. Ramanathan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Optical Systems ◽  
High Resolution Data ◽  
Sensing Data ◽  
Resolution Data

In recent days, requirement of high spatial resolution remote sensing data in various fields has increased tremendously.  High resolution satellite remote sensing data is obtained with long focal length optical systems and low altitude. As fabrication of high-resolution optical system and accommodating on the satellite is a challenging task, various alternate methods are being explored to get high resolution imageries. Alternately the high-resolution data can be obtained from super resolution techniques. The super resolution technique uses single or multiple low-resolution mis-registered data sets to generate high resolution data set.  Various algorithms are employed in super resolution technique to derive high spatial resolution. In this paper we have compared two methods namely overlapping and interleaving methods and their capability in generating high resolution data are presented.

scholarly journals Ten simple rules for working with high resolution remote sensing data

2021 ◽  
Author(s):  
Maxwell Benjamin Joseph ◽  
Anna Spiers ◽  
Michael J. Koontz ◽  
Nayani Ilangakoon ◽  
Kylen Solvik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Environmental Science ◽  
Ad Hoc ◽  
Remote Sensing Data ◽  
Trial And Error ◽  
High Resolution Data ◽  
Sensing Data ◽  
Simple Rules ◽  
Resolution Data

Researchers in Earth and environmental science can extract incredible value from high resolution remote sensing data, but these data can be hard to use. Pain free use requires skills from remote sensing and the data sciences that are seldom taught together. In practice, many researchers teach themselves how to use high resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. Here we outline ten “rules” with examples from Earth and environmental science to help applied researchers work more effectively with high resolution data.

scholarly journals Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring

2019 ◽  
Author(s):  
Xiaowei Jia ◽  
Mengdie Wang ◽  
Ankush Khandelwal ◽  
Anuj Karpatne ◽  
Vipin Kumar
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Temporal Patterns ◽  
Small Scale ◽  
High Resolution Data ◽  
Sensing Data ◽  
Coarse Resolution ◽  
Sequential Models ◽  
Resolution Data

Effective and timely monitoring of croplands is critical for managing food supply. While remote sensing data from earth-observing satellites can be used to monitor croplands over large regions, this task is challenging for small-scale croplands as they cannot be captured precisely using coarse-resolution data. On the other hand, the remote sensing data in higher resolution are collected less frequently and contain missing or disturbed data. Hence, traditional sequential models cannot be directly applied on high-resolution data to extract temporal patterns, which are essential to identify crops. In this work, we propose a generative model to combine multi-scale remote sensing data to detect croplands at high resolution. During the learning process, we leverage the temporal patterns learned from coarse-resolution data to generate missing high-resolution data. Additionally, the proposed model can track classification confidence in real time and potentially lead to an early detection. The evaluation in an intensively cultivated region demonstrates the effectiveness of the proposed method in cropland detection.

scholarly journals Long-term analysis of irrational water use processes based on cartographic materials and remote sensing data

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Andrey Medvedev ◽  
Natalia Alekseenko ◽  
Natalia Telnova ◽  
Alexander Koshkarev
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Water Level ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Topographic Maps ◽  
High Resolution Data ◽  
Sensing Data ◽  
The Past ◽  
Resolution Data

<p><strong>Abstract.</strong> Assessment and monitoring of environmental features based on large-scale and ultra-high resolution data, including remote sensing data, which have advantages in the repeatability of information and the speed of processing of incoming data, often face issues of completeness and duration of time series in retrospective analysis. Cartographic materials and remote sensing data allow monitoring for rapidly changing natural and anthropogenic features in the study areas, but very often face a problem when an event or phenomenon occurred many years ago and it is necessary to make a complete chronology.</p><p>Ultra-high-resolution data, remote sensing data and the results of the subsequent geoinformation analysis are widely used to solve problems in a number of socio-economic areas of territorial development, in particular:</p><ul><li>in environmental studies &amp;ndash; identification of local sources of water pollution, the consequences of their impact onecosystems, synthetic assessment of the ecological state of the territories and their comfort;</li><li>in the management of various resources, including water &amp;ndash; determination of biological productivity of water bodies, identification of water bioresources, detection of anthropogenically provoked and natural changes in water mass,implementation for glaciological studies, etc.</li></ul><p>Within the framework of the current study, a multi-time analysis of the water area and the coastal strip of Lake Sevan (the Republic of Armenia) at an altitude of about 1900 m above sea level, was carried out. The lake has repeatedly beensubjected to changes in the water level of the reservoir in the past. The 1930s and in the period between 1949 to 1962 were noted by the most intense drop in water level (more than 10 meters). In the 1990s, there was a slight increase inthe level, and then until 2001, the level of the lake continued to decrease.</p><p>The main factors affecting aquatic ecosystems and the overall ecological status of the lake are:</p><ol><li>Repeated changes in the water level of the reservoir in the past and its expected fluctuations in the future.</li><li>The uncontrolled discharge of harmful substances caused great damage to the lake, which affected the water qualityand biodiversity of this unique natural site.</li><li>Untimely cleaning of flooded forests, which increases the risk of eutrophication of the lake.</li><li>The poorly organized system of waste disposal and unauthorized landfills of municipal solid waste, as well as animalwaste.</li><li>Unauthorized construction of recreational facilities and capital structures in the coastal and water protection zonewhich may be flooded.</li></ol><p> The information support of the study is based on the materials of satellite imagery from the worldview2, SPOT 5/6,Resurs-P, Canopus-B, materials from the international space station (ISS), materials of archival aerial photography anddata obtained from the UAVs, in combination with other map data sources in the range of scales 1&amp;thinsp;:&amp;thinsp;5&amp;thinsp;000 &amp;ndash; 1&amp;thinsp;:&amp;thinsp;100&amp;thinsp;000,including digital topographic maps, land use maps, statistical and literary data. In fact, cartographic materials andremote sensing data provide a time history of 75 years, from large-scale topographic maps of 1942&amp;ndash;1943 to highlydetailed images of 2017&amp;ndash;2018.</p><p>According to the results of the study, it was possible to establish the position of the coastline for different time periods.The period between 1949 and 1962, when there was the most critical drop in the water level, was especially interestingand had not been studied before. Archival aerial photographs for 1943 and 1963 allowed to reconstruct the position ofthe coastline for almost every year of irrational water use.</p>

scholarly journals How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

2020 ◽  
Vol 12 (3) ◽  
pp. 417 ◽  
Author(s):  
Xin Zhang ◽  
Liangxiu Han ◽  
Lianghao Han ◽  
Liang Zhu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Sensing Data

Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

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