321 research outputs found

    Removal of Thin Clouds in Landsat-8 OLI Data with Independent Component Analysis

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    An approach to remove clouds in Landsat-8 operational land imager (OLI) data was developed with independent component analysis (ICA). Within cloud-covered areas, histograms were derived to quantify changes of the reflectance values before and after the use of the algorithm. Referred to a cloud-free image, changes of histogram curves validated the algorithm. Scatterplots were generated and linear regression performed for the reflectance values of each band before and after the algorithm, and compared to those of the reference image. Band-by-band, results in cloud removal were acceptable. The algorithm had little effect on pixels in cloud-free areas after the analyses of histograms, scatterplots, and linear regression equations. Finally, the algorithm was applied to various land use and land cover types and cloud conditions, and to a full Landsat-8 scene yielding satisfactory results efficiently

    Cloud removal from optical remote sensing images

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    Optical remote sensing images used for Earth surface observations are constantly contaminated by cloud cover. Clouds dynamically affect the applications of optical data and increase the difficulty of image analysis. Therefore, cloud is considered as one of the sources of noise in optical image data, and its detection and removal need to be operated as a pre-processing step in most remote sensing image processing applications. This thesis investigates the current cloud detection and removal algorithms and develops three new cloud removal methods to improve the accuracy of the results. A thin cloud removal method based on signal transmission principles and spectral mixture analysis (ST-SMA) for pixel correction is developed in the first contribution. This method considers not only the additive reflectance from the clouds but also the energy absorption when solar radiation passes through them. Data correction is achieved by subtracting the product of the cloud endmember signature and the cloud abundance and rescaling according to the cloud thickness. The proposed method has no requirement for meteorological data and does not rely on reference images. The experimental results indicate that the proposed approach is able to perform effective removal of thin clouds in different scenarios. In the second study, an effective cloud removal method is proposed by taking advantage of the noise-adjusted principal components transform (CR-NAPCT). It is found that the signal-to-noise ratio (S/N) of cloud data is higher than data without cloud contamination, when spatial correlation is considered and are shown in the first NAPCT component (NAPC1) in the NAPCT data. An inverse transformation with a modified first component is then applied to generate the cloud free image. The effectiveness of the proposed method is assessed by performing experiments on simulated and real data to compare the quantitative and qualitative performance of the proposed approach. The third study of this thesis deals with both cloud and cloud shadow problems with the aid of an auxiliary image in a clear sky condition. A new cloud removal approach called multitemporal dictionary learning (MDL) is proposed. Dictionaries of the cloudy areas (target data) and the cloud free areas (reference data) are learned separately in the spectral domain. An online dictionary learning method is then applied to obtain the two dictionaries in this method. The removal process is conducted by using the coefficients from the reference image and the dictionary learned from the target image. This method is able to recover the data contaminated by thin and thick clouds or cloud shadows. The experimental results show that the MDL method is effective from both quantitative and qualitative viewpoints

    Landsat Program

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    Landsat initiated the revolution in moderate resolution Earth remote sensing in the 1970s. With seven successful missions over 40+ years, Landsat has documented - and continues to document - the global Earth land surface and its evolution. The Landsat missions and sensors have evolved along with the technology from a demonstration project in the analog world of visual interpretation to an operational mission in the digital world, with incremental improvements along the way in terms of spectral, spatial, radiometric and geometric performance as well as acquisition strategy, data availability, and products

    Mapping areas of Great Artesian Basin diffuse discharge

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    Lake Eyre Basin Springs AssessmentDorothy Turner, Kenneth Clarke, Davina White & Megan Lewi

    Mapping methods and observations of surficial snow/ice cover at Redoubt and Pavlof volcanoes, Alaska using optical satellite imagery

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    Thesis (M.S.) University of Alaska Fairbanks, 2014Alaska is a natural laboratory for the study of how active volcanism interacts with underlying seasonal snow, perennial snow, and glacial ice cover. While over half of the historically active volcanoes in Alaska have some degree of perennial snow or glacial ice, all Alaskan volcanoes have a covering of seasonal snow for a period of time throughout the year. Previous research has centered on how volcanic deposits erode away the underlying snow/ice cover during an eruption, producing volcanic mudflows called lahars. Less emphasis has been placed on how variations in the snow/ice cover substrate effect the efficiency of meltwater generation during a volcanic eruption. Glacial ice, perennial snow, and seasonal snow can all contribute significantly to meltwater, and therefore the variations in the types of snow/ice cover present at Alaskan volcanoes must be analyzed. By examining the changing spatial extent of seasonal snow present at a volcano during multiple Alaskan summers, the approximate boundaries of perennial snow and ice can be mapped as the snow/ice cover consistently present at the end of each ablation season. In this study, two methods of snow/ice cover mapping for Redoubt and Pavlof volcanoes are analyzed for efficiency and accuracy. Identification of the best method allows for mapping of the snow/ice cover consistently present during each Alaskan summer month over at least two different years. These maps can serve as approximations for the snow/ice cover likely to be present at both volcanoes during each summer month. Volcanic deposits produced during the 2009 Redoubt and 2013 Pavlof eruptions are spatially linked to these snow/ice cover maps so that future research can focus on the interaction between deposits and type of snow/ice substrate. Additional observations and conclusions are made regarding how the visible snow/ice cover varies during and after each eruption.Chapter 1. Introduction -- 1.1. Background -- 1.2. Comparison of snow/ice cover mapping methods for Alaskan volcanoes -- 1.3. Mapping snow/ice on Redoubt and Pavlof during quiescence and eruption -- 1.4. Summary of final outcomes -- 1.5. References -- Chapter 2. Methods for snow/ice cover mapping of Redoubt and Pavlof volcanoes using optical satellite imagery -- 2.1. Introduction -- 2.1.1. Satellite remote sensing of glaciers and snow cover in Alaska -- 2.1.2. Previous work and methods for studying snow/ice on volcanoes -- 2.1.3. Challenges of mapping snow/ice cover at Alaskan volcanoes -- 2.2. Setting of Redoubt volcano -- 2.2.1. Basic setting of Redoubt volcano -- 2.3. Setting of Pavlof volcano -- 2.3.1. Basic setting of Pavlof volcano -- 2.4. Methods -- 2.4.1. Previous work in snow/ice cover mapping using satellite imagery -- 2.4.2. Sensors used for snow/ice cover mapping -- 2.4.3. Pre-processing of satellite imagery -- 2.4.4. Methods used to map snow/ice cover at Redoubt and Pavlof -- 2.4.5. Technique 1: band ratios -- 2.4.6. Technique 2: principal component analysis -- 2.4.7. Technique 3: linear spectral unmixing -- 2.5. Results and discussion -- 2.5.1. Snow/ice cover mapping using threshold method -- 2.5.2. Snow/ice cover mapping using linear spectral unmixing method -- 2.5.3. Improvements to linear spectral unmixing method for snow/ice cover mapping -- 2.5.4. Validation of results -- 2.6. Conclusion -- 2.7. Figures -- 2.8. Tables -- 2.9. References -- Chapter 3. Observations of surficial snow/ice cover changes due to seasonal and eruptive influences on Redoubt and Pavlof volcanoes, Alaska using optical remote sensing -- 3.1. Introduction -- 3.1.1. Alaskan volcanoes -- 3.2. Volcano-snow/ice interactions -- 3.2.1. Short term interactions -- 3.2.2. Long term interactions -- 3.2.3. Lahar formation and hazards -- 3.2.4. Influence of snow/ice substrate type on lahar generation -- 3.3. Background on Redoubt volcano -- 3.3.1. Setting of Redoubt volcano -- 3.3.2. Recent eruptions at Redoubt volcano -- 3.3.3. Eruption effects on Drift Glacier -- 3.3.4. Lahar hazards at Redoubt volcano -- 3.4. Background on Pavlof volcano -- 3.4.1. Setting of Pavlof volcano -- 3.4.2. Recent eruptions at Pavlof volcano -- 3.4.3. Lahar hazards at Pavlof volcano -- 3.5. Methods -- 3.5.1. Sensors used to create Products 1, 2, and 3 -- 3.5.2. Methods used to produce Product 1: individual snow/ice cover maps -- 3.5.3. Methods used to produce Product 2: snow/ice cover summary maps -- 3.5.4. Methods used to produce Product 3: composite maps of eruptive deposits and snow/ice cover -- 3.6. Results and discussion -- 3.6.1. Product 1: individual snow/ice cover maps of Redoubt subset -- 3.6.2. Product 2: snow/ice cover summary maps of Redoubt subset -- 3.6.3. Product 3: composite maps of eruptive deposits and snow/ice cover of Redoubt subset -- 3.6.4. Product 1: individual snow/ice cover maps of Pavlof subset -- 3.6.5. Product 2: snow/ice cover maps of Pavlof subset -- 3.6.6. Product 3: composite maps of eruptive deposits and snow/ice cover of Pavlof subset -- 3.7. Conclusion -- 3.8. Figures -- 3.9. Tables -- 3.10. References -- Chapter 4. Conclusion -- 4.1. Comparison of snow/ice cover mapping methods for Alaskan volcanoes -- 4.2. Mapping snow/ice on Redoubt and Pavlof during quiescence and eruption -- 4.3. Limitations and future work -- 4.4. References

    Remote sensing technology applications in forestry and REDD+

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    Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

    Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

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    Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable

    Enhancing Landsat time series through multi-sensor fusion and integration of meteorological data

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    Over 50 years ago, the United States Interior Secretary, Stewart Udall, directed space agencies to gather "facts about the natural resources of the earth." Today global climate change and human modification make earth observations from all variety of sensors essential to understand and adapt to environmental change. The Landsat program has been an invaluable source for understanding the history of the land surface, with consistent observations from the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors since 1982. This dissertation develops and explores methods for enhancing the TM/ETM+ record by fusing other data sources, specifically, Landsat 8 for future continuity, radar data for tropical forest monitoring, and meteorological data for semi-arid vegetation dynamics. Landsat 8 data may be incorporated into existing time series of Landsat 4-7 data for applications like change detection, but vegetation trend analysis requires calibration, especially when using the near-infrared band. The improvements in radiometric quality and cloud masking provided by Landsat 8 data reduce noise compared to previous sensors. Tropical forests are notoriously difficult to monitor with Landsat alone because of clouds. This dissertation developed and compared two approaches for fusing Synthetic Aperture Radar (SAR) data from the Advanced Land Observation Satellite (ALOS-1) with Landsat in Peru, and found that radar data increased accuracy of deforestation. Simulations indicate that the benefit of using radar data increased with higher cloud cover. Time series analysis of vegetation indices from Landsat in semi-arid environments is complicated by the response of vegetation to high variability in timing and amount of precipitation. We found that quantifying dynamics in precipitation and drought index data improved land cover change detection performance compared to more traditional harmonic modeling for grasslands and shrublands in California. This dissertation enhances the value of Landsat data by combining it with other data sources, including other optical sensors, SAR data, and meteorological data. The methods developed here show the potential for data fusion and are especially important in light of recent and upcoming missions, like Sentinel-1, Sentinel-2, and NASA-ISRO Synthetic Aperture Radar (NISAR)
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