scholarly journals INTEGRATION OF DATA OF THE REMOTE SENSING, GIS, AND GAMMA-SPECTROMETRIC ANALYSIS TO STUDY SOIL MATERIAL REDISTRIBUTION

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 147-152
Author(s):  
L. N. Trofimetz ◽  
A. A. Kolesnikov ◽  
E. A. Panidi ◽  
P. M. Kikin
Keyword(s):  
Remote Sensing ◽  
Integrated Analysis ◽  
Spectrometric Analysis ◽  
Complex Data ◽  
Soil Material ◽  
Temporary Streams ◽  
Soil Runoff ◽  
High Resolution Satellite Imagery ◽  
Gis Environment ◽  
Map Data

Abstract. The paper discusses a problem of complex data application when accounting erosion network elements to study soil runoff and soil material redistribution on arable slopes. It is needed to estimate and account contribution of microrelief landforms to the sediment (washed out soil material) redistribution on arable areas to enhance accuracy of estimation of the soil runoff and accumulation. However, microrelief landforms are hardly detected on topographic maps and plans used traditionally in land management. For example, temporary streams formed in plowing furrows (in the case of along-slope plowing) can be detected only when survey and soil sampling data are attracted, or (partially) using remote sensing data.Due to such a context, we discover integrated analysis of map data (digital maps represented and processed in GIS environment), data of gamma-spectrometric analysis of the soil samples, and very high resolution satellite imagery, which is aimed onto detection of the role of stable and dynamically changing microrelief landforms in soil material redistribution.

scholarly journals Remote Sensing and GIS for Integrated Modeling and Analysis for Mapping of Groundwater Potential Zones in Sarada River Basin, Visakhapatnam, India

2020 ◽  
Vol 78 ◽  
pp. 1-13
Author(s):  
Nooka Ratnam Kinthada ◽  
Murali Krishna Gurram
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Groundwater Potential ◽  
Remote Sensing And Gis ◽  
Integrated Analysis ◽  
Groundwater Potential Zones ◽  
Gis Technology ◽  
Modeling And Analysis ◽  
Valley Fills ◽  
Gis Environment

The study aimed at identifying and mapping groundwater potential zones in agricultural intensive Sarada river basin using Remote sensing and GIS technology. Zones of water potentiality were mapped integrating various information layers in GIS environment which eventually helped weighted modeling to arrive at the final outcome. Hydrogeomorphic units such as alluvial plains, valley fills, shallow weathered pediplains and deeply weathered pediplainswere mapped. Eventually water potential zones in the basin were mapped and categorised them in to ‘excellent’, ‘good’, ‘moderate’ and ‘poor’. The study highlighted the effective use of Remote sensing and GIS technology for integrated analysis, identification and mapping of the groundwater potential zones in the Sarada river basin.

Assessment and Bibliometric Review of Scientific Achievements Published in the Archives of Photogrammetry, Cartography and Remote Sensing

2019 ◽  
Vol 31 (1) ◽  
pp. 145-166
Author(s):  
Krzysztof Bakuła ◽  
Zdzisław Kurczyński
Keyword(s):  
Remote Sensing ◽  
Statistical Data ◽  
Spatial Information ◽  
Technological Development ◽  
Scientific Work ◽  
Scientific Institutions ◽  
Bibliometric Review ◽  
History Of ◽  
Gis Environment ◽  
Scientific Achievements

Abstract The Archives of Photogrammetry, Cartography and Remote Sensing is a journal which, in the era of technological development of photogrammetry and remote sensing and changes related to cartography in the field of common digitization of sources and processing of spatial information in GIS environment, has been one of the most popular places for publishing articles in this field in Poland for years. Thirty volumes published throughout 25 years have provided nearly 1000 scientific articles and monographic studies summarizing the scientific work of several hundred authors from dozens of scientific institutions and production companies in Poland. This article is an attempt to summarize the achievements published in the journal in the field of bibliometric evaluation and statistical data of the publications from the time of the existence of this inter-association journal. The text quotes the history of the journal, indicates statistics on the number of articles, their citation with the most popular items, authors, reviewers. This evaluation was compared with other national and foreign journals.

scholarly journals A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing

Data ◽  
2021 ◽  
Vol 6 (10) ◽  
pp. 108
Author(s):  
Carmine Gambardella ◽  
Rosaria Parente ◽  
Alessandro Ciambrone ◽  
Marialaura Casbarra
Keyword(s):  
Remote Sensing ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Spectral Characteristics ◽  
Spectral Signature ◽  
Complex Data ◽  
Ancillary Data ◽  
Spectral Angle Mapper ◽  
Visible Images ◽  
Components Analysis

Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania, is the focus of the research proposed by the multidisciplinary group of the Benecon University Consortium. In this study, principal components analysis (PCA) was used to remove redundant spectral information from multiband datasets. This makes it easier to identify the most prevalent spectral characteristics in most bands and those that are specific to only a few bands. The survey and airborne monitoring by hyperspectral sensors is carried out with an Itres CASI 1500 sensor owned by Benecon, characterized by a spectral range of 380–1050 nm and 288 configurable channels. The spectral configuration adopted for the research was developed specifically to maximize the spectral separability of cannabis. The ground resolution of the georeferenced cartographic data varies according to the flight planning, inserted in the aerial platform of an Italian Guardia di Finanza’s aircraft, in relation to the orography of the sites under investigation. The geodatabase, wherein the processing of hyperspectral and visible images converge, contains ancillary data such as digital aeronautical maps, digital terrain models, color orthophoto, topographic data and in any case a significant amount of data so that they can be processed synergistically. The goal is to create maps and predictive scenarios, through the application of the spectral angle mapper algorithm, of the cannabis plantations scattered throughout the area. The protocol consists of comparing the spectral data acquired with the CASI1500 airborne sensor and the spectral signature of the cannabis leaves that have been acquired in the laboratory with ASD Fieldspec PRO FR spectrometers. These scientific studies have demonstrated how it is possible to achieve ex ante control of the evolution of the phenomenon itself for monitoring the cultivation of cannabis plantations.

scholarly journals LANDSLIDE HAZARD ZONATION MAPPING OF CHAMOLI LANDSLIDES IN REMOTE SENSING AND GIS ENVIRONMENT

2018 ◽  
Vol XLII-5 ◽  
pp. 475-480
Author(s):  
M. K. Tripathi ◽  
H. Govil ◽  
P. K. Champati ray ◽  
I. C. Das
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Landslide Hazard ◽  
Information Value ◽  
Slope Aspect ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Thematic Layers ◽  
Zonation Map ◽  
Gis Environment

<p><strong>Abstract.</strong> Landslides are very common problem in hilly terrain. Chamoli region of Himalaya is highest sensitive zone of the landslide hazards. The purpose of Chamoli landslide study, to observe the important terrain factors and parameters responsible for landslide initiation. Lithological, geomorphological, slope, aspect, landslide, drainage density and lineament density map generated in remote sensing and GIS environment. Data information of related geological terrain obtain through topographic maps, remote sensing images, field visits and geological maps. Geodatabases of all thematic layers prepared through digitization of topographic map and satellite imageries (LISS-III, LISS-IV &amp;amp; ASTER DEM). Integrated all thematic layers applying information value method under GIS environment to map the zonation of landslide hazard zonation map validation and verification completed by field visit. The landslide hazard zonation map classified in four classes very high, high, medium and low.</p>

Contribution of urbanization and climate variability on surface water depletion across USA watersheds

2021 ◽  
Author(s):  
Serena Ceola ◽  
Irene Palazzoli
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Water Resources ◽  
Climate Variability ◽  
Water Loss ◽  
The United States ◽  
Future Climate Change ◽  
Integrated Analysis ◽  
World Population ◽  
Surface Water Resources

&lt;p&gt;Surface water resources are extremely vulnerable to climate variability and are seriously threatened by human activities. The depletion of surface water is expected to rapidly increase due to the combination of future climate change and world population growth projections. Under this scenario, the impacts of climate and human dynamics on surface water resources represent a global issue, requiring the definition of adequate management strategies that prevent water crisis and guarantee equitable access to freshwater resources. Remote sensing provides data that allow to monitor environmental change processes, such as changes in climatic conditions, land use, and spatial allocation of human settlements and activities. Although many products describing surface water dynamics and urban growth obtained from satellite imagery are available, an integrated analysis of such geospatial information has not been performed yet. Here, we explore the driving role of the variation in key climatic variables (e.g., &amp;#160;precipitation, temperature, and soil moisture) and the extent of urban areas in the depletion of surface water across the watersheds in the United States by using data derived from remote sensing images and performing a correlation analysis. From our preliminary results, we observe that there is a positive correlation between surface water loss and the level of urbanization in each basin of our study area, meaning that surface water loss increases with the extent of urban area. On the contrary, we find that the correlation between surface water loss and precipitation has a counter-intuitive trend which needs to be further examined.&lt;/p&gt;

Integrated analysis of remote sensing and numerical oil drift simulations for improved oil spill preparedness capabilities

2021 ◽  
Author(s):  
Camilla Brekke ◽  
Martine Espeseth ◽  
Knut-Frode Dagestad ◽  
Johannes Röhrs ◽  
Lars Hole ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soybean Oil ◽  
Oil Spill ◽  
Mineral Oil ◽  
The Arctic ◽  
Optical Data ◽  
Integrated Analysis ◽  
Geophysical Research ◽  
Oil Emulsion ◽  
Detection Capabilities

&lt;p&gt;&lt;strong&gt;Integrated analysis of remote sensing and numerical oil drift simulations for &lt;/strong&gt;&lt;strong&gt;improved&amp;#160;&lt;/strong&gt;&lt;strong&gt;oil spill preparedness capabilities&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Camilla Brekke&lt;sup&gt;1&lt;/sup&gt;, Martine M. Espeseth&lt;sup&gt;1&lt;/sup&gt;, Knut-Frode Dagestad&lt;sup&gt;2&lt;/sup&gt;, Johannes R&amp;#246;hrs&lt;sup&gt;2&lt;/sup&gt;, Lars Robert Hole&lt;sup&gt;2&lt;/sup&gt;, and Andreas Reigber&lt;sup&gt;3&lt;/sup&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;sup&gt;1&lt;/sup&gt;UiT The Arctic University of Norway, Troms&amp;#248;, Norway&lt;/p&gt;&lt;p&gt;&lt;sup&gt;2&lt;/sup&gt;The Norwegian Meteorological Institute, Oslo, Norway&lt;/p&gt;&lt;p&gt;&lt;sup&gt;3&lt;/sup&gt;DLR, Microwaves and Radar Institute, Oberpfaffenhofen-We&amp;#223;ling, Germany&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We present results from a successfully conducted free-floating oil spill field experiment followed by an integrated analysis of remotely sensed data and drift simulations. The experiment took place in the North Sea in the summer of 2019 during Norwegian Clean Seas Association for Operating Companies&amp;#8217; annual oil-on-water exercise. Two types of oils were applied: a mineral oil emulsion and a soybean oil emulsion. The dataset collected contains a collection of close-in-time radar (aircraft and space-borne) and optical data (aircraft, aerostat, and drone) acquisitions of the slicks. We compare oil drift simulations, applying various configurations of wind, wave, and current information, with observed slick positions and shape. We describe trajectories and dynamics of the spills, slick extent, and their evolution, and the differences in detection capabilities in optical instruments versus multifrequency quad-polarimetric synthetic aperture radar (SAR) imagery acquired by DLRs large-scale airborne SAR facility (F-SAR). When using the best available forcing from in situ data and forecast models, good agreement with the observed position and extent are found in this study. The appearance in the optical images and the SAR time series from F-SAR were found to be different between the soybean and mineral oil types. Differences in mineral oil detection capabilities are found between SAR and optical imagery of thinner sheen regions. From a drifting perspective, the biological oil emulsions could replace the viscous similar mineral oil emulsion in future oil spill preparedness campaigns. However, from a remote sensing and wildlife perspective, the two oils have different properties. Depending on the practical application, further investigation on how the soybean oil impact the seabirds must be conducted in order to recommend the soybean oil as a viable substitute for mineral oil.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;This study is published as open access in Journalof Geophysical Research: Oceans[1], and we encourage the audience to read this article for detailed acquaintance with the work.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Reference:&lt;/p&gt;&lt;p&gt;[1]Brekke, C., Espeseth, M. M., Dagestad, K.-F., R&amp;#246;hrs, J., Hole, L. R., &amp; Reigber,A. (2021). Integrated analysis of multisensor datasets and oil driftsimulations&amp;#8212;a free-floating oil experiment in the open ocean. Journalof Geophysical Research: Oceans, 126, e2020JC016499. https://doi.org/10.1029/2020JC016499&lt;/p&gt;

scholarly journals Remote sensing and gis applications in determining shoreline and surface water quality changes in Bayelsa state, Nigeria

1970 ◽  
Vol 10 (5) ◽  
pp. 572-587
Author(s):  
A.O. Adebola ◽  
T.H.T Ogunribido ◽  
S.A. Adegboyega ◽  
M.O. Ibitoye ◽  
A.A Adeseko
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Landsat Imagery ◽  
Surface Water Quality ◽  
Remote Sensing And Gis ◽  
Shoreline Changes ◽  
Gis Applications ◽  
Gis Environment ◽  
Coastal Landform

The study of shoreline changes is essential for updating the changes in shoreline maps and management of natural resources as the shoreline is one of the most important features on the earth’s surface. Shorelines are the key element in coastal GIS that provide information on coastal landform dynamics. The purpose of this paper is to investigate shoreline changes in the study area and how it affects surface water quality using Landsat imagery from 1987 to 2016. The image processing techniques adopted involves supervised classification, object-based image analysis, shoreline extraction and image enhancement. The data obtained was analyzed and maps were generated and then integrated in a GIS environment. The results indicate that LULC changes in wetland areas increases rapidly during the years (1987-2016) from 34.83 to 38.96%, vegetation cover reduces drastically through the year which range from 30% to 20%. Polluted surface water was observed to have decreased from 30% to 20% during 1984-2010 and reduced by about 3% in 2016. In addition, the result revealed the highest level of erosion from 1987 to 2016 which is -49.60% against the highest level of accretion of 13.39% EPR and NSM -1400 erosion against 350 accretions. It was also observed that variations in shoreline changes affect the quality of surface water possibly due to shoreline movement hinterland. This study has demonstrated that through satellite remote sensing and GIS techniques, the Nigerian coastline can adequately be monitored for various changes that have taken place over the years.Key Words: Shoreline, Remote Sensing, Erosion, Accretion, GIS 

scholarly journals Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

2020 ◽  
Vol 12 (16) ◽  
pp. 2626 ◽  
Author(s):  
Qingting Li ◽  
Zhengchao Chen ◽  
Bing Zhang ◽  
Baipeng Li ◽  
Kaixuan Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Spatial Scales ◽  
Detection Accuracy ◽  
Single Shot ◽  
Tailings Dams ◽  
High Resolution Satellite Imagery ◽  
Tailings Ponds

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

Sign in / Sign up

Export Citation Format

Share Document