| 1. |
- Guptha, Guru Chythanya, et al.
(författare)
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Evaluation of an urban drainage system and its resilience using remote sensing and GIS
- 2021
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Ingår i: Remote Sensing Applications: Society and Environment. - : Elsevier. - 2352-9385. ; 23
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Tidskriftsartikel (refereegranskat)abstract
- The increasing number of pluvial floods due to extreme climatic events or poor maintenance of the drainage networks urge for assessing the performance of the urban drainage system (UDS). This paper presents a comprehensive evaluation of the UDS of Gurugram City, India. While the limited availability of sub-hourly precipitation and finer resolution geospatial data pose major challenges in the detailed analyses through Storm Water Management Model (SWMM), it was circumvented by utilizing the high-resolution remotely sensed datasets viz., IMERG (half-hourly precipitation data from 2000 to 2019), ALOS PALSAR (Digital Elevation Model) and Sentinel-2 (land use/land cover). Functional failure scenarios (i.e., the combinations of climate change and urbanization) were simulated to assess the impacts on the resilience of the UDS. The modelling results showed that individually, climate change would impose a more serious threat than urbanization, whereas their combinations would significantly hamper the resilience of the UDS. The structural failure (only single link-failure) scenarios were analyzed, and 11 out of 25 conduits were identified to be non-resilient. The study highlights the importance of the readily available remote sensing datasets, which fill the gap of non-availability of ground-based datasets at desirable resolutions, especially in developing countries.
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| 2. |
- Kadhim Al-lami, Ahmed, et al.
(författare)
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Using Vegetation Indices for monitoring the spread of Nile Rose plant in the Tigris River within Wasit province, Iraq
- 2021
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Ingår i: Remote Sensing Applications: Society and Environment. - : Elsevier. - 2352-9385. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- The Nile rose or water hyacinth (Eichhornia crassipes) is an aquatic species threaten socio-economic and ecological systems, by invading freshwater ecosystems, affecting their productivity and functionality, as well as causing unfixable damage to key hydrological processes. Spectral signature differences can play a common role through using remote identification for these invasive plants, by using hyperspectral data, while many other studies showed that textural and phrenological differences are also can be considered as an effective strategy in this critical problem. New generation sensors like Sentinel 2 and Landsat 8 sensors of recently launched crop with improved sensing characteristics, unlike the previous broadband multispectral sensors has been provided untapped prospective alternatives. New insights were introduced in the detecting, mapping, and monitoring the spread of Nile Rose aquatic plant in the Tigris River at Wasit province in Iraq which has caused damage to fishing nets and make it difficult for fishermen to paddle on the river. Vegetation indices have been used to assess the impacts on major socio-economic activities in the study area. Spectral reflectance of Landsat 8 operational land imager OLI (acquired at 6 Oct 2016) was used to differentiate the spectral signature of the water hyacinth from other plants. These indices recorded the highest reflection of the Nile Rose plant relative to the rest of the plants. The result showed that the green Chlorophyll Index (CL Green) with overall accuracy of 89% which proved that this study has established a promising method for monitoring the invasion of the Nile Rose in the Tigris River to insure the availability of safe drinking water as a main source for the people such as in the study area nearby the part of Tigris River.
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| 3. |
- Rudke, A. P., et al.
(författare)
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Mapping past landscapes using landsat data : Upper Paraná River Basin in 1985
- 2021
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Ingår i: Remote Sensing Applications: Society and Environment. - : Elsevier BV. - 2352-9385. ; 21
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Tidskriftsartikel (refereegranskat)abstract
- During the last decades, the science of remote sensing of the Earth's surface has produced an enormous amount of data. In parallel, with the increase in computational capacity, several classification methods have been applied to the satellite retrievals. This timely combination allows recovering more accurate knowledge about the land cover maps of past times. Therefore, the main goal of this work was to develop a land cover product for the year 1985 in the Upper Paraná River Basin (UPRB-1985), one of the largest and most economically important river basins in the world. The land cover map was developed using a supervised classifier - SVM (Support Vector Machine) applied to data from Landsat TM (Thematic Mapper) sensor. The classification process was carried out based on 52 scenes collected during 1985 and a total of 17,040 training samples across the basin. Pixel and Object-based methods were used to classify Landsat scenes. The generated mapping accuracy was assessed using statistical criteria adopted in the literature - Global Accuracy and Kappa Index. The McNemar's test result showed no significant differences (at the 5% level) between the Pixel-based and Object-based classifications, even with the Object-based classification accuracy was slightly higher (Global Accuracy of 79.8%). However, some relationship between the relief and the classification approach was observed. In sub-basins with high slopes, the mean overall accuracy values of the Pixel-based classification approach were 13.1% higher than the Object-based approach. By mapping past land cover, this work is strategic information to understand ongoing processes, as well as to assess changes in land cover that have occurred over time and evaluate to what extent they explain the variability in the hydrology of the region.
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