The Experts below are selected from a list of 61383 Experts worldwide ranked by ideXlab platform
Yong Yao Lee - One of the best experts on this subject based on the ideXlab platform.
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Daily imaging scheduling of an earth Observation Satellite
IEEE Transactions on Systems Man and Cybernetics Part A:Systems and Humans., 2005Co-Authors: Wei-cheng Lin, Chung Yang Liu, Da Yin Liao, Yong Yao LeeAbstract:This work presents the development of a daily imaging scheduling system for a low-orbit, Earth Observation Satellite. The daily imaging scheduling problem of Satellite considers various imaging requests with different reward opportunities, changeover efforts between two consecutive imaging tasks, cloud-coverage effects, and the availability of the spacecraft resource. It belongs to a class of single-machine scheduling problems with salient features of sequence-dependent setup, job assembly, and the constraint of operating time windows. The scheduling problem is formulated as an integer-programming problem, which is NP-hard in computational complexity. Lagrangian relaxation and linear search techniques are adopted to solve this problem. In order to demonstrate the efficiency and effectiveness of our solution methodology, a Tabu search-based algorithm is implemented, which is modified from the algorithm in Vasquez and Hao, 2001. Numerical results indicate that the approach is very effective to generate a near-optimal, feasible schedule for the imaging operations of the Satellite. It is efficient in applications to the real problems. The Lagrangian-relaxation approach is superior to the Tabu search one in both optimality and computation time.
Da Yin Liao - One of the best experts on this subject based on the ideXlab platform.
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Imaging order scheduling of an earth Observation Satellite
IEEE Transactions on Systems Man and Cybernetics Part C: Applications and Reviews, 2007Co-Authors: Da Yin Liao, Yu Tsung YangAbstract:This paper presents the research and development of an imaging order scheduler for FORMOSAT-2, an Earth Observation Satellite taking images of ocean and landmass in the vicinity of Taiwan according to customers' requests. The quality of Satellite image pictures depends heavily on the posture and position of the spacecraft as well as on the weather condition of the target area when taking the images. The Satellite imaging order scheduler considers current and future weather conditions to provide an imaging plan that satisfies customer requirements with quality imaging pictures. The Satellite imaging order scheduling problem is formulated as a stochastic integer programming problem. We adopt the rolling horizon approach to solve this problem. A nominal plan is generated based on the forecast weather conditions. This nominal plan is adjusted with the updated information during its execution. Lagrangian relaxation is used to solve for a nominal imaging plan. Numerical results indicate that our imaging order scheduler is effective and efficient to generate good imaging plans for realistic problems. We also conduct experiments to further explore the algorithmic features. Our analysis of variance (ANOVA) study concludes that the performance of our solution algorithm significantly depends on the number of jobs, as well as the rewards of order completion. However, the effect caused by weather conditions is not obvious.
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Daily imaging scheduling of an earth Observation Satellite
IEEE Transactions on Systems Man and Cybernetics Part A:Systems and Humans., 2005Co-Authors: Wei-cheng Lin, Chung Yang Liu, Da Yin Liao, Yong Yao LeeAbstract:This work presents the development of a daily imaging scheduling system for a low-orbit, Earth Observation Satellite. The daily imaging scheduling problem of Satellite considers various imaging requests with different reward opportunities, changeover efforts between two consecutive imaging tasks, cloud-coverage effects, and the availability of the spacecraft resource. It belongs to a class of single-machine scheduling problems with salient features of sequence-dependent setup, job assembly, and the constraint of operating time windows. The scheduling problem is formulated as an integer-programming problem, which is NP-hard in computational complexity. Lagrangian relaxation and linear search techniques are adopted to solve this problem. In order to demonstrate the efficiency and effectiveness of our solution methodology, a Tabu search-based algorithm is implemented, which is modified from the algorithm in Vasquez and Hao, 2001. Numerical results indicate that the approach is very effective to generate a near-optimal, feasible schedule for the imaging operations of the Satellite. It is efficient in applications to the real problems. The Lagrangian-relaxation approach is superior to the Tabu search one in both optimality and computation time.
Rochelle Schneider Dos Santos - One of the best experts on this subject based on the ideXlab platform.
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estimating spatio temporal air temperature in london uk using machine learning and earth Observation Satellite data
International Journal of Applied Earth Observation and Geoinformation, 2020Co-Authors: Rochelle Schneider Dos SantosAbstract:Abstract Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urban–rural air temperature (Ta) difference, thus generating the Urban Heat Island (UHI) phenomenon. Ta is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of Ta in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum Ta (Tmax) at high spatio-temporal resolution using Earth Observation (EO) Satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of Tmax for London at 1 km2 during the summertime between 2006 and 2017 using advanced statistical techniques and Satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate Tmax for an urban area. In this work, 6 regression models were calibrated with 6 Satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict Tmax: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R² = 0.68, MAE = 1.60 °C, and RMSE = 2.03 °C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of Tmax, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population.
Chandra Giri - One of the best experts on this subject based on the ideXlab platform.
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global land cover mapping using earth Observation Satellite data recent progresses and challenges
Isprs Journal of Photogrammetry and Remote Sensing, 2015Co-Authors: Yifang Ban, Peng Gong, Chandra GiriAbstract:Global land cover mapping using earth Observation Satellite data : recent progresses and challenges
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Status and distribution of mangrove forests of the world using earth Observation Satellite data
Global Ecology and Biogeography, 2011Co-Authors: Chandra Giri, E. Ochieng, Larry L Tieszen, Tracey Loveland, A. Singh, Z Zhu, N. DukeAbstract:Aim Our scientific understanding of the extent and distribution of mangrove forests of the world is inadequate. The available global mangrove databases, com- piled using disparate geospatial data sources and national statistics, need to be improved.Here,we mapped the status and distributions of global mangroves using recently available Global Land Survey (GLS) data and the Landsat archive. Methods We interpreted approximately 1000 Landsat scenes using hybrid super- vised and unsupervised digital image classification techniques. Each image was normalized for variation in solar angle and earth–sun distance by converting the digital number values to the top-of-the-atmosphere reflectance.Ground truth data and existing maps and databases were used to select training samples and also for iterative labelling. Results were validated using existing GIS data and the published literature to map ‘true mangroves’. Results The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world’s mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude. Main conclusions We report that the remaining area of mangrove forest in the world is less than previously thought. Our estimate is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations.We present the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created.We developed and used better mapping techniques and data sources and mapped mangroves with better spatial and thematic details than previous studies.
Wei-cheng Lin - One of the best experts on this subject based on the ideXlab platform.
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Daily imaging scheduling of an earth Observation Satellite
IEEE Transactions on Systems Man and Cybernetics Part A:Systems and Humans., 2005Co-Authors: Wei-cheng Lin, Chung Yang Liu, Da Yin Liao, Yong Yao LeeAbstract:This work presents the development of a daily imaging scheduling system for a low-orbit, Earth Observation Satellite. The daily imaging scheduling problem of Satellite considers various imaging requests with different reward opportunities, changeover efforts between two consecutive imaging tasks, cloud-coverage effects, and the availability of the spacecraft resource. It belongs to a class of single-machine scheduling problems with salient features of sequence-dependent setup, job assembly, and the constraint of operating time windows. The scheduling problem is formulated as an integer-programming problem, which is NP-hard in computational complexity. Lagrangian relaxation and linear search techniques are adopted to solve this problem. In order to demonstrate the efficiency and effectiveness of our solution methodology, a Tabu search-based algorithm is implemented, which is modified from the algorithm in Vasquez and Hao, 2001. Numerical results indicate that the approach is very effective to generate a near-optimal, feasible schedule for the imaging operations of the Satellite. It is efficient in applications to the real problems. The Lagrangian-relaxation approach is superior to the Tabu search one in both optimality and computation time.
