Wyniki wyszukiwania - Biblioteka Nauki

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River Cross-Section Addition from the Google Earth Engine in Downstream of the Ba River

100%

Nguyen B. D. , Dang T. M.

Inżynieria Mineralna

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2024

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tom Vol. 2, no. 1

35--40

EN

Riverbed depth is the main and essential data to conduct hydrodynamic modeling research. Typically, riverbed topography data is collected directly from cross-sections arranged along the river at relatively distant intervals. This paper presents the results of applying Google Earth Engine technology and high-resolution Sentinel 2 remote sensing images combined with digital elevation model data and field-measured cross-sections to supplement the cross-sections of the downstream Ba River. The reliability of the cross-sections obtained using this technology has been verified against actual measurements at several locations on the mainstream of the Ba River. The research results indicate that most of the interpolated cross-sections are consistent with the actual measured data.

PL

Głównymi i niezbędnymi danymi do prowadzenia badań modelowania hydrodynamicznego jest głębokość koryta rzeki. Zazwyczaj dane o topografii koryta zbierane są bezpośrednio z przekrojów rozmieszczonych wzdłuż rzeki w stosunkowo odległych odstępach. W artykule przedstawiono wyniki zastosowania technologii Google Earth Engine i wysokiej rozdzielczości obrazów teledetekcyjnych Sentinel 2 w połączeniu z danymi cyfrowego modelu wysokości i przekrojami pomierzonymi w terenie w celu uzupełnienia przekrojów poprzecznych dolnego biegu rzeki Ba. Wiarygodność przekrojów uzyskanych tą technologią została zweryfikowana w oparciu o rzeczywiste pomiary w kilku miejscach głównego nurtu rzeki Ba. Wyniki badań wskazują, że większość interpolowanych przekrojów jest zgodna z rzeczywistymi danymi pomiarowymi.

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Spatiotemporal mapping of inundation area at Lake Limboto in Gorontalo, Indonesia, using cloud computing technology

71%

Lahay R. J. , Koem S.

Journal of Water and Land Development

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2022

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tom no. 52

27--33

EN

Monitoring activities on the dynamics of water shrinkage at Lake Limboto are essential to the lake’s ecosystem’s recovery. A remote sensing technology functions to monitor the dynamics of lake inundation area; this allows one to produce a comprehensive set of spatial and temporal data. Such complex satellite dataset demands extra time, greater storage resources, and greater computing capacity. The Google Earth Engine platform emerges as the alternative to tackle such problems. The present study aims to explore the capability of Google Earth Engine in formulating spatial and temporal maps of the inundation area at Lake Limboto. A total of 345 scenes of Landsat image on the study area (available during the period of 1989–2019) were involved in generating a quick inundation area map of the lake. The whole processes (pre-processing, processing, analysing, and evaluating) were automatized by using the Google Earth Engine interface. The evaluation of mapping result accuracy indicated that the average score of F1-score and Intersection over Union (IoU) was at 0.88 and 0.91, respectively. Moreover, the mapping results of the lake’s inundation area from 1989 to 2019 showed that the inundation area tended to decrease significantly in size over time. During the period, the lake’s area also shrank from 3023.8 ha in 1989 to 1275.0 ha in 2019. All in all, the spatiotemporal information about the changes in lake area may be treated as a reference for decision-making processes of lake management in the future.

3

Mapping Environmental Impacts in North-Western Algeria through Multivariate Spatio-Temporal Analysis Using Remote Sensing and Geographic Information System

48%

Mahcer I. , Baahmed D. , Chemirik C. H. K. , Nedjai R.

Ecological Engineering & Environmental Technology

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2024

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tom Vol. 25, iss. 5

42--60

EN

The interactions between the normalised difference vegetation index (NDVI), the normalised difference built-up index (NDBI), and land surface temperature (LST) are complex. The assessment of land use/land cover (LULC) changes in the North-western region of Algeria between 1995 and 2021 confirms the direct influence of these factors on surface thermal processes. The use of new information technologies, particularly remote sensing coupled with GIS, favourably contributes to the processing of a large volume of data and to the use of specific methods aimed at confirming and/or disproving the hypotheses put forward. The application of LULC classification methods clearly highlights the magnitude of transformations, predominantly in favour of intensified urbanisation over the past two decades. Indeed, agricultural lands have experienced a reduction of 17.45%, while urbanised areas have nearly doubled. This phenomenon can, in part, be attributed to the mass migration of populations from inland areas to the coast, not only due to climate change: secondary for political problems between 1990 and 2001. Similarly, barren lands have increased by 10.45%. These changes have real implications for ecosystems (mainly loss of biodiversity) and the climate (pollution, GHG emissions, and rising ambient temperatures). The estimation of average LST from multiple satellite scenes reveals an increasing trend, rising from 36.6 °C in 1995 to 40.35 °C in 2021. The direct relationship between LST and NDVI and between LST and NDBI confirms the close association between land use change and increasing surface temperatures. The Pearson coefficient between LST and NDVI showed a negative correlation, ranging between -0.52 and -0.47, while it was positively correlated between LST and NDBI, with values around 0.66. The emergence of hotspots in the region, confirmed by the results of analysis employing the Getis-Ord G* method, is marked by clearly increasing spatial envelopes. This phenomenon is associated with a distinct reduction in vegetation cover density, coupled with an increased vulnerability to drought conditions. These initial results argue in favour of preserving green and blue networks and, more largely, ecosystems.