Investigating anthropogenically transformed landscapes with remote sensing

Belenok, V. Yu.; Noszczyk, Tomasz; Hebryn-Baidy, Liliia; Kryachok, Serhiy

doi:10.1016/j.rsase.2021.100635

Cited by 16 publications

(21 citation statements)

References 43 publications

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“…Among the existing modern methods of obtaining information, the most efficient one is remote sensing from space (Keshava, 2004;Richards and Jia, 2006;Bofana et al, 2020). The information obtained in this way not only facilitates continuous monitoring of urban areas and controlling potentially dangerous areas, but also creating databases of space images from different times, which are the basis for statistical research, modeling, assessment, and forecasting of the condition of urban areas (Zatserkovny et al, 2019;Martins et al, 2020;Luo, Tong and Pan, 2021;Belenok, Noszczyk, Hebryn-Baidy and Kryachok, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…There is a man-made burden on the landscapes of Ukraine due to environmental pollution by chemical, metallurgical and mining industries, nuclear and thermal power plants, sugar factories, vehicles, land reclamation systems, etc. Industry affects landscapes mainly through their direct destruction, especially mining (quarries, dumps, heaps), and as a result of emissions of pollutants into the atmosphere and hydrosphere, which are spread over the atmosphere over long distances, and enter almost all landscapes (Sokolovskaya, 2014;Belenok, Noszczyk, Hebryn-Baidy and Kryachok, 2021). According to Academician Vernadsky, man is currently one of the landscape-forming factors, responsible for transforming all natural landscapes.…”

Section: Introductionmentioning

confidence: 99%

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Geoinformation Mapping of Anthropogenically Transformed Landscapes of Bila Tserkva (Ukraine)

Belenok¹,

Hebryn-Baidy²,

Bіelousova³

et al. 2022

ASP.FC

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Aim of the study: The main purpose of the study is the analysis and assessment of anthropogenically transformed landscapes of Bila Tserkva (Ukraine) based on a combination of remote sensing methods and GIS mapping Material and methods: Usage of geoinformatics methods for mapping anthropogenically transformed landscapes of Bila Tserkva is studied. The data was downloaded and processed using the Semi-Automatic Classification Plugin QGIS for the supervised classification of remote sensing data. Satellite images were radiometrically calibrated and atmospherically corrected, followed by a controlled classification with signature creation, visualization of spectral profiles, quality assessment and post-processing Results and conclusions: The main methods of landscape research are analyzed. The conclusion is made about the expediency of using spectrophotometry of satellite images in order to identify different types of landscapes based on satellite data. An supervised classification of satellite images different-time images was performed, as a result of which the main Bila Tserkva landscape types were identified. Those identified types are: water bodies, vegetation (grass, forest, parks) urban areas and bare soils. Spatio-temporal changes of landscapes are studied and these changes are described in quantitative indicators

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geoinformation Mapping of Anthropogenically Transformed Landscapes of Bila Tserkva (Ukraine)

Belenok¹,

Hebryn-Baidy²,

Bіelousova³

et al. 2022

ASP.FC

View full text Add to dashboard Cite

show abstract

“…In contrast, the regularities are clearly outlined in the studied habitats, and the agricultural area shows a homogeneous picture since the harvest of the arable field shows a negative NDVI value [30,31]. Paths formed due to anthropogenic factors can also be detected with the help of satellite data, which has already been examined [33] based on the NDVI index. Of the indices used in our study, MNDWI proved to be the most suitable for detecting roads.…”

Section: Discussionmentioning

confidence: 92%

“…The dominant taxon in the more closed stands habitat (H2b) was Festuca pseudovina, which also indicates the degradation of the vegetation [14,32]. The occurrence of Festuca rupicola suggests cooler environmental conditions and climatic effects [33]. Complex patches appear in the deeper areas (D34), mostly characteristic of individual Hungarian habitats [1,34,35]; and wet, swampy, marshy, or water-bound vegetation (D34) patches appear in the depressions on-site [36][37][38].…”

Section: Discussionmentioning

confidence: 98%

Habitat Changes along Ipoly River Valley (Hungary) in Extreme Wet and Dry Years

Járdi

Saláta

Falusi

et al. 2022

Water

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The Ipoly Valley is a natural habitat along the Ipoly River, only slightly affected by water management; therefore, this is an especially sensitive area and reflects well the vegetation changes in the driest (2020) and wettest (2010) years. The study’s aim is to identify natural changes within habitats and the evaluation of habitat types’ boundaries. For the study, a hand-held GPS device was applied on-site. The habitat identification is based on the General National Habitat Classification System (Á-NÉR). In addition to on-site data, Sentinel-2A satellite data were used to compare different extreme years 2020 and 2021—changes in different habitat patches using different vegetation indices. A change in precipitation causes shifts in the vegetation, this is shown on a map. As we predicted, decreasing the precipitation results in a decrease of the area of wet habitat patches. The satellite image shows a more accurate picture of the real location of the associations, which is important for long-term research. In addition, we can get accurate data on the situation of areas (roads and paths) affected by anthropogenic factors.

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“…RS data contain valuable knowledge on the earth's surface that can be coupled with image classification techniques to extract the LULC information [19]. In addition, with the advent of image processing technologies using GIS, image classification methods have made a significant advancement in the conduct of LULC mapping [20,21] including assessment and identification of LULC change [22]. Undoubtedly, the LULC maps are some of the most important documents that provide information for various applications in land use policy development, agricultural monitoring, urban planning, ecosystem services, conservation, and LULC dynamic assessment [23].…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery

Tempa

Aryal²

2022

SN Appl. Sci.

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The study of land use land cover has become increasingly significant with the availability of remote sensing data. The main objective of this study is to delineate geohazard-prone areas using semi-automatic classification technique and Sentinel-2 satellite imagery in Bhutan. An open-source, semi-automatic classification plugin tools in QGIS software enabled efficient and rapid conduct of land cover classification. Band sets 2-8, 8A, and 11-12 are utilized and the virtual colour composites have been used for the clustering and creation of training samples or regions of interest. An iterative self-organizing data analysis technique is used for clustering and the image is classified by a minimum distance algorithm in the unsupervised classification. The Random Forest (RF) classifier is used for the supervised classification. The unsupervised classification shows an overall accuracy of 85.47% (Kappa coefficient = 0.71) and the RF classifier resulted in an accuracy of 92.62% (Kappa coefficient = 0.86). A comparison of the classification shows a higher overall accuracy of the RF classifier with an improvement of 7.15%. The study highlights 35.59% (512,100 m2) of the study area under the geohazard-prone area. The study also overlaid the major landslide polygons to roughly validate the landslide hazards. Article highlights Semi-automatic classification technique was applied to delineate the geohazard-prone area in the heterogeneous region of Bhutan Himalaya. Unsupervised and supervised classification technique were used to perform land cover classification using the semi-automatic classification plugin (SCP). The Random Forest classifier predicted higher accuracy and the application is rapid and efficient compared to the unsupervised classification.

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Investigating anthropogenically transformed landscapes with remote sensing

Cited by 16 publications

References 43 publications

Geoinformation Mapping of Anthropogenically Transformed Landscapes of Bila Tserkva (Ukraine)

Geoinformation Mapping of Anthropogenically Transformed Landscapes of Bila Tserkva (Ukraine)

Habitat Changes along Ipoly River Valley (Hungary) in Extreme Wet and Dry Years

Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery

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