2,935 resources related to MODIS
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All fields of satellite, airborne and ground remote sensing.
To promote awareness, understanding, advancement and application of ocean engineering and marine technology. This includes all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.
The scope of the conference are astronomy and astrophysics, atmospheric and magnetospheric sciences, geosciences and remote sensing, satellite and communication technology and interdisciplinary space sciences.
The conference scope would be covering latest technological advances and research results in the fields of theoretical, experimental and applied signal, image and video processing. Hence, IEEE ICSIPA conference seeks original high quality submissions addressing innovative research in the broad field of signal, image and video processing. These broad fields include but not limited to 1) Acquisition, Storage, Retrieval and Display, 2) Computer Vision Processing and Analysis, 3) Information Forensics and Security, 4) Biomedical Signal Processing, 5)Applied Signal and Speech Processing, and 6) Emerging Technologies
ISAPE, a serial symposium on antennas, propagation, and EM theory, offers an active forum for exchanging creative ideas and experiences on the latest developments and designs in the areas of antennas, propagation, and electromagnetic theory for professors, researchers, engineers, and excellent students all over the world.
It is expected that GRS Letters will apply to a wide range of remote sensing activities looking to publish shorter, high-impact papers. Topics covered will remain within the IEEE Geoscience and Remote Sensing Societys field of interest: the theory, concepts, and techniques of science and engineering as they apply to the sensing of the earth, oceans, atmosphere, and space; and ...
Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.
The Field of Interest of the IEEE Sensors Journal is the science and applications of sensing phenomena, including theory, design, and application of devices for sensing and transducing physical, chemical, and biological phenomena. The emphasis is on the electronics, physics, biology, and intelligence aspects of sensors and integrated sensor-actuators. (IEEE Guide for Authors) (The fields of interest of the IEEE ...
IEEE Transactions on Geoscience and Remote Sensing, 2005
IEEE Expert, 1995
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Sea ice concentration (SIC) is an important sea ice parameter of the atmosphere-ice-ocean system in the polar region. Daily 6.25 km AMSR-E/AMSR2 SIC from Bremen University (UB) is one of the widely used SIC products. In this paper, MODIS data and aerial image are used to validate this product. The results show that the daily mean AMSR-E ASI products underestimate ...
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
A new multilayer IST-albedo Moderate Resolution Imaging Spectroradiometer (MODIS) product of Greenland was developed to meet the needs of the ice sheet modeling community. The multiple layers of the product enable the relationship between IST and albedo to be evaluated easily. Surface temperature is a fundamental input for dynamical ice sheet models because it is a component of the ice ...
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010
Canopy spectral invariant variables, escape probability and recollision probability, are wavelength independent and intrinsic canopy structure properties. They provide a physical interpretation of the correlation between canopy architecture and multi-angle spectral data. The 500m Moderate resolution Imaging Spectroadiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product from study sites at Howland Forest, Maine are used to develop multivariate linear regression models ...
Sea ice concentration (SIC) is an important sea ice parameter of the atmosphere-ice-ocean system in the polar region. Daily 6.25 km AMSR-E/AMSR2 SIC from Bremen University (UB) is one of the widely used SIC products. In this paper, MODIS data and aerial image are used to validate this product. The results show that the daily mean AMSR-E ASI products underestimate SICs about 17.9% based on the aerial image, and underestimate SICs about 8.5% based on MODIS image. The sea ice extent (SIE) and sea ice area (SIA) which are derived from SIC by ASI algorithm, Dynamic Tie-point ASI algorithm (DT-ASI) as well as NT algorithm are compared.
A new multilayer IST-albedo Moderate Resolution Imaging Spectroradiometer (MODIS) product of Greenland was developed to meet the needs of the ice sheet modeling community. The multiple layers of the product enable the relationship between IST and albedo to be evaluated easily. Surface temperature is a fundamental input for dynamical ice sheet models because it is a component of the ice sheet radiation budget and mass balance. Albedo influences absorption of incoming solar radiation. The daily product will combine the existing standard MODIS Collection-6 ice-surface temperature, derived melt maps, snow albedo and water vapor products. The new product is available in a polar stereographic projection in NetCDF format. The product will ultimately extend from March 2000 through the end of 2017.
Canopy spectral invariant variables, escape probability and recollision probability, are wavelength independent and intrinsic canopy structure properties. They provide a physical interpretation of the correlation between canopy architecture and multi-angle spectral data. The 500m Moderate resolution Imaging Spectroadiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product from study sites at Howland Forest, Maine are used to develop multivariate linear regression models to estimate canopy vertical structure using both escape probabilities and directional reflectance. These are compared with canopy height information which has been retrieved from the airborne Laser Vegetation Imaging Sensor (LVIS) at a finer scale spatial resolution. Both the escape probability and the directional reflectance approaches achieve similar results with correlation coefficients of 0.63-0.66. This suggests that the MODIS 500m BRDF data can be useful in extrapolating limited lidar information on canopy vertical structure to larger regional areas.
Satellite images can improve the possibilities for the detection of oil spills as they cover large areas and offer an economical and easier way of continuous coast areas patrolling. There are many common techniques to detect dark formations on the SAR images. This paper mainly focuses on method with spot feature extraction and global thresholding. The main approach used in this paper is detecting the dark spots, using local and global threshold algorithms. For each dark spot, a number of features are calculated in order to classify the slick as either oil or other possible geographical or natural components of water. The proposed threshold algorithm, initially analyzes the SAR images, and then assigns a probability to the dark spot to indicate whether it is an oil spill or look alike.
Remote Sensing provides the only practical means to monitor changes over large areas. This paper describes the development of a generic algorithm designed to map the temporal occurrence and spatial extent of areas exhibiting sudden change. The algorithm is demonstrated here applied to the problem of mapping fire-affected areas. The research further develops the work of, which implemented a bi-directional reflectance (BRDF) model-based change detection algorithm to map the approximate day and location of burning, using daily 500 m MODIS surface reflectance data. An original algorithm assumption is that the surface state remains static prior to the changes of interest. This is problematic in the presence of underlying change (for example, due to vegetation phenology) especially when there are missing and/or cloudy data. In an attempt to deal with this issue, an additional kernel has been added to the BRDF model in the form of a cubic function of time. In addition, a step function kernel has been introduced in order to more robustly detect step-like changes. These modifications and preliminary results over southern Africa using daily MODIS land surface reflectance data are presented
Utilization of satellite date from multiple platforms increases our chances of more frequent and accurate observations of the Earth's surface in both global and regional scale For the purpose of vegetation monitoring, this will be particularly true by combining the data from sensors of various spatial, spectral, and temporal resolutions, e.g. the combinations of data from AVHRR (broad band), MODIS (narrow band) and ETM+ (higher spatial resolution). Even though the same spectral vegetation index can be obtained from these sensors, the two main issues need to be considered, one is the systematic differences caused by the spectral response functions, and the other is the differences in spatial resolutions. This paper investigates the spectral issue and its role in the spectral calibration of NDVI among sensors. Hyperspectral data from Hyperion onboard the EO-1 platform were used to simulate outputs from various sensors by band convolution. The data were initially corrected for Rayleigh scattering and Ozone absorption to produce the top-of-the-canopy reflectance as a starting point. The technique first designs a sensor-specific vegetation index (VI) and background brightness index (BI) by accounting for the differences in band-pass filters. These VIs and BIs are then used to estimate the common parameters (sensor independent parameters) attributed to vegetation amount and background brightness. Finally, these parameters are used for the translation of VI among sensors.
In this paper, the internal operations of an Extended Kalman Filter is investigated to see if any useful information can be derived to detect land cover change in a MODIS time series. The Extended Kalman Filter expands its internal covariance if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human settlements, with a corresponding false alarm rate below 11%.
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