Готові списки джерел за темами / Remote Sensing; Landsat-8 OLI; Water Indices

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Добірка наукової літератури з теми "Remote Sensing; Landsat-8 OLI; Water Indices"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Remote Sensing; Landsat-8 OLI; Water Indices"

Al-Fartusi, Adel Jassim, Mutasim Ibrahim Malik, and Hameed Majeed Abduljabbar. "Utilizing Spectral Indices to Estimate Total Dissolved Solids in Water Body Northwest Arabian Gulf." ILMU KELAUTAN: Indonesian Journal of Marine Sciences 28, no. 3 (2023): 217–24. http://dx.doi.org/10.14710/ik.ijms.28.3.217-224.

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Remote sensing techniques have made it possible to monitor an important parameter of water quality, total dissolved solids (TDS), more appropriately and regularly. The research aims to assess Landsat 8 OLI images' ability to expose TDS on the sea surface in Iraqi marine waters. six band combinations were employed in the correlation analysis between band values and six dissolved solids samples collected during the fieldwork in three sampling stations to determine the amount of total dissolved solids (TDS): st1, st2, and st3 in December 2014 (26,38,36.9) g.L-1 and mid-January 2022 (27.4,37.9,37)

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Ghofrani, Zahra, Victor Sposito, and Robert Faggian. "Improving flood monitoring in rural areas using remote sensing." Water Practice and Technology 14, no. 1 (2018): 160–71. http://dx.doi.org/10.2166/wpt.2018.118.

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Abstract Precise information on the extent of inundated land is required for flood monitoring, relief, and protective measures. In this paper, two spectral indices, Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), were used to identify inundated areas during heavy rainfall events in the Tarwin catchment, Victoria, Australia, using Landsat-8 OLI imagery. By integrating the assessed condition of levees, this research also explains the inefficiency of the flood control measures of this region of Australia. NDWI and MNDWI indices performed well, but

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Synan, Haley E., Brian L. Howes, Sara Sampieri, and Steven E. Lohrenz. "Water Quality Monitoring Using Landsat 8 OLI in Pleasant Bay, Massachusetts, USA." Remote Sensing 17, no. 4 (2025): 638. https://doi.org/10.3390/rs17040638.

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Water quality monitoring is essential to assess and manage anthropogenic eutrophication, especially for coastal estuaries in heavily populated areas. Current monitoring techniques rely on in situ sampling, which can be expensive and limited in spatial and temporal coverage. Satellite remote sensing, using the Landsat 8 (Operational Land Imager, OLI) platform, has the potential to provide more extensive coverage than traditional methods. Coastal waters are optically more complex and often shallower and more enclosed than the open ocean, presenting conditions that pose challenges to remote sensi

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Chen, Jie, Yankun Wang, Jingzhe Wang, et al. "The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis." Remote Sensing 16, no. 11 (2024): 1984. http://dx.doi.org/10.3390/rs16111984.

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The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comp

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Mancino, Giuseppe, Agostino Ferrara, Antonietta Padula, and Angelo Nolè. "Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment." Remote Sensing 12, no. 2 (2020): 291. http://dx.doi.org/10.3390/rs12020291.

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Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comp

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Moradi, M., M. Sahebi, and M. Shokri. "MODIFIED OPTIMIZATION WATER INDEX (MOWI) FOR LANDSAT-8 OLI/TIRS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 185–90. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-185-2017.

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Water is one of the most important resources that essential need for human life. Due to population growth and increasing need of human to water, proper management of water resources will be one of the serious challenges of next decades. Remote sensing data is the best way to the management of water resources due time and cost effectiveness over a greater range of temporal and spatial scales. Between many kinds of satellite data, from SAR to optic or from high resolution to low resolution, Landsat imagery is more interesting data for water detection and management of earth surface water. Landsa

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Zhao, Minyan, and Fiachra O’Loughlin. "Mapping Irish Water Bodies: Comparison of Platforms, Indices and Water Body Type." Remote Sensing 15, no. 14 (2023): 3677. http://dx.doi.org/10.3390/rs15143677.

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Accurate monitoring of water bodies is essential for the management and regulation of water resources. Traditional methods for measuring water quality are always time-consuming and expensive; furthermore, it can be very difficult capture the full spatiotemporal variations across regions. Many studies have shown the possibility of remote-sensing-based water monitoring work in many areas, especially for water quality monitoring. However, the use of optical remotely sensed imagery depends on several factors, including weather, quality of images and the size of water bodies. Hence, in this study,

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Yousefian, F., M. Sahebi, M. Shokri, and M. Moradi. "A NOVEL WATER INDEX (SWI) FOR SALTY WATER FROM LANDSAT 8 OLI/TIRS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 1097–105. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-1097-2019.

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Abstract. Monitoring natural resources is one of the most important tasks in earth observation and remote sensing satellites. Water resources play a crucial role in the life of human on the planet. Among the water resources, salty lakes are of particular importance in biological, physical and environmental issues. In this study, a new Salty Water Index (SWI) for Landsat 8 Operational Land Imager (OLI) images is proposed based on salty lakes by particle swarm optimization (PSO), where water doesn’t combine by cloud, shadow, and salty areas. SWI is implemented on four famous and important salty

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Emmanuel, Chigozie Dike* Bright Godfrey Ameme Evangeline Nkiruka Le-ol Anthony. "Comparative Analysis of Multi-Spectral Shoreline Delineation Using Landsat-8, Sentinel-2, and PlanetScope Imageries in Coastal Environments of Nigeria." International Journal of Scientific Research and Technology 2, no. 2 (2025): 159–74. https://doi.org/10.5281/zenodo.14907334.

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Changes in the shoreline position are a result of climate change-induced sea level rise and morphological changes caused by coastal processes. The delineation of shoreline positions relies on robust techniques and data sources, with remote sensing being particularly advantageous due to its cost-effectiveness and technological advancements. The study focuses on the eastern Niger Delta region of Nigeria, utilising mid-resolution multispectral datasets from Landsat-8 OLI, Sentinel-2 MSI, and PlanetScope to compare shoreline positions derived from different water indices (NDVI and NDWI) and classi

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Mail, Abd Al Salam Mohammed. "Desertification Detected in the Udhaim River Basin, Iraq Based on Spectral Indices Derived from Remote Sensing Images." Miscellanea Geographica 21, no. 3 (2017): 124–31. http://dx.doi.org/10.1515/mgrsd-2017-0007.

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AbstractIn this study, changes in Land Use Land Cover (LULC) have been investigated over the Udhaim River Basin in Iraq by using spectral indices. NDVI, NDBI, NDWI, NDBaI, and CI represent respectively the vegetation, built-up, water bodies, bare-land, and soil crust of LULC. Two different images were acquired for the analysis, namely a Landsat 5 TM image from 1 July 2007 and a Landsat 8 OLI from 5 June 2015, both representing summer conditions. Results show that the percentages of vegetated land and water body areas have decreased. On the contrary, the percentages of built-up, bare land and s

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Більше джерел

Дисертації з теми "Remote Sensing; Landsat-8 OLI; Water Indices"

Blackmore, Debra Sue. "Use of Water Indices Derived from Landsat OLI Imagery and GIS to Estimate the Hydrologic Connectivity of Wetlands in the Tualatin River National Wildlife Refuge." Thesis, Portland State University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10191067.

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<p> This study compared two remote sensing water indices: the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI). Both indices were calculated using publically-available data from the Landsat 8 Operational Land Imager (OLI). The research goal was to determine whether the indices are effective in locating open water and measuring surface soil moisture. To demonstrate the application of water indices, analysis was conducted for freshwater wetlands in the Tualatin River Basin in northwestern Oregon to estimate hydrologic connectivity and hydrological permanence between these w

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Bernardo, Nariane Marselhe Ribeiro. "A semianalytical algorithm to retrieve the suspended particulate matter in a cascade reservoir system with widely differing optical properties /." Presidente Prudente, 2019. http://hdl.handle.net/11449/190950.

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Orientador: Enner Herenio de Alcântara<br>Resumo: O Material Particulado em Suspensão (MPS) é o principal componente em sistemas aquáticos. Elevadas concentrações de MPS implicam na atenuação da luz, e ocasionam alterações das taxas fotossintéticas. Além disso, a presença de MPS no sistema aquático pode aumentar os níveis de turbidez, absorver poluentes e podem ser considerados como um indicativo de descargas de escoamento superficial. Portanto, monitorar as concentrações de MPS é essencial para a gerar informações técnicas que subsidiem o correto manejo dos recursos aquáticos, prevenindo cola

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Частини книг з теми "Remote Sensing; Landsat-8 OLI; Water Indices"

Geng, Haoran, and Jing Zhang. "Inversion Study of Baiyangdian Water Quality Parameters Based on Different Remote Sensing Data Sources." In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde230308.

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Water environment monitoring is very important for the synergistic development of humans and nature, and remote sensing provides a method that can monitor water quality in a large area, at low cost and high frequency. However, the spectral resolution and temporal resolution vary among different satellites, so it is especially important to find the satellite remote sensing data suitable for the study area. In this paper, taking Baiyangdian as an example, we collected and compiled the measured water quality data from 4 stations of Baiyangdian from 2021 to 2022, as well as Landsat 8-9 OLI and Sen

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Seenipandi, Kaliraj, K. K. Ramachandran, Prashant Ghadei, and Sulochana Shekhar. "Seasonal variability of sea surface temperature in Southern Indian coastal water using Landsat 8 OLI/TIRS images." In Remote Sensing of Ocean and Coastal Environments. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-819604-5.00016-0.

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Seenipandi, Kaliraj, K. K. Ramachandran, Prashant Ghadei, and Sulochana Shekhar. "Ocean remote sensing of suspended sediment variability in Southern Indian coastal water region using Landsat 8 OLI images." In Remote Sensing of Ocean and Coastal Environments. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-819604-5.00017-2.

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Seenipandi, Kaliraj, K. K. Ramachandran, Prashant Ghadei, and Sulochana Shekhar. "Ocean remote sensing for seasonal predictability of phytoplankton (chl-a) biomass in the Southern Indian coastal water region using Landsat 8 OLI images." In Remote Sensing of Ocean and Coastal Environments. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-819604-5.00003-2.

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Kumar, Nirmal, S. K. Singh, G. P. Obi Reddy, and R. K. Naitam. "Developing Logistic Regression Models to Identify Salt-Affected Soils Using Optical Remote Sensing." In Interdisciplinary Approaches to Information Systems and Software Engineering. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7784-3.ch010.

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A major part of Indo-Gangetic plain is affected with soil salinity/alkalinity. Information on spatial distribution of soil salinity is important for planning management practices for its restoration. Remote sensing has proven to be a powerful tool in quantifying and monitoring the development of soil salinity. The chapter aims to develop logistic regression models, using Landsat 8 data, to identify salt affected soils in Indo-Gangetic plain. Logistic regression models based on Landsat 8 bands and several salinity indices were developed, individually and in combination. The bands capable of dif

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Misbari, S., J. I. A. Gisen, and M. Hashim. "DETECTION AND QUANTIFICATION OF SUBMERGED SEAGRASS TOTAL ABOVEGROUND BIOMASS CHANGES IN TINGGI ISLAND, JOHOR USING REMOTE SENSING DATA." In Construction Engineering and Management. PENERBIT UNIVERSITI MALAYSIA PAHANG, 2022. http://dx.doi.org/10.15282/cem.1.04.2022.02.05.

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Tinggi Island area is gazetted as Johor Marine Park while unpredictable natural phenomenon claimed as a primary threat that caused reduction of submerged seagrass total aboveground biomass (SSTAGB). This study aims to (a) detect and quantify SSTAGB in clear water of the Tinggi Island area using satellite data, and (b) assess SSTAGB changes in 2009 and 2014. Algorithm of Bottom Reflectance Index is implemented on Landsat 8 OLI image of 2009 and 2014 to detect and study spatial distribution of multi-species submerged seagrass. Field data sampling was conducted to validate the classified satellit

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Тези доповідей конференцій з теми "Remote Sensing; Landsat-8 OLI; Water Indices"

pu, zhengpeng, Le Liu, Wen Zou, et al. "Construction and application of a comprehensive urban water ecological index based on landsat 8-9 OLI remote sensing data: a case study of the Hefei urban area." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070615.

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Alkhatlan, A., A. Bannari, T. S. Ali, A. Abahussain, and N. Hameid. "Mapping Submerged Aquatic Vegetation in Shallow Water of Arabian Gulf Using Water Spectral Indices, Field Observations and Landsat-OLI Data." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898883.

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Da Silva, Gabriel M., Egidio Arai, Tânia B. Hoffmann, et al. "Land use and Land Cover Classification in São Paulo, Brazil, Using Landsat-8 Oli Images and Derived Spectral Indices." In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023. http://dx.doi.org/10.1109/igarss52108.2023.10283440.

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Mondal, Rajdeep, Jit Mukherjee, and Jayanta Mukhopadhyay. "Automated Coastline Detection from Landsat 8 Oli/Tirs Images with the Presence of Inland Water Bodies in Andaman." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9324366.

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Mishra, Vikash K., and T. Pant. "Monitoring the Change in Water Class of Two Rivers in Sangam Region, Prayagraj, India using Landsat-8 OLI Imagery." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8897973.

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Rivani, Anggia, and Pramaditya Wicaksono. "Water Trophic Status Mapping of Tecto-Volcanic Maninjau Lake during Algae Bloom using Landsat 8 OLI Satellite Imagery." In 2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES). IEEE, 2018. http://dx.doi.org/10.1109/icares.2018.8547055.

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Mukherjee, Jit, Jayanta Mukhopadhyay, and Debashish Chakravarty. "Investigation of Seasonal Separation in Mine and Non Mine Water Bodies Using Local Feature Analysis of Landsat 8 OLI/TIRS Images." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8517579.

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Yin, Zhikui, Xiang Xie, Wenming Duan, Dongdong Zhang, Zhengkun Zhang, and Huan Li. "Long time series monitoring method of soil and water loss based on Landsat 8 OLI satellite remote sensing data." In International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), edited by Chuan Qin and Huiyu Zhou. SPIE, 2024. http://dx.doi.org/10.1117/12.3035423.

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Lanera, Pasquale, Luigi Dattola, Francesco Sante Rende, et al. "Comparison of Sentinel-2 and Landsat-8 OLI satellite images vs. high spatial resolution images (MIVIS and WorldView-2) for mapping Posidonia oceanica meadows." In Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018, edited by Charles R. Bostater, Stelios P. Mertikas, and Xavier Neyt. SPIE, 2018. http://dx.doi.org/10.1117/12.2326798.

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Dudumashe, N., and A. Thomas. "ASSESSMENT OF LAND SURFACE TEMPERATURE AND DROUGHT INDICES FOR THE KLERKSDORP-ORKNEY-STILFONTEIN-HARTEBEESFONTEIN (KOSH) REGION." In Лесные экосистемы в условиях изменения климата: биологическая продуктивность и дистанционный мониторинг. Crossref, 2020. http://dx.doi.org/10.25686/7230.2020.6.58828.

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Land surface temperature (LST) is a key calculator of local climate, vegetation growth, and urban change. Spatial and temporal variation of LST over land use/land cover (LULC) features results in changes in environmental factors that influence the characteristics of the land surface. In this study, some remote sensing techniques have been applied to Landsat 8 data acquired during summer and spring seasons of years 2019, 2018, and 2013 to estimate normalized difference vegetation index (NDVI), LST, normalized difference built-up index (NDBI), three drought indices viz. vegetation supply water i

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Звіти організацій з теми "Remote Sensing; Landsat-8 OLI; Water Indices"

Johansen, Richard A., Christina L. Saltus, Molly K. Reif, and Kaytee L. Pokrzywinski. A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing. U.S. Army Engineer Research and Development Center, 2022. http://dx.doi.org/10.21079/11681/44523.

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Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25

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