54,476 research outputs found
Planet X probe: A fresh new look at an old familiar place
Planet X Probe utilizes a Get Away Special (GAS) payload to provide a large student population with a remote Earth sensing experimental package. To provide a cooperative as well as a competitive environment, the effort is targeted at all grade levels and at schools in different geographical regions. LANDSAT capability allows students to investigate the Earth, its physical makeup, its resources, and the impact of man. This project also serves as an educational device to get students to stand back and take a fresh look at their home planet. The key element is to treat the familiar Earth as an unknown planet with knowledge based only on what is observable and provable from the images obtained. Through participation, a whole range of experiences will include: (1) mission planning; (2) research and pilot projects to train teams; (3) identification and recruitment of scientific mentors and dialogue; (4) selection of a student advisory team to be available during the mission; (5) analysis of data and compilation of findings; (6) report preparation, constucted along sound scientific principles; and (7) presentation and defense of findings before a meeting of competitive student groups and scientist in the field
Space-Based Lasers for Remote Sensing Applications
There are currently three operational lidar systems orbiting the Earth, the Moon and the planet Mercury gathering scientific data and images to form a better understanding of our Earth and solar system. In this paper we will present an overview of the spacebome laser programs and offer insights into future spacebome lasers for remote sensing applications
THE ROLE OF GIS AND REMOTE SENSING IN MAPPING THE DISTRIBUTION OF GREENHOUSE GASES
Technology offers a means to assess, plan, and implement sustainable programmes that can affect us into the future. A GIS-based framework helps gain a scientific understanding of the earth at a truly global scale. GIS with updated data helps people to know what happens in our planet, how climate takes place and where impacts of climate change affect people. Remote sensing was also identified as a foundational technology. Tying in remote sensing technologies and data with GIS is a powerful combination of understanding spatial patterns in the earth’s ever changing surface. Combining Remote sensing information in a GIS allows us to track, model, and observe climate trends across the planet’s surface. (Jack Dangermond, 2010)
Beam scanning offset Cassegrain reflector antennas by subreflector movement
In 1987 a NASA panel recommended the creation of the Mission to Planet Earth. This mission was intended to apply to remote sensing experience of the space community to earth remote sensing to enhance the understanding of the climatological processes of our planet and to determine if, and to what extent, the hydrological cycle of Earth is being affected by human activity. One of the systems required for the mission was a wide scanning, high gain reflector antenna system for use in radiometric remote sensing from geostationary orbit. This work describes research conducted at Virginia Tech into techniques for beam scanning offset Cassegrain reflector antennas by subreflector translation and rotation. Background material relevant to beam scanning antenna systems and offset Cassegrain reflector antenna system is presented. A test case is developed based on the background material. The test case is beam scanned using two geometrical optics methods of determining the optimum subreflector position for the desired scanned beam direction. Physical optics far-field results are given for the beam scanned systems. The test case system is found to be capable of beam scanning over a range of 35 half-power beamwidths while maintaining a 90 percent beam efficiency or 50 half-power beamwidths while maintaining less than l dB of gain loss during scanning
FOREWORD: Satellite Remote Sensing Beyond 2015
Satellite remote sensing has progressed tremendously since the first Landsat was launched on June 23, 1972. Since the 1970s, satellite remote sensing and associated airborne and in situ measurements have resulted in vital and indispensable observations for understanding our planet through time. These observations have also led to dramatic improvements in numerical simulation models of the coupled atmosphere-land-ocean systems at increasing accuracies and predictive capability. The same observations document the Earth's climate and are driving the consensus that Homo sapiens is changing our climate through greenhouse gas emissions. These accomplishments are the combined work of many scientists from many countries and a dedicated cadre of engineers who build the instruments and satellites that collect Earth observation data from satellites, all working toward the goal of improving our understanding of the Earth. This edition of the Remote Sensing Handbook (Vol. I, II, and III) is a compendium of information for many research areas of our Planet that have contributed to our substantial progress since the 1970s. Remote sensing community is now using multiple sources of satellite and in situ data to advance our studies, what ever they might be. In the following paragraphs, I will illustrate how valuable and pivotal role satellite remote sensing has played in climate system study over last five decades, The Chapters in the Remote Sensing Handbook (Vol. I, II, and III) provides many other specific studies on land, water, and other applications using EO data of last five decades, The Landsat system of Earth-observing satellites has led the way in pioneering sustained observations of our planet. From 1972 to the present, at least one and sometimes two Landsat satellites have been in operation. Starting with the launch of the first NOAA-NASA Polar Orbiting Environmental Satellites NOAA-6 in 1978, improved imaging of land, clouds, and oceans and atmospheric soundings of temperature were accomplished. The NOAA system of polar-orbiting meteorological satellites has continued uninterrupted since that time, providing vital observations for numerical weather prediction. These same satellites are also responsible for the remarkable records of sea surface temperature and land vegetation index from the Advanced Very High Resolution Radiometers (AVHRR) that now span more than 33 years, although no one anticipated these valuable climate records from this instrument before the launch of NOAA-7 in 1981. The success of data from the AVHRR led to the design of the MODIS instruments on NASA's Earth Observing System of satellite platforms that improved substantially upon the AVHRR. The first of the EOS platforms, Terra, was launched in 2000 and the second of these platforms, Aqua, was launched in 2002
Satellite-Respondent Buoys Identify Ocean Debris
NASA operates a series of Earth-observing satellites, which help scientists learn more about our home planet. Through partnerships with universities and other government agencies, like the National Oceanic and Atmospheric Administration (NOAA), the Space Agency helps scientists around the world capture precise movements of the Earth s crust to learn more about the underground processes related to earthquakes and volcanic eruptions, create accurate assessments of wind resources for future energy use, and preserve endangered species by generating much-needed data about their environments. This work, done primarily from space with satellites using a variety of complex instruments to take readings of the surface below, generates leagues of valuable data that aid scientists on the ground - or in some cases on the water. As much of the Earth is covered in water liquid, frozen, saltwater, or fresh much of NASA s remote sensing work focuses on the oceans and their health. This valuable, mammoth (yet fragile) resource provides insight into the overall health of our planet, as water, in addition to being abundant, is a key ingredient to all known life on Earth. As part of its ocean-observing work, NASA partnered with NOAA and private industry to develop remote sensing technologies for protecting the seas of the North Pacific from a nefarious and pervasive problem: derelict fishing gear
Report of the panel on lithospheric structure and evolution, section 3
The panel concluded that NASA can contribute to developing a refined understanding of the compositional, structural, and thermal differences between continental and oceanic lithosphere through a vigorous program in solid Earth science with the following objectives: determine the most fundamental geophysical property of the planet; determine the global gravity field to an accuracy of a few milliGals at wavelengths of 100 km or less; determine the global lithospheric magnetic field to a few nanoTeslas at a wavelength of 100 km; determine how the lithosphere has evolved to its present state via acquiring geologic remote sensing data over all the continents
An Overview of Deep Learning Networks for Remote Sensing Applications
To study and understand the world around us, remote sensing specialists rely on aerial and satellite photographs. Today, deep learning models necessitating extensive data or specialised data are employed in many remote sensing applications. Sometimes, the spatial and spectral resolution of Observation satellites of the planet earth will fall short of requirements due to technological constraints in optics and sensors, as well as the expensive expense of upgrading sensors and equipment. Insufficient information might reduce a model's efficiency. The efficiency of deep learning frameworks that rely on data can be improved by the use of a adversarial networks, which is a type of technique that can generate synthetic data. This is one of the best innovative developments in Deep Learning in past decade. GANs have seen rapid adoption and widespread success in the Remote Sensing sector. GANs can also perform picture-to-image translation, such as clearing cloud cover from a satellite image.This paper aims to investigate the applications of different Adversarial Networks in the remote sensing area and the databases used for training of GANs and metrics of evaluation
EarthNets: Empowering AI in Earth Observation
Earth observation (EO), aiming at monitoring the state of planet Earth using
remote sensing data, is critical for improving our daily lives and living
environment. With a growing number of satellites in orbit, an increasing number
of datasets with diverse sensors and research domains are being published to
facilitate the research of the remote sensing community. This paper presents a
comprehensive review of more than 500 publicly published datasets, including
research domains like agriculture, land use and land cover, disaster
monitoring, scene understanding, vision-language models, foundation models,
climate change, and weather forecasting. We systematically analyze these EO
datasets from four aspects: volume, resolution distributions, research domains,
and the correlation between datasets. Based on the dataset attributes, we
propose to measure, rank, and select datasets to build a new benchmark for
model evaluation. Furthermore, a new platform for EO, termed EarthNets, is
released to achieve a fair and consistent evaluation of deep learning methods
on remote sensing data. EarthNets supports standard dataset libraries and
cutting-edge deep learning models to bridge the gap between the remote sensing
and machine learning communities. Based on this platform, extensive
deep-learning methods are evaluated on the new benchmark. The insightful
results are beneficial to future research. The platform and dataset collections
are publicly available at https://earthnets.github.io.Comment: 30 page
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