Temat: landsat - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A study of the climate and human impact on the future survival of the Al-Sannya marsh in Iraq
Autorzy:: Hatem, Amal Jabbar
Al-Jasim, Ali Adnan N.
Abduljabbar, Hameed Majeed
Powiązania:: https://bibliotekanauki.pl/articles/2048498.pdf
Data publikacji:: 2021
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: climate change
human impact
Landsat 5
Landsat 8
maximum likelihood classification
south of Iraq
Opis:: The marshes are the most abundant water sources and ecological rich communities. They have a significant impact on the ecological and economic well-being of the communities surrounding them. However, climatic changes directly impact these bodies of water, especially those marshes which depend on rainwater and flooding for their survival. The Al-Sannya marsh is used as the example of marshes in Southern Iraq for this study between 1987-2017. The research takes place throughout the winter season due to the revival of marshes in southern Iraq at this time of year. The years 1987, 1990, 1995, 2000, 2007, 2014, 2017 are the focus of this study. Satellite imagery from the Landsat 5 (TM) and Landsat 8 (OLI) and the meteorological parameters affecting the marsh were acquired from NASA. The calculation of the areas of water bodies after classification using satellite imagery is done using the maximum likelihood method and comparing it with meteorological parameters. These results showed that these marshes are facing extinction due to the general change of climate and the interference of humans in utilising the drylands of the marsh for agricultural purposes. The vegetation area can be seen to have decreased from 51.15 km2 in 2000 to 8.77 km2 in 2017.
Źródło:: Journal of Water and Land Development; 2021, 51; 168-173
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A study of the climate and human impact on the future survival of the Al-Sannya marsh in Iraq
Autorzy:: Hatem, Amal Jabbar
Al-Jasim, Ali Adnan N.
Abduljabbar, Hameed Majeed
Powiązania:: https://bibliotekanauki.pl/articles/2048508.pdf
Data publikacji:: 2021
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: climate change
human impact
Landsat 5
Landsat 8
maximum likelihood classification
south of Iraq
Opis:: The marshes are the most abundant water sources and ecological rich communities. They have a significant impact on the ecological and economic well-being of the communities surrounding them. However, climatic changes directly impact these bodies of water, especially those marshes which depend on rainwater and flooding for their survival. The Al-Sannya marsh is used as the example of marshes in Southern Iraq for this study between 1987-2017. The research takes place throughout the winter season due to the revival of marshes in southern Iraq at this time of year. The years 1987, 1990, 1995, 2000, 2007, 2014, 2017 are the focus of this study. Satellite imagery from the Landsat 5 (TM) and Landsat 8 (OLI) and the meteorological parameters affecting the marsh were acquired from NASA. The calculation of the areas of water bodies after classification using satellite imagery is done using the maximum likelihood method and comparing it with meteorological parameters. These results showed that these marshes are facing extinction due to the general change of climate and the interference of humans in utilising the drylands of the marsh for agricultural purposes. The vegetation area can be seen to have decreased from 51.15 km2 in 2000 to 8.77 km2 in 2017.
Źródło:: Journal of Water and Land Development; 2021, 51; 168-173
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Evaluating four remote sensing based models to estimate latent heat flux in semi-arid climate for heterogeneous surface coverage of western Algeria
Autorzy:: Oualid, Tewfik A.
Hamimed, Abderahmane
Khaldi, Abdelkader
Powiązania:: https://bibliotekanauki.pl/articles/2174320.pdf
Data publikacji:: 2022
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: Algeria
energy balance
evapotranspiration
Landsat
METRIC
SPARSE
TIM
TSEB
Opis:: Optimal estimation of water balance components at the local and regional scales is essential for many applications such as integrated water resources management, hydrogeological modelling and irrigation scheduling. Evapotranspiration is a very important component of the hydrological cycle at the soil surface, particularly in arid and semi-arid lands. Mapping evapotranspiration at high resolution with internalised calibration (METRIC), trapezoid interpolation model (TIM), two-source energy balance (TSEB), and soil-plant-atmosphere and remote sensing evapotranspiration (SPARSE) models were applied using Landsat 8 images for four dates during 2014-2015 and meteorological data. Surface energy maps were then generated. Latent heat flux estimated by four models was then compared and evaluated with those measured by applying the method of Bowen ratio for the various days. In warm periods with high water stress differences and with important surface temperature differences, METRIC proves to be the most robust with the root-mean-square error (RMSE) less than 40 W∙m-2. However, during the periods with no significant surface temperature and soil humidity differences, SPARSE model is superior with the RMSE of 35 W∙m-2. The results of TIM are close to METRIC, since both models are sensitive to the difference in surface temperature. However, SPARSE remains reliable with the RMSE of 55 W∙m-2 unlike TSEB, which has a large deviation from the other models. On the other hand, during the days when the temperature difference is small, SPARSE and TSEB are superior, with a clear advantage of SPARSE serial version, where temperature differences are less important.
Źródło:: Journal of Water and Land Development; 2022, 55; 259--275
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Object-oriented classification of Landsat ETM+ satellite image
Klasyfikacja obiektowa zdjęcia satelitarnego Landsat ETM+
Autorzy:: Lewiński, S.
Powiązania:: https://bibliotekanauki.pl/articles/293332.pdf
Data publikacji:: 2006
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: klasyfikacja obiektowa
Landsat ETM+
użytkowanie ziemi
object-oriented classification
land use
Opis:: This paper presents results of object-oriented classification of Landsat ETM+ satellite image conducted using eCognition software. The classified image was acquired on 7 May 2000. In this particular study, an area of 423 km² within the borders of Legionowo Community near Warsaw is considered. Prior to classification, segmentation of the Landsat ETM+ image is performed using panchromatic channel, fused multispectral and panchromatic data. The applied methods of classification enabled the identification of 18 land cover and land use classes. After the classification, generalization and raster to vector conversion, verification and accuracy assessment are performed by means of visual interpretation. Overall accuracy of the classification reached 94.6%. The verification and classification results are combined to form the final database. This is followed by comparing the object-oriented with traditional pixel-based classification. The latter is performed using the so-called hybrid classification based on both supervised and unsupervised classification approaches. The traditional pixel-based approach identified only 8 classes. Comparison of the pixel-based classification with the database obtained using the object-oriented approach revealed that the former reached 72% and 61% accuracy, according to the applied method.
W artykule przedstawiono wyniki klasyfikacji obiektowej zdjęcia satelitarnego Landsat ETM+, uzyskane z zastosowaniem specjalistycznego oprogramowania eCognition. Klasyfikację wykonano na przykładzie zdjęcia zarejestrowanego 7 maja 2000 r., obrazującego obszar badawczy o powierzchni 423 km², znajdujący się w granicach powiatu legionowskiego w pobliżu Warszawy. Proces klasyfikacji obiektowej polega na rozpoznaniu obiektów, którymi są grupy pikseli spełniające założone kryterium jednorodności. Granice obiektów zostały zdefiniowane w czasie segmentacji zdjęcia, wykonanej na podstawie wartości pikseli kanału panchromatycznego skanera ETM+ oraz danych uzyskanych w wyniku połączenia wybranych kanałów wielospektralnych z kanałem panchromatycznym. Zastosowane metody klasyfikacyjne, związane nie tylko z wartościami spektralnymi charakteryzującymi poszczególne obiekty, lecz również z kryteriami parametrycznymi, umożliwiły identyfikację 18 klas pokrycia i użytkowania ziemi. Następnie wynik klasyfikacji został przetworzony funkcjami: generalizacji, konwersji formatu danych oraz poddany weryfikacji. Generalizację wykonano z zastosowaniem jednostki odniesienia wynoszącej 1 ha dla klas zabudowy i wody oraz 4 ha dla pozostałych klas. Następnie format klasyfikacji został zmieniony z rastrowego na wektorowy, w którym wykonano wygładzenie granic wydzieleń. Klasyfikacja została zakończona weryfikacją wektorowej bazy danych metodą interpretacji wizualnej. Całkowita dokładność klasyfikacji została oceniona na poziomie 94.6%. Po uwzględnieniu zmian wprowadzonych w czasie weryfikacji uzyskano końcową postać bazy danych. Wyniki klasyfikacji obiektowej zostały porównane z wynikami tradycyjnej (pikselowej) klasyfikacji, wykonanej z zastosowaniem algorytmu tzw. klasyfikacji hybrydowej, składającej się z następujących po sobie klasyfikacji nadzorowanej i nienadzorowanej. Rozpoznano jedynie 8 klas pokrycia i użytkowania ziemi. Dokładność tradycyjnej klasyfikacji oceniono przez porównanie jej z wynikami klasyfikacji obiektowej. Uzyskano wyniki na niskim poziomie, wynoszącym jedynie 72% i 61%, w zależności od przyjętej metody oceny.
Źródło:: Journal of Water and Land Development; 2006, 10; 91-106
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Spatiotemporal mapping of inundation area at Lake Limboto in Gorontalo, Indonesia, using cloud computing technology
Autorzy:: Lahay, Rakhmat Jaya
Koem, Syahrizal
Powiązania:: https://bibliotekanauki.pl/articles/2073728.pdf
Data publikacji:: 2022
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: Google Earth Engine
Lake Limboto
lake shrinkage
Landsat
remote sensing
water index
Opis:: 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.
Źródło:: Journal of Water and Land Development; 2022, 52; 27--33
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Extraction of urban construction development with using Landsat satellite images and geoinformation systems
Autorzy:: Arifjanov, Aybek M.
Akmalov, Shamshodbek B.
Samiev, Luqmon N.
Powiązania:: https://bibliotekanauki.pl/articles/1844383.pdf
Data publikacji:: 2021
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: ArcGIS
geographic information system
GIS
Landsat satellite image
remote sensing
RS
urban
Opis:: In recent times there have been many changes on Earth, which have appeared after anthropogenic impact. Finding solutions to problems in the environment requires studying the problems quickly, make proper conclusions and creating safe and useful measures. Humanity has always had an effect on the environment. There can be many changes on the Earth because of direct and indirect effects of humans on nature. Determining these changes at the right time and organizing meas-urements of them requires the creation of quick analysing methods. This development has improved specialists’ interest for remote sensing (RS) imagery. Moreover, in accordance with analysis of literature sources, agriculture, irrigation and ecology have the most demand for RS imagery. This article is about using geographic information system (GIS) and RS technologies in cadastre and urban construction branches. This article covers a newly created automated method for the calculation of artificial surface area based on satellite images. Accuracy of the analysis is verified according to the field experiments. Accuracy of analysis is 95%. According to the analysis from 1972 to 2019 artificial area enlargement is 13.44%. This method is very simple and easy to use. Using this data, the analysis method can decrease economical costs for field measures. Using this method and these tools in branches also allows for greater efficiency in time and resources.
Źródło:: Journal of Water and Land Development; 2021, 48; 65-69
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: GIS and geodata contribution to the cartographic modelling of blue-green infrastructure in urbanised areas
Autorzy:: Czyża, Szymon
Kowalczyk, Anna M.
Powiązania:: https://bibliotekanauki.pl/articles/28411648.pdf
Data publikacji:: 2023
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: blue-green infrastructure
drainage system
GIS
Landsat 8 images
map
reference databases
retention
spatial analysis
Opis:: The development of cities and peri-urban areas is exerting an increasingly strong impact on the natural environment and, at the same time, on the living conditions and health of people. Problems and challenges that need to be addressed include increasing air pollution in these areas, formation of a surface urban heat island (SUHI), water management disruptions (water scarcity or excess), and the destruction of natural habitats. One of the solutions that contributes to climate change mitigation is the introduction of blue-green infrastructure into the city space and urbanised areas. The research objective was to identify spatial features (geodata) that determine the optimum location of selected blue-green infrastructure (BGI) components, acquire them, and then use the Geographical Information System (GIS) to determine their optimum locations. As the first step, cartographic models were developed which indicated areas that enable the development of selected blue-green infrastructure components in the Olsztyn city area, Warmińsko-Mazurskie Province, Poland. The models were juxtaposed with other two models developed by the authors, i.e. a surface urban heat island model and a demographic model that showed the age structure of the city’s population. Consequently, maps with potential locations for the blue-green infrastructure were developed, while taking into account reference data from the National Land Surveying and Cartographic Resource and Landsat 8 images. Keywords: blue-green infrastructure, drainage system, GIS, Landsat 8 images, map, reference databases, retention, spatial analysis
Źródło:: Journal of Water and Land Development; 2023, 59; 183--194
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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