Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

PubMed

Bradbury, Kyle; Saboo, Raghav; L Johnson, Timothy; Malof, Jordan M; Devarajan, Arjun; Zhang, Wuming; M Collins, Leslie; G Newell, Richard

2016-12-06

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The data assimilation problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three-dimensional concentration fields from atmospheric diffusion models. General conditions were derived for the reconstructability of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data was developed.

Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three dimensional concentration fields from atmospheric diffusion models. General conditions are derived for the "reconstructability' of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data is developed.

Planetary Remote Sensing Science Enabled by MIDAS (Multiple Instrument Distributed Aperture Sensor)

NASA Technical Reports Server (NTRS)

Pitman, Joe; Duncan, Alan; Stubbs, David; Sigler, Robert; Kendrick, Rick; Chilese, John; Lipps, Jere; Manga, Mike; Graham, James; dePater, Imke

2004-01-01

The science capabilities and features of an innovative and revolutionary approach to remote sensing imaging systems, aimed at increasing the return on future space science missions many fold, are described. Our concept, called Multiple Instrument Distributed Aperture Sensor (MIDAS), provides a large-aperture, wide-field, diffraction-limited telescope at a fraction of the cost, mass and volume of conventional telescopes, by integrating optical interferometry technologies into a mature multiple aperture array concept that addresses one of the highest needs for advancing future planetary science remote sensing.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1992-01-01

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation.

PubMed

Qin, Changbo; Jia, Yangwen; Su, Z; Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

2008-07-29

This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems.

Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation

PubMed Central

Qin, Changbo; Jia, Yangwen; Su, Z.(Bob); Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

2008-01-01

This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems. PMID:27879946

Construction of Green Tide Monitoring System and Research on its Key Techniques

NASA Astrophysics Data System (ADS)

Xing, B.; Li, J.; Zhu, H.; Wei, P.; Zhao, Y.

2018-04-01

As a kind of marine natural disaster, Green Tide has been appearing every year along the Qingdao Coast, bringing great loss to this region, since the large-scale bloom in 2008. Therefore, it is of great value to obtain the real time dynamic information about green tide distribution. In this study, methods of optical remote sensing and microwave remote sensing are employed in Green Tide Monitoring Research. A specific remote sensing data processing flow and a green tide information extraction algorithm are designed, according to the optical and microwave data of different characteristics. In the aspect of green tide spatial distribution information extraction, an automatic extraction algorithm of green tide distribution boundaries is designed based on the principle of mathematical morphology dilation/erosion. And key issues in information extraction, including the division of green tide regions, the obtaining of basic distributions, the limitation of distribution boundary, and the elimination of islands, have been solved. The automatic generation of green tide distribution boundaries from the results of remote sensing information extraction is realized. Finally, a green tide monitoring system is built based on IDL/GIS secondary development in the integrated environment of RS and GIS, achieving the integration of RS monitoring and information extraction.

OhioView: Distribution of Remote Sensing Data Across Geographically Distributed Environments

NASA Technical Reports Server (NTRS)

Ramos, Calvin T.

1998-01-01

Various issues associated with the distribution of remote sensing data across geographically distributed environments are presented in viewgraph form. Specific topics include: 1) NASA education program background; 2) High level architectures, technologies and applications; 3) LeRC internal architecture and role; 4) Potential GIBN interconnect; 5) Potential areas of network investigation and research; 6) Draft of OhioView data model; and 7) the LeRC strategy and roadmap.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).

PubMed

Reichenau, Tim G; Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

PubMed Central

Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI. PMID:27391858

Cloud-top height retrieval from polarizing remote sensor POLDER

NASA Astrophysics Data System (ADS)

He, Xianqiang; Pan, Delu; Yan, Bai; Mao, Zhihua

2006-10-01

A new cloud-top height retrieval method is proposed by using polarizing remote sensing. In cloudy conditions, it shows that, in purple and blue bands, linear polarizing radiance at the top-of-atmosphere (TOA) is mainly contributed by Rayleigh scattering of the atmosphere's molecules above cloud, and the contribution by cloud reflection and aerosol scattering can be neglected. With such characteristics, the basis principle and method of cloud-top height retrieval using polarizing remote sensing are presented in detail, and tested by the polarizing remote sensing data of POLDER. The satellite-derived cloud-top height product can not only show the distribution of global cloud-top height, but also obtain the cloud-top height distribution of moderate-scale meteorological phenomena like hurricanes and typhoons. This new method is promising to become the operational algorithm for cloud-top height retrieval for POLDER and the future polarizing remote sensing satellites.

The U.S. Geological Survey Land Remote Sensing Program

USGS Publications Warehouse

,

2003-01-01

In 2002, the U. S. Geological Survey (USGS) launched a program to enhance the acquisition, preservation, and use of remotely sensed data for USGS science programs, as well as for those of cooperators and customers. Remotely sensed data are fundamental tools for studying the Earth's land surface, including coastal and near-shore environments. For many decades, the USGS has been a leader in providing remotely sensed data to the national and international communities. Acting on its historical topographic mapping mission, the USGS has archived and distributed aerial photographs of the United States for more than half a century. Since 1972, the USGS has acquired, processed, archived, and distributed Landsat and other satellite and airborne remotely sensed data products to users worldwide. Today, the USGS operates and manages the Landsats 5 and 7 missions and cooperates with the National Aeronautics and Space Administration (NASA) to define and implement future satellite missions that will continue and expand the collection of moderate-resolution remotely sensed data. In addition to being a provider of remotely sensed data, the USGS is a user of these data and related remote sensing technology. These data are used in natural resource evaluations for energy and minerals, coastal environmental surveys, assessments of natural hazards (earthquakes, volcanoes, and landslides), biological surveys and investigations, water resources status and trends analyses and studies, and geographic and cartographic applications, such as wildfire detection and tracking and as a source of information for The National Map. The program furthers these distinct but related roles by leading the USGS activities in providing remotely sensed data while advancing applications of such data for USGS programs and a wider user community.

The Large Area Crop Inventory Experiment (LACIE). Part 3: A systematic approach to the practical application of remote-sensing technology

NASA Technical Reports Server (NTRS)

Murphy, J. D.; Dideriksen, R. I.

1975-01-01

The application of remote sensing technology by the U.S. Department of Agriculture (USDA) is examined. The activities of the USDA Remote-Sensing User Requirement Task Force which include cataloging USDA requirements for earth resources data, determining those requirements that would return maximum benefits by using remote sensing technology and developing a plan for acquiring, processing, analyzing, and distributing data to satisfy those requirements are described. Emphasis is placed on the large area crop inventory experiment and its relationship to the task force.

Review of Remote Sensing Needs and Applications in Africa

NASA Technical Reports Server (NTRS)

Brown, Molly E.

2007-01-01

Remote sensing data has had an important role in identifying and responding to inter-annual variations in the African environment during the past three decades. As a largely agricultural region with diverse but generally limited government capacity to acquire and distribute ground observations of rainfall, temperature and other parameters, remote sensing is sometimes the only reliable measure of crop growing conditions in Africa. Thus, developing and maintaining the technical and scientific capacity to analyze and utilize satellite remote sensing data in Africa is critical to augmenting the continent's local weather/climate observation networks as well as its agricultural and natural resource development and management. The report Review of Remote Sensing Needs and Applications in Africa' has as its central goal to recommend to the US Agency for International Development an appropriate approach to support sustainable remote sensing applications at African regional remote sensing centers. The report focuses on "RS applications" to refer to the acquisition, maintenance and archiving, dissemination, distribution, analysis, and interpretation of remote sensing data, as well as the integration of interpreted data with other spatial data products. The report focuses on three primary remote sensing centers: (1) The AGRHYMET Regional Center in Niamey, Niger, created in 1974, is a specialized institute of the Permanent Interstate Committee for Drought Control in the Sahel (CILSS), with particular specialization in science and techniques applied to agricultural development, rural development, and natural resource management. (2) The Regional Centre for Maiming of Resources for Development (RCMRD) in Nairobi, Kenya, established in 1975 under the auspices of the United Nations Economic Commission for Africa and the Organization of African Unity (now the African Union), is an intergovernmental organization, with 15 member states from eastern and southern Africa. (3) The Regional Remote Sensing Unit (RRSU) in Gaborone, Botswana, began work in June 1988 and operates under the Agriculture Information Management System (AIMS), as part of the Food, Agriculture and Natural Resources (FANR) Directorate, based at the Southern Africa Development Community (SADC) Secretariat.

A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing prospective

NASA Astrophysics Data System (ADS)

Shafique, Muhammad; van der Meijde, Mark; Khan, M. Asif

2016-03-01

The 8th October 2005 Kashmir earthquake, in northern Pakistan has triggered thousands of landslides, which was the second major factor in the destruction of the build-up environment, after earthquake-induced ground shaking. Subsequent to the earthquake, several researchers from home and abroad applied a variety of remote sensing techniques, supported with field observations, to develop inventories of the earthquake-triggered landslides, analyzed their spatial distribution and subsequently developed landslide-susceptibility maps. Earthquake causative fault rupture, geology, anthropogenic activities and remote sensing derived topographic attributes were observed to have major influence on the spatial distribution of landslides. These were subsequently used to develop a landslide susceptibility map, thereby demarcating the areas prone to landsliding. Temporal studies monitoring the earthquake-induced landslides shows that the earthquake-induced landslides are stabilized, contrary to earlier belief, directly after the earthquake. The biggest landslide induced dam, as a result of the massive Hattian Bala landslide, is still posing a threat to the surrounding communities. It is observed that remote sensing data is effectively and efficiently used to assess the landslides triggered by the Kashmir earthquake, however, there is still a need of more research to understand the mechanism of intensity and distribution of landslides; and their continuous monitoring using remote sensing data at a regional scale. This paper, provides an overview of remote sensing and GIS applications, for the Kashmir-earthquake triggered landslides, derived outputs and discusses the lessons learnt, advantages, limitations and recommendations for future research.

The U.S. Geological Survey land remote sensing program

USGS Publications Warehouse

Saunders, T.; Feuquay, J.; Kelmelis, J.A.

2003-01-01

The U.S. Geological Survey has been a provider of remotely sensed information for decades. As the availability and use of satellite data has grown, USGS has placed increasing emphasis on expanding the knowledge about the science of remote sensing and on making remotely sensed data more accessible. USGS encourages widespread availability and distribution of these data and through its programs, encourages and enables a variety of research activities and the development of useful applications of the data. The science of remote sensing has great potential for assisting in the monitoring and assessment of the impacts of natural disasters, management and analysis of environmental, biological, energy, and mineral investigations, and supporting informed public policy decisions. By establishing the Land Remote Sensing Program (LRS) as a major unit of the USGS Geography Program, USGS has taken the next step to further increase support for the accessibility, understanding, and use of remotely sensed data. This article describes the LRS Program, its mission and objectives, and how the program has been structured to accomplish its goals.

Remote sensing of high-latitude ionization profiles by ground-based and spaceborne instrumentation

NASA Technical Reports Server (NTRS)

Vondrak, R. R.

1981-01-01

Ionospheric specification and modeling are now largely based on data provided by active remote sensing with radiowave techniques (ionosondes, incoherent-scatter radars, and satellite beacons). More recently, passive remote sensing techniques have been developed that can be used to monitor quantitatively the spatial distribution of high-latitude E-region ionization. These passive methods depend on the measurement, or inference, of the energy distribution of precipitating kilovolt electrons, the principal source of the nighttime E-region at high latitudes. To validate these techniques, coordinated measurements of the auroral ionosphere have been made with the Chatanika incoherent-scatter radar and a variety of ground-based and spaceborne sensors

Active-Passive Microwave Remote Sensing of Martian Permafrost and Subsurface Water

NASA Technical Reports Server (NTRS)

Raizer, V.; Linkin, V. M.; Ozorovich, Y. R.; Smythe, W. D.; Zoubkov, B.; Babkin, F.

2000-01-01

The investigation of permafrost formation global distribution and their appearance in h less than or equal 1 m thick subsurface layer would be investigated successfully by employment of active-passive microwave remote sensing techniques.

Two Optical Atmospheric Remote Sensing Techniques and AN Associated Analytic Solution to a Class of Integral Equations

NASA Astrophysics Data System (ADS)

Manning, Robert Michael

This work concerns itself with the analysis of two optical remote sensing methods to be used to obtain parameters of the turbulent atmosphere pertinent to stochastic electromagnetic wave propagation studies, and the well -posed solution to a class of integral equations that are central to the development of these remote sensing methods. A remote sensing technique is theoretically developed whereby the temporal frequency spectrum of the scintillations of a stellar source or a point source within the atmosphere, observed through a variable radius aperture, is related to the space-time spectrum of atmospheric scintillation. The key to this spectral remote sensing method is the spatial filtering performed by a finite aperture. The entire method is developed without resorting to a priori information such as results from stochastic wave propagation theory. Once the space-time spectrum of the scintillations is obtained, an application of known results of atmospheric wave propagation theory and simple geometric considerations are shown to yield such important information such as the spectrum of atmospheric turbulence, the cross-wind velocity, and the path profile of the atmospheric refractive index structure parameter. A method is also developed to independently verify the Taylor frozen flow hypothesis. The success of the spectral remote sensing method relies on the solution to a Fredholm integral equation of the first kind. An entire class of such equations, that are peculiar to inverse diffraction problems, is studied and a well-posed solution (in the sense of Hadamard) is obtained and probed. Conditions of applicability are derived and shown not to limit the useful operating range of the spectral remote sensing method. The general integral equation solution obtained is then applied to another remote sensing problem having to do with the characterization of the particle size distribution to atmospheric aerosols and hydrometeors. By measuring the diffraction pattern in the focal plane of a lens created by the passage of a laser beam through a distribution of particles, it is shown that the particle-size distribution of the particles can be obtained. An intermediate result of the analysis also gives the total volume concentration of the particles.

Distribution of chlorophyll and harmful algal blooms (HABs): A review on space based studies in the coastal environments of Chinese marginal seas

NASA Astrophysics Data System (ADS)

Wei, Guifeng; Tang, Danling; Wang, Sufen

Monitoring of spatial and temporal distribution of chlorophyll (Chl-a) concentrations in the aquatic milieu is always challenging and often interesting. However, the recent advancements in satellite digital data play a significant role in providing outstanding results for the marine environmental investigations. The present paper is aimed to review ‘remote sensing research in Chinese seas’ within the period of 24 years from 1978 to 2002. Owing to generalized distributional pattern, the Chl-a concentrations are recognized high towards northern Chinese seas than the southern. Moreover, the coastal waters, estuaries, and upwelling zones always exhibit relatively high Chl-a concentrations compared with offshore waters. On the basis of marine Chl-a estimates obtained from satellite and other field measured environmental parameters, we have further discussed on the applications of satellite remote sensing in the fields of harmful algal blooms (HABs), primary production and physical oceanographic currents of the regional seas. Concerned with studies of HABs, satellite remote sensing proved more advantageous than any other conventional methods for large-scale applications. Probably, it may be the only source of authentic information responsible for the evaluation of new research methodologies to detect HABs. At present, studies using remote sensing methods are mostly confined to observe algal bloom occurrences, hence, it is essential to coordinate the mechanism of marine ecological and oceanographic dynamic processes of HABs using satellite remote sensing data with in situ measurements of marine environmental parameters. The satellite remote sensing on marine environment and HABs is believed to have a great improvement with popular application of technology.

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 4 : web-based bridge information database--visualization analytics and distributed sensing.

DOT National Transportation Integrated Search

2012-03-01

This report introduces the design and implementation of a Web-based bridge information visual analytics system. This : project integrates Internet, multiple databases, remote sensing, and other visualization technologies. The result : combines a GIS ...

Remote-sensing supported monitoring of global biodiversity change

NASA Astrophysics Data System (ADS)

Jetz, W.; Tuanmu, M. N.; W, A.; Melton, F. S.; Parmentier, B.; Amatulli, G.; Guzman, A.

2016-12-01

Remote sensing combined with biodiversity observation offers an unrivalled tool for understanding and predicting species distributions and their changes at the planetary scale. I will illustrate recently developed high-resolution remote-sensing based layers targeted for spatiotemporal biodiversity modeling, addressing climate, environment, topography, and habitat heterogeneity. In particular, I will illustrate the development and use of global MODIS-derived environmental layers for biodiversity assessment and change monitoring. Remote-sensing based capture of these putative predictors of biodiversity dynamics provides more a reliable signal than spatially interpolated layers and avoids inflated spatial autocorrelation. The layers result in more accurate models of species occurrence and are more readily able to address the scale of processes underpinning species distributions, e.g. when combined with emerging hierarchical, cross-scale models. I illustrate the multiple ways in which this type of information, based on continuously collected data, supports the prediction of not just spatial but also temporal variation in biodiversity. Using implementations in the Map of Life infrastructure I will showcase new indicators of species distribution and change that demonstrate these new opportunities.

Remote sensing of on-road vehicle emissions: Mechanism, applications and a case study from Hong Kong

NASA Astrophysics Data System (ADS)

Huang, Yuhan; Organ, Bruce; Zhou, John L.; Surawski, Nic C.; Hong, Guang; Chan, Edward F. C.; Yam, Yat Shing

2018-06-01

Vehicle emissions are a major contributor to air pollution in cities and have serious health impacts to their inhabitants. On-road remote sensing is an effective and economic tool to monitor and control vehicle emissions. In this review, the mechanism, accuracy, advantages and limitations of remote sensing were introduced. Then the applications and major findings of remote sensing were critically reviewed. It was revealed that the emission distribution of on-road vehicles was highly skewed so that the dirtiest 10% vehicles accounted for over half of the total fleet emissions. Such findings highlighted the importance and effectiveness of using remote sensing for in situ identification of high-emitting vehicles for further inspection and maintenance programs. However, the accuracy and number of vehicles affected by screening programs were greatly dependent on the screening criteria. Remote sensing studies showed that the emissions of gasoline and diesel vehicles were significantly reduced in recent years, with the exception of NOx emissions of diesel vehicles in spite of greatly tightened automotive emission regulations. Thirdly, the experience and issues of using remote sensing for identifying high-emitting vehicles in Hong Kong (where remote sensing is a legislative instrument for enforcement purposes) were reported. That was followed by the first time ever identification and discussion of the issue of frequent false detection of diesel high-emitters using remote sensing. Finally, the challenges and future research directions of on-road remote sensing were elaborated.

The investigation of advanced remote sensing techniques for the measurement of aerosol characteristics

NASA Technical Reports Server (NTRS)

Deepak, A.; Becher, J.

1979-01-01

Advanced remote sensing techniques and inversion methods for the measurement of characteristics of aerosol and gaseous species in the atmosphere were investigated. Of particular interest were the physical and chemical properties of aerosols, such as their size distribution, number concentration, and complex refractive index, and the vertical distribution of these properties on a local as well as global scale. Remote sensing techniques for monitoring of tropospheric aerosols were developed as well as satellite monitoring of upper tropospheric and stratospheric aerosols. Computer programs were developed for solving multiple scattering and radiative transfer problems, as well as inversion/retrieval problems. A necessary aspect of these efforts was to develop models of aerosol properties.

Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

2017-12-01

Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological regime and the Lake Turkana level variability.

Estimation of Snow Parameters Based on Passive Microwave Remote Sensing and Meteorological Information

NASA Technical Reports Server (NTRS)

Tsang, Leung; Hwang, Jenq-Neng

1996-01-01

A method to incorporate passive microwave remote sensing measurements within a spatially distributed snow hydrology model to provide estimates of the spatial distribution of Snow Water Equivalent (SWE) as a function of time is implemented. The passive microwave remote sensing measurements are at 25 km resolution. However, in mountain regions the spatial variability of SWE over a 25 km footprint is large due to topographic influences. On the other hand, the snow hydrology model has built-in topographic information and the capability to estimate SWE at a 1 km resolution. In our work, the snow hydrology SWE estimates are updated and corrected using SSM/I passive microwave remote sensing measurements. The method is applied to the Upper Rio Grande River Basin in the mountains of Colorado. The change in prediction of SWE from hydrology modeling with and without updating is compared with measurements from two SNOTEL sites in and near the basin. The results indicate that the method incorporating the remote sensing measurements into the hydrology model is able to more closely estimate the temporal evolution of the measured values of SWE as a function of time.

An Open Source Software and Web-GIS Based Platform for Airborne SAR Remote Sensing Data Management, Distribution and Sharing

NASA Astrophysics Data System (ADS)

Changyong, Dou; Huadong, Guo; Chunming, Han; Ming, Liu

2014-03-01

With more and more Earth observation data available to the community, how to manage and sharing these valuable remote sensing datasets is becoming an urgent issue to be solved. The web based Geographical Information Systems (GIS) technology provides a convenient way for the users in different locations to share and make use of the same dataset. In order to efficiently use the airborne Synthetic Aperture Radar (SAR) remote sensing data acquired in the Airborne Remote Sensing Center of the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), a Web-GIS based platform for airborne SAR data management, distribution and sharing was designed and developed. The major features of the system include map based navigation search interface, full resolution imagery shown overlaid the map, and all the software adopted in the platform are Open Source Software (OSS). The functions of the platform include browsing the imagery on the map navigation based interface, ordering and downloading data online, image dataset and user management, etc. At present, the system is under testing in RADI and will come to regular operation soon.

Upper Kalamazoo watershed land cover inventory. [based on remote sensing

NASA Technical Reports Server (NTRS)

Richason, B., III; Enslin, W.

1973-01-01

Approximately 1000 square miles of the eastern portion of the watershed were inventoried based on remote sensing imagery. The classification scheme, imagery and interpretation procedures, and a cost analysis are discussed. The distributions of land cover within the area are tabulated.

Get Close to Glaciers with Satellite Imagery.

ERIC Educational Resources Information Center

Hall, Dorothy K.

1986-01-01

Discusses the use of remote sensing from satellites to monitor glaciers. Discusses efforts to use remote sensing satellites of the Landsat series for examining the global distribution, mass, balance, movements, and dynamics of the world's glaciers. Includes several Landsat images of various glaciers. (TW)

Synoptic thermal and oceanographic parameter distributions in the New York Bight Apex

NASA Technical Reports Server (NTRS)

Johnson, R. W.; Bahn, G. S.; Thomas, J. P.

1981-01-01

Concurrent surface water measurements made from a moving oceanographic research vessel were used to calibrate and interpret remotely sensed data collected over a plume in the New York Bight Apex on 23 June 1977. Multiple regression techniques were used to develop equations to map synoptic distributions of chlorophyll a and total suspended matter in the remotely sensed scene. Thermal (which did not have surface calibration values) and water quality parameter distributions indicated a cold mass of water in the Bight Apex with an overflowing nutrient-rich warm water plume that originated in the Sandy Hook Bay and flowed south near the New Jersey shoreline. Data analysis indicates that remotely sensed data may be particularly useful for studying physical and biological processes in the top several metres of surface water at plume boundaries.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Understanding Local Structure Globally in Earth Science Remote Sensing Data Sets

NASA Technical Reports Server (NTRS)

Braverman, Amy; Fetzer, Eric

2007-01-01

Empirical probability distributions derived from the data are the signatures of physical processes generating the data. Distributions defined on different space-time windows can be compared and differences or changes can be attributed to physical processes. This presentation discusses on ways to reduce remote sensing data in a way that preserves information, focusing on the rate-distortion theory and using the entropy-constrained vector quantization algorithm.

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

NASA Technical Reports Server (NTRS)

Zhang, Xiaodong; Kirilenko, Andrei; Lim, Howe; Teng, Williams

2010-01-01

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

Remote rainfall sensing for landslide hazard analysis

USGS Publications Warehouse

Wieczorek, Gerald F.; McWreath, Harry; Davenport, Clay

2001-01-01

Methods of assessing landslide hazards and providing warnings are becoming more advanced as remote sensing of rainfall provides more detailed temporal and spatial data on rainfall distribution. Two recent landslide disasters are examined noting the potential for using remotely sensed rainfall data for landslide hazard analysis. For the June 27, 1995, storm in Madison County, Virginia, USA, National Weather Service WSR-88D Doppler radar provided rainfall estimates based on a relation between cloud reflectivity and moisture content on a 1 sq. km. resolution every 6 minutes. Ground-based measurements of rainfall intensity and precipitation total, in addition to landslide timing and distribution, were compared with the radar-derived rainfall data. For the December 14-16, 1999, storm in Vargas State, Venezuela, infrared sensing from the GOES-8 satellite of cloud top temperatures provided the basis for NOAA/NESDIS rainfall estimates on a 16 sq. km. resolution every 30 minutes. These rainfall estimates were also compared with ground-based measurements of rainfall and landslide distribution. In both examples, the remotely sensed data either overestimated or underestimated ground-based values by up to a factor of 2. The factors that influenced the accuracy of rainfall data include spatial registration and map projection, as well as prevailing wind direction, cloud orientation, and topography.

Development of multi-mission satellite data systems at the German Remote Sensing Data Centre

NASA Astrophysics Data System (ADS)

Lotz-Iwen, H. J.; Markwitz, W.; Schreier, G.

1998-11-01

This paper focuses on conceptual aspects of the access to multi-mission remote sensing data by online catalogue and information systems. The system ISIS of the German Remote Sensing Data Centre is described as an example of a user interface to earth observation data. ISIS has been designed to support international scientific research as well as operational applications by offering online access to the database via public networks. It provides catalogue retrieval, visualisation and transfer of image data, and is integrated in international activities dedicated to catalogue and archive interoperability. Finally, an outlook is given on international projects dealing with access to remote sensing data in distributed archives.

Design and Verification of Remote Sensing Image Data Center Storage Architecture Based on Hadoop

NASA Astrophysics Data System (ADS)

Tang, D.; Zhou, X.; Jing, Y.; Cong, W.; Li, C.

2018-04-01

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

HABSEED: a Simple Spatially Explicit Meta-Populations Model Using Remote Sensing Derived Habitat Quality Data

NASA Astrophysics Data System (ADS)

Heumann, B. W.; Guichard, F.; Seaquist, J. W.

2005-05-01

The HABSEED model uses remote sensing derived NPP as a surrogate for habitat quality as the driving mechanism for population growth and local seed dispersal. The model has been applied to the Sahel region of Africa. Results show that the functional response of plants to habitat quality alters population distribution. Plants more tolerant of medium quality habitat have greater distributions to the North while plants requiring only the best habitat are limited to the South. For all functional response types, increased seed production results in diminishing returns. Functional response types have been related to life history tradeoffs and r-K strategies based on the results. Results are compared to remote sensing derived vegetation land cover.

Use of UAS remote sensing data to estimate crop ET at high spatial resolution

USDA-ARS?s Scientific Manuscript database

Estimation of the spatial distribution of evapotranspiration (ET) based on remotely sensed imagery has become useful for managing water in irrigated agricultural at various spatial scales. However, data acquired by conventional satellites (Landsat, ASTER, etc.) lack the spatial resolution to capture...

Natural resource inventory for urban planning utilizing remote sensing techniques

NASA Technical Reports Server (NTRS)

Foster, K. E.; Mackey, P. F.; Bonham, C. D.

1972-01-01

Remote sensing techniques were applied to the lower Pantano Wash area to acquire data for planning an ecological balance between the expanding Tucson metropolitan area and its environment. The types and distribution of vegetation are discussed along with the hydrologic aspects of the Wash.

Ten ways remote sensing can contribute to conservation

USGS Publications Warehouse

Rose, Robert A.; Byler, Dirck; Eastman, J. Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A.; Laporte, Nadine; Leidner, Allison K.; Leimgruber, Peter; Morisette, Jeffrey T.; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C.; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2014-01-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Ten ways remote sensing can contribute to conservation.

PubMed

Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; Leimgruber, Peter; Morisette, Jeffrey; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2015-04-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions? © 2014 Society for Conservation Biology.

Remote sensing in biological oceanography

NASA Technical Reports Server (NTRS)

Esaias, W. E.

1981-01-01

The main attribute of remote sensing is seen as its ability to measure distributions over large areas on a synoptic basis and to repeat this coverage at required time periods. The way in which the Coastal Zone Color Scanner, by showing the distribution of chlorophyll a, can locate areas productive in both phytoplankton and fishes is described. Lidar techniques are discussed, and it is pointed out that lidar will increase the depth range for observations.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Initial results of the spatial distribution of rubber trees in Peninsular Malaysia using remotely sensed data for biomass estimate

NASA Astrophysics Data System (ADS)

Shidiq, I. P. A.; Ismail, M. H.; Kamarudin, N.

2014-02-01

The preservation and sustainable management of forest and other land cover ecosystems such as rubber trees will help addressing two major recent issues: climate change and bio-resource energy. The rubber trees are dominantly distributed in the Negeri Sembilan and Kedah on the west coast side of Peninsular Malaysia. This study is aimed to analyse the spatial distribution and biomass of rubber trees in Peninsular Malaysia with special emphasis in Negeri Sembilan State. Geospatial data from remote sensors are used to tackle the time and labour consuming problem due to the large spatial coverage and the need of continuous temporal data. Remote sensing imagery used in this study is a Landsat 5 TM. The image from optical sensor was used to sense the rubber trees and further classified rubber tree by different age.

[Investigation on remote measurement of air pollution by a method of infrared passive scanning imaging].

PubMed

Jiao, Yang; Xu, Liang; Gao, Min-Guang; Feng, Ming-Chun; Jin, Ling; Tong, Jing-Jing; Li, Sheng

2012-07-01

Passive remote sensing by Fourier-transform infrared (FTIR) spectrometry allows detection of air pollution. However, for the localization of a leak and a complete assessment of the situation in the case of the release of a hazardous cloud, information about the position and the distribution of a cloud is essential. Therefore, an imaging passive remote sensing system comprising an interferometer, a data acquisition and processing software, scan system, a video system, and a personal computer has been developed. The remote sensing of SF6 was done. The column densities of all directions in which a target compound has been identified may be retrieved by a nonlinear least squares fitting algorithm and algorithm of radiation transfer, and a false color image is displayed. The results were visualized by a video image, overlaid by false color concentration distribution image. The system has a high selectivity, and allows visualization and quantification of pollutant clouds.

THE SPATIAL DISTRIBUTION OF PHYTOPLANKTON CHLOROPHYLL CONCENTRATIONS IN NARRAGANSETT BAY USING AIRCRAFT REMOTE SENSING

EPA Science Inventory

During the summer of 2002, phytoplankton chlorophyll concentrations were determined in Narragansett Bay, Rhode Island using a light aircraft equipped with the MicroSAS remote sensing system. From an altitude of 300 m, the three sensor system measured sea surface radiance (Lt), sk...

Coupling fine-scale root and canopy structure using ground-based remote sensing

Treesearch

Brady Hardiman; Christopher Gough; John Butnor; Gil Bohrer; Matteo Detto; Peter Curtis

2017-01-01

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in...

Natural Resource Monitoring of Rheum tanguticum by Multilevel Remote Sensing

PubMed Central

Xie, Caixiang; Song, Jingyuan; Suo, Fengmei; Li, Xiwen; Li, Ying; Yu, Hua; Xu, Xiaolan; Luo, Kun; Li, Qiushi; Xin, Tianyi; Guan, Meng; Xu, Xiuhai; Miki, Eiji; Takeda, Osami; Chen, Shilin

2014-01-01

Remote sensing has been extensively applied in agriculture for its objectiveness and promptness. However, few applications are available for monitoring natural medicinal plants. In the paper, a multilevel monitoring system, which includes satellite and aerial remote sensing, as well as ground investigation, was initially proposed to monitor natural Rheum tanguticum resource in Baihe Pasture, Zoige County, Sichuan Province. The amount of R. tanguticum from images is M = S*ρ and S is vegetation coverage obtained by satellite imaging, whereas ρ is R. tanguticum density obtained by low-altitude imaging. Only the R. tanguticum which coverages exceeded 1 m2 could be recognized from the remote sensing image because of the 0.1 m resolution of the remote sensing image (called effective resource at that moment), and the results of ground investigation represented the amounts of R. tanguticum resource in all sizes (called the future resource). The data in paper showed that the present available amount of R. tanguticum accounted for 4% to 5% of the total quantity. The quantity information and the population structure of R. tanguticum in the Baihe Pasture were initially confirmed by this system. It is feasible to monitor the quantitative distribution for natural medicinal plants with scattered distribution. PMID:25101134

Measuring grassland structure for recovery of grassland species at risk

NASA Astrophysics Data System (ADS)

Guo, Xulin; Gao, Wei; Wilmshurst, John

2005-09-01

An action plan for recovering species at risk (SAR) depends on an understanding of the plant community distribution, vegetation structure, quality of the food source and the impact of environmental factors such as climate change at large scale and disturbance at small scale, as these are fundamental factors for SAR habitat. Therefore, it is essential to advance our knowledge of understanding the SAR habitat distribution, habitat quality and dynamics, as well as developing an effective tool for measuring and monitoring SAR habitat changes. Using the advantages of non-destructive, low cost, and high efficient land surface vegetation biophysical parameter characterization, remote sensing is a potential tool for helping SAR recovery action. The main objective of this paper is to assess the most suitable techniques for using hyperspectral remote sensing to quantify grassland biophysical characteristics. The challenge of applying remote sensing in semi-arid and arid regions exists simply due to the lower biomass vegetation and high soil exposure. In conservation grasslands, this problem is enhanced because of the presence of senescent vegetation. Results from this study demonstrated that hyperspectral remote sensing could be the solution for semi-arid grassland remote sensing applications. Narrow band raw data and derived spectral vegetation indices showed stronger relationships with biophysical variables compared to the simulated broad band vegetation indices.

Research for applications of remote sensing to state and local governments (ARSIG)

NASA Technical Reports Server (NTRS)

Foster, K. E.; Johnson, J. D.

1973-01-01

Remote sensing and its application to problems confronted by local and state planners are reported. The added dimension of remote sensing as a data gathering tool has been explored identifying pertinent land use factors associated with urban growth such as soil associations, soil capability, vegetation distribution, and flood prone areas. Remote sensing within rural agricultural setting has also been utilized to determine irrigation runoff volumes, cropping patterns, and land use. A variety of data sources including U-2 70 mm multispectral black and white photography, RB-57 9-inch color IR, HyAC panoramic color IR and ERTS-1 imagery have been used over selected areas of Arizona including Tucson, Arizona (NASA Test Site #30) and the Sulphur Springs Valley.

An integrated approach to the remote sensing of floating ice

NASA Technical Reports Server (NTRS)

Campbell, W. J.; Ramseier, R. O.; Weeks, W. F.; Gloersen, P.

1976-01-01

Review article on remote sensing applications to glaciology. Ice parameters sensed include: ice cover vs open water, ice thickness, distribution and morphology of ice formations, vertical resolution of ice thickness, ice salinity (percolation and drainage of brine; flushing of ice body with fresh water), first-year ice and multiyear ice, ice growth rate and surface heat flux, divergence of ice packs, snow cover masking ice, behavior of ice shelves, icebergs, lake ice and river ice; time changes. Sensing techniques discussed include: satellite photographic surveys, thermal IR, passive and active microwave studies, microwave radiometry, microwave scatterometry, side-looking radar, and synthetic aperture radar. Remote sensing of large aquatic mammals and operational ice forecasting are also discussed.

NDSI products system based on Hadoop platform

NASA Astrophysics Data System (ADS)

Zhou, Yan; Jiang, He; Yang, Xiaoxia; Geng, Erhui

2015-12-01

Snow is solid state of water resources on earth, and plays an important role in human life. Satellite remote sensing is significant in snow extraction with the advantages of cyclical, macro, comprehensiveness, objectivity, timeliness. With the continuous development of remote sensing technology, remote sensing data access to the trend of multiple platforms, multiple sensors and multiple perspectives. At the same time, in view of the remote sensing data of compute-intensive applications demand increase gradually. However, current the producing system of remote sensing products is in a serial mode, and this kind of production system is used for professional remote sensing researchers mostly, and production systems achieving automatic or semi-automatic production are relatively less. Facing massive remote sensing data, the traditional serial mode producing system with its low efficiency has been difficult to meet the requirements of mass data timely and efficient processing. In order to effectively improve the production efficiency of NDSI products, meet the demand of large-scale remote sensing data processed timely and efficiently, this paper build NDSI products production system based on Hadoop platform, and the system mainly includes the remote sensing image management module, NDSI production module, and system service module. Main research contents and results including: (1)The remote sensing image management module: includes image import and image metadata management two parts. Import mass basis IRS images and NDSI product images (the system performing the production task output) into HDFS file system; At the same time, read the corresponding orbit ranks number, maximum/minimum longitude and latitude, product date, HDFS storage path, Hadoop task ID (NDSI products), and other metadata information, and then create thumbnails, and unique ID number for each record distribution, import it into base/product image metadata database. (2)NDSI production module: includes the index calculation, production tasks submission and monitoring two parts. Read HDF images related to production task in the form of a byte stream, and use Beam library to parse image byte stream to the form of Product; Use MapReduce distributed framework to perform production tasks, at the same time monitoring task status; When the production task complete, calls remote sensing image management module to store NDSI products. (3)System service module: includes both image search and DNSI products download. To image metadata attributes described in JSON format, return to the image sequence ID existing in the HDFS file system; For the given MapReduce task ID, package several task output NDSI products into ZIP format file, and return to the download link (4)System evaluation: download massive remote sensing data and use the system to process it to get the NDSI products testing the performance, and the result shows that the system has high extendibility, strong fault tolerance, fast production speed, and the image processing results with high accuracy.

Preliminary results of fisheries investigation associated with Skylab-3. [remotely sensed distribution and abundance of gamefish in Gulf of Mexico

NASA Technical Reports Server (NTRS)

Savastano, K. J. (Principal Investigator); Pastula, E. J., Jr.; Woods, G.; Faller, K.

1974-01-01

The author has identified the following significant results. This investigation is to establish the feasibility of utilizing remotely sensed data acquired from aircraft and satellite platforms to provide information concerning the distribution and abundance of oceanic gamefish. Data from the test area in the northeastern Gulf of Mexico has made possible the identification of fisheries significant environmental parameters for white marlin. Predictive models based on catch data and surface truth information have been developed and have demonstrated potential for reducing search significantly by identifying areas which have a high probability of being productive. Three of the parameters utilized by the model, chlorophyll-a, sea surface temperature, and turbidity have been inferred from aircraft sensor data. Cloud cover and delayed receipt have inhibited the use of Skylab data. The first step toward establishing the feasibility of utilizing remotely sensed data to assess amd monitor the distribution of ocean gamefish has been taken with the successful identification of fisheries significant oceanographic parameters and the demonstration of the capability of measuring most of these parameters remotely.

Effect of the revisit interval on the accuracy of remote sensing-based estimates of evapotranspiration at field scales

USDA-ARS?s Scientific Manuscript database

Accurate spatially distributed estimates of evapotranspiration (ET) derived from remotely sensed data are critical to a broad range of practical and operational applications. However, due to lengthy return intervals and cloud cover, data acquisition is not continuous over time. To fill the data gaps...

Using remote sensing and machine learning for the spatial modelling of a bluetongue virus vector

NASA Astrophysics Data System (ADS)

Van doninck, J.; Peters, J.; De Baets, B.; Ducheyne, E.; Verhoest, N. E. C.

2012-04-01

Bluetongue is a viral vector-borne disease transmitted between hosts, mostly cattle and small ruminants, by some species of Culicoides midges. Within the Mediterranean basin, C. imicola is the main vector of the bluetongue virus. The spatial distribution of this species is limited by a number of environmental factors, including temperature, soil properties and land cover. The identification of zones at risk of bluetongue outbreaks thus requires detailed information on these environmental factors, as well as appropriate epidemiological modelling techniques. We here give an overview of the environmental factors assumed to be constraining the spatial distribution of C. imicola, as identified in different studies. Subsequently, remote sensing products that can be used as proxies for these environmental constraints are presented. Remote sensing data are then used together with species occurrence data from the Spanish Bluetongue National Surveillance Programme to calibrate a supervised learning model, based on Random Forests, to model the probability of occurrence of the C. imicola midge. The model will then be applied for a pixel-based prediction over the Iberian peninsula using remote sensing products for habitat characterization.

Analysis of Human Activities in Nature Reserves Based on Nighttime Light Remote Sensing and Microblogging Data - by the Case of National Nature Reserves in Jiangxi Province

NASA Astrophysics Data System (ADS)

Shi, F.; Li, X.; Xu, H.

2017-09-01

The study used the mainstream social media in china - Sina microblogging data combined with nighttime light remote sensing and various geographical data to reveal the pattern of human activities and light pollution of the Jiangxi Provincial National Nature Reserves. Firstly, we performed statistical analysis based on both functional areas and km-grid from the perspective of space and time, and selected the key areas for in-depth study. Secondly, the relationship between microblogging data and nighttime light remote sensing, population, GDP, road coverage, road distance and road type in nature reserves was analyzed by Spearman correlation coefficient method, so the distribution pattern and influencing factors of the microblogging data were explored. Thirdly, a region where the luminance value was greater than 0.2 was defined as a light region. We evaluated the management status by analyzing the distribution of microblogging data in both light area and non-light area. Final results showed that in all nature reserves, the top three were the Lushan Nature Reserve, the Jinggangshan Nature Reserve, the Taohongling National Nature Reserve of Sikas both on the total number and density of microblogging ; microblogging had a significant correlation with nighttime light remote sensing , the GDP, population, road and other factors; the distribution of microblogging near roads in protected area followed power laws; luminous radiance of Lushan Nature Reserve was the highest, with 43 percent of region was light at night; analysis combining nighttime light remote sensing with microblogging data reflected the status of management of nature reserves.

Can we infer plant facilitation from remote sensing? A test across global drylands

PubMed Central

Xu, Chi; Holmgren, Milena; Van Nes, Egbert H.; Maestre, Fernando T.; Soliveres, Santiago; Berdugo, Miguel; Kéfi, Sonia; Marquet, Pablo A.; Abades, Sebastian; Scheffer, Marten

2016-01-01

Facilitation is a major force shaping the structure and diversity of plant communities in terrestrial ecosystems. Detecting positive plant-plant interactions relies on the combination of field experimentation and the demonstration of spatial association between neighboring plants. This has often restricted the study of facilitation to particular sites, limiting the development of systematic assessments of facilitation over regional and global scales. Here we explore whether the frequency of plant spatial associations detected from high-resolution remotely-sensed images can be used to infer plant facilitation at the community level in drylands around the globe. We correlated the information from remotely-sensed images freely available through Google Earth™ with detailed field assessments, and used a simple individual-based model to generate patch-size distributions using different assumptions about the type and strength of plant-plant interactions. Most of the patterns found from the remotely-sensed images were more right-skewed than the patterns from the null model simulating a random distribution. This suggests that the plants in the studied drylands show stronger spatial clustering than expected by chance. We found that positive plant co-occurrence, as measured in the field, was significantly related to the skewness of vegetation patch-size distribution measured using Google Earth™ images. Our findings suggest that the relative frequency of facilitation may be inferred from spatial pattern signals measured from remotely-sensed images, since facilitation often determines positive co-occurrence among neighboring plants. They pave the road for a systematic global assessment of the role of facilitation in terrestrial ecosystems. PMID:26552256

Applications of synergistic combination of remote sensing and in-situ measurements on urban monitoring of air quality

NASA Astrophysics Data System (ADS)

Diaz, Adrian; Dominguez, Victor; Campmier, Mark; Wu, Yonghua; Arend, Mark; Vladutescu, Daniela Viviana; Gross, Barry; Moshary, Fred

2017-08-01

In this study, multiple remote sensing and in-situ measurements are combined in order to obtain a comprehensive understanding of the aerosol distribution in New York City. Measurement of the horizontal distribution of aerosols is performed using a scanning eye-safe elastic-backscatter micro-pulse lidar. Vertical distribution of aerosols is measured with a co-located ceilometer. Furthermore, our analysis also includes in-situ measurements of particulate matter and wind speed and direction. These observations combined show boundary layer dynamics as well as transport and inhomogeneous spatial distribution of aerosols, which are of importance for air quality monitoring.

Remote sensing and human health: new sensors and new opportunities.

PubMed

Beck, L R; Lobitz, B M; Wood, B L

2000-01-01

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Système Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

The integrated design and archive of space-borne signal processing and compression coding

NASA Astrophysics Data System (ADS)

He, Qiang-min; Su, Hao-hang; Wu, Wen-bo

2017-10-01

With the increasing demand of users for the extraction of remote sensing image information, it is very urgent to significantly enhance the whole system's imaging quality and imaging ability by using the integrated design to achieve its compact structure, light quality and higher attitude maneuver ability. At this present stage, the remote sensing camera's video signal processing unit and image compression and coding unit are distributed in different devices. The volume, weight and consumption of these two units is relatively large, which unable to meet the requirements of the high mobility remote sensing camera. This paper according to the high mobility remote sensing camera's technical requirements, designs a kind of space-borne integrated signal processing and compression circuit by researching a variety of technologies, such as the high speed and high density analog-digital mixed PCB design, the embedded DSP technology and the image compression technology based on the special-purpose chips. This circuit lays a solid foundation for the research of the high mobility remote sensing camera.

Remote sensing and human health: new sensors and new opportunities

NASA Technical Reports Server (NTRS)

Beck, L. R.; Lobitz, B. M.; Wood, B. L.

2000-01-01

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

Role of remote sensing in Bay measurements

NASA Technical Reports Server (NTRS)

Mugler, J. P., Jr.; Godfrey, J. P.; Hickman, G. D.; Hovis, W. G.; Pearson, A. O.; Weaver, K. N.

1978-01-01

Remote measurements of a number of surface or near surface parameters for baseline definition and specialized studies, remote measurements of episodic events, and remote measurements of the Bay lithosphere are considered in terms of characterizing and understanding the ecology of the Chesapeake Bay. Geologic processes and features best suited for information enhancement by remote sensing methods are identified. These include: (1) rates of sedimentation in the Bay; (2) rates of erosion of Bay shorelines; (3) spatial distribution and geometry of aquifers; (4) mapping of Karst terrain (sinkholes); and (5) mapping of fracture patterns. Recommendations for studying problem areas identified are given.

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

Impact of 3D Canopy Structure on Remote Sensing Vegetation Index and Solar Induced Chlorophyll Fluorescence

NASA Astrophysics Data System (ADS)

Zeng, Y.; Berry, J. A.; Jing, L.; Qinhuo, L.

2017-12-01

Terrestrial ecosystem plays a critical role in removing CO2 from atmosphere by photosynthesis. Remote sensing provides a possible way to monitor the Gross Primary Production (GPP) at the global scale. Vegetation Indices (VI), e.g., NDVI and NIRv, and Solar Induced Fluorescence (SIF) have been widely used as a proxy for GPP, while the impact of 3D canopy structure on VI and SIF has not be comprehensively studied yet. In this research, firstly, a unified radiative transfer model for visible/near-infrared reflectance and solar induced chlorophyll fluorescence has been developed based on recollision probability and directional escape probability. Then, the impact of view angles, solar angles, weather conditions, leaf area index, and multi-layer leaf angle distribution (LAD) on VI and SIF has been studied. Results suggest that canopy structure plays a critical role in distorting pixel-scale remote sensing signal from leaf-scale scattering. In thin canopy, LAD affects both of the remote sensing estimated GPP and real GPP, while in dense canopy, SIF variations are mainly due to canopy structure, instead of just due to physiology. At the microscale, leaf angle reflects the plant strategy to light on the photosynthesis efficiency, and at the macroscale, a priori knowledge of leaf angle distribution for specific species can improve the global GPP estimation by remote sensing.

The added value of remote sensing products in constraining hydrological models

NASA Astrophysics Data System (ADS)

Nijzink, Remko C.; Almeida, Susana; Pechlivanidis, Ilias; Capell, René; Gustafsson, David; Arheimer, Berit; Freer, Jim; Han, Dawei; Wagener, Thorsten; Sleziak, Patrik; Parajka, Juraj; Savenije, Hubert; Hrachowitz, Markus

2017-04-01

The calibration of a hydrological model still depends on the availability of streamflow data, even though more additional sources of information (i.e. remote sensed data products) have become more widely available. In this research, the model parameters of four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) were constrained with remotely sensed products. The models were applied over 27 catchments across Europe to cover a wide range of climates, vegetation and landscapes. The fluxes and states of the models were correlated with the relevant products (e.g. MOD10A snow with modelled snow states), after which new a-posteriori parameter distributions were determined based on a weighting procedure using conditional probabilities. Briefly, each parameter was weighted with the coefficient of determination of the relevant regression between modelled states/fluxes and products. In this way, final feasible parameter sets were derived without the use of discharge time series. Initial results show that improvements in model performance, with regard to streamflow simulations, are obtained when the models are constrained with a set of remotely sensed products simultaneously. In addition, we present a more extensive analysis to assess a model's ability to reproduce a set of hydrological signatures, such as rising limb density or peak distribution. Eventually, this research will enhance our understanding and recommendations in the use of remotely sensed products for constraining conceptual hydrological modelling and improving predictive capability, especially for data sparse regions.

Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images.

PubMed

Zou, Zhengxia; Shi, Zhenwei

2018-03-01

We propose a new paradigm for target detection in high resolution aerial remote sensing images under small target priors. Previous remote sensing target detection methods frame the detection as learning of detection model + inference of class-label and bounding-box coordinates. Instead, we formulate it from a Bayesian view that at inference stage, the detection model is adaptively updated to maximize its posterior that is determined by both training and observation. We call this paradigm "random access memories (RAM)." In this paradigm, "Memories" can be interpreted as any model distribution learned from training data and "random access" means accessing memories and randomly adjusting the model at detection phase to obtain better adaptivity to any unseen distribution of test data. By leveraging some latest detection techniques e.g., deep Convolutional Neural Networks and multi-scale anchors, experimental results on a public remote sensing target detection data set show our method outperforms several other state of the art methods. We also introduce a new data set "LEarning, VIsion and Remote sensing laboratory (LEVIR)", which is one order of magnitude larger than other data sets of this field. LEVIR consists of a large set of Google Earth images, with over 22 k images and 10 k independently labeled targets. RAM gives noticeable upgrade of accuracy (an mean average precision improvement of 1% ~ 4%) of our baseline detectors with acceptable computational overhead.

Copyright protection of remote sensing imagery by means of digital watermarking

NASA Astrophysics Data System (ADS)

Barni, Mauro; Bartolini, Franco; Cappellini, Vito; Magli, Enrico; Olmo, Gabriella; Zanini, R.

2001-12-01

The demand for remote sensing data has increased dramatically mainly due to the large number of possible applications capable to exploit remotely sensed data and images. As in many other fields, along with the increase of market potential and product diffusion, the need arises for some sort of protection of the image products from unauthorized use. Such a need is a very crucial one even because the Internet and other public/private networks have become preferred and effective means of data exchange. An important issue arising when dealing with digital image distribution is copyright protection. Such a problem has been largely addressed by resorting to watermarking technology. Before applying watermarking techniques developed for multimedia applications to remote sensing applications, it is important that the requirements imposed by remote sensing imagery are carefully analyzed to investigate whether they are compatible with existing watermarking techniques. On the basis of these motivations, the contribution of this work is twofold: (1) assessment of the requirements imposed by the characteristics of remotely sensed images on watermark-based copyright protection; (2) discussion of a case study where the performance of two popular, state-of-the-art watermarking techniques are evaluated by the light of the requirements at the previous point.

Validation plays the role of a "bridge" in connecting remote sensing research and applications

NASA Astrophysics Data System (ADS)

Wang, Zhiqiang; Deng, Ying; Fan, Yida

2018-07-01

Remote sensing products contribute to improving earth observations over space and time. Uncertainties exist in products of different levels; thus, validation of these products before and during their applications is critical. This study discusses the meaning of validation in depth and proposes a new definition of reliability for use with such products. In this context, validation should include three aspects: a description of the relevant uncertainties, quantitative measurement results and a qualitative judgment that considers the needs of users. A literature overview is then presented evidencing improvements in the concepts associated with validation. It shows that the root mean squared error (RMSE) is widely used to express accuracy; increasing numbers of remote sensing products have been validated; research institutes contribute most validation efforts; and sufficient validation studies encourage the application of remote sensing products. Validation plays a connecting role in the distribution and application of remote sensing products. Validation connects simple remote sensing subjects with other disciplines, and it connects primary research with practical applications. Based on the above findings, it is suggested that validation efforts that include wider cooperation among research institutes and full consideration of the needs of users should be promoted.

Remote sensing of vegetation pattern and condition to monitor changes in Everglades biogeochemistry

USGS Publications Warehouse

Jones, John W.

2011-01-01

Ground-based studies of biogeochemistry and vegetation patterning yield process understanding, but the amount of information gained by ground-based studies can be greatly enhanced by efficient, synoptic, and temporally resolute monitoring afforded by remote sensing. The variety of presently available Everglades vegetation maps reflects both the wide range of application requirements and the need to balance cost and capability. More effort needs to be applied to documenting and understanding vegetation distribution and condition as indicators of biogeochemistry and contamination. Ground-based and remote sensing studies should be modified to maximize their synergy and utility for adaptive management.

Multispectral analysis of ocean dumped materials

NASA Technical Reports Server (NTRS)

Johnson, R. W.

1977-01-01

Experiments conducted in the Atlantic coastal zone indicated that plumes resulting from ocean dumping of acid wastes and sewage sludge have unique spectral characteristics. Remotely sensed wide area synoptic coverage provided information on these pollution features that was not readily available from other sources. Aircraft remotely sensed photographic and multispectral scanner data were interpreted by two methods. First, qualitative analyses in which pollution features were located, mapped, and identified without concurrent sea truth and, second, quantitative analyses in which concurrently collected sea truth was used to calibrate the remotely sensed data and to determine quantitative distributions of one or more parameters in a plume.

The investigation of advanced remote sensing, radiative transfer and inversion techniques for the measurement of atmospheric constituents

NASA Technical Reports Server (NTRS)

Deepak, Adarsh; Wang, Pi-Huan

1985-01-01

The research program is documented for developing space and ground-based remote sensing techniques performed during the period from December 15, 1977 to March 15, 1985. The program involved the application of sophisticated radiative transfer codes and inversion methods to various advanced remote sensing concepts for determining atmospheric constituents, particularly aerosols. It covers detailed discussions of the solar aureole technique for monitoring columnar aerosol size distribution, and the multispectral limb scattered radiance and limb attenuated radiance (solar occultation) techniques, as well as the upwelling scattered solar radiance method for determining the aerosol and gaseous characteristics. In addition, analytical models of aerosol size distribution and simulation studies of the limb solar aureole radiance technique and the variability of ozone at high altitudes during satellite sunrise/sunset events are also described in detail.

Entropy Masking

NASA Technical Reports Server (NTRS)

Watson, Andrew B.; Stone, Leland (Technical Monitor)

1997-01-01

This paper details two projects that use the World Wide Web (WWW) for dissemination of curricula that focus on remote sensing. 1) Presenting grade-school students with the concepts used in remote sensing involves educating the teacher and then providing the teacher with lesson plans. In a NASA-sponsored project designed to introduce students in grades 4 through 12 to some of the ideas and terminology used in remote sensing, teachers from local grade schools and middle schools were recruited to write lessons about remote sensing concepts they could use in their classrooms. Twenty-two lessons were produced and placed in seven modules that include: the electromagnetic spectrum, two- and three-dimensional perception, maps and topography, scale, remote sensing, biotic and abiotic concepts, and landscape chi rise. Each lesson includes a section that evaluates what students have learned by doing the exercise. The lessons, instead of being published in a workbook and distributed to a limited number of teachers, have been placed on a WWW server, enabling much broader access to the package. This arrangement also allows for the lessons to be modified after feedback from teachers accessing the package. 2) Two-year colleges serve to teach trade skills, prepare students for enrollment in senior institutions of learning, and more and more, retrain students who have college degrees in new technologies and skills. A NASA-sponsored curriculum development project is producing a curriculum using remote sensing analysis an Earth science applications. The project has three major goals. First, it will implement the use of remote sensing data in a broad range of community college courses. Second, it will create curriculum modules and classes that are transportable to other community colleges. Third, the project will be an ongoing source of data and curricular materials to other community colleges. The curriculum will have these course pathways to a certificate; a) a Science emphasis, b) an Arts and Letters emphasis, and c) a Computer Science emphasis Each pathway includes course work in remote sensing, geographical information systems (GIS), computer science, Earth science, software and technology utilization, and communication. Distribution of products from this project to other two-year colleges will be accomplished using the WWW.

Spatial Inference for Distributed Remote Sensing Data

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Katzfuss, M.; Nguyen, H.

2014-12-01

Remote sensing data are inherently spatial, and a substantial portion of their value for scientific analyses derives from the information they can provide about spatially dependent processes. Geophysical variables such as atmopsheric temperature, cloud properties, humidity, aerosols and carbon dioxide all exhibit spatial patterns, and satellite observations can help us learn about the physical mechanisms driving them. However, remote sensing observations are often noisy and incomplete, so inferring properties of true geophysical fields from them requires some care. These data can also be massive, which is both a blessing and a curse: using more data drives uncertainties down, but also drives costs up, particularly when data are stored on different computers or in different physical locations. In this talk I will discuss a methodology for spatial inference on massive, distributed data sets that does not require moving large volumes of data. The idea is based on a combination of ideas including modeling spatial covariance structures with low-rank covariance matrices, and distributed estimation in sensor or wireless networks.

A Comparative Distributed Evaluation of the NWS-RDHM using Shape Matching and Traditional Measures with In Situ and Remotely Sensed Information

NASA Astrophysics Data System (ADS)

KIM, J.; Bastidas, L. A.

2011-12-01

We evaluate, calibrate and diagnose the performance of National Weather Service RDHM distributed model over the Durango River Basin in Colorado using simultaneously in situ and remotely sensed information from different discharge gaging stations (USGS), information about snow cover (SCV) and snow water equivalent (SWE) in situ from several SNOTEL sites and snow information distributed over the catchment from remotely sensed information (NOAA-NASA). In the process of evaluation we attempt to establish the optimal degree of parameter distribution over the catchment by calibration. A multi-criteria approach based on traditional measures (RMSE) and similarity based pattern comparisons using the Hausdorff and Earth Movers Distance approaches is used for the overall evaluation of the model performance. These pattern based approaches (shape matching) are found to be extremely relevant to account for the relatively large degree of inaccuracy in the remotely sensed SWE (judged inaccurate in terms of the value but reliable in terms of the distribution pattern) and the high reliability of the SCV (yes/no situation) while at the same time allow for an evaluation that quantifies the accuracy of the model over the entire catchment considering the different types of observations. The Hausdorff norm, due to its intrinsically multi-dimensional nature, allows for the incorporation of variables such as the terrain elevation as one of the variables for evaluation. The EMD, because of its extremely high computational overburden, requires the mapping of the set of evaluation variables into a two dimensional matrix for computation.

Mississippi Sound remote sensing study. [NASA Earth Resources Laboratory seasonal experiments

NASA Technical Reports Server (NTRS)

Atwell, B. H.; Thomann, G. C.

1973-01-01

A study of the Mississippi Sound was initiated in early 1971 by personnel of NASA Earth Resources Laboratory. Four separate seasonal experiments consisting of quasi-synoptic remote and surface measurements over the entire area were planned. Approximately 80 stations distributed throughout Mississippi Sound were occupied. Surface water temperature and secchi extinction depth were measured at each station and water samples were collected for water quality analyses. The surface distribution of three water parameters of interest from a remote sensing standpoint - temperature, salinity and chlorophyll content - are displayed in map form. Areal variations in these parameters are related to tides and winds. A brief discussion of the general problem of radiative measurements of water temperature is followed by a comparison of remotely measured temperatures (PRT-5) to surface vessel measurements.

The location and scope of geographic remote sensing training in the United States

NASA Technical Reports Server (NTRS)

Hawley, A. J.

1981-01-01

Maps displaying the distribution of graduate departments of geography in the United States and enrollments in remote sensing courses in all geography departments during the past two calendar years were compiled. It was anticipated that the two distributions would show a marked similarity since remote sensing is a relatively new geographic tool requiring specialized training to use as well as equipment not normally found in most geography departments. Thus only the larger graduate departments can afford to devote time and resources to this specialty. A broad correspondence does exist between the graduate departments of geography and the courses in remote ensing. However, the correlation is far from complete and the exceptions are frequent and large enough to cast doubt upon the accuracy of the original hypothesis. Whereas many large departments do offer courses in remote sensing, many smaller colleges and universities do also. A number of possible explanations can be offered for the discrepancies: (1) course titles, (2) the liberal arts orientation of geography departments in many universities, (3) job-oriented skills which many smaller departments have emphasized, and (4) in the tight job market many new graduates of even the larger departments have had to accept position in smaller departments and colleges.

Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature. Part 1: Measurement of Evapotranspiration at the Environmental Research Center and Determination of Priestley-taylor Parameter

NASA Technical Reports Server (NTRS)

Kotada, K.; Nakagawa, S.; Kai, K.; Yoshino, M. M.; Takeda, K.; Seki, K.

1985-01-01

In order to study the distribution of evapotranspiration in the humid region using remote sensing technology, the parameter (alpha) in the Priestley-Taylor model was determined. The daily means of the parameter alpha = 1.14 can be available from summer to autumn and alpha = to approximately 2.0 in winter. The results of the satellite and the airborne sensing done on 21st and 22nd January, 1983, are described. Using the vegetation distribution in the Tsukuba Academic New Town, as well as the radiation temperature obtained by remote sensing and the radiation data observed at the ground surface, the evapotranspiration was calculated for each vegetation type by the Priestley-Taylor method. The daily mean evapotranspiration on 22nd January, 1983, was approximately 0.4 mm/day. The differences in evapotranspiration between the vegetation types were not detectable, because the magnitude of evapotranspiration is very little in winter.

Remote-sensing based approach to forecast habitat quality under climate change scenarios.

PubMed

Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Remote-sensing based approach to forecast habitat quality under climate change scenarios

PubMed Central

Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios. PMID:28257501

Application of Lidar remote sensing to the estimation of forest canopy and stand structure

NASA Astrophysics Data System (ADS)

Lefsky, Michael Andrew

A new remote sensing instrument, SLICER (Scanning Lidar Imager of Canopies by Echo Recovery), has been applied to the problem of remote sensing the canopy and stand structure of two groups of deciduous forests, Tulip Poplar-Oak stands in the vicinity of Annapolis, MD. and bottomland hardwood stands near Williamston, NC. The ability of the SLICER instrument to remotely sense the vertical distribution of canopy structure (Canopy Height Profile), bulk canopy transmittance, and several indices of canopy height has been successfully validated using twelve stands with coincident field and SLICER estimates of canopy structure. Principal components analysis has been applied to canopy height profiles from both field sites, and three significant factors were identified, each closely related to the amount of foliage in a recognizable layer of the forest, either understory, midstory, or overstory. The distribution of canopy structure to these layers is significantly correlated with the size and number of stems supporting them. The same layered structure was shown to apply to both field and SLICER remotely sensed canopy height profiles, and to apply to SLICER remotely sensed canopy profiles from both the bottomland hardwood stands in the coastal plain of North Carolina, and to mesic Tulip-Poplars stands in the upland coastal plain of Maryland. Linear regressions have demonstrated that canopy and stand structure are correlated to both a statistically significant and useful degree. Stand age and stem density is more highly correlated to stand height, while stand basal area and aboveground biomass are more closely related to a new measure of canopy structure, the quadratic mean canopy height. A geometric model of canopy structure has been shown to explain the differing relationships between canopy structure and stand basal area for stands of Eastern Deciduous Forest and Douglas Fir Forest.

USGS Provision of Near Real Time Remotely Sensed Imagery for Emergency Response

NASA Astrophysics Data System (ADS)

Jones, B. K.

2014-12-01

The use of remotely sensed imagery in the aftermath of a disaster can have an important impact on the effectiveness of the response for many types of disasters such as floods, earthquakes, volcanic eruptions, landslides, and other natural or human-induced disasters. Ideally, responders in areas that are commonly affected by disasters would have access to archived remote sensing imagery plus the ability to easily obtain the new post event data products. The cost of obtaining and storing the data and the lack of trained professionals who can process the data into a mapping product oftentimes prevent this from happening. USGS Emergency Operations provides remote sensing and geospatial support to emergency managers by providing access to satellite images from numerous domestic and international space agencies including those affiliated with the International Charter Space and Major Disasters and their space-based assets and by hosting and distributing thousands of near real time event related images and map products through the Hazards Data Distribution System (HDDS). These data may include digital elevation models, hydrographic models, base satellite images, vector data layers such as roads, aerial photographs, and other pre and post disaster data. These layers are incorporated into a Web-based browser and data delivery service, the Hazards Data Distribution System (HDDS). The HDDS can be made accessible either to the general public or to specific response agencies. The HDDS concept anticipates customer requirements and provides rapid delivery of data and services. This presentation will provide an overview of remotely sensed imagery that is currently available to support emergency response operations and examples of products that have been created for past events that have provided near real time situational awareness for responding agencies.

An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks

NASA Astrophysics Data System (ADS)

Holben, Brent N.; Kim, Jhoon; Sano, Itaru; Mukai, Sonoyo; Eck, Thomas F.; Giles, David M.; Schafer, Joel S.; Sinyuk, Aliaksandr; Slutsker, Ilya; Smirnov, Alexander; Sorokin, Mikhail; Anderson, Bruce E.; Che, Huizheng; Choi, Myungje; Crawford, James H.; Ferrare, Richard A.; Garay, Michael J.; Jeong, Ukkyo; Kim, Mijin; Kim, Woogyung; Knox, Nichola; Li, Zhengqiang; Lim, Hwee S.; Liu, Yang; Maring, Hal; Nakata, Makiko; Pickering, Kenneth E.; Piketh, Stuart; Redemann, Jens; Reid, Jeffrey S.; Salinas, Santo; Seo, Sora; Tan, Fuyi; Tripathi, Sachchida N.; Toon, Owen B.; Xiao, Qingyang

2018-01-01

Over the past 24 years, the AErosol RObotic NETwork (AERONET) program has provided highly accurate remote-sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution including all continents and many oceanic island and coastal sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial-resolution ground-based remote-sensing networks. An effort to address these needs resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote-sensing comparison and analysis of local to mesoscale variability in aerosol properties. This paper describes the DRAGON deployments that will continue to contribute to the growing body of research related to meso- and microscale aerosol features and processes. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, Jin AU; Shin, Robert T.; Nghiem, Son V.; Yueh, Herng-Aung; Han, Hsiu C.; Lim, Harold H.; Arnold, David V.

1990-01-01

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images.

Relationships between remotely sensed fisheries distribution information and selected oceanographic parameters in the Mississippi Sound

NASA Technical Reports Server (NTRS)

Kemmerer, A. J.; Benigno, J. A.

1973-01-01

The author has identified the following significant results. A feasibility study to demonstrate the potential of satellites for providing fisheries significant information was conducted in the Mississippi Sound and adjacent offshore waters. Attempts were made to relate satellite acquired imagery to selected oceanographic parameters and then to relate these parameters to aircraft remotely sensed distribution patterns of resident surface schooling fishes. Initial results suggest that this approach is valid and that the satellite acquired imagery may have important fisheries resource assessment implications.

Determination of phytoplankton chlorophyll concentrations in the Chesapeake Bay with aircraft remote sensing

NASA Technical Reports Server (NTRS)

Harding, Lawrence W., Jr.; Itsweire, Eric C.; Esaias, Wayne E.

1992-01-01

Remote sensing measurements of the distribution of phytoplankton chlorophyll concentrations in Chesapeake Bay during 1989 are described. It is shown that remote sensing from light aircraft can complement and extend measurements made from traditional platforms and provide data of improved temporal and spatial resolution, leading to a better understanding of phytoplankton dynamics in the estuary. The developments of the winter-spring diatom bloom in the polyhaline to mesohaline regions of the estuary and of the late-spring and summer dinoflagellate blooms in oligohaline and mesohaline regions are traced. The study presents the local chlorophyll algorithm developed using the NASA Ocean Data Acquisition System data and in situ chlorophyll data, interpolated maps of chlorophyll concentration generated by applying the algorithm to aircraft radiance data, ancillary in situ data on nutrients, turbidity, streamflow, and light availability, and an interpretation of phytoplankton dynamics in terms of the chlorophyll distribution in Chesapeake Bay during 1989.

Potential for Remotely Sensed Soil Moisture Data in Hydrologic Modeling

NASA Technical Reports Server (NTRS)

Engman, Edwin T.

1997-01-01

Many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface soil moisture and portray the spatial heterogeneity of hydrologic processes and properties that one encounters in drainage basins. The hydrologic processes that may be detected include ground water recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential ET, and information about the hydrologic properties of soils and heterogeneity of hydrologic parameters. Microwave remote sensing has the potential to detect these signatures within a basin in the form of volumetric soil moisture measurements in the top few cm. These signatures should provide information on how and where to apply soil physical parameters in distributed and lumped parameter models and how to subdivide drainage basins into hydrologically similar sub-basins.

Remote sensing of vegetation pattern and condition to monitor changes in everglades biogeochemistry

USGS Publications Warehouse

Jones, J.W.

2011-01-01

Ground-based studies of biogeochemistry and vegetation patterning yield process understanding, but the amount of information gained by ground-based studies can be greatly enhanced by efficient, synoptic, and temporally resolute monitoring afforded by remote sensing. The variety of presently available Everglades vegetation maps reflects both the wide range of application requirements and the need to balance cost and capability. More effort needs to be applied to documenting and understanding vegetation distribution and condition as indicators of biogeochemistry and contamination. Ground-based and remote sensing studies should be modified to maximize their synergy and utility for adaptive management. Copyright ?? 2011 Taylor & Francis Group, LLC.

Information Extraction of Tourist Geological Resources Based on 3d Visualization Remote Sensing Image

NASA Astrophysics Data System (ADS)

Wang, X.

2018-04-01

Tourism geological resources are of high value in admiration, scientific research and universal education, which need to be protected and rationally utilized. In the past, most of the remote sensing investigations of tourism geological resources used two-dimensional remote sensing interpretation method, which made it difficult for some geological heritages to be interpreted and led to the omission of some information. This aim of this paper is to assess the value of a method using the three-dimensional visual remote sensing image to extract information of geological heritages. skyline software system is applied to fuse the 0.36 m aerial images and 5m interval DEM to establish the digital earth model. Based on the three-dimensional shape, color tone, shadow, texture and other image features, the distribution of tourism geological resources in Shandong Province and the location of geological heritage sites were obtained, such as geological structure, DaiGu landform, granite landform, Volcanic landform, sandy landform, Waterscapes, etc. The results show that using this method for remote sensing interpretation is highly recognizable, making the interpretation more accurate and comprehensive.

Mapping CDOM Concentration in Waters Influenced by the Mississippi River Plume

NASA Technical Reports Server (NTRS)

Miller, Richard L.; DelCastillo, Carlos E.; Powell, Rodney T.; DSa, Eurico; Spiering, Bruce

2002-01-01

Colored dissolved organic matter (CDOM) is often an important component of the organic carbon pool in river-dominated coastal margins. CDOM directly influences remote sensing applications through its strong absorption in the UV and blue regions of the spectrum. This effect can complicate the use of chlorophyll a retrieval algorithms and phytoplankton production models that are based on remotely sensed ocean color. As freshwater input is the principle source of CDOM in coastal margins, CDOM distribution can often be described by conservative mixing with open ocean waters and may serve as an optical tracer of riverine water. Hence, there is considerable interest in the ability to accurately measure and map CDOM concentrations as well as understand the processes that govern the optical properties and distribution of CDOM in coastal environments. We are examining CDOM dynamics in the waters influenced by the Mississippi River plume. Our program incorporates discrete samples, flow-through measurements, and remote sensing. CDOM absorption spectra of discrete samples are measured at sea using a portable, multiple pathlength waveguide system. A SAFire multi-spectral fluorescence meter provides spectral characterization of CDOM (fluorescence and absorption) using a ship flow-through system for continuous surface mapping. In situ reflectance spectra are obtained by a hand held spectroradiometer. Remotely sensed images are obtained from the SeaWiFS and CRIS (Coastal Research Imaging Spectrometer) instruments. We describe here the instruments used, sampling protocols employed, and the relationships derived between in situ measurements and remotely sensed data for this optically complex environment.

Multi-resource data-based research on remote sensing monitoring over the green tide in the Yellow Sea

NASA Astrophysics Data System (ADS)

Gao, Zhiqiang; Xu, Fuxiang; Song, Debin; Zheng, Xiangyu; Chen, Maosi

2017-09-01

This paper conducted dynamic monitoring over the green tide (large green alga—Ulva prolifera) occurred in the Yellow Sea in 2014 to 2016 by the use of multi-source remote sensing data, including GF-1 WFV, HJ-1A/1B CCD, CBERS-04 WFI, Landsat-7 ETM+ and Landsta-8 OLI, and by the combination of VB-FAH (index of Virtual-Baseline Floating macroAlgae Height) with manual assisted interpretation based on remote sensing and geographic information system technologies. The result shows that unmanned aerial vehicle (UAV) and shipborne platform could accurately monitor the distribution of Ulva prolifera in small spaces, and therefore provide validation data for the result of remote sensing monitoring over Ulva prolifera. The result of this research can provide effective information support for the prevention and control of Ulva prolifera.

Cooperative remote sensing and actuation using networked unmanned vehicles

NASA Astrophysics Data System (ADS)

Chao, Haiyang

This dissertation focuses on how to design and employ networked unmanned vehicles for remote sensing and distributed control purposes in the current information-rich world. The target scenarios are environmental or agricultural applications such as river/reservoir surveillance, wind profiling measurement, and monitoring/control of chemical leaks, etc. AggieAir, a small and low-cost unmanned aircraft system, is designed based on the remote sensing requirements from environmental monitoring missions. The state estimation problem and the advanced lateral flight controller design problem are further attacked focusing on the small unmanned aerial vehicle (UAV) platform. Then the UAV-based remote sensing problem is focused with further flight test results. Given the measurements from unmanned vehicles, the actuation algorithms are needed for missions like the diffusion control. A consensus-based central Voronoi tessellation (CVT) algorithm is proposed for better control of the diffusion process. Finally, the dissertation conclusion and some new research suggestions are presented.

Commercial potential of remote sensing data from the Earth observing system

NASA Technical Reports Server (NTRS)

Merry, Carolyn J.; Tomlin, Sandra M.

1992-01-01

The purpose was to assess the market potential of remote sensing value-added products from the Earth Observing System (EOS) platform. Sensors on the EOS platform were evaluated to determine which qualities and capabilities could be useful to the commercial user. The approach was to investigate past and future satellite data distribution programs. A questionnaire was developed for use in a telephone survey. Based on the results of the survey of companies that add value to remotely sensed data, conversations with the principal investigators in charge of each EOS sensor, a study of past commercial satellite data ventures, and reading from the commercial remote sensing industry literature, three recommendations were developed: develop a strategic plan for commercialization of EOS data, define a procedure for commercial users within the EOS data stream, and develop an Earth Observations Commercial Applications Program-like demonstration program within NASA using EOS simulated data.

Application of remote sensing to monitoring and studying dispersion in ocean dumping

NASA Technical Reports Server (NTRS)

Johnson, R. W.; Ohlhorst, C. W.

1981-01-01

Remotely sensed wide area synoptic data provides information on ocean dumping that is not readily available by other means. A qualitative approach has been used to map features, such as river plumes. Results of quantitative analyses have been used to develop maps showing quantitative distributions of one or more water quality parameters, such as suspended solids or chlorophyll a. Joint NASA/NOAA experiments have been conducted at designated dump areas in the U.S. coastal zones to determine the applicability of aircraft remote sensing systems to map plumes resulting from ocean dumping of sewage sludge and industrial wastes. A second objective is related to the evaluation of previously developed quantitative analysis techniques for studying dispersion of materials in these plumes. It was found that plumes resulting from dumping of four waste materials have distinctive spectral characteristics. The development of a technology for use in a routine monitoring system, based on remote sensing techniques, is discussed.

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

USGS Publications Warehouse

West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-01-01

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

PubMed

West, Amanda M; Evangelista, Paul H; Jarnevich, Catherine S; Young, Nicholas E; Stohlgren, Thomas J; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-10-11

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

A remote-sensing, GIS-based approach to identify, characterize, and model spawning habitat for fall-run chum salmon in a sub-arctic, glacially fed river

USGS Publications Warehouse

Wirth, Lisa; Rosenberger, Amanda; Prakash, Anupma; Gens, Rudiger; Margraf, F. Joseph; Hamazaki, Toshihide

2012-01-01

At northern limits of a species’ distribution, fish habitat requirements are often linked to thermal preferences, and the presence of overwintering habitat. However, logistical challenges and hydrologic processes typical of glacial systems could compromize the identification of these habitats, particularly in large river environments. Our goal was to identify and characterize spawning habitat for fall-run chum salmon Oncorhynchus keta and model habitat selection from spatial distributions of tagged individuals in the Tanana River, Alaska using an approach that combined ground surveys with remote sensing. Models included braiding, sinuosity, ice-free water surface area (indicating groundwater influence), and persistent ice-free water (i.e., consistent presence of ice-free water for a 12-year period according to satellite imagery). Candidate models containing persistent ice-free water were selected as most likely, highlighting the utility of remote sensing for monitoring and identifying salmon habitat in remote areas. A combination of ground and remote surveys revealed spatial and temporal thermal characteristics of these habitats that could have strong biological implications. Persistent ice-free sites identified using synthetic aperture radar appear to serve as core areas for spawning fall chum salmon, and the importance of stability through time suggests a legacy of successful reproductive effort for this homing species. These features would not be captured with a one-visit traditional survey but rather required remote-sensing monitoring of the sites through time.

Use hyperspectral remote sensing technique to monitoring pine wood nomatode disease preliminary

NASA Astrophysics Data System (ADS)

Qin, Lin; Wang, Xianghong; Jiang, Jing; Yang, Xianchang; Ke, Daiyan; Li, Hongqun; Wang, Dingyi

2016-10-01

The pine wilt disease is a devastating disease of pine trees. In China, the first discoveries of the pine wilt disease on 1982 at Dr. Sun Yat-sen's Mausoleum in Nanjing. It occurred an area of 77000 hm2 in 2005, More than 1540000 pine trees deaths in the year. Many districts of Chongqing in Three Gorges Reservoir have different degrees of pine wilt disease occurrence. It is a serious threat to the ecological environment of the reservoir area. Use unmanned airship to carry high spectrum remote sensing monitoring technology to develop the study on pine wood nematode disease early diagnosis and early warning and forecasting in this study. The hyper spectral data and the digital orthophoto map data of Fuling District Yongsheng Forestry had been achieved In September 2015. Using digital image processing technology to deal with the digital orthophoto map, the number of disease tree and its distribution is automatic identified. Hyper spectral remote sensing data is processed by the spectrum comparison algorithm, and the number and distribution of disease pine trees are also obtained. Two results are compared, the distribution area of disease pine trees are basically the same, indicating that using low air remote sensing technology to monitor the pine wood nematode distribution is successful. From the results we can see that the hyper spectral data analysis results more accurate and less affected by environmental factors than digital orthophoto map analysis results, and more environment variable can be extracted, so the hyper spectral data study is future development direction.

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

Treesearch

Steven H. Ackers; Raymond J. Davis; Keith A. Olsen; Katie M. Dugger

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last fewdecades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often...

Probability theory for 3-layer remote sensing radiative transfer model: univariate case.

PubMed

Ben-David, Avishai; Davidson, Charles E

2012-04-23

A probability model for a 3-layer radiative transfer model (foreground layer, cloud layer, background layer, and an external source at the end of line of sight) has been developed. The 3-layer model is fundamentally important as the primary physical model in passive infrared remote sensing. The probability model is described by the Johnson family of distributions that are used as a fit for theoretically computed moments of the radiative transfer model. From the Johnson family we use the SU distribution that can address a wide range of skewness and kurtosis values (in addition to addressing the first two moments, mean and variance). In the limit, SU can also describe lognormal and normal distributions. With the probability model one can evaluate the potential for detecting a target (vapor cloud layer), the probability of observing thermal contrast, and evaluate performance (receiver operating characteristics curves) in clutter-noise limited scenarios. This is (to our knowledge) the first probability model for the 3-layer remote sensing geometry that treats all parameters as random variables and includes higher-order statistics. © 2012 Optical Society of America

Remote Sensing of Vegetation Nitrogen Content for Spatially Explicit Carbon and Water Cycle Estimation

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Miller, J. R.; Chen, J. M.

2009-05-01

Foliage nitrogen concentration is a determinant of photosynthetic capacity of leaves, thereby an important input to ecological models for estimating terrestrial carbon and water budgets. Recently, spectrally continuous airborne hyperspectral remote sensing imagery has proven to be useful for retrieving an important related parameter, total chlorophyll content at both leaf and canopy scales. Thus remote sensing of vegetation biochemical parameters has promising potential for improving the prediction of global carbon and water balance patterns. In this research, we explored the feasibility of estimating leaf nitrogen content using hyperspectral remote sensing data for spatially explicit estimation of carbon and water budgets. Multi-year measurements of leaf biochemical contents of seven major boreal forest species were carried out in northeastern Ontario, Canada. The variation of leaf chlorophyll and nitrogen content in response to various growth conditions, and the relationship between them,were investigated. Despite differences in plant type (deciduous and evergreen), leaf age, stand growth conditions and developmental stages, leaf nitrogen content was strongly correlated with leaf chlorophyll content on a mass basis during the active growing season (r2=0.78). With this general correlation, leaf nitrogen content was estimated from leaf chlorophyll content at an accuracy of RMSE=2.2 mg/g, equivalent to 20.5% of the average measured leaf nitrogen content. Based on this correlation and a hyperspectral remote sensing algorithm for leaf chlorophyll content retrieval, the spatial variation of leaf nitrogen content was inferred from the airborne hyperspectral remote sensing imagery acquired by Compact Airborne Spectrographic Imager (CASI). A process-based ecological model Boreal Ecosystem Productivity Simulator (BEPS) was used for estimating terrestrial carbon and water budgets. In contrast to the scenario with leaf nitrogen content assigned as a constant value without differentiation between and within vegetation types for calculating the photosynthesis rate, we incorporated the spatial distribution of leaf nitrogen content in the model to estimate net primary productivity and evaportranspiration of boreal ecosystem. These regional estimates of carbon and water budgets with and without N mapping are compared, and the importance of this leaf biochemistry information derived from hyperspectral remote sensing in regional mapping of carbon and water fluxes is quantitatively assessed. Keywords: Remote Sensing, Leaf Nitrogen Content, Spatial Distribution, Carbon and Water Budgets, Estimation

The feasibility of utilizing remotely sensed data to assess and monitor oceanic gamefish

NASA Technical Reports Server (NTRS)

Savastano, K. J.; Leming, T. D.

1975-01-01

An investigation was conducted to establish the feasibility of utilizing remotely sensed data acquired from aircraft and satellite platforms to provide information concerning the distribution and abundance of oceanic gamefish. The data from the test area was jointly acquired by NASA, the Navy, the Air Force and NOAA/NMFS elements and private and professional fishermen in the northeastern Gulf of Mexico. The data collected has made it possible to identify fisheries significant environmental parameters for white marlin. Prediction models, based on catch data and surface truth information, were developed and demonstrated a potential for significantly reducing search by identifying areas that have a high probability of productivity. Three of the parameters utilized by the models, chlorophyll-a, sea surface temperature, and turbidity were inferred from aircraft sensor data and were tested. Effective use of Skylab data was inhibited by cloud cover and delayed delivery. Initial efforts toward establishing the feasibility of utilizing remotely sensed data to assess and monitor the distribution of oceanic gamefish has successfully identified fisheries significant oceanographic parameters and demonstrated the capability of remotely measuring most of the parameters.

State estimation for distributed systems with sensing delay

NASA Astrophysics Data System (ADS)

Alexander, Harold L.

1991-08-01

Control of complex systems such as remote robotic vehicles requires combining data from many sensors where the data may often be delayed by sensory processing requirements. The number and variety of sensors make it desirable to distribute the computational burden of sensing and estimation among multiple processors. Classic Kalman filters do not lend themselves to distributed implementations or delayed measurement data. The alternative Kalman filter designs presented in this paper are adapted for delays in sensor data generation and for distribution of computation for sensing and estimation over a set of networked processors.

An Interactive Web-Based Analysis Framework for Remote Sensing Cloud Computing

NASA Astrophysics Data System (ADS)

Wang, X. Z.; Zhang, H. M.; Zhao, J. H.; Lin, Q. H.; Zhou, Y. C.; Li, J. H.

2015-07-01

Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write complex data processing code on the web directly, so they can design their own data processing algorithm.

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

PubMed

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

PubMed Central

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. PMID:25405760

Producing fractional rangeland component predictions in a sagebrush ecosystem, a Wyoming sensitivity analysis

USGS Publications Warehouse

Xian, George; Homer, Collin G.; Granneman, Brian; Meyer, Debra K.

2012-01-01

Remote sensing information has been widely used to monitor vegetation condition and variations in a variety of ecosystems, including shrublands. Careful application of remotely sensed imagery can provide additional spatially explicit, continuous, and extensive data on the composition and condition of shrubland ecosystems. Historically, the most widely available remote sensing information has been collected by Landsat, which has offered large spatial coverage and moderate spatial resolution data globally for nearly three decades. Such medium-resolution satellite remote sensing information can quantify the distribution and variation of terrestrial ecosystems. Landsat imagery has been frequently used with other high-resolution remote sensing data to classify sagebrush components and quantify their spatial distributions (Ramsey and others, 2004; Seefeldt and Booth, 2004; Stow and others, 2008; Underwood and others, 2007). Modeling algorithms have been developed to use field measurements and satellite remote sensing data to quantify the extent and evaluate the quality of shrub ecosystem components in large geographic areas (Homer and others, 2009). The percent cover of sagebrush ecosystem components, including bare-ground, herbaceous, litter, sagebrush, and shrub, have been quantified for entire western states (Homer and others, 2012). Furthermore, research has demonstrated the use of current measurements with historical archives of Landsat imagery to quantify the variations of these components for the last two decades (Xian and others, 2012). The modeling method used to quantify the extent and spatial distribution of sagebrush components over a large area also has required considerable amounts of training data to meet targeted accuracy requirements. These training data have maintained product accuracy by ensuring that they are derived from good quality field measurements collected during appropriate ecosystem phenology and subsequently maximized by extrapolation on high-resolution remote sensing data (Homer and others, 2012). This method has proven its utility; however, to develop these products across even larger areas will require additional cost efficiencies to ensure that an adequate product can be developed for the lowest cost possible. Given the vast geographic extent of shrubland ecosystems in the western United States, identifying cost efficiencies with optimal training data development and subsequent application to medium resolution satellite imagery provide the most likely areas for methodological efficiency gains. The primary objective of this research was to conduct a series of sensitivity tests to evaluate the most optimal and practical way to develop Landsat scale information for estimating the extent and distribution of sagebrush ecosystem components over large areas in the conterminous United States. An existing dataset of sagebrush components developed from extensive field measurements, high-resolution satellite imagery, and medium resolution Landsat imagery in Wyoming was used as the reference database (Homer and others, 2012). Statistical analysis was performed to analyze the relation between the accuracy of sagebrush components and the amount and distribution of training data on Landsat scenes needed to obtain accurate predictions.

Optimizing remote sensing and GIS tools for mapping and managing the distribution of an invasive mangrove (Rhizophora mangle) on South Molokai, Hawaii

USGS Publications Warehouse

D'Iorio, M.; Jupiter, S.D.; Cochran, S.A.; Potts, D.C.

2007-01-01

In 1902, the Florida red mangrove, Rhizophora mangle L., was introduced to the island of Molokai, Hawaii, and has since colonized nearly 25% of the south coast shoreline. By classifying three kinds of remote sensing imagery, we compared abilities to detect invasive mangrove distributions and to discriminate mangroves from surrounding terrestrial vegetation. Using three analytical techniques, we compared mangrove mapping accuracy for various sensor-technique combinations. ANOVA of accuracy assessments demonstrated significant differences among techniques, but no significant differences among the three sensors. We summarize advantages and disadvantages of each sensor and technique for mapping mangrove distributions in tropical coastal environments.

Remote measurement of atmospheric pollutants

NASA Technical Reports Server (NTRS)

Allario, F.; Hoell, J.; Seals, R. K.

1979-01-01

The concentration and vertical distribution of atmospheric ammonia and ozone are remotely sensed, using dual-C02-laser multichannel infrared Heterodyne Spectrometer (1HS). Innovation makes atmospheric pollution measurements possible with nearly-quantum-noise-limited sensitivity and ultrafine spectral resolution.

Remote Sensing-Based, 5-m, Vegetation Distributions, Kougarok Study Site, Seward Peninsula, Alaska, ca. 2009 - 2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Langford, Zachary; Kumar, Jitendra; Hoffman, Forrest

A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5~m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. This data was developed using the fusion of hyperspectral, multispectral, and terrain datasets. The current data is located in the Kougarok watershed but we plan to expand this over the Seward Peninsula.

Remote sensing for industrial applications in the energy business: digital territorial data integration for planning of overhead power transmission lines (OHTLs)

NASA Astrophysics Data System (ADS)

Terrazzino, Alfonso; Volponi, Silvia; Borgogno Mondino, Enrico

2001-12-01

An investigation has been carried out, concerning remote sensing techniques, in order to assess their potential application to the energy system business: the most interesting results concern a new approach, based on digital data from remote sensing, to infrastructures with a large territorial distribution: in particular OverHead Transmission Lines, for the high voltage transmission and distribution of electricity on large distances. Remote sensing could in principle be applied to all the phases of the system lifetime, from planning to design, to construction, management, monitoring and maintenance. In this article, a remote sensing based approach is presented, targeted to the line planning: optimization of OHTLs path and layout, according to different parameters (technical, environmental and industrial). Planning new OHTLs is of particular interest in emerging markets, where typically the cartography is missing or available only on low accuracy scale (1:50.000 and lower), often not updated. Multi- spectral images can be used to generate thematic maps of the region of interest for the planning (soil coverage). Digital Elevation Models (DEMs), allow the planners to easily access the morphologic information of the surface. Other auxiliary information from local laws, environmental instances, international (IEC) standards can be integrated in order to perform an accurate optimized path choice and preliminary spotting of the OHTLs. This operation is carried out by an ABB proprietary optimization algorithm: the output is a preliminary path that bests fits the optimization parameters of the line in a life cycle approach.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-11-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Poudyal, R.; Singh, M.; Gautam, R.; Gatebe, C. K.

2016-12-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR)- http://car.gsfc.nasa.gov/. Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements

NASA Technical Reports Server (NTRS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-01-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Assimilation of remote sensing observations into a sediment transport model of China's largest freshwater lake: spatial and temporal effects.

PubMed

Zhang, Peng; Chen, Xiaoling; Lu, Jianzhong; Zhang, Wei

2015-12-01

Numerical models are important tools that are used in studies of sediment dynamics in inland and coastal waters, and these models can now benefit from the use of integrated remote sensing observations. This study explores a scheme for assimilating remotely sensed suspended sediment (from charge-coupled device (CCD) images obtained from the Huanjing (HJ) satellite) into a two-dimensional sediment transport model of Poyang Lake, the largest freshwater lake in China. Optimal interpolation is used as the assimilation method, and model predictions are obtained by combining four remote sensing images. The parameters for optimal interpolation are determined through a series of assimilation experiments evaluating the sediment predictions based on field measurements. The model with assimilation of remotely sensed sediment reduces the root-mean-square error of the predicted sediment concentrations by 39.4% relative to the model without assimilation, demonstrating the effectiveness of the assimilation scheme. The spatial effect of assimilation is explored by comparing model predictions with remotely sensed sediment, revealing that the model with assimilation generates reasonable spatial distribution patterns of suspended sediment. The temporal effect of assimilation on the model's predictive capabilities varies spatially, with an average temporal effect of approximately 10.8 days. The current velocities which dominate the rate and direction of sediment transport most likely result in spatial differences in the temporal effect of assimilation on model predictions.

[Modeling of species distribution using topography and remote sensing data, with vascular plants of the Tukuringra Range low mountain belt (Zeya state Nature Reserve, Amur Region) as a case study].

PubMed

Dudov, S V

2016-01-01

On the basis of maximum entropy method embedded in MaxEnt software, the cartographic models are designed for spatial distribution of 63 species of vascular plants inhabiting low mountain belt of the Tukuringra Range. Initial data for modeling were actual points of a species occurrence, data on remote sensing (multispectral space snapshots by Landsat), and a digital topographic model. It is found out that the structure of factors contributing to the model is related to species ecological amplitude. The distribution of stenotopic species is determined, mainly, by the topography, which thermal and humidity conditions of habitats are associated with. To the models for eurytopic species, variables formed on the basis of remote sensing contribute significantly, those variables encompassing the parameters of the soil-vegetable cover. In course of the obtained models analyzing, three principal groups of species are revealed that have similar distribution pattern. Species of the first group are restricted in their distribution by the slopes of the. River Zeya and River Giluy gorges. Species of the second group are associated with the southern macroslope of the range and with southern slopes of large rivers' valleys. The third group incorporates those species that are distributed over the whole territory under study.

Remote sensing of forest insect disturbances: Current state and future directions

NASA Astrophysics Data System (ADS)

Senf, Cornelius; Seidl, Rupert; Hostert, Patrick

2017-08-01

Insect disturbance are important agents of change in forest ecosystems around the globe, yet their spatial and temporal distribution and dynamics are not well understood. Remote sensing has gained much attention in mapping and understanding insect outbreak dynamics. Consequently, we here review the current literature on the remote sensing of insect disturbances. We suggest to group studies into three insect types: bark beetles, broadleaved defoliators, and coniferous defoliators. By so doing, we systematically compare the sensors and methods used for mapping insect disturbances within and across insect types. Results suggest that there are substantial differences between methods used for mapping bark beetles and defoliators, and between methods used for mapping broadleaved and coniferous defoliators. Following from this, we highlight approaches that are particularly suited for each insect type. Finally, we conclude by highlighting future research directions for remote sensing of insect disturbances. In particular, we suggest to: 1) Separate insect disturbances from other agents; 2) Extend the spatial and temporal domain of analysis; 3) Make use of dense time series; 4) Operationalize near-real time monitoring of insect disturbances; 5) Identify insect disturbances in the context of coupled human-natural systems; and 6) Improve reference data for assessing insect disturbances. Since the remote sensing of insect disturbances has gained much interest beyond the remote sensing community recently, the future developments identified here will help integrating remote sensing products into operational forest management. Furthermore, an improved spatiotemporal quantification of insect disturbances will support an inclusion of these processes into regional to global ecosystem models.

Simple luminosity normalization of greenness, yellowness and redness/greenness for comparison of leaf spectral profiles in multi-temporally acquired remote sensing images.

PubMed

Doi, Ryoichi

2012-09-01

Observation of leaf colour (spectral profiles) through remote sensing is an effective method of identifying the spatial distribution patterns of abnormalities in leaf colour, which enables appropriate plant management measures to be taken. However, because the brightness of remote sensing images varies with acquisition time, in the observation of leaf spectral profiles in multi-temporally acquired remote sensing images, changes in brightness must be taken into account. This study identified a simple luminosity normalization technique that enables leaf colours to be compared in remote sensing images over time. The intensity values of green and yellow (green+red) exhibited strong linear relationships with luminosity (R2 greater than 0.926) when various invariant rooftops in Bangkok or Tokyo were spectralprofiled using remote sensing images acquired at different time points. The values of the coefficient and constant or the coefficient of the formulae describing the intensity of green or yellow were comparable among the single Bangkok site and the two Tokyo sites, indicating the technique's general applicability. For single rooftops, the values of the coefficient of variation for green, yellow, and red/green were 16% or less (n=6-11), indicating an accuracy not less than those of well-established remote sensing measures such as the normalized difference vegetation index. After obtaining the above linear relationships, raw intensity values were normalized and a temporal comparison of the spectral profiles of the canopies of evergreen and deciduous tree species in Tokyo was made to highlight the changes in the canopies' spectral profiles. Future aspects of this technique are discussed herein.

Remote sensing of forest insect disturbances: Current state and future directions.

PubMed

Senf, Cornelius; Seidl, Rupert; Hostert, Patrick

2017-08-01

Insect disturbance are important agents of change in forest ecosystems around the globe, yet their spatial and temporal distribution and dynamics are not well understood. Remote sensing has gained much attention in mapping and understanding insect outbreak dynamics. Consequently, we here review the current literature on the remote sensing of insect disturbances. We suggest to group studies into three insect types: bark beetles, broadleaved defoliators, and coniferous defoliators. By so doing, we systematically compare the sensors and methods used for mapping insect disturbances within and across insect types. Results suggest that there are substantial differences between methods used for mapping bark beetles and defoliators, and between methods used for mapping broadleaved and coniferous defoliators. Following from this, we highlight approaches that are particularly suited for each insect type. Finally, we conclude by highlighting future research directions for remote sensing of insect disturbances. In particular, we suggest to: 1) Separate insect disturbances from other agents; 2) Extend the spatial and temporal domain of analysis; 3) Make use of dense time series; 4) Operationalize near-real time monitoring of insect disturbances; 5) Identify insect disturbances in the context of coupled human-natural systems; and 6) Improve reference data for assessing insect disturbances. Since the remote sensing of insect disturbances has gained much interest beyond the remote sensing community recently, the future developments identified here will help integrating remote sensing products into operational forest management. Furthermore, an improved spatiotemporal quantification of insect disturbances will support an inclusion of these processes into regional to global ecosystem models.

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

PubMed Central

Qi, Baogui; Zhuang, Yin; Chen, He; Chen, Liang

2018-01-01

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited. PMID:29693585

Watermarking techniques for electronic delivery of remote sensing images

NASA Astrophysics Data System (ADS)

Barni, Mauro; Bartolini, Franco; Magli, Enrico; Olmo, Gabriella

2002-09-01

Earth observation missions have recently attracted a growing interest, mainly due to the large number of possible applications capable of exploiting remotely sensed data and images. Along with the increase of market potential, the need arises for the protection of the image products. Such a need is a very crucial one, because the Internet and other public/private networks have become preferred means of data exchange. A critical issue arising when dealing with digital image distribution is copyright protection. Such a problem has been largely addressed by resorting to watermarking technology. A question that obviously arises is whether the requirements imposed by remote sensing imagery are compatible with existing watermarking techniques. On the basis of these motivations, the contribution of this work is twofold: assessment of the requirements imposed by remote sensing applications on watermark-based copyright protection, and modification of two well-established digital watermarking techniques to meet such constraints. More specifically, the concept of near-lossless watermarking is introduced and two possible algorithms matching such a requirement are presented. Experimental results are shown to measure the impact of watermark introduction on a typical remote sensing application, i.e., unsupervised image classification.

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery.

PubMed

Qi, Baogui; Shi, Hao; Zhuang, Yin; Chen, He; Chen, Liang

2018-04-25

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks.

PubMed

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-04-26

With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction.

A revised surface resistance parameterisation for estimating latent heat flux from remotely sensed data

NASA Astrophysics Data System (ADS)

Song, Yi; Wang, Jiemin; Yang, Kun; Ma, Mingguo; Li, Xin; Zhang, Zhihui; Wang, Xufeng

2012-07-01

Estimating evapotranspiration (ET) is required for many environmental studies. Remote sensing provides the ability to spatially map latent heat flux. Many studies have developed approaches to derive spatially distributed surface energy fluxes from various satellite sensors with the help of field observations. In this study, remote-sensing-based λE mapping was conducted using a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image. The remotely sensed data and field observations employed in this study were obtained from Watershed Allied Telemetry Experimental Research (WATER). A biophysics-based surface resistance model was revised to account for water stress and temperature constraints. The precision of the results was validated using 'ground truth' data obtained by eddy covariance (EC) system. Scale effects play an important role, especially for parameter optimisation and validation of the latent heat flux (λE). After considering the footprint of EC, the λE derived from the remote sensing data was comparable to the EC measured value during the satellite's passage. The results showed that the revised surface resistance parameterisation scheme was useful for estimating the latent heat flux over cropland in arid regions.

Sustainable transport planning using GIS and remote sensing: an integrated approach

NASA Astrophysics Data System (ADS)

Giorgoudis, Marios D.; Hadjimitsis, Diofantos G.; Shiftan, Yoram

2014-08-01

The main advantage of using GIS is its ability to access and analyze spatially distributed data. The applications of GIS to transportation can be viewed as involving either on data retrieval; data integrator; or data analysis. The use of remote sensing can assist the retrieval of land use changes. Indeed, the integration of GIS and remote sensing will be used to fill the gap in the smart transport planning. A four step research is going to be done in order to try to integrate the usage of GIS and remote sensing to sustainable transport planning. The proposed research will be held in the city of Limassol, Cyprus. The data that are going to be used are data that are going to be collected through questionnaires, and other available data from the Cyprus Public Works Department and from the Remote Sensing Laboratory and Geo-Environment Research Lab of the Cyprus University of Technology. Overall, statistical analysis and market segmentation of data will be done, the land usage will be examined, and a scenario building on mode choice will be held. This paper presents an overview of the methodology that will be adopted.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks

PubMed Central

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-01-01

Simple Summary The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. Abstract With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction. PMID:29701686

On the sensitivity of numerical weather prediction to remotely sensed marine surface wind data - A simulation study

NASA Technical Reports Server (NTRS)

Cane, M. A.; Cardone, V. J.; Halem, M.; Halberstam, I.

1981-01-01

The reported investigation has the objective to assess the potential impact on numerical weather prediction (NWP) of remotely sensed surface wind data. Other investigations conducted with similar objectives have not been satisfactory in connection with a use of procedures providing an unrealistic distribution of initial errors. In the current study, care has been taken to duplicate the actual distribution of information in the conventional observing system, thus shifting the emphasis from accuracy of the data to the data coverage. It is pointed out that this is an important consideration in assessing satellite observing systems since experience with sounder data has shown that improvements in forecasts due to satellite-derived information is due less to a general error reduction than to the ability to fill data-sparse regions. The reported study concentrates on the evaluation of the observing system simulation experimental design and on the assessment of the potential of remotely sensed marine surface wind data.

Capturing the fugitive: Applying remote sensing to terrestrial animal distribution and diversity

NASA Astrophysics Data System (ADS)

Leyequien, Euridice; Verrelst, Jochem; Slot, Martijn; Schaepman-Strub, Gabriela; Heitkönig, Ignas M. A.; Skidmore, Andrew

2007-02-01

Amongst many ongoing initiatives to preserve biodiversity, the Millennium Ecosystem Assessment again shows the importance to slow down the loss of biological diversity. However, there is still a gap in the overview of global patterns of species distributions. This paper reviews how remote sensing has been used to assess terrestrial faunal diversity, with emphasis on proxies and methodologies, while exploring prospective challenges for the conservation and sustainable use of biodiversity. We grouped and discussed papers dealing with the faunal taxa mammals, birds, reptiles, amphibians, and invertebrates into five classes of surrogates of animal diversity: (1) habitat suitability, (2) photosynthetic productivity, (3) multi-temporal patterns, (4) structural properties of habitat, and (5) forage quality. It is concluded that the most promising approach for the assessment, monitoring, prediction, and conservation of faunal diversity appears to be the synergy of remote sensing products and auxiliary data with ecological biodiversity models, and a subsequent validation of the results using traditional observation techniques.

Hillslope characterization: Identifying key controls on local-scale plant communities' distribution using remote sensing and subsurface data fusion.

NASA Astrophysics Data System (ADS)

Falco, N.; Wainwright, H. M.; Dafflon, B.; Leger, E.; Peterson, J.; Steltzer, H.; Wilmer, C.; Williams, K. H.; Hubbard, S. S.

2017-12-01

Mountainous watershed systems are characterized by extreme heterogeneity in hydrological and pedological properties that influence biotic activities, plant communities and their dynamics. To gain predictive understanding of how ecosystem and watershed system evolve under climate change, it is critical to capture such heterogeneity and to quantify the effect of key environmental variables such as topography, and soil properties. In this study, we exploit advanced geophysical and remote sensing techniques - coupled with machine learning - to better characterize and quantify the interactions between plant communities' distribution and subsurface properties. First, we have developed a remote sensing data fusion framework based on the random forest (RF) classification algorithm to estimate the spatial distribution of plant communities. The framework allows the integration of both plant spectral and structural information, which are derived from multispectral satellite images and airborne LiDAR data. We then use the RF method to evaluate the estimated plant community map, exploiting the subsurface properties (such as bedrock depth, soil moisture and other properties) and geomorphological parameters (such as slope, curvature) as predictors. Datasets include high-resolution geophysical data (electrical resistivity tomography) and LiDAR digital elevation maps. We demonstrate our approach on a mountain hillslope and meadow within the East River watershed in Colorado, which is considered to be a representative headwater catchment in the Upper Colorado Basin. The obtained results show the existence of co-evolution between above and below-ground processes; in particular, dominant shrub communities in wet and flat areas. We show that successful integration of remote sensing data with geophysical measurements allows identifying and quantifying the key environmental controls on plant communities' distribution, and provides insights into their potential changes in the future climate conditions.

Analysis of Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing monitoring key technology in coastal wetland

NASA Astrophysics Data System (ADS)

Ma, Yi; Zhang, Jie; Zhang, Jingyu

2016-01-01

The coastal wetland, a transitional zone between terrestrial ecosystems and marine ecosystems, is the type of great value to ecosystem services. For the recent 3 decades, area of the coastal wetland is decreasing and the ecological function is gradually degraded with the rapid development of economy, which restricts the sustainable development of economy and society in the coastal areas of China in turn. It is a major demand of the national reality to carry out the monitoring of coastal wetlands, to master the distribution and dynamic change. UAV, namely unmanned aerial vehicle, is a new platform for remote sensing. Compared with the traditional satellite and manned aerial remote sensing, it has the advantage of flexible implementation, no cloud cover, strong initiative and low cost. Image-spectrum merging is one character of high spectral remote sensing. At the same time of imaging, the spectral curve of each pixel is obtained, which is suitable for quantitative remote sensing, fine classification and target detection. Aimed at the frontier and hotspot of remote sensing monitoring technology, and faced the demand of the coastal wetland monitoring, this paper used UAV and the new remote sensor of high spectral imaging instrument to carry out the analysis of the key technologies of monitoring coastal wetlands by UAV on the basis of the current situation in overseas and domestic and the analysis of developing trend. According to the characteristic of airborne hyperspectral data on UAV, that is "three high and one many", the key technology research that should develop are promoted as follows: 1) the atmosphere correction of the UAV hyperspectral in coastal wetlands under the circumstance of complex underlying surface and variable geometry, 2) the best observation scale and scale transformation method of the UAV platform while monitoring the coastal wetland features, 3) the classification and detection method of typical features with high precision from multi scale hyperspectral images based on time sequence. The research results of this paper will help to break the traditional concept of remote sensing monitoring coastal wetlands by satellite and manned aerial vehicle, lead the trend of this monitoring technology, and put forward a new technical proposal for grasping the distribution of the coastal wetland and the changing trend and carrying out the protection and management of the coastal wetland.

A New Airborne Lidar for Remote Sensing of Canopy Fluorescence and Vertical Profile

NASA Astrophysics Data System (ADS)

Ounis, A.; Bach, J.; Mahjoub, A.; Daumard, F.; Moya, I.; Goulas, Y.

2016-06-01

We report the development of a new lidar system for airborne remote sensing of chlorophyll fluorescence (ChlF) and vertical profile of canopies. By combining laserinduced fluorescence (LIF), sun-induced fluorescence (SIF) and canopy height distribution, the new instrument will low the simultaneous assessment of gross primary production (GPP), photosynthesis efficiency and above ground carbon stocks. Technical issues of the lidar development are discussed and expected performances are presented.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

PubMed Central

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2018-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions. PMID:29657544

High resolution remote sensing information identification for characterizing uranium mineralization setting in Namibia

NASA Astrophysics Data System (ADS)

Zhang, Jie-Lin; Wang, Jun-hu; Zhou, Mi; Huang, Yan-ju; Xuan, Yan-xiu; Wu, Ding

2011-11-01

The modern Earth Observation System (EOS) technology takes important role in the uranium geological exploration, and high resolution remote sensing as one of key parts of EOS is vital to characterize spectral and spatial information of uranium mineralization factors. Utilizing satellite high spatial resolution and hyperspectral remote sensing data (QuickBird, Radarsat2, ASTER), field spectral measurement (ASD data) and geological survey, this paper established the spectral identification characteristics of uranium mineralization factors including six different types of alaskite, lower and upper marble of Rössing formation, dolerite, alkali metasomatism, hematization and chloritization in the central zone of Damara Orogen, Namibia. Moreover, adopted the texture information identification technology, the geographical distribution zones of ore-controlling faults and boundaries between the different strata were delineated. Based on above approaches, the remote sensing geological anomaly information and image interpretation signs of uranium mineralization factors were extracted, the metallogenic conditions were evaluated, and the prospective areas have been predicted.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards.

PubMed

Wright, Daniel B; Mantilla, Ricardo; Peters-Lidard, Christa D

2017-04-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.

Sensible Success

NASA Technical Reports Server (NTRS)

2001-01-01

Commercial remote sensing uses satellite imagery to provide valuable information about the planet's features. By capturing light reflected from the Earth's surface with cameras or sensor systems, usually mounted on an orbiting satellite, data is obtained for business enterprises with an interest in land feature distribution. Remote sensing is practical when applied to large-area coverage, such as agricultural monitoring, regional mapping, environmental assessment, and infrastructure planning. For example, cellular service providers use satellite imagery to select the most ideal location for a communication tower. Crowsey Incorporated has the ability to use remote sensing capabilities to conduct spatial geographic visualizations and other remote-sensing services. Presently, the company has found a demand for these services in the area of litigation support. By using spatial information and analyses, Crowsey helps litigators understand and visualize complex issues and then to communicate a clear argument, with complete indisputable evidence. Crowsey Incorporated is a proud partner in NASA's Mississippi Space Commerce Initiative, with research offices at the John C. Stennis Space Center.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

NASA Technical Reports Server (NTRS)

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2017-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, Rainy Day can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, Rainy Day can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. Rainy Day can be useful for hazard modeling under nonstationary conditions.

Remote Sensing of Aerosol and their Radiative Forcing of Climate

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Tanre, Didier; Remer, Lorraine A.

1999-01-01

Remote sensing of aerosol and aerosol radiative forcing of climate is going through a major transformation. The launch in next few years of new satellites designed specifically for remote sensing of aerosol is expected to further revolutionized aerosol measurements: until five years ago satellites were not designed for remote sensing of aerosol. Aerosol optical thickness was derived as a by product, only over the oceans using one AVHRR channel with errors of approx. 50%. However it already revealed a very important first global picture of the distribution and sources of aerosol. In the last 5 years we saw the introduction of polarization and multi-view observations (POLDER and ATSR) for satellite remote sensing of aerosol over land and ocean. Better products are derived from AVHRR using its two channels. The new TOMS aerosol index shows the location and transport of aerosol over land and ocean. Now we anticipate the launch of EOS-Terra with MODIS, MISR and CERES on board for multi-view, multi-spectral remote sensing of aerosol and its radiative forcing. This will allow application of new techniques, e.g. using a wide spectral range (0.55-2.2 microns) to derive precise optical thickness, particle size and mass loading. Aerosol is transparent in the 2.2 microns channel, therefore this channel can be used to detect surface features that in turn are used to derive the aerosol optical thickness in the visible part of the spectrum. New techniques are developed to derive the aerosol single scattering albedo, a measure of absorption of sunlight, and techniques to derive directly the aerosol forcing at the top of the atmosphere. In the last 5 years a global network of sun/sky radiometers was formed, designed to communicate in real time the spectral optical thickness from 50-80 locations every day, every 15 minutes. The sky angular and spectral information is also measured and used to retrieve the aerosol size distribution, refractive index, single scattering albedo and the spectral flux reaching the surface. Effort to introduce remote sensing from lidars will literally additional dimension to aerosol remote sensing. The vertical dimension is a critical link between the global satellite observations and modeling of aerosol transport. Lidars are also critical to study aerosol impact on cloud microphysics and reflectance. Both lidar ground networks and satellite systems are in development. This new capability is expected to put remote sensing in the forefront of aerosol and climate studies. Together with field experiments, chemical analysis and chemical transport models we anticipate, in the next decade, to be able to resolve some of the outstanding questions regarding the role of aerosol in climate, in atmospheric chemistry and its influence on human health and life on this planet.

Utilizing multisource remotely sensed data to dynamically monitor drought in China

NASA Astrophysics Data System (ADS)

Liu, Sanchao; Li, Wenbo

2011-12-01

Drought is one of major nature disaster in the world and China. China has a vast territory and very different spatio-temporal distribution weather condition. Therefore, drought disasters occur frequently throughout China, which may affect large areas and cause great economic loss every year. In this paper, geostationary meteorological remote sensing data, FY-2C/D/E VISSR and three quantitative remotely sensed models including Cloud Parameters Method (CPM), Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI) have been used to dynamically monitor severe drought in southwest China from 2009 to 2010. The results have effectively revealed the occurrence, development and disappearance of this drought event. The monitoring results can be used for the relevant disaster management departments' decision-making works.

Research of BRDF effects on remote sensing imagery

NASA Astrophysics Data System (ADS)

Nina, Peng; Kun, Wang; Tao, Li; Yang, Pan

2011-08-01

The gray distribution and contrast of the optical satellite remote sensing imagery in the same kind of ground surface acquired by sensor is quite different, it depends not only on the satellite's observation and the sun incidence orientation but also the structural and optical properties of the surface. Therefore, the objectives of this research are to analyze the different BRDF characters of soil, vegetation, water and urban surface and also their BRDF effects on the quality of satellite image through 6S radiative transfer model. Furthermore, the causation of CCD blooming and spilling by ground reflectance is discussed by using QUICKBIRD image data and the corresponding ground image data. The general conclusion of BRDF effects on remote sensing imagery is proposed.

Particle size distribution of river-suspended sediments determined by in situ measured remote-sensing reflectance.

PubMed

Zhang, Yuanzhi; Huang, Zhaojun; Chen, Chuqun; He, Yijun; Jiang, Tingchen

2015-07-10

Suspended sediments in water bodies are classified into organic and inorganic matter and have been investigated by remote-sensing technology for years. Focusing on inorganic matter, however, detailed information such as the grain size of this matter has not been provided yet. In this study, we present a new solution for estimating inorganic suspended sediments' size distribution in highly complex Case 2 waters by using a simple spectrometer sensor rather than a backscattering sensor. An experiment was carried out in the Pearl River Estuary (PRE) in the dry season to collect the remote-sensing reflectance (R_rs) and particle size distribution (PSD) of inorganic suspended sediments. Based on Mie theory, PSDs in the PRE waters were retrieved by R_rs, colored dissolved organic matter, and phytoplankton. The retrieved median diameters in 12 stations show good agreement with those of laboratory analysis at root mean square error of 2.604 μm (27.63%), bias of 1.924 μm (20.42%), and mean absolute error of 2.298 μm (24.37%). The retrieved PSDs and previous PSDs were compared, and the features of PSDs in the PRE waters were concluded.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

Remote sensing strategies for global resource exploration and environmental management

NASA Astrophysics Data System (ADS)

Henderson, Frederick B.

Since 1972, satellite remote sensing, when integrated with other exploration techniques, has demonstrated operational exploration and engineering cost savings and reduced exploration risks through improved geological mapping. Land and ocean remote sensing satellite systems under development for the 1990's by the United States, France, Japan, Canada, ESA, Russia, China, and others, will significantly increase our ability to explore for, develop, and manage energy and mineral resources worldwide. A major difference between these systems is the "Open Skies" and "Non-Discriminatory Access to Data" policies as have been practiced by the U.S. and France and the restrictive nationalistic data policies as have been practiced by Russia and India. Global exploration will use satellite remote sensing to better map regional structural and basin-like features that control the distribution of energy and mineral resources. Improved sensors will better map lithologic and stratigraphic units and identify alteration effects in rocks, soils, and vegetation cover indicative of undiscovered subsurface resources. These same sensors will also map and monitor resource development. The use of satellite remote sensing data will grow substantially through increasing integration with other geophysical, geochemical, and geologic data using improved geographic information systems (GIS). International exploration will focus on underdeveloped countries rather than on mature exploration areas such as the United States, Europe, and Japan. Energy and mineral companies and government agencies in these countries and others will utilize available remote sensing data to acquire economic intelligence on global resources. If the "Non-Discriminatory Access to Data" principle is observed by satellite producing countries, exploration will remain competitive "on the ground". In this manner, remote sensing technology will continue to be developed to better explore for and manage the world's needed resources. If, however, satellite producing countries follow the Russian and Indian lead and restrict civil satellite data as tools of their national security and economic policies, remote sensing technology may become internationally competitive in space, redundant, prohibitively expensive, and generally unavailable to the world community.

Modeling Atmospheric CO2 Processes to Constrain the Missing Sink

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Denning, A. S.; Erickson, D. J.; Collatz, J. C.; Pawson, S.

2005-01-01

We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.

The role of remote sensing and GIS for spatial prediction of vector-borne diseases transmission: a systematic review.

PubMed

Palaniyandi, M

2012-12-01

There have been several attempts made to the appreciation of remote sensing and GIS for the study of vectors, biodiversity, vector presence, vector abundance and the vector-borne diseases with respect to space and time. This study was made for reviewing and appraising the potential use of remote sensing and GIS applications for spatial prediction of vector-borne diseases transmission. The nature of the presence and the abundance of vectors and vector-borne diseases, disease infection and the disease transmission are not ubiquitous and are confined with geographical, environmental and climatic factors, and are localized. The presence of vectors and vector-borne diseases is most complex in nature, however, it is confined and fueled by the geographical, climatic and environmental factors including man-made factors. The usefulness of the present day availability of the information derived from the satellite data including vegetation indices of canopy cover and its density, soil types, soil moisture, soil texture, soil depth, etc. is integrating the information in the expert GIS engine for the spatial analysis of other geoclimatic and geoenvironmental variables. The present study gives the detailed information on the classical studies of the past and present, and the future role of remote sensing and GIS for the vector-borne diseases control. The ecological modeling directly gives us the relevant information to understand the spatial variation of the vector biodiversity, vector presence, vector abundance and the vector-borne diseases in association with geoclimatic and the environmental variables. The probability map of the geographical distribution and seasonal variations of horizontal and vertical distribution of vector abundance and its association with vector -borne diseases can be obtained with low cost remote sensing and GIS tool with reliable data and speed.

Developing particle emission inventories using remote sensing (PEIRS).

PubMed

Tang, Chia-Hsi; Coull, Brent A; Schwartz, Joel; Lyapustin, Alexei I; Di, Qian; Koutrakis, Petros

2017-01-01

Information regarding the magnitude and distribution of PM 2.5 emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time-consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially resolved emission inventories for PM 2.5 . This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeastern United States during the period 2002-2013 using high-resolution 1 km × 1 km aerosol optical depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R 2 = 0.66-0.71, CV = 17.7-20%). Predicted emissions are found to correlate with land use parameters, suggesting that our method can capture emissions from land-use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively. We present a novel method, particle emission inventories using remote sensing (PEIRS), using remote sensing data to construct spatially resolved PM 2.5 emission inventories. Both primary emissions and secondary formations are captured and predicted at a high spatial resolution of 1 km × 1 km. Using PEIRS, large and comprehensive data sets can be generated cost-effectively and can inform development of air quality regulations.

Hyperspectral remote sensing study of harmful algal blooms in the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Nie, Yixiang

Recent development of hyperspectral remote sensing provides capability to identify and classify harmful algal blooms beyond the estimation of chlorophyll concentrations. This study uses hyperspectral data to extract spectral signatures, classify algal blooms, and map the spatial distribution of the algal blooms in the upper Chesapeake Bay. Furthermore, water quality parameters from ground stations have been used together with remote sensing data to provide better understanding of the formation and transformation of the life cycle of harmful algal blooms, and the cause of their outbreaks in the upper Chesapeake Bay. The present results show a strong and significant positive correlation between chlorophyll concentrations and total organic nitrogen concentrations. This relation suggests that total organic nitrogen played an important role in triggering the harmful algal blooms in the upper Chesapeake Bay in this study. This study establishes an integrated approach which combines hyperspectral imaging with multispectral ocean color remote sensing data and traditional water quality monitoring system in the study of harmful algal blooms in small water bodies such as the Chesapeake Bay. Presently, remote sensing is well integrated into the research community, but is less commonly used by resource managers. This dissertation couples remote sensing technologies with specific monitoring programs. The present results will help natural resource managers, local authorities, and the public to utilize an integrated approach in order to better understand, evaluate, preserve, and restore the health of the Chesapeake Bay waters and habitats.

[Analysis of related factors of slope plant hyperspectral remote sensing].

PubMed

Sun, Wei-Qi; Zhao, Yun-Sheng; Tu, Lin-Ling

2014-09-01

In the present paper, the slope gradient, aspect, detection zenith angle and plant types were analyzed. In order to strengthen the theoretical discussion, the research was under laboratory condition, and modeled uniform slope for slope plant. Through experiments we found that these factors indeed have influence on plant hyperspectral remote sensing. When choosing slope gradient as the variate, the blade reflection first increases and then decreases as the slope gradient changes from 0° to 36°; When keeping other factors constant, and only detection zenith angle increasing from 0° to 60°, the spectral characteristic of slope plants do not change significantly in visible light band, but decreases gradually in near infrared band; With only slope aspect changing, when the dome meets the light direction, the blade reflectance gets maximum, and when the dome meets the backlit direction, the blade reflectance gets minimum, furthermore, setting the line of vertical intersection of incidence plane and the dome as an axis, the reflectance on the axis's both sides shows symmetric distribution; In addition, spectral curves of different plant types have a lot differences between each other, which means that the plant types also affect hyperspectral remote sensing results of slope plants. This research breaks through the limitations of the traditional vertical remote sensing data collection and uses the multi-angle and hyperspectral information to analyze spectral characteristics of slope plants. So this research has theoretical significance to the development of quantitative remote sensing, and has application value to the plant remote sensing monitoring.

Scintillometer networks for calibration and validation of energy balance and soil moisture remote sensing algorithms

NASA Astrophysics Data System (ADS)

Hendrickx, Jan M. H.; Kleissl, Jan; Gómez Vélez, Jesús D.; Hong, Sung-ho; Fábrega Duque, José R.; Vega, David; Moreno Ramírez, Hernán A.; Ogden, Fred L.

2007-04-01

Accurate estimation of sensible and latent heat fluxes as well as soil moisture from remotely sensed satellite images poses a great challenge. Yet, it is critical to face this challenge since the estimation of spatial and temporal distributions of these parameters over large areas is impossible using only ground measurements. A major difficulty for the calibration and validation of operational remote sensing methods such as SEBAL, METRIC, and ALEXI is the ground measurement of sensible heat fluxes at a scale similar to the spatial resolution of the remote sensing image. While the spatial length scale of remote sensing images covers a range from 30 m (LandSat) to 1000 m (MODIS) direct methods to measure sensible heat fluxes such as eddy covariance (EC) only provide point measurements at a scale that may be considerably smaller than the estimate obtained from a remote sensing method. The Large Aperture scintillometer (LAS) flux footprint area is larger (up to 5000 m long) and its spatial extent better constraint than that of EC systems. Therefore, scintillometers offer the unique possibility of measuring the vertical flux of sensible heat averaged over areas comparable with several pixels of a satellite image (up to about 40 Landsat thermal pixels or about 5 MODIS thermal pixels). The objective of this paper is to present our experiences with an existing network of seven scintillometers in New Mexico and a planned network of three scintillometers in the humid tropics of Panama and Colombia.

[Combustion temperature measurement of pyrotechnic composition using remote sensing Fourier transform infrared spectrometry].

PubMed

Zhou, Xin-li; Li, Yan; Liu, Zu-liang; Zhu, Chang-jiang; Wang, Jun-de; Lu, Chun-xu

2002-10-01

In this paper, combustion characterization of pyrotechnic composition is investigated using a remote sensing Fourier transform infrared spectrometry. The emission spectra have been recorded between 4,700 and 740 cm-1 with a spectral resolution of 4 cm-1. The combustion temperature can be determined remotely from spectral line intensity distribution of the fine structure of the emission fundamental band of gaseous products such as HF. The relationship between combustion temperature and combustion time has been given. Results show that there is a violent mutative temperature field with bigger temperature gradient near combustion surface. It reveals that the method of temperature measurement using remote sensing FTIR for flame temperature of unstable, violent and short time combustion on real time is a rapid, accurate and sensitive technique without interference the flame temperature field. Potential prospects of temperature measurement, gas product concentration measurement and combustion mechanism are also revealed.

Remote Sensing Systems to Detect and Analyze Oil Spills on the U.S. Outer Continental Shelf - A State of the Art Assessment

DTIC Science & Technology

2016-08-18

multi- sensor remote sensing approach to describe the distribution of oil from the DWH spill. They used airborne and satellite , multi- and hyperspectral...Experimental Sensors e.g., Acoustic and Nuclear Magnetic Resonance (NMR) (Fingas and Brown, 2012; Puestow et al., 2013). These are further...ship, aerial - aircraft, aerostat or UAV, or satellite ), among other classification criteria. A comprehensive review of sensor categories employed

Application of remote sensing to the geological study of the alkaline complex region of Itatiaia. [Brazil

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Rodrigues, J. E.

1980-01-01

The methodology of remote sensing applied to geological study in a complex area was evaluated. Itatiaia was selected as a test area, which covers the alkaline massives and its precambrian basement. LANDSAT-MSS and radar mosaic of the RADAMBRASIL Project were used for photointerpretation. Previous geological works were consulted and many discrepancies in the distribution of stratigraphic units were found. Moreover, structural lineaments and talus deposits were clearly delineated.

Spatial analysis of vector-borne infectious diseases and ecological indicators using GIS and remote sensing

NASA Astrophysics Data System (ADS)

Anh, N. K.; Liou, Y. A.

2017-12-01

Ecological and climate indicators play a vital role in defining patterns of human activities and behaviors, such as seasonal features, migration, winter-summer lifestyles, which in turn might be associated with vector-borne disease habitats and transmission risks. Remote sensing has been instrumental in deriving environmental variables and indicators. GIS is shown to be a powerful tool in spatiotemporal visualization and distribution of vector-borne diseases and for analysis of associations between environmental conditions and characteristics of vector-borne habitats. Vietnam is in the sub-tropical climate zone with high humidity and abundant precipitation, while the distribution of precipitation is uneven leading to frequently annual occurrence of drought and flood disasters. Moreover, urban heat island effect is significantly enhanced in urbanized areas in recent years. The increase in the frequency and magnitude of severity of weather extremes that are potentially linked to climate change and anthropogenic processes have highlighted the demand of research into health risk assessment and adaptive capacity. This research focuses on the analysis of physical features of environmental indicators and its association with vector-borne diseases as well as adaptive capacity. The study illustrates how remotely sensed data has been utilized in geohealth applications, surveillance, and health risk mapping. In addition, promising possibilities of allowing disease early-warning systems with citizen participation platform will be proposed. Keywords: Vector-borne diseases; environmental indicators; remote sensing; GIS; Vietnam.

Cooling effect of rivers on metropolitan Taipei using remote sensing.

PubMed

Chen, Yen-Chang; Tan, Chih-Hung; Wei, Chiang; Su, Zi-Wen

2014-01-23

This study applied remote sensing technology to analyze how rivers in the urban environment affect the surface temperature of their ambient areas. While surface meteorological stations can supply accurate data points in the city, remote sensing can provide such data in a two-dimensional (2-D) manner. The goal of this paper is to apply the remote sensing technique to further our understanding of the relationship between the surface temperature and rivers in urban areas. The 2-D surface temperature data was retrieved from Landsat-7 thermal infrared images, while data collected by Formosat-2 was used to categorize the land uses in the urban area. The land surface temperature distribution is simulated by a sigmoid function with nonlinear regression analysis. Combining the aforementioned data, the range of effect on the surface temperature from rivers can be derived. With the remote sensing data collected for the Taipei Metropolitan area, factors affecting the surface temperature were explored. It indicated that the effect on the developed area was less significant than on the ambient nature zone; moreover, the size of the buffer zone between the river and city, such as the wetlands or flood plain, was found to correlate with the affected distance of the river surface temperature.

Cooling Effect of Rivers on Metropolitan Taipei Using Remote Sensing

PubMed Central

Chen, Yen-Chang; Tan, Chih-Hung; Wei, Chiang; Su, Zi-Wen

2014-01-01

This study applied remote sensing technology to analyze how rivers in the urban environment affect the surface temperature of their ambient areas. While surface meteorological stations can supply accurate data points in the city, remote sensing can provide such data in a two-dimensional (2-D) manner. The goal of this paper is to apply the remote sensing technique to further our understanding of the relationship between the surface temperature and rivers in urban areas. The 2-D surface temperature data was retrieved from Landsat-7 thermal infrared images, while data collected by Formosat-2 was used to categorize the land uses in the urban area. The land surface temperature distribution is simulated by a sigmoid function with nonlinear regression analysis. Combining the aforementioned data, the range of effect on the surface temperature from rivers can be derived. With the remote sensing data collected for the Taipei Metropolitan area, factors affecting the surface temperature were explored. It indicated that the effect on the developed area was less significant than on the ambient nature zone; moreover, the size of the buffer zone between the river and city, such as the wetlands or flood plain, was found to correlate with the affected distance of the river surface temperature. PMID:24464232

Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H2O,δD} pairs - a review

NASA Astrophysics Data System (ADS)

Schneider, Matthias; Wiegele, Andreas; Barthlott, Sabine; González, Yenny; Christner, Emanuel; Dyroff, Christoph; García, Omaira E.; Hase, Frank; Blumenstock, Thomas; Sepúlveda, Eliezer; Mengistu Tsidu, Gizaw; Takele Kenea, Samuel; Rodríguez, Sergio; Andrey, Javier

2016-07-01

In the lower/middle troposphere, {H2O,δD} pairs are good proxies for moisture pathways; however, their observation, in particular when using remote sensing techniques, is challenging. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating the remote sensing with in situ measurement techniques. The aim is to retrieve calibrated tropospheric {H2O,δD} pairs from the middle infrared spectra measured from ground by FTIR (Fourier transform infrared) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper, we present the final MUSICA products, and discuss the characteristics and potential of the NDACC/FTIR and MetOp/IASI {H2O,δD} data pairs. First, we briefly resume the particularities of an {H2O,δD} pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H2O and δD in situ profile references measured in the subtropics, between 0 and 7 km. Third, we reveal that the {H2O,δD} pair distributions obtained from the different remote sensors are consistent and allow distinct lower/middle tropospheric moisture pathways to be identified in agreement with multi-year in situ references. Fourth, we document the possibilities of the NDACC/FTIR instruments for climatological studies (due to long-term monitoring) and of the MetOp/IASI sensors for observing diurnal signals on a quasi-global scale and with high horizontal resolution. Fifth, we discuss the risk of misinterpreting {H2O,δD} pair distributions due to incomplete processing of the remote sensing products.

Remote sensing of vegetation canopy photosynthetic and stomatal conductance efficiencies

NASA Technical Reports Server (NTRS)

Myneni, R. B.; Ganapol, B. D.; Asrar, G.

1992-01-01

The problem of remote sensing the canopy photosynthetic and stomatal conductance efficiencies is investigated with the aid of one- and three-dimensional radiative transfer methods coupled to a semi-empirical mechanistic model of leaf photosynthesis and stomatal conductance. Desertlike vegetation is modeled as clumps of leaves randomly distributed on a bright dry soil with partial ground cover. Normalized difference vegetation index (NDVI), canopy photosynthetic (Ep), and stomatal efficiencies (Es) are calculated for various geometrical, optical, and illumination conditions. The contribution of various radiative fluxes to estimates of Ep is evaluated and the magnitude of errors in bulk canopy formulation of problem parameters are quantified. The nature and sensitivity of the relationship between Ep and Es to NDVI is investigated, and an algorithm is proposed for use in operational remote sensing.

Three optical methods for remotely measuring aerosol size distributions.

NASA Technical Reports Server (NTRS)

Reagan, J. A.; Herman, B. M.

1971-01-01

Three optical probing methods for remotely measuring atmospheric aerosol size distributions are discussed and contrasted. The particular detection methods which are considered make use of monostatic lidar (laser radar), bistatic lidar, and solar radiometer sensing techniques. The theory of each of these measurement techniques is discussed briefly, and the necessary constraints which must be applied to obtain aerosol size distribution information from such measurements are pointed out. Theoretical and/or experimental results are also presented which demonstrate the utility of the three proposed probing methods.

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

Contents: Multi-sensors and systems in remote sensing ; Radar sensing systems over land; Remote sensing techniques in oceanography; Influence of...propagation media and background; Infrared techniques in remote sensing ; Photography in remote sensing ; Analytical studies in remote sensing .

Echo the Bat and the Pigeon Adventure

NASA Technical Reports Server (NTRS)

Butcher, Ginger

2000-01-01

A multimedia, CD ROM to teach 2nd graders about remote sensing was created and developed into a web site. Distribution was expanded for Grades K-4 or 5-8. The idea was to have a story introduction, interactive story and a teacher's website. Interactive Multimedia Adventures in Grade School Education using Remote Sensing (I.M.A.G.E.R.S.) was created. The lessons are easy to use, readily available and aligned with national standards. This resource combines hands-on activities with an interactive web site

Water resources by orbital remote sensing: Examples of applications

NASA Technical Reports Server (NTRS)

Martini, P. R. (Principal Investigator)

1984-01-01

Selected applications of orbital remote sensing to water resources undertaken by INPE are described. General specifications of Earth application satellites and technical characteristics of LANDSAT 1, 2, 3, and 4 subsystems are described. Spatial, temporal and spectral image attributes of water as well as methods of image analysis for applications to water resources are discussed. Selected examples are referred to flood monitoring, analysis of water suspended sediments, spatial distribution of pollutants, inventory of surface water bodies and mapping of alluvial aquifers.

The quantitative control and matching of an optical false color composite imaging system

NASA Astrophysics Data System (ADS)

Zhou, Chengxian; Dai, Zixin; Pan, Xizhe; Li, Yinxi

1993-10-01

Design of an imaging system for optical false color composite (OFCC) capable of high-precision density-exposure time control and color balance is presented. The system provides high quality FCC image data that can be analyzed using a quantitative calculation method. The quality requirement to each part of the image generation system is defined, and the distribution of satellite remote sensing image information is analyzed. The proposed technology makes it possible to present the remote sensing image data more effectively and accurately.

Non-Lambertian effects on remote sensing of surface reflectance and vegetation index

NASA Technical Reports Server (NTRS)

Lee, T. Y.; Kaufman, Y. J.

1986-01-01

This paper discusses the effects of non-Lambertian reflection from a homogeneous surface on remote sensing of the surface reflectance and vegetation index from a satellite. Remote measurement of the surface characteristics is perturbed by atmospheric scattering of sun light. This scattering tends to smooth the angular dependence of non-Lambertian surface reflectances, an effect that is not present in the case of Lambertian surfaces. This effect is calculated to test the validity of a Lambertian assumption used in remote sensing. For the three types of vegetations considered in this study, the assumption of Lambertian surface can be used satisfactorily in the derivation of surface reflectance from remotely measured radiance for a view angle outside the backscattering region. Within the backscattering region, however, the use of the assumption can result in a considerable error in the derived surface reflectance. Accuracy also deteriorates with increasing solar zenith angle. The angular distribution of the surface reflectance derived from remote measurements is smoother than that at the surface. The effect of surface non-Lambertianity on remote sensing of vegetation index is very weak. Since the effect is similiar in the visible and near infrared part of the solar spectrum for the vegetations treated in this study, it is canceled in deriving the vegetation index. The effect of the diffuse skylight on surface reflectance measurements at ground level is also discussed.

Global Validation of MODIS Atmospheric Profile-Derived Near-Surface Air Temperature and Dew Point Estimates

NASA Astrophysics Data System (ADS)

Famiglietti, C.; Fisher, J.; Halverson, G. H.

2017-12-01

This study validates a method of remote sensing near-surface meteorology that vertically interpolates MODIS atmospheric profiles to surface pressure level. The extraction of air temperature and dew point observations at a two-meter reference height from 2001 to 2014 yields global moderate- to fine-resolution near-surface temperature distributions that are compared to geographically and temporally corresponding measurements from 114 ground meteorological stations distributed worldwide. This analysis is the first robust, large-scale validation of the MODIS-derived near-surface air temperature and dew point estimates, both of which serve as key inputs in models of energy, water, and carbon exchange between the land surface and the atmosphere. Results show strong linear correlations between remotely sensed and in-situ near-surface air temperature measurements (R2 = 0.89), as well as between dew point observations (R2 = 0.77). Performance is relatively uniform across climate zones. The extension of mean climate-wise percent errors to the entire remote sensing dataset allows for the determination of MODIS air temperature and dew point uncertainties on a global scale.

Networking Technologies Enable Advances in Earth Science

NASA Technical Reports Server (NTRS)

Johnson, Marjory; Freeman, Kenneth; Gilstrap, Raymond; Beck, Richard

2004-01-01

This paper describes an experiment to prototype a new way of conducting science by applying networking and distributed computing technologies to an Earth Science application. A combination of satellite, wireless, and terrestrial networking provided geologists at a remote field site with interactive access to supercomputer facilities at two NASA centers, thus enabling them to validate and calibrate remotely sensed geological data in near-real time. This represents a fundamental shift in the way that Earth scientists analyze remotely sensed data. In this paper we describe the experiment and the network infrastructure that enabled it, analyze the data flow during the experiment, and discuss the scientific impact of the results.

Survey of remote sensing applications

USGS Publications Warehouse

Deutsch, Morris

1974-01-01

Data from the first earth resources technology satellite (ERTS) as well as from NASA and other aircraft, contain much of the information indicative of the distribution of groundwater and the extent of its utilization. Thermal infrared imagery from aircraft is particularly valuable in studying groundwater discharge to the sea and other surface water bodies. Color infrared photography from aircraft and space is also used to locate areas of potential groundwater development. Anomalies in vegetation, soils, moisture, and their pattern of distribution may be indicative of underlying groundwater conditions. Remote sensing may be used directly or indirectly to identify stream reaches for test holes or production wells. Similarly, location of submarine springs increase effectiveness of groundwater exploration in the coastal zone.

Spatial distribution and ecological environment analysis of great gerbil in Xinjiang Plague epidemic foci based on remote sensing

NASA Astrophysics Data System (ADS)

Gao, Mengxu; Li, Qun; Cao, Chunxiang; Wang, Juanle

2014-03-01

Yersinia pestis (Plague bacterium) from great gerbil was isolated in 2005 in Xinjiang Dzungarian Basin, which confirmed the presence of the plague epidemic foci. This study analysed the spatial distribution and suitable habitat of great gerbil based on the monitoring data of great gerbil from Chinese Center for Disease Control and Prevention, as well as the ecological environment elements obtained from remote sensing products. The results showed that: (1) 88.5% (277/313) of great gerbil distributed in the area of elevation between 200 and 600 meters. (2) All the positive points located in the area with a slope of 0-3 degree, and the sunny tendency on aspect was not obvious. (3) All 313 positive points of great gerbil distributed in the area with an average annual temperature from 5 to 11 °C, and 165 points with an average annual temperature from 7 to 9 °C. (4) 72.8% (228/313) of great gerbil survived in the area with an annual precipitation of 120-200mm. (5) The positive points of great gerbil increased correspondingly with the increasing of NDVI value, but there is no positive point when NDVI is higher than 0.521, indicating the suitability of vegetation for great gerbil. This study explored a broad and important application for the monitoring and prevention of plague using remote sensing and geographic information system.

[Application of small remote sensing satellite constellations for environmental hazards in wetland landscape mapping: taking Liaohe Delta, Liaoning Province of Northeast China as a case].

PubMed

Yang, Yuan-Zheng; Chang, Yu; Hu, Yuan-Man; Liu, Miao; Li, Yue-Hui

2011-06-01

To timely and accurately acquire the spatial distribution pattern of wetlands is of significance for the dynamic monitoring, conservation, and sustainable utilization of wetlands. The small remote sensing satellite constellations A/B stars (HJ-1A/1B stars) for environmental hazards were launched by China for monitoring terrestrial resources, which could provide a new data source of remote sensing image acquisition for retrieving wetland types. Taking Liaohe Delta as a case, this paper compared the accuracy of wetland classification map and the area of each wetland type retrieved from CCD data (HJ CCD data) and TM5 data, and validated and explored the applicability and the applied potential of HJ CCD data in wetland resources dynamic monitoring. The results showed that HJ CCD data could completely replace Landsat TM5 data in feature extraction and remote sensing classification. In real-time monitoring, due to its 2 days of data acquisition cycle, HJ CCD data had the priority to Landsat TM5 data (16 days of data acquisition cycle).

U.S. Geological Survey, remote sensing, and geoscience data: Using standards to serve us all

USGS Publications Warehouse

Benson, Michael G.; Faundeen, John L.

2000-01-01

The U.S. Geological Survey (USGS) advocates the use of standards with geosciences and remotely sensed data and metadata for its own purposes and those of its customers. In activities that range from archiving data to making a product, the incorporation of standards makes these functions repeatable and understandable. More important, when accepted standards are followed, data discovery and sharing can be more efficient and the overall value to society can be expanded. The USGS archives many terabytes of digital geoscience and remotely sensed data. Several million photographs are also available to the research community. To manage these vast holdings and ensure that strict preservation and high usability criteria are observed, the USGS uses standards within the archival, data management, public access and ordering, and data distribution areas. The USGS uses Federal and international standards in performing its role as the U.S. National Satellite Land Remote Sensing Data Archive and in its mission as the long-term archive and production center for aerial photographs and cartographic data covering the United States.

Shaping NASA's Earth Science Enterprise Workforce Development Initiative to Address Industry Needs

NASA Technical Reports Server (NTRS)

Rosage, David; Meeson, Blanche W. (Technical Monitor)

2001-01-01

It has been well recognized that the commercial remote sensing industry will expand in new directions, resulting in new applications, thus requiring a larger, more skilled workforce to fill the new positions. In preparation for this change, NASA has initiated a Remote Sensing Professional Development Program to address the workforce needs of this emerging industry by partnering with the private sector, academia, relevant professional societies, and other R&D organizations. Workforce needs will in part include understanding current industry concerns, personnel competencies, current and future skills, growth rates, geographical distributions, certifications, and sources of pre-service and in-service personnel. Dave Rosage of the NASA Goddard Space Flight Center and a panel of MAPPS members will lead a discussion to help NASA specifically address private firms' near and long-term personnel needs to be included in NASA's Remote Sensing Professional Development Program. In addition, Dave Rosage will present perspectives on how remote sensing technologies are evolving, new NASA instruments being developed, and what future workforce skills are expected to support these new developments.

Multitask SVM learning for remote sensing data classification

NASA Astrophysics Data System (ADS)

Leiva-Murillo, Jose M.; Gómez-Chova, Luis; Camps-Valls, Gustavo

2010-10-01

Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.

Watermarking-based protection of remote sensing images: requirements and possible solutions

NASA Astrophysics Data System (ADS)

Barni, Mauro; Bartolini, Franco; Cappellini, Vito; Magli, Enrico; Olmo, Gabriella

2001-12-01

Earth observation missions have recently attracted ag rowing interest form the scientific and industrial communities, mainly due to the large number of possible applications capable to exploit remotely sensed data and images. Along with the increase of market potential, the need arises for the protection of the image products from non-authorized use. Such a need is a very crucial one even because the Internet and other public/private networks have become preferred means of data exchange. A crucial issue arising when dealing with digital image distribution is copyright protection. Such a problem has been largely addressed by resorting to watermarking technology. A question that obviously arises is whether the requirements imposed by remote sensing imagery are compatible with existing watermarking techniques. On the basis of these motivations, the contribution of this work is twofold: i) assessment of the requirements imposed by the characteristics of remotely sensed images on watermark-based copyright protection ii) analysis of the state-of-the-art, and performance evaluation of existing algorithms in terms of the requirements at the previous point.

Performance of One-Class Classifiers for Invasive Species Mapping using Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Skowronek, S.; Asner, G. P.; Feilhauer, H.

2016-12-01

Reliable distribution maps are crucial for the monitoring and management of invasive plant species. Remote sensing can provide such maps for larger areas. However, most remote sensing approaches focus on species in a prominent phenological stage, and a systematic assessment of the performance of different one-class classifiers for mapping species in a more inconspicuous phenological stage is missing so far. In this study, we used hyperspectral remote sensing data to detect the invasive grass Phalaris aquatica and the invasive herb Centaurea solstitialisin a pre-flowering stage in the Jasper Ridge Biological Preserve in California. We collected presence-only data, 66 plots for C. solstitialis and 30 plots for P. aquatica, to calibrate a distribution model and additional presence-absence data (166 / 173 plots) to validate model performance. All plots have a size of 3 m x 3 m. The hyperspectral remote sensing imagery was acquired using the Carnegie Airborne Observatory (CAO) visible to shortwave infrared (VSWIR) imaging spectrometer (400-2500 nm range) in May 2015 with a ground sampling distance (pixel size) of 1 m x 1 m. To find the best approach for mapping these species, we compared the performance of three different state-of-the-art classifiers working with presence-only data: Maxent, biased support vector machines and boosted regression trees. The resulting overall accuracies were 72 - 74% for C. solstitialis, and 83 - 88% for P. aquatica. For both species the overall performance was slightly better for Maxent and BRT than for biased SVM. The detection rates for low cover plots were considerably higher for C. solstitialis than for P. aquatica. For C. solstitalis, they ranged between 71 and 75% for plots with less than 15% cover, highlighting the potential of remote sensing to contribute to an early detection. The models relied on different areas of the spectrum, but still produced the same general pattern, which implies that more than one property of a species or a mixed plot can be used to create a viable model. We conclude that the different one-class classifiers we tested do allow detecting the target species in a more inconspicuous phenological stage, with similar success rates.

Analysis of malaria endemic areas on the Indochina Peninsula using remote sensing.

PubMed

Nihei, Naoko; Hashida, Yoshihiko; Kobayashi, Mutsuo; Ishii, Akira

2002-10-01

We applied remote sensing using satellite images capable of obtaining data over a broad range, transcending national borders, as a method of rapidly, precisely, and safely increasing our understanding of the potential distribution of malaria. Our target region was the so-called Mekong malaria region on the Indochina Peninsula. As a malaria index, we used existing distribution maps of total reported malaria cases, malaria mortality, vivax malaria and falciparum malaria incidences, and so forth for 1997 and 1998. We produced monthly distribution maps of a normalized difference vegetation index (NDVI) with values of 0.2+, 0.3+, 0.35+, and 0.4+ using the geographical information system/remote sensing software based on the East Asia monthly NDVI maps of 1997. These maps were overlaid with various malaria index distribution maps, and cross-tabulations were carried out. The resulting maps with NDVI values of 0.3+ and 0.4+ matched the falciparum malaria distribution well, and we realized, in particular, that falciparum malaria is prevalent in regions in which NDVI values of 0.4+ continue for 6 months or more, while cases are fewer in regions with NDVI values of 0.4+ that continue for 5 months or less. It will be necessary in the future to examine the relationship between NDVI values and the habitats of the various vector mosquitoes using high-resolution satellite images and to implement detailed forecasts for malaria endemic areas by means of NDVI.

Into the environment of mosquito-borne disease: A spatial analysis of vector distribution using traditional and remotely sensed methods

NASA Astrophysics Data System (ADS)

Brown, Heidi E.

Spatially explicit information is increasingly available for infectious disease modeling. However, such information is reluctantly or inappropriately incorporated. My dissertation research uses spatially explicit data to assess relationships between landscape and mosquito species distribution and discusses challenges regarding accurate predictive risk modeling. The goal of my research is to use remotely sensed environmental information and spatial statistical methods to better understand mosquito-borne disease epidemiology for improvement of public health responses. In addition to reviewing the progress of spatial infectious disease modeling, I present four research projects. I begin by evaluating the biases in surveillance data and build up to predictive modeling of mosquito species presence. In the first study I explore how mosquito surveillance trap types influence estimations of mosquito populations. Then. I use county-based human surveillance data and landscape variables to identify risk factors for West Nile virus disease. The third study uses satellite-based vegetation indices to identify spatial variation among West Nile virus vectors in an urban area and relates the variability to virus transmission dynamics. Finally, I explore how information from three satellite sensors of differing spatial and spectral resolution can be used to identify and distinguish mosquito habitat across central Connecticut wetlands. Analyses presented here constitute improvements to the prediction of mosquito distribution and therefore identification of disease risk factors. Current methods for mosquito surveillance data collection are labor intensive and provide an extremely limited, incomplete picture of the species composition and abundance. Human surveillance data offers additional challenges with respect to reporting bias and resolution, but is nonetheless informative in identifying environmental risk factors and disease transmission dynamics. Remotely sensed imagery supports mosquito and human disease surveillance data by providing spatially explicit, line resolution information about environmental factors relevant to vector-borne disease processes. Together, surveillance and remotely sensed environmental data facilitate improved description and modeling of disease transmission. Remote sensing can be used to develop predictive maps of mosquito distribution in relation to disease risk. This has implications for increased accuracy of mosquito control efforts. The projects presented in this dissertation enhance current public health capacities by examining the applications of spatial modeling with respect to mosquito-borne disease.

Estimating spatially distributed soil texture using time series of thermal remote sensing - a case study in central Europe

NASA Astrophysics Data System (ADS)

Müller, Benjamin; Bernhardt, Matthias; Jackisch, Conrad; Schulz, Karsten

2016-09-01

For understanding water and solute transport processes, knowledge about the respective hydraulic properties is necessary. Commonly, hydraulic parameters are estimated via pedo-transfer functions using soil texture data to avoid cost-intensive measurements of hydraulic parameters in the laboratory. Therefore, current soil texture information is only available at a coarse spatial resolution of 250 to 1000 m. Here, a method is presented to derive high-resolution (15 m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288 km2) from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. A principle component analysis of the images reveals the most dominant thermal patterns (principle components, PCs) that are related to 212 fractional soil texture samples. Within a multiple linear regression framework, distributed soil texture information is estimated and related uncertainties are assessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp) lies well within and even below the range of recent studies on soil texture estimation, while requiring sparser sample setups and a less diverse set of basic spatial input. This approach will improve the generation of spatially distributed topsoil maps, particularly for hydrologic modeling purposes, and will expand the usage of thermal remote sensing products.

Calibration of a distributed hydrologic model for six European catchments using remote sensing data

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, M. C.; Mendiguren González, G.; Kumar, R.; Rakovec, O.; Samaniego, L. E.

2017-12-01

While observed streamflow has been the single reference for most conventional hydrologic model calibration exercises, the availability of spatially distributed remote sensing observations provide new possibilities for multi-variable calibration assessing both spatial and temporal variability of different hydrologic processes. In this study, we first identify the key transfer parameters of the mesoscale Hydrologic Model (mHM) controlling both the discharge and the spatial distribution of actual evapotranspiration (AET) across six central European catchments (Elbe, Main, Meuse, Moselle, Neckar and Vienne). These catchments are selected based on their limited topographical and climatic variability which enables to evaluate the effect of spatial parameterization on the simulated evapotranspiration patterns. We develop a European scale remote sensing based actual evapotranspiration dataset at a 1 km grid scale driven primarily by land surface temperature observations from MODIS using the TSEB approach. Using the observed AET maps we analyze the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mHM model. This model allows calibrating one-basin-at-a-time or all-basins-together using its unique structure and multi-parameter regionalization approach. Results will indicate any tradeoffs between spatial pattern and discharge simulation during model calibration and through validation against independent internal discharge locations. Moreover, added value on internal water balances will be analyzed.

Snowpack spatial variability: Towards understanding its effect on remote sensing measurements and snow slope stability

NASA Astrophysics Data System (ADS)

Marshall, Hans-Peter

The distribution of water in the snow-covered areas of the world is an important climate change indicator, and it is a vital component of the water cycle. At local and regional scales, the snow water equivalent (SWE), the amount of liquid water a given area of the snowpack represents, is very important for water resource management, flood forecasting, and prediction of available hydropower energy. Measurements from only a few automatic weather stations, such as the SNOTEL network, or sparse manual snowpack measurements are typically extrapolated for estimating SWE over an entire basin. Widespread spatial variability in the distribution of SWE and snowpack stratigraphy at local scales causes large errors in these basin estimates. Remote sensing measurements offer a promising alternative, due to their large spatial coverage and high temporal resolution. Although snow cover extent can currently be estimated from remote sensing data, accurately quantifying SWE from remote sensing measurements has remained difficult, due to a high sensitivity to variations in grain size and stratigraphy. In alpine snowpacks, the large degree of spatial variability of snowpack properties and geometry, caused by topographic, vegetative, and microclimatic effects, also makes prediction of snow avalanches very difficult. Ground-based radar and penetrometer measurements can quickly and accurately characterize snowpack properties and SWE in the field. A portable lightweight radar was developed, and allows a real-time estimate of SWE to within 10%, as well as measurements of depths of all major density transitions within the snowpack. New analysis techniques developed in this thesis allow accurate estimates of mechanical properties and an index of grain size to be retrieved from the SnowMicroPenetrometer. These two tools together allow rapid characterization of the snowpack's geometry, mechanical properties, and SWE, and are used to guide a finite element model to study the stress distribution on a slope. The ability to accurately characterize snowpack properties at much higher resolutions and spatial extent than previously possible will hopefully help lead to a more complete understanding of spatial variability, its effect on remote sensing measurements and snow slope stability, and result in improvements in avalanche prediction and accuracy of SWE estimates from space.

Earth view: A business guide to orbital remote sensing

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1990-01-01

The following subject areas are covered: Earth view - a guide to orbital remote sensing; current orbital remote sensing systems (LANDSAT, SPOT image, MOS-1, Soviet remote sensing systems); remote sensing satellite; and remote sensing organizations.

Remote sensing of Essential Biodiversity Variables: new measurements linking ecosystem structure, function and composition

NASA Astrophysics Data System (ADS)

Schimel, D.; Pavlick, R.; Stavros, E. N.; Townsend, P. A.; Ustin, S.; Thompson, D. R.

2017-12-01

Remote sensing can inform a wide variety of essential biodiversity variables, including measurements that define primary productivity, forest structure, biome distribution, plant communities, land use-land cover change and climate drivers of change. Emerging remote sensing technologies can add significantly to remote sensing of EBVs, providing new, large scale insights on plant and habitat diversity itself, as well as causes and consequences of biodiversity change. All current biodiversity assessments identify major data gaps, with insufficient coverage in critical regions, limited observations to monitor change over time, with very limited revisit of sample locations, as well as taxon-specific biased biases. Remote sensing cannot fill many of the gaps in global biodiversity observations, but spectroscopic measurements in terrestrial and marine environments can aid in assessing plant/phytoplankton functional diversity and efficiently reveal patterns in space, as well as changes over time, and, by making use of chlorophyll fluorescence, reveal associated patterns in photosynthesis. LIDAR and RADAR measurements quantify ecosystem structure, and can precisely define changes due to growth, disturbance and land use. Current satellite-based EBVs have taken advantage of the extraordinary time series from LANDSAT and MODIS, but new measurements more directly reveal ecosystem structure, function and composition. We will present results from pre-space airborne studies showing the synergistic ability of a suite of new remote observation techniques to quantify biodiversity and ecosystem function and show how it changes during major disturbance events.

Biological and remote sensing perspectives of pigmentation in coral reef organisms.

PubMed

Hedley, John D; Mumby, Peter J

2002-01-01

Coral reef communities face unprecedented pressures on local, regional and global scales as a consequence of climate change and anthropogenic disturbance. Optical remote sensing, from satellites or aircraft, is possibly the only means of measuring the effects of such stresses at appropriately large spatial scales (many thousands of square kilometres). To map key variables such as coral community structure, percentages of living coral or percentages of dead coral, a remote sensing instrument must be able to distinguish the reflectance spectra (i.e. "spectral signature", reflected light as a function of wavelength) of each category. For biotic classes, reflectance is a complex function of pigmentation, structure and morphology. Studies of coral "colour" fall into two disparate but potentially complementary types. Firstly, biological studies tend to investigate the structure and significance of pigmentation in reef organisms. These studies often lack details that would be useful from a remote sensing perspective such as intraspecific variation in pigment concentration or the contribution of fluorescence to reflectance. Secondly, remote sensing studies take empirical measurements of spectra and seek wavelengths that discriminate benthic categories. Benthic categories used in remote sensing sometimes consist of species groupings that are biologically or spectrally inappropriate (e.g. merging of algal phyla with distinct pigments). Here, we attempt to bridge the gap between biological and remote sensing perspectives of pigmentation in reef taxa. The aim is to assess the extent to which spectral discrimination can be given a biological foundation, to reduce the ad hoc nature of discriminatory criteria, and to understand the fundamental (biological) limitations in the spectral separability of biotic classes. Sources of pigmentation in reef biota are reviewed together with remote sensing studies where spectral discrimination has been effectively demonstrated between benthic categories. The basis of reflectance is considered as the sum of pigmented components, such as zooxanthellae, host tissues and skeletons of corals. Problems in the empirical in situ measurement of reflectance are identified, such as the differing types of reflectance which can be measured, the interaction of the light field with morphology, and depth-dependent variability of measured reflectance due to fluorescence. The latter is estimated in some cases to introduce an error of up to 20% when depth differs by 8 m. Spectral features useful in discriminating reef benthos are identified and related to pigmentation. The slope in the reflectance spectra between 650 and 690 nm is dependent on chlorophyll-a concentration and can be used to discriminate bare sand with no algal component from chlorophyll-a containing benthos (algae, corals). The slope in reflectance at various locations between 500 and 560 nm can be useful in discriminating bleached and unbleached corals, possibly due to reduced peridinin concentration. Rhodophyta may be discernible by the presence of a dip in reflectance at 570 nm, due to a phycoerythrin absorption peak. However, the utility of some discriminatory criteria in deeper waters is mitigated by the relatively poor transmission of light through water at longer wavelengths (especially > 600 nm). Contrary to suggested categorizations of fluorescent pigments in coral host tissues, it is shown that these pigments form an almost continuous distribution with respect to their excitation and emission peaks. Remote sensing by induced fluorescence is a promising approach, but further details about the variation and distribution of these pigments are required. It is hoped that this review will promote cross-disciplinary collaboration between pigment biologists and the reef remote sensing community. Where possible, the discriminative criteria adopted in remote sensing should be related to biological phenomena, thus lending an intuitive, process-orientated basis for interpreting spectral data. Similarly, remote sensing may provide a novel scaling perspective to biological studies of pigmentation in reef organisms.

Space-Derived Imagery and a Commercial Remote Sensing Industry: Impossible Dream or Inevitable Reality?

NASA Astrophysics Data System (ADS)

Murray, Felsher

Landsat-1 was launched in 1972 as a research satellite. Many of us viewed this satellite as a precursor to remote sensing "commercialization." Indeed since that time, the birth, growth and maturation of a remote sensing "industry" has been an ongoing objective for much of the U.S. private sector engaged in space and ground-segment activities related to the acquisition, analysis, and dissemination of imagery. In September 1999 a U.S. commercial entity, Space Imaging, Inc. launched its 1-meter pan/4-meter multispectral IKONOS sensor. DigitalGlobe, Inc. (nee EarthWatch, Inc.) matched this feat in October 2001. Thus, a full 30 years later, we are finally on the brink of building a true remote sensing information industry based on the global availability of competitively-priced space- derived imagery of the Earth. The upcoming availability of similar imagery from non-U.S. sources as ImageSat and U.S. sources as ORBIMAGE will only strengthen that reality. However, a remote sensing industry can only grow by allowing these entities (in times of peace) unencumbered access to a world market. And that market continues to expand -- up 11% in 2001, with gross revenues of U.S. commercial remote sensing firms alone reaching 2.44 billion, according to a joint NASA/ASPRS industry survey. However, the 30-year gap between the research-labeled Landsat-1 and our current commercial successes was not technology-driven. That lacuna was purely political -- driven by valid concerns related to national security. Although the world's governments have cooperated thoroughly and completely in areas related to satellite telecommunications, cooperation in space-derived image information is still today done cautiously and on a case-by-case basis -- and then only for science- based undertakings. It is still a fact that, except for the United States, all other Earth-imaging satellites/sensors flying today are owned, operated, and their products disseminated, by national governments -- and not private sector entities. Will the template now fashioned by the U.S. -- that of licensing private industry to build, fly, and operate remote sensing satellites as well as to distribute their imagery worldwide -- be replicated by other nations? Eventually, yes. Availability of the World Wide Web is an international communications reality. Availability of world wide imaging will be just as real. And much of that imagery will be marketed, sold, and distributed via that same global Internet. I feel that as an expected outcome of our technological age, we can ensure not only our own national security but international security as well, by assuring worldwide accessibility to worldwide space- derived image information. This requires -- in fact demands -- the presence of a viable international remote sensing industry. It is not impossible; It is inevitable.

Discharge prediction in the Upper Senegal River using remote sensing data

NASA Astrophysics Data System (ADS)

Ceccarini, Iacopo; Raso, Luciano; Steele-Dunne, Susan; Hrachowitz, Markus; Nijzink, Remko; Bodian, Ansoumana; Claps, Pierluigi

2017-04-01

The Upper Senegal River, West Africa, is a poorly gauged basin. Nevertheless, discharge predictions are required in this river for the optimal operation of the downstream Manantali reservoir, flood forecasting, development plans for the entire basin and studies for adaptation to climate change. Despite the need for reliable discharge predictions, currently available rainfall-runoff models for this basin provide only poor performances, particularly during extreme regimes, both low-flow and high-flow. In this research we develop a rainfall-runoff model that combines remote-sensing input data and a-priori knowledge on catchment physical characteristics. This semi-distributed model, is based on conceptual numerical descriptions of hydrological processes at the catchment scale. Because of the lack of reliable input data from ground observations, we use the Tropical Rainfall Measuring Mission (TRMM) remote-sensing data for precipitation and the Global Land Evaporation Amsterdam Model (GLEAM) for the terrestrial potential evaporation. The model parameters are selected by a combination of calibration, by match of observed output and considering a large set of hydrological signatures, as well as a-priori knowledge on the catchment. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to choose the most likely range in which the parameter sets belong. Analysis of different experiments enhances our understanding on the added value of distributed remote-sensing data and a-priori information in rainfall-runoff modelling. Results of this research will be used for decision making at different scales, contributing to a rational use of water resources in this river.

Using Remote Sensing Data to Constrain Models of Fault Interactions and Plate Boundary Deformation

NASA Astrophysics Data System (ADS)

Glasscoe, M. T.; Donnellan, A.; Lyzenga, G. A.; Parker, J. W.; Milliner, C. W. D.

2016-12-01

Determining the distribution of slip and behavior of fault interactions at plate boundaries is a complex problem. Field and remotely sensed data often lack the necessary coverage to fully resolve fault behavior. However, realistic physical models may be used to more accurately characterize the complex behavior of faults constrained with observed data, such as GPS, InSAR, and SfM. These results will improve the utility of using combined models and data to estimate earthquake potential and characterize plate boundary behavior. Plate boundary faults exhibit complex behavior, with partitioned slip and distributed deformation. To investigate what fraction of slip becomes distributed deformation off major faults, we examine a model fault embedded within a damage zone of reduced elastic rigidity that narrows with depth and forward model the slip and resulting surface deformation. The fault segments and slip distributions are modeled using the JPL GeoFEST software. GeoFEST (Geophysical Finite Element Simulation Tool) is a two- and three-dimensional finite element software package for modeling solid stress and strain in geophysical and other continuum domain applications [Lyzenga, et al., 2000; Glasscoe, et al., 2004; Parker, et al., 2008, 2010]. New methods to advance geohazards research using computer simulations and remotely sensed observations for model validation are required to understand fault slip, the complex nature of fault interaction and plate boundary deformation. These models help enhance our understanding of the underlying processes, such as transient deformation and fault creep, and can aid in developing observation strategies for sUAV, airborne, and upcoming satellite missions seeking to determine how faults behave and interact and assess their associated hazard. Models will also help to characterize this behavior, which will enable improvements in hazard estimation. Validating the model results against remotely sensed observations will allow us to better constrain fault zone rheology and physical properties, having implications for the overall understanding of earthquake physics, fault interactions, plate boundary deformation and earthquake hazard, preparedness and risk reduction.

Thermokarst Characteristics and Distribution in a Transitional Arctic Biome: New Discoveries and Possible Monitoring Directions in a Climate Change Scenario

NASA Astrophysics Data System (ADS)

Balser, A. W.; Gooseff, M. N.; Jones, J. B.; Bowden, W. B.; Sanzone, D. M.; Allen, A.; Larouche, J. R.

2006-12-01

In arctic regions, climate warming is leading to permafrost melting and wide-scale ecosystem alteration. A prominent pathway of permafrost loss is through thermokarst, which includes the catastrophic loss of soil structure and rapid subsidence. The regional-scale distribution of thermokarst is poorly documented throughout arctic regions. Remote landscapes and a lack of reliable, regional-scale detection techniques severely hamper our understanding of past prevalence and present distribution patterns. Intensive field campaigns are providing key data to bolster our understanding of the distribution and the characteristics of thermokarst formation, and enabling comprehensive method studies to develop remotely-sensed detection techniques. The Noatak Valley in northwestern Alaska's Brooks Range mountains harbors a transitional landscape from arctic and alpine tundra to boreal forest, all contained in a single 7,000,000 acre watershed. Preliminary field investigations augmented by photogrammetric measurements in 2006 revealed consistent patterns in the distribution of classifiable thermokarst feature types in a 2300 square-mile study area in the middle Noatak basin. Four distinct classes of thermokarst show remarkably tight relationships with ambient slope and local landcover. These investigations tie to larger efforts to document past and present regional distribution, testing remotely sensed data analysis techniques for baseline metrics and a future monitoring scheme.

Remote Sensing Proxies for Vector-borne Disease Risk Assessment (Invited)

NASA Astrophysics Data System (ADS)

Anyamba, A.

2010-12-01

The spread of re-emerging vector-borne diseases such Rift Valley fever (RVF) and Chikungunya (CHIK) is a major issue of global public health concern. This combined with a variable climate regime has opened an avenue for satellite remote sensing to contribute towards a comprehensive understanding of some of the drivers influencing such vector-borne disease outbreaks. Satellite derived measurements such as vegetation indices, rainfall estimates, and land-surface temperature; can be used to infer the complex mosaic of factors that influence ecology and habitat suitability, emergence and population dynamics of disease vectors. However, there are still some gaps in application including appropriate temporal resolution of remote sensing measurements, the complexity of the virus-vector-disease-ecology system and human components that contribute to disease risk that need to be addressed. Geographic Distribution of Recent Rift Valley fever oubreaks

Passive remote sensing of large-scale methane emissions from Oil Fields in California's San Joaquin Valley and validation by airborne in-situ measurements - Results from COMEX

NASA Astrophysics Data System (ADS)

Gerilowski, Konstantin; Krautwurst, Sven; Thompson, David R.; Thorpe, Andrew K.; Kolyer, Richard W.; Jonsson, Haflidi; Krings, Thomas; Frankenberg, Christian; Horstjann, Markus; Leifer, Ira; Eastwood, Michael; Green, Robert O.; Vigil, Sam; Fladeland, Matthew; Schüttemeyer, Dirk; Burrows, John P.; Bovensmann, Heinrich

2016-04-01

The CO2 and MEthane EXperiment (COMEX) was a NASA and ESA funded campaign in support of the HyspIRI and CarbonSat mission definition activities. As a part of this effort, seven flights were performed between June 3 and September 4, 2014 with the Methane Airborne MAPper (MAMAP) remote sensing instrument (operated by the University of Bremen in cooperation with the German Research Centre for Geosciences - GFZ) over the Kern River, Kern Front, and Poso Creek Oil Fields located in California's San Joaquin Valley. MAMAP was installed for the flights aboard the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft, together with: a Picarro fast in-situ greenhouse gas (GHG) analyzer operated by the NASA Ames Research Center, ARC; a 5-hole turbulence probe; and an atmospheric measurement package operated by CIRPAS measuring aerosols, temperature, dew-point, and other atmospheric parameters. Three of the flights were accompanied by the Next Generation Airborne Visual InfraRed Imaging Spectrometer (AVIRIS-NG), operated by the Jet Propulsion Laboratory (JPL), California Institute of Technology, installed aboard a second Twin Otter aircraft. Large-scale, high-concentration CH4 plumes were detected by the MAMAP instrument over the fields and tracked over several kilometers. The spatial distribution of the MAMAP observed plumes was compared to high spatial resolution CH4 anomaly maps derived by AVIRIS-NG imaging spectroscopy data. Remote sensing data collected by MAMAP was used to infer CH4 emission rates and their distributions over the three fields. Aggregated emission estimates for the three fields were compared to aggregated emissions inferred by subsequent airborne in-situ validation measurements collected by the Picarro instrument. Comparison of remote sensing and in-situ flux estimates will be presented, demonstrating the ability of airborne remote sensing data to provide accurate emission estimates for concentrations above the detection limit. This opens new applications of airborne atmospheric remote sensing in the area of anthropogenic top-down emission monitoring as well as for atmospheric CH4 leakage monitoring during accidents like the Elgin blow-out (March 2012) in the North Sea or the recent Aliso Canyon gas leak incident (2015/2016) in California.

[Remote sensing monitoring and screening for urban black and odorous water body: A review.

PubMed

Shen, Qian; Zhu, Li; Cao, Hong Ye

2017-10-01

Continuous improvement of urban water environment and overall control of black and odorous water body are not merely national strategic needs with the action plan for prevention and treatment of water pollution, but also the hot issues attracting the attention of people. Most previous researches concentrated on the study of cause, evaluation and treatment measures of this phenomenon, and there are few researches on the monitoring using remote sensing, which is often a strain to meet the national needs of operational monitoring. This paper mainly summarized the urgent research problems, mainly including the identification and classification standard, research on the key technologies, and the frame of remote sensing screening systems for the urban black and odorous water body. The main key technologies were concluded too, including the high spatial resolution image preprocessing and extraction technique for black and odorous water body, the extraction of water information in city zones, the classification of the black and odorous water, and the identification and classification technique based on satellite-sky-ground remote sensing. This paper summarized the research progress and put forward research ideas of monitoring and screening urban black and odorous water body via high spatial resolution remote sensing technology, which would be beneficial to having an overall grasp of spatial distribution and improvement progress of black and odorous water body, and provide strong technical support for controlling urban black and odorous water body.

Geomorphic Processes and Remote Sensing Signatures of Alluvial Fans in the Kun Lun Mountains, China

NASA Technical Reports Server (NTRS)

Farr, Tom G.; Chadwick, Oliver A.

1996-01-01

The timing of alluvial deposition in arid and semiarid areas is tied to land-surface instability caused by regional climate changes. The distribution pattern of dated deposits provides maps of regional land-surface response to past climate change. Sensitivity to differences in surface roughness and composition makes remote sensing techniques useful for regional mapping of alluvial deposits. Radar images from the Spaceborne Radar Laboratory and visible wavelength images from the French SPOT satellite were used to determine remote sensing signatures of alluvial fan units for an area in the Kun Lun Mountains of northwestern China. These data were combined with field observations to compare surface processes and their effects on remote sensing signatures in northwestern China and the southwestern United States. Geomorphic processes affecting alluvial fans in the two areas include aeolian deposition, desert varnish, and fluvial dissection. However, salt weathering is a much more important process in the Kun Lun than in the southwestern United States. This slows the formation of desert varnish and prevents desert pavement from forming. Thus the Kun Lun signatures are characteristic of the dominance of salt weathering, while signatures from the southwestern United States are characteristic of the dominance of desert varnish and pavement processes. Remote sensing signatures are consistent enough in these two regions to be used for mapping fan units over large areas.

Airborne remote sensing for geology and the environment; present and future

USGS Publications Warehouse

Watson, Ken; Knepper, Daniel H.

1994-01-01

In 1988, a group of leading experts from government, academia, and industry attended a workshop on airborne remote sensing sponsored by the U.S. Geological Survey (USGS) and hosted by the Branch of Geophysics. The purpose of the workshop was to examine the scientific rationale for airborne remote sensing in support of government earth science in the next decade. This report has arranged the six resulting working-group reports under two main headings: (1) Geologic Remote Sensing, for the reports on geologic mapping, mineral resources, and fossil fuels and geothermal resources; and (2) Environmental Remote Sensing, for the reports on environmental geology, geologic hazards, and water resources. The intent of the workshop was to provide an evaluation of demonstrated capabilities, their direct extensions, and possible future applications, and this was the organizational format used for the geologic remote sensing reports. The working groups in environmental remote sensing chose to present their reports in a somewhat modified version of this format. A final section examines future advances and limitations in the field. There is a large, complex, and often bewildering array of remote sensing data available. Early remote sensing studies were based on data collected from airborne platforms. Much of that technology was later extended to satellites. The original 80-m-resolution Landsat Multispectral Scanner System (MSS) has now been largely superseded by the 30-m-resolution Thematic Mapper (TM) system that has additional spectral channels. The French satellite SPOT provides higher spatial resolution for channels equivalent to MSS. Low-resolution (1 km) data are available from the National Oceanographic and Atmospheric Administration's AVHRR system, which acquires reflectance and day and night thermal data daily. Several experimental satellites have acquired limited data, and there are extensive plans for future satellites including those of Japan (JERS), Europe (ESA), Canada (Radarsat), and the United States (EOS). There are currently two national airborne remote sensing programs (photography, radar) with data archived at the USGS' EROS Data Center. Airborne broadband multispectral data (comparable to Landsat MSS and TM but involving several more channels) for limited geographic areas also are available for digital processing and analysis. Narrow-band imaging spectrometer data are available for some NASA experiment sites and can be acquired for other locations commercially. Remote sensing data and derivative images, because of the uniform spatial coverage, availability at different resolutions, and digital format, are becoming important data sets for geographic information system (GIS) analyses. Examples range from overlaying digitized geologic maps on remote sensing images and draping these over topography, to maps of mineral distribution and inferred abundance. A large variety of remote sensing data sets are available, with costs ranging from a few dollars per square mile for satellite digital data to a few hundred dollars per square mile for airborne imaging spectrometry. Computer processing and analysis costs routinely surpass these expenses because of the equipment and expertise necessary for information extraction and interpretation. Effective use requires both an understanding of the current methodology and an appreciation of the most cost-effective solution.

Crosscutting Airborne Remote Sensing Technologies for Oil and Gas and Earth Science Applications

NASA Technical Reports Server (NTRS)

Aubrey, A. D.; Frankenberg, C.; Green, R. O.; Eastwood, M. L.; Thompson, D. R.; Thorpe, A. K.

2015-01-01

Airborne imaging spectroscopy has evolved dramatically since the 1980s as a robust remote sensing technique used to generate 2-dimensional maps of surface properties over large spatial areas. Traditional applications for passive airborne imaging spectroscopy include interrogation of surface composition, such as mapping of vegetation diversity and surface geological composition. Two recent applications are particularly relevant to the needs of both the oil and gas as well as government sectors: quantification of surficial hydrocarbon thickness in aquatic environments and mapping atmospheric greenhouse gas components. These techniques provide valuable capabilities for petroleum seepage in addition to detection and quantification of fugitive emissions. New empirical data that provides insight into the source strength of anthropogenic methane will be reviewed, with particular emphasis on the evolving constraints enabled by new methane remote sensing techniques. Contemporary studies attribute high-strength point sources as significantly contributing to the national methane inventory and underscore the need for high performance remote sensing technologies that provide quantitative leak detection. Imaging sensors that map spatial distributions of methane anomalies provide effective techniques to detect, localize, and quantify fugitive leaks. Airborne remote sensing instruments provide the unique combination of high spatial resolution (<1 m) and large coverage required to directly attribute methane emissions to individual emission sources. This capability cannot currently be achieved using spaceborne sensors. In this study, results from recent NASA remote sensing field experiments focused on point-source leak detection, will be highlighted. This includes existing quantitative capabilities for oil and methane using state-of-the-art airborne remote sensing instruments. While these capabilities are of interest to NASA for assessment of environmental impact and global climate change, industry similarly seeks to detect and localize leaks of both oil and methane across operating fields. In some cases, higher sensitivities desired for upstream and downstream applications can only be provided by new airborne remote sensing instruments tailored specifically for a given application. There exists a unique opportunity for alignment of efforts between commercial and government sectors to advance the next generation of instruments to provide more sensitive leak detection capabilities, including those for quantitative source strength determination.

Technology study of quantum remote sensing imaging

NASA Astrophysics Data System (ADS)

Bi, Siwen; Lin, Xuling; Yang, Song; Wu, Zhiqiang

2016-02-01

According to remote sensing science and technology development and application requirements, quantum remote sensing is proposed. First on the background of quantum remote sensing, quantum remote sensing theory, information mechanism, imaging experiments and prototype principle prototype research situation, related research at home and abroad are briefly introduced. Then we expounds compress operator of the quantum remote sensing radiation field and the basic principles of single-mode compression operator, quantum quantum light field of remote sensing image compression experiment preparation and optical imaging, the quantum remote sensing imaging principle prototype, Quantum remote sensing spaceborne active imaging technology is brought forward, mainly including quantum remote sensing spaceborne active imaging system composition and working principle, preparation and injection compression light active imaging device and quantum noise amplification device. Finally, the summary of quantum remote sensing research in the past 15 years work and future development are introduced.

Remote Sensing of Spatial Distributions of Greenhouse Gases in the Los Angles Basin

NASA Technical Reports Server (NTRS)

Fu, Dejian; Pongetti, Thomas J.; Sander, Stanley P.; Cheung, Ross; Stutz, Jochen; Park, Chang Hyoun; Li, Qinbin

2011-01-01

The Los Angeles air basin is a significant anthropogenic source of greenhouse gases and pollutants including CO2, CH4, N2O, and CO, contributing significantly to regional and global climate change. Recent legislation in California, the California Global Warming Solutions Act (AB32), established a statewide cap for greenhouse gas emissions for 2020 based on 1990 emissions. Verifying the effectiveness of regional greenhouse gas emissions controls requires high-precision, regional-scale measurement methods combined with models that capture the principal anthropogenic and biogenic sources and sinks. We present a novel approach for monitoring the spatial distributions of greenhouse gases in the Los Angeles basin using high resolution remote sensing spectroscopy. We participated in the CalNex 2010 campaign to provide greenhouse gas distributions for comparison between top-down and bottom-up emission estimates.

Remote Sensing of Spatial Distributions of Greenhouse Gases in the Los Angeles Basin

NASA Technical Reports Server (NTRS)

Fu, Dejian; Sander, Stanley P.; Pongetti, Thomas J.; Cheung, Ross; Stutz, Jochen

2010-01-01

The Los Angeles air basin is a significant anthropogenic source of greenhouse gasses and pollutants including CO2, CH4, N2O, and CO, contributing significantly to regional and global climate change. Recent legislation in California, the California Global Warning Solutions Act (AB32), established a statewide cap for greenhouse gas emissions for 2020 based on 1990 emissions. Verifying the effectiveness of regional greenhouse gas emissions controls requires high-precision, regional-scale measurement methods combined with models that capture the principal anthropogenic and biogenic sources and sinks. We present a novel approach for monitoring the spatial distribution of greenhouse gases in the Los Angeles basin using high resolution remote sensing spectroscopy. We participated in the CalNex 2010 campaign to provide greenhouse gas distributions for comparison between top-down and bottom-up emission estimates.

Simulating vegetation controls on hurricane-induced shallow landslides with a distributed ecohydrological model

Treesearch

Taehee Hwang; Lawrence E. Band; T. C. Hales; Chelcy F. Miniat; James M. Vose; Paul V. Bolstad; Brian Miles; Katie Price

2015-01-01

The spatial distribution of shallow landslides in steep forested mountains is strongly controlled by aboveground and belowground biomass, including the distribution of root cohesion. While remote sensing of aboveground canopy properties is relatively advanced, estimating the spatial distribution of root cohesion at the forest landscape scale remains challenging. We...

[Study on suitable distribution areas of Grifola umbellate in Sichuan province based on remote sensing and GIS].

PubMed

Zhang, You; Wang, Juan; Zhang, Jie; Peng, Wen-Fu; Xu, Xin-Liang; Fang, Qing-Mao

2016-09-01

Grifola umbellate is the important medicinal materials in China which has a very high medicinal value. This study analyzedthe suitable distribution areasof G. umbellate and provided scientific basis for determining G. umbellate planting regions and planning production distribution reasonably. The suitable distribution areas of G. umbellate in Sichuan province was researched based on TM, ETM+, and DEM data,the key ecological factors that affect the growth of G. umbellate were extracted, including elevation, slope, aspect, average annual temperature,average annual precipitation,forest information,soil information, following remote sensing and GIS techniques, combining field researchdata. The results showed that the G. umbellate resources in Sichuan province were mainly distributed in Pingwu, Beichuan, Licountry, Yanyuan, Xichang, Dechang, Yanbian, Miyi, Huidong, Panzhihua and so on, the suitability distribution areas is 276.214 4 km² approximately and accounting for more than 0.143 3% of the total area.According to the related document information and the field investigation, showed that the suitability distribution based on RS and GIS were corresponded with the actual distribution areas of G. umbellate. Copyright© by the Chinese Pharmaceutical Association.

A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

NASA Astrophysics Data System (ADS)

Mui, Amy B.

Freshwater turtles are a globally declining taxa, and estimates of population status are not available for many species. Primary causes of decline stem from widespread habitat loss and degradation, and obtaining spatially-explicit information on remaining habitat across a relevant spatial scale has proven challenging. The discipline of remote sensing science has been employed widely in studies of biodiversity conservation, but it has not been utilized as frequently for cryptic, and less vagile species such as turtles, despite their vulnerable status. The work presented in this thesis investigates how multi-temporal remote sensing imagery can contribute key information for building spatially-explicit and temporally dynamic models of habitat and connectivity for the threatened, Blanding's turtle (Emydoidea blandingii) in southern Ontario, Canada. I began with outlining a methodological approach for delineating freshwater wetlands from high spatial resolution remote sensing imagery, using a geographic object-based image analysis (GEOBIA) approach. This method was applied to three different landscapes in southern Ontario, and across two biologically relevant seasons during the active (non-hibernating) period of Blanding's turtles. Next, relevant environmental variables associated with turtle presence were extracted from remote sensing imagery, and a boosted regression tree model was developed to predict the probability of occurrence of this species. Finally, I analysed the movement potential for Blanding's turtles in a disturbed landscape using a combination of approaches. Results indicate that (1) a parsimonious GEOBIA approach to land cover mapping, incorporating texture, spectral indices, and topographic information can map heterogeneous land cover with high accuracy, (2) remote-sensing derived environmental variables can be used to build habitat models with strong predictive power, and (3) connectivity potential is best estimated using a variety of approaches, though accurate estimates across human-altered landscapes is challenging. Overall, this body of work supports the use of remote sensing imagery in species distribution models to strengthen the precision, and power of predictive models, and also draws attention to the need to consider a multi-temporal examination of species habitat requirements.

Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing

PubMed Central

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Vounatsou, Penelope; Poda, Jean-Noël; N'Goran, Eliézer K.; Utzinger, Jürg; Raso, Giovanna

2015-01-01

Background Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. Methodology We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d’Ivoire and validated against readily available survey data from school-aged children. Principal Findings Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d’Ivoire. Conclusions/Significance A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data. PMID:26587839

Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing.

PubMed

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Vounatsou, Penelope; Poda, Jean-Noël; N'Goran, Eliézer K; Utzinger, Jürg; Raso, Giovanna

2015-11-01

Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d'Ivoire and validated against readily available survey data from school-aged children. Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d'Ivoire. A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.

Studies on remote sensing method of particle size and water density distribution in mists and clouds using laser radar techniques

NASA Technical Reports Server (NTRS)

Shimizu, H.; Kobayasi, T.; Inaba, H.

1979-01-01

A method of remote measurement of the particle size and density distribution of water droplets was developed. In this method, the size of droplets is measured from the Mie scattering parameter which is defined as the total-to-backscattering ratio of the laser beam. The water density distribution is obtained by a combination of the Mie scattering parameter and the extinction coefficient of the laser beam. This method was examined experimentally for the mist generated by an ultrasonic mist generator and applied to clouds containing rain and snow. Compared with the conventional sampling method, the present method has advantages of remote measurement capability and improvement in accuracy.

Remote sensing of earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1988-01-01

Two monographs and 85 journal and conference papers on remote sensing of earth terrain have been published, sponsored by NASA Contract NAG5-270. A multivariate K-distribution is proposed to model the statistics of fully polarimetric data from earth terrain with polarizations HH, HV, VH, and VV. In this approach, correlated polarizations of radar signals, as characterized by a covariance matrix, are treated as the sum of N n-dimensional random vectors; N obeys the negative binomial distribution with a parameter alpha and mean bar N. Subsequently, and n-dimensional K-distribution, with either zero or non-zero mean, is developed in the limit of infinite bar N or illuminated area. The probability density function (PDF) of the K-distributed vector normalized by its Euclidean norm is independent of the parameter alpha and is the same as that derived from a zero-mean Gaussian-distributed random vector. The above model is well supported by experimental data provided by MIT Lincoln Laboratory and the Jet Propulsion Laboratory in the form of polarimetric measurements.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

A remote sensing research agenda for mapping and monitoring biodiversity

NASA Technical Reports Server (NTRS)

Stoms, D. M.; Estes, J. E.

1993-01-01

A remote sensing research agenda designed to expand the knowledge of the spatial distribution of species richness and its ecological determinants and to predict its response to global change is proposed. Emphasis is placed on current methods of mapping species richness of both plants and animals, hypotheses concerning the biophysical factors believed to determine patterns of species richness, and anthropogenic processes causing the accelerating rate of extinctions. It is concluded that biodiversity should be incorporated more prominently into the global change and earth system science paradigms.

A methodology for mapping forest latent heat flux densities using remote sensing

NASA Technical Reports Server (NTRS)

Pierce, Lars L.; Congalton, Russell G.

1988-01-01

Surface temperatures and reflectances of an upper elevation Sierran mixed conifer forest were monitored using the Thematic Mapper Simulator sensor during the summer of 1985 in order to explore the possibility of using remote sensing to determine the distribution of solar energy on forested watersheds. The results show that the method is capable of quantifying the relative energy allocation relationships between the two cover types defined in the study. It is noted that the method also has the potential to map forest latent heat flux densities.

Illumination invariant feature point matching for high-resolution planetary remote sensing images

NASA Astrophysics Data System (ADS)

Wu, Bo; Zeng, Hai; Hu, Han

2018-03-01

Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

Ground-based remote sensing of tropospheric water vapour isotopologues within the project MUSICA

NASA Astrophysics Data System (ADS)

Schneider, M.; Barthlott, S.; Hase, F.; González, Y.; Yoshimura, K.; García, O. E.; Sepúlveda, E.; Gomez-Pelaez, A.; Gisi, M.; Kohlhepp, R.; Dohe, S.; Blumenstock, T.; Wiegele, A.; Christner, E.; Strong, K.; Weaver, D.; Palm, M.; Deutscher, N. M.; Warneke, T.; Notholt, J.; Lejeune, B.; Demoulin, P.; Jones, N.; Griffith, D. W. T.; Smale, D.; Robinson, J.

2012-12-01

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologue data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remote sensing datasets. We demonstrate that the MUSICA humidity profiles are representative for most of the troposphere with a vertical resolution ranging from about 2 km (in the lower troposphere) to 8 km (in the upper troposphere) and with an estimated precision of better than 10%. We find that the sensitivity with respect to the isotopologue composition is limited to the lower and middle troposphere, whereby we estimate a precision of about 30‰ for the ratio between the two isotopologues HD16O and H216O. The measurement noise, the applied atmospheric temperature profiles, the uncertainty in the spectral baseline, and the cross-dependence on humidity are the leading error sources. We introduce an a posteriori correction method of the cross-dependence on humidity, and we recommend applying it to isotopologue ratio remote sensing datasets in general. In addition, we present mid-infrared CO2 retrievals and use them for demonstrating the MUSICA network-wide data consistency. In order to indicate the potential of long-term isotopologue remote sensing data if provided with a well-documented quality, we present a climatology and compare it to simulations of an isotope incorporated AGCM (Atmospheric General Circulation Model). We identify differences in the multi-year mean and seasonal cycles that significantly exceed the estimated errors, thereby indicating deficits in the modeled atmospheric water cycle.

Ground-based remote sensing of tropospheric water vapour isotopologues within the project MUSICA

NASA Astrophysics Data System (ADS)

Schneider, M.; Barthlott, S.; Hase, F.; González, Y.; Yoshimura, K.; García, O. E.; Sepúlveda, E.; Gomez-Pelaez, A.; Gisi, M.; Kohlhepp, R.; Dohe, S.; Blumenstock, T.; Strong, K.; Weaver, D.; Palm, M.; Deutscher, N. M.; Warneke, T.; Notholt, J.; Lejeune, B.; Demoulin, P.; Jones, N.; Griffith, D. W. T.; Smale, D.; Robinson, J.

2012-08-01

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologues data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remote sensing datasets. We demonstrate that the MUSICA humidity profiles are representative for most of the troposphere with a vertical resolution ranging from about 2 km (in the lower troposphere) to 8 km (in the upper troposphere) and with an estimated precision of better than 10%. We find that the sensitivity with respect to the isotopologue composition is limited to the lower and middle troposphere, whereby we estimate a precision of about 30‰ for the ratio between the two isotopologues HD16O and H216O. The measurement noise, the applied atmospheric temperature profiles, the uncertainty in the spectral baseline, and interferences from humidity are the leading error sources. We introduce an a posteriori correction method of the humidity interference error and we recommend applying it for isotopologue ratio remote sensing datasets in general. In addition, we present mid-infrared CO2 retrievals and use them for demonstrating the MUSICA network-wide data consistency. In order to indicate the potential of long-term isotopologue remote sensing data if provided with a well-documented quality, we present a climatology and compare it to simulations of an isotope incorporated AGCM (Atmospheric General Circulation Model). We identify differences in the multi-year mean and seasonal cycles that significantly exceed the estimated errors, thereby indicating deficits in the modeled atmospheric water cycle.

The research of road and vehicle information extraction algorithm based on high resolution remote sensing image

NASA Astrophysics Data System (ADS)

Zhou, Tingting; Gu, Lingjia; Ren, Ruizhi; Cao, Qiong

2016-09-01

With the rapid development of remote sensing technology, the spatial resolution and temporal resolution of satellite imagery also have a huge increase. Meanwhile, High-spatial-resolution images are becoming increasingly popular for commercial applications. The remote sensing image technology has broad application prospects in intelligent traffic. Compared with traditional traffic information collection methods, vehicle information extraction using high-resolution remote sensing image has the advantages of high resolution and wide coverage. This has great guiding significance to urban planning, transportation management, travel route choice and so on. Firstly, this paper preprocessed the acquired high-resolution multi-spectral and panchromatic remote sensing images. After that, on the one hand, in order to get the optimal thresholding for image segmentation, histogram equalization and linear enhancement technologies were applied into the preprocessing results. On the other hand, considering distribution characteristics of road, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used to suppress water and vegetation information of preprocessing results. Then, the above two processing result were combined. Finally, the geometric characteristics were used to completed road information extraction. The road vector extracted was used to limit the target vehicle area. Target vehicle extraction was divided into bright vehicles extraction and dark vehicles extraction. Eventually, the extraction results of the two kinds of vehicles were combined to get the final results. The experiment results demonstrated that the proposed algorithm has a high precision for the vehicle information extraction for different high resolution remote sensing images. Among these results, the average fault detection rate was about 5.36%, the average residual rate was about 13.60% and the average accuracy was approximately 91.26%.

A Citizen Science Campaign to Validate Snow Remote-Sensing Products

NASA Astrophysics Data System (ADS)

Wikstrom Jones, K.; Wolken, G. J.; Arendt, A. A.; Hill, D. F.; Crumley, R. L.; Setiawan, L.; Markle, B.

2017-12-01

The ability to quantify seasonal water retention and storage in mountain snow packs has implications for an array of important topics, including ecosystem function, water resources, hazard mitigation, validation of remote sensing products, climate modeling, and the economy. Runoff simulation models, which typically rely on gridded climate data and snow remote sensing products, would be greatly improved if uncertainties in estimates of snow depth distribution in high-elevation complex terrain could be reduced. This requires an increase in the spatial and temporal coverage of observational snow data in high-elevation data-poor regions. To this end, we launched Community Snow Observations (CSO). Participating citizen scientists use Mountain Hub, a multi-platform mobile and web-based crowdsourcing application that allows users to record, submit, and instantly share geo-located snow depth, snow water equivalence (SWE) measurements, measurement location photos, and snow grain information with project scientists and other citizen scientists. The snow observations are used to validate remote sensing products and modeled snow depth distribution. The project's prototype phase focused on Thompson Pass in south-central Alaska, an important infrastructure corridor that includes avalanche terrain and the Lowe River drainage and is essential to the City of Valdez and the fisheries of Prince William Sound. This year's efforts included website development, expansion of the Mountain Hub tool, and recruitment of citizen scientists through a combination of social media outreach, community presentations, and targeted recruitment of local avalanche professionals. We also conducted two intensive field data collection campaigns that coincided with an aerial photogrammetric survey. With more than 400 snow depth observations, we have generated a new snow remote-sensing product that better matches actual SWE quantities for Thompson Pass. In the next phase of the citizen science portion of this project we will focus on expanding our group of participants to a larger geographic area in Alaska, further develop our partnership with Mountain Hub, and build relationships in new communities as we conduct a photogrammetric survey in a different region next year.

Flood Management Enhancement Using Remotely Sensed Data

NASA Technical Reports Server (NTRS)

Romanowski, Gregory J.

1997-01-01

SENTAR, Inc., entered into a cooperative agreement with NASA Goddard Space Flight Center (GSFC) in December 1994. The intent of the NASA Cooperative Agreement was to stimulate broad public use, via the Internet, of the very large remote sensing databases maintained by NASA and other agencies, thus stimulating U.S. economic growth, improving the quality of life, and contributing to the implementation of a National Information Infrastructure. SENTAR headed a team of collaborating organizations in meeting the goals of this project. SENTAR's teammates were the NASA Marshall Space Flight Center (MSFC) Global Hydrology and Climate Center (GHCC), the U.S. Army Space and Strategic Defense Command (USASSDC), and the Alabama Emergency Management Agency (EMA). For this cooperative agreement, SENTAR and its teammates accessed remotely sensed data in the Distributed Active Archive Centers, and other available sources, for use in enhancing the present capabilities for flood disaster management by the Alabama EMA. The project developed a prototype software system for addressing prediction, warning, and damage assessment for floods, though it currently focuses on assessment. The objectives of the prototype system were to demonstrate the added value of remote sensing data for emergency management operations during floods and the ability of the Internet to provide the primary communications medium for the system. To help achieve these objectives, SENTAR developed an integrated interface for the emergency operations staff to simplify acquiring and manipulating source data and data products for use in generating new data products. The prototype system establishes a systems infrastructure designed to expand to include future flood-related data and models or to include other disasters with their associated remote sensing data requirements and distributed data sources. This report covers the specific work performed during the seventh, and final, milestone period of the project, which began on 1 October 1996 and ended on 31 January 1997. In addition, it provides a summary of the entire project.

Intensity-Duration-Frequency curves from remote sensing datasets: direct comparison of weather radar and CMORPH over the Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Morin, Efrat; Marra, Francesco; Peleg, Nadav; Mei, Yiwen; Anagnostou, Emmanouil N.

2017-04-01

Rainfall frequency analysis is used to quantify the probability of occurrence of extreme rainfall and is traditionally based on rain gauge records. The limited spatial coverage of rain gauges is insufficient to sample the spatiotemporal variability of extreme rainfall and to provide the areal information required by management and design applications. Conversely, remote sensing instruments, even if quantitative uncertain, offer coverage and spatiotemporal detail that allow overcoming these issues. In recent years, remote sensing datasets began to be used for frequency analyses, taking advantage of increased record lengths and quantitative adjustments of the data. However, the studies so far made use of concepts and techniques developed for rain gauge (i.e. point or multiple-point) data and have been validated by comparison with gauge-derived analyses. These procedures add further sources of uncertainty and prevent from isolating between data and methodological uncertainties and from fully exploiting the available information. In this study, we step out of the gauge-centered concept presenting a direct comparison between at-site Intensity-Duration-Frequency (IDF) curves derived from different remote sensing datasets on corresponding spatial scales, temporal resolutions and records. We analyzed 16 years of homogeneously corrected and gauge-adjusted C-Band weather radar estimates, high-resolution CMORPH and gauge-adjusted high-resolution CMORPH over the Eastern Mediterranean. Results of this study include: (a) good spatial correlation between radar and satellite IDFs ( 0.7 for 2-5 years return period); (b) consistent correlation and dispersion in the raw and gauge adjusted CMORPH; (c) bias is almost uniform with return period for 12-24 h durations; (d) radar identifies thicker tail distributions than CMORPH and the tail of the distributions depends on the spatial and temporal scales. These results demonstrate the potential of remote sensing datasets for rainfall frequency analysis for management (e.g. warning and early-warning systems) and design (e.g. sewer design, large scale drainage planning)

Applications of Remote Sensing to Emergency Management.

DTIC Science & Technology

1980-02-15

Contents: Foundations of Remote Sensing : Data Acquisition and Interpretation; Availability of Remote Sensing Technology for Disaster Response...Imaging Systems, Current and Near Future Satellite and Aircraft Remote Sensing Systems; Utilization of Remote Sensing in Disaster Response: Categories of...Disasters, Phases of Monitoring Activities; Recommendations for Utilization of Remote Sensing Technology in Disaster Response; Selected Reading List.

On validating remote sensing simulations using coincident real data

NASA Astrophysics Data System (ADS)

Wang, Mingming; Yao, Wei; Brown, Scott; Goodenough, Adam; van Aardt, Jan

2016-05-01

The remote sensing community often requires data simulation, either via spectral/spatial downsampling or through virtual, physics-based models, to assess systems and algorithms. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is one such first-principles, physics-based model for simulating imagery for a range of modalities. Complex simulation of vegetation environments subsequently has become possible, as scene rendering technology and software advanced. This in turn has created questions related to the validity of such complex models, with potential multiple scattering, bidirectional distribution function (BRDF), etc. phenomena that could impact results in the case of complex vegetation scenes. We selected three sites, located in the Pacific Southwest domain (Fresno, CA) of the National Ecological Observatory Network (NEON). These sites represent oak savanna, hardwood forests, and conifer-manzanita-mixed forests. We constructed corresponding virtual scenes, using airborne LiDAR and imaging spectroscopy data from NEON, ground-based LiDAR data, and field-collected spectra to characterize the scenes. Imaging spectroscopy data for these virtual sites then were generated using the DIRSIG simulation environment. This simulated imagery was compared to real AVIRIS imagery (15m spatial resolution; 12 pixels/scene) and NEON Airborne Observation Platform (AOP) data (1m spatial resolution; 180 pixels/scene). These tests were performed using a distribution-comparison approach for select spectral statistics, e.g., established the spectra's shape, for each simulated versus real distribution pair. The initial comparison results of the spectral distributions indicated that the shapes of spectra between the virtual and real sites were closely matched.

Discrimination of fluoride and phosphate contamination in central Florida for analyses of environmental effects

NASA Technical Reports Server (NTRS)

Coker, A. E.; Marshall, R.; Thomson, F.

1972-01-01

A study was made of the spatial registration of fluoride and phosphate pollution parameters in central Florida by utilizing remote sensing techniques. Multispectral remote sensing data were collected over the area and processed to produce multispectral recognition maps. These processed data were used to map land areas and waters containing concentrations of fluoride and phosphate. Maps showing distribution of affected and unaffected vegetation were produced. In addition, the multispectral data were processed by single band radiometric slicing to produce radiometric maps used to delineate areas of high ultraviolet radiance, which indicates high fluoride concentrations. The multispectral parameter maps and radiometric maps in combination showed distinctive patterns, which are correlated with areas known to be affected by fluoride and phosphate contamination. These remote sensing techniques have the potential for regional use to assess the environmental impact of fluoride and phosphate wastes in central Florida.

An Uncertainty Quantification Framework for Remote Sensing Retrievals

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Hobbs, J.

2017-12-01

Remote sensing data sets produced by NASA and other space agencies are the result of complex algorithms that infer geophysical state from observed radiances using retrieval algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified by stepping outside the assumptions that underlie the retrieval methodology. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach, and provide a case-study example from the Orbiting Carbon Observatory 2 mission.

The acquisition, storage, and dissemination of LANDSAT and other LACIE support data

NASA Technical Reports Server (NTRS)

Abbotts, L. F.; Nelson, R. M. (Principal Investigator)

1979-01-01

Activities performed at the LACIE physical data library are described. These include the researching, acquisition, indexing, maintenance, distribution, tracking, and control of LACIE operational data and documents. Much of the data available can be incorporated into an Earth resources data base. Elements of the data collection that can support future remote sensing programs include: (1) the LANDSAT full-frame image files; (2) the microfilm file of aerial and space photographic and multispectral maps and charts that encompasses a large portion of the Earth's surface; (3) the map/chart collection that includes various scale maps and charts for a good portion of the U.S. and the LACIE area in foreign countries; (4) computer-compatible tapes of good quality LANDSAT scenes; (5) basic remote sensing data, project data, reference material, and associated publications; (6) visual aids to support presentation on remote sensing projects; and (7) research acquisition and handling procedures for managing data.

Integration of remote sensing and hydrologic modeling through multi-disciplinary semiarid field campaigns: Moonsoon 1990, Walnut Gulch 1992, and SALSA-MEX

NASA Technical Reports Server (NTRS)

Moran, M. S.; Goodrich, D. C.; Kustas, W. P.

1994-01-01

A research and modeling strategy is presented for development of distributed hydrologic models given by a combination of remotely sensed and ground based data. In support of this strategy, two experiments Moonsoon'90 and Walnut Gulch'92 were conducted in a semiarid rangeland southeast of Tucson, Arizona, (U.S.) and a third experiment, the SALSA-MEX (Semi Arid Land Surface Atmospheric Mountain Experiment) was proposed. Results from the Moonsoon'90 experiment substantially advanced the understanding of the hydrologic and atmospheric fluxes in an arid environment and provided insight into the use of remote sensing data for hydrologic modeling. The Walnut Gulch'92 experiment addressed the seasonal hydrologic dynamics of the region and the potential of combined optical microwave remote sensing for hydrologic applications. SALSA-MEX will combine measurements and modeling to study hydrologic processes influenced by surrounding mountains, such as enhanced precipitation, snowmelt and recharge to ground water aquifers. The results from these experiments, along with the extensive experimental data bases, should aid the research community in large scale modeling of mass and energy exchanges across the soil-plant-atmosphere interface.

Tools and Services for Working with Multiple Land Remote Sensing Data Products

NASA Astrophysics Data System (ADS)

Krehbiel, C.; Friesz, A.; Harriman, L.; Quenzer, R.; Impecoven, K.; Maiersperger, T.

2016-12-01

The availability of increasingly large and diverse satellite remote sensing datasets provides both an opportunity and a challenge across broad Earth science research communities. On one hand, the extensive assortment of available data offer unprecedented opportunities to improve our understanding of Earth science and enable data use across a multitude of science disciplines. On the other hand, increasingly complex formats, data structures, and metadata can be an obstacle to data use for the broad user community that is interested in incorporating remote sensing Earth science data into their research. NASA's Land Processes Distributed Active Archive Center (LP DAAC) provides easy to use Python notebook tutorials for services such as accessing land remote sensing data from the LP DAAC Data Pool and interpreting data quality information from MODIS. We use examples to demonstrate the capabilities of the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS), such as spatially and spectrally subsetting data, decoding valuable quality information, and exploring initial analysis results within the user interface. We also show data recipes for R and Python scripts that help users process ASTER L1T and ASTER Global Emissivity Datasets.

Airborne Particles: What We Have Learned About Their Role in Climate from Remote Sensing, and Prospects for Future Advances

NASA Technical Reports Server (NTRS)

Kahn, Ralph A.

2013-01-01

Desert dust, wildfire smoke, volcanic ash, biogenic and urban pollution particles, all affect the regional-scale climate of Earth in places and at times; some have global-scale impacts on the column radiation balance, cloud properties, atmospheric stability structure, and circulation patterns. Remote sensing has played a central role in identifying the sources and transports of airborne particles, mapping their three-dimensional distribution and variability, quantifying their amount, and constraining aerosol air mass type. The measurements obtained from remote sensing have strengths and limitations, and their value for characterizing Earths environment is enhanced immensely when they are combined with direct, in situ observations, and used to constrain aerosol transport and climate models. A similar approach has been taken to study the role particles play in determining the climate of Mars, though based on far fewer observations. This presentation will focus what we have learned from remote sensing about the impacts aerosol have on Earths climate; a few points about how aerosols affect the climate of Mars will also be introduced, in the context of how we might assess aerosol-climate impacts more generally on other worlds.

Historical ruins of remote sensing archaeology in arid desertified environment, northwestern China

NASA Astrophysics Data System (ADS)

Hu, N. K.; Li, X.

2017-02-01

Silk Road is an important exchange channel for human communication and culture propagation between ancient China and the West during historical periods. A lot of human activities performed in Silk Road and many historical ruins leave behind to present. Archaeological ruins can play a significant role in studying and restoring the past human activities, as well as understanding regional environmental changes. There were many flourishingly human activities during different historical periods that were developed in ancient Juyan Oasis in the downstream of the Heihe River Basin. A large number of historical ruins that reflect past human activities preserved between numerous of the nebkhas and sand dunes. In this study, combined high-resolution remote sensing imageries with in situ truths investigated during the fieldwork, certain unknown ruins were identified according to the image features of historical ruins that appear in remotely sensed data, which were undiscovered during the previous field archaeological investigations and unreported in the past public literatures. Almost all of the newly discovered ruins that were identified using remote sensing images are distributed in the Lvcheng and BJ2008 surroundings. Newly findings supplement the missing gaps that were not taking into account during the previous field surveys.

Growth pattern research on the modern deposition of Ganjiang delta in Poyang lake basin by spatio-temporal remote sensing images

NASA Astrophysics Data System (ADS)

Zhou, Hongying; Yuan, Xuanjun; Zhang, Youyan; Dong, Wentong; Liu, Song

2016-11-01

It is of great importance for petroleum exploration to study the sedimentary features and the growth pattern of shoal water deltas in lake basins. Taking spatio-temporal remote sensing images as the principal data source, combined with field sedimentation survey, a quantitative research on the modern deposition of Ganjiang delta in the Poyang Lake Basin is described in this paper. Using 76 multi-temporal and multi-type remote sensing images acquired from 1973 to 2015, combined with field sedimentation survey, remote sensing interpretation analysis was conducted on the sedimentary facies of the Ganjiang delta. It is found that that the current Poyang Lake mainly consists of three types of sand body deposits including deltaic deposit, overflow channel deposit, and aeolian deposit, and the distribution of sand bodies was affected by the above three types of depositions jointly. The mid-branch channels of the Ganjiang delta increased on an exponential growth rhythm. The main growth pattern of the Ganjiang delta is dendritic and reticular, and the distributary channel mostly arborizes at lake inlet and was reworked to be reticulatus at late stage.

Combining remote sensing image with DEM to identify ancient Minqin Oasis, northwest of China

NASA Astrophysics Data System (ADS)

Xie, Yaowen

2008-10-01

The developing and desertification process of Minqin oasis is representative in the whole arid area of northwest China. Combining Remote Sensing image with Digital Elevation Model (DEM) can produce the three-dimensional image of the research area which can give prominence to the spatial background of historical geography phenomenon's distribution, providing the conditions for extracting and analyzing historical geographical information thoroughly. This research rebuilds the ancient artificial Oasis based on the three-dimensional images produced by the TM digital Remote Sensing image and DEM created using 1:100000 topographic maps. The result indicates that the whole area of the ancient artificial oasis in Minqin Basin over the whole historical period reaches 321km2, in the form of discontinuous sheet, separated on the two banks of ancient Shiyang River and its branches, namely, Xishawo area, west to modern Minqin Basin and Zhongshawo area, in the center of the oasis. Except for a little of the ancient oasis unceasingly used by later people, most of it became desert. The combination of digital Remote Sensing image and DEM can integrate the advantages of both in identifying ancient oasis and improve the interpreting accuracy greatly.

The AmericaView Project - Putting the Earth into Your Hands

USGS Publications Warehouse

,

2005-01-01

The U.S. Geological Survey (USGS) is a leader in collecting, archiving, and distributing geospatial data and information about the Earth. Providing quick, reliable access to remotely sensed images and geospatial data is the driving principle behind the AmericaView Project. A national not-for-profit organization, AmericaView, Inc. was established and is supported by the USGS to coordinate the activities of a national network of university-led consortia with the primary objective of the advancement of the science of remote sensing. Individual consortia members include academic institutions, as well as state, local, and tribal government agencies. AmericaView's focus is to expand the understanding and use of remote sensing through education and outreach efforts and to provide affordable, integrated remote sensing information access and delivery to the American public. USGS's Landsat and NASA's Earth Observing System (EOS) satellite data are downlinked from satellites or transferred from other facilities to the USGS Center for Earth Resources Observation and Science (EROS) ground receiving station in Sioux Falls, South Dakota. The data can then be transferred over high-speed networks to consortium members, where it is archived and made available for public use.

Intelligent services for discovery of complex geospatial features from remote sensing imagery

NASA Astrophysics Data System (ADS)

Yue, Peng; Di, Liping; Wei, Yaxing; Han, Weiguo

2013-09-01

Remote sensing imagery has been commonly used by intelligence analysts to discover geospatial features, including complex ones. The overwhelming volume of routine image acquisition requires automated methods or systems for feature discovery instead of manual image interpretation. The methods of extraction of elementary ground features such as buildings and roads from remote sensing imagery have been studied extensively. The discovery of complex geospatial features, however, is still rather understudied. A complex feature, such as a Weapon of Mass Destruction (WMD) proliferation facility, is spatially composed of elementary features (e.g., buildings for hosting fuel concentration machines, cooling towers, transportation roads, and fences). Such spatial semantics, together with thematic semantics of feature types, can be used to discover complex geospatial features. This paper proposes a workflow-based approach for discovery of complex geospatial features that uses geospatial semantics and services. The elementary features extracted from imagery are archived in distributed Web Feature Services (WFSs) and discoverable from a catalogue service. Using spatial semantics among elementary features and thematic semantics among feature types, workflow-based service chains can be constructed to locate semantically-related complex features in imagery. The workflows are reusable and can provide on-demand discovery of complex features in a distributed environment.

Assimilation of remote sensing observations into a continuous distributed hydrological model: impacts on the hydrologic cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Campo, Lorenzo; Cenci, Luca; Silvestro, Francesco; Delogu, Fabio; Boni, Giorgio; Rudari, Roberto

2015-04-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce in-situ data. This work investigates the impact of the assimilation of different remote sensing products on the hydrological cycle by using a continuous physically based distributed hydrological model. Three soil moisture products derived by ASCAT (Advanced SCATterometer) are used to update the model state variables. The satellite-derived products are assimilated into the hydrological model using different assimilation techniques: a simple nudging and the Ensemble Kalman Filter. Moreover two assimilation strategies are evaluated to assess the impact of assimilating the satellite products at model spatial resolution or at the satellite scale. The experiments are carried out for three Italian catchments on multi year period. The benefits on the model predictions of discharge, LST, evapotranspiration and soil moisture dynamics are tested and discussed.

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

Runoff simulation sensitivity to remotely sensed initial soil water content

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Schmugge, T. J.; Jackson, T. J.; Unkrich, C. L.; Keefer, T. O.; Parry, R.; Bach, L. B.; Amer, S. A.

1994-05-01

A variety of aircraft remotely sensed and conventional ground-based measurements of volumetric soil water content (SW) were made over two subwatersheds (4.4 and 631 ha) of the U.S. Department of Agriculture's Agricultural Research Service Walnut Gulch experimental watershed during the 1990 monsoon season. Spatially distributed soil water contents estimated remotely from the NASA push broom microwave radiometer (PBMR), an Institute of Radioengineering and Electronics (IRE) multifrequency radiometer, and three ground-based point methods were used to define prestorm initial SW for a distributed rainfall-runoff model (KINEROS; Woolhiser et al., 1990) at a small catchment scale (4.4 ha). At a medium catchment scale (631 ha or 6.31 km2) spatially distributed PBMR SW data were aggregated via stream order reduction. The impacts of the various spatial averages of SW on runoff simulations are discussed and are compared to runoff simulations using SW estimates derived from a simple daily water balance model. It was found that at the small catchment scale the SW data obtained from any of the measurement methods could be used to obtain reasonable runoff predictions. At the medium catchment scale, a basin-wide remotely sensed average of initial water content was sufficient for runoff simulations. This has important implications for the possible use of satellite-based microwave soil moisture data to define prestorm SW because the low spatial resolutions of such sensors may not seriously impact runoff simulations under the conditions examined. However, at both the small and medium basin scale, adequate resources must be devoted to proper definition of the input rainfall to achieve reasonable runoff simulations.

REMOTE SENSING TECHNOLOGIES APPLICATIONS RESEARCH

EPA Science Inventory

Remote sensing technologies applications research supports the ORD Landscape Sciences Program (LSP) in two separate areas: operational remote sensing, and remote sensing research and development. Operational remote sensing is provided to the LSP through the use of current and t...

St. Louis Encephalitis virus mosquito vectors dynamics in three different environments in relation to remotely sensed environmental conditions.

PubMed

Batallán, Gonzalo P; Estallo, Elizabet L; Flores, Fernando S; Sartor, Paolo; Contigiani, Marta S; Almirón, Walter R

2015-06-01

In Argentina the St. Louis Encephalitis virus (SLEV) is an endemic and widely distributed pathogen transmitted by the cosmopolitan mosquito Culex quinquefasciatus. During two outbreaks in Córdoba city, in 2005 and 2010, Culex interfor was also found infected, but its role as vector of SLEV is poorly known. This mosquito species is distributed from central Argentina to southern Brazil. The primary aim of this study was to analyze the population dynamic of Cx. interfor and Cx. quinquefasciatus in three different environments (urban, suburban and non-urban) in relation to remotely sensed environmental data for vegetation (NDVI and NDWI) and temperature (brightness temperature). Cx. quinquefasciatus and Cx. interfor were found at the three sampled sites, being both the most abundant Culex species, with peaks in early and midsummer. Temporal distribution patterns of both mosquito species were highly correlated in a non-urban area of high SLEV risk transmission. Cx. quinquefasciatus and Cx. interfor were associated with the most urbanized site and the non-urban environment, respectively; high significant correlations were detected between vegetation indices and abundance of both mosquito species confirming these associations. These data provide a foundation for building density maps of these two SLEV mosquito vectors using remotely sensed data to help inform vector control programs. Copyright © 2015 Elsevier B.V. All rights reserved.

Estimation of sulphur dioxide emission rate from a power plant based on the remote sensing measurement with an imaging-DOAS instrument

NASA Astrophysics Data System (ADS)

Chong, Jihyo; Kim, Young J.; Baek, Jongho; Lee, Hanlim

2016-10-01

Major anthropogenic sources of sulphur dioxide in the troposphere include point sources such as power plants and combustion-derived industrial sources. Spatially resolved remote sensing of atmospheric trace gases is desirable for better estimation and validation of emission from those sources. It has been reported that Imaging Differential Optical Absorption Spectroscopy (I-DOAS) technique can provide the spatially resolved two-dimensional distribution measurement of atmospheric trace gases. This study presents the results of I-DOAS observations of SO2 from a large power plant. The stack plume from the Taean coal-fired power plant was remotely sensed with an I-DOAS instrument. The slant column density (SCD) of SO2 was derived by data analysis of the absorption spectra of the scattered sunlight measured by an I-DOAS over the power plant stacks. Two-dimensional distribution of SO2 SCD was obtained over the viewing window of the I-DOAS instrument. The measured SCDs were converted to mixing ratios in order to estimate the rate of SO2 emission from each stack. The maximum mixing ratio of SO2 was measured to be 28.1 ppm with a SCD value of 4.15×1017 molecules/cm2. Based on the exit velocity of the plume from the stack, the emission rate of SO2 was estimated to be 22.54 g/s. Remote sensing of SO2 with an I-DOAS instrument can be very useful for independent estimation and validation of the emission rates from major point sources as well as area sources.

The application of unmanned aerial vehicle remote sensing for monitoring secondary geological disasters after earthquakes

NASA Astrophysics Data System (ADS)

Lei, Tianjie; Zhang, Yazhen; Wang, Xingyong; Fu, Jun'e.; Li, Lin; Pang, Zhiguo; Zhang, Xiaolei; Kan, Guangyuan

2017-07-01

Remote sensing system fitted on Unmanned Aerial Vehicle (UAV) can obtain clear images and high-resolution aerial photographs. It has advantages of strong real-time, flexibility and convenience, free from influence of external environment, low cost, low-flying under clouds and ability to work full-time. When an earthquake happened, it could go deep into the places safely and reliably which human staff can hardly approach, such as secondary geological disasters hit areas. The system can be timely precise in response to secondary geological disasters monitoring by a way of obtaining first-hand information as quickly as possible, producing a unique emergency response capacity to provide a scientific basis for overall decision-making processes. It can greatly enhance the capability of on-site disaster emergency working team in data collection and transmission. The great advantages of UAV remote sensing system played an irreplaceable role in monitoring secondary geological disaster dynamics and influences. Taking the landslides and barrier lakes for example, the paper explored the basic application and process of UAV remote sensing in the disaster emergency relief. UAV high-resolution remote sensing images had been exploited to estimate the situation of disaster-hit areas and monitor secondary geological disasters rapidly, systematically and continuously. Furthermore, a rapid quantitative assessment on the distribution and size of landslides and barrier lakes was carried out. Monitoring results could support relevant government departments and rescue teams, providing detailed and reliable scientific evidence for disaster relief and decision-making.

Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

NASA Technical Reports Server (NTRS)

Zhou, Yuyu; Weng, Qihao; Gurney, Kevin R.; Shuai, Yanmin; Hu, Xuefei

2012-01-01

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.

Modeling α- and β-diversity in a tropical forest from remotely sensed and spatial data

NASA Astrophysics Data System (ADS)

Hernández-Stefanoni, J. Luis; Gallardo-Cruz, J. Alberto; Meave, Jorge A.; Rocchini, Duccio; Bello-Pineda, Javier; López-Martínez, J. Omar

2012-10-01

Comprehensive information on species distribution and species composition patterns of plant communities is required for effective conservation and management of biodiversity. Remote sensing offers an inexpensive means of attaining complete spatial coverage for large areas, at regular time intervals, and can therefore be extremely useful for estimating both species richness and spatial variation of species composition (α- and β-diversity). An essential step to map such attributes is to identify and understand their main drivers. We used remotely sensed data as a surrogate of plant productivity and habitat structure variables for explaining α- and β-diversity, and evaluated the relative roles of productivity-habitat structure and spatial variables in explaining observed patterns of α- and β-diversity by using a Principal Coordinates of Neighbor Matrices analysis. We also examined the relationship between remotely sensed and field data, in order to map α- and β-diversity at the landscape-level in the Yucatan Peninsula, using a regression kriging procedure. These two procedures integrate the relationship of species richness and spatial species turnover both with remotely sensed data and spatial structure. The empirical models so obtained can be used to predict species richness and variation in species composition, and they can be regarded as valuable tools not only for identifying areas with high local species richness (α-diversity), but also areas with high species turnover (β-diversity). Ultimately, information obtained in this way can help maximize the number of species preserved in a landscape.

Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling.

PubMed

Walz, Yvonne; Wegmann, Martin; Leutner, Benjamin; Dech, Stefan; Vounatsou, Penelope; N'Goran, Eliézer K; Raso, Giovanna; Utzinger, Jürg

2015-11-30

Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d'Ivoire using high- and moderate-resolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixel-based modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.

Mapping of risk prone areas of kala-azar (Visceral leishmaniasis) in parts of Bihar State, India: an RS and GIS approach.

PubMed

Sudhakar, S; Srinivas, T; Palit, A; Kar, S K; Battacharya, S K

2006-09-01

The kala-azar fever (Visceral leishmaniasis) is continuing unabated in India for over a century, now being largely confined to the eastern part of India mainly in Bihar state and to some extent in its bordering states like West Bengal and Uttar Pradesh. Two study sites namely Patepur block in Vaishali district with high endemicity in northern part and Lohardagga block in Lohardagga district with absolute non-endemicity in southern part of Bihar were selected for the study with the following objectives : (i) to study the macro-ecosystem in relation to distribution of vector -Phlebotomus argentipes; (ii) to identify/map the risk prone areas or villages in a block for quick remedial measures; and (iii) to make use of satellite remote sensing and GIS to demonstrate the utility for rapid assessment of landuse/landcover and their relation with the incidence of kalaazar leading to the mapping of risk prone areas. Indian Remote Sensing (IRS)-1D LISS III satellite data for the periods of March and November 2000 were analysed in Silicon graphic image processing system using ERDAS software. False color composites (FCC) were generated and landuse/landcover was assessed using Maximum likelihood supervised classification techniques based on ground truth training sets. During the study the GIS functions are used to quantify the remotely sensed landscape proportions of 5 km2 buffer surrounding each known group of villages of high occurrence of sandflies in endemic and nonendemic study sites. Instead of traditional ground based survey methods to vector surveillance, the present study used a combination of remote sensing (RS) and geographical information system (GIS) approach to develop landscape predictors of sandfly abundance-an indicator of human vector contact and as a measure of risk prone areas. Statistical analysis using the remotely sensed landscape variables showed that rural villages surrounded by higher proportion of transitional swamps with soft stemmed edible plants and banana, sugarcane plantations had higher sandfly abundance and would, therefore, be at higher risk prone areas for man-vector contact. The present study clearly brought out the usefulness of satellite remote sensing technology in generating the crucial information on spatial distribution of landuse/landcover classes with special emphasis on indicator landcover classes thereby helping in prioritising the area to identify risk prone areas of kala-azar through GIS application tools.

USGS remote sensing coordination for the 2010 Haiti earthquake

USGS Publications Warehouse

Duda, Kenneth A.; Jones, Brenda

2011-01-01

In response to the devastating 12 January 2010, earthquake in Haiti, the US Geological Survey (USGS) provided essential coordinating services for remote sensing activities. Communication was rapidly established between the widely distributed response teams and data providers to define imaging requirements and sensor tasking opportunities. Data acquired from a variety of sources were received and archived by the USGS, and these products were subsequently distributed using the Hazards Data Distribution System (HDDS) and other mechanisms. Within six weeks after the earthquake, over 600,000 files representing 54 terabytes of data were provided to the response community. The USGS directly supported a wide variety of groups in their use of these data to characterize post-earthquake conditions and to make comparisons with pre-event imagery. The rapid and continuing response achieved was enabled by existing imaging and ground systems, and skilled personnel adept in all aspects of satellite data acquisition, processing, distribution and analysis. The information derived from image interpretation assisted senior planners and on-site teams to direct assistance where it was most needed.

Tunnel-Site Selection by Remote Sensing Techniques

DTIC Science & Technology

A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined...conventional remote sensing . Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave

System and method for evaluating wind flow fields using remote sensing devices

DOEpatents

Schroeder, John; Hirth, Brian; Guynes, Jerry

2016-12-13

The present invention provides a system and method for obtaining data to determine one or more characteristics of a wind field using a first remote sensing device and a second remote sensing device. Coordinated data is collected from the first and second remote sensing devices and analyzed to determine the one or more characteristics of the wind field. The first remote sensing device is positioned to have a portion of the wind field within a first scanning sector of the first remote sensing device. The second remote sensing device is positioned to have the portion of the wind field disposed within a second scanning sector of the second remote sensing device.

Exploring Models and Data for Remote Sensing Image Caption Generation

NASA Astrophysics Data System (ADS)

Lu, Xiaoqiang; Wang, Binqiang; Zheng, Xiangtao; Li, Xuelong

2018-04-01

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal

Research on active imaging information transmission technology of satellite borne quantum remote sensing

NASA Astrophysics Data System (ADS)

Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

2017-08-01

According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

Integration of Remote Sensing Data In Operational Flood Forecast In Southwest Germany

NASA Astrophysics Data System (ADS)

Bach, H.; Appel, F.; Schulz, W.; Merkel, U.; Ludwig, R.; Mauser, W.

Methods to accurately assess and forecast flood discharge are mandatory to minimise the impact of hydrological hazards. However, existing rainfall-runoff models rarely accurately consider the spatial characteristics of the watershed, which is essential for a suitable and physics-based description of processes relevant for runoff formation. Spatial information with low temporal variability like elevation, slopes and land use can be mapped or extracted from remote sensing data. However, land surface param- eters of high temporal variability, like soil moisture and snow properties are hardly available and used in operational forecasts. Remote sensing methods can improve flood forecast by providing information on the actual water retention capacities in the watershed and facilitate the regionalisation of hydrological models. To prove and demonstrate this, the project 'InFerno' (Integration of remote sensing data in opera- tional water balance and flood forecast modelling) has been set up, funded by DLR (50EE0053). Within InFerno remote sensing data (optical and microwave) are thor- oughly processed to deliver spatially distributed parameters of snow properties and soil moisture. Especially during the onset of a flood this information is essential to estimate the initial conditions of the model. At the flood forecast centres of 'Baden- Württemberg' and 'Rheinland-Pfalz' (Southwest Germany) the remote sensing based maps on soil moisture and snow properties will be integrated in the continuously op- erated water balance and flood forecast model LARSIM. The concept is to transfer the developed methodology from the Neckar to the Mosel basin. The major challenges lie on the one hand in the implementation of algorithms developed for a multisensoral synergy and the creation of robust, operationally applicable remote sensing products. On the other hand, the operational flood forecast must be adapted to make full use of the new data sources. In the operational phase of the project ESA's ENVISAT satellite, which will be launched in 2002, will serve as remote sensing data source. Until EN- VISAT data is available, algorithm retrieval, software development and product gener- ation is performed using existing sensors with ENVISAT-like specifications. Based on these data sets test cases and demonstration runs are conducted and will be presented to prove the advantages of the approach.

Controlling Malaria and Other Diseases Using Remote Sensing

NASA Technical Reports Server (NTRS)

Kiang, Richard K.; Wharton, Stephen W. (Technical Monitor)

2001-01-01

Remote sensing offers the vantage of monitoring a vast area of the Earth continuously. Once developed and launched, a satellite gives years of service in collecting data from the land, the oceans, and the atmosphere. Since the 1980s, attempts have been made to relate disease occurrence with remotely sensed environmental and geophysical parameters, using data from Landsat, SPOT, AVHRR, and other satellites. With higher spatial resolution, the recent satellite sensors provide a new outlook for disease control. At sub-meter to I 10m resolution, surface types associated with disease carriers can be identified more accurately. The Ikonos panchromatic sensor with I m resolution, and the Advanced Land Imager with 1 Om resolution on the newly launched Earth Observing-1, both have displayed remarkable mapping capabilities. In addition, an entire array of geophysical parameters can now be measured or inferred from various satellites. Airborne remote sensing, with less concerns on instrument weight, size, and power consumption, also offers a low-cost alternative for regional applications. NASA/GSFC began to collaborate with the Mahidol University on malaria and filariasis control using remote sensing in late 2000. The objectives are: (1) To map the breeding sites for the major vector species; (2) To identify the potential sites for larvicide and insecticide applications; (3) To explore the linkage of vector population and transmission intensity to environmental variables; (4) To monitor the impact of climate change and human activities on vector population and transmission; and (5) To develop a predictive model for disease distribution. Field studies are being conducted in several provinces in Thailand. Data analyses will soon begin. Malaria data in South Korea are being used as surrogates for developing classification techniques. GIS has been shown to be invaluable in making the voluminous remote sensing data more readily understandable. It will be used throughout this study to clearly demonstrate the spatial relationship between the disease intensities, geophysical variables, and socioeconomic parameters. Asides from malaria and filariasis, application of remote sensing to the control of other diseases have been vigorously pursued by NASA's Environment and Health Initiative. The current program includes projects on Rift Valley fever, St. Louis encephalitis, dengue fever, ebola, African dust and diseases, meningitis, asthma, bartonellosis, cholera, and urban health concerns. Results from these projects indicate that remote sensing will play an increasingly important role in disease control in the future.

Application of future remote sensing systems to irrigation

NASA Technical Reports Server (NTRS)

Miller, L. D.

1982-01-01

Area estimates of irrigated crops and knowledge of crop type are required for modeling water consumption to assist farmers, rangers, and agricultural consultants in scheduling irrigation for distributed management of crop yields. Information on canopy physiology and soil moisture status on a spatial basis is potentially available from remote sensors, so the questions to be addressed relate to: (1) timing (data frequency, instantaneous and integrated measurement); and scheduling (widely distributed spatial demands); (2) spatial resolution; (3) radiometric and geometric accuracy and geoencoding; and (4) information/data distribution. This latter should be overnight, with no central storage, onsite capture, and low cost.

Introduction to the physics and techniques of remote sensing

NASA Technical Reports Server (NTRS)

Elachi, Charles

1987-01-01

This book presents a comprehensive overview of the basics behind remote-sensing physics, techniques, and technology. The physics of wave/matter interactions, techniques of remote sensing across the electromagnetic spectrum, and the concepts behind remote sensing techniques now established and future ones under development are discussed. Applications of remote sensing are described for a wide variety of earth and planetary atmosphere and surface sciences. Solid surface sensing across the electromagnetic spectrum, ocean surface sensing, basic principles of atmospheric sensing and radiative transfer, and atmospheric remote sensing in the microwave, millimeter, submillimeter, and infrared regions are examined.

Use of remote-sensing techniques to survey the physical habitat of large rivers

USGS Publications Warehouse

Edsall, Thomas A.; Behrendt, Thomas E.; Cholwek, Gary; Frey, Jeffery W.; Kennedy, Gregory W.; Smith, Stephen B.; Edsall, Thomas A.; Behrendt, Thomas E.; Cholwek, Gary; Frey, Jeffrey W.; Kennedy, Gregory W.; Smith, Stephen B.

1997-01-01

Remote-sensing techniques that can be used to quantitatively characterize the physical habitat in large rivers in the United States where traditional survey approaches typically used in small- and medium-sized streams and rivers would be ineffective or impossible to apply. The state-of-the-art remote-sensing technologies that we discuss here include side-scan sonar, RoxAnn, acoustic Doppler current profiler, remotely operated vehicles and camera systems, global positioning systems, and laser level survey systems. The use of these technologies will permit the collection of information needed to create computer visualizations and hard copy maps and generate quantitative databases that can be used in real-time mode in the field to characterize the physical habitat at a study location of interest and to guide the distribution of sampling effort needed to address other habitat-related study objectives. This report augments habitat sampling and characterization guidance provided by Meador et al. (1993) and is intended for use primarily by U.S. Geological Survey National Water Quality Assessment program managers and scientists who are documenting water quality in streams and rivers of the United States.

Calculations of atmospheric refraction for spacecraft remote-sensing applications

NASA Technical Reports Server (NTRS)

Chu, W. P.

1983-01-01

Analytical solutions to the refraction integrals appropriate for ray trajectories along slant paths through the atmosphere are derived in this paper. This type of geometry is commonly encountered in remote-sensing applications utilizing an occultation technique. The solutions are obtained by evaluating higher-order terms from expansion of the refraction integral and are dependent on the vertical temperature distributions. Refraction parameters such as total refraction angles, air masses, and path lengths can be accurately computed. It is also shown that the method can be used for computing refraction parameters in astronomical refraction geometry for large zenith angles.

Advances in Spatial Data Infrastructure, Acquisition, Analysis, Archiving and Dissemination

NASA Technical Reports Server (NTRS)

Ramapriyan, Hampapuran K.; Rochon, Gilbert L.; Duerr, Ruth; Rank, Robert; Nativi, Stefano; Stocker, Erich Franz

2010-01-01

The authors review recent contributions to the state-of-thescience and benign proliferation of satellite remote sensing, spatial data infrastructure, near-real-time data acquisition, analysis on high performance computing platforms, sapient archiving, multi-modal dissemination and utilization for a wide array of scientific applications. The authors also address advances in Geoinformatics and its growing ubiquity, as evidenced by its inclusion as a focus area within the American Geophysical Union (AGU), European Geosciences Union (EGU), as well as by the evolution of the IEEE Geoscience and Remote Sensing Society's (GRSS) Data Archiving and Distribution Technical Committee (DAD TC).

Schistosomes, snails and satellites.

PubMed

Brooker, S

2002-05-01

This paper gives an overview of the recent progress made in the use and application of geographical information systems (GIS) and remotely sensed (RS) satellite sensor data for the epidemiology and control of schistosomiasis in sub-Saharan Africa. Details are given of the use of GIS to collate, map and analyse available parasitological data. The use of RS data to understand better the broad scale environmental factors influencing schistosome distribution is defined and examples detailed for the prediction of schistosomiasis in unsampled areas. Finally, the current practical application of GIS and remote sensing are reviewed in the context of national control programmes.

Remote sensing of the atmosphere from environmental satellites

NASA Technical Reports Server (NTRS)

Allison, L. J.; Wexler, R.; Laughlin, C. R.; Bandeen, W. R.

1977-01-01

Various applications of satellite remote sensing of the earth are reviewed, including (1) the use of meteorological satellites to obtain photographic and radiometric data for determining weather conditions; (2) determination of the earth radiation budget from measurements of reflected solar radiation and emitted long wave terrestrial radiation; (3) the use of microwave imagery for measuring ice and snow cover; (4) LANDSAT visual and near infrared observation of floods and crop growth; and (5) the use of the Nimbus 4 backscatter ultraviolet instrument to measure total ozone and vertical ozone distribution. Plans for future activities are also discussed.

Using Remote Sensing, Weather, and Demographic Data to Create Risk Maps for Zika, Dengue, and Chikungunya in Brazil

NASA Astrophysics Data System (ADS)

Manore, C.; Conrad, J.; Del Valle, S.; Ziemann, A.; Fairchild, G.; Generous, E. N.

2017-12-01

Mosquito-borne diseases such as Zika, dengue, and chikungunya viruses have dynamics coupled to weather, ecology, human infrastructure, socio-economic demographics, and behavior. We use time-varying remote sensing and weather data, along with demographics and ecozones to predict risk through time for Zika, dengue, and chikungunya outbreaks in Brazil. We use distributed lag methods to quantify the lag between outbreaks and weather. Our statistical model indicates that the relationships between the variables are complex, but that quantifying risk is possible with the right data at appropriate spatio-temporal scales.

Geological applications and training in remote sensing

NASA Technical Reports Server (NTRS)

Sabins, F. F., Jr.

1981-01-01

Some of the experiences, methods, and opinions developed during 15 years of teaching an introductory course in remote sensing at several universities in the Southern California area are related. Although the course is offered in Geology departments, every class includes significant numbers of students from other disciplines including geography, computer science, biology, and environmental science. The instructor or teaching assistant provides a few hours of tutorial lectures (outside of regular class time) on basic geology for these nongeologists. This approach is successful because the grade distribution for nongeologists is similar to that for geologists. The schedule for a typical one-semester course is given.

A extract method of mountainous area settlement place information from GF-1 high resolution optical remote sensing image under semantic constraints

NASA Astrophysics Data System (ADS)

Guo, H., II

2016-12-01

Spatial distribution information of mountainous area settlement place is of great significance to the earthquake emergency work because most of the key earthquake hazardous areas of china are located in the mountainous area. Remote sensing has the advantages of large coverage and low cost, it is an important way to obtain the spatial distribution information of mountainous area settlement place. At present, fully considering the geometric information, spectral information and texture information, most studies have applied object-oriented methods to extract settlement place information, In this article, semantic constraints is to be added on the basis of object-oriented methods. The experimental data is one scene remote sensing image of domestic high resolution satellite (simply as GF-1), with a resolution of 2 meters. The main processing consists of 3 steps, the first is pretreatment, including ortho rectification and image fusion, the second is Object oriented information extraction, including Image segmentation and information extraction, the last step is removing the error elements under semantic constraints, in order to formulate these semantic constraints, the distribution characteristics of mountainous area settlement place must be analyzed and the spatial logic relation between settlement place and other objects must be considered. The extraction accuracy calculation result shows that the extraction accuracy of object oriented method is 49% and rise up to 86% after the use of semantic constraints. As can be seen from the extraction accuracy, the extract method under semantic constraints can effectively improve the accuracy of mountainous area settlement place information extraction. The result shows that it is feasible to extract mountainous area settlement place information form GF-1 image, so the article proves that it has a certain practicality to use domestic high resolution optical remote sensing image in earthquake emergency preparedness.

[Thematic Issue: Remote Sensing.

ERIC Educational Resources Information Center

Howkins, John, Ed.

1978-01-01

Four of the articles in this publication discuss the remote sensing of the Earth and its resources by satellites. Among the topics dealt with are the development and management of remote sensing systems, types of satellites used for remote sensing, the uses of remote sensing, and issues involved in using information obtained through remote…

75 FR 65304 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-22

... Commercial Remote Sensing (ACCRES); Request for Nominations AGENCY: National Oceanic and Atmospheric... Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was... Atmosphere, on matters relating to the U.S. commercial remote sensing industry and NOAA's activities to carry...

Development and Operation of the Americas ALOS Data Node

NASA Astrophysics Data System (ADS)

Arko, S. A.; Marlin, R. H.; La Belle-Hamer, A. L.

2004-12-01

In the spring of 2005, the Japanese Aerospace Exploration Agency (JAXA) will launch the next generation in advanced, remote sensing satellites. The Advanced Land Observing Satellite (ALOS) includes three sensors, two visible imagers and one L-band polarimetric SAR, providing high-quality remote sensing data to the scientific and commercial communities throughout the world. Focusing on remote sensing and scientific pursuits, ALOS will image nearly the entire Earth using all three instruments during its expected three-year lifetime. These data sets offer the potential for data continuation of older satellite missions as well as new products for the growing user community. One of the unique features of the ALOS mission is the data distribution approach. JAXA has created a worldwide cooperative data distribution network. The data nodes are NOAA /ASF representing the Americas ALOS Data Node (AADN), ESA representing the ALOS European and African Node (ADEN), Geoscience Australia representing Oceania and JAXA representing the Asian continent. The AADN is the sole agency responsible for archival, processing and distribution of L0 and L1 products to users in both North and South America. In support of this mission, AADN is currently developing a processing and distribution infrastructure to provide easy access to these data sets. Utilizing a custom, grid-based process controller and media generation system, the overall infrastructure has been designed to provide maximum throughput while requiring a minimum of operator input and maintenance. This paper will present an overview of the ALOS system, details of each sensor's capabilities and of the processing and distribution system being developed by AADN to provide these valuable data sets to users throughout North and South America.

a Kml-Based Approach for Distributed Collaborative Interpretation of Remote Sensing Images in the Geo-Browser

NASA Astrophysics Data System (ADS)

Huang, L.; Zhu, X.; Guo, W.; Xiang, L.; Chen, X.; Mei, Y.

2012-07-01

Existing implementations of collaborative image interpretation have many limitations for very large satellite imageries, such as inefficient browsing, slow transmission, etc. This article presents a KML-based approach to support distributed, real-time, synchronous collaborative interpretation for remote sensing images in the geo-browser. As an OGC standard, KML (Keyhole Markup Language) has the advantage of organizing various types of geospatial data (including image, annotation, geometry, etc.) in the geo-browser. Existing KML elements can be used to describe simple interpretation results indicated by vector symbols. To enlarge its application, this article expands KML elements to describe some complex image processing operations, including band combination, grey transformation, geometric correction, etc. Improved KML is employed to describe and share interpretation operations and results among interpreters. Further, this article develops some collaboration related services that are collaboration launch service, perceiving service and communication service. The launch service creates a collaborative interpretation task and provides a unified interface for all participants. The perceiving service supports interpreters to share collaboration awareness. Communication service provides interpreters with written words communication. Finally, the GeoGlobe geo-browser (an extensible and flexible geospatial platform developed in LIESMARS) is selected to perform experiments of collaborative image interpretation. The geo-browser, which manage and visualize massive geospatial information, can provide distributed users with quick browsing and transmission. Meanwhile in the geo-browser, GIS data (for example DEM, DTM, thematic map and etc.) can be integrated to assist in improving accuracy of interpretation. Results show that the proposed method is available to support distributed collaborative interpretation of remote sensing image

Literature relevant to remote sensing of water quality

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Marcell, R. F.

1983-01-01

References relevant to remote sensing of water quality were compiled, organized, and cross-referenced. The following general categories were included: (1) optical properties and measurement of water characteristics; (2) interpretation of water characteristics by remote sensing, including color, transparency, suspended or dissolved inorganic matter, biological materials, and temperature; (3) application of remote sensing for water quality monitoring; (4) application of remote sensing according to water body type; and (5) manipulation, processing and interpretation of remote sensing digital water data.

Learning Methods of Remote Sensing In the 2013 Curriculum of Secondary School

NASA Astrophysics Data System (ADS)

Lili Somantri, Nandi

2016-11-01

The new remote sensing material included in the subjects of geography in the curriculum of 1994. For geography teachers generation of 90s and over who in college do not get the material remote sensing, for teaching is a tough matter. Most teachers only give a theoretical matter, and do not carry out practical reasons in the lack of facilities and infrastructure of computer laboratories. Therefore, in this paper studies the importance about the method or manner of teaching remote sensing material in schools. The purpose of this paper is 1) to explain the position of remote sensing material in the study of geography, 2) analyze the Geography Curriculum 2013 Subjects related to remote sensing material, 3) describes a method of teaching remote sensing material in schools. The method used in this paper is a descriptive analytical study supported by the literature. The conclusion of this paper that the position of remote sensing in the study of geography is a method or a way to obtain spatial data earth's surface. In the 2013 curriculum remote sensing material has been applied to the study of land use and transportation. Remote sensing methods of teaching must go through a practicum, which starts from the introduction of the theory of remote sensing, data extraction phase of remote sensing imagery to produce maps, both visually and digitally, field surveys, interpretation of test accuracy, and improved maps.

JPRS Report, Science & Technology, China, Remote Sensing Systems, Applications.

DTIC Science & Technology

1991-01-17

Partial Contents: Short Introduction to Nation’s Remote Sensing Units, Domestic Airborne Remote - Sensing System, Applications in Monitoring Natural...Disasters, Applications of Imagery From Experimental Satellites Launched in 1985, 1986, Current Status, Future Prospects for Domestic Remote - Sensing -Satellite...Ground Station, and Radar Remote - Sensing Technology Used to Monitor Yellow River Delta,

CHARACTERISTIC LENGTH SCALE OF INPUT DATA IN DISTRIBUTED MODELS: IMPLICATIONS FOR MODELING GRID SIZE. (R824784)

EPA Science Inventory

The appropriate spatial scale for a distributed energy balance model was investigated by: (a) determining the scale of variability associated with the remotely sensed and GIS-generated model input data; and (b) examining the effects of input data spatial aggregation on model resp...

[A review on polarization information in the remote sensing detection].

PubMed

Gong, Jie-Qiong; Zhan, Hai-Gang; Liu, Da-Zhao

2010-04-01

Polarization is one of the inherent characteristics. Because the surface of the target structure, internal structure, and the angle of incident light are different, the earth's surface and any target in atmosphere under optical interaction process will have their own characteristic nature of polarization. Polarimetric characteristics of radiation energy from the targets are used in polarization remote sensing detection as detective information. Polarization remote sensing detection can get the seven-dimensional information of targets in complicated backgrounds, detect well-resolved outline of targets and low-reflectance region of objectives, and resolve the problems of atmospheric detection and identification camouflage detection which the traditional remote sensing detection can not solve, having good foreground in applications. This paper introduces the development of polarization information in the remote sensing detection from the following four aspects. The rationale of polarization remote sensing detection is the base of polarization remote sensing detection, so it is firstly introduced. Secondly, the present researches on equipments that are used in polarization remote sensing detection are particularly and completely expatiated. Thirdly, the present exploration of theoretical simulation of polarization remote sensing detection is well detailed. Finally, the authors present the applications research home and abroad of the polarization remote sensing detection technique in the fields of remote sensing, atmospheric sounding, sea surface and underwater detection, biology and medical diagnosis, astronomical observation and military, summing up the current problems in polarization remote sensing detection. The development trend of polarization remote sensing detection technology in the future is pointed out in order to provide a reference for similar studies.

Long-term monitoring on environmental disasters using multi-source remote sensing technique

NASA Astrophysics Data System (ADS)

Kuo, Y. C.; Chen, C. F.

2017-12-01

Environmental disasters are extreme events within the earth's system that cause deaths and injuries to humans, as well as causing damages and losses of valuable assets, such as buildings, communication systems, farmlands, forest and etc. In disaster management, a large amount of multi-temporal spatial data is required. Multi-source remote sensing data with different spatial, spectral and temporal resolutions is widely applied on environmental disaster monitoring. With multi-source and multi-temporal high resolution images, we conduct rapid, systematic and seriate observations regarding to economic damages and environmental disasters on earth. It is based on three monitoring platforms: remote sensing, UAS (Unmanned Aircraft Systems) and ground investigation. The advantages of using UAS technology include great mobility and availability in real-time rapid and more flexible weather conditions. The system can produce long-term spatial distribution information from environmental disasters, obtaining high-resolution remote sensing data and field verification data in key monitoring areas. It also supports the prevention and control on ocean pollutions, illegally disposed wastes and pine pests in different scales. Meanwhile, digital photogrammetry can be applied on the camera inside and outside the position parameters to produce Digital Surface Model (DSM) data. The latest terrain environment information is simulated by using DSM data, and can be used as references in disaster recovery in the future.

[The polarization characteristics distribution and correction method of the polarization coupling error in ocean remote sensing system].

PubMed

Gao, Jun; Wang, Shu-Peng; Gu, Xing-Fa; Yu, Tao; Fang, Li

2012-06-01

With the development of the quantitative researches using ocean color remote sensing data sets, study on reducing the uncertainty of the response of the ocean color remote sensors to the polarization characteristics of the target has been attracting more and more attention recently. Taking MODIS as an example, the polarization distribution in the whole field of view was analyzed. For the atmosphere path radiance and the apparent radiance considering the coupling between ocean surface and atmosphere, the polarization distribution has a strong relation with the imaging geometry. Compared to the contribution of the polarization from the rough sea surface, the contribution from the atmosphere is dominated. Based on the polarization characteristics in the field of view, the influence of the polarization coupling error on the quality of the satellite data was studied with the assumption of different polarization sensitivities. It was found that errors due to polarization sensitivity in the field of view are lower than water leaving radiance only when the polarization sensitivity is less than 2%. And in this case it can meet the need of the retrieval of water leaving radiative products. The method of the compensation for the polarization coupling error due to the atmosphere is proposed, which proved to be effective to improve the utilization of satellite data and the accuracy of measured radiance by remote sensor.

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

PubMed Central

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G.; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level. PMID:22235254

Developing a flood monitoring system from remotely sensed data for the Limpopo basin

USGS Publications Warehouse

Asante, K.O.; Macuacua, R.D.; Artan, G.A.; Lietzow, R.W.; Verdin, J.P.

2007-01-01

This paper describes the application of remotely sensed precipitation to the monitoring of floods in a region that regularly experiences extreme precipitation and flood events, often associated with cyclonic systems. Precipitation data, which are derived from spaceborne radar aboard the National Aeronautics and Space Administration's Tropical Rainfall Measuring Mission and from National Oceanic and Atmospheric Administration's infrared-based products, are used to monitor areas experiencing extreme precipitation events that are defined as exceedance of a daily mean areal average value of 50 mm over a catchment. The remotely sensed precipitation data are also ingested into a hydrologic model that is parameterized using spatially distributed elevation, soil, and land cover data sets that are available globally from remote sensing and in situ sources. The resulting stream-flow is classified as an extreme flood event when flow anomalies exceed 1.5 standard deviations above the short-term mean. In an application in the Limpopo basin, it is demonstrated that the use of satellite-derived precipitation allows for the identification of extreme precipitation and flood events, both in terms of relative intensity and spatial extent. The system is used by water authorities in Mozambique to proactively initiate independent flood hazard verification before generating flood warnings. The system also serves as a supplementary information source when in situ gauging systems are disrupted. This paper concludes that remotely sensed precipitation and derived products greatly enhance the ability of water managers in the Limpopo basin to monitor extreme flood events and provide at-risk communities with early warning information. ?? 2007 IEEE.

Mapping migratory bird prevalence using remote sensing data fusion.

PubMed

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

[Maximum entropy model versus remote sensing-based methods for extracting Oncomelania hupensis snail habitats].

PubMed

Cong-Cong, Xia; Cheng-Fang, Lu; Si, Li; Tie-Jun, Zhang; Sui-Heng, Lin; Yi, Hu; Ying, Liu; Zhi-Jie, Zhang

2016-12-02

To explore the technique of maximum entropy model for extracting Oncomelania hupensis snail habitats in Poyang Lake zone. The information of snail habitats and related environment factors collected in Poyang Lake zone were integrated to set up the maximum entropy based species model and generate snail habitats distribution map. Two Landsat 7 ETM+ remote sensing images of both wet and drought seasons in Poyang Lake zone were obtained, where the two indices of modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI) were applied to extract snail habitats. The ROC curve, sensitivities and specificities were applied to assess their results. Furthermore, the importance of the variables for snail habitats was analyzed by using Jackknife approach. The evaluation results showed that the area under receiver operating characteristic curve (AUC) of testing data by the remote sensing-based method was only 0.56, and the sensitivity and specificity were 0.23 and 0.89 respectively. Nevertheless, those indices above-mentioned of maximum entropy model were 0.876, 0.89 and 0.74 respectively. The main concentration of snail habitats in Poyang Lake zone covered the northeast part of Yongxiu County, northwest of Yugan County, southwest of Poyang County and middle of Xinjian County, and the elevation was the most important environment variable affecting the distribution of snails, and the next was land surface temperature (LST). The maximum entropy model is more reliable and accurate than the remote sensing-based method for the sake of extracting snail habitats, which has certain guiding significance for the relevant departments to carry out measures to prevent and control high-risk snail habitats.

A survey of natural aggregate properties and characteristics important in remote sensing and airborne geophysics

USGS Publications Warehouse

Knepper, D.H.; Langer, W.H.; Miller, S.

1995-01-01

Natural aggregate is vital to the construction industry. Although natural aggregate is a high volume/low value commodity that is abundant, new sources are becoming increasingly difficult to find and develop because of rigid industry specifications, political considerations, development and transportation costs, and environmental concerns. There are two primary sources of natural aggregate: (1) exposed or near-surface bedrock that can be crushed, and (2) deposits of sand and gravel. Remote sensing and airborne geophysics detect surface and near-surface phenomena, and may be useful for detecting and mapping potential aggregate sources; however, before a methodology for applying these techniques can be developed, it is necessary to understand the type, distribution, physical properties, and characteristics of natural aggregate deposits. The distribution of potential aggregate sources is closely tied to local geologic history. Conventional exploration for natural aggregate deposits has been largely a ground-based operation, although aerial photographs and topographic maps have been extensively used to target possible deposits. Today, the exploration process also considers factors such as the availability of the land, space and water supply for processing, political and environmental factors, and distance from the market; exploration and planning cannot be separated. There are many physical properties and characteristics by which to judge aggregate material for specific applications; most of these properties and characteristics pertain only to individual aggregate particles. The application of remote sensing and airborne geophysical measurements to detecting and mapping potential aggregate sources, however, is based on intrinsic bulk physical properties and extrinsic characteristics of the deposits that can be directly measured, mathematically derived from measurement, or interpreted with remote sensing and geophysical data. ?? 1995 Oxford UniversityPress.

Hyperspectral remote sensing of wild oyster reefs

NASA Astrophysics Data System (ADS)

Le Bris, Anthony; Rosa, Philippe; Lerouxel, Astrid; Cognie, Bruno; Gernez, Pierre; Launeau, Patrick; Robin, Marc; Barillé, Laurent

2016-04-01

The invasion of the wild oyster Crassostrea gigas along the western European Atlantic coast has generated changes in the structure and functioning of intertidal ecosystems. Considered as an invasive species and a trophic competitor of the cultivated conspecific oyster, it is now seen as a resource by oyster farmers following recurrent mass summer mortalities of oyster spat since 2008. Spatial distribution maps of wild oyster reefs are required by local authorities to help define management strategies. In this work, visible-near infrared (VNIR) hyperspectral and multispectral remote sensing was investigated to map two contrasted intertidal reef structures: clusters of vertical oysters building three-dimensional dense reefs in muddy areas and oysters growing horizontally creating large flat reefs in rocky areas. A spectral library, collected in situ for various conditions with an ASD spectroradiometer, was used to run Spectral Angle Mapper classifications on airborne data obtained with an HySpex sensor (160 spectral bands) and SPOT satellite HRG multispectral data (3 spectral bands). With HySpex spectral/spatial resolution, horizontal oysters in the rocky area were correctly classified but the detection was less efficient for vertical oysters in muddy areas. Poor results were obtained with the multispectral image and from spatially or spectrally degraded HySpex data, it was clear that the spectral resolution was more important than the spatial resolution. In fact, there was a systematic mud deposition on shells of vertical oyster reefs explaining the misclassification of 30% of pixels recognized as mud or microphytobenthos. Spatial distribution maps of oyster reefs were coupled with in situ biomass measurements to illustrate the interest of a remote sensing product to provide stock estimations of wild oyster reefs to be exploited by oyster producers. This work highlights the interest of developing remote sensing techniques for aquaculture applications in coastal areas.

Motion Trajectories for Wide-area Surveying with a Rover-based Distributed Spectrometer

NASA Technical Reports Server (NTRS)

Tunstel, Edward; Anderson, Gary; Wilson, Edmond

2006-01-01

A mobile ground survey application that employs remote sensing as a primary means of area coverage is highlighted. It is distinguished from mobile robotic area coverage problems that employ contact or proximity-based sensing. The focus is on a specific concept for performing mobile surveys in search of biogenic gases on planetary surfaces using a distributed spectrometer -- a rover-based instrument designed for wide measurement coverage of promising search areas. Navigation algorithms for executing circular and spiral survey trajectories are presented for widearea distributed spectroscopy and evaluated based on area covered and distance traveled.

Estimation of atmospheric columnar organic matter (OM) mass concentration from remote sensing measurements of aerosol spectral refractive indices

NASA Astrophysics Data System (ADS)

Zhang, Ying; Li, Zhengqiang; Sun, Yele; Lv, Yang; Xie, Yisong

2018-04-01

Aerosols have adverse effects on human health and air quality, changing Earth's energy balance and lead to climate change. The components of aerosol are important because of the different spectral characteristics. Based on the low hygroscopic and high scattering properties of organic matter (OM) in fine modal atmospheric aerosols, we develop an inversion algorithm using remote sensing to obtain aerosol components including black carbon (BC), organic matter (OM), ammonium nitrate-like (AN), dust-like (DU) components and aerosol water content (AW). In the algorithm, the microphysical characteristics (i.e. volume distribution and complex refractive index) of particulates are preliminarily separated to fine and coarse modes, and then aerosol components are retrieved using bimodal parameters. We execute the algorithm using remote sensing measurements of sun-sky radiometer at AERONET site (Beijing RADI) in a period from October of 2014 to January of 2015. The results show a reasonable distribution of aerosol components and a good fit for spectral feature calculations. The mean OM mass concentration in atmospheric column is account for 14.93% of the total and 56.34% of dry and fine-mode aerosol, being a fairly good correlation (R = 0.56) with the in situ observations near the surface layer.

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies.

PubMed

Yang, Xiaohuan; Huang, Yaohuan; Dong, Pinliang; Jiang, Dong; Liu, Honghui

2009-01-01

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

NASA Astrophysics Data System (ADS)

Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

2017-09-01

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

24 CFR 3280.609 - Water distribution systems.

Code of Federal Regulations, 2010 CFR

2010-04-01

... and temperature relief valve installed for this purpose shall have the temperature sensing element...) Size of branch. Start at the most remote outlet on any branch of the hot or cold water piping and...

An investigation of the distribution of eruptive products on the shield volcanoes of the western Galapagos Islands using remotely sensed data

NASA Technical Reports Server (NTRS)

Munro, Duncan C.; Rowland, Scott K.; Mouginis-Mark, Peter J.; Wilson, Lionel; Oviedo-Perez, Victor-Hugo

1991-01-01

Recent volcanic activity in the Galapagos Islands is concentrated on the two westernmost islands, Isla Isabela and Isla Fernandina. Difficult access has thus far prevented comprehensive geological field studies, so we examine the potential of remotely sensed data as a means of studying volcanic processes in the region. Volcan Wolf is used as an example of the analysis of SPOT HRV-1 data undertaken for each volcano. Landsat TM data are analyzed in an attempt to construct a relative age sequence for the recent eruptive activity on Isla Fernandina. No systematic variation in the surface reflectance of lava flows as a function of age could be detected with these data. Thus it was not possible to complete a study of the temporal distribution of volcanic activity.

Recovering of images degraded by atmosphere

NASA Astrophysics Data System (ADS)

Lin, Guang; Feng, Huajun; Xu, Zhihai; Li, Qi; Chen, Yueting

2017-08-01

Remote sensing images are seriously degraded by multiple scattering and bad weather. Through the analysis of the radiative transfer procedure in atmosphere, an image atmospheric degradation model considering the influence of atmospheric absorption multiple scattering and non-uniform distribution is proposed in this paper. Based on the proposed model, a novel recovering method is presented to eliminate atmospheric degradation. Mean-shift image segmentation and block-wise deconvolution are used to reduce time cost, retaining a good result. The recovering results indicate that the proposed method can significantly remove atmospheric degradation and effectively improve contrast compared with other removal methods. The results also illustrate that our method is suitable for various degraded remote sensing, including images with large field of view (FOV), images taken in side-glance situations, image degraded by atmospheric non-uniform distribution and images with various forms of clouds.

Observing Changing Ecological Diversity in the Anthropocene

NASA Technical Reports Server (NTRS)

Schimel, David S.; Asner, Gregory P.; Moorcroft, Paul

2012-01-01

As the world enters the Anthropocene, the planet's environment is changing rapidly, putting critical ecosystem services at risk. Understanding and forecasting how ecosystems will change over the coming decades requires understanding the sensitivity of species to environmental change. The extant distribution of species and functional groups contains valuable information about the performance of different species in different environments. However, with high rates of environmental change, information inherent in ranges of many species will disappear, since that information exists only under quasi-equilibrium conditions. The information content of distributional data obtained now is greater than data obtained in the future. New remote sensing technologies can map chemical and structural traits of plant canopies and allow inference of trait and in many cases, species ranges. Current satellite remote sensing data can only produce relatively simple classifications, but new techniques have dramatically higher biological information content.

Monthly AOD maps combining strengths of remote sensing products

NASA Astrophysics Data System (ADS)

Kinne, Stefan

2010-05-01

The mid-visible aerosol optical depth (AOD) is the most prominent property to quantify aerosol amount the atmospheric column. Almost all aerosol retrievals of satellite sensors provide estimates for this property, however, often with limited success. As sensors differ in capabilities individual retrievals have local and regional strengths and weaknesses. Focusing on individual retrieval strengths a satellite based AOD composite has been constructed. Hereby, every retrieval performance has been assessed in statistical comparisons to ground-based sun-photometry, which provide highly accurate references though only at few globally distributed monitoring sites. Based on these comparisons, which consider bias as well as spatial patterns and seasonality, the regionally best performing satellite AOD products are combined. The resulting remote sensing AOD composite provide a general reference for the spatial and temporal AOD distribution on an (almost) global basis - solely tied to sensor data.

Quantifying uncertainties in radar forward models through a comparison between CloudSat and SPartICus reflectivity factors

NASA Astrophysics Data System (ADS)

Mascio, Jeana; Mace, Gerald G.

2017-02-01

Interpretations of remote sensing measurements collected in sample volumes containing ice-phase hydrometeors are very sensitive to assumptions regarding the distributions of mass with ice crystal dimension, otherwise known as mass-dimensional or m-D relationships. How these microphysical characteristics vary in nature is highly uncertain, resulting in significant uncertainty in algorithms that attempt to derive bulk microphysical properties from remote sensing measurements. This uncertainty extends to radar reflectivity factors forward calculated from model output because the statistics of the actual m-D in nature is not known. To investigate the variability in m-D relationships in cirrus clouds, reflectivity factors measured by CloudSat are combined with particle size distributions (PSDs) collected by coincident in situ aircraft by using an optimal estimation-based (OE) retrieval of the m-D power law. The PSDs were collected by 12 flights of the Stratton Park Engineering Company Learjet during the Small Particles in Cirrus campaign. We find that no specific habit emerges as preferred, and instead, we find that the microphysical characteristics of ice crystal populations tend to be distributed over a continuum-defying simple categorization. With the uncertainties derived from the OE algorithm, the uncertainties in forward-modeled backscatter cross section and, in turn, radar reflectivity is calculated by using a bootstrapping technique, allowing us to infer the uncertainties in forward-modeled radar reflectivity that would be appropriately applied to remote sensing simulator algorithms.

[High Resolution Remote Sensing Monitoring and Assessment of Secondary Geological Disasters Triggered by the Lushan Earthquake].

PubMed

Wang, Fu-tao; Wang, Shi-xin; Zhou, Yi; Wang, Li-tao; Yan, Fu-li; Li, Wen-jun; Liu, Xiong-fei

2016-01-01

The secondary geological disasters triggered by the Lushan earthquake on April 20, 2013, such as landslides, collapses, debris flows, etc., had caused great casualties and losses. We monitored the number and spatial distribution of the secondary geological disasters in the earthquake-hit area from airborne remote sensing images, which covered areas about 3 100 km2. The results showed that Lushan County, Baoxing County and Tianquan County were most severely affected; there were 164, 126 and 71 secondary geological disasters in these regions. Moreover, we analyzed the relationship between the distribution of the secondary geological disasters, geological structure and intensity. The results indicate that there were 4 high-hazard zones in the monitored area, one focused within six kilometers from the epicenter, and others are distributed along the two main fault zones of the Longmen Mountain. More than 97% secondary geological disasters occurred in zones with a seismic intensity of VII to IX degrees, a slope between 25 A degrees and 50 A degrees, and an altitude of between 800 and 2 000 m. At last, preliminary suggestions were proposed for the rehabilitation and reconstruction planning of Lushan earthquake. According to the analysis result, airborne and space borne remote sensing can be used accurately and effectively in almost real-time to monitor and assess secondary geological disasters, providing a scientific basis and decision making support for government emergency command and post-disaster reconstruction.

Remote sensing supported surveillance and characterization of tailings behavior at a gold mine site, Finland.

NASA Astrophysics Data System (ADS)

Rauhala, Anssi; Tuomela, Anne; Rossi, Pekka M.; Davids, Corine

2017-04-01

The management of vast amounts of tailings produced is one of the key issues in mining operations. The effective and economic disposal of the waste requires knowledge concerning both basic physical properties of the tailings as well as more complex aspects such as consolidation behavior. The behavior of tailings in itself is a very complex issue that can be affected by flocculation, sedimentation, consolidation, segregation, deposition, freeze-thaw, and desiccation phenomena. The utilization of remote sensing in an impoundment-scale monitoring of tailings could benefit the management of tailings, and improve our knowledge on tailings behavior. In order to gain better knowledge of tailings behavior in cold climate, we have utilized both modern remote sensing techniques and more traditional in situ and laboratory measurements in characterizing thickened gold tailings behavior at a Finnish gold mine site, where the production has been halted due to low gold prices. The remote sensing measurements consisted of elevation datasets collected from unmanned aerial vehicles during summers 2015 and 2016, and a further campaign is planned for the summer 2017. The ongoing traditional measurements include for example particle-size distribution, frost heave, frost depth, water retention, temperature profile, and rheological measurements. Initial results from the remote sensing indicated larger than expected settlements on parts of the tailings impoundment, and also highlighted some of the complexities related to data processing. The interpretation of the results and characterization of the behavior is in this case complicated by possible freeze-thaw effects and potential settlement of the impoundment bottom structure consisting of natural peat. Experiments with remote sensing and unmanned aerial vehicles indicate that they could offer potential benefits in frequent mine site monitoring, but there is a need towards more robust and streamlined data acquisition and processing. The gathered data and obtained results form the basis for further modelling efforts which aim at better management of tailings storage facilities.

Project MEDSAT: The design of a remote sensing platform for malaria research and control

NASA Astrophysics Data System (ADS)

1991-04-01

Project MEDSAT was proposed with the specific goal of designing a satellite to remotely sense pertinent information useful in establishing strategies to control malaria. The 340 kg MEDSAT satellite is to be inserted into circular earth orbit aboard the Pegasus Air-Launched Space Booster at an inclination of 21 degrees and an altitude of 473 km. It is equipped with a synthetic aperture radar and a visible thermal/infrared sensor to remotely sense conditions at the target area of Chiapas, Mexico. The orbit is designed so that MEDSAT will pass over the target site twice each day. The data from each scan will be downlinked to Hawaii for processing, resulting in maps indicating areas of high malaria risk. These will be distributed to health officials at the target site. A relatively inexpensive launch by Pegasus and a design using mainly proven, off-the-shelf technology permit a low mission cost, while innovations in the satellite controls and the scientific instruments allow a fairly complex mission.

Remote Sensing of Aerosol and Aerosol Radiative Forcing of Climate from EOS Terra MODIS Instrument

NASA Technical Reports Server (NTRS)

Kaufman, Yoram; Tanre, Didier; Remer, Lorraine; Einaudi, Franco (Technical Monitor)

2000-01-01

The recent launch of EOS-Terra into polar orbit has begun to revolutionize remote sensing of aerosol and their effect on climate. Terra has five instruments, two of them,Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectro-Radiometer (MISR) are designed to monitor global aerosol in two different complementary ways. Here we shall discuss the use of the multispectral measurements of MODIS to derive: (1) the global distribution of aerosol load (and optical thickness) over ocean and land; (2) to measure the impact of aerosol on reflection of sunlight to space; and (3) to measure the ability of aerosol to absorb solar radiation. These measurements have direct applications on the understanding of the effect of aerosol on climate, the ability to predict climate change, and on the monitoring of dust episodes and man-made pollution. Principles of remote sensing of aerosol from MODIS will be discussed and first examples of measurements from MODIS will be provided.

a Novel Framework for Remote Sensing Image Scene Classification

NASA Astrophysics Data System (ADS)

Jiang, S.; Zhao, H.; Wu, W.; Tan, Q.

2018-04-01

High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57 % and 83.35 % respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

Retrieving background surface reflectance of Himawari-8/AHI using BRDF modeling

NASA Astrophysics Data System (ADS)

Choi, Sungwon; Seo, Minji; Lee, Kyeong-sang; Han, Kyung-soo

2017-04-01

In these days, remote sensing is more important than past. And retrieving surface reflectance in remote sensing is also important. So there are many ways to retrieve surface reflectance by my countries with polar orbit and geostationary satellite. We studied Bidirectional Reflectance Distribution Function (BRDF) which is used to retrieve surface reflectance. In BRDF equation, we calculate surface reflectance using BRD components and angular data. BRD components are to calculate 3 of scatterings, isotropic geometric and volumetric scattering. To make Background Surface Reflectance (BSR) of Himawari-8/AHI. We used 5 bands (band1, band2, band3, band4, band5) with BRDF. And we made 5 BSR for 5 channels. For validation, we compare BSR with Top of canopy (TOC) reflectance of AHI. As a result, bias are from -0.00223 to 0.008328 and Root Mean Square Error (RMSE) are from 0.045 to 0.049. We think BSR can be used to replace TOC reflectance in remote sensing to improve weakness of TOC reflectance.

Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery.

PubMed

Sánchez-Azofeifa, Arturo; Rivard, Benoit; Wright, Joseph; Feng, Ji-Lu; Li, Peijun; Chong, Mei Mei; Bohlman, Stephanie A

2011-01-01

Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments.

Research on the Construction of Remote Sensing Automatic Interpretation Symbol Big Data

NASA Astrophysics Data System (ADS)

Gao, Y.; Liu, R.; Liu, J.; Cheng, T.

2018-04-01

Remote sensing automatic interpretation symbol (RSAIS) is an inexpensive and fast method in providing precise in-situ information for image interpretation and accuracy. This study designed a scientific and precise RSAIS data characterization method, as well as a distributed and cloud architecture massive data storage method. Additionally, it introduced an offline and online data update mode and a dynamic data evaluation mechanism, with the aim to create an efficient approach for RSAIS big data construction. Finally, a national RSAIS database with more than 3 million samples covering 86 land types was constructed during 2013-2015 based on the National Geographic Conditions Monitoring Project of China and then annually updated since the 2016 period. The RSAIS big data has proven to be a good method for large scale image interpretation and field validation. It is also notable that it has the potential to solve image automatic interpretation with the assistance of deep learning technology in the remote sensing big data era.

BOREAS Landsat MSS Imagery: Digital Counts

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Strub, Richard; Newcomer, Jeffrey A.

2000-01-01

The Boreal Ecosystem-Atmospheric Study (BOREAS) Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. The Earth Resources Technology Satellite (ERTS) Program launched the first of a series of satellites (ERTS-1) in 1972. Part of the NASA Earth Resources Survey Program, the ERTS Program and the ERTS satellites were later renamed Landsat to better represent the civil satellite program's prime emphasis on remote sensing of land resources. Landsat satellites 1 through 5 carry the Multispectral Scanner (MSS) sensor. Canada for Remote Sensing (CCRS) and BOREAS personnel gathered a set of MSS images of the BOREAS region from Landsat satellites 1, 2, 4, and 5 covering the dates of 21 Aug 1972 to 05 Sep 1988. The data are provided in binary image format files of various formats. The Landsat MSS imagery is available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Estimation of the Distribution of Tabebuia guayacan (Bignoniaceae) Using High-Resolution Remote Sensing Imagery

PubMed Central

Sánchez-Azofeifa, Arturo; Rivard, Benoit; Wright, Joseph; Feng, Ji-Lu; Li, Peijun; Chong, Mei Mei; Bohlman, Stephanie A.

2011-01-01

Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments. PMID:22163825

Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes.

PubMed

Leong, Misha; Roderick, George K

2015-01-01

Global change has led to shifts in phenology, potentially disrupting species interactions such as plant-pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to other communities in the ecosystem. Here, we investigated the phenological diversity of the vegetation across a human-altered landscape including urban, agricultural, and natural land use types. We found that the patterns of change in the vegetation indices (EVI and NDVI) of human-altered landscapes are out of synchronization with the phenology in neighboring natural California grassland habitat. Comparing these findings to a spatio-temporal pollinator distribution dataset, EVI and NDVI were significant predictors of total bee abundance, a relationship that improved with time lags. This evidence supports the importance of differences in temporal dynamics between land use types. These findings also highlight the potential to utilize remote sensing data to make predictions for components of biodiversity that have tight vegetation associations, such as pollinators.

Determination of Winter Wheat Phenology in Bavaria- A Contribution to Regional Crop Health Monitoring from Space

NASA Astrophysics Data System (ADS)

Bruggemann, Lena; Bach, Heike; Ruf, Tobias; Appel, Florian; Migdall, Silke; Hank, Tobias; Mauser, Wolfram; Eiblmeier, Peter

2016-08-01

The central topic of this study is the monitoring of winter wheat phenology and the detection of anthesis (flowering) using remotely sensed data as well as crop growth modeling. It is not possible to directly observe the flowering of wheat with optical satellite sensors. Thus, an approach that combines crop growth modeling with remote sensing data covering optical and microwave spectral ranges was developed. This was done in three steps: The hydro-agroecological land surface model PROMET was first run in a stand-alone version for selected sites distributed throughout Bavaria using only static input parameters (e.g. soil map) and current meteorological data as driving factors. Thus, multitemporal information from optical remote sensing data was assimilated into the model runs in a second step to improve the accuracy of the results. Finally, the use of radar data for anthesis detection in winter wheat was tested using Sentinel-1 data of 2015 in dual polarization mode (VV+VH).

Project MEDSAT: The design of a remote sensing platform for malaria research and control

NASA Technical Reports Server (NTRS)

1991-01-01

Project MEDSAT was proposed with the specific goal of designing a satellite to remotely sense pertinent information useful in establishing strategies to control malaria. The 340 kg MEDSAT satellite is to be inserted into circular earth orbit aboard the Pegasus Air-Launched Space Booster at an inclination of 21 degrees and an altitude of 473 km. It is equipped with a synthetic aperture radar and a visible thermal/infrared sensor to remotely sense conditions at the target area of Chiapas, Mexico. The orbit is designed so that MEDSAT will pass over the target site twice each day. The data from each scan will be downlinked to Hawaii for processing, resulting in maps indicating areas of high malaria risk. These will be distributed to health officials at the target site. A relatively inexpensive launch by Pegasus and a design using mainly proven, off-the-shelf technology permit a low mission cost, while innovations in the satellite controls and the scientific instruments allow a fairly complex mission.

Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario

Treesearch

Peter T. Wolter; Philip A. Townsend; Brian R. Sturtevant; Clayton C. Kingdon

2008-01-01

Insects and disease affect large areas of forest in the U.S. and Canada. Understanding ecosystem impacts of such disturbances requires knowledge of host species distribution patterns on the landscape. In this study, we mapped the distribution and abundance of host species for the spruce budworm (Choristoneura fumiferana) to facilitate landscape scale...

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

Powers, W. F.

1972-01-01

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Operational programs in forest management and priority in the utilization of remote sensing

NASA Technical Reports Server (NTRS)

Douglass, R. W.

1978-01-01

A speech is given on operational remote sensing programs in forest management and the importance of remote sensing in forestry is emphasized. Forest service priorities in using remote sensing are outlined.

Remote sensing, land use, and demography - A look at people through their effects on the land

NASA Technical Reports Server (NTRS)

Paul, C. K.; Landini, A. J.

1976-01-01

Relevant causes of failure by the remote sensing community in the urban scene are analyzed. The reasons for the insignificant role of remote sensing in urban land use data collection are called the law of realism, the incompatibility of remote sensing and urban management system data formats is termed the law of nominal/ordinal systems compatibility, and the land use/population correlation dilemma is referred to as the law of missing persons. The study summarizes the three laws of urban land use information for which violations, avoidance, or ignorance have caused the decline of present remote sensing research. Particular attention is given to the rationale for urban land use information and for remote sensing. It is shown that remote sensing of urban land uses compatible with the three laws can be effectively developed by realizing the 10 percent contribution of remote sensing to urban land use planning data collection.

Thematic Conference on Geologic Remote Sensing, 8th, Denver, CO, Apr. 29-May 2, 1991, Proceedings. Vols. 1 & 2

NASA Technical Reports Server (NTRS)

1991-01-01

The proceedings contain papers discussing the state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing, along with the research and development activities aimed at increasing the future capabilities of this technology. The following topics are addressed: spectral geology, U.S. and international hydrocarbon exporation, radar and thermal infrared remote sensing, engineering geology and hydrogeology, mineral exploration, remote sensing for marine and environmental applications, image processing and analysis, geobotanical remote sensing, and data integration and geographic information systems. Particular attention is given to spectral alteration mapping with imaging spectrometers, mapping the coastal plain of the Congo with airborne digital radar, applications of remote sensing techniques to the assessment of dam safety, remote sensing of ferric iron minerals as guides for gold exploration, principal component analysis for alteration mappping, and the application of remote sensing techniques for gold prospecting in the north Fujian province.

Methods of training the graduate level and professional geologist in remote sensing technology

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1981-01-01

Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism.

PubMed

Carvalho, Omar S; Scholte, Ronaldo G C; Guimarães, Ricardo J P S; Freitas, Corina C; Drummond, Sandra C; Amaral, Ronaldo S; Dutra, Luciano V; Oliveira, Guilherme; Massara, Cristiano L; Enk, Martin J

2010-07-01

Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R(2) = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.

A comparison of minimum distance and maximum likelihood techniques for proportion estimation

NASA Technical Reports Server (NTRS)

Woodward, W. A.; Schucany, W. R.; Lindsey, H.; Gray, H. L.

1982-01-01

The estimation of mixing proportions P sub 1, P sub 2,...P sub m in the mixture density f(x) = the sum of the series P sub i F sub i(X) with i = 1 to M is often encountered in agricultural remote sensing problems in which case the p sub i's usually represent crop proportions. In these remote sensing applications, component densities f sub i(x) have typically been assumed to be normally distributed, and parameter estimation has been accomplished using maximum likelihood (ML) techniques. Minimum distance (MD) estimation is examined as an alternative to ML where, in this investigation, both procedures are based upon normal components. Results indicate that ML techniques are superior to MD when component distributions actually are normal, while MD estimation provides better estimates than ML under symmetric departures from normality. When component distributions are not symmetric, however, it is seen that neither of these normal based techniques provides satisfactory results.

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

PubMed

Wilson, Adam M; Jetz, Walter

2016-03-01

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

Remote sensing by satellite - Technical and operational implications for international cooperation

NASA Technical Reports Server (NTRS)

Doyle, S. E.

1976-01-01

International cooperation in the U.S. Space Program is discussed and related to the NASA program for remote sensing of the earth. Satellite remote sensing techniques are considered along with the selection of the best sensors and wavelength bands. The technology of remote sensing satellites is considered with emphasis on the Landsat system configuration. Future aspects of remote sensing satellites are considered.

Searching for Water Ice at the Lunar North Pole Using High-Resolution Images and Radar

NASA Technical Reports Server (NTRS)

Mitchell, J. L.; Lawrence, S. J.; Robinson, M. S.; Speyerer, E. J.; Denevi, B. W.

2017-01-01

Permanently shadowed regions (PSRs) at the lunar poles are potential reservoirs of frozen volatiles, and are therefore high-priority exploration targets. PSRs trap water and other volatiles because their annual maximum temperatures (40-100K) are lower than the sublimation temperatures of these species (i.e. H2O approx.104K). Previous studies using various remote sensing techniques have not been able to definitively characterize the distribution or abundance of ice in lunar PSRs. The purpose of this study is to search for signs of ice in PSRs using two complimentary remote sensing techniques: radar and visible images.

Assessment of Remote Sensing Technologies for Location of Hydrogen and Helium Leaks

NASA Technical Reports Server (NTRS)

Sellar, R. Glenn; Sohn, Yongho; Mathur, Varun; Reardon, Peter

2001-01-01

In Phase 1 of this project, a hierarchy of techniques for H2 and He leak location was developed. A total of twelve specific remote sensing techniques were evaluated; the results are summarized. A basic diffusion model was also developed to predict the concentration and distribution of H2 or He resulting from a leak. The objectives of Phase 2 of the project consisted of the following four tasks: Advance Rayleigh Doppler technique from TRL 1 to TRL 2; Plan to advance Rayleigh Doppler technique from TRL 2 to TRL 3; Advance researchers and resources for further advancement; Extend diffusion model.

An Examination of the Impact of Drizzle Drops on Satellite-Retrieved Effective Particle Sizes

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Arduini, Robert F.; Young, David F.; Ayers, J, Kirk; Albrecht, Bruce A.; Sharon, Tarah; Stevens, Bjorn

2004-01-01

In general, cloud effective droplet radii are remotely sensed in the near-infrared using the assumption of a monomodal droplet size distribution. It has been observed in many instances, especially in relatively pristine marine environments, that cloud effective droplet radii derived from satellite data often exceed 15 m or more. Comparisons of remotely sensed and in situ retrievals indicate that the former often overestimates the latter in clouds with drizzle-size droplets. To gain a better understanding of this discrepancy, this paper performs a theoretical and empirical evaluation of the impact of drizzle drops on the derived effective radius.

Oil pollution signatures by remote sensing.

NASA Technical Reports Server (NTRS)

Catoe, C. E.; Mclean, J. T.

1972-01-01

Study of the possibility of developing an effective remote sensing system for oil pollution monitoring which would be capable of detecting oil films on water, mapping the areal extent of oil slicks, measuring slick thickness, and identifying the oil types. In the spectral regions considered (ultraviolet, visible, infrared, microwave, and radar), the signatures were sufficiently unique when compared to the background so that it was possible to detect and map oil slicks. Both microwave and radar techniques are capable of operating in adverse weather. Fluorescence techniques show promise in identifying oil types. A multispectral system will be required to detect oil, map its distribution, estimate film thickness, and characterize the oil pollutant.

Remote sensing in operational range management programs in Western Canada

NASA Technical Reports Server (NTRS)

Thompson, M. D.

1977-01-01

A pilot program carried out in Western Canada to test remote sensing under semi-operational conditions and display its applicability to operational range management programs was described. Four agencies were involved in the program, two in Alberta and two in Manitoba. Each had different objectives and needs for remote sensing within its range management programs, and each was generally unfamiliar with remote sensing techniques and their applications. Personnel with experience and expertise in the remote sensing and range management fields worked with the agency personnel through every phase of the pilot program. Results indicate that these agencies have found remote sensing to be a cost effective tool and will begin to utilize remote sensing in their operational work during ensuing seasons.

Scale as the common language for soil variations revealed with geophysics, biophysics, and remote sensing

USDA-ARS?s Scientific Manuscript database

Quantification and estimation of crop response to management are important for efficient use of resources. Because the spatial distribution of crop response is related to the distribution of soil properties, crop response to management practices will also have a strong spatial component. Most plot r...

An Interoperable, Agricultural Information System Based on Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Teng, William; Chiu, Long; Doraiswamy, Paul; Kempler, Steven; Liu, Zhong; Pham, Long; Rui, Hualan

2005-01-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.

Visual analytics of inherently noisy crowdsourced data on ultra high resolution displays

NASA Astrophysics Data System (ADS)

Huynh, Andrew; Ponto, Kevin; Lin, Albert Yu-Min; Kuester, Falko

The increasing prevalence of distributed human microtasking, crowdsourcing, has followed the exponential increase in data collection capabilities. The large scale and distributed nature of these microtasks produce overwhelming amounts of information that is inherently noisy due to the nature of human input. Furthermore, these inputs create a constantly changing dataset with additional information added on a daily basis. Methods to quickly visualize, filter, and understand this information over temporal and geospatial constraints is key to the success of crowdsourcing. This paper present novel methods to visually analyze geospatial data collected through crowdsourcing on top of remote sensing satellite imagery. An ultra high resolution tiled display system is used to explore the relationship between human and satellite remote sensing data at scale. A case study is provided that evaluates the presented technique in the context of an archaeological field expedition. A team in the field communicated in real-time with and was guided by researchers in the remote visual analytics laboratory, swiftly sifting through incoming crowdsourced data to identify target locations that were identified as viable archaeological sites.

Mapping the Distribution of Potential Land Drought in Batam Island Using the Integration of Remote Sensing and Geographic Information Systems (GIS)

NASA Astrophysics Data System (ADS)

Lubis, M. Z.; Taki, H. M.; Anurogo, W.; Pamungkas, D. S.; Wicaksono, P.; Aprilliyanti, T.

2017-12-01

Potential land drought mapping on Batam is needed to determine the distribution of areas that are very potential to the physical drought of the land. It is because the drought is always threatening on the long dry season. This research integrates remote sensing science with Geographic Information System (GIS). This research aims to map the distribution of land drought potential in Batam Island. The parameters used in this research are land use, Land Surface Temperature (LST), Potential dryness of land on the Batam island. The resulting map indicates the existence of five potential drought classes on the island of Batam. The area of very low drought potential is 2629.45 ha, mostly located in the Sungai Beduk sub-district. High drought potential with an area of 7081.39 ha is located in Sekupang sub-district. The distribution of very high land drought potential is in Batam city and Nongsa sub-district with area of 15600.12 ha. The coefficient of determination (R 2) is 0.6279. This indicates a strong positive relationship between field LST and modelled LST.

PROCEEDINGS OF THE FOURTH SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT; 12, 13, 14 APRIL 1966.

DTIC Science & Technology

The symposium was conducted as part of a continuing program investigating the field of remote sensing , its potential in scientific research and...information on all aspects of remote sensing , with special emphasis on such topics as needs for remotely sensed data, data management, and the special... remote sensing programs, data acquisition, data analysis and application, and equipment design, were presented. (Author)

Remote sensing and image interpretation

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Kiefer, R. W. (Principal Investigator)

1979-01-01

A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.

Assessing the Rayleigh Intensity Remote Leak Detection Technique

NASA Technical Reports Server (NTRS)

Clements, Sandra

2001-01-01

Remote sensing technologies are being considered for efficient, low cost gas leak detection. An exploratory project to identify and evaluate remote sensing technologies for application to gas leak detection is underway. During Phase 1 of the project, completed last year, eleven specific techniques were identified for further study. One of these, the Rayleigh Intensity technique, would make use of changes in the light scattered off of gas molecules to detect and locate a leak. During the 10-week Summer Faculty Fellowship Program, the scatter of light off of gas molecules was investigated. The influence of light scattered off of aerosols suspended in the atmosphere was also examined to determine if this would adversely affect leak detection. Results of this study indicate that in unconditioned air, it will be difficult, though perhaps not impossible, to distinguish between a gas leak and natural variations in the aerosol content of the air. Because information about the particle size distribution in clean room environments is incomplete, the applicability in clean rooms is uncertain though more promising than in unconditioned environments. It is suggested that problems caused by aerosols may be overcome by using the Rayleigh Intensity technique in combination with another remote sensing technique, the Rayleigh Doppler technique.

Mineralogy and Astrobiology Detection Using Laser Remote Sensing Instrument

NASA Technical Reports Server (NTRS)

Abedin, M. Nurul; Bradley, Arthur T.; Sharma, Shiv K.; Misra, Anupam K.; Lucey, Paul G.; Mckay, Chistopher P.; Ismail, Syed; Sandford, Stephen P.

2015-01-01

A multispectral instrument based on Raman, laser-induced fluorescence (LIF), laser-induced breakdown spectroscopy (LIBS), and a lidar system provides high-fidelity scientific investigations, scientific input, and science operation constraints in the context of planetary field campaigns with the Jupiter Europa Robotic Lander and Mars Sample Return mission opportunities. This instrument conducts scientific investigations analogous to investigations anticipated for missions to Mars and Jupiter's icy moons. This combined multispectral instrument is capable of performing Raman and fluorescence spectroscopy out to a >100 m target distance from the rover system and provides single-wavelength atmospheric profiling over long ranges (>20 km). In this article, we will reveal integrated remote Raman, LIF, and lidar technologies for use in robotic and lander-based planetary remote sensing applications. Discussions are focused on recently developed Raman, LIF, and lidar systems in addition to emphasizing surface water ice, surface and subsurface minerals, organics, biogenic, biomarker identification, atmospheric aerosols and clouds distributions, i.e., near-field atmospheric thin layers detection for next robotic-lander based instruments to measure all the above-mentioned parameters. OCIS codes: (120.0280) Remote sensing and sensors; (130.0250) Optoelectronics; (280.3640) Lidar; (300.2530) Fluorescence, laser-induced; (300.6450) Spectroscopy, Raman; (300.6365) Spectroscopy, laser induced breakdown

Modelling Hydrologic Processes in the Mekong River Basin Using a Distributed Model Driven by Satellite Precipitation and Rain Gauge Observations

PubMed Central

Wang, Wei; Lu, Hui; Yang, Dawen; Sothea, Khem; Jiao, Yang; Gao, Bin; Peng, Xueting; Pang, Zhiguo

2016-01-01

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998–2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002–2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability. PMID:27010692

Modelling Hydrologic Processes in the Mekong River Basin Using a Distributed Model Driven by Satellite Precipitation and Rain Gauge Observations.

PubMed

Wang, Wei; Lu, Hui; Yang, Dawen; Sothea, Khem; Jiao, Yang; Gao, Bin; Peng, Xueting; Pang, Zhiguo

2016-01-01

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998-2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002-2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability.

Geotechnical applications of remote sensing and remote data transmission; Proceedings of the Symposium, Cocoa Beach, FL, Jan. 31-Feb. 1, 1986

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, A.I.; Pettersson, C.B.

1988-01-01

Papers and discussions concerning the geotechnical applications of remote sensing and remote data transmission, sources of remotely sensed data, and glossaries of remote sensing and remote data transmission terms, acronyms, and abbreviations are presented. Aspects of remote sensing use covered include the significance of lineaments and their effects on ground-water systems, waste-site use and geotechnical characterization, the estimation of reservoir submerging losses using CIR aerial photographs, and satellite-based investigation of the significance of surficial deposits for surface mining operations. Other topics presented include the location of potential ground subsidence and collapse features in soluble carbonate rock, optical Fourier analysis ofmore » surface features of interest in geotechnical engineering, geotechnical applications of U.S. Government remote sensing programs, updating the data base for a Geographic Information System, the joint NASA/Geosat Test Case Project, the selection of remote data telemetry methods for geotechnical applications, the standardization of remote sensing data collection and transmission, and a comparison of airborne Goodyear electronic mapping system/SAR with satelliteborne Seasat/SAR radar imagery.« less

Education in Environmental Remote Sensing: Potentials and Problems.

ERIC Educational Resources Information Center

Kiefer, Ralph W.; Lillesand, Thomas M.

1983-01-01

Discusses remote sensing principles and applications and the status and needs of remote sensing education in the United States. A summary of the fundamental policy issues that will determine remote sensing's future role in environmental and resource managements is included. (Author/BC)

THE EPA REMOTE SENSING ARCHIVE

EPA Science Inventory

What would you do if you were faced with organizing 30 years of remote sensing projects that had been haphazardly stored at two separate locations for years then combined? The EPA Remote Sensing Archive, currently located in Las Vegas, Nevada. contains the remote sensing data and...

Modelling population distribution using remote sensing imagery and location-based data

NASA Astrophysics Data System (ADS)

Song, J.; Prishchepov, A. V.

2017-12-01

Detailed spatial distribution of population density is essential for city studies such as urban planning, environmental pollution and city emergency, even estimate pressure on the environment and human exposure and risks to health. However, most of the researches used census data as the detailed dynamic population distribution are difficult to acquire, especially in microscale research. This research describes a method using remote sensing imagery and location-based data to model population distribution at the function zone level. Firstly, urban functional zones within a city were mapped by high-resolution remote sensing images and POIs. The workflow of functional zones extraction includes five parts: (1) Urban land use classification. (2) Segmenting images in built-up area. (3) Identification of functional segments by POIs. (4) Identification of functional blocks by functional segmentation and weight coefficients. (5) Assessing accuracy by validation points. The result showed as Fig.1. Secondly, we applied ordinary least square and geographically weighted regression to assess spatial nonstationary relationship between light digital number (DN) and population density of sampling points. The two methods were employed to predict the population distribution over the research area. The R²of GWR model were in the order of 0.7 and typically showed significant variations over the region than traditional OLS model. The result showed as Fig.2.Validation with sampling points of population density demonstrated that the result predicted by the GWR model correlated well with light value. The result showed as Fig.3. Results showed: (1) Population density is not linear correlated with light brightness using global model. (2) VIIRS night-time light data could estimate population density integrating functional zones at city level. (3) GWR is a robust model to map population distribution, the adjusted R2 of corresponding GWR models were higher than the optimal OLS models, confirming that GWR models demonstrate better prediction accuracy. So this method provide detailed population density information for microscale citizen studies.

Lidar investigations of ozone in the upper troposphere - lower stratosphere: technique and results of measurements

NASA Astrophysics Data System (ADS)

Romanovskii, O. A.; Burlakov, V. D.; Dolgii, S. I.; Nevzorov, A. A.; Nevzorov, A. V.; Kharchenko, O. V.

2016-12-01

Prediction of atmospheric ozone layer, which is the valuable and irreplaceable geo asset, is currently the important scientific and engineering problem. The relevance of the research is caused by the necessity to develop laser remote methods for sensing ozone to solve the problems of controlling the environment and climatology. The main aim of the research is to develop the technique for laser remote ozone sensing in the upper troposphere - lower stratosphere by differential absorption method for temperature and aerosol correction and analysis of measurement results. The report introduces the technique of recovering profiles of ozone vertical distribution considering temperature and aerosol correction in atmosphere lidar sounding by differential absorption method. The temperature correction of ozone absorption coefficients is introduced in the software to reduce the retrieval errors. The authors have determined wavelengths, promising to measure ozone profiles in the upper troposphere - lower stratosphere. We present the results of DIAL measurements of the vertical ozone distribution at the Siberian lidar station in Tomsk. Sensing is performed according to the method of differential absorption at wavelength pair of 299/341 nm, which are, respectively, the first and second Stokes components of SRS conversion of 4th harmonic of Nd:YAG laser (266 nm) in hydrogen. Lidar with receiving mirror 0.5 m in diameter is used to implement sensing of vertical ozone distribution in altitude range of 6-18 km. The recovered ozone profiles were compared with IASI satellite data and Kruger model. The results of applying the developed technique to recover the profiles of ozone vertical distribution considering temperature and aerosol correction in the altitude range of 6-18 km in lidar atmosphere sounding by differential absorption method confirm the prospects of using the selected wavelengths of ozone sensing 341 and 299 nm in the ozone lidar.

Research on remote sensing image pixel attribute data acquisition method in AutoCAD

NASA Astrophysics Data System (ADS)

Liu, Xiaoyang; Sun, Guangtong; Liu, Jun; Liu, Hui

2013-07-01

The remote sensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remote sensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remote sensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remote sensing image processing algorithms.

Bibliography of Remote Sensing Techniques Used in Wetland Research.

DTIC Science & Technology

1993-01-01

remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.... Change detection, Wetland assessment, Remote sensing ,

Remote Distributed Vibration Sensing Through Opaque Media Using Permanent Magnets

DOE PAGES

Chen, Yi; Mazumdar, Anirban; Brooks, Carlton F.; ...

2018-04-05

Vibration sensing is critical for a variety of applications from structural fatigue monitoring to understanding the modes of airplane wings. In particular, remote sensing techniques are needed for measuring the vibrations of multiple points simultaneously, assessing vibrations inside opaque metal vessels, and sensing through smoke clouds and other optically challenging environments. Here, in this paper, we propose a method which measures high-frequency displacements remotely using changes in the magnetic field generated by permanent magnets. We leverage the unique nature of vibration tracking and use a calibrated local model technique developed specifically to improve the frequency-domain estimation accuracy. The results showmore » that two-dimensional local models surpass the dipole model in tracking high-frequency motions. A theoretical basis for understanding the effects of electronic noise and error due to correlated variables is generated in order to predict the performance of experiments prior to implementation. Simultaneous measurements of up to three independent vibrating components are shown. The relative accuracy of the magnet-based displacement tracking with respect to the video tracking ranges from 40 to 190 μm when the maximum displacements approach ±5 mm and when sensor-to-magnet distances vary from 25 to 36 mm. Finally, vibration sensing inside an opaque metal vessel and mode shape changes due to damage on an aluminum beam are also studied using the wireless permanent-magnet vibration sensing scheme.« less

Remote Distributed Vibration Sensing Through Opaque Media Using Permanent Magnets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yi; Mazumdar, Anirban; Brooks, Carlton F.

Vibration sensing is critical for a variety of applications from structural fatigue monitoring to understanding the modes of airplane wings. In particular, remote sensing techniques are needed for measuring the vibrations of multiple points simultaneously, assessing vibrations inside opaque metal vessels, and sensing through smoke clouds and other optically challenging environments. Here, in this paper, we propose a method which measures high-frequency displacements remotely using changes in the magnetic field generated by permanent magnets. We leverage the unique nature of vibration tracking and use a calibrated local model technique developed specifically to improve the frequency-domain estimation accuracy. The results showmore » that two-dimensional local models surpass the dipole model in tracking high-frequency motions. A theoretical basis for understanding the effects of electronic noise and error due to correlated variables is generated in order to predict the performance of experiments prior to implementation. Simultaneous measurements of up to three independent vibrating components are shown. The relative accuracy of the magnet-based displacement tracking with respect to the video tracking ranges from 40 to 190 μm when the maximum displacements approach ±5 mm and when sensor-to-magnet distances vary from 25 to 36 mm. Finally, vibration sensing inside an opaque metal vessel and mode shape changes due to damage on an aluminum beam are also studied using the wireless permanent-magnet vibration sensing scheme.« less

Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea.

PubMed

Simis, Stefan G H; Ylöstalo, Pasi; Kallio, Kari Y; Spilling, Kristian; Kutser, Tiit

2017-01-01

Optical-biogeochemical relationships of particulate and dissolved organic matter are presented in support of remote sensing of the Baltic Sea pelagic. This system exhibits strong seasonality in phytoplankton community composition and wide gradients of chromophoric dissolved organic matter (CDOM), properties which are poorly handled by existing remote sensing algorithms. Absorption and scattering properties of particulate matter reflected the seasonality in biological (phytoplankton succession) and physical (thermal stratification) processes. Inherent optical properties showed much wider variability when normalized to the chlorophyll-a concentration compared to normalization to either total suspended matter dry weight or particulate organic carbon. The particle population had the largest optical variability in summer and was dominated by organic matter in both seasons. The geographic variability of CDOM and relationships with dissolved organic carbon (DOC) are also presented. CDOM dominated light absorption at blue wavelengths, contributing 81% (median) of the absorption by all water constituents at 400 nm and 63% at 442 nm. Consequentially, 90% of water-leaving radiance at 412 nm originated from a layer (z90) no deeper than approximately 1.0 m. With water increasingly attenuating light at longer wavelengths, a green peak in light penetration and reflectance is always present in these waters, with z90 up to 3.0-3.5 m depth, whereas z90 only exceeds 5 m at biomass < 5 mg Chla m-3. High absorption combined with a weakly scattering particle population (despite median phytoplankton biomass of 14.1 and 4.3 mg Chla m-3 in spring and summer samples, respectively), characterize this sea as a dark water body for which dedicated or exceptionally robust remote sensing techniques are required. Seasonal and regional optical-biogeochemical models, data distributions, and an extensive set of simulated remote-sensing reflectance spectra for testing of remote sensing algorithms are provided as supplementary data.

Remote sensing studies of the northeastern portion of the lunar nearside

NASA Technical Reports Server (NTRS)

Hawke, B. R.; Blewett, D. T.; Lucey, P. G.; Taylor, G. J.; Peterson, C. A.; Bell, J. F.; Robinson, M. S.; Bell, J. F., III; Coombs, C. R.; Jaumann, R.

1993-01-01

During the Galileo spacecraft encounter with the Earth-Moon system in December, 1992, a variety of spectral data and imagery were obtained for the eastern limb region as well as much of the lunar nearside. In order to support this encounter, we have been collecting near-infrared spectra and other remote sensing data for that portion of the northeastern nearside (NEM region) for which the highest resolution Galileo data were obtained. Analysis of spectra obtained for highlands units in the NEN region indicates that most surface units are dominated by anorthositic norite. To date, no pure anorthosites have been identified in the region. Several dark-haloed impact craters have exposed mare material from beneath highlands-rich surface units. Hence, ancient mare volcanism occurred in at least a portion of the NEN region. Endogenic dark-haloed craters in the region are the source of localized dark mantle deposits (LDMD) of pyroclastic origin and at least two compositional groups are present. The Galileo spacecraft obtained very high-resolution remote sensing data for the northeastern part of the nearside of the Moon. In order to prepare for and support this encounter, we have collected and analyzed a variety of spectral data for the NEN region. Numerous unanswered questions exist for this region. These include: (1) the composition and stratigraphy of the local highlands crust, (2) the nature and mode of formation of regional light plains, (3) the composition of localized pyroclastic deposits, and (4) the distribution of possible cryptomare in the region. The purpose of this paper is to present the preliminary results of our analyzes of remote sensing data of remote sensing data obtained for the NEN region.

Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms

NASA Astrophysics Data System (ADS)

Shahtahmassebi, Amir Reza; Song, Jie; Zheng, Qing; Blackburn, George Alan; Wang, Ke; Huang, Ling Yan; Pan, Yi; Moore, Nathan; Shahtahmassebi, Golnaz; Sadrabadi Haghighi, Reza; Deng, Jing Song

2016-04-01

A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hotspot regions in the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re-densification mechanisms. Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms.

LIDAR Remote Sensing of Particulate Matter Emissions from On-Road Vehicles

NASA Astrophysics Data System (ADS)

Keislar, R. E.; Kuhns, H.; Mazzoleni, C.; Moosmuller, H.; Watson, J.

2002-12-01

DRI has developed a remote sensing method for on-road particulate matter emissions from gasoline-powered and diesel-powered vehicles called the Vehicle Emissions Remote Sensing System (VERSS). Remote sensing of gaseous pollutants in vehicle exhaust is a well-established, economical way to determine on-road emissions for thousands of vehicles per day. The VERSS adds a particulate matter channel to complement gaseous pollutant measurements. The VERSS uses 266-nm ultraviolet laser light to achieve greater sensitivity than visible light to sub-micrometer particles, where the greatest mass fraction has been reported. The VERSS system integrates the lidar channel with a commercial remote sensing device (RSD) for gaseous pollutants, and the RSD CO2 measurement can be used to estimate fuel-based particle mass emissions. We describe the interpretation and processing of lidar returns from field measurements taken by the combined VERSS during the Southern Nevada Air Quality Study (SNAQS), conducted in the Las Vegas area. With suitable assumptions regarding size distribution and particle composition, the lidar backscatter signal and the RSD yield three basic measurements of particulate matter in the exhaust plume. For each passing vehicle, these three channels are: 1) Columnar extinction in the infrared (IR at 3.9 micrometers) 2) Columnar extinction in the ultraviolet (UV at 266 nm) 3) Range-resolved backscatter at 266 nm (horizontal spatial resolution of 20-25 cm) The 3.9-micrometer channel is a good surrogate for absorption by elemental carbon (EC) in tailpipe emissions and has been utilized in previous studies. Opacity measurements at 266 nm provide optical extinction due to scattering from tailpipe organic carbon (OC) and EC emissions.

Use of remote sensing and a geographical information system in a national helminth control programme in Chad.

PubMed Central

Brooker, Simon; Beasley, Michael; Ndinaromtan, Montanan; Madjiouroum, Ester Mobele; Baboguel, Marie; Djenguinabe, Elie; Hay, Simon I.; Bundy, Don A. P.

2002-01-01

OBJECTIVE: To design and implement a rapid and valid epidemiological assessment of helminths among schoolchildren in Chad using ecological zones defined by remote sensing satellite sensor data and to investigate the environmental limits of helminth distribution. METHODS: Remote sensing proxy environmental data were used to define seven ecological zones in Chad. These were combined with population data in a geographical information system (GIS) in order to define a sampling protocol. On this basis, 20 schools were surveyed. Multilevel analysis, by means of generalized estimating equations to account for clustering at the school level, was used to investigate the relationship between infection patterns and key environmental variables. FINDINGS: In a sample of 1023 schoolchildren, 22.5% were infected with Schistosoma haematobium and 32.7% with hookworm. None were infected with Ascaris lumbricoides or Trichuris trichiura. The prevalence of S. haematobium and hookworm showed marked geographical heterogeneity and the observed patterns showed a close association with the defined ecological zones and significant relationships with environmental variables. These results contribute towards defining the thermal limits of geohelminth species. Predictions of infection prevalence were made for each school surveyed with the aid of models previously developed for Cameroon. These models correctly predicted that A. lumbricoides and T. trichiura would not occur in Chad but the predictions for S. haematobium were less reliable at the school level. CONCLUSION: GIS and remote sensing can play an important part in the rapid planning of helminth control programmes where little information on disease burden is available. Remote sensing prediction models can indicate patterns of geohelminth infection but can only identify potential areas of high risk for S. haematobium. PMID:12471398

Use of multispectral satellite remote sensing to assess mixing of suspended sediment downstream of large river confluences

NASA Astrophysics Data System (ADS)

Umar, M.; Rhoads, Bruce L.; Greenberg, Jonathan A.

2018-01-01

Although past work has noted that contrasts in turbidity often are detectable on remotely sensed images of rivers downstream from confluences, no systematic methodology has been developed for assessing mixing over distance of confluent flows with differing surficial suspended sediment concentrations (SSSC). In contrast to field measurements of mixing below confluences, satellite remote-sensing can provide detailed information on spatial distributions of SSSC over long distances. This paper presents a methodology that uses remote-sensing data to estimate spatial patterns of SSSC downstream of confluences along large rivers and to determine changes in the amount of mixing over distance from confluences. The method develops a calibrated Random Forest (RF) model by relating training SSSC data from river gaging stations to derived spectral indices for the pixels corresponding to gaging-station locations. The calibrated model is then used to predict SSSC values for every river pixel in a remotely sensed image, which provides the basis for mapping of spatial variability in SSSCs along the river. The pixel data are used to estimate average surficial values of SSSC at cross sections spaced uniformly along the river. Based on the cross-section data, a mixing metric is computed for each cross section. The spatial pattern of change in this metric over distance can be used to define rates and length scales of surficial mixing of suspended sediment downstream of a confluence. This type of information is useful for exploring the potential influence of various controlling factors on mixing downstream of confluences, for evaluating how mixing in a river system varies over time and space, and for determining how these variations influence water quality and ecological conditions along the river.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

Results from the July 1981 Workshop on Passive Remote Sensing of the Troposphere

NASA Technical Reports Server (NTRS)

Keafer, L. S., Jr.; Reichle, H. G., Jr.

1982-01-01

Potential roles of passive remote sensors in the study of the chemistry and related dynamics of the lower atmosphere were defined by a Tropospheric Passive Remote Sensing Workshop, and technology advances required to implement these roles were identified. A promising role is in making global-scale, multilayer measurements of the more abundant trace tropospheric gaseous species (e.g., O3, CO, CH4, HNO3) and of aerosol thickness and size distribution. It includes both nadirand limb-viewing measurements. Technology advances focus on both scanning- and fixed-spectra, nadir-viewing techniques with resolutions of 0.1 kaysers or better. Balloon- and Shuttle-borne experiments should be performed to study the effects of instrument noise and background fluctuations on data inversion and to determine the utility of simultaneously obtained nadir- and limb-viewing data.

Kite Aerial Photography as a Tool for Remote Sensing

ERIC Educational Resources Information Center

Sallee, Jeff; Meier, Lesley R.

2010-01-01

As humans, we perform remote sensing nearly all the time. This is because we acquire most of our information about our surroundings through the senses of sight and hearing. Whether viewed by the unenhanced eye or a military satellite, remote sensing is observing objects from a distance. With our current technology, remote sensing has become a part…

AN APPROACH TO TRANSGENIC CROP MONITORING

EPA Science Inventory

Remote sensing by aerial or satellite images may provide a method of identifying transgenic pesticidal crop distribution in the landscape. Genetically engineered crops containing bacterial gene(s) that express an insecticidal protein from Bacillus thuringiensis (Bt) are regulated...

Tight real-time synchronization of a microwave clock to an optical clock across a turbulent air path

PubMed Central

Bergeron, Hugo; Sinclair, Laura C.; Swann, William C.; Nelson, Craig W.; Deschênes, Jean-Daniel; Baumann, Esther; Giorgetta, Fabrizio R.; Coddington, Ian; Newbury, Nathan R.

2018-01-01

The ability to distribute the precise time and frequency from an optical clock to remote platforms could enable future precise navigation and sensing systems. Here we demonstrate tight, real-time synchronization of a remote microwave clock to a master optical clock over a turbulent 4-km open air path via optical two-way time-frequency transfer. Once synchronized, the 10-GHz frequency signals generated at each site agree to 10−14 at one second and below 10−17 at 1000 seconds. In addition, the two clock times are synchronized to ±13 fs over an 8-hour period. The ability to phase-synchronize 10-GHz signals across platforms supports future distributed coherent sensing, while the ability to time-synchronize multiple microwave-based clocks to a high-performance master optical clock supports future precision navigation/timing systems. PMID:29607352

MODIS Cloud Products Derived from Terra and Aqua During CRYSTAL-FACE

NASA Technical Reports Server (NTRS)

King, Michael D.; Platnick, S.; Riedi, J. C.; Ackerman, S. A.; Menzel, W. P.

2003-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS), developed as part of the Earth Observing System (EOS) and launched on Terra in December 1999 and Aqua in May 2002, is designed to meet the scientific needs for satellite remote sensing of clouds, aerosols, water vapor, and land and ocean surface properties. During the CRYSTAL-FACE experiment, numerous aircraft coordinated both in situ and remote sensing observations with the Terra and Aqua spacecraft. In this paper we will emphasize the optical, microphysical, and physical properties of both liquid water and ice clouds obtained from an analysis of the satellite observations over Florida and the Gulf of Mexico during July 2002. We will present the frequency distribution of liquid water and ice cloud microphysical properties throughout the region, separating the results over land and ocean. Probability distributions of effective radius and cloud optical thickness will also be shown.

Integrating Remote and Social Sensing Data for a Scenario on Secure Societies in Big Data Platform

NASA Astrophysics Data System (ADS)

Albani, Sergio; Lazzarini, Michele; Koubarakis, Manolis; Taniskidou, Efi Karra; Papadakis, George; Karkaletsis, Vangelis; Giannakopoulos, George

2016-08-01

In the framework of the Horizon 2020 project BigDataEurope (Integrating Big Data, Software & Communities for Addressing Europe's Societal Challenges), a pilot for the Secure Societies Societal Challenge was designed considering the requirements coming from relevant stakeholders. The pilot is focusing on the integration in a Big Data platform of data coming from remote and social sensing.The information on land changes coming from the Copernicus Sentinel 1A sensor (Change Detection workflow) is integrated with information coming from selected Twitter and news agencies accounts (Event Detection workflow) in order to provide the user with multiple sources of information.The Change Detection workflow implements a processing chain in a distributed parallel manner, exploiting the Big Data capabilities in place; the Event Detection workflow implements parallel and distributed social media and news agencies monitoring as well as suitable mechanisms to detect and geo-annotate the related events.

Application of symmetry properties to polarimetric remote sensing with JPL AIRSAR data

NASA Technical Reports Server (NTRS)

Nghiem, S. V.; Yueh, Simon H.; Kwok, R.; Li, F. K.

1992-01-01

Based on symmetry properties, polarimetric remote sensing of geophysical media is studied. From the viewpoint of symmetry groups, media with reflection, rotation, azimuthal, and centrical symmetries are considered. The symmetries impose relations among polarimetric scattering coefficients, which are valid to all scattering mechanisms in the symmetrical configurations. Various orientation distributions of non-spherical scatterers can be identified from the scattering coefficients by a comparison with the symmetry calculations. Experimental observations are then analyzed for many geophysical scenes acquired with the Jet Propulsion Laboratory (JPL) airborne polarimetric SAR at microwave frequencies over sea ice and vegetation. Polarimetric characteristics of different ice types are compared with symmetry behaviors. The polarimetric response of a tropical rain forest reveals characteristics close to the centrical symmetry properties, which can be used as a distributed target to relatively calibrate polarimetric radars without any deployment of manmade calibration targets.

Tight real-time synchronization of a microwave clock to an optical clock across a turbulent air path.

PubMed

Bergeron, Hugo; Sinclair, Laura C; Swann, William C; Nelson, Craig W; Deschênes, Jean-Daniel; Baumann, Esther; Giorgetta, Fabrizio R; Coddington, Ian; Newbury, Nathan R

2016-04-01

The ability to distribute the precise time and frequency from an optical clock to remote platforms could enable future precise navigation and sensing systems. Here we demonstrate tight, real-time synchronization of a remote microwave clock to a master optical clock over a turbulent 4-km open air path via optical two-way time-frequency transfer. Once synchronized, the 10-GHz frequency signals generated at each site agree to 10 -14 at one second and below 10 -17 at 1000 seconds. In addition, the two clock times are synchronized to ±13 fs over an 8-hour period. The ability to phase-synchronize 10-GHz signals across platforms supports future distributed coherent sensing, while the ability to time-synchronize multiple microwave-based clocks to a high-performance master optical clock supports future precision navigation/timing systems.

Remote sensing for detecting and mapping whitefly (Bemisia tabaci) infestations

USDA-ARS?s Scientific Manuscript database

Remote sensing technology has long been used for detecting insect infestations on agricultural crops. With recent advances in remote sensing sensors and other spatial information technologies such as Global Position Systems (GPS) and Geographic Information Systems (GIS), remote sensing is finding mo...

Reflections on Earth--Remote-Sensing Research from Your Classroom.

ERIC Educational Resources Information Center

Campbell, Bruce A.

2001-01-01

Points out the uses of remote sensing in different areas, and introduces the program "Reflections on Earth" which provides access to basic and instructional information on remote sensing to students and teachers. Introduces students to concepts related to remote sensing and measuring distances. (YDS)

Remote-Sensing Practice and Potential

DTIC Science & Technology

1974-05-01

Six essential processes that must be accomplished if use of a remote - sensing system is to result in useful information are defined as problem...to be useful in remote - sensing projects are described. An overview of the current state-of-the-art of remote sensing is presented.

History and future of remote sensing technology and education

NASA Technical Reports Server (NTRS)

Colwell, R. N.

1980-01-01

A historical overview of the discovery and development of photography, related sciences, and remote sensing technology is presented. The role of education to date in the development of remote sensing is discussed. The probable future and potential of remote sensing and training is described.

Spatial modelling of evapotranspiration in the Luquillo experimental forest of Puerto Rico using remotely-sensed data.

Treesearch

Wei Wu; Charles A.S. Hall; Frederick N. Scatena; Lindi J. Quackenbush

2006-01-01

Summary Actual evapotranspiration (aET) and related processes in tropical forests can explain 70% of the lateral global energy transport through latent heat, and therefore are very important in the redistribution of water on the Earth’s surface [Mauser, M., Scha¨dlich, S., 1998. Modelling the spatial distribution of evapotranspiration on different scales using remote...

Inverse analysis of non-uniform temperature distributions using multispectral pyrometry

NASA Astrophysics Data System (ADS)

Fu, Tairan; Duan, Minghao; Tian, Jibin; Shi, Congling

2016-05-01

Optical diagnostics can be used to obtain sub-pixel temperature information in remote sensing. A multispectral pyrometry method was developed using multiple spectral radiation intensities to deduce the temperature area distribution in the measurement region. The method transforms a spot multispectral pyrometer with a fixed field of view into a pyrometer with enhanced spatial resolution that can give sub-pixel temperature information from a "one pixel" measurement region. A temperature area fraction function was defined to represent the spatial temperature distribution in the measurement region. The method is illustrated by simulations of a multispectral pyrometer with a spectral range of 8.0-13.0 μm measuring a non-isothermal region with a temperature range of 500-800 K in the spot pyrometer field of view. The inverse algorithm for the sub-pixel temperature distribution (temperature area fractions) in the "one pixel" verifies this multispectral pyrometry method. The results show that an improved Levenberg-Marquardt algorithm is effective for this ill-posed inverse problem with relative errors in the temperature area fractions of (-3%, 3%) for most of the temperatures. The analysis provides a valuable reference for the use of spot multispectral pyrometers for sub-pixel temperature distributions in remote sensing measurements.

Remote Sensing Derived Fire Frequency, Soil Moisture and Ecosystem Productivity Explain Regional Movements in Emu over Australia

PubMed Central

Madani, Nima; Kimball, John S.; Nazeri, Mona; Kumar, Lalit; Affleck, David L. R.

2016-01-01

Species distribution modeling has been widely used in studying habitat relationships and for conservation purposes. However, neglecting ecological knowledge about species, e.g. their seasonal movements, and ignoring the proper environmental factors that can explain key elements for species survival (shelter, food and water) increase model uncertainty. This study exemplifies how these ecological gaps in species distribution modeling can be addressed by modeling the distribution of the emu (Dromaius novaehollandiae) in Australia. Emus cover a large area during the austral winter. However, their habitat shrinks during the summer months. We show evidence of emu summer habitat shrinkage due to higher fire frequency, and low water and food availability in northern regions. Our findings indicate that emus prefer areas with higher vegetation productivity and low fire recurrence, while their distribution is linked to an optimal intermediate (~0.12 m3 m-3) soil moisture range. We propose that the application of three geospatial data products derived from satellite remote sensing, namely fire frequency, ecosystem productivity, and soil water content, provides an effective representation of emu general habitat requirements, and substantially improves species distribution modeling and representation of the species’ ecological habitat niche across Australia. PMID:26799732

Remote Sensing Derived Fire Frequency, Soil Moisture and Ecosystem Productivity Explain Regional Movements in Emu over Australia.

PubMed

Madani, Nima; Kimball, John S; Nazeri, Mona; Kumar, Lalit; Affleck, David L R

2016-01-01

Species distribution modeling has been widely used in studying habitat relationships and for conservation purposes. However, neglecting ecological knowledge about species, e.g. their seasonal movements, and ignoring the proper environmental factors that can explain key elements for species survival (shelter, food and water) increase model uncertainty. This study exemplifies how these ecological gaps in species distribution modeling can be addressed by modeling the distribution of the emu (Dromaius novaehollandiae) in Australia. Emus cover a large area during the austral winter. However, their habitat shrinks during the summer months. We show evidence of emu summer habitat shrinkage due to higher fire frequency, and low water and food availability in northern regions. Our findings indicate that emus prefer areas with higher vegetation productivity and low fire recurrence, while their distribution is linked to an optimal intermediate (~0.12 m3 m(-3)) soil moisture range. We propose that the application of three geospatial data products derived from satellite remote sensing, namely fire frequency, ecosystem productivity, and soil water content, provides an effective representation of emu general habitat requirements, and substantially improves species distribution modeling and representation of the species' ecological habitat niche across Australia.

Role of remote sensing in documenting living resources

NASA Technical Reports Server (NTRS)

Wagner, P. E.; Anderson, R. R.; Brun, B.; Eisenberg, M.; Genys, J. B.; Lear, D. W., Jr.; Miller, M. H.

1978-01-01

Specific cases of known or potentially useful applications of remote sensing in assessing biological resources are discussed. It is concluded that the more usable remote sensing techniques relate to the measurement of population fluctuations in aquatic systems. Sensing of the flora and the fauna of the Bay is considered with emphasis on direct sensing of aquatic plant populations and of water quality. Recommendations for remote sensing projects are given.

Commercial future: making remote sensing a media event

NASA Astrophysics Data System (ADS)

Lurie, Ian

1999-12-01

The rapid growth of commercial remote sensing has made high quality digital sensing data widely available -- now, remote sensing must become and remain a strong, commercially viable industry. However, this new industry cannot survive without an educated consumer base. To access markets, remote sensing providers must make their product more accessible, both literally and figuratively: Potential customers must be able to find the data they require, when they require it, and they must understand the utility of the information available to them. The Internet and the World Wide Web offer the perfect medium to educate potential customers and to sell remote sensing data to those customers. A well-designed web presence can provide both an information center and a market place for companies offering their data for sale. A very high potential web-based market for remote sensing lies in media. News agencies, web sites, and a host of other visual media services can use remote sensing data to provide current, relevant information regarding news around the world. This paper will provide a model for promotion and sale of remote sensing data via the Internet.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Analysis of flood inundation in ungauged basins based on multi-source remote sensing data.

PubMed

Gao, Wei; Shen, Qiu; Zhou, Yuehua; Li, Xin

2018-02-09

Floods are among the most expensive natural hazards experienced in many places of the world and can result in heavy losses of life and economic damages. The objective of this study is to analyze flood inundation in ungauged basins by performing near-real-time detection with flood extent and depth based on multi-source remote sensing data. Via spatial distribution analysis of flood extent and depth in a time series, the inundation condition and the characteristics of flood disaster can be reflected. The results show that the multi-source remote sensing data can make up the lack of hydrological data in ungauged basins, which is helpful to reconstruct hydrological sequence; the combination of MODIS (moderate-resolution imaging spectroradiometer) surface reflectance productions and the DFO (Dartmouth Flood Observatory) flood database can achieve the macro-dynamic monitoring of the flood inundation in ungauged basins, and then the differential technique of high-resolution optical and microwave images before and after floods can be used to calculate flood extent to reflect spatial changes of inundation; the monitoring algorithm for the flood depth combining RS and GIS is simple and easy and can quickly calculate the depth with a known flood extent that is obtained from remote sensing images in ungauged basins. Relevant results can provide effective help for the disaster relief work performed by government departments.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

[Crop geometry identification based on inversion of semiempirical BRDF models].

PubMed

Zhao, Chun-jiang; Huang, Wen-jiang; Mu, Xu-han; Wang, Jin-diz; Wang, Ji-hua

2009-09-01

With the rapid development of remote sensing technology, the application of remote sensing has extended from single view angle to multi-view angles. It was studied for the qualitative and quantitative effect of average leaf angle (ALA) on crop canopy reflected spectrum. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of leaf area index (LAI) and monitoring of crop growth condition by remote sensing technology. Investigations of the effect of erective and horizontal varieties were conducted by bidirectional canopy reflected spectrum and semiempirical bidirectional reflectance distribution function (BRDF) models. The sensitive analysis was done based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso) at red band (680 nm) and near infrared band (800 nm). By combining the weights of the red and near-infrared bands, the semiempirical models can obtain structural information by retrieving biophysical parameters from the physical BRDF model and a number of bidirectional observations. So, it will allow an on-site and non-sampling mode of crop ALA identification, which is useful for using remote sensing for crop growth monitoring and for improving the LAI inversion accuracy, and it will help the farmers in guiding the fertilizer and irrigation management in the farmland without a priori knowledge.

77 FR 39220 - Advisory Committee on Commercial Remote Sensing (ACCRES); Charter Renewal

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-02

... Commercial Remote Sensing (ACCRES); Charter Renewal AGENCY: National Oceanic and Atmospheric Administration... Committee on Commercial Remote Sensing (ACCRES) was renewed on March 14, 2012. SUPPLEMENTARY INFORMATION: In... Commercial Remote Sensing (ACCRES) is in the public interest in connection with the performance of duties...

76 FR 66042 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-25

... Commercial Remote Sensing (ACCRES); Request for Nominations ACTION: Notice requesting nominations for the Advisory Committee on Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was established to advise the Secretary of Commerce, through the Under Secretary...

An introduction to quantitative remote sensing. [data processing

NASA Technical Reports Server (NTRS)

Lindenlaub, J. C.; Russell, J.

1974-01-01

The quantitative approach to remote sensing is discussed along with the analysis of remote sensing data. Emphasis is placed on the application of pattern recognition in numerically oriented remote sensing systems. A common background and orientation for users of the LARS computer software system is provided.

Characterization Of Industrial And Background Aerosols In The RhÔne-alpes Region Using Laser Remote Sensing Device.

NASA Astrophysics Data System (ADS)

Geffroy, S.; Rairoux, P.; Mondelain, D.; Boutou, V.; Wolf, J.-P.; Frejafon, E.

Lack of reliable database on aerosol emission and dispersion is one of the main rea- sons for the incertitude of the impact of aerosol on the climate change. International statements and policies requested improvement on the global and on the regional scale. This new project is related to the characterisation of the spatial and time distribution of the aerosols in the Rhône-Alpes region. Actually, aerosols monitoring is mainly performed at ground level in this region and only few studies have been performed on the 3D distribution of urban aerosols (soot) using remote sensing laser device. The Rhône-Alpes region is representative for the regional impact of industry and traffic emission and also for the long-range transport of pollution over the East part of the Alps. The environmental situation of the region in term of sources and localization is especially dominated by: heavy traffic with several motorways (A6 from Paris, A7 to Marseille - both downtown - and A43 to the Alps and Italy) and industrial pollu- tion in particular for Lyon (refinery and several chemistry plants) and Saint Etienne agglomerations, which have a direct impact on the local air quality and also on the regional and national scale. Characterization of the aerosol load and dispersion in this region will be achieved applying two schemes. The first one will be related to the 3D quantitative characterization of diffuse aerosol emission in the industrial areas. Mon- itoring will be performed using a UV-infrared lidar remote sensing device. Emission cadastre elaboration and microphysical characterisation of the emission will be estab- lished. Direct access to several aerosol distribution modes will be used to describe the aerosol population dynamic: sedimentation, transport and aggregation. Studies on the direct impact of the emission on the region will be achieved coupling the 3D and ground level monitoring with dispersion model. The second scheme will be related to the long term remote sensing of the atmospheric background aerosols. Monitoring of the vertical and time distribution of their optical properties will be performed and this at 6 channels laying from the UV to the infrared spectral region. A high priority will be set on the data quality control and assurance in order to elaborate a reliable database. Several analyses will be performed with this dataset: the characterization of the microphysical properties of the aerosols. The regional and continental impact of the aerosols coupling the data with back-trajectories calculation and the validation of radiative model. By achieving a sufficient data quality, a proposition will be made to integrate the data into the European network Earlinet, which establishes a quantita- tive comprehensive statistical data base of both horizontal and vertical distributions of aerosols on a continental scale using a network of advanced laser remote sensing stations distributed all over Europe. This project will begin in summer 2002 and it will be taking place in cooperation with the national office INERIS.

Application of remote thermal scanning to the NASA energy conservation program

NASA Technical Reports Server (NTRS)

Bowman, R. L.; Jack, J. R.

1977-01-01

Airborne thermal scans of all NASA centers were made during 1975 and 1976. The remotely sensed data were used to identify a variety of heat losses, including those from building roofs and central heating system distribution lines. Thermal imagery from several NASA centers is presented to demonstrate the capability of detecting these heat losses remotely. Many heat loss areas located by the scan data were verified by ground surveys. At this point, at least for such energy-intensive areas, thermal scanning is an excellent means of detecting many possible energy losses.

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

PubMed Central

Wang, Kai; Franklin, Steven E.; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS). PMID:22163432

Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

PubMed

Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

Remote Sensing and Reflectance Profiling in Entomology.

PubMed

Nansen, Christian; Elliott, Norman

2016-01-01

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Mapping multi-scale vascular plant richness in a forest landscape with integrated LiDAR and hyperspectral remote-sensing.

PubMed

Hakkenberg, C R; Zhu, K; Peet, R K; Song, C

2018-02-01

The central role of floristic diversity in maintaining habitat integrity and ecosystem function has propelled efforts to map and monitor its distribution across forest landscapes. While biodiversity studies have traditionally relied largely on ground-based observations, the immensity of the task of generating accurate, repeatable, and spatially-continuous data on biodiversity patterns at large scales has stimulated the development of remote-sensing methods for scaling up from field plot measurements. One such approach is through integrated LiDAR and hyperspectral remote-sensing. However, despite their efficiencies in cost and effort, LiDAR-hyperspectral sensors are still highly constrained in structurally- and taxonomically-heterogeneous forests - especially when species' cover is smaller than the image resolution, intertwined with neighboring taxa, or otherwise obscured by overlapping canopy strata. In light of these challenges, this study goes beyond the remote characterization of upper canopy diversity to instead model total vascular plant species richness in a continuous-cover North Carolina Piedmont forest landscape. We focus on two related, but parallel, tasks. First, we demonstrate an application of predictive biodiversity mapping, using nonparametric models trained with spatially-nested field plots and aerial LiDAR-hyperspectral data, to predict spatially-explicit landscape patterns in floristic diversity across seven spatial scales between 0.01-900 m 2 . Second, we employ bivariate parametric models to test the significance of individual, remotely-sensed predictors of plant richness to determine how parameter estimates vary with scale. Cross-validated results indicate that predictive models were able to account for 15-70% of variance in plant richness, with LiDAR-derived estimates of topography and forest structural complexity, as well as spectral variance in hyperspectral imagery explaining the largest portion of variance in diversity levels. Importantly, bivariate tests provide evidence of scale-dependence among predictors, such that remotely-sensed variables significantly predict plant richness only at spatial scales that sufficiently subsume geolocational imprecision between remotely-sensed and field data, and best align with stand components including plant size and density, as well as canopy gaps and understory growth patterns. Beyond their insights into the scale-dependent patterns and drivers of plant diversity in Piedmont forests, these results highlight the potential of remotely-sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air- and space-borne platforms. © 2017 by the Ecological Society of America.

Remote Sensing in Geography in the New Millennium: Prospects, Challenges, and Opportunities

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Jensen, John R.; Morain, Stanley A.; Walsh, Stephen J.; Ridd, Merrill K.

1999-01-01

Remote sensing science contributes greatly to our understanding of the Earth's ecosystems and cultural landscapes. Almost all the natural and social sciences, including geography, rely heavily on remote sensing to provide quantitative, and indispensable spatial information. Many geographers have made significant contributions to remote sensing science since the 1970s, including the specification of advanced remote sensing systems, improvements in analog and digital image analysis, biophysical modeling, and terrain analysis. In fact, the Remote Sensing Specialty Group (RSSG) is one of the largest specialty groups within the AAG with over 500 members. Remote sensing in concert with a geographic information systems, offers much value to geography as both an incisive spatial-analytical tool and as a scholarly pursuit that adds to the body of geographic knowledge on the whole. The "power" of remote sensing as a research endeavor in geography lies in its capabilities for obtaining synoptic, near-real time data at many spatial and temporal scales, and in many regions of the electromagnetic spectrum - from microwave, to RADAR, to visible, and reflective and thermal infrared. In turn, these data present a vast compendium of information for assessing Earth attributes and characte6stics that are at the very core of geography. Here we revisit how remote sensing has become a fundamental and important tool for geographical research, and how with the advent of new and improved sensing systems to be launched in the near future, remote sensing will further advance geographical analysis in the approaching New Millennium.

Agricultural Production Monitoring in the Sahel Using Remote Sensing: Present Possibilities and Research Needs

DTIC Science & Technology

1993-01-01

during the agricultural season. Satellite remote sensing can contribute significantly to such a system by collecting information on crops and on...well as techniques to derive biophysical variables from remotely-sensed data. Finally, the integration of these remote - sensing techniques with crop

Effects of molecular and particle scatterings on the model parameter for remote-sensing reflectance.

PubMed

Lee, ZhongPing; Carder, Kendall L; Du, KePing

2004-09-01

For optically deep waters, remote-sensing reflectance (r(rs)) is traditionally expressed as the ratio of the backscattering coefficient (b(b)) to the sum of absorption and backscattering coefficients (a + b(b)) that multiples a model parameter (g, or the so-called f'/Q). Parameter g is further expressed as a function of b(b)/(a + b(b)) (or b(b)/a) to account for its variation that is due to multiple scattering. With such an approach, the same g value will be derived for different a and b(b) values that provide the same ratio. Because g is partially a measure of the angular distribution of upwelling light, and the angular distribution from molecular scattering is quite different from that of particle scattering; g values are expected to vary with different scattering distributions even if the b(b)/a ratios are the same. In this study, after numerically demonstrating the effects of molecular and particle scatterings on the values of g, an innovative r(rs) model is developed. This new model expresses r(rs) in two separate terms: one governed by the phase function of molecular scattering and one governed by the phase function of particle scattering, with a model parameter introduced for each term. In this way the phase function effects from molecular and particle scatterings are explicitly separated and accounted for. This new model provides an analytical tool to understand and quantify the phase-function effects on r(rs), and a platform to calculate r(rs) spectrum quickly and accurately that is required for remote-sensing applications.

Estimating the spatial distribution of field-applied mushroom compost in the Brandywine-Christina River Basin using multispectral remote sensing

NASA Astrophysics Data System (ADS)

Moxey, Kelsey A.

The world's greatest concentration of mushroom farms is settled within the Brandywine-Christina River Basin in Chester County in southeastern Pennsylvania. This industry produces a nutrient-rich byproduct known as spent mushroom compost, which has been traditionally applied to local farm fields as an organic fertilizer and soil amendment. While mushroom compost has beneficial properties, the possible over-application to farm fields could potentially degrade stream water quality. The goal of this study was to estimate the spatial extent and intensity of field-applied mushroom compost. We applied a remote sensing approach using Landsat multispectral imagery. We utilized the soil line technique, using the red and near-infrared bands, to estimate differences in soil wetness as a result of increased soil organic matter content from mushroom compost. We validated soil wetness estimates by examining the spectral response of references sites. We performed a second independent validation analysis using expert knowledge from agricultural extension agents. Our results showed that the soil line based wetness index worked well. The spectral validation illustrated that compost changes the spectral response of soil because of changes in wetness. The independent expert validation analysis produced a strong significant correlation between our remotely-sensed wetness estimates and the empirical ratings of compost application intensities. Overall, the methodology produced realistic spatial distributions of field-applied compost application intensities across the study area. These spatial distributions will be used for follow-up studies to assess the effect of spent mushroom compost on stream water quality.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kassianov, Evgueni I.; Riley, Erin A.; Kleiss, Jessica

Cloud amount is an essential and extensively used macrophysical parameter of cumulus clouds. It is commonly defined as a cloud fraction (CF) from zenith-pointing ground-based active and passive remote sensing. However, conventional retrievals of CF from the remote sensing data with very narrow field-of-view (FOV) may not be representative of the surrounding area. Here we assess its representativeness using an integrated dataset collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site in Oklahoma, USA. For our assessment with focus on selected days with single-layer cumulus clouds (2005-2016), we include the narrow-FOVmore » ARM Active Remotely Sensed Clouds Locations (ARSCL) and large-FOV Total Sky Imager (TSI) cloud products, the 915-MHz Radar Wind Profiler (RWP) measurements of wind speed and direction, and also high-resolution satellite images from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that a root-mean-square difference (RMSD) between the 15-min averaged ARSCL cloud fraction (CF) and the 15-min averaged TSI fractional sky cover (FSC) is large (up to 0.3). We also discuss how the horizontal distribution of clouds can modify the obtained large RMSD using a new uniformity metric. The latter utilizes the spatial distribution of the FSC over the 100° FOV TSI images obtained with high temporal resolution (30 sec sampling). We demonstrate that cases with more uniform spatial distribution of FSC show better agreement between the narrow-FOV CF and large-FOV FSC, reducing the RMSD by up to a factor of 2.« less

Ground-based hyperspectral imaging and terrestrial laser scanning for fracture characterization in the Mississippian Boone Formation

NASA Astrophysics Data System (ADS)

Sun, Lei; Khan, Shuhab D.; Sarmiento, Sergio; Lakshmikantha, M. R.; Zhou, Huawei

2017-12-01

Petroleum geoscientists have been using cores and well logs to study source rocks and reservoirs, however, the inherent discontinuous nature of these data cannot account for horizontal heterogeneities. Modern exploitation requires better understanding of important source rocks and reservoirs at outcrop scale. Remote sensing of outcrops is becoming a first order tool for reservoir analog studies including horizontal heterogeneities. This work used ground-based hyperspectral imaging, terrestrial laser scanning (TLS), and high-resolution photography to study a roadcut of the Boone Formation at Bella Vista, northwest Arkansas, and developed an outcrop model for reservoir analog analyses. The petroliferous Boone Formation consists of fossiliferous limestones interbedded with chert of early Mississippian age. We used remote sensing techniques to identify rock types and to collect 3D geometrical data. Mixture tuned matched filtering classification of hyperspectral data show that the outcrop is mostly limestones with interbedded chert nodules. 1315 fractures were classified according to their strata-bounding relationships, among these, larger fractures are dominantly striking in ENE - WSW directions. Fracture extraction data show that chert holds more fractures than limestones, and both vertical and horizontal heterogeneities exist in chert nodule distribution. Utilizing ground-based remote sensing, we have assembled a virtual outcrop model to extract mineral composition as well as fracture data from the model. We inferred anisotropy in vertical fracture permeability based on the dominancy of fracture orientations, the preferential distribution of fractures and distribution of chert nodules. These data are beneficial in reservoir analogs to study rock mechanics and fluid flow, and to improve well performances.

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

PubMed Central

Callister, Kate E.; Griffioen, Peter A.; Avitabile, Sarah C.; Haslem, Angie; Kelly, Luke T.; Kenny, Sally A.; Nimmo, Dale G.; Farnsworth, Lisa M.; Taylor, Rick S.; Watson, Simon J.; Bennett, Andrew F.; Clarke, Michael F.

2016-01-01

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km2 study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery. PMID:27029046

Identification of understory invasive exotic plants with remote sensing in urban forests

NASA Astrophysics Data System (ADS)

Shouse, Michael; Liang, Liang; Fei, Songlin

2013-04-01

Invasive exotic plants (IEP) pose a significant threat to many ecosystems. To effectively manage IEP, it is important to efficiently detect their presences and determine their distribution patterns. Remote sensing has been a useful tool to map IEP but its application is limited in urban forests, which are often the sources and sinks for IEP. In this study, we examined the feasibility and tradeoffs of species level IEP mapping using multiple remote sensing techniques in a highly complex urban forest setting. Bush honeysuckle (Lonicera maackii), a pervasive IEP in eastern North America, was used as our modeling species. Both medium spatial resolution (MSR) and high spatial resolution (HSR) imagery were employed in bush honeysuckle mapping. The importance of spatial scale was also examined using an up-scaling simulation from the HSR object based classification. Analysis using both MSR and HSR imagery provided viable results for IEP distribution mapping in urban forests. Overall mapping accuracy ranged from 89.8% to 94.9% for HSR techniques and from 74.6% to 79.7% for MSR techniques. As anticipated, classification accuracy reduces as pixel size increases. HSR based techniques produced the most desirable results, therefore is preferred for precise management of IEP in heterogeneous environment. However, the use of MSR techniques should not be ruled out given their wide availability and moderate accuracy.

Developing Particle Emission Inventories Using Remote Sensing (PEIRS)

NASA Technical Reports Server (NTRS)

Tang, Chia-Hsi; Coull, Brent A.; Schwartz, Joel; Lyapustin, Alexei I.; Di, Qian; Koutrakis, Petros

2016-01-01

Information regarding the magnitude and distribution of PM(sub 2.5) emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially-resolved emission inventories for PM(sub 2.5). This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeast United States during the period of 2002-2013 using high- resolution 1 km x 1 km Aerosol Optical Depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R(sup2) = 0.66 approx. 0.71, CV = 17.7 approx. 20%). Predicted emissions are found to correlate with land use parameters suggesting that our method can capture emissions from land use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively.

Developing Particle Emission Inventories Using Remote Sensing (PEIRS)

PubMed Central

Tang, Chia-Hsi; Coull, Brent A.; Schwartz, Joel; Lyapustin, Alexei I.; Di, Qian; Koutrakis, Petros

2018-01-01

Information regarding the magnitude and distribution of PM2.5 emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially-resolved emission inventories for PM2.5. This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeast United States during the period of 2002–2013 using high- resolution 1 km × 1km Aerosol Optical Depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R2=0.66~0.71, CV = 17.7~20%). Predicted emissions are found to correlate with land use parameters suggesting that our method can capture emissions from land use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively. PMID:27653469

A Student-Friendly Graphical User Interface to Extract Data from Remote Sensing Level-2 Products.

NASA Astrophysics Data System (ADS)

Bernardello, R.

2016-02-01

Remote sensing era has provided an unprecedented amount of publicly available data. The United States National Aeronautics and Space Administration Goddard Space Flight Center (NASA-GSFC) has achieved remarkable results in the distribution of these data to the scientific community through the OceanColor web page (http://oceancolor.gsfc.nasa.gov/). However, the access to these data, is not straightforward and needs a certain investment of time in learning the use of existing software. Satellite sensors acquire raw data that are processed through several steps towards a format usable by the scientific community. These products are distributed in Hierarchical Data Format (HDF) which often represents the first obstacle for students, teachers and scientists not used to deal with extensive matrices. We present here SATellite data PROcessing (SATPRO) a newly developed Graphical User Interface (GUI) designed in MATLAB environment to provide an easy, immediate yet reliable way to select and extract Level-2 data from NASA SeaWIFS and MODIS-Aqua databases for oceanic surface temperature and chlorophyll. Since no previous experience with MATLAB is required, SATPRO allows the user to explore the available dataset without investing any software-learning time. SATPRO is an ideal tool to introduce undergraduate students to the use of remote sensing data in oceanography and can also be useful for research projects at the graduate level.

Associations between breeding bird abundance and stand structure in the White Mountains, New Hampshire and Maine, USA

Treesearch

Richard M. DeGraaf; Jay B. Hestbeck; Mariko Yamasaki

1998-01-01

Assessment of faunal distribution in relation to landscape features is becoming increasingly popular. Technological advances in remote sensing have encouraged regional analyses of the distributions of terrestrial vertebrates. Comparisons of the strength of association of habitat characteristics at various scales of measurement of habitat structure are rare. We compared...

Operating tool for a distributed data and information management system

NASA Astrophysics Data System (ADS)

Reck, C.; Mikusch, E.; Kiemle, S.; Wolfmüller, M.; Böttcher, M.

2002-07-01

The German Remote Sensing Data Center has developed the Data Information and Management System DIMS which provides multi-mission ground system services for earth observation product processing, archiving, ordering and delivery. DIMS successfully uses newest technologies within its services. This paper presents the solution taken to simplify operation tasks for this large and distributed system.

Method of determining forest production from remotely sensed forest parameters

DOEpatents

Corey, J.C.; Mackey, H.E. Jr.

1987-08-31

A method of determining forest production entirely from remotely sensed data in which remotely sensed multispectral scanner (MSS) data on forest 5 composition is combined with remotely sensed radar imaging data on forest stand biophysical parameters to provide a measure of forest production. A high correlation has been found to exist between the remotely sensed radar imaging data and on site measurements of biophysical 10 parameters such as stand height, diameter at breast height, total tree height, mean area per tree, and timber stand volume.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-01

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Proceedings of the 2004 High Spatial Resolution Commercial Imagery Workshop

NASA Technical Reports Server (NTRS)

2006-01-01

Topics covered include: NASA Applied Sciences Program; USGS Land Remote Sensing: Overview; QuickBird System Status and Product Overview; ORBIMAGE Overview; IKONOS 2004 Calibration and Validation Status; OrbView-3 Spatial Characterization; On-Orbit Modulation Transfer Function (MTF) Measurement of QuickBird; Spatial Resolution Characterization for QuickBird Image Products 2003-2004 Season; Image Quality Evaluation of QuickBird Super Resolution and Revisit of IKONOS: Civil and Commercial Application Project (CCAP); On-Orbit System MTF Measurement; QuickBird Post Launch Geopositional Characterization Update; OrbView-3 Geometric Calibration and Geopositional Accuracy; Geopositional Statistical Methods; QuickBird and OrbView-3 Geopositional Accuracy Assessment; Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images; Laboratory Measurement of Bidirectional Reflectance of Radiometric Tarps; Stennis Space Center Verification and Validation Capabilities; Joint Agency Commercial Imagery Evaluation (JACIE) Team; Adjacency Effects in High Resolution Imagery; Effect of Pulse Width vs. GSD on MTF Estimation; Camera and Sensor Calibration at the USGS; QuickBird Geometric Verification; Comparison of MODTRAN to Heritage-based Results in Vicarious Calibration at University of Arizona; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Estimating Sub-Pixel Proportions of Sagebrush with a Regression Tree; How Do YOU Use the National Land Cover Dataset?; The National Map Hazards Data Distribution System; Recording a Troubled World; What Does This-Have to Do with This?; When Can a Picture Save a Thousand Homes?; InSAR Studies of Alaska Volcanoes; Earth Observing-1 (EO-1) Data Products; Improving Access to the USGS Aerial Film Collections: High Resolution Scanners; Improving Access to the USGS Aerial Film Collections: Phoenix Digitizing System Product Distribution; System and Product Characterization: Issues Approach; Innovative Approaches to Analysis of Lidar Data for the National Map; Changes in Imperviousness near Military Installations; Geopositional Accuracy Evaluations of QuickBird and OrbView-3: Civil and Commercial Applications Project (CCAP); Geometric Accuracy Assessment: OrbView ORTHO Products; QuickBird Radiometric Calibration Update; OrbView-3 Radiometric Calibration; QuickBird Radiometric Characterization; NASA Radiometric Characterization; Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards; QuickBird Applications; Airport Mapping and Perpetual Monitoring Using IKONOS; OrbView-3 Relative Accuracy Results and Impacts on Exploitation and Accuracy Improvement; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Applying High-Resolution Satellite Imagery and Remotely Sensed Data to Local Government Applications: Sioux Falls, South Dakota; Automatic Co-Registration of QuickBird Data for Change Detection Applications; Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources; Automated, Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery; Science Applications of High Resolution Imagery at the USGS EROS Data Center; Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research; Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems; Determining Regional Arctic Tundra Carbon Exchange: A Bottom-Up Approach; Using IKONOS Imagery to Assess Impervious Surface Area, Riparian Buffers and Stream Health in the Mid-Atlantic Region; Commercial Remote Sensing Space Policy Civil Implementation Update; USGS Commercial Remote Sensing Data Contracts (CRSDC); and Commercial Remote Sensing Space Policy (CRSSP): Civil Near-Term Requirements Collection Update.

Remote Sensing Decision Support System for Optimal Access Restoration in Post Disaster Environments

DOT National Transportation Integrated Search

2017-01-01

Access restoration is an extremely important part of disaster response. Without access to the site, critically important emergency functions like search and rescue, emergency evacuation, and relief distribution, cannot commence. Frequently, roads are...

Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

NASA Astrophysics Data System (ADS)

Herman, Matthew R.; Nejadhashemi, A. Pouyan; Abouali, Mohammad; Hernandez-Suarez, Juan Sebastian; Daneshvar, Fariborz; Zhang, Zhen; Anderson, Martha C.; Sadeghi, Ali M.; Hain, Christopher R.; Sharifi, Amirreza

2018-01-01

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73-0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error - observations standard deviation ratio (RSR) values <0.7 (0.39-0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32-0.52, PBIAS: ±32.73%, and RSR: 0.63-0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59-0.61, PBIAS: ±13.70%, and RSR: 0.63-0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.

Field Data Collection: an Essential Element in Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Pettinger, L. R.

1971-01-01

Field data collected in support of remote sensing projects are generally used for the following purposes: (1) calibration of remote sensing systems, (2) evaluation of experimental applications of remote sensing imagery on small test sites, and (3) designing and evaluating operational regional resource studies and inventories which are conducted using the remote sensing imagery obtained. Field data may be used to help develop a technique for a particular application, or to aid in the application of that technique to a resource evaluation or inventory problem for a large area. Scientists at the Forestry Remote Sensing Laboratory have utilized field data for both purposes. How meaningful field data has been collected in each case is discussed.

Remote sensing and eLearning 2.0 for school education

NASA Astrophysics Data System (ADS)

Voss, Kerstin; Goetzke, Roland; Hodam, Henryk

2010-10-01

The "Remote Sensing in Schools" project aims at improving the integration of "Satellite remote sensing" into school teaching. Therefore, it is the project's overall objective to teach students in primary and secondary schools the basics and fields of application of remote sensing. Existing results show that many teachers are interested in remote sensing and at same time motivated to integrate it into their teaching. Despite the good intention, in the end, the implementation often fails due to the complexity and poor set-up of the information provided. Therefore, a comprehensive and well-structured learning platform on the topic of remote sensing is developed. The platform shall allow a structured introduction to the topic.

Remote sensing study of Maumee River effects of Lake Erie

NASA Technical Reports Server (NTRS)

Svehla, R.; Raquet, C.; Shook, D.; Salzman, J.; Coney, T.; Wachter, D.; Gedney, R.

1975-01-01

The effects of river inputs on boundary waters were studied in partial support of the task to assess the significance of river inputs into receiving waters, dispersion of pollutants, and water quality. The effects of the spring runoff of the Maumee River on Lake Erie were assessed by a combination of ship survey and remote sensing techniques. The imagery obtained from a multispectral scanner of the west basin of Lake Erie is discussed: this clearly showed the distribution of particulates throughout the covered area. This synoptic view, in addition to its qualitative value, is very useful in selecting sampling stations for shipboard in situ measurements, and for extrapolating these quantitative results throughout the area of interest.

Mathematical model investigation of long-term transport of ocean-dumped sewage sludge related to remote sensing

NASA Technical Reports Server (NTRS)

Kuo, C. Y.; Modena, T. D.

1979-01-01

An existing, three-dimensional, Eulerian-Lagrangian finite-difference model was modified and used to examine the transport processes of dumped sewage sludge in the New York Bight. Both in situ and laboratory data were utilized in an attempt to approximate model inputs such as mean current speed, horizontal diffusion coefficients, particle size distributions, and specific gravities. The results presented are a quantitative description of the fate of a negatively buoyant sewage sludge plume resulting from continuous and instantaneous barge releases. Concentrations of the sludge near the surface were compared qualitatively with those remotely sensed. Laboratory study was performed to investigate the behavior of sewage sludge dumping in various ambient density conditions.

BOREAS RSS-7 Regional LAI and FPAR Images From 10-Day AVHRR-LAC Composites

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Chen, Jing; Cihlar, Josef

2000-01-01

The BOReal Ecosystem-Atmosphere Study Remote Sensing Science (BOREAS RSS-7) team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of Leaf Area Index (LAI) from remotely sensed images. Advanced Very High Resolution Radiometer (AVHRR) level-4c 10-day composite Normalized Difference Vegetation Index (NDVI) images produced at CCRS were used to produce images of LAI and the Fraction of Photosynthetically Active Radiation (FPAR) absorbed by plant canopies for the three summer IFCs in 1994 across the BOREAS region. The algorithms were developed based on ground measurements and Landsat Thematic Mapper (TM) images. The data are stored in binary image format files.

Remote sensing programs and courses in engineering and water resources

NASA Technical Reports Server (NTRS)

Kiefer, R. W.

1981-01-01

The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

Remote sensing research in geographic education: An alternative view

NASA Technical Reports Server (NTRS)

Wilson, H.; Cary, T. K.; Goward, S. N.

1981-01-01

It is noted that within many geography departments remote sensing is viewed as a mere technique a student should learn in order to carry out true geographic research. This view inhibits both students and faculty from investigation of remotely sensed data as a new source of geographic knowledge that may alter our understanding of the Earth. The tendency is for geographers to accept these new data and analysis techniques from engineers and mathematicians without questioning the accompanying premises. This black-box approach hinders geographic applications of the new remotely sensed data and limits the geographer's contribution to further development of remote sensing observation systems. It is suggested that geographers contribute to the development of remote sensing through pursuit of basic research. This research can be encouraged, particularly among students, by demonstrating the links between geographic theory and remotely sensed observations, encouraging a healthy skepticism concerning the current understanding of these data.

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management

USDA-ARS?s Scientific Manuscript database

Unmanned aerial vehicles (UAV) provide a unique platform for remote sensing to monitor crop fields that complements remote sensing from satellite, aircraft and ground-based platforms. The UAV-based remote sensing is versatile at ultra-low altitude to be able to provide an ultra-high-resolution imag...

75 FR 32360 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-08

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and.... Abstract NOAA has established requirements for the licensing of private operators of remote-sensing space... Land Remote- Sensing Policy Act of 1992 and with the national security and international obligations of...

78 FR 44536 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-24

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and... for the licensing of private operators of remote-sensing space systems. The information in applications and subsequent reports is needed to ensure compliance with the Land Remote- Sensing Policy Act of...

Advancement of China’s Visible Light Remote Sensing Technology In Aerospace,

DTIC Science & Technology

1996-03-19

Aerospace visible light film systems were among the earliest space remote sensing systems to be developed in China. They have been applied very well...makes China the third nation in the world to master space remote sensing technology, it also puts recoverable remote sensing satellites among the first

Polarimetric passive remote sensing of periodic surfaces

NASA Technical Reports Server (NTRS)

Veysoglu, Murat E.; Yueh, H. A.; Shin, R. T.; Kong, J. A.

1991-01-01

The concept of polarimetry in active remote sensing is extended to passive remote sensing. The potential use of the third and fourth Stokes parameters U and V, which play an important role in polarimetric active remote sensing, is demonstrated for passive remote sensing. It is shown that, by the use of the reciprocity principle, the polarimetric parameters of passive remote sensing can be obtained through the solution of the associated direct scattering problem. These ideas are applied to study polarimetric passive remote sensing of periodic surfaces. The solution of the direct scattering problem is obtained by an integral equation formulation which involves evaluation of periodic Green's functions and normal derivative of those on the surface. Rapid evaluation of the slowly convergent series associated with these functions is observed to be critical for the feasibility of the method. New formulas, which are rapidly convergent, are derived for the calculation of these series. The study has shown that the brightness temperature of the Stokes parameter U can be significant in passive remote sensing. Values as high as 50 K are observed for certain configurations.

From planets to crops and back: Remote sensing makes sense

NASA Astrophysics Data System (ADS)

Mustard, John F.

2017-04-01

Remotely sensed data and the instruments that acquire them are core parts of Earth and planetary observation systems. They are used to quantify the Earth's interconnected systems, and remote sensing is the only way to get a daily, or more frequent, snapshot of the status of the Earth. It really is the Earth's stethoscope. In a similar manner remote sensing is the rock hammer of the planetary scientist and the only way comprehensive data sets can be acquired. To risk offending many remotely sensed data acquired across the electromagnetic spectrum, it is the tricorder to explore known and unknown planets. Arriving where we are today in the use of remotely sensed data in the solar system has been a continually evolving synergy between Earth observation, planetary exploration, and fundamental laboratory work.

Remote sensing of natural resources: Quarterly literature review

NASA Technical Reports Server (NTRS)

1976-01-01

A quarterly review of technical literature concerning remote sensing techniques is presented. The format contains indexed and abstracted materials with emphasis on data gathering techniques performed or obtained remotely from space, aircraft, or ground-based stations. Remote sensor applications including the remote sensing of natural resources are presented.

High resolution remote sensing of densely urbanised regions: a case study of Hong Kong.

PubMed

Nichol, Janet E; Wong, Man Sing

2009-01-01

Data on the urban environment such as climate or air quality is usually collected at a few point monitoring stations distributed over a city. However, the synoptic viewpoint of satellites where a whole city is visible on a single image permits the collection of spatially comprehensive data at city-wide scale. In spite of rapid developments in remote sensing systems, deficiencies in image resolution and algorithm development still exist for applications such as air quality monitoring and urban heat island analysis. This paper describes state-of-the-art techniques for enhancing and maximising the spatial detail available from satellite images, and demonstrates their applications to the densely urbanised environment of Hong Kong. An Emissivity Modulation technique for spatial enhancement of thermal satellite images permits modelling of urban microclimate in combination with other urban structural parameters at local scale. For air quality monitoring, a Minimum Reflectance Technique (MRT) has been developed for MODIS 500 m images. The techniques described can promote the routine utilization of remotely sensed images for environmental monitoring in cities of the 21(st) century.

When Models and Observations Collide: Journeying towards an Integrated Snow Depth Product

NASA Astrophysics Data System (ADS)

Webster, M.; Petty, A.; Boisvert, L.; Markus, T.; Kurtz, N. T.; Kwok, R.; Perovich, D. K.

2017-12-01

Knowledge of snow depth is essential for assessing changes in sea ice mass balance due to snow's insulating and reflective properties. In remote sensing applications, the accuracy of sea ice thickness retrievals from altimetry crucially depends on snow depth. Despite the need for snow depth data, we currently lack continuous observations that capture the basin-scale snow depth distribution and its seasonal evolution. Recent in situ and remote sensing observations are sparse in space and time, and contain uncertainties, caveats, and/or biases that often require careful interpretation. Likewise, using model output for remote sensing applications is limited due to uncertainties in atmospheric forcing and different treatments of snow processes. Here, we summarize our efforts in bringing observational and model data together to develop an approach for an integrated snow depth product. We start with a snow budget model and incrementally incorporate snow processes to determine the effects on snow depth and to assess model sensitivity. We discuss lessons learned in model-observation integration and ideas for potential improvements to the treatment of snow in models.

Multi-decadal Arctic sea ice roughness.

NASA Astrophysics Data System (ADS)

Tsamados, M.; Stroeve, J.; Kharbouche, S.; Muller, J. P., , Prof; Nolin, A. W.; Petty, A.; Haas, C.; Girard-Ardhuin, F.; Landy, J.

2017-12-01

The transformation of Arctic sea ice from mainly perennial, multi-year ice to a seasonal, first-year ice is believed to have been accompanied by a reduction of the roughness of the ice cover surface. This smoothening effect has been shown to (i) modify the momentum and heat transfer between the atmosphere and ocean, (ii) to alter the ice thickness distribution which in turn controls the snow and melt pond repartition over the ice cover, and (iii) to bias airborne and satellite remote sensing measurements that depend on the scattering and reflective characteristics over the sea ice surface topography. We will review existing and novel remote sensing methodologies proposed to estimate sea ice roughness, ranging from airborne LIDAR measurement (ie Operation IceBridge), to backscatter coefficients from scatterometers (ASCAT, QUICKSCAT), to multi angle maging spectroradiometer (MISR), and to laser (Icesat) and radar altimeters (Envisat, Cryosat, Altika, Sentinel-3). We will show that by comparing and cross-calibrating these different products we can offer a consistent multi-mission, multi-decadal view of the declining sea ice roughness. Implications for sea ice physics, climate and remote sensing will also be discussed.

Application of Multitemporal Remotely Sensed Soil Moisture for the Estimation of Soil Physical Properties

NASA Technical Reports Server (NTRS)

Mattikalli, N. M.; Engman, E. T.; Jackson, T. J.; Ahuja, L. R.

1997-01-01

This paper demonstrates the use of multitemporal soil moisture derived from microwave remote sensing to estimate soil physical properties. The passive microwave ESTAR instrument was employed during June 10-18, 1992, to obtain brightness temperature (TB) and surface soil moisture data in the Little Washita watershed, Oklahoma. Analyses of spatial and temporal variations of TB and soil moisture during the dry-down period revealed a direct relationship between changes in T and soil moisture and soil physical (viz. texture) and hydraulic (viz. saturated hydraulic conductivity, K(sat)) properties. Statistically significant regression relationships were developed for the ratio of percent sand to percent clay (RSC) and K(sat), in terms of change components of TB and surface soil moisture. Validation of results using field measured values and soil texture map indicated that both RSC and K(sat) can be estimated with reasonable accuracy. These findings have potential applications of microwave remote sensing to obtain quick estimates of the spatial distributions of K(sat), over large areas for input parameterization of hydrologic models.

High Resolution Remote Sensing of Densely Urbanised Regions: a Case Study of Hong Kong

PubMed Central

Nichol, Janet E.; Wong, Man Sing

2009-01-01

Data on the urban environment such as climate or air quality is usually collected at a few point monitoring stations distributed over a city. However, the synoptic viewpoint of satellites where a whole city is visible on a single image permits the collection of spatially comprehensive data at city-wide scale. In spite of rapid developments in remote sensing systems, deficiencies in image resolution and algorithm development still exist for applications such as air quality monitoring and urban heat island analysis. This paper describes state-of-the-art techniques for enhancing and maximising the spatial detail available from satellite images, and demonstrates their applications to the densely urbanised environment of Hong Kong. An Emissivity Modulation technique for spatial enhancement of thermal satellite images permits modelling of urban microclimate in combination with other urban structural parameters at local scale. For air quality monitoring, a Minimum Reflectance Technique (MRT) has been developed for MODIS 500 m images. The techniques described can promote the routine utilization of remotely sensed images for environmental monitoring in cities of the 21st century. PMID:22408549

Progress in remote sensing (1972-1976)

USGS Publications Warehouse

Fischer, W. A.; Hemphill, W.R.; Kover, Allan

1976-01-01

This report concerns the progress in remote sensing during the period 1972–1976. Remote sensing has been variously defined but is basically the art or science of telling something about an object without touching it. During the past four years, the major research thrusts have been in three areas: (1) computer-assisted enhancement and interpretation systems; (2) earth science applications of Landsat data; (3) and investigations of the usefulness of observations of luminescence, thermal infrared, and microwave energies. Based on the data sales at the EROS Data Center, the largest users of the Landsat data are industrial companies, followed by government agencies (both national and foreign), and academic institutions. Thermal surveys from aircraft have become largely operational, however, significant research is being undertaken in the field of thermal modeling and analysis of high altitude images. Microwave research is increasing rapidly and programs are being developed for satellite observations. Microwave research is concentrating on oil spill detection, soil moisture measurement, and observations of ice distributions. Luminescence investigations offer promise for becoming a quantitative method of assessing vegetation stress and pollutant concentrations.

Optical Remote Sensing Measurements of Air Pollution in Mexico City During MCMA- 2006

NASA Astrophysics Data System (ADS)

Galle, B.; Mellqvist, J.; Johansson, M.; Rivera, C.; Samuelsson, J.; Zhang, Y.

2007-05-01

During March 2006 the Optical Remote sensing group at Chalmers University of Technology participated in the MCMA-2006 field campaign in Mexico City, performing measurements of air pollution using a set of different optical remote sensing instruments. This poster gives an overview of the techniques applied and results obtained. The techniques applied were: Solar Occultation FTIR and UV spectroscopy from fixed locations throughout the MCMA area, yielding total columns of CO, CH2O, SO2 and NO2. Long Path FTIR measurements from site T0 located in the north part of central Mexico City. With this instrument line-averaged concentration measurements of CO and CO2 was obtained in parallel with DOAS measurements performed by other partners. MAX-DOAS measurements from site T0, yielding total column and spatial distributions of SO2 and NO2. Mobile DOAS scattered Sunlight measurements of total columns of SO2 and NO2 in and around the MCMA area. Mobile and stationary DOAS measurements in the vicinity of Tula and Popocatépetl in order to quantify emissions from industry and volcano.

A Brief History of Fire, Heat and Their Manifestations in Remote Sensing

NASA Astrophysics Data System (ADS)

Alavipanah, S. K.; Attarchi, S.

2015-12-01

The discovery of fire was one of the earliest of human discoveries. At the beginning, man sensed heat on his skin and then perceived the concept of fire and temperature. Fire and its manifestation in form of light and heat have contributed in many literary sources and religious books. It has being interpreted in various manners and construed by different explanations. Some of these definitions have resemblances with today's human findings in the argument about heat, temperature, light and their spectra. In this work, we reviewed a broad range of literary, historical, religious and cultural sources to gain deeper insight into the meaning of fire and heat in human's thought, beliefs and myths from the past to today. We found a close linkage between predecessor's perception and impression about heat and what is known today as thermal energy. It should be mentioned that we strictly deny the claim of their awareness of modern concepts such as energy or thermodynamics. However, we interfere that they perceived these conceptions. We cannot clearly explain how our predecessor shaped their impression about fire and heat without any knowledge of the nature of new science such as energy, temperature and thermal remote sensing. Nevertheless, their though compromise with modern science. According to the recent findings, temperature play important role as an efficient indicator of sustainability in landscape. Magnitude and distribution of temperature and its changes over time - which could be traced by thermal remote sensing- are of great importance. A concise literature review relating to fire and heat will broaden our knowledge about temperature and thermal remote sensing.

Multi- and hyperspectral remote sensing of tropical marine benthic habitats

NASA Astrophysics Data System (ADS)

Mishra, Deepak R.

Tropical marine benthic habitats such as coral reef and associated environments are severely endangered because of the environmental degradation coupled with hurricanes, El Nino events, coastal pollution and runoff, tourism, and economic development. To monitor and protect this diverse environment it is important to not only develop baseline maps depicting their spatial distribution but also to document their changing conditions over time. Remote sensing offers an important means of delineating and monitoring coral reef ecosystems. Over the last twenty years the scientific community has been investigating the use and potential of remote sensing techniques to determine the conditions of the coral reefs by analyzing their spectral characteristics from space. One of the problems in monitoring coral reefs from space is the effect of the water column on the remotely sensed signal. When light penetrates water its intensity decreases exponentially with increasing depth. This process, known as water column attenuation, exerts a profound effect on remotely sensed data collected over water bodies. The approach presented in this research focuses on the development of semi-analytical models that resolves the confounding influence water column attenuation on substrate reflectance to characterize benthic habitats from high resolution remotely sensed imagery on a per-pixel basis. High spatial resolution satellite and airborne imagery were used as inputs in the models to derive water depth and water column optical properties (e.g., absorption and backscattering coefficients). These parameters were subsequently used in various bio-optical algorithms to deduce bottom albedo and then to classify the benthos, generating a detailed map of benthic habitats. IKONOS and QuickBird multispectral satellite data and AISA Eagle hyperspectral airborne data were used in this research for benthic habitat mapping along the north shore of Roatan Island, Honduras. The AISA Eagle classification was consistently more accurate (84%) including finer definition of geomorphological features than the satellite sensors. IKONOS (81%) and QuickBird (81%) sensors showed similar accuracy to AISA, however, such similarity was only reached at the coarse classification levels of 5 and 6 habitats. These results confirm the potential of an effective combination of high spectral and spatial resolution sensor, for accurate benthic habitat mapping.

Dual use of distributed remote sensing satellites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Canavan, G.H.

1992-12-02

Satellites can serve both defense and the environment, simultaneously monitoring preparations for aggression, the environment, pollution, and natural disasters. These applications have been discussed extensively in international meetings, which have produced specific projects for cooperation and growing acceptance of dual-use concepts.

Dual use of distributed remote sensing satellites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Canavan, G.H.

1993-03-01

Satellites can serve both defense and the environment, simultaneously monitoring preparations for aggression, the environment, pollution, and natural disasters. These applications have been discussed extensively in international meetings, which have produced specific projects for cooperation and growing acceptance of dual-use concepts.

SCALE PROBLEMS IN REPORTING LANDSCAPE PATTERN AT THE REGIONAL SCALE

EPA Science Inventory

Remotely sensed data for Southeastern United States (Standard Federal Region 4) are used to examine the scale problems involved in reporting landscape pattern for a large, heterogeneous region. Frequency distributions of landscape indices illustrate problems associated with the g...

Dual use of distributed remote sensing satellites

NASA Astrophysics Data System (ADS)

Canavan, G. H.

1992-12-01

Satellites can serve both defense and the environment, simultaneously monitoring preparations for aggression, the environment, pollution, and natural disasters. These applications have been discussed extensively in international meetings, which have produced specific projects for cooperation and growing acceptance of dual-use concepts.

Forest mensuration with remote sensing: A retrospective and a vision for the future

Treesearch

Randolph H. Wynne

2004-01-01

Remote sensing, while occasionally oversold, has clear potential to reduce the overall cost of traditional forest inventories. Perhaps most important, some of the information needed for more intensive, rather than extensive, forest management is available from remote sensing. These new information needs may justify increased use and the increased cost of remote sensing...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Remote Sensing: Analyzing Satellite Images to Create Higher Order Thinking Skills.

ERIC Educational Resources Information Center

Marks, Steven K.; And Others

1996-01-01

Presents a unit that uses remote-sensing images from satellites and other spacecraft to provide new perspectives of the earth and generate greater global awareness. Relates the levels of Bloom's hierarchy to different aspects of the remote sensing unit to confirm that the concepts and principles of remote sensing and related images belong in…

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Annotated bibliography of remote sensing methods for monitoring desertification

USGS Publications Warehouse

Walker, A.S.; Robinove, Charles J.

1981-01-01

Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

Applied Remote Sensing Program (ARSP)

NASA Technical Reports Server (NTRS)

Johnson, J. D.; Foster, K. E.; Mouat, D. A.; Miller, D. A.; Conn, J. S.

1976-01-01

The activities and accomplishments of the Applied Remote Sensing Program during FY 1975-1976 are reported. The principal objective of the Applied Remote Sensing Program continues to be designed projects having specific decision-making impacts as a principal goal. These projects are carried out in cooperation and collaboration with local, state and federal agencies whose responsibilities lie with planning, zoning and environmental monitoring and/or assessment in the application of remote sensing techniques. The end result of the projects is the use by the involved agencies of remote sensing techniques in problem solving.

Communicating remote sensing concepts in an interdisciplinary environment

NASA Technical Reports Server (NTRS)

Chung, R.

1981-01-01

Although remote sensing is currently multidisciplinary in its applications, many of its terms come from the engineering sciences, particularly from the field of pattern recognition. Scholars from fields such as the social sciences, botany, and biology, may experience initial difficulty with remote sensing terminology, even though parallel concepts exist in their own fields. Some parallel concepts and terminologies from nonengineering fields, which might enhance the understanding of remote sensing concepts in an interdisciplinary situation are identified. Feedbacks which this analogue strategy might have on remote sensing itself are explored.

People, Places and Pixels: Remote Sensing in the Service of Society

NASA Technical Reports Server (NTRS)

Lulla, Kamlesh

2003-01-01

What is the role of Earth remote sensing and other geospatial technologies in our society? Recent global events have brought into focus the role of geospatial science and technology such as remote sensing, GIS, GPS in assisting the professionals who are responsible for operations such as rescue and recovery of sites after a disaster or a terrorist act. This paper reviews the use of recent remote sensing products from satellites such as IKONOS in these efforts. Aerial and satellite imagery used in land mine detection has been evaluated and the results of this evaluation will be discussed. Synopsis of current and future ISS Earth Remote Sensing capabilities will be provided. The role of future missions in humanitarian use of remote sensing will be explored.

Remote temperature distribution sensing using permanent magnets

DOE PAGES

Chen, Yi; Guba, Oksana; Brooks, Carlton F.; ...

2016-10-31

Remote temperature sensing is essential for applications in enclosed vessels where feedthroughs or optical access points are not possible. A unique sensing method for measuring the temperature of multiple closely-spaced points is proposed using permanent magnets and several three-axis magnetic field sensors. The magnetic field theory for multiple magnets is discussed and a solution technique is presented. Experimental calibration procedures, solution inversion considerations and methods for optimizing the magnet orientations are described in order to obtain low-noise temperature estimates. The experimental setup and the properties of permanent magnets are shown. Finally, experiments were conducted to determine the temperature of ninemore » magnets in different configurations over a temperature range of 5 to 60 degrees Celsius and for a sensor-to-magnet distance of up to 35 mm. Furthermore, to show the possible applications of this sensing system for measuring temperatures through metal walls, additional experiments were conducted inside an opaque 304 stainless steel cylinder.« less

Object-based vegetation classification with high resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions such as (1) whether the sample characteristics affect the classification accuracy and how significant if it does; (2) how much variance of classification uncertainty can be explained by above factors. This research is carried out on a hilly peninsular area in Mediterranean climate, Point Reyes National Seashore (PRNS) in Northern California. The area mainly consists of a heterogeneous, semi-natural broadleaf and conifer woodland, shrub land, and annual grassland. A detailed list of vegetation alliances is used in this study. Research results from the first phase indicates that vegetation spatial variation as reflected by the average local variance (ALV) keeps a high level of magnitude between 1 m and 4 m resolution. (Abstract shortened by UMI.)

Quaternary sediment architecture in the Orkhon Valley (central Mongolia) inferred from capacitive coupled resistivity and Georadar measurements

NASA Astrophysics Data System (ADS)

Mackens, Sonja; Klitzsch, Norbert; Grützner, Christoph; Klinger, Riccardo

2017-09-01

Detailed information on shallow sediment distribution in basins is required to achieve solutions for problems in Quaternary geology, geomorphology, neotectonics, (geo)archaeology, and climatology. Usually, detailed information is obtained by studying outcrops and shallow drillings. Unfortunately, such data are often sparsely distributed and thus cannot characterise entire basins in detail. Therefore, they are frequently combined with remote sensing methods to overcome this limitation. Remote sensing can cover entire basins but provides information of the land surface only. Geophysical methods can close the gap between detailed sequences of the shallow sediment inventory from drillings at a few spots and continuous surface information from remote sensing. However, their interpretation in terms of sediment types is often challenging, especially if permafrost conditions complicate their interpretation. Here we present an approach for the joint interpretation of the geophysical methods ground penetrating radar (GPR) and capacitive coupled resistivity (CCR), drill core, and remote sensing data. The methods GPR and CCR were chosen because they allow relatively fast surveying and provide complementary information. We apply the approach to the middle Orkhon Valley in central Mongolia where fluvial, alluvial, and aeolian processes led to complex sediment architecture. The GPR and CCR data, measured on profiles with a total length of about 60 km, indicate the presence of two distinct layers over the complete surveying area: (i) a thawed layer at the surface, and (ii) a frozen layer below. In a first interpretation step, we establish a geophysical classification by considering the geophysical signatures of both layers. We use sedimentological information from core logs to relate the geophysical classes to sediment types. This analysis reveals internal structures of Orkhon River sediments, such as channels and floodplain sediments. We also distinguish alluvial fan deposits and aeolian sediments by their distinct geophysical signature. With this procedure we map aeolian sediments, debris flow sediments, floodplains, and channel sediments along the measured profiles in the entire basin. We show that the joint interpretation of drillings and geophysical profile measurements matches the information from remote sensing data, i.e., the sediment architecture of vast areas can be characterised by combining these techniques. The method presented here proves powerful for characterising large areas with minimal effort and can be applied to similar settings.

Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model

USGS Publications Warehouse

Byrd, Kristin B.; Windham-Myers, Lisamarie; Leeuw, Thomas; Downing, Bryan D.; Morris, James T.; Ferner, Matthew C.

2016-01-01

Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise scenarios. We tested the feasibility of deriving two key MEM inputs—average annual suspended sediment concentration (SSC) and aboveground peak biomass—from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, DigitalGlobe WorldView-2, and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable although peak biomass from Landsat 8 best matched field-measured values. The Portable Remote Imaging Spectrometer SSC model was most accurate, although a Landsat 8 time series provided annual average SSC estimates. Landsat 8-measured peak biomass values were randomly distributed, and annual average SSC (30 mg/L) was well represented in the main channels (IQR: 29–32 mg/L), illustrating the suitability of these inputs across the model domain. Trend response surface analysis identified significant diversion between field and remote sensing-based model runs at 60 yr due to model sensitivity at the marsh edge (80–140 cm NAVD88), although at 100 yr, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150–200 cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape-scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency, and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage, and flood protection.

Palm Swamp Wetland Ecosystems of the Upper Amazon: Characterizing their Distribution and Inundation State Using Multiple Resolution Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schröder, R.; Pinto, N.; Zimmermann, R.; Horna, V.

2011-12-01

Palm swamp wetlands are prevalent in the Amazon basin, including extensive regions in northern Peru. These ecosystems are characterized by constant surface inundation and moderate seasonal water level variation. The combination of constantly saturated soils, giving rise to low oxygen conditions, and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, knowledge of their spatial extent and inundation state is crucial for assessing the associated land-atmosphere carbon exchange. Precise spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We are developing a remote sensing methodology using multiple resolution microwave remote sensing data to determine palm swamp distribution and inundation state over focus regions in the Amazon basin in northern Peru. For this purpose, two types of multi-temporal microwave data are used: 1) high-resolution (100 m) data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR) to derive maps of palm swamp extent and inundation from dual-polarization fine-beam and multi-temporal HH-polarized ScanSAR, and 2) coarse resolution (25 km) combined active and passive microwave data from QuikSCAT and AMSR-E to derive inundated area fraction on a weekly basis. We compare information content and accuracy of the coarse resolution products to the PALSAR-based datasets to ensure information harmonization. The synergistic combination of high and low resolution datasets will allow for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA/EORC. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Feasibility Study of Space-based CO2 Remote Sensing Using Pulsed 2-micron Integrated Path Differential Absorption Lidar

NASA Astrophysics Data System (ADS)

Singh, U. N.; Refaat, T. F.; Ismail, S.; Davis, K. J.; Kawa, S. R.; Menzies, R. T.; Petros, M.; Yu, J.

2016-12-01

Carbon dioxide (CO2) is recognized as the most important anthropogenic greenhouse gas. While CO2 concentration is rapidly increasing, understanding of the global carbon cycle remains a primary scientific challenge. This is mainly due to the lack of full characterization of CO2 sources and sinks. Quantifying the current global distribution of CO2 sources and sinks with sufficient accuracy and spatial resolution is a critical requirement for improving models of carbon-climate interactions and for attributing them to specific biogeochemical processes. This requires sustained atmospheric CO2 observations with high precision, and low bias for high accuracy, and spatial and temporal dense representation that cannot be fully realized with current CO2 observing systems, including existing satellite CO2 passive remote sensors. Progress in 2-micron instrument technologies, airborne testing, and system performance simulations indicates that the necessary lower tropospheric weighted CO2 measurements can be achieved from space using new high pulse energy 2-micron direct detection active remote sensing. Advantages of the CO2 active remote sensing include low bias measurements that are independent of sun light or Earth's radiation and day/night coverage over all latitudes and seasons. In addition, the direct detection system provides precise ranging with simultaneous measurement of aerosol and cloud distributions. The 2-micron active remote sensing offers strong CO2 absorption lines with optimum low tropospheric and near surface weighting. A feasibility study, including system optimization and sensitivity analysis of a space-based 2-micron pulsed IPDA lidar for CO2 measurement, is presented. This is based on the successful demonstration of the CO2 double-pulse IPDA lidar and the technology maturation of the triple-pulse IPDA lidar, currently under development at NASA Langley Research Center. Preliminary simulations indicate CO2 random measurement errors of 0.71, 0.35 and 0.13 ppm for snow, ocean surface, and desert surface reflectivity, respectively. These simulations assume a 400 km altitude polar orbit, 100 mJ pulse energy, a 1.5 m telescope, a 6.2 MHz detection bandwidth, 0.05 aerosol optical depth and 7 second data average.

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions

PubMed Central

Wilson, Adam M.; Jetz, Walter

2016-01-01

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties. PMID:27031693

Factors influencing the at-sea distribution of Cassin's Auklets (Ptychoramphus aleuticus) that breed in the Channel Islands, California

USGS Publications Warehouse

Adams, Josh; Takekawa, John Y.; Carter, Harry R.; Yee, Julie L.

2010-01-01

We used radiotelemetry to evaluate at-sea habitat use by Cassin's Auklets (Ptychoramphus aleuticus) that bred at Prince Island, off southern California, from 1999 through 2001. We used logistic regression to compare paired radiotelemetry (presence) with random (pseudo-absence) location-associated habitat variables derived from (1) satellite remote-sensing of sea surface temperature and chlorophyll-a concentration and (2) bathymetry. Compared with random locations within their foraging area and after controlling for distance to colony, odds ratios indicated that Cassin's Auklets with dependent young occurred in relatively shallower, warmer, and chlorophyll-rich water associated with chlorophyll fronts near the insular shelf break. These oceanographic features characterize habitats that support key euphausiid prey (e.g., Thysanoessa spinifera) and also other krill predators. Radiotelemetry combined with satellite remote-sensing of the ocean provides an alternative to vessel-based surveys for evaluating seabird foraging habitats. In the absence of information on the actual distribution, abundance, and, hence, availability of Zooplankton prey for seabirds, environmental factors can serve as proxies to help elucidate distributional patterns of seabirds at sea.

Satellite Remote Sensing of Atmospheric Pollution: the Far-Reaching Impact of Burning in Southern Africa

NASA Technical Reports Server (NTRS)

Fishman, Jack; Al-Saadi, Jassim A.; Neil, Doreen O.; Creilson, John K.; Severance, Kurt; Thomason, Larry W.; Edwards, David R.

2008-01-01

When the first observations of a tropospheric trace gas were obtained in the 1980s, carbon monoxide enhancements from tropical biomass burning dominated the observed features. In 2005, an active remote-sensing system to provide detailed information on the vertical distribution of aerosols and clouds was launched, and again, one of the most imposing features observed was the presence of emissions from tropical biomass burning. This paper presents a brief overview of space-borne observations of the distribution of trace gases and aerosols and how tropical biomass burning, primarily in the Southern Hemisphere, has provided an initially surprising picture of the distribution of these species and how they have evolved from prevailing transport patterns in that hemisphere. We also show how interpretation of these observations has improved significantly as a result of the improved capability of trajectory modeling in recent years and how information from this capability has provided additional insight into previous measurements form satellites. Key words: pollution; biomass burning; aerosols; tropical trace gas emissions; Southern Hemisphere; carbon monoxide.

A method to analyze "source-sink" structure of non-point source pollution based on remote sensing technology.

PubMed

Jiang, Mengzhen; Chen, Haiying; Chen, Qinghui

2013-11-01

With the purpose of providing scientific basis for environmental planning about non-point source pollution prevention and control, and improving the pollution regulating efficiency, this paper established the Grid Landscape Contrast Index based on Location-weighted Landscape Contrast Index according to the "source-sink" theory. The spatial distribution of non-point source pollution caused by Jiulongjiang Estuary could be worked out by utilizing high resolution remote sensing images. The results showed that, the area of "source" of nitrogen and phosphorus in Jiulongjiang Estuary was 534.42 km(2) in 2008, and the "sink" was 172.06 km(2). The "source" of non-point source pollution was distributed mainly over Xiamen island, most of Haicang, east of Jiaomei and river bank of Gangwei and Shima; and the "sink" was distributed over southwest of Xiamen island and west of Shima. Generally speaking, the intensity of "source" gets weaker along with the distance from the seas boundary increase, while "sink" gets stronger. Copyright © 2013 Elsevier Ltd. All rights reserved.

Alteration mineral mapping and metallogenic prediction using CASI/SASI airborne hyperspectral data in Mingshujing area of Gansu Province, NW China

NASA Astrophysics Data System (ADS)

Sun, Yu; Zhao, Yingjun; Qin, Kai; Tian, Feng

2016-04-01

Hyperspectral remote sensing is a frontier of remote sensing. Due to its advantage of integrated image with spectrum, it can realize objects identification, superior to objects classification of multispectral remote sensing. Taken the Mingshujing area in Gansu Province of China as an example, this study extracted the alteration minerals and thus to do metallogenic prediction using CASI/SASI airborne hyperspectral data. The Mingshujing area, located in Liuyuan region of Gansu Province, is dominated by middle Variscan granites and Indosinian granites, with well developed EW- and NE-trending faults. In July 2012, our project team obtained the CASI/SASI hyperspectral data of Liuyuan region by aerial flight. The CASI hyperspectral data have 32 bands and the SASI hyperspectral data have 88 bands, with spectral resolution of 15nm for both. The hyperspectral raw data were first preprocessed, including radiometric correction and geometric correction. We then conducted atmospheric correction using empirical line method based on synchronously measured ground spectra to obtain hyperspectral reflectance data. Spectral dimension of hyperspectral data was reduced by the minimum noise fraction transformation method, and then purity pixels were selected. After these steps, image endmember spectra were obtained. We used the endmember spectrum election method based on expert knowledge to analyze the image endmember spectra. Then, the mixture tuned matched filter (MTMF) mapping method was used to extract mineral information, including limonite, Al-rich sericite, Al-poor sericite and chlorite. Finally, the distribution of minerals in the Mingshujing area was mapped. According to the distribution of limonite and Al-rich sericite mapped by CASI/SASI hyperspectral data, we delineated five gold prospecting areas, and further conducted field verification in these areas. It is shown that there are significant gold mineralized anomalies in surface in the Baixianishan and Xitan prospecting areas. The application of CASI/SASI airborne hyperspectral remote sensing data in the metallogenic prediction of the Mingshujing area has achieved ideal results, indicative of their wide application potential in geological research.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

Modeling light scattering by mineral dust particles using spheroids

NASA Astrophysics Data System (ADS)

Merikallio, Sini; Nousiainen, Timo

Suspended dust particles have a considerable influence on light scattering in both terrestrial and planetary atmospheres and can therefore have a large effect on the interpretation of remote sensing measurements. Assuming dust particles to be spherical is known to produce inaccurate results when modeling optical properties of real mineral dust particles. Yet this approximation is widely used for its simplicity. Here, we simulate light scattering by mineral dust particles using a distribution of model spheroids. This is done by comparing scattering matrices calculated from a dust optical database of Dubovik et al. [2006] with those measured in the laboratory by Volten et al. [2001]. Wavelengths of 441,6 nm and 632,8 nm and refractive indexes of Re = 1.55 -1.7 and Im = 0.001i -0.01i were adopted in this study. Overall, spheroids are found to fit the measurements significantly better than Mie spheres. Further, we confirm that the shape distribution parametrization developed in Nousiainen et al. (2006) significantly improves the accuracy of simulated single-scattering for small mineral dust particles. The spheroid scheme should therefore yield more reliable interpretations of remote sensing data from dusty planetary atmospheres. While the spheroidal scheme is superior to spheres in remote sensing applications, its performance is far from perfect especially for samples with large particles. Thus, additional advances are clearly possible. Further studies of the Martian atmosphere are currently under way. Dubovik et al. (2006) Application of spheroid models to account for aerosol particle nonspheric-ity in remote sensing of desert dust, JGR, Vol. 111, D11208 Volten et al. (2001) Scattering matrices of mineral aerosol particles at 441.6 nm and 632.8 nm, JGR, Vol. 106, No. D15, pp. 17375-17401 Nousiainen et al. (2006) Light scattering modeling of small feldspar aerosol particles using polyhedral prisms and spheroids, JQSRT 101, pp. 471-487

Symmetry in polarimetric remote sensing

NASA Technical Reports Server (NTRS)

Nghiem, S. V.; Yueh, S. H.; Kwok, R.

1993-01-01

Relationships among polarimetric backscattering coefficients are derived from the viewpoint of symmetry groups. For both reciprocal and non-reciprocal media, symmetry encountered in remote sensing due to reflection, rotation, azimuthal, and centrical symmetry groups is considered. The derived properties are general and valid to all scattering mechanisms, including volume and surface scatterings and their interactions, in a given symmetrical configuration. The scattering coefficients calculated from theoretical models for layer random media and rough surfaces are shown to obey the symmetry relations. Use of symmetry properties in remote sensing of structural and environmental responses of scattering media is also discussed. Orientations of spheroidal scatterers described by spherical, uniform, planophile, plagiothile, erectophile, and extremophile distributions are considered to derive their polarimetric backscattering characteristics. These distributions can be identified from the observed scattering coefficients by comparison with theoretical symmetry calculations. A new parameter is then defined to study scattering structures in geophysical media. Observations from polarimetric data acquired by the Jet Propulsion Laboratory airborne synthetic aperture radar over forests, sea ice, and sea surface are presented. Experimental evidences of the symmetry relationships are shown and their use in polarimetric remote sensing is illustrated. For forests, the coniferous forest in Mt. Shasta area (California) and mixed forest near Presque Isle (Maine) exhibit characteristics of the centrical symmetry at C-band. For sea ice in the Beaufort Sea, multi-year sea ice has a cross-polarized ratio e close to e(sub 0), calculated from symmetry, due to the randomness in the scattering structure. First-year sea ice has e much smaller than e(sub 0) due to the preferential alignment of the columnar structure of the ice. From polarimetric data of a sea surface in the Bering Sea, it is observed that e and e(sub 0) are increasing with incident angle and e is greater than e(sub 0) at L-band because of the directional feature of sea surface waves. Symmetry properties of geophysical media can also be used to calibrate polarimetric radars.

Remotely Sensed Data Informs Red List Evaluations and Conservation Priorities in Southeast Asia.

PubMed

Li, Binbin V; Hughes, Alice C; Jenkins, Clinton N; Ocampo-Peñuela, Natalia; Pimm, Stuart L

2016-01-01

The IUCN Red List has assessed the global distributions of the majority of the world's amphibians, birds and mammals. Yet these assessments lack explicit reference to widely available, remotely-sensed data that can sensibly inform a species' risk of extinction. Our first goal is to add additional quantitative data to the existing standardised process that IUCN employs. Secondly, we ask: do our results suggest species of concern-those at considerably greater risk than hitherto appreciated? Thirdly, these assessments are not only important on a species-by-species basis. By combining distributions of species of concern, we map conservation priorities. We ask to what degree these areas are currently protected and how might knowledge from remote sensing modify the priorities? Finally, we develop a quick and simple method to identify and modify the priority setting in a landscape where natural habitats are disappearing rapidly and so where conventional species' assessments might be too slow to respond. Tropical, mainland Southeast Asia is under exceptional threat, yet relatively poorly known. Here, additional quantitative measures may be particularly helpful. This region contains over 122, 183, and 214 endemic mammals, birds, and amphibians, respectively, of which the IUCN considers 37, 21, and 37 threatened. When corrected for the amount of remaining natural habitats within the known elevation preferences of species, the average sizes of species ranges shrink to <40% of their published ranges. Some 79 mammal, 49 bird, and 184 amphibian ranges are <20,000km2-an area at which IUCN considers most other species to be threatened. Moreover, these species are not better protected by the existing network of protected areas than are species that IUCN accepts as threatened. Simply, there appear to be considerably more species at risk than hitherto appreciated. Furthermore, incorporating remote sensing data showing where habitat loss is prevalent changes the locations of conservation priorities.

Analytical inversions in remote sensing of particle size distributions. IV - Comparison of Fymat and Box-McKellar solutions in the anomalous diffraction approximation

NASA Technical Reports Server (NTRS)

Fymat, A. L.; Smith, C. B.

1979-01-01

It is shown that the inverse analytical solutions, provided separately by Fymat and Box-McKellar, for reconstructing particle size distributions from remote spectral transmission measurements under the anomalous diffraction approximation can be derived using a cosine and a sine transform, respectively. Sufficient conditions of validity of the two formulas are established. Their comparison shows that the former solution is preferable to the latter in that it requires less a priori information (knowledge of the particle number density is not needed) and has wider applicability. For gamma-type distributions, and either a real or a complex refractive index, explicit expressions are provided for retrieving the distribution parameters; such expressions are, interestingly, proportional to the geometric area of the polydispersion.

Estimating individual tree mid- and understory rank-size distributions from airborne laser scanning in semi-arid forests

Treesearch

Tyson L. Swetnam; Donald A. Falk; Ann M. Lynch; Stephen R. Yool

2014-01-01

Limitations inherent to airborne laser scanning (ALS) technology and the complex sorting and packing relationships of forests complicate accurate remote sensing of mid- and understory trees, especially in denser forest stands. Self-similarities in rank-sized individual tree distributions (ITD), e.g. bole diameter or height, are a well-understood property of natural,...

The application of remote sensing techniques to the study of ophiolites

NASA Astrophysics Data System (ADS)

Khan, Shuhab D.; Mahmood, Khalid

2008-08-01

Satellite remote sensing methods are a powerful tool for detailed geologic analysis, especially in inaccessible regions of the earth's surface. Short-wave infrared (SWIR) bands are shown to provide spectral information bearing on the lithologic, structural, and geochemical character of rock bodies such as ophiolites, allowing for a more comprehensive assessment of the lithologies present, their stratigraphic relationships, and geochemical character. Most remote sensing data are widely available for little or no cost, along with user-friendly software for non-specialists. In this paper we review common remote sensing systems and methods that allow for the discrimination of solid rock (lithologic) components of ophiolite complexes and their structural relationships. Ophiolites are enigmatic rock bodies which associated with most, if not all, plate collision sutures. Ophiolites are ideal for remote sensing given their widely recognized diversity of lithologic types and structural relationships. Accordingly, as a basis for demonstrating the utility of remote sensing techniques, we briefly review typical ophiolites in the Tethyan tectonic belt. As a case study, we apply integrated remote sensing studies of a well-studied example, the Muslim Bagh ophiolite, located in Balochistan, western Pakistan. On this basis, we attempt to demonstrate how remote sensing data can validate and reconcile existing information obtained from field studies. The lithologic and geochemical diversity of Muslim Bagh are representative of Tethyan ophiolites. Despite it's remote location it has been extensively mapped and characterized by structural and geochemical studies, and is virtually free of vegetative cover. Moreover, integrating the remote sensing data with 'ground truth' information thus offers the potential of an improved template for interpreting remote sensing data sets of other ophiolites for which little or no field information is available.

Basic technologies of web services framework for research, discovery, and processing the disparate massive Earth observation data from heterogeneous sources

NASA Astrophysics Data System (ADS)

Savorskiy, V.; Lupyan, E.; Balashov, I.; Burtsev, M.; Proshin, A.; Tolpin, V.; Ermakov, D.; Chernushich, A.; Panova, O.; Kuznetsov, O.; Vasilyev, V.

2014-04-01

Both development and application of remote sensing involves a considerable expenditure of material and intellectual resources. Therefore, it is important to use high-tech means of distribution of remote sensing data and processing results in order to facilitate access for as much as possible number of researchers. It should be accompanied with creation of capabilities for potentially more thorough and comprehensive, i.e. ultimately deeper, acquisition and complex analysis of information about the state of Earth's natural resources. As well objective need in a higher degree of Earth observation (EO) data assimilation is set by conditions of satellite observations, in which the observed objects are uncontrolled state. Progress in addressing this problem is determined to a large extent by order of the distributed EO information system (IS) functioning. Namely, it is largely dependent on reducing the cost of communication processes (data transfer) between spatially distributed IS nodes and data users. One of the most effective ways to improve the efficiency of data exchange processes is the creation of integrated EO IS optimized for running procedures of distributed data processing. The effective EO IS implementation should be based on specific software architecture.

Evaluation of a spatially-distributed Thornthwaite water-balance model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lough, J.A.

1993-03-01

A small watershed of low relief in coastal New Hampshire was divided into hydrologic sub-areas in a geographic information system on the basis of soils, sub-basins and remotely-sensed landcover. Three variables were spatially modeled for input to 49 individual water-balances: available water content of the root zone, water input and potential evapotranspiration (PET). The individual balances were weight-summed to generate the aggregate watershed-balance, which saw 9% (48--50 mm) less annual actual-evapotranspiration (AET) compared to a lumped approach. Analysis of streamflow coefficients suggests that the spatially-distributed approach is more representative of the basin dynamics. Variation of PET by landcover accounted formore » the majority of the 9% AET reduction. Variation of soils played a near-negligible role. As a consequence of the above points, estimates of landcover proportions and annual PET by landcover are sufficient to correct a lumped water-balance in the Northeast. If remote sensing is used to estimate the landcover area, a sensor with a high spatial resolution is required. Finally, while the lower Thornthwaite model has conceptual limitations for distributed application, the upper Thornthwaite model is highly adaptable to distributed problems and may prove useful in many earth-system models.« less

[Monitoring the thermal plume from coastal nuclear power plant using satellite remote sensing data: modeling, and validation].

PubMed

Zhu, Li; Zhao, Li-Min; Wang, Qiao; Zhang, Ai-Ling; Wu, Chuan-Qing; Li, Jia-Guo; Shi, Ji-Xiang

2014-11-01

Thermal plume from coastal nuclear power plant is a small-scale human activity, mornitoring of which requires high-frequency and high-spatial remote sensing data. The infrared scanner (IRS), on board of HJ-1B, has an infrared channel IRS4 with 300 m and 4-days as its spatial and temporal resolution. Remote sensing data aquired using IRS4 is an available source for mornitoring thermal plume. Retrieval pattern for coastal sea surface temperature (SST) was built to monitor the thermal plume from nuclear power plant. The research area is located near Guangdong Daya Bay Nuclear Power Station (GNPS), where synchronized validations were also implemented. The National Centers for Environmental Prediction (NCEP) data was interpolated spatially and temporally. The interpolated data as well as surface weather conditions were subsequently employed into radiative transfer model for the atmospheric correction of IRS4 thermal image. A look-up-table (LUT) was built for the inversion between IRS4 channel radiance and radiometric temperature, and a fitted function was also built from the LUT data for the same purpose. The SST was finally retrieved based on those preprocessing procedures mentioned above. The bulk temperature (BT) of 84 samples distributed near GNPS was shipboard collected synchronically using salinity-temperature-deepness (CTD) instruments. The discrete sample data was surface interpolated and compared with the satellite retrieved SST. Results show that the average BT over the study area is 0.47 degrees C higher than the retrieved skin temperature (ST). For areas far away from outfall, the ST is higher than BT, with differences less than 1.0 degrees C. The main driving force for temperature variations in these regions is solar radiation. For areas near outfall, on the contrary, the retrieved ST is lower than BT, and greater differences between the two (meaning > 1.0 degrees C) happen when it gets closer to the outfall. Unlike the former case, the convective heat transfer resulting from the thermal plume is the primary reason leading to the temperature variations. Temperature rising (TR) distributions obtained from remote sensing data and in-situ measurements are consistent, except that the interpolated BT shows more level details (> 5 levels) than that of the ST (up to 4 levels). The areas with higher TR levels (> 2) are larger on BT maps, while for lower TR levels (≤ 2), the two methods perform with no obvious differences. Minimal errors for satellite-derived SST occur regularly around local time 10 a. m. This makes the remote sensing results to be substitutes for in-situ measurements. Therefore, for operational applications of HJ-1B IRS4, remote sensing technique can be a practical approach to monitoring the nuclear plant thermal pollution around this time period.

Interinstrument comparison of remote-sensing devices and a new method for calculating on-road nitrogen oxides emissions and validation of vehicle-specific power.

PubMed

Rushton, Christopher E; Tate, James E; Shepherd, Simon P; Carslaw, David C

2018-02-01

Emissions of nitrogen oxides (NOx) by vehicles in real driving environments are only partially understood. This has been brought to the attention of the world with recent revelations of the cheating of the type of approval tests exposed in the dieselgate scandal. Remote-sensing devices offer investigators an opportunity to directly measure in situ real driving emissions of tens of thousands of vehicles. Remote-sensing NO 2 measurements are not as widely available as would be desirable. The aim of this study is to improve the ability of investigators to estimate the NO 2 emissions and to improve the confidence of the total NOx results calculated from standard remote-sensing device (RSD) measurements. The accuracy of the RSD speed and acceleration module was also validated using state-of-the-art onboard global positioning system (GPS) tracking. Two RSDs used in roadside vehicle emissions surveys were tested side by side under off-carriageway conditions away from transient pollution sources to ascertain the consistency of their measurements. The speed correlation was consistent across the range of measurements at 95% confidence and the acceleration correlation was consistent at 95% confidence intervals for all but the most extreme acceleration cases. VSP was consistent at 95% confidence across all measurements except for those at VSP ≥ 15 kW t -1 , which show a small underestimate. The controlled distribution gas nitric oxide measurements follow a normal distribution with 2σ equal to 18.9% of the mean, compared to 15% observed during factory calibration indicative of additional error introduced into the system. Systematic errors of +84 ppm were observed but within the tolerance of the control gas. Interinstrument correlation was performed, with the relationship between the FEAT and the RSD4600 being linear with a gradient of 0.93 and an R 2 of 0.85, indicating good correlation. A new method to calculate NOx emissions using fractional NO 2 combined with NO measurements made by the RSD4600 was constructed, validated, and shown to be more accurate than previous methods. Synchronized remote-sensing measurements of NO were taken using two different remote-sensing devices in an off-road study. It was found that the measurements taken by both instruments were well correlated. Fractional NO 2 measurements from a prior study, measurable on only one device, were used to create new NO x emission factors for the device that could not be measured by the second device. These estimates were validated against direct measurement of total NO x emission factors and shown to be an improvement on previous methodologies. Validation of vehicle-specific power was performed with good correlation observed.

Remote Sensing and Remote Control Activities in Europe and America: Part 2--Remote Sensing Ground Stations in Europe,

DTIC Science & Technology

1996-04-08

Development tasks and products of remote sensing ground stations in Europe are represented by the In-Sec Corporation and the Schlumberger Industries Corporation. The article presents the main products of these two corporations.

[Estimation of desert vegetation coverage based on multi-source remote sensing data].

PubMed

Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

2012-12-01

Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

An Approach of Registration between Remote Sensing Image and Electronic Chart Based on Coastal Line

NASA Astrophysics Data System (ADS)

Li, Ying; Yu, Shuiming; Li, Chuanlong

Remote sensing plays an important role marine oil spill emergency. In order to implement a timely and effective countermeasure, it is important to provide exact position of oil spills. Therefore it is necessary to match remote sensing image and electronic chart properly. Variance ordinarily exists between oil spill image and electronic chart, although geometric correction is applied to remote sensing image. It is difficult to find the steady control points on sea to make exact rectification of remote sensing image. An improved relaxation algorithm was developed for finding the control points along the coastline since oil spills occurs generally near the coast. A conversion function is created with the least square, and remote sensing image can be registered with the vector map based on this function. SAR image was used as the remote sensing data and shape format map as the electronic chart data. The results show that this approach can guarantee the precision of the registration, which is essential for oil spill monitoring.

UAV-based remote sensing of the Heumoes landslide, Austria Vorarlberg

NASA Astrophysics Data System (ADS)

Niethammer, U.; Joswig, M.

2009-04-01

The Heumoes landslide, is located in the eastern Vorarlberg Alps, Austria, 10 km southeast of Dornbirn. The extension of the landslide is about 2000 m in west to east direction and about 500 m at its widest extent in north to south direction. It occurs between an elevation of 940 m in the east and 1360 m in the west, slope angles of more than 60 % can be observed as well as almost flat areas. Its total volume is estimated to be 9.400.000 cubic meters and its average velocities amount to some centimeter per year. Surface signatures or 'photolineations' of creeping landslides, e.g. fractures and rupture lines in sediments and street pavings, and vegetation contrasts by changes of water table in shallow vegetation in principle can be resolved by remote sensing. The necessary ground cell resolution of few centimeters, however, generally can't be achieved by routine areal or satellite imagery. The fast technological progress of unmanned areal vehicles (UAV) and the reduced payload by miniaturized optical cameras now allow for UAV remote sensing applications that are below the high financial limits of military intelligence. Even with 'low-cost' equipment, the necessary centimeter-scale ground cell resolution can be achieved by adapting the flight altitude to some ten to one hundred meters. Operated by scientists experienced in remote-control flight models, UAV remote sensing can now be performed routinely, and campaign-wise after any significant event of, e.g., heavy rainfall, or partial mudflow. We have investigated a concept of UAV-borne remote sensing based on motorized gliders, and four-propeller helicopters or 'quad-rotors'. Several missions were flown over the Heumoes landslide. Between 2006 and 2008 three series UAV-borne photographs of the Heumoes landslide were taken and could be combined to orto-mosaics of the slope area within few centimeters ground cell resolution. We will present the concept of our low cost quad-rotor UAV system and first results of the image-processing based evaluation of the acquired images to characterize spatial and temporal details of landslide behaviour. We will also sketch first schemes of joint interpretation or 'data fusion' of UAV-based remote sensing with the results from geophysical mapping of underground distribution of soil moisture and fracture processes (Walter & Joswig, EGU 2009).

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

NASA Technical Reports Server (NTRS)

Yueh, Simon H.

2004-01-01

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Remote Sensing and the Environment.

ERIC Educational Resources Information Center

Osmers, Karl

1991-01-01

Suggests using remote sensing technology to help students make sense of the natural world. Explains that satellite information allows observation of environmental changes over time. Identifies possible student projects based on remotely sensed data. Recommends obtaining the assistance of experts and seeking funding through effective project…

The pan-sharpening of satellite and UAV imagery for agricultural applications

NASA Astrophysics Data System (ADS)

Jenerowicz, Agnieszka; Woroszkiewicz, Malgorzata

2016-10-01

Remote sensing techniques are widely used in many different areas of interest, i.e. urban studies, environmental studies, agriculture, etc., due to fact that they provide rapid, accurate and information over large areas with optimal time, spatial and spectral resolutions. Agricultural management is one of the most common application of remote sensing methods nowadays. Monitoring of agricultural sites and creating information regarding spatial distribution and characteristics of crops are important tasks to provide data for precision agriculture, crop management and registries of agricultural lands. For monitoring of cultivated areas many different types of remote sensing data can be used- most popular are multispectral satellites imagery. Such data allow for generating land use and land cover maps, based on various methods of image processing and remote sensing methods. This paper presents fusion of satellite and unnamed aerial vehicle (UAV) imagery for agricultural applications, especially for distinguishing crop types. Authors in their article presented chosen data fusion methods for satellite images and data obtained from low altitudes. Moreover the authors described pan- sharpening approaches and applied chosen pan- sharpening methods for multiresolution image fusion of satellite and UAV imagery. For such purpose, satellite images from Landsat- 8 OLI sensor and data collected within various UAV flights (with mounted RGB camera) were used. In this article, the authors not only had shown the potential of fusion of satellite and UAV images, but also presented the application of pan- sharpening in crop identification and management.

A Decision Support Information System for Urban Landscape Management Using Thermal Infrared Data

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.; Rickman, Douglas L.; Estes, Maurice G., Jr.; Laymon, Charles A.; Howell, Burgess F.

2000-01-01

In this paper, we describe efforts to use remote sensing data within the purview of an information support system, to assess urban thermal landscape characteristics as a means for developing more robust models of the Urban Heat Island (UHI) effect. We also present a rationale on how we have successfully translated the results from the study of urban thermal heating and cooling regimes as identified from remote sensing data, to decision-makers, planners, government officials, and the public at large in several US cities to facilitate better understanding of how the UHI affects air quality. Additionally, through the assessment of the spatial distribution of urban thermal landscape characteristics using remote sensing data, it is possible to develop strategies to mitigate the UHI that hopefully will in turn, drive down ozone levels and improve overall urban air quality. Four US cities have been the foci for intensive analysis as part of our studies: Atlanta, GA, Baton Rouge, LA, Salt Lake City, UT, and Sacramento, CA. The remote sensing data for each of these cities has been used to generate a number of products for use by "stakeholder" working groups to convey information on what the effects are of the UHI and what measures can be taken to mitigate it. In turn, these data products are used to both educate and inform policy-makers, planners, and the general public about what kinds of UHI mitigation strategies are available.

Improved Wetland Mapping Through the use of Advanced Geospatial Technologies

USDA-ARS?s Scientific Manuscript database

For the United States to effectively manage its remaining wetlands, their abundance, distribution, boundaries, and inherent characteristics must be better understood. As natural resource management becomes more holistic and moves towards ecosystem management, the synoptic view that remotely sensed d...

Satellite remote sensing of submerged aquatic vegetation distribution and status in the Currituck Sound, NC.

DOT National Transportation Integrated Search

2012-11-01

Submerged Aquatic Vegetation (SAV) is an important component in any estuarine ecosystem. As such, it is regulated by federal and state agencies as a jurisdictional resource, where impacts to SAV are compensated through mitigation. Historically, tradi...

In-database processing of a large collection of remote sensing data: applications and implementation

NASA Astrophysics Data System (ADS)

Kikhtenko, Vladimir; Mamash, Elena; Chubarov, Dmitri; Voronina, Polina

2016-04-01

Large archives of remote sensing data are now available to scientists, yet the need to work with individual satellite scenes or product files constrains studies that span a wide temporal range or spatial extent. The resources (storage capacity, computing power and network bandwidth) required for such studies are often beyond the capabilities of individual geoscientists. This problem has been tackled before in remote sensing research and inspired several information systems. Some of them such as NASA Giovanni [1] and Google Earth Engine have already proved their utility for science. Analysis tasks involving large volumes of numerical data are not unique to Earth Sciences. Recent advances in data science are enabled by the development of in-database processing engines that bring processing closer to storage, use declarative query languages to facilitate parallel scalability and provide high-level abstraction of the whole dataset. We build on the idea of bridging the gap between file archives containing remote sensing data and databases by integrating files into relational database as foreign data sources and performing analytical processing inside the database engine. Thereby higher level query language can efficiently address problems of arbitrary size: from accessing the data associated with a specific pixel or a grid cell to complex aggregation over spatial or temporal extents over a large number of individual data files. This approach was implemented using PostgreSQL for a Siberian regional archive of satellite data products holding hundreds of terabytes of measurements from multiple sensors and missions taken over a decade-long span. While preserving the original storage layout and therefore compatibility with existing applications the in-database processing engine provides a toolkit for provisioning remote sensing data in scientific workflows and applications. The use of SQL - a widely used higher level declarative query language - simplifies interoperability between desktop GIS, web applications and geographic web services and interactive scientific applications (MATLAB, IPython). The system is also automatically ingesting direct readout data from meteorological and research satellites in near-real time with distributed acquisition workflows managed by Taverna workflow engine [2]. The system has demonstrated its utility in performing non-trivial analytic processing such as the computation of the Robust Satellite Technique (RST) indices [3]. It had been useful in different tasks such as studying urban heat islands, analyzing patterns in the distribution of wildfire occurrences, detecting phenomena related to seismic and earthquake activity. Initial experience has highlighted several limitations of the proposed approach yet it has demonstrated ability to facilitate the use of large archives of remote sensing data by geoscientists. 1. J.G. Acker, G. Leptoukh, Online analysis enhances use of NASA Earth science data. EOS Trans. AGU, 2007, 88(2), P. 14-17. 2. D. Hull, K. Wolsfencroft, R. Stevens, C. Goble, M.R. Pocock, P. Li and T. Oinn, Taverna: a tool for building and running workflows of services. Nucleic Acids Research. 2006. V. 34. P. W729-W732. 3. V. Tramutoli, G. Di Bello, N. Pergola, S. Piscitelli, Robust satellite techniques for remote sensing of seismically active areas // Annals of Geophysics. 2001. no. 44(2). P. 295-312.

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

Pettry, D. E.; Powell, N. L.; Newhouse, M. E.

1974-01-01

Remote sensing studies in Virginia and Chesapeake Bay areas to investigate soil and plant conditions via remote sensing technology are reported ant the results given. Remote sensing techniques and interactions are also discussed. Specific studies on the effects of soil moisture and organic matter on energy reflection of extensively occurring Sassafras soils are discussed. Greenhouse and field studies investigating the effects of chlorophyll content of Irish potatoes on infrared reflection are presented. Selected ground truth and environmental monitoring data are shown in summary form. Practical demonstrations of remote sensing technology in agriculture are depicted and future use areas are delineated.

Applications of remote sensing to watershed management

NASA Technical Reports Server (NTRS)

Rango, A.

1975-01-01

Aircraft and satellite remote sensing systems which are capable of contributing to watershed management are described and include: the multispectral scanner subsystem on LANDSAT and the basic multispectral camera array flown on high altitude aircraft such as the U-2. Various aspects of watershed management investigated by remote sensing systems are discussed. Major areas included are: snow mapping, surface water inventories, flood management, hydrologic land use monitoring, and watershed modeling. It is indicated that technological advances in remote sensing of hydrological data must be coupled with an expansion of awareness and training in remote sensing techniques of the watershed management community.

NASA Glenn OHIOVIEW FY01/02 Project

NASA Technical Reports Server (NTRS)

2003-01-01

The results of the research performed by the university principal investigators are herein compiled. OhioView's general goals were: 1) To increase remote sensing education for Ohio s undergraduate and graduate students, and also enhancing curriculum in the mathematics and science for K-12 students using the capabilities of remote sensing; 2) To conduct advanced research to develop novel remote sensing applications, i.e. to turn data into information for more applications; 3) To maximize the use of remote sensing technology by the general public through outreach and the development of tools for more user-friendly access to remote sensing data.

The availability of conventional forms of remotely sensed data

USGS Publications Warehouse

Sturdevant, James A.; Holm, Thomas M.

1982-01-01

For decades Federal and State agencies have been collecting aerial photographs of various film types and scales over parts of the United States. More recently, worldwide Earth resources data acquired by orbiting satellites have inundated the remote sensing community. Determining the types of remotely sensed data that are publicly available can be confusing to the land-resource manager, planner, and scientist. This paper is a summary of the more commonly used types of remotely sensed data (aircraft and satellite) and their public availability. Special emphasis is placed on the National High-Altitude Photography (NHAP) program and future remote-sensing satellites.

Impact of microwave derived soil moisture on hydrologic simulations using a spatially distributed water balance model

NASA Technical Reports Server (NTRS)

Lin, D. S.; Wood, E. F.; Famiglietti, J. S.; Mancini, M.

1994-01-01

Spatial distributions of soil moisture over an agricultural watershed with a drainage area of 60 ha were derived from two NASA microwave remote sensors, and then used as a feedback to determine the initial condition for a distributed water balance model. Simulated hydrologic fluxes over a period of twelve days were compared with field observations and with model predictions based on a streamflow derived initial condition. The results indicated that even the low resolution remotely sensed data can improve the hydrologic model's performance in simulating the dynamics of unsaturated zone soil moisture. For the particular watershed under study, the simulated water budget was not sensitive to the resolutions of the microwave sensors.

Remote sensing of ferric iron minerals as guides for gold exploration

NASA Technical Reports Server (NTRS)

Taranik, Dan L.; Kruse, Fred A.; Goetz, Alexander F. H.; Atkinson, William W.

1991-01-01

The relationship between the surficial iron mineralogy and economic mineralization is investigated, using data from an airborne imaging spectrometer (the 63-channel Geophysical and Environmental Research Imaging Spectrometer) to map the distribution of iron minerals in the Cripple Creek mining district in Colorado. The airborne image data were coregistered with the field map data for the distribution of iron oxides in the district, in a geographic information computer system, in order to compare their information content. It is shown that the remote imagery was able to uniquely identify the mineral hematite, a mixture of goethite/jarosite, and a mixture of hematite/goethite.

NASA's Applied Remote Sensing Training (ARSET) Webinar Series

Atmospheric Science Data Center

2016-07-12

NASA's Applied Remote Sensing Training (ARSET) Webinar Series Tuesday, July 12, 2016 ... you of a free training opportunity: Introduction to Remote Sensing for Air Quality Applications Webinar Series Beginning in ...

Tropospheric Passive Remote Sensing

NASA Technical Reports Server (NTRS)

Keafer, L. S., Jr. (Editor)

1982-01-01

The long term role of airborne/spaceborne passive remote sensing systems for tropospheric air quality research and the identification of technology advances required to improve the performance of passive remote sensing systems were discussed.

Remote Sensing as a Demonstration of Applied Physics.

ERIC Educational Resources Information Center

Colwell, Robert N.

1980-01-01

Provides information about the field of remote sensing, including discussions of geo-synchronous and sun-synchronous remote-sensing platforms, the actual physical processes and equipment involved in sensing, the analysis of images by humans and machines, and inexpensive, small scale methods, including aerial photography. (CS)

Opportunities and problems in introducing or expanding the teaching of remote sensing in universities

NASA Technical Reports Server (NTRS)

Maxwell, E. L.

1980-01-01

The need for degree programs in remote sensing is considered. Any education program which claims to train remote sensing specialists must include expertise in the physical principles upon which remote sensing is based. These principles dictate the limits of engineering and design, computer analysis, photogrammetry, and photointerpretation. Faculty members must be hired to provide emphasis in those five areas.

Remote sensing of vegetation fires and its contribution to a fire management information system

Treesearch

Stephane P. Flasse; Simon N. Trigg; Pietro N. Ceccato; Anita H. Perryman; Andrew T. Hudak; Mark W. Thompson; Bruce H. Brockett; Moussa Drame; Tim Ntabeni; Philip E. Frost; Tobias Landmann; Johan L. le Roux

2004-01-01

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the types of information remote sensing can provide to the fire community. First, it considers fire management information needs in the context of a fire management information system. An introduction to remote sensing then...

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER... electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Basic Remote Sensing Investigations for Beach Reconnaissance.

DTIC Science & Technology

Progress is reported on three tasks designed to develop remote sensing beach reconnaissance techniques applicable to the benthic, beach intertidal...and beach upland zones. Task 1 is designed to develop remote sensing indicators of important beach composition and physical parameters which will...ultimately prove useful in models to predict beach conditions. Task 2 is designed to develop remote sensing techniques for survey of bottom features in

Bridging the Scales from Field to Region with Practical Tools to Couple Time- and Space-Synchronized Data from Flux Towers and Networks with Proximal and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Burba, G. G.; Avenson, T.; Burkart, A.; Gamon, J. A.; Guan, K.; Julitta, T.; Pastorello, G.; Sakowska, K.

2017-12-01

Many hundreds of flux towers are presently operational as standalone projects and as parts of regional networks. However, the vast majority of these towers do not allow straightforward coupling with remote sensing (drone, aircraft, satellite, etc.) data, and even fewer have optical sensors for validation of remote sensing products, and upscaling from field to regional levels. In 2016-2017, new tools to collect, process, and share time-synchronized flux data from multiple towers were developed and deployed globally. Originally designed to automate site and data management, and to streamline flux data analysis, these tools allow relatively easy matching of tower data with remote sensing data: GPS-driven PTP time protocol synchronizes instrumentation within the station, different stations with each other, and all of these to remote sensing data to precisely align remote sensing and flux data in time Footprint size and coordinates computed and stored with flux data help correctly align tower flux footprints and drone, aircraft or satellite motion to precisely align optical and flux data in space Full snapshot of the remote sensing pixel can then be constructed, including leaf-level, ground optical sensor, and flux tower measurements from the same footprint area, closely coupled with the remote sensing measurements to help interpret remote sensing data, validate models, and improve upscaling Additionally, current flux towers can be augmented with advanced ground optical sensors and can use standard routines to deliver continuous products (e.g. SIF, PRI, NDVI, etc.) based on automated field spectrometers (e.g., FloX and RoX, etc.) and other optical systems. Several dozens of new towers already operational globally can be readily used for the proposed workflow. Over 500 active traditional flux towers can be updated to synchronize their data with remote sensing measurements. This presentation will show how the new tools are used by major networks, and describe how this approach can be utilized for matching remote sensing and tower data to aid in ground truthing, improve scientific interactions, and promote joint grant writing and other forms of collaboration between the flux and remote sensing communities.

Monitoring Crop Phenology and Growth Stages from Space: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.; Mladenova, I. E.; Kustas, W. P.; Alfieri, J. G.

2014-12-01

Crop growth stages in concert with weather and soil moisture conditions can have a significant impact on crop yields. In the U.S., crop growth stages and conditions are reported by farmers at the county level. These reports are somewhat subjective and fluctuate between different reporters, locations and times. Remote sensing data provide an alternative approach to monitoring crop growth over large areas in a more consistent and quantitative way. In the recent years, remote sensing data have been used to detect vegetation phenology at 1-km spatial resolution globally. However, agricultural applications at field scale require finer spatial resolution remote sensing data. Landsat (30-m) data have been successfully used for agricultural applications. There are many medium resolution sensors available today or in near future. These include Landsat, SPOT, RapidEye, ASTER and future Sentinel-2 etc. Approaches have been developed in the past several years to integrate remote sensing data from different sensors which may have different sensor characteristics, and spatial and temporal resolutions. This allows us opportunities today to map crop growth stages and conditions using dense time-series remote sensing at field scales. However, remotely sensed phenology (or phenological metrics) is normally derived based on the mathematical functions of the time-series data. The phenological metrics are determined by either identifying inflection (curvature) points or some pre-defined thresholds in the remote sensing phenology algorithms. Furthermore, physiological crop growth stages may not be directly correlated to the remotely sensed phenology. The relationship between remotely sensed phenology and crop growth stages is likely to vary for specific crop types and varieties, growing stages, conditions and even locations. In this presentation, we will examine the relationship between remotely sensed phenology and crop growth stages using in-situ measurements from Fluxnet sites and crop progress reports from USDA NASS. We will present remote sensing approaches and focus on: 1) integrating multiple sources of remote sensing data; and 2) extracting crop phenology at field scales. An example in the U.S. Corn Belt area will be presented and analyzed. Future directions for mapping crop growth stages will be discussed.

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

NASA Astrophysics Data System (ADS)

Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, Arijit; Singh, Sarnam; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, Ch. Sudhakar; Gupta, Stutee; Pujar, Girish; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, Poonam; Singh, J. S.; Chitale, Vishwas; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, Deepak; Karnatak, Harish; Saran, Sameer; Giriraj, A.; Padalia, Hitendra; Kale, Manish; Nandy, Subrato; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, Chiranjibi; Singh, D. K.; Devagiri, G. M.; Talukdar, Gautam; Panigrahy, Rabindra K.; Singh, Harnam; Sharma, J. R.; Haridasan, K.; Trivedi, Shivam; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, Madhura; Nagabhatla, Nidhi; Prasad, Nupoor; Tripathi, O. P.; Prasad, P. Rama Chandra; Dash, Pushpa; Qureshi, Qamer; Tripathi, S. K.; Ramesh, B. R.; Gowda, Balakrishnan; Tomar, Sanjay; Romshoo, Shakil; Giriraj, Shilpa; Ravan, Shirish A.; Behera, Soumit Kumar; Paul, Subrato; Das, Ashesh Kumar; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, Uma; Menon, A. R. R.; Srivastava, Gaurav; Neeti; Sharma, Subrat; Mohapatra, U. B.; Peddi, Ashok; Rashid, Humayun; Salroo, Irfan; Krishna, P. Hari; Hajra, P. K.; Vergheese, A. O.; Matin, Shafique; Chaudhary, Swapnil A.; Ghosh, Sonali; Lakshmi, Udaya; Rawat, Deepshikha; Ambastha, Kalpana; Malik, Akhtar H.; Devi, B. S. S.; Gowda, Balakrishna; Sharma, K. C.; Mukharjee, Prashant; Sharma, Ajay; Davidar, Priya; Raju, R. R. Venkata; Katewa, S. S.; Kant, Shashi; Raju, Vatsavaya S.; Uniyal, B. P.; Debnath, Bijan; Rout, D. K.; Thapa, Rajesh; Joseph, Shijo; Chhetri, Pradeep; Ramachandran, Reshma M.

2015-07-01

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge's life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).

Characterizing the interface between wild ducks and poultry to evaluate the potential of transmission of avian pathogens.

PubMed

Cappelle, Julien; Gaidet, Nicolas; Iverson, Samuel A; Takekawa, John Y; Newman, Scott H; Fofana, Bouba; Gilbert, Marius

2011-11-15

Characterizing the interface between wild and domestic animal populations is increasingly recognized as essential in the context of emerging infectious diseases (EIDs) that are transmitted by wildlife. More specifically, the spatial and temporal distribution of contact rates between wild and domestic hosts is a key parameter for modeling EIDs transmission dynamics. We integrated satellite telemetry, remote sensing and ground-based surveys to evaluate the spatio-temporal dynamics of indirect contacts between wild and domestic birds to estimate the risk that avian pathogens such as avian influenza and Newcastle viruses will be transmitted between wildlife to poultry. We monitored comb ducks (Sarkidiornis melanotos melanotos) with satellite transmitters for seven months in an extensive Afro-tropical wetland (the Inner Niger Delta) in Mali and characterise the spatial distribution of backyard poultry in villages. We modelled the spatial distribution of wild ducks using 250-meter spatial resolution and 8-days temporal resolution remotely-sensed environmental indicators based on a Maxent niche modelling method. Our results show a strong seasonal variation in potential contact rate between wild ducks and poultry. We found that the exposure of poultry to wild birds was greatest at the end of the dry season and the beginning of the rainy season, when comb ducks disperse from natural water bodies to irrigated areas near villages. Our study provides at a local scale a quantitative evidence of the seasonal variability of contact rate between wild and domestic bird populations. It illustrates a GIS-based methodology for estimating epidemiological contact rates at the wildlife and livestock interface integrating high-resolution satellite telemetry and remote sensing data.

Bio-Optical Properties of the Arabian Sea as Determined by In Situ and Sea WiFS Data

NASA Technical Reports Server (NTRS)

Trees, Charles C.

1997-01-01

The overall objective of this work was to characterize optical and fluorescence properties in the euphotic zone during two British Ocean Flux Study (BOFS) Arabian Sea cruises. This was later expanded in 1995 to include three U.S. JGOFS Arabian Sea Cruises. The region was to be divided into one or more "bio-optical provinces," within each of which a single set of regression models was to be developed to relate the vertical distribution of irradiance attenuation and normalized fluorescence (SF and NF) to remote sensing reflectance and diffuse attenuation coefficient. The working hypothesis was that over relatively large spatial and temporal scales, the vertical profiles of bio-optical properties were predictable. The specific technical objectives were: (1) To characterize the vertical distribution of the inherent and apparent optical properties by measuring downwelling and upwelling irradiances, upwelling radiances, scalar irradiance of PAR, and beam transmissions at each station - from these data, spectral diffuse attenuation coefficients, irradiance reflectances, remote sensing reflectances, surface-leaving radiances and beam attenuation coefficients were determined; (2) To characterize the spectral absorption of total particulate, detrital, and dissolved organic material at each station from discrete water samples; (3) To describe the vertical distribution of photoadaptive properties in the water column by measuring profiles of stimulated (SF) and natural (NF) fluorescence and examining relationships between SF and NF as a function of diffuse optical depth, pigment biomass and primary productivity; and (4) To establish locally derived, in-water algorithms relating remote sensing reflectance spectra to diffuse attenuation coefficients, phytoplankton pigment concentrations and primary productivity, through intercomparisons with in situ measurements, for application to SeaWiFS data.

Remote Sensing: A Film Review.

ERIC Educational Resources Information Center

Carter, David J.

1986-01-01

Reviews the content of 19 films on remote sensing published between 1973 and 1980. Concludes that they are overly simplistic, notably outdated, and generally too optimistic about the potential of remote sensing from space for resource exploration and environmental problem-solving. Provides names and addresses of more current remote sensing…

Geostatistics and remote sensing using NOAA-AVHRR satellite imagery as predictive tools in tick distribution and habitat suitability estimations for Boophilus microplus (Acari: Ixodidae) in South America. National Oceanographic and Atmosphere Administration-Advanced Very High Resolution Radiometer.

PubMed

Estrada-Peña, A

1999-02-01

Remote sensing based on NOAA (National Oceanographic and Atmosphere Administration) satellite imagery was used, together with geostatistics (cokriging) to model the correlation between the temperature and vegetation variables and the distribution of the cattle tick, Boophilus microplus (Canestrini), in the Neotropical region. The results were used to map the B. microplus habitat suitability on a continental scale. A database of B. microplus capture localities was used, which was tabulated with the AVHRR (Advanced Very High Resolution Radiometer) images from the NOAA satellite series. They were obtained at 10 days intervals between 1983 and 1994, with an 8 km resolution. A cokriging system was generated to extrapolate the results. The data for habitat suitability obtained through two vegetation and four temperature variables were strongly correlated with the known distribution of B. microplus (sensitivity 0.91; specificity 0.88) and provide a good estimation of the tick habitat suitability. This model could be used as a guide to the correct interpretation of the distribution limits of B. microplus. It can be also used to prepare eradication campaigns or to make predictions about the effects of global change on the distribution of the parasite.

Estimation of rainfall using remote sensing for Riyadh climate, KSA

NASA Astrophysics Data System (ADS)

AlHassoun, Saleh A.

2013-05-01

Rainfall data constitute an important parameter for studying water resources-related problems. Remote sensing techniques could provide rapid and comprehensive overview of the rainfall distribution in a given area. Thus, the infrared data from the LandSat satellite in conjunction with the Scofield-oliver method were used to monitor and model rainfall in Riyadh area as a resemble of any area in the Kingdom of Saudi Arabia(KSA). Four convective clouds that covered two rain gage stations were analyzed. Good estimation of rainfall was obtained from satellite images. The results showed that the satellite rainfall estimations were well correlated to rain gage measurements. The satellite climate data appear to be useful for monitoring and modeling rainfall at any area where no rain gage is available.

The IEEE GRSS Standardized Remote Sensing Data Website: A Step Towards "Science 2.0" in Remote Sensing

NASA Astrophysics Data System (ADS)

Dell'Acqua, Fabio; Iannelli, Gianni Cristian; Kerekes, John; Lisini, Gianni; Moser, Gabriele; Ricardi, Niccolo; Pierce, Leland

2016-08-01

The issue of homogeneity in performance assessment of proposed algorithms for information extraction is generally perceived also in the Earth Observation (EO) domain. Different authors propose different datasets to test their developed algorithms and to the reader it is frequently difficult to assess which is better for his/her specific application, given the wide variability in test sets that makes pure comparison of e.g. accuracy values less meaningful than one would desire. With our work, we gave a modest contribution to ease the problem by making it possible to automatically distribute a limited set of possible "standard" open datasets, together with some ground truth info, and automatically assess processing results provided by the users.

Remote sensing of the Fram Strait marginal ice zone

USGS Publications Warehouse

Shuchman, R.A.; Burns, B.A.; Johannessen, O.M.; Josberger, E.G.; Campbell, W.J.; Manley, T.O.; Lannelongue, N.

1987-01-01

Sequential remote sensing images of the Fram Strait marginal ice zone played a key role in elucidating the complex interactions of the atmosphere, ocean, and sea ice. Analysis of a subset of these images covering a 1-week period provided quantitative data on the mesoscale ice morphology, including ice edge positions, ice concentrations, floe size distribution, and ice kinematics. The analysis showed that, under light to moderate wind conditions, the morphology of the marginal ice zone reflects the underlying ocean circulation. High-resolution radar observations showed the location and size of ocean eddies near the ice edge. Ice kinematics from sequential radar images revealed an ocean eddy beneath the interior pack ice that was verified by in situ oceanographic measurements.