A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

USGS Publications Warehouse

Giri, C.; Zhu, Z.; Reed, B.

2005-01-01

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced

Globally scalable generation of high-resolution land cover from multispectral imagery

NASA Astrophysics Data System (ADS)

Stutts, S. Craig; Raskob, Benjamin L.; Wenger, Eric J.

2017-05-01

We present an automated method of generating high resolution ( 2 meter) land cover using a pattern recognition neural network trained on spatial and spectral features obtained from over 9000 WorldView multispectral images (MSI) in six distinct world regions. At this resolution, the network can classify small-scale objects such as individual buildings, roads, and irrigation ponds. This paper focuses on three key areas. First, we describe our land cover generation process, which involves the co-registration and aggregation of multiple spatially overlapping MSI, post-aggregation processing, and the registration of land cover to OpenStreetMap (OSM) road vectors using feature correspondence. Second, we discuss the generation of land cover derivative products and their impact in the areas of region reduction and object detection. Finally, we discuss the process of globally scaling land cover generation using cloud computing via Amazon Web Services (AWS).

NASA Satellite Captures Super Bowl Cities - Denver, CO

NASA Image and Video Library

2016-02-06

Landsat 7 image of Denver area acquired Nov 3, 2015. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD...Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Seattle [annotated

NASA Image and Video Library

2015-01-30

Landsat 7 image of Seattle, Washington acquired August 23, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD. Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Boston/Providence

NASA Image and Video Library

2015-01-30

Landsat 7 image of Boston/Providence area acquired August 25, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD...Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Phoenix [annotated

NASA Image and Video Library

2015-01-30

Landsat 7 image of Phoenix, Arizona acquired November 28, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD. Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data

USGS Publications Warehouse

Giri, Chandra; Long, Jordan

2014-01-01

Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

NASA Satellite Captures Super Bowl Cities - Charlotte, NC

NASA Image and Video Library

2016-02-06

Landsat 7 image of the Charlotte, NC area acquired Oct 18, 2015. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD...Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

The Bidirectional Reflectance Distribution Function and Albedo Analysis for Various Land-Cover Types in the Midwestern United States for the Tempo Satellite

NASA Astrophysics Data System (ADS)

Marshall, Bethany

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite, scheduled to be launched around the year 2020, is the first phase of NASA's next generation of missions that will study the composition of the Earth's atmosphere. TEMPO provides a new set of high-resolution ( 0.4 nm) spectral data in the ultraviolet and visible portion of the electromagnetic spectrum that can be used to measure atmospheric pollutants. Due to its high spectral resolution and hourly temporal resolution covering entire North America, TEMPO data can also be used along with the calculation of spectral indices using the bidirectional reflection distribution functions (BRDF) or albedo to improve crop growth and yield monitoring for regional food security. The objectives of this thesis research were to 1) characterize BRDF/albedo of various land-cover types in Midwestern United States that can be used to remove land surface competent from at-sensor TEMPO radiances for accurate estimation of atmospheric chemistry and 2) evaluation of TEMPO data for regional agro-ecosystem studies. To this end, we: (1) collected 461 upwelling and downwelling solar irradiances and spectral albedo of various land-cover types (e.g., grapevine, maize, soybean, tomato, rock, asphalt road and concrete pave way, clean and turbid waters) at 110 sites in the States of Missouri, Illinois, Indiana, and Colorado using a PSR-3500 hand-held Spectroradiometer; (2) conducted a field and manned aircraft data collection campaign in Maryland Heights, Missouri using the Geo-TASO flight instrument flown onboard the NASA HU-25C Falcon aircraft on August 13, 2014; and (3) utilized Ross-Li Kernel BRDF model and MODTRAN radiative transfer simulations to characterize BRDF/albedo of various land-cover types. TEMPO retrieval of atmospheric gases must account for the effects of surface BRDF/albedo. Since BRDF is an inherent optical properties of surface, this research will contribute to the TEMPO mission by providing high

NASA Satellite Captures Super Bowl Cities - Boston/Providence [annotated

NASA Image and Video Library

2015-01-30

Landsat 7 image of Boston/Providence area acquired August 25, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD. Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Multi Satellites Monitoring of Land Use/Cover Change and Its Driving Forces in Kashgar Region, China

NASA Astrophysics Data System (ADS)

Maimaitiaili, Ayisulitan; Aji, xiaokaiti; Kondoh, Akihiko

2016-04-01

Multi Satellites Monitoring of Land Use/Cover Change and Its Driving Forces in Kashgar Region, China Ayisulitan Maimaitiaili1, Xiaokaiti Aji2 Akihiko Kondoh2 1Graduate School of Science, Chiba University, Japan 2Center for Environmental Remote Sensing, Chiba University The spatio-temporal changes of Land Use/Cover (LUCC) and its driving forces in Kashgar region, Xinjiang Province, China, are investigated by using satellite remote sensing and a geographical information system (GIS). Main goal of this paper is to quantify the drivers of LUCC. First, considering lack of the Land Cover (LC) map in whole study area, we produced LC map by using Landsat images. Land use information from Landsat data was collected using maximum likelihood classification method. Land use change was studied based on the change detection method of land use types. Second, because the snow provides a key water resources for stream flow, agricultural production and drinking water for sustaining large population in Kashgar region, snow cover are estimated by Spot Vegetation data. Normalized Difference Snow Index (NDSI) algorithm are applied to make snow cover map, which is used to screen the LUCC and climate change. The best agreement is found with threshold value of NDSI≥0.2 to generate multi-temporal snow cover and snowmelt maps. Third, driving forces are systematically identified by LC maps and statistical data such as climate and socio-economic data, regarding to i) the climate changes and ii) socioeconomic development that the spatial correlation among LUCC, snow cover change, climate and socioeconomic changes are quantified by using liner regression model and negative / positive trend analysis. Our results showed that water bodies, bare land and grass land have decreasing notably. By contrast, crop land and urban area have continually increasing significantly, which are dominated in study area. The area of snow/ice have fluctuated and has strong seasonal trends, total annual snow cover

Mapping of land cover in northern California with simulated hyperspectral satellite imagery

NASA Astrophysics Data System (ADS)

Clark, Matthew L.; Kilham, Nina E.

2016-09-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Analysis of hyperspectral, or imaging spectrometer, imagery has shown an impressive capacity to map a wide range of natural and anthropogenic land cover. Applications have been mostly with single-date imagery from relatively small spatial extents. Future hyperspectral satellites will provide imagery at greater spatial and temporal scales, and there is a need to assess techniques for mapping land cover with these data. Here we used simulated multi-temporal HyspIRI satellite imagery over a 30,000 km2 area in the San Francisco Bay Area, California to assess its capabilities for mapping classes defined by the international Land Cover Classification System (LCCS). We employed a mapping methodology and analysis framework that is applicable to regional and global scales. We used the Random Forests classifier with three sets of predictor variables (reflectance, MNF, hyperspectral metrics), two temporal resolutions (summer, spring-summer-fall), two sample scales (pixel, polygon) and two levels of classification complexity (12, 20 classes). Hyperspectral metrics provided a 16.4-21.8% and 3.1-6.7% increase in overall accuracy relative to MNF and reflectance bands, respectively, depending on pixel or polygon scales of analysis. Multi-temporal metrics improved overall accuracy by 0.9-3.1% over summer metrics, yet increases were only significant at the pixel scale of analysis. Overall accuracy at pixel scales was 72.2% (Kappa 0.70) with three seasons of metrics. Anthropogenic and homogenous natural vegetation classes had relatively high confidence and producer and user accuracies were over 70%; in comparison, woodland and forest classes had considerable confusion. We next focused on plant functional types with relatively pure spectra by removing open-canopy shrublands

Modelling land cover change in the Ganga basin

NASA Astrophysics Data System (ADS)

Moulds, S.; Tsarouchi, G.; Mijic, A.; Buytaert, W.

2013-12-01

Over recent decades the green revolution in India has driven substantial environmental change. Modelling experiments have identified northern India as a 'hot spot' of land-atmosphere coupling strength during the boreal summer. However, there is a wide range of sensitivity of atmospheric variables to soil moisture between individual climate models. The lack of a comprehensive land cover change dataset to force climate models has been identified as a major contributor to model uncertainty. In this work a time series dataset of land cover change between 1970 and 2010 is constructed for northern India to improve the quantification of regional hydrometeorological feedbacks. The MODIS instrument on board the Aqua and Terra satellites provides near-continuous remotely sensed datasets from 2000 to the present day. However, the quality of satellite products before 2000 is poor. To complete the dataset MODIS images are extrapolated back in time using the Conversion of Land Use and its Effects at small regional extent (CLUE-s) modelling framework. Non-spatial estimates of land cover area from national agriculture and forest statistics, available on a state-wise, annual basis, are used as a direct model input. Land cover change is allocated spatially as a function of biophysical and socioeconomic drivers identified using logistic regression. This dataset will provide an essential input to a high resolution, physically based land surface model to generate the lower boundary condition to assess the impact of land cover change on regional climate.

Land-Cover Trends of the Southern California Mountains Ecoregion

USGS Publications Warehouse

Soulard, Christopher E.; Raumann, Christian G.; Wilson, Tamara S.

2007-01-01

This report presents an assessment of land-use and land-cover (LU/LC) change in the Southern California Mountains ecoregion for the period 1973-2001. The Southern California Mountains is one of 84 Level-III ecoregions as defined by the U.S. Environmental Protection Agency (EPA). Ecoregions have served as a spatial framework for environmental resource management, denoting areas that contain a geographically distinct assemblage of biotic and abiotic phenomena including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The established Land Cover Trends methodology generates estimates of change for ecoregions using a probability sampling approach and change-detection analysis of thematic land-cover images derived from Landsat satellite imagery.

A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi

USGS Publications Warehouse

Smith, Kathryn E.L.; Nayegandhi, Amar; Brock, John C.

2010-01-01

INTRODUCTION Land-use and land-cover (LULC) data provide important information for environmental management. Data pertaining to land-cover and land-management activities are a common requirement for spatial analyses, such as watershed modeling, climate change, and hazard assessment. In coastal areas, land development, storms, and shoreline modification amplify the need for frequent and detailed land-cover datasets. The northern Gulf of Mexico coastal area is no exception. The impact of severe storms, increases in urban area, dramatic changes in land cover, and loss of coastal-wetland habitat all indicate a vital need for reliable and comparable land-cover data. Four main attributes define a land-cover dataset: the date/time of data collection, the spatial resolution, the type of classification, and the source data. The source data are the foundation dataset used to generate LULC classification and are typically remotely sensed data, such as aerial photography or satellite imagery. These source data have a large influence on the final LULC data product, so much so that one can classify LULC datasets into two general groups: LULC data derived from aerial photography and LULC data derived from satellite imagery. The final LULC data can be converted from one format to another (for instance, vector LULC data can be converted into raster data for analysis purposes, and vice versa), but each subsequent dataset maintains the imprint of the source medium within its spatial accuracy and data features. The source data will also influence the spatial and temporal resolution, as well as the type of classification. The intended application of the LULC data typically defines the type of source data and methodology, with satellite imagery being selected for large landscapes (state-wide, national data products) and repeatability (environmental monitoring and change analysis). The coarse spatial scale and lack of refined land-use categories are typical drawbacks to satellite

Land use and land cover change in the North Central Appalachians ecoregion

USGS Publications Warehouse

Napton, D.E.; Sohl, Terry L.; Auch, Roger F.; Loveland, Thomas R.

2003-01-01

The North Central Appalachians ecoregion, spanning northern Pennsylvania and southern New York, has a long history of land use and land cover change. Turn-of-the-century logging dramatically altered the natural landscape of the ecoregion, but subsequent regeneration returned the ecoregion to a forest dominated condition. To understand contemporary land use and land cover changes, the U.S. Geological Survey with NASA and the U.S. Environmental Protection Agency used a random sample of satellite remotely sensed data for 1973, 1980, 1986, 1992, and 2000 to estimate the rates and assess the primary drivers of change in the North Central Appalachians. The overall change was 6.2%. The 1973-1980 period had the lowest rate of change (1.5%); the highest rate (2.9%) occurred during the 1992-2000 period. The primary conversions were deforestation through harvesting and natural disturbance (i.e., tornados) followed by regeneration, and conversion of forests to mining and urban lands. The primary drivers of the change included changes in access, energy and forest prices, and attitudes toward the environment.

NASA Satellite Captures Super Bowl Cities - Santa Clara, CA

NASA Image and Video Library

2017-12-08

Landsat 7 image of the Santa Clara area acquired Nov 16, 2015. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD...Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

A global dataset of crowdsourced land cover and land use reference data.

PubMed

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-06-13

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

A global dataset of crowdsourced land cover and land use reference data

PubMed Central

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F.; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-01-01

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general. PMID:28608851

Land Cover Analysis of Temperate Asia

NASA Technical Reports Server (NTRS)

Justice, Chris

1998-01-01

Satellite data from the advanced very high resolution radiometer (AVHRR) instrument were used to produce a general land cover distribution of temperate Asia (referred to hence as Central Asia) from 1982, starting with the NOAA-7 satellite, and continuing through 1991, ending with the NOAA-11 satellite. Emphasis was placed upon delineating the and and semi-arid zones of Central Asia (largely Mongolia and adjacent areas), mapping broad categories of aggregated land cover, and upon studying photosynthetic capacity increases in Central Asia from 1982 to 1991.

Developed land cover of Puerto Rico

Treesearch

William A. Gould; Sebastian Martinuzzi; Olga M. Ramos Gonzalez

2008-01-01

This map shows the distribution of developed land cover in Puerto Rico (Martinuzzi et al. 2007). Developed land cover refers to urban, built-up and non-vegetated areas that result from human activity. These typically include built structures, concrete, asphalt, and other infrastructure. The developed land cover was estimated using Landsat 7 ETM+ satellite images pan...

Characterizing Satellite Rainfall Errors based on Land Use and Land Cover and Tracing Error Source in Hydrologic Model Simulation

NASA Astrophysics Data System (ADS)

Gebregiorgis, A. S.; Peters-Lidard, C. D.; Tian, Y.; Hossain, F.

2011-12-01

Hydrologic modeling has benefited from operational production of high resolution satellite rainfall products. The global coverage, near-real time availability, spatial and temporal sampling resolutions have advanced the application of physically based semi-distributed and distributed hydrologic models for wide range of environmental decision making processes. Despite these successes, the existence of uncertainties due to indirect way of satellite rainfall estimates and hydrologic models themselves remain a challenge in making meaningful and more evocative predictions. This study comprises breaking down of total satellite rainfall error into three independent components (hit bias, missed precipitation and false alarm), characterizing them as function of land use and land cover (LULC), and tracing back the source of simulated soil moisture and runoff error in physically based distributed hydrologic model. Here, we asked "on what way the three independent total bias components, hit bias, missed, and false precipitation, affect the estimation of soil moisture and runoff in physically based hydrologic models?" To understand the clear picture of the outlined question above, we implemented a systematic approach by characterizing and decomposing the total satellite rainfall error as a function of land use and land cover in Mississippi basin. This will help us to understand the major source of soil moisture and runoff errors in hydrologic model simulation and trace back the information to algorithm development and sensor type which ultimately helps to improve algorithms better and will improve application and data assimilation in future for GPM. For forest and woodland and human land use system, the soil moisture was mainly dictated by the total bias for 3B42-RT, CMORPH, and PERSIANN products. On the other side, runoff error was largely dominated by hit bias than the total bias. This difference occurred due to the presence of missed precipitation which is a major

Consequences of land use and land cover change

USGS Publications Warehouse

Slonecker, E. Terrence; Barnes, Christopher; Karstensen, Krista; Milheim, Lesley E.; Roig-Silva, Coral M.

2013-01-01

The U.S. Geological Survey (USGS) Climate and Land Use Change Mission Area is one of seven USGS mission areas that focuses on making substantial scientific "...contributions to understanding how Earth systems interact, respond to, and cause global change". Using satellite and other remotely sensed data, USGS scientists monitor patterns of land cover change over space and time at regional, national, and global scales. These data are analyzed to understand the causes and consequences of changing land cover, such as economic impacts, effects on water quality and availability, the spread of invasive species, habitats and biodiversity, carbon fluctuations, and climate variability. USGS scientists are among the leaders in the study of land cover, which is a term that generally refers to the vegetation and artificial structures that cover the land surface. Examples of land cover include forests, grasslands, wetlands, water, crops, and buildings. Land use involves human activities that take place on the land. For example, "grass" is a land cover, whereas pasture and recreational parks are land uses that produce a cover of grass.

Next-Generation NASA Earth-Orbiting Relay Satellites: Fusing Optical and Microwave Communications

NASA Technical Reports Server (NTRS)

Israel, David J.; Shaw, Harry

2018-01-01

NASA is currently considering architectures and concepts for the generation of relay satellites that will replace the Tracking and Data Relay Satellite (TDRS) constellation, which has been flying since 1983. TDRS-M, the last of the second TDRS generation, launched in August 2017, extending the life of the TDRS constellation beyond 2030. However, opportunities exist to re-engineer the concepts of geosynchronous Earth relay satellites. The needs of the relay satellite customers have changed dramatically over the last 34 years since the first TDRS launch. There is a demand for greater bandwidth as the availability of the traditional RF spectrum for space communications diminishes and the demand for ground station access grows. The next generation of NASA relay satellites will provide for operations that have factored in these new constraints. In this paper, we describe a heterogeneous constellation of geosynchronous relay satellites employing optical and RF communications. The new constellation will enable new optical communications services formed by user-to-space relay, space relay-to-space relay and space relay-to-ground links. It will build upon the experience from the Lunar Laser Communications Demonstration from 2013 and the Laser Communications Relay Demonstration to be launched in 2019.Simultaneous to establishment of the optical communications space segment, spacecraft in the TDRS constellation will be replaced with RF relay satellites with targeted subsets of the TDRS capabilities. This disaggregation of the TDRS service model will allow for flexibility in replenishing the needs of legacy users as well as addition of new capabilities for future users. It will also permit the U.S. government access to launch capabilities such as rideshare and to hosted payloads that were not previously available.In this paper, we also explore how the next generation of Earth relay satellites provides a significant boost in the opportunities for commercial providers to the

Next-Generation NASA Earth-Orbiting Relay Satellites: Fusing Microwave and Optical Communications

NASA Technical Reports Server (NTRS)

Israel, David J.

2018-01-01

NASA is currently considering architectures and concepts for the generation of relay satellites that will replace the Tracking and Data Relay Satellite (TDRS) constellation, which has been flying since 1983. TDRS-M, the last of the second TDRS generation, launched in August 2017, extending the life of the TDRS constellation beyond 2030. However, opportunities exist to re-engineer the concepts of geosynchronous Earth relay satellites. The needs of the relay satellite customers have changed dramatically over the last 34 years since the first TDRS launch. There is a demand for greater bandwidth as the availability of the traditional RF spectrum for space communications diminishes and the demand for ground station access grows. The next generation of NASA relay satellites will provide for operations that have factored in these new constraints. In this paper, we describe a heterogeneous constellation of geosynchronous relay satellites employing optical and RF communications. The new constellation will enable new optical communications services formed by user-to-space relay, space relay-to-space relay and space relay-to-ground links. It will build upon the experience from the Lunar Laser Communications Demonstration from 2013 and the Laser Communications Relay Demonstration to be launched in 2019.Simultaneous to establishment of the optical communications space segment, spacecraft in the TDRS constellation will be replaced with RF relay satellites with targeted subsets of the TDRS capabilities. This disaggregation of the TDRS service model will allow for flexibility in replenishing the needs of legacy users as well as addition of new capabilities for future users. It will also permit the U.S. government access to launch capabilities such as rideshare and to hosted payloads that were not previously available. In this paper, we also explore how the next generation of Earth relay satellites provides a significant boost in the opportunities for commercial providers to the

Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

NASA Astrophysics Data System (ADS)

Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

2016-06-01

The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

Land cover changes in central Sonora Mexico

Treesearch

Diego Valdez-Zamudio; Alejandro Castellanos-Villegas; Stuart Marsh

2000-01-01

Remote sensing techniques have been demonstrated to be very effective tools to help detect, analyze, and evaluate land cover changes in natural areas of the world. Changes in land cover can generally be attributed to either natural or anthropogenic forces. Multitemporal satellite imagery and airborne videography were used to detect, analyze, and evaluate land cover...

Built-Up Area and Land Cover Extraction Using High Resolution Pleiades Satellite Imagery for Midrand, in Gauteng Province, South Africa

NASA Astrophysics Data System (ADS)

Fundisi, E.; Musakwa, W.

2017-09-01

Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.

Impacts of land cover transitions on surface temperature in China based on satellite observations

NASA Astrophysics Data System (ADS)

Zhang, Yuzhen; Liang, Shunlin

2018-02-01

China has experienced intense land use and land cover changes during the past several decades, which have exerted significant influences on climate change. Previous studies exploring related climatic effects have focused mainly on one or two specific land use changes, or have considered all land use and land cover change types together without distinguishing their individual impacts, and few have examined the physical processes of the mechanism through which land use changes affect surface temperature. However, in this study, we considered satellite-derived data of multiple land cover changes and transitions in China. The objective was to obtain observational evidence of the climatic effects of land cover transitions in China by exploring how they affect surface temperature and to what degree they influence it through the modification of biophysical processes, with an emphasis on changes in surface albedo and evapotranspiration (ET). To achieve this goal, we quantified the changes in albedo, ET, and surface temperature in the transition areas, examined their correlations with temperature change, and calculated the contributions of different land use transitions to surface temperature change via changes in albedo and ET. Results suggested that land cover transitions from cropland to urban land increased land surface temperature (LST) during both daytime and nighttime by 0.18 and 0.01 K, respectively. Conversely, the transition of forest to cropland tended to decrease surface temperature by 0.53 K during the day and by 0.07 K at night, mainly through changes in surface albedo. Decreases in both daytime and nighttime LST were observed over regions of grassland to forest transition, corresponding to average values of 0.44 and 0.20 K, respectively, predominantly controlled by changes in ET. These results highlight the necessity to consider the individual climatic effects of different land cover transitions or conversions in climate research studies. This short

Land cover mapping of North and Central America—Global Land Cover 2000

USGS Publications Warehouse

Latifovic, Rasim; Zhu, Zhi-Liang

2004-01-01

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

Towards monitoring land-cover and land-use changes at a global scale: the global land survey 2005

USGS Publications Warehouse

Gutman, G.; Byrnes, Raymond A.; Masek, J.; Covington, S.; Justice, C.; Franks, S.; Headley, Rachel

2008-01-01

Land cover is a critical component of the Earth system, infl uencing land-atmosphere interactions, greenhouse gas fl uxes, ecosystem health, and availability of food, fi ber, and energy for human populations. The recent Integrated Global Observations of Land (IGOL) report calls for the generation of maps documenting global land cover at resolutions between 10m and 30m at least every fi ve years (Townshend et al., in press). Moreover, despite 35 years of Landsat observations, there has not been a unifi ed global analysis of land-cover trends nor has there been a global assessment of land-cover change at Landsat-like resolution. Since the 1990s, the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) have supported development of data sets based on global Landsat observations (Tucker et al., 2004). These land survey data sets, usually referred to as GeoCover ™, provide global, orthorectifi ed, typically cloud-free Landsat imagery centered on the years 1975, 1990, and 2000, with a preference for leaf-on conditions. Collectively, these data sets provided a consistent set of observations to assess land-cover changes at a decadal scale. These data are freely available via the Internet from the USGS Center for Earth Resources Observation and Science (EROS) (see http://earthexplorer.usgs.gov or http://glovis.usgs.gov). This has resulted in unprecedented downloads of data, which are widely used in scientifi c studies of land-cover change (e.g., Boone et al., 2007; Harris et al., 2005; Hilbert, 2006; Huang et al. 2007; Jantz et al., 2005, Kim et al., 2007; Leimgruber, 2005; Masek et al., 2006). NASA and USGS are continuing to support land-cover change research through the development of GLS2005 - an additional global Landsat assessment circa 20051 . Going beyond the earlier initiatives, this data set will establish a baseline for monitoring changes on a 5-year interval and will pave the way toward continuous global land-cover

How Scientists Differentiate Between Land Cover Types

NASA Technical Reports Server (NTRS)

2002-01-01

Before scientists can transform raw satellite image data into land cover maps, they must decide on what categories of land cover they would like to use. Categories are simply the types of landscape that the scientists are trying to map and can vary greatly from map to map. For flood maps, there may be only two categories-dry land and wet land-while a standard global land cover map may have seventeen categories including closed shrub lands, savannas, evergreen needle leaf forest, urban areas, and ice/snow. The only requirement for any land cover category is that it have a distinct spectral signature that a satellite can record. As can be seen through a prism, many different colors (wavelengths) make up the spectra of sunlight. When sunlight strikes objects, certain wavelengths are absorbed and others are reflected or emitted. The unique way in which a given type of land cover reflects and absorbs light is known as its spectral signature. Anyone who has flown over the midwestern United States has seen evidence of this phenomenon. From an airplane window, the ground appears as a patchwork of different colors formed by the fields of crops planted there. The varying pigments of the leaves, the amount of foliage per square foot, the age of the plants, and many other factors create this tapestry. Most imaging satellites are sensitive to specific wavelengths of light, including infrared wavelengths that cannot be seen with the naked eye. Passive satellite remote sensors-such as those flown on Landsat 5, Landsat 7, and Terra-have a number of light detectors (photoreceptors) on board that measure the energy reflected or emitted by the Earth. One light detector records only the blue part of the spectrum coming off the Earth. Another observes all the yellow-green light and still another picks up on all the near-infrared light. The detectors scan the Earth's surface as the satellite travels in a circular orbit very nearly from pole-to-pole. To differentiate between types of

Advances in Land Data Assimilation at the NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Reichle, Rolf

2009-01-01

Research in land surface data assimilation has grown rapidly over the last decade. In this presentation we provide a brief overview of key research contributions by the NASA Goddard Space Flight Center (GSFC). The GSFC contributions to land assimilation primarily include the continued development and application of the Land Information System (US) and the ensemble Kalman filter (EnKF). In particular, we have developed a method to generate perturbation fields that are correlated in space, time, and across variables and that permit the flexible modeling of errors in land surface models and observations, along with an adaptive filtering approach that estimates observation and model error input parameters. A percentile-based scaling method that addresses soil moisture biases in model and observational estimates opened the path to the successful application of land data assimilation to satellite retrievals of surface soil moisture. Assimilation of AMSR-E surface soil moisture retrievals into the NASA Catchment model provided superior surface and root zone assimilation products (when validated against in situ measurements and compared to the model estimates or satellite observations alone). The multi-model capabilities of US were used to investigate the role of subsurface physics in the assimilation of surface soil moisture observations. Results indicate that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Building on this experience, GSFC leads the development of the Level 4 Surface and Root-Zone Soil Moisture (L4_SM) product for the planned NASA Soil-Moisture-Active-Passive (SMAP) mission. A key milestone was the design and execution of an Observing System Simulation Experiment that quantified the contribution of soil moisture retrievals to land data assimilation products as a function of retrieval and land model skill and yielded an estimate of the error budget for the

Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites

NASA Astrophysics Data System (ADS)

Belward, Alan S.; Skøien, Jon O.

2015-05-01

This paper presents a compendium of satellites under civilian and/or commercial control with the potential to gather global land-cover observations. From this we show that a growing number of sovereign states are acquiring capacity for space based land-cover observations and show how geopolitical patterns of ownership are changing. We discuss how the number of satellites flying at any time has progressed as a function of increased launch rates and mission longevity, and how the spatial resolutions of the data they collect has evolved. The first such satellite was launched by the USA in 1972. Since then government and/or private entities in 33 other sovereign states and geopolitical groups have chosen to finance such missions and 197 individual satellites with a global land-cover observing capacity have been successfully launched. Of these 98 were still operating at the end of 2013. Since the 1970s the number of such missions failing within 3 years of launch has dropped from around 60% to less than 20%, the average operational life of a mission has almost tripled, increasing from 3.3 years in the 1970s to 8.6 years (and still lengthening), the average number of satellites launched per-year/per-decade has increased from 2 to 12 and spatial resolution increased from around 80 m to less than 1 m multispectral and less than half a meter for panchromatic; synthetic aperture radar resolution has also fallen, from 25 m in the 1970s to 1 m post 2007. More people in more countries have access to data from global land-cover observing spaceborne missions at a greater range of spatial resolutions than ever before. We provide a compendium of such missions, analyze the changes and shows how innovation, the need for secure data-supply, national pride, falling costs and technological advances may underpin the trends we document.

Generating Land Surface Reflectance for the New Generation of Geostationary Satellite Sensors with the MAIAC Algorithm

NASA Astrophysics Data System (ADS)

Wang, W.; Wang, Y.; Hashimoto, H.; Li, S.; Takenaka, H.; Higuchi, A.; Lyapustin, A.; Nemani, R. R.

2017-12-01

The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor land surface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of-atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked ("remembered") over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance

Citizen science land cover classification based on ground and satellite imagery: Case study Day River in Vietnam

NASA Astrophysics Data System (ADS)

Nguyen, Son Tung; Minkman, Ellen; Rutten, Martine

2016-04-01

Citizen science is being increasingly used in the context of environmental research, thus there are needs to evaluate cognitive ability of humans in classifying environmental features. With the focus on land cover, this study explores the extent to which citizen science can be applied in sensing and measuring the environment that contribute to the creation and validation of land cover data. The Day Basin in Vietnam was selected to be the study area. Different methods to examine humans' ability to classify land cover were implemented using different information sources: ground based photos - satellite images - field observation and investigation. Most of the participants were solicited from local people and/or volunteers. Results show that across methods and sources of information, there are similar patterns of agreement and disagreement on land cover classes among participants. Understanding these patterns is critical to create a solid basis for implementing human sensors in earth observation. Keywords: Land cover, classification, citizen science, Landsat 8

Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods

USGS Publications Warehouse

Xian, George; Homer, Collin G.; Fry, Joyce

2009-01-01

The recent release of the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001, which represents the nation's land cover status based on a nominal date of 2001, is widely used as a baseline for national land cover conditions. To enable the updating of this land cover information in a consistent and continuous manner, a prototype method was developed to update land cover by an individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season in 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, land cover classifications at the full NLCD resolution for 2006 areas of change were completed by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain several metropolitan areas including Seattle, Washington; San Diego, California; Sioux Falls, South Dakota; Jackson, Mississippi; and Manchester, New Hampshire. Results from the five study areas show that the vast majority of land cover change was captured and updated with overall land cover classification accuracies of 78.32%, 87.5%, 88.57%, 78.36%, and 83.33% for these areas. The method optimizes mapping efficiency and has the potential to provide users a flexible method to generate updated land cover at national and regional scales by using NLCD 2001 as the baseline.

Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2017-04-01

The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

NASA mobile satellite program

NASA Technical Reports Server (NTRS)

Knouse, G.; Weber, W.

1985-01-01

A three phase development program for ground and space segment technologies which will enhance and enable the second and third generation mobile satellite systems (MSS) is outlined. Phase 1, called the Mobile Satellite Experiment (MSAT-X), is directed toward the development of ground segment technology needed for future MSS generations. Technology validation and preoperational experiments with other government agencies will be carried out during the two year period following launch. The satellite channel capacity needed to carry out these experiments will be obtained from industry under a barter type agreement in exchange for NASA provided launch services. Phase 2 will develop and flight test the multibeam spacecraft antenna technology needed to obtain substantial frequency reuse for second generation commercial systems. Industry will provide the antenna, and NASA will fly it on the Shuttle and test it in orbit. Phase 3 is similar to Phase 2 but will develop an even larger multibeam antenna and test it on the space station.

NASA mobile satellite program

NASA Astrophysics Data System (ADS)

Knouse, G.; Weber, W.

1985-04-01

A three phase development program for ground and space segment technologies which will enhance and enable the second and third generation mobile satellite systems (MSS) is outlined. Phase 1, called the Mobile Satellite Experiment (MSAT-X), is directed toward the development of ground segment technology needed for future MSS generations. Technology validation and preoperational experiments with other government agencies will be carried out during the two year period following launch. The satellite channel capacity needed to carry out these experiments will be obtained from industry under a barter type agreement in exchange for NASA provided launch services. Phase 2 will develop and flight test the multibeam spacecraft antenna technology needed to obtain substantial frequency reuse for second generation commercial systems. Industry will provide the antenna, and NASA will fly it on the Shuttle and test it in orbit. Phase 3 is similar to Phase 2 but will develop an even larger multibeam antenna and test it on the space station.

Land cover

USGS Publications Warehouse

Jorgenson, Janet C.; Joria, Peter C.; Douglas, David C.; Douglas, David C.; Reynolds, Patricia E.; Rhode, E.B.

2002-01-01

Documenting the distribution of land-cover types on the Arctic National Wildlife Refuge coastal plain is the foundation for impact assessment and mitigation of potential oil exploration and development. Vegetation maps facilitate wildlife studies by allowing biologists to quantify the availability of important wildlife habitats, investigate the relationships between animal locations and the distribution or juxtaposition of habitat types, and assess or extrapolate habitat characteristics across regional areas.To meet the needs of refuge managers and biologists, satellite imagery was chosen as the most cost-effective method for mapping the large, remote landscape of the 1002 Area.Objectives of our study were the following: 1) evaluate a vegetation classification scheme for use in mapping. 2) determine optimal methods for producing a satellite-based vegetation map that adequately met the needs of the wildlife research and management objectives; 3) produce a digital vegetation map for the Arctic Refuge coastal plain using Lands at-Thematic Mapper(TM) satellite imagery, existing geobotanical classifications, ground data, and aerial photographs, and 4) perform an accuracy assessment of the map.

Land-Cover Trends of the Central Basin and Range Ecoregion

USGS Publications Warehouse

Soulard, Christopher E.

2006-01-01

a geographically distinct assemblage of biotic and abiotic phenomena including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The established Land Cover Trends methodology generates estimates of LU/LC change using a probability sampling approach and change-detection analysis of thematic land-cover images derived from Landsat satellite imagery.

Monitoring urban land cover change by updating the national land cover database impervious surface products

USGS Publications Warehouse

Xian, George Z.; Homer, Collin G.

2009-01-01

The U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001 is widely used as a baseline for national land cover and impervious conditions. To ensure timely and relevant data, it is important to update this base to a more recent time period. A prototype method was developed to update the land cover and impervious surface by individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season from both 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, impervious surface was estimated for areas of change by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain a variety of metropolitan areas. Results from the five study areas show that the vast majority of impervious surface changes associated with urban developments were accurately captured and updated. The approach optimizes mapping efficiency and can provide users a flexible method to generate updated impervious surface at national and regional scales.

High spatial resolution satellite observations for validation of MODIS land products: IKONOS observations acquired under the NASA scientific data purchase.

Treesearch

Jeffrey T. Morisette; Jaime E. Nickeson; Paul Davis; Yujie Wang; Yuhong Tian; Curtis E. Woodcock; Nikolay Shabanov; Matthew Hansen; Warren B. Cohen; Doug R. Oetter; Robert E. Kennedy

2003-01-01

Phase 1I of the Scientific Data Purchase (SDP) has provided NASA investigators access to data from four different satellite and airborne data sources. The Moderate Resolution Imaging Spectrometer (MODIS) land discipline team (MODLAND) sought to utilize these data in support of land product validation activities with a lbcus on tile EOS Land Validation Core Sites. These...

Landsat continuity: Issues and opportunities for land cover monitoring

USGS Publications Warehouse

Wulder, M.A.; White, Joanne C.; Goward, S.N.; Masek, J.G.; Irons, J.R.; Herold, M.; Cohen, W.B.; Loveland, Thomas R.; Woodcock, C.E.

2008-01-01

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures

Generation and Evaluation of a Global Land Surface Phenology Product from Suomi-NPP VIIRS Observations

NASA Astrophysics Data System (ADS)

Zhang, X.; Liu, L.; Yan, D.; Moon, M.; Liu, Y.; Henebry, G. M.; Friedl, M. A.; Schaaf, C.

2017-12-01

Land surface phenology (LSP) datasets have been produced from a variety of coarse spatial resolution satellite observations at both regional and global scales and spanning different time periods since 1982. However, the LSP product generated from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data at a spatial resolution of 500m, which is termed Land Cover Dynamics (MCD12Q2), is the only global product operationally produced and freely accessible at annual time steps from 2001. Because MODIS instrument is aging and will be replaced by the Visible Infrared Imaging Radiometer Suite (VIIRS), this research focuses on the generation and evaluation of a global LSP product from Suomi-NPP VIIRS time series observations that provide continuity with the MCD12Q2 product. Specifically, we generate 500m VIIRS global LSP data using daily VIIRS Nadir BRDF (bidirectional reflectance distribution function)-Adjusted reflectances (NBAR) in combination with land surface temperature, snow cover, and land cover type as inputs. The product provides twelve phenological metrics (seven phenological dates and five phenological greenness magnitudes), along with six quality metrics characterizing the confidence and quality associated with phenology retrievals at each pixel. In this paper, we describe the input data and algorithms used to produce this new product, and investigate the impact of VIIRS data time series quality on phenology detections across various climate regimes and ecosystems. As part of our analysis, the VIIRS LSP is evaluated using PhenoCam imagery in North America and Asia, and using higher spatial resolution satellite observations from Landsat 8 over an agricultural area in the central USA. We also explore the impact of high frequency cloud cover on the VIIRS LSP product by comparing with phenology detected from the Advanced Himawari Imager (AHI) onboard Himawari-8. AHI is a new geostationary sensor that observes land surface every 10 minutes, which increases

Land cover mapping of Greater Mesoamerica using MODIS data

USGS Publications Warehouse

Giri, Chandra; Jenkins, Clinton N.

2005-01-01

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: (i) qualitative assessment provided good insight in identifying and correcting gross errors; (ii) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest (r2 = 0.88), shrubland (r2 = 0.75), and cropland (r2 = 0.97) but weak positive association for grassland (r2 = 0.26); and (iii) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Comparison of U.S. Forest Land AreaEstimates From Forest Inventory and Analysis, National Resources Inventory, and Four Satellite Image-Derived Land Cover Data Sets

Treesearch

Mark D. Nelson; Ronald E. McRoberts; Veronica C. Lessard

2005-01-01

Our objective was to test one application of remote sensing technology for complementing forest resource assessments by comparing a variety of existing satellite image-derived land cover maps with national inventory-derived estimates of United States forest land area. National Resources Inventory (NRI) 1997 estimates of non-Federal forest land area differed by 7.5...

United states national land cover data base development? 1992-2001 and beyond

USGS Publications Warehouse

Yang, L.

2008-01-01

An accurate, up-to-date and spatially-explicate national land cover database is required for monitoring the status and trends of the nation's terrestrial ecosystem, and for managing and conserving land resources at the national scale. With all the challenges and resources required to develop such a database, an innovative and scientifically sound planning must be in place and a partnership be formed among users from government agencies, research institutes and private sectors. In this paper, we summarize major scientific and technical issues regarding the development of the NLCD 1992 and 2001. Experiences and lessons learned from the project are documented with regard to project design, technical approaches, accuracy assessment strategy, and projecti imiplementation.Future improvements in developing next generation NLCD beyond 2001 are suggested, including: 1) enhanced satellite data preprocessing in correction of atmospheric and adjacency effect and the topographic normalization; 2) improved classification accuracy through comprehensive and consistent training data and new algorithm development; 3) multi-resolution and multi-temporal database targeting major land cover changes and land cover database updates; 4) enriched database contents by including additional biophysical parameters and/or more detailed land cover classes through synergizing multi-sensor, multi-temporal, and multi-spectral satellite data and ancillary data, and 5) transform the NLCD project into a national land cover monitoring program. ?? 2008 IEEE.

Regional forest land cover characterisation using medium spatial resolution satellite data

USGS Publications Warehouse

Huang, Chengquan; Homer, Collin G.; Yang, Limin; Wulder, Michael A.; Franklin, Steven E.

2003-01-01

Increasing demands on forest resources require comprehensive, consistent and up-to-date information on those resources at spatial scales appropriate for management decision-making and for scientific analysis. While such information can be derived using coarse spatial resolution satellite data (e.g. Tucker et al. 1984; Zhu and Evans 1994; Cihlar et al. 1996; Cihlar et al., Chapter 12), many regional applications require more spatial and thematic details than can be derived by using coarse resolution imagery. High spatial resolution satellite data such as IKONOS and Quick Bird images (Aplin et al. 1997), though usable for deriving detailed forest information (Culvenor, Chapter 9), are currently not feasible for wall-to-wall regional applications because of extremely high data cost, huge data volume, and lack of contiguous coverage over large areas. Forest studies over large areas have often been accomplished using data acquired by intermediate spatial resolution sensor systems, including the Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and the Enhanced Thematic Mapper Plus (ETM+) of Landsat, the High Resolution Visible (HRV) of the Systeme Pour l'Observation de la Terre (SPOT), and the Linear Image Self-Scanner (LISS) of the Indian Remote Sensing satellite. These sensor systems are more appropriate for regional applications because they can routinely produce spatially contiguous data over large areas at relatively low cost, and can be used to derive a host of forest attributes (e.g. Cohen et al. 1995; Kimes et al. 1999; Cohen et al. 2001; Huang et al. 2001; Sugumaran 2001). Of the above intermediate spatial resolution satellites, Landsat is perhaps the most widely used in various types of land remote sensing applications, in part because it has provided more extensive spatial and temporal coverage of the globe than any other intermediate resolution satellite. Spatially contiguous Landsat data have been developed for many regions of the globe (e.g. Lunetta and

Characterization and classification of South American land cover types using satellite data

NASA Technical Reports Server (NTRS)

Townshend, J. R. G.; Justice, C. O.; Kalb, V.

1987-01-01

Various methods are compared for carrying out land cover classifications of South America using multitemporal Advanced Very High Resolution Radiometer data. Fifty-two images of the normalized difference vegetation index (NDVI) from a 1-year period are used to generate multitemporal data sets. Three main approaches to land cover classification are considered, namely the use of the principal components transformed images, the use of a characteristic curves procedure based on NDVI values plotted against time, and finally application of the maximum likelihood rule to multitemporal data sets. Comparison of results from training sites indicates that the last approach yields the most accurate results. Despite the reliance on training site figures for performance assessment, the results are nevertheless extremely encouraging, with accuracies for several cover types exceeding 90 per cent.

User Generated Spatial Content Sources for Land Use/Land Cover Validation Purposes: Suitability Analysis and Integration Model

NASA Astrophysics Data System (ADS)

Estima, Jacinto Paulo Simoes

Traditional geographic information has been produced by mapping agencies and corporations, using high skilled people as well as expensive precision equipment and procedures, in a very costly approach. The production of land use and land cover databases are just one example of such traditional approach. On the other side, The amount of Geographic Information created and shared by citizens through the Web has been increasing exponentially during the last decade, resulting from the emergence and popularization of technologies such as the Web 2.0, cloud computing, GPS, smart phones, among others. Such comprehensive amount of free geographic data might have valuable information to extract and thus opening great possibilities to improve significantly the production of land use and land cover databases. In this thesis we explored the feasibility of using geographic data from different user generated spatial content initiatives in the process of land use and land cover database production. Data from Panoramio, Flickr and OpenStreetMap were explored in terms of their spatial and temporal distribution, and their distribution over the different land use and land cover classes. We then proposed a conceptual model to integrate data from suitable user generated spatial content initiatives based on identified dissimilarities among a comprehensive list of initiatives. Finally we developed a prototype implementing the proposed integration model, which was then validated by using the prototype to solve four identified use cases. We concluded that data from user generated spatial content initiatives has great value but should be integrated to increase their potential. The possibility of integrating data from such initiatives in an integration model was proved. Using the developed prototype, the relevance of the integration model was also demonstrated for different use cases. None None None

Monitoring land use/land cover changes using CORINE land cover data: a case study of Silivri coastal zone in Metropolitan Istanbul.

PubMed

Yilmaz, Rüya

2010-06-01

The objective of the present study was to assess changes in land use/land cover patterns in the coastal town of Silivri, a part of greater Istanbul administratively. In the assessment, remotely sensed data, in the form of satellite images, and geographic information systems were used. Types of land use/land cover were designated as the percentage of the total area studied. Results calculated from the satellite data for land cover classification were compared successfully with the database Coordination of Information on the Environment (CORINE). This served as a reference to appraise the reliability of the study presented here. The CORINE Program was established by the European Commission to create a harmonized Geographical Information System on the state of the environment in the European Community. Unplanned urbanization is causing land use changes mainly in developing countries such as Turkey. This situation in Turkey is frequently observed in the city of Istanbul. There are only a few studies of land use-land cover changes which provide an integrated assessment of the biophysical and societal causes and consequences of environmental degradation in Istanbul. The research area comprised greater Silivri Town which is situated by the coast of Marmara Sea, and it is located approximately 60 km west of Istanbul. The city of Istanbul is one of the largest metropolises in Europe with ca. 15 million inhabitants. Additionally, greater Silivri is located near the terminal point of the state highway connecting Istanbul with Europe. Measuring of changes occurring in land use would help control future planning of settlements; hence, it is of importance for the Greater Silivri and Silivri Town. Following our evaluations, coastal zone of Silivri was classified into the land use groups of artificial surfaces agricultural areas and forests and seminatural areas with 47.1%, 12.66%, and 22.62%, respectively.

Revealing Land Cover Change in California With Satellite Data

NASA Astrophysics Data System (ADS)

Potter, Christopher; Genovese, Vanessa; Gross, Peggy; Boriah, Shyam; Steinbach, Michael; Kumar, Vipin

2007-06-01

The conversion of natural land cover into human-dominated cover types continues to be a change of global proportions with many unknown environmental consequences. Noteworthy conversions of this type include tree stand harvests in forested regions, urbanization, and agricultural intensification in former woodland and natural grassland areas. Determining where, when, and why natural ecosystem conversions occur is a crucial scientific concern [Foley et al., 2005]. Characteristics of the land cover can have important impacts on local climate, radiation balance, biogeochemistry, hydrology and the diversity and abundance of terrestrial species [Randerson et al., 2006]. Consequently, understanding trends in land cover conversion at local scales is a requirement for making useful numerical predictions about other regional and global changes. It is urgent that accurate, timely, and economical tools be made available to document these conversions and aid in the management of their impacts.

NASA Sees Hurricane Arthur's Cloud-Covered Eye

NASA Image and Video Library

2014-07-03

This visible image of Tropical Storm Arthur was taken by the MODIS instrument aboard NASA's Aqua satellite on July 2 at 18:50 UTC (2:50 p.m. EDT). A cloud-covered eye is clearly visible. Credit: NASA Goddard MODIS Rapid Response Team Read more: www.nasa.gov/content/goddard/arthur-atlantic/ NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Forman, Barton A.; Draper, Clara S.; Liu, Qing

2013-01-01

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Decadal land cover change dynamics in Bhutan.

PubMed

Gilani, Hammad; Shrestha, Him Lal; Murthy, M S R; Phuntso, Phuntso; Pradhan, Sudip; Bajracharya, Birendra; Shrestha, Basanta

2015-01-15

Land cover (LC) is one of the most important and easily detectable indicators of change in ecosystem services and livelihood support systems. This paper describes the decadal dynamics in LC changes at national and sub-national level in Bhutan derived by applying object-based image analysis (OBIA) techniques to 1990, 2000, and 2010 Landsat (30 m spatial resolution) data. Ten LC classes were defined in order to give a harmonized legend land cover classification system (LCCS). An accuracy of 83% was achieved for LC-2010 as determined from spot analysis using very high resolution satellite data from Google Earth Pro and limited field verification. At the national level, overall forest increased from 25,558 to 26,732 km(2) between 1990 and 2010, equivalent to an average annual growth rate of 59 km(2)/year (0.22%). There was an overall reduction in grassland, shrubland, and barren area, but the observations were highly dependent on time of acquisition of the satellite data and climatic conditions. The greatest change from non-forest to forest (277 km(2)) was in Bumthang district, followed by Wangdue Phodrang and Trashigang, with the least (1 km(2)) in Tsirang. Forest and scrub forest covers close to 75% of the land area of Bhutan, and just over half of the total area (51%) has some form of conservation status. This study indicates that numerous applications and analyses can be carried out to support improved land cover and land use (LCLU) management. It will be possible to replicate this study in the future as comparable new satellite data is scheduled to become available. Copyright © 2014 Elsevier Ltd. All rights reserved.

Trends in NASA communication satellites

NASA Technical Reports Server (NTRS)

Sivo, J. N.; Robbins, W. H.; Stretchberry, D. M.

1972-01-01

Satellite telecommunications can help to satisfy several national needs such as education, health care, cultural opportunities, and data transfer. There are current experiments being conducted with NASA spacecraft ATS 1, 3, and 5 in an attempt to satisfy these national needs. Future experiments are planned for the ATS F/G and CTS spacecrafts. The next generation of communications satellites must provide multiple region coverage, multichannel capability, high quality TV pictures, and must allow low cost ground receivers to be used. The proposed NASA spacecrafts, ATS H/I, will satisfy these requirements. Other countries of the world can benefit from ATS H/I technology.

NASA's Earth Observing System (EOS): Observing the Atmosphere, Land, Oceans, and Ice from Space

NASA Technical Reports Server (NTRS)

King, Michael D.

2004-01-01

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year, the last of the first series of EOS missions, Aura, was launched. Aura is designed exclusively to conduct research on the composition, chemistry, and dynamics of the Earth's upper and lower atmosphere, employing multiple instruments on a single spacecraft. Aura is the third in a series of major Earth observing satellites to study the environment and climate change and is part of NASA's Earth Science Enterprise. The first and second missions, Terra and Aqua, are designed to study the land, oceans, atmospheric constituents (aerosols, clouds, temperature, and water vapor), and the Earth's radiation budget. The other seven EOS spacecraft include satellites to study (i) land cover & land use change, (ii) solar irradiance and solar spectral variation, (iii) ice volume, (iv) ocean processes (vector wind and sea surface topography), and (v) vertical variations of clouds, water vapor, and aerosols up to and including the stratosphere. Aura's chemistry measurements will also follow up on measurements that began with NASA's Upper Atmosphere Research Satellite and continue the record of satellite ozone data collected from the TOMS missions. In this presentation I will describe how scientists are using EOS data to examine the health of the earth's atmosphere, including atmospheric chemistry, aerosol properties, and cloud properties, with a special but not exclusive look at the latest earth observing mission, Aura.

NASA's Earth Observing System (EOS): Observing the Atmosphere, Land, Oceans, and Ice from Space

NASA Technical Reports Server (NTRS)

King, Michael D.

2005-01-01

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by whch scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year, the last of the first series of EOS missions, Aura, was launched. Aura is designed exclusively to conduct research on the composition, chemistry, and dynamics of the Earth's upper and lower atmosphere, employing multiple instruments on a single spacecraft. Aura is the third in a series of major Earth observing satellites to study the environment and climate change and is part of NASA's Earth Science Enterprise. The first and second missions, Terra and Aqua, are designed to study the land, oceans, atmospheric constituents (aerosols, clouds, temperature, and water vapor), and the Earth's radiation budget. The other seven EOS spacecraft include satellites to study (i) land cover & land use change, (ii) solar irradiance and solar spectral variation, (iii) ice volume, (iv) ocean processes (vector wind and sea surface topography), and (v) vertical variations of clouds, water vapor, and aerosols up to and including the stratosphere. Aura's chemistry measurements will also follow up on measurements that began with NASA's Upper Atmosphere Research Satellite and continue the record of satellite ozone data collected from the TOMS missions. In this presentation I will describe how scientists are using EOS data to examine the health of the earth's atmosphere, including atmospheric chemistry, aerosol properties, and cloud properties, with a special look at the latest earth observing mission, Aura.

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

USGS Publications Warehouse

Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

Land cover change of watersheds in Southern Guam from 1973 to 2001.

PubMed

Wen, Yuming; Khosrowpanah, Shahram; Heitz, Leroy

2011-08-01

Land cover change can be caused by human-induced activities and natural forces. Land cover change in watershed level has been a main concern for a long time in the world since watersheds play an important role in our life and environment. This paper is focused on how to apply Landsat Multi-Spectral Scanner (MSS) satellite image of 1973 and Landsat Thematic Mapper (TM) satellite image of 2001 to determine the land cover changes of coastal watersheds from 1973 to 2001. GIS and remote sensing are integrated to derive land cover information from Landsat satellite images of 1973 and 2001. The land cover classification is based on supervised classification method in remote sensing software ERDAS IMAGINE. Historical GIS data is used to replace the areas covered by clouds or shadows in the image of 1973 to improve classification accuracy. Then, temporal land cover is utilized to determine land cover change of coastal watersheds in southern Guam. The overall classification accuracies for Landsat MSS image of 1973 and Landsat TM image of 2001 are 82.74% and 90.42%, respectively. The overall classification of Landsat MSS image is particularly satisfactory considering its coarse spatial resolution and relatively bad data quality because of lots of clouds and shadows in the image. Watershed land cover change in southern Guam is affected greatly by anthropogenic activities. However, natural forces also affect land cover in space and time. Land cover information and change in watersheds can be applied for watershed management and planning, and environmental modeling and assessment. Based on spatio-temporal land cover information, the interaction behavior between human and environment may be evaluated. The findings in this research will be useful to similar research in other tropical islands.

Recent land-use/land-cover change in the Central California Valley

USGS Publications Warehouse

Soulard, Christopher E.; Wilson, Tamara S.

2013-01-01

Open access to Landsat satellite data has enabled annual analyses of modern land-use and land-cover change (LULCC) for the Central California Valley ecoregion between 2005 and 2010. Our annual LULCC estimates capture landscape-level responses to water policy changes, climate, and economic instability. From 2005 to 2010, agriculture in the region fluctuated along with regulatory-driven changes in water allocation as well as persistent drought conditions. Grasslands and shrublands declined, while developed lands increased in former agricultural and grassland/shrublands. Development rates stagnated in 2007, coinciding with the onset of the historic foreclosure crisis in California and the global economic downturn. We utilized annual LULCC estimates to generate interval-based LULCC estimates (2000–2005 and 2005–2010) and extend existing 27 year interval-based land change monitoring through 2010. Resulting change data provides insights into the drivers of landscape change in the Central California Valley ecoregion and represents the first, continuous, 37 year mapping effort of its kind.

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer; Livingston, Gerry P.; Gore, Warren J. (Technical Monitor)

1998-01-01

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Computer generated maps from digital satellite data - A case study in Florida

NASA Technical Reports Server (NTRS)

Arvanitis, L. G.; Reich, R. M.; Newburne, R.

1981-01-01

Ground cover maps are important tools to a wide array of users. Over the past three decades, much progress has been made in supplementing planimetric and topographic maps with ground cover details obtained from aerial photographs. The present investigation evaluates the feasibility of using computer maps of ground cover from satellite input tapes. Attention is given to the selection of test sites, a satellite data processing system, a multispectral image analyzer, general purpose computer-generated maps, the preliminary evaluation of computer maps, a test for areal correspondence, the preparation of overlays and acreage estimation of land cover types on the Landsat computer maps. There is every indication to suggest that digital multispectral image processing systems based on Landsat input data will play an increasingly important role in pattern recognition and mapping land cover in the years to come.

An Inventory of Ohio's Land Use/Land Cover as Seen by Landsat

NASA Technical Reports Server (NTRS)

Schaal, Gary M.

1977-01-01

LANDSAT 2 (Land Satellite) was launched at Vandenburg, AFB, California on January 22, 1975. The satellite orbits the earth at an altitude of about 920 km (570 miles) and scans the earth's surface in a continuous track 185 km (115 miles) wide. LANDSAT 2 passes over the same spot every 18 days transmitting scanned data to receiving stations scattered around the globe. LANDSAT's continuously-scanning sensors provide useful information about the earth, one of the most important categories being land use. The statistics contained in the appendices of this report represent acreage and percentage of seven types of land cover in Ohio as seen by LANDSAT. The inventory represents a trial effort at determining the Sate's land cover by a method which is inexpensive, reliable, accurate and rapid. Given a successful method, the inventory and periodic updates could provide information to land use decision-makers and, over a period of time, would reveal patterns of land use change. Technical aspects of the project (process, methodology, and verification) are discussed in Schaal (1977) and Schmidt (1976).

Comparison of Surface Ground Temperature from Satellite Observations and the Off-Line Land Surface GEOS Assimilation System

NASA Technical Reports Server (NTRS)

Yang, R.; Houser, P.; Joiner, J.

1998-01-01

The surface ground temperature (Tg) is an important meteorological variable, because it represents an integrated thermal state of the land surface determined by a complex surface energy budget. Furthermore, Tg affects both the surface sensible and latent heat fluxes. Through these fluxes. the surface budget is coupled with the atmosphere above. Accurate Tg data are useful for estimating the surface radiation budget and fluxes, as well as soil moisture. Tg is not included in conventional synoptical weather station reports. Currently, satellites provide Tg estimates globally. It is necessary to carefully consider appropriate methods of using these satellite data in a data assimilation system. Recently, an Off-line Land surface GEOS Assimilation (OLGA) system was implemented at the Data Assimilation Office at NASA-GSFC. One of the goals of OLGA is to assimilate satellite-derived Tg data. Prior to the Tg assimilation, a thorough investigation of satellite- and model-derived Tg, including error estimates, is required. In this study we examine the Tg from the n Project (ISCCP DI) data and the OLGA simulations. The ISCCP data used here are 3-hourly DI data (2.5x2.5 degree resolution) for 1992 summer months (June, July, and August) and winter months (January and February). The model Tg for the same periods were generated by OLGA. The forcing data for this OLGA 1992 simulation were generated from the GEOS-1 Data Assimilation System (DAS) at Data Assimilation Office NASA-GSFC. We examine the discrepancies between ISCCP and OLGA Tg with a focus on its spatial and temporal characteristics, particularly on the diurnal cycle. The error statistics in both data sets, including bias, will be estimated. The impact of surface properties, including vegetation cover and type, topography, etc, on the discrepancies will be addressed.

Research on snow cover monitoring of Northeast China using Fengyun Geostationary Satellite

NASA Astrophysics Data System (ADS)

Wu, Tong; Gu, Lingjia; Ren, Ruizhi; Zhou, TIngting

2017-09-01

Snow cover information has great significance for monitoring and preventing snowstorms. With the development of satellite technology, geostationary satellites are playing more important roles in snow monitoring. Currently, cloud interference is a serious problem for obtaining accurate snow cover information. Therefore, the cloud pixels located in the MODIS snow products are usually replaced by cloud-free pixels around the day, which ignores snow cover dynamics. FengYun-2(FY-2) is the first generation of geostationary satellite in our country which complements the polar orbit satellite. The snow cover monitoring of Northeast China using FY-2G data in January and February 2016 is introduced in this paper. First of all, geometric and radiometric corrections are carried out for visible and infrared channels. Secondly, snow cover information is extracted according to its characteristics in different channels. Multi-threshold judgment methods for the different land types and similarity separation techniques are combined to discriminate snow and cloud. Furthermore, multi-temporal data is used to eliminate cloud effect. Finally, the experimental results are compared with the MOD10A1 and MYD10A1 (MODIS daily snow cover) product. The MODIS product can provide higher resolution of the snow cover information in cloudless conditions. Multi-temporal FY-2G data can get more accurate snow cover information in cloudy conditions, which is beneficial for monitoring snowstorms and climate changes.

Using the NASA Giovanni DICCE Portal to Investigate Land-Ocean Linkages with Satellite and Model Data

NASA Technical Reports Server (NTRS)

Acker, James G.; Zalles, Daniel; Krumhansl, Ruth

2012-01-01

Data-enhanced Investigations for Climate Change Education (DICCE), a NASA climate change education project, employs the NASA Giovanni data system to enable teachers to create climate-related classroom projects using selected satellite and assimilated model data. The easy-to-use DICCE Giovanni portal (DICCE-G) provides data parameters relevant to oceanic, terrestrial, and atmospheric processes. Participants will explore land-ocean linkages using the available data in the DICCE-G portal, in particular focusing on temperature, ocean biology, and precipitation variability related to El Ni?o and La Ni?a events. The demonstration includes the enhanced information for educators developed for the DICCE-G portal. The prototype DICCE Learning Environment (DICCE-LE) for classroom project development will also be demonstrated.

Effects of rainfall patterns and land cover on the subsurface flow generation of sloping Ferralsols in southern China

PubMed Central

Yang, Jie; Tang, Chongjun; Chen, Lihua; Liu, Yaojun; Wang, Lingyun

2017-01-01

Rainfall patterns and land cover are two important factors that affect the runoff generation process. To determine the surface and subsurface flows associated with different rainfall patterns on sloping Ferralsols under different land cover types, observational data related to surface and subsurface flows from 5 m × 15 m plots were collected from 2010 to 2012. The experiment was conducted to assess three land cover types (grass, litter cover and bare land) in the Jiangxi Provincial Soil and Water Conservation Ecological Park. During the study period, 114 natural rainfall events produced subsurface flow and were divided into four groups using k-means clustering according to rainfall duration, rainfall depth and maximum 30-min rainfall intensity. The results showed that the total runoff and surface flow values were highest for bare land under all four rainfall patterns and lowest for the covered plots. However, covered plots generated higher subsurface flow values than bare land. Moreover, the surface and subsurface flows associated with the three land cover types differed significantly under different rainfall patterns. Rainfall patterns with low intensities and long durations created more subsurface flow in the grass and litter cover types, whereas rainfall patterns with high intensities and short durations resulted in greater surface flow over bare land. Rainfall pattern I had the highest surface and subsurface flow values for the grass cover and litter cover types. The highest surface flow value and lowest subsurface flow value for bare land occurred under rainfall pattern IV. Rainfall pattern II generated the highest subsurface flow value for bare land. Therefore, grass or litter cover are able to convert more surface flow into subsurface flow under different rainfall patterns. The rainfall patterns studied had greater effects on subsurface flow than on total runoff and surface flow for covered surfaces, as well as a greater effect on surface flows associated

Seasonal land-cover regions of the United States

USGS Publications Warehouse

Loveland, Thomas R.; Merchant, James W.; Brown, Jesslyn F.; Ohlen, Donald O.; Reed, Bradley C.; Olson, Paul; Hutchinson, John

1995-01-01

Global-change investigations have been hindered by deficiencies in the availability and quality of land-cover data. The U.S. Geological Survey and the University of Nebraska-Lincoln have collaborated on the development of a new approach to land-cover characterization that attempts to address requirements of the global-change research community and others interested in regional patterns of land cover. An experimental 1 -kilometer-resolution database of land-cover characteristics for the coterminous U.S. has been prepared to test and evaluate the approach. Using multidate Advanced Very High Resolution Radiometer (AVHRR) satellite data complemented by elevation, climate, ecoregions, and other digital spatial datasets, the authors define 152, seasonal land-cover regions. The regionalization is based on a taxonomy of areas with respect to data on land cover, seasonality or phenology, and relative levels of primary production. The resulting database consists of descriptions of the vegetation, land cover, and seasonal, spectral, and site characteristics for each region. These data are used in the construction of an illustrative 1:7,500,000-scaIe map of the seasonal land-cover regions as well as of smaller-scale maps portraying general land cover and seasonality. The seasonal land-cover characteristics database can also be tailored to provide a broad range of other landscape parameters useful in national and global-scale environmental modeling and assessment.

Global land cover mapping using Earth observation satellite data: Recent progresses and challenges

NASA Astrophysics Data System (ADS)

Ban, Yifang; Gong, Peng; Giri, Chandra

2015-05-01

Land cover is an important variable for many studies involving the Earth surface, such as climate, food security, hydrology, soil erosion, atmospheric quality, conservation biology, and plant functioning. Land cover not only changes with human caused land use changes, but also changes with nature. Therefore, the state of land cover is highly dynamic. In winter snow shields underneath various other land cover types in higher latitudes. Floods may persist for a long period in a year over low land areas in the tropical and subtropical regions. Forest maybe burnt or clear cut in a few days and changes to bare land. Within several months, the coverage of crops may vary from bare land to nearly 100% crops and then back to bare land following harvest. The highly dynamic nature of land cover creates a challenge in mapping and monitoring which remains to be adequately addressed. As economic globalization continues to intensify, there is an increasing trend of land cover/land use change, environmental pollution, land degradation, biodiversity loss at the global scale, timely and reliable information on global land cover and its changes is urgently needed to mitigate the negative impact of global environment change.

Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

NASA Astrophysics Data System (ADS)

Höhle, J.

2014-09-01

A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.

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.

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

Land Cover Change in the Boston Mountains, 1973-2000

USGS Publications Warehouse

Karstensen, Krista A.

2009-01-01

The U.S. Geological Survey (USGS) Land Cover Trends project is focused on understanding the rates, trends, causes, and consequences of contemporary U.S. land-cover change. The objectives of the study are to: (1) to develop a comprehensive methodology for using sampling and change analysis techniques and Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data to measure regional land-cover change across the United States; (2) to characterize the types, rates, and temporal variability of change for a 30-year period; (3) to document regional driving forces and consequences of change; and (4) to prepare a national synthesis of land-cover change (Loveland and others, 1999). The 1999 Environmental Protection Agency (EPA) Level III ecoregions derived from Omernik (1987) provide the geographic framework for the geospatial data collected between 1973 and 2000. The 27-year study period was divided into five temporal periods: 1973-1980, 1980-1986, 1986-1992, 1992-2000, and 1973-2000, and the data are evaluated using a modified Anderson Land Use Land Cover Classification System (Anderson and others, 1976) for image interpretation. The rates of land-cover change are estimated using a stratified, random sampling of 10-kilometer (km) by 10-km blocks allocated within each ecoregion. For each sample block, satellite images are used to interpret land-cover change for the five time periods previously mentioned. Additionally, historic aerial photographs from similar time frames and other ancillary data, such as census statistics and published literature, are used. The sample block data are then incorporated into statistical analyses to generate an overall change matrix for the ecoregion. Field data of the sample blocks include direct measurements of land cover, particularly ground-survey data collected for training and validation of image classifications (Loveland and others, 2002). The field experience allows for additional

How well do we characterize the biophysical effects of vegetation cover change? Benchmarking land surface models against satellite observations.

NASA Astrophysics Data System (ADS)

Duveiller, Gregory; Forzieri, Giovanni; Robertson, Eddy; Georgievski, Goran; Li, Wei; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

2017-04-01

Changes in vegetation cover can affect the climate by altering the carbon, water and energy cycles. The main tools to characterize such land-climate interactions for both the past and future are land surface models (LSMs) that can be embedded in larger Earth System models (ESMs). While such models have long been used to characterize the biogeochemical effects of vegetation cover change, their capacity to model biophysical effects accurately across the globe remains unclear due to the complexity of the phenomena. The result of competing biophysical processes on the surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and on the background climate (e.g. presence of snow or soil moisture). Here we present a global scale benchmarking exercise of four of the most commonly used LSMs (JULES, ORCHIDEE, JSBACH and CLM) against a dedicated dataset of satellite observations. To facilitate the understanding of the causes that lead to discrepancies between simulated and observed data, we focus on pure transitions amongst major plant functional types (PFTs): from different tree types (evergreen broadleaf trees, deciduous broadleaf trees and needleleaf trees) to either grasslands or crops. From the modelling perspective, this entails generating a separate simulation for each PFT in which all 1° by 1° grid cells are uniformly covered with that PFT, and then analysing the differences amongst them in terms of resulting biophysical variables (e.g net radiation, latent and sensible heat). From the satellite perspective, the effect of pure transitions is obtained by unmixing the signal of different 0.05° spatial resolution MODIS products (albedo, latent heat, upwelling longwave radiation) over a local moving window using PFT maps derived from the ESA Climate Change Initiative land cover map. After aggregating to a common spatial support, the observation and model-driven datasets are confronted and

Application of satellite and GIS technologies for land-cover and land-use mapping at the rural-urban fringe - A case study

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

Treitz, P.M.; Howarth, P.J.; Gong, Peng

1992-04-01

SPOT HRV multispectral and panchromatic data were recorded and coregistered for a portion of the rural-urban fringe of Toronto, Canada. A two-stage digital analysis algorithm incorporating a spectral-class frequency-based contextual classification of eight land-cover and land-use classes resulted in an overall Kappa coefficient of 82.2 percent for training-area data and a Kappa coefficient of 70.3 percent for test-area data. A matrix-overlay analysis was then performed within the geographic information system (GIS) to combine the land-cover and land-use classes generated from the SPOT digital classification with zoning information for the area. The map that was produced has an estimated interpretation accuracymore » of 78 percent. Global Positioning System (GPS) data provided a positional reference for new road networks. These networks, in addition to the new land-cover and land-use map derived from the SPOT HRV data, provide an up-to-date synthesis of change conditions in the area. 51 refs.« less

NASA CYGNSS Satellite Measurements and Applications

NASA Astrophysics Data System (ADS)

Murray, J. J.; Ruf, C. S.; Baker, N. L.; Green, D. S.; Stough, T.

2017-12-01

NASA launched the CYGNSS mission 15 December 2016 which comprises a constellation of eight satellites flying in a low inclination (tropical) Earth orbit. Each satellite measures up to four independent GPS signals scattered by the ocean, to obtain surface roughness, near surface wind speed, and air-sea latent heat flux. Utilizing such a large number of satellites, these measurements which are uniquely able to penetrate clouds and heavy precipitation, allows CYGNSS to frequently sample tropical cyclone intensification and of the diurnal cycle of winds. Additionally, data retrievals over land have proven effective to map surface water and soil moisture. Engineering commissioning of the constellation was successfully completed in March 2017 and the mission is now conducting science measurements. An overview of the CYGNSS system, mission and measurement concept will be presented, together with highlights of early on-orbit performance. Scientific results obtained during the 2017 hurricane season and featured at the NASA CYGNSS Applications Workshop in Monterey, CA 31 October - 2 November 2, 2017 will also be presented.

Comprehensive data set of global land cover change for land surface model applications

NASA Astrophysics Data System (ADS)

Sterling, Shannon; Ducharne, AgnèS.

2008-09-01

To increase our understanding of how humans have altered the Earth's surface and to facilitate land surface modeling experiments aimed to elucidate the direct impact of land cover change on the Earth system, we create and analyze a database of global land use/cover change (LUCC). From a combination of sources including satellite imagery and other remote sensing, ecological modeling, and country surveys, we adapt and synthesize existing maps of potential land cover and layers of the major anthropogenic land covers, including a layer of wetland loss, that are then tailored for land surface modeling studies. Our map database shows that anthropogenic land cover totals to approximately 40% of the Earth's surface, consistent with literature estimates. Almost all (92%) of the natural grassland on the Earth has been converted to human use, mostly grazing land, and the natural temperate savanna with mixed C3/C4 is almost completely lost (˜90%), due mostly to conversion to cropland. Yet the resultant change in functioning, in terms of plant functional types, of the Earth system from land cover change is dominated by a loss of tree cover. Finally, we identify need for standardization of percent bare soil for global land covers and for a global map of tree plantations. Estimates of land cover change are inherently uncertain, and these uncertainties propagate into modeling studies of the impact of land cover change on the Earth system; to begin to address this problem, modelers need to document fully areas of land cover change used in their studies.

New NASA Satellite Zooms in on Tornado Swath

NASA Technical Reports Server (NTRS)

2002-01-01

A number of severe thunder storms swept through the mid-Atlantic states on April 28, bringing high winds, hailstones, and heavy rains to many areas. The intense storms spawned at least two tornadoes, one of which was classified as an F4 twister. The powerful tornado touched down in southern Maryland and ripped through the town of La Plata, destroying most of the historic downtown. The twister-the strongest ever recorded to hit the state and perhaps the strongest ever recorded in the eastern U.S.-flattened everything in its path along a 24-mile (39 km) swath running west to east through the state. The tornado's path can be seen clearly in this band-sharpened color image acquired on May 1 by the Advanced Land Imager (ALI), flying aboard NASA's EO-1 satellite. La Plata is situated toward the lefthand side of this scene and the twister's swath is the bright stripe passing through the town and running eastward 6 miles (10 km) toward the Patuxent River beyond the righthand side of the image. This stripe is the result of the vegetation flattened by the storm. The flattened vegetation reflects more light than untouched vegetation. EO-1 is the first Earth observing satellite launched as part of NASA's New Millennium Program. This program is designed to spearhead development and testing of a new generation of satellite remote sensing technologies for future Earth and space science missions. The ALI is designed to improve upon and extend the measurement heritage begun by the Landsat series of satellites well into the 21st Century. For more images of the tornado's path, including Landsat, visit Tornado Hits La Plata, Maryland in the Natural Hazards section of the Earth Observatory. Image courtesy Lawrence Ong, EO-1 Mission Science Office, NASA GSFC

Enhancement of spectral quality of archival aerial photographs using satellite imagery for detection of land cover

NASA Astrophysics Data System (ADS)

Siok, Katarzyna; Jenerowicz, Agnieszka; Woroszkiewicz, Małgorzata

2017-07-01

Archival aerial photographs are often the only reliable source of information about the area. However, these data are single-band data that do not allow unambiguous detection of particular forms of land cover. Thus, the authors of this article seek to develop a method of coloring panchromatic aerial photographs, which enable increasing the spectral information of such images. The study used data integration algorithms based on pansharpening, implemented in commonly used remote sensing programs: ERDAS, ENVI, and PCI. Aerial photos and Landsat multispectral data recorded in 1987 and 2016 were chosen. This study proposes the use of modified intensity-hue-saturation and Brovey methods. The use of these methods enabled the addition of red-green-blue (RGB) components to monochrome images, thus enhancing their interpretability and spectral quality. The limitations of the proposed method relate to the availability of RGB satellite imagery, the accuracy of mutual orientation of the aerial and the satellite data, and the imperfection of archival aerial photographs. Therefore, it should be expected that the results of coloring will not be perfect compared to the results of the fusion of recent data with a similar ground sampling resolution, but still, they will allow a more accurate and efficient classification of land cover registered on archival aerial photographs.

Ecoregions and land cover trends in Senegal

USGS Publications Warehouse

Tappan, G. Gray; Sall, M.; Wood, E.C.; Cushing, Matthew

2004-01-01

This study examines long-term changes in Senegal's natural resources. We monitor and quantify land use and land cover changes occurring across Senegal using nearly 40 years of satellite imagery, aerial surveys, and fieldwork. We stratify Senegal into ecological regions and present land use and land cover trends for each region, followed by a national summary. Results aggregated to the national level show moderate change, with a modest decrease in savannas from 74 to 70 percent from 1965 to 2000, and an expansion of cropland from 17 to 21 percent. However, at the ecoregion scale, we observed rapid change in some and relative stability in others. One particular concern is the decline in Senegal's biodiverse forests. However, in the year 2000, Senegal's savannas, woodlands, and forests still cover more than two-thirds of the country, and the rate of agricultural expansion has slowed.

An advanced generation land mobile satellite system and its critical technologies

NASA Technical Reports Server (NTRS)

Naderi, F.

1982-01-01

A conceptual design for a Land Mobile Satellite System (LMSS) for the 1990s is presented. LMSS involves small tranceivers accessing satellites directly, with ground reception through small car-top antennas. The satellite would have a large antenna and blanket coverage areas in the UHF. The call may originate from a home, be carried by wire to a gateway, transmitted to satellite on the S-band, converted to UHF on the satellite, and transmitted to the vehicle. The system design is constrained by the number of users in an area during the busiest hours, Shuttle storage, controllability factors, and the total area served. A 55-m antenna has been selected, with 87 spot beams and two 10 MHz UHF bands in the 806-890 MHz band. A 17 dB interbeam isolation level is required, implying that sufficient sub-bands can be generated to assure 8265 total channels. The mobile satellite (MSAT) would have an 83 m mast lower segment, a 34 m upper segment, and a second, 10 m antenna made of a deployable mesh. Various antenna function modes are considered.

Digital elevation data as an aid to land use and land cover classification

USGS Publications Warehouse

Colvocoresses, Alden P.

1981-01-01

In relatively well mapped areas such as the United States and Europe, digital data can be developed from topographic maps or from the stereo aerial photographic movie. For poorer mapped areas (which involved most of the world's land areas), a satellite designed to obtain stereo data offers the best hope for a digital elevation database. Such a satellite, known as Mapsat, has been defined by the U.S. Geological Survey. Utilizing modern solid state technology, there is no reason why such stereo data cannot be acquired simultaneously with the multispectral response, thus simplifying the overall problem of land use and land cover classification.

Land Cover Influence on Wet Season Storm Runoff Generation and Hydrologic Flowpaths in Central Panama

NASA Astrophysics Data System (ADS)

Birch, A. L.; Stallard, R. F.; Barnard, H. R.

2017-12-01

While relationships between land use/land cover and hydrology are well studied and understood in temperate parts of the world, little research exists in the humid tropics, where hydrologic research is often decades behind. Specifically, quantitative information on how physical and biological differences across varying land covers influence runoff generation and hydrologic flowpaths in the humid tropics is scarce; frequently leading to poorly informed hydrologic modelling and water policy decision making. This research effort seeks to quantify how tropical land cover change may alter physical hydrologic processes in the economically important Panama Canal Watershed (Republic of Panama) by separating streamflow into its different runoff components using end member mixing analysis. The samples collected for this project come from small headwater catchments of four varying land covers (mature tropical forest, young secondary forest, active pasture, recently clear-cut tropical forest) within the Smithsonian Tropical Research Institute's Agua Salud Project. During the past three years, samples have been collected at the four study catchments from streamflow and from a number of water sources within hillslope transects, and have been analyzed for stable water isotopes, major cations, and major anions. Major ion analysis of these samples has shown distinct geochemical differences for the potential runoff generating end members sampled (soil moisture/ preferential flow, groundwater, overland flow, throughfall, and precipitation). Based on this finding, an effort was made from May-August 2017 to intensively sample streamflow during wet season storm events, yielding a total of 5 events of varying intensity in each land cover/catchment, with sampling intensity ranging from sub-hourly to sub-daily. The focus of this poster presentation will be to present the result of hydrograph separation's done using end member mixing analysis from this May-August 2017 storm dataset. Expected

NASA's mobile satellite communications program; ground and space segment technologies

NASA Technical Reports Server (NTRS)

Naderi, F.; Weber, W. J.; Knouse, G. H.

1984-01-01

This paper describes the Mobile Satellite Communications Program of the United States National Aeronautics and Space Administration (NASA). The program's objectives are to facilitate the deployment of the first generation commercial mobile satellite by the private sector, and to technologically enable future generations by developing advanced and high risk ground and space segment technologies. These technologies are aimed at mitigating severe shortages of spectrum, orbital slot, and spacecraft EIRP which are expected to plague the high capacity mobile satellite systems of the future. After a brief introduction of the concept of mobile satellite systems and their expected evolution, this paper outlines the critical ground and space segment technologies. Next, the Mobile Satellite Experiment (MSAT-X) is described. MSAT-X is the framework through which NASA will develop advanced ground segment technologies. An approach is outlined for the development of conformal vehicle antennas, spectrum and power-efficient speech codecs, and modulation techniques for use in the non-linear faded channels and efficient multiple access schemes. Finally, the paper concludes with a description of the current and planned NASA activities aimed at developing complex large multibeam spacecraft antennas needed for future generation mobile satellite systems.

How NASA Sees the Earth and Its Climate

NASA Technical Reports Server (NTRS)

BrowndeColstoun, Eric

2012-01-01

NASA Research Addresses Broad Questions: (1) How are global ecosystems changing? (2) What changes are occurring in global land cover and land use and what are their causes? (3) How is the Earth s surface being transformed and how can such information be used to predict future changes? (4) What are the consequences of land cover and land use change for the sustainability of ecosystems and economic productivity? NASA uses the view from above to monitor our changing home. Different satellites help us study the various systems of the Earth. No one system can do it all. NASA tools and science helps us to understand how the planet is changing and what the changes mean for us.

Land Data Assimilation of Satellite-Based Soil Moisture Products Using the Land Information System Over the NLDAS Domain

NASA Technical Reports Server (NTRS)

Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.

2011-01-01

This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.

High Resolution Land Use Land Cover Classification using Landsat Earth Observation Data for the Continental Africa

NASA Astrophysics Data System (ADS)

Midekisa, A.; Bennet, A.; Gething, P. W.; Holl, F.; Andrade-Pacheco, R.; Savory, D. J.; Hugh, S. J.

2016-12-01

Spatially detailed and temporally dynamic land use land cover data is necessary to monitor the state of the land surface for various applications. Yet, such data at a continental to global scale is lacking. Here, we developed high resolution (30 meter) annual land use land cover layers for the continental Africa using Google Earth Engine. To capture ground truth training data, high resolution satellite imageries were visually inspected and used to identify 7, 212 sample Landsat pixels that were comprised entirely of one of seven land use land cover classes (water, man-made impervious surface, high biomass, low biomass, rock, sand and bare soil). For model validation purposes, 80% of points from each class were used as training data, with 20% withheld as a validation dataset. Cloud free Landsat 7 annual composites for 2000 to 2015 were generated and spectral bands from the Landsat images were then extracted for each of the training and validation sample points. In addition to the Landsat spectral bands, spectral indices such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used as covariates in the model. Additionally, calibrated night time light imageries from the National Oceanic and Atmospheric Administration (NOAA) were included as a covariate. A decision tree classification algorithm was applied to predict the 7 land cover classes for the periods 2000 to 2015 using the training dataset. Using the validation dataset, classification accuracy including omission error and commission error were computed for each land cover class. Model results showed that overall accuracy of classification was high (88%). This high resolution land cover product developed for the continental Africa will be available for public use and can potentially enhance the ability of monitoring and studying the state of the Earth's surface.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

PubMed

Midekisa, Alemayehu; Holl, Felix; Savory, David J; Andrade-Pacheco, Ricardo; Gething, Peter W; Bennett, Adam; Sturrock, Hugh J W

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

PubMed Central

Holl, Felix; Savory, David J.; Andrade-Pacheco, Ricardo; Gething, Peter W.; Bennett, Adam; Sturrock, Hugh J. W.

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth’s land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. PMID:28953943

Land-cover change in the Ozark Highlands, 1973-2000

USGS Publications Warehouse

Karstensen, Krista A.

2010-01-01

Led by the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS) in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA), the Land-Cover Trends Project was initiated in 1999 and aims to document the types, geographic distributions, and rates of land-cover change on a region by region basis for the conterminous United States, and to determine some of the key drivers and consequences of the change (Loveland and others, 2002). For 1973, 1980, 1986, 1992, and 2000 land-cover maps derived from the Landsat series are classified by visual interpretation, inspection of historical aerial photography and ground survey, into 11 land-cover classes. The classes are defined to capture land cover that is discernable in Landsat data. A stratified probability-based sampling methodology undertaken within the 84 Omernik Level III Ecoregions (Omernik, 1987) was used to locate the blocks, with 9 to 48 blocks per ecoregion. The sampling was designed to enable a statistically robust 'scaling up' of the sample-classification data to estimate areal land-cover change within each ecoregion (Loveland and others, 2002; Stehman and others, 2005). At the time of writing, approximately 90 percent of the 84 conterminous United States ecoregions have been processed by the Land-Cover Trends Project. Results from these completed ecoregions illustrate that across the conterminous United States there is no single profile of land-cover/land-use change, rather, there are varying pulses affected by clusters of change agents (Loveland and others, 2002). Land-Cover Trends Project results for the conterminous United States to-date are being used for collaborative environmental change research with partners such as; the National Science Foundation, the National Oceanic and Atmospheric Administration, and the U.S. Fish and Wildlife Service. The strategy has also been adapted for use in a NASA global

Land cover mapping of the National Park Service northwest Alaska management area using Landsat multispectral and thematic mapper satellite data

USGS Publications Warehouse

Markon, C.J.; Wesser, Sara

1998-01-01

A land cover map of the National Park Service northwest Alaska management area was produced using digitally processed Landsat data. These and other environmental data were incorporated into a geographic information system to provide baseline information about the nature and extent of resources present in this northwest Alaskan environment.This report details the methodology, depicts vegetation profiles of the surrounding landscape, and describes the different vegetation types mapped. Portions of nine Landsat satellite (multispectral scanner and thematic mapper) scenes were used to produce a land cover map of the Cape Krusenstern National Monument and Noatak National Preserve and to update an existing land cover map of Kobuk Valley National Park Valley National Park. A Bayesian multivariate classifier was applied to the multispectral data sets, followed by the application of ancillary data (elevation, slope, aspect, soils, watersheds, and geology) to enhance the spectral separation of classes into more meaningful vegetation types. The resulting land cover map contains six major land cover categories (forest, shrub, herbaceous, sparse/barren, water, other) and 19 subclasses encompassing 7 million hectares. General narratives of the distribution of the subclasses throughout the project area are given along with vegetation profiles showing common relationships between topographic gradients and vegetation communities.

The Regional Land Cover Monitoring System: Building regional capacity through innovative land cover mapping approaches

NASA Astrophysics Data System (ADS)

Saah, D.; Tenneson, K.; Hanh, Q. N.; Aekakkararungroj, A.; Aung, K. S.; Goldstein, J.; Cutter, P. G.; Maus, P.; Markert, K. N.; Anderson, E.; Ellenburg, W. L.; Ate, P.; Flores Cordova, A. I.; Vadrevu, K.; Potapov, P.; Phongsapan, K.; Chishtie, F.; Clinton, N.; Ganz, D.

2017-12-01

Earth observation and Geographic Information System (GIS) tools, products, and services are vital to support the environmental decision making by governmental institutions, non-governmental agencies, and the general public. At the heart of environmental decision making is the monitoring land cover and land use change (LCLUC) for land resource planning and for ecosystem services, including biodiversity conservation and resilience to climate change. A major challenge for monitoring LCLUC in developing regions, such as Southeast Asia, is inconsistent data products at inconsistent intervals that have different typologies across the region and are typically made in without stakeholder engagement or input. Here we present the Regional Land Cover Monitoring System (RLCMS), a novel land cover mapping effort for Southeast Asia, implemented by SERVIR-Mekong, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries. The RLCMS focuses on mapping biophysical variables (e.g. canopy cover, tree height, or percent surface water) at an annual interval and in turn using those biophysical variables to develop land cover maps based on stakeholder definitions of land cover classes. This allows for flexible and consistent land cover classifications that can meet the needs of different institutions across the region. Another component of the RLCMS production is the stake-holder engagement through co-development. Institutions that directly benefit from this system have helped drive the development for regional needs leading to services for their specific uses. Examples of services for regional stakeholders include using the RLCMS to develop maps using the IPCC classification scheme for GHG emission reporting and developing custom annual maps as an input to hydrologic modeling/flood forecasting systems. In addition to the implementation of this system and the service stemming from the RLCMS in Southeast Asia, it is

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

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

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

ASSESSING THE ACCURACY OF SATELLITE-DERIVED LAND COVER CLASSIFICATION USING HISTORICAL AERIAL PHOTOGRAPHY DIGITAL ORTHOPHOTO QUADRANGLES, AND AIRBORNE VIDEO DATA

EPA Science Inventory

As the rapidly growing archives of satellite remote sensing imagery now span decades'worth of data, there is increasing interest in the study of long-term regional land cover change across multiple image dates. In most cases, however, temporally coincident ground sampled data are...

ASSESSING THE ACCURACY OF SATELLITE-DERIVED LAND COVER CLASSIFICATION USING HISTORICAL AERIAL PHOTOGRAPHY, DIGITAL ORTHOPHOTO QUADDRANGLES, AND AIRBORNE VIDEO DATA

EPA Science Inventory

As the rapidly growing archives of satellite remote sensing imagery now span decades'worth of data, there is increasing interest in the study of long-term regional land cover change across multiple image dates. In most cases, however, temporally coincident ground sampled data are...

ASSESSING THE ACCURACY OF SATELLITE-DERIVED LAND COVER CLASSIFICATION USING HISTORICAL AERIAL PHOTOGRAPHY, DIGITAL ORTHOPHOTO QUADRANGLES, AND AIRBORNE VIDEO DATA

EPA Science Inventory

As the rapidly growing archives of satellite remote sensing imagery now span decades' worth of data, there is increasing interest in the study of long-term regional land cover change across multiple image dates. In most cases, however, temporally coincident ground sampled data ar...

Applications of national land cover maps in United States forestry

Treesearch

Kurt H. Riitters; Gregory A. Reams

2008-01-01

Land cover maps derived from satellite imagery have a long and varied history of uses in United States forestry science and management. This article reviews recent developments concerning the use of national- to continental-scale land cover maps for inventory, monitoring, and resource assessment in the U.S. Forest Service. The use of mid-scale digital resolution...

Land use and land cover change in the Greater Yellowstone Ecosystem: 1975-1995

USGS Publications Warehouse

Parmenter, A.W.; Hansen, A.; Kennedy, R.E.; Cohen, W.; Langner, U.; Lawrence, R.; Maxwell, B.; Gallant, Alisa; Aspinall, R.

2003-01-01

Shifts in the demographic and economic character of the Greater Yellowstone Ecosystem (GYE) are driving patterns of land cover and land use change in the region. Such changes may have important consequences for ecosystem functioning. The objective of this paper is to quantify the trajectories and rates of change in land cover and use across the GYE for the period 1975-1995 using satellite imagery. Spectral and geographic variables were used as inputs to classification tree regression analysis (CART) to find "rules" which defined land use and land cover classes on the landscape. The resulting CART functions were used to map land cover and land use across seven Landsat TM scenes for 1995. We then used a thresholding technique to identify locations that differed in spectral properties between the 1995 and 1985 time periods. These "changed" locations were classified using CART functions derived from spectral and geographic data from 1985. This was similarly done for the year 1975 based on Landsat MSS data. Differences between the 1975, 1985, and 1995 maps were considered change in land cover and use. We calibrated and tested the accuracy of our models using data acquired through manual interpretation of aerial photos. Elevation and vegetative indices derived from the remotely sensed satellite imagery explained the most variance in the land use and land cover classes (-i.e., defined the "rules" most often). Overall accuracies from our study were good, ranging from 94% at the coarsest level of detail to 74% at the finest. The largest changes over the study period were the increases in burned, urban, and mixed conifer-herbaceous classes and decreases in woody deciduous, mixed woody deciduous-herbaceous, and conifer habitats. These changes have important implications for ecological function and biodiversity. The expansion of mixed conifer classes may increase fuel loads and enhance risk to the growing number of rural homes. The reduction of woody deciduous cover types is

A multi-temporal analysis approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.

2012-06-01

Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.

Land-Use and Land-Cover Change around Mobile Bay, Alabama from 1974-2008

NASA Technical Reports Server (NTRS)

Ellis, Jean; Spruce, Joseph P.; Swann, Roberta; Smooth, James C.

2009-01-01

This document summarizes the major findings of a Gulf of Mexico Application Pilot project led by NASA Stennis Space Center (SSC) in conjunction with a regional collaboration network of the Gulf of Mexico Alliance (GOMA). NASA researchers processed and analyzed multi-temporal Landsat data to assess land-use and land-cover (LULC) changes in the coastal counties of Mobile and Baldwin, AL between 1974 and 2008. Our goal was to create satellite-based LULC data products using methods that could be transferable to other coastal areas of concern within the Gulf of Mexico. The Mobile Bay National Estuary Program (MBNEP) is the primary end-user, however, several other state and local groups may benefit from the project s data products that will be available through NOAA-NCDDC s Regional Ecosystem Data Management program. Mobile Bay is a critical ecologic and economic region in the Gulf of Mexico and to the entire country. Mobile Bay was designated as an estuary of national significance in 1996. This estuary receives the fourth largest freshwater inflow in the United States. It provides vital nursery habitat for commercially and recreationally important fish species. It has exceptional aquatic and terrestrial bio-diversity, however, its estuary health is influenced by changing LULC patterns, such as urbanization. Mobile and Baldwin counties have experienced a population growth of 1.1% and 20.5% from 2000-2006. Urban expansion and population growth are likely to accelerate with the construction and operation of the ThyssenKrupp steel mill in the northeast portion of Mobile County. Land-use and land-cover change can negatively impact Gulf coast water quality and ecological resources. The conversion of forest to urban cover types impacts the carbon cycle and increases the freshwater and sediment in coastal waters. Increased freshwater runoff decreases salinity and increases the turbidity of coastal waters, thus impacting the growth potential of submerged aquatic vegetation (SAV

Application of spectrometer cropscan MSR 16R and Landsat imagery for identification the spectral characteristics of land cover

NASA Astrophysics Data System (ADS)

Tampubolon, Togi; Abdullah, Khiruddin bin; San, Lim Hwee

2013-09-01

The spectral characteristics of land cover are basic references in classifying satellite image for geophysics analysis. It can be obtained from the measurements using spectrometer and satellite image processing. The aims of this study to investigate the spectral characteristics of land cover based on the results of measurement using Spectrometer Cropscan MSR 16R and Landsat satellite imagery. The area of study in this research is in Medan, (Deli Serdang, North Sumatera) Indonesia. The scope of this study is the basic survey from the measurements of spectral land cover which is covered several type of land such as a cultivated and managed terrestrial areas, natural and semi-natural, cultivated aquatic or regularly flooded areas, natural and semi-natural aquatic, artificial surfaces and associated areas, bare areas, artificial waterbodies and natural waterbodies. The measurement and verification were conducted using a spectrometer provided their spectral characteristics and Landsat imagery, respectively. The results of the spectral characteristics of land cover shows that each type of land cover have a unique characteristic. The correlation of spectral land cover based on spectrometer Cropscan MSR 16R and Landsat satellite image are above 90 %. However, the land cover of artificial waterbodiese have a correlation under 40 %. That is because the measurement of spectrometer Cropscan MSR 16R and acquisition of Landsat satellite imagery has a time different.

Fire Monitoring from the New Generation of US Polar and Geostationary Satellites

NASA Astrophysics Data System (ADS)

Csiszar, I.; Justice, C. O.; Prins, E.; Schroeder, W.; Schmidt, C.; Giglio, L.

2012-04-01

Sensors on the new generation of US operational environmental satellites will provide measurements suitable for active fire detection and characterization. The NPOESS Preparatory Project (NPP) satellite, launched on October 28, 2011, carries the Visible Infrared Imager Radiometer Suite (VIIRS), which is expected to continue the active fire data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System Terra and Aqua Satellites. Early evaluation of the VIIRS active fire product, including comparison to near-simultaneous MODIS data, is underway. The new generation of Geostationary Operational Environmental Satellite (GOES) series, starting with GOES-R to be launched in 2015, will carry the Advanced Baseline Imager (ABI), providing higher spatial and temporal resolution than the current GOES imager. The ABI will also include a dedicated band to provide radiance observations over a wider dynamic range to detect and characterize hot targets. In this presentation we discuss details of the monitoring capabilities from both VIIRS and ABI and the current status of the corresponding algorithm development and testing efforts. An integral part of this activity is explicit product validation, utilizing high resolution satellite and airborne imagery as reference data. The new capabilities also represent challenges to establish continuity with data records from heritage missions, and to coordinate compatible international missions towards a global multi-platform fire monitoring system. These objectives are pursued by the Fire Mapping and Monitoring Implementation Team of the Global Observation of Forest and Land Cover Dynamics (GOFC-GOLD) program, which also provides coordinated contribution to relevant initiatives by the Committee on Earth Observation Satellites (CEOS), the Coordination Group for Meteorological Satellites (CGMS) and the Global Climate Observing System (GCOS).

Specifications for updating USGS land use and land cover maps

USGS Publications Warehouse

Milazzo, Valerie A.

1983-01-01

To meet the increasing demands for up-to-date land use and land cover information, a primary goal of the U.S. Geological Survey's (USGS) national land use and land cover mapping program is to provide for periodic updating of maps and data in a timely and uniform manner. The technical specifications for updating existing USGS land use and land cover maps that are presented here cover both the interpretive aspects of detecting and identifying land use and land cover changes and the cartographic aspects of mapping and presenting the change data in conventional map format. They provide the map compiler with the procedures and techniques necessary to then use these change data to update existing land use and land cover maps in a manner that is both standardized and repeatable. Included are specifications for the acquisition of remotely sensed source materials, selection of compilation map bases, handling of data base corrections, editing and quality control operations, generation of map update products for USGS open file, and the reproduction and distribution of open file materials. These specifications are planned to become part of the National Mapping Division's Technical Instructions.

Land cover mapping at sub-pixel scales

NASA Astrophysics Data System (ADS)

Makido, Yasuyo Kato

One of the biggest drawbacks of land cover mapping from remotely sensed images relates to spatial resolution, which determines the level of spatial details depicted in an image. Fine spatial resolution images from satellite sensors such as IKONOS and QuickBird are now available. However, these images are not suitable for large-area studies, since a single image is very small and therefore it is costly for large area studies. Much research has focused on attempting to extract land cover types at sub-pixel scale, and little research has been conducted concerning the spatial allocation of land cover types within a pixel. This study is devoted to the development of new algorithms for predicting land cover distribution using remote sensory imagery at sub-pixel level. The "pixel-swapping" optimization algorithm, which was proposed by Atkinson for predicting sub-pixel land cover distribution, is investigated in this study. Two limitations of this method, the arbitrary spatial range value and the arbitrary exponential model of spatial autocorrelation, are assessed. Various weighting functions, as alternatives to the exponential model, are evaluated in order to derive the optimum weighting function. Two different simulation models were employed to develop spatially autocorrelated binary class maps. In all tested models, Gaussian, Exponential, and IDW, the pixel swapping method improved classification accuracy compared with the initial random allocation of sub-pixels. However the results suggested that equal weight could be used to increase accuracy and sub-pixel spatial autocorrelation instead of using these more complex models of spatial structure. New algorithms for modeling the spatial distribution of multiple land cover classes at sub-pixel scales are developed and evaluated. Three methods are examined: sequential categorical swapping, simultaneous categorical swapping, and simulated annealing. These three methods are applied to classified Landsat ETM+ data that has

Measuring land-use and land-cover change using the U.S. department of agriculture's cropland data layer: Cautions and recommendations

NASA Astrophysics Data System (ADS)

Lark, Tyler J.; Mueller, Richard M.; Johnson, David M.; Gibbs, Holly K.

2017-10-01

Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps.

Multi-Sourced Satellite Observations of Land Cover and Land Use Change in South and Southeast Asia with Challenging Environmental and Socioeconomic Impacts

NASA Astrophysics Data System (ADS)

Nghiem, S. V.; Small, C.; Jacobson, M. Z.; Brakenridge, G. R.; Balk, D.; Sorichetta, A.; Masetti, M.; Gaughan, A. E.; Stevens, F. R.; Mathews, A.; Frazier, A. E.; Das, N. N.

2017-12-01

An innovative paradigm to observe the rural-urban transformation over the landscape using multi-sourced satellite data is formulated as a time and space continuum, extensively in space across South and Southeast Asia and in time over a decadal scale. Rather than a disparate array of individual cities and their vicinities in separated areas and in a discontinuous collection of points in time, the time-space continuum paradigm enables significant advances in addressing rural-urban change as a continuous gradient across the landscape from the wilderness to rural to urban areas to study challenging environmental and socioeconomic issues. We use satellite data including QuikSCAT scatterometer, SRTM and Sentinel-1 SAR, Landsat, WorldView, MODIS, and SMAP together with environmental and demographic data and modeling products to investigate land cover and land use change in South and Southeast Asia and associated impacts. Utilizing the new observational advances and effectively capitalizing current capabilities, we will present interdisciplinary results on urbanization in three dimensions, flood and drought, wildfire, air and water pollution, urban change, policy effects, population dynamics and vector-borne disease, agricultural assessment, and land degradation and desertification.

Typhoon Chan-Hom "Eyes" NASA's Aqua Satellite

NASA Image and Video Library

2017-12-08

Typhoon Chan-Hom's eye was visible from space when NASA's Aqua satellite passed overhead early on July 8, 2015. The MODIS instrument, known as the Moderate Resolution Imaging Spectrometer, flies aboard NASA's Aqua satellite. When Aqua passed over Typhoon Chan-Hom on July 8 at 04:25 UTC (12:25 a.m. EDT), MODIS captured a visible-light image of the storm that clearly showed its eye. The MODIS image also a ring of powerful thunderstorms surrounding the eye of the storm, and the bulk of thunderstorms wrapping around the system from west to east, along the southern side. At 0900 UTC (5 a.m. EDT), Typhoon Chan-Hom's maximum sustained winds were near 85 knots (97.8 mph/157.4 kph). Tropical-storm-force winds extended 145 nautical miles (166.9 miles/268.5 km) from the center, making the storm almost 300 nautical miles (345 miles/555 km) in diameter. Typhoon-force winds extended out to 35 nautical miles (40 miles/64.8 km) from the center. Chan-Hom's eye was centered near 20.5 North latitude and 132.7 East longitude, about 450 nautical miles (517.9 miles/833.4 km) southeast of Kadena Air Base, Iwo To, Japan. Chan-Hom was moving to the northwest at 11 knots (12.6 mph/20.3 kph). The typhoon was generating very rough seas with wave heights to 28 feet (8.5 meters). The Joint Typhoon Warning Center expects Chan-Hom to continue tracking northwestward over the next three days under the steering influence of a sub-tropical ridge (elongated area of high pressure). Chan-Hom is expected to intensify steadily peaking at 120 knots (138.1 mph/222.2 kph) on July 10. The JTWC forecast predicts that Chan-Hom will make landfall near Wenzhou, Zhejiang, China and begin decaying due to land interaction. For updated warnings and watches from China's National Meteorological Centre, visit: www.cma.gov.cn/en/WeatherWarnings/. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team b>NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific

Improving Water Management Decision Support Tools Using NASA Satellite and Modeling Data

NASA Astrophysics Data System (ADS)

Toll, D. L.; Arsenault, K.; Nigro, J.; Pinheiro, A.; Engman, E. T.; Triggs, J.; Cosgrove, B.; Alonge, C.; Boyle, D.; Allen, R.; Townsend, P.; Ni-Meister, W.

2006-05-01

One of twelve Applications of National priority within NASA's Applied Science Program, the Water Management Program Element addresses concerns and decision making related to water availability, water forecast and water quality. The goal of the Water Management Program Element is to encourage water management organizations to use NASA Earth science data, models products, technology and other capabilities in their decision support tools for problem solving. The Water Management Program Element partners with Federal agencies, academia, private firms, and may include international organizations. This paper further describes the Water Management Program with the objective of informing the applications community of the potential opportunities for using NASA science products for problem solving. We will illustrate some ongoing and application Water Management projects evaluating and benchmarking NASA data with partnering federal agencies and their decision support tools: 1) Environmental Protection Agency for water quality; 2) Bureau of Reclamation for water supply, demand and forecast; and 3) NOAA National Weather Service for improved weather prediction. Examples of the types of NASA contributions to the these agency decision support tools include: 1) satellite observations within models assist to estimate water storage, i.e., snow water equivalent, soil moisture, aquifer volumes, or reservoir storages; 2) model derived products, i.e., evapotranspiration, precipitation, runoff, ground water recharge, and other 4-dimensional data assimilation products; 3) improve water quality, assessments by using improved inputs from NASA models (precipitation, evaporation) and satellite observations (e.g., temperature, turbidity, land cover) to nonpoint source models; and 4) water (i.e., precipitation) and temperature predictions from days to decades over local, regional and global scales.

Generating local scale land use/cover change scenarios: case studies of high-risk mountain areas

NASA Astrophysics Data System (ADS)

Malek, Žiga; Glade, Thomas; Boerboom, Luc

2014-05-01

The relationship between land use/cover changes and consequences to human well-being is well acknowledged and has led to higher interest of both researchers and decision makers in driving forces and consequences of such changes. For example, removal of natural vegetation cover or urban expansion resulting in new elements at risk can increase hydro-meteorological risk. This is why it is necessary to study how the land use/cover could evolve in the future. Emphasis should especially be given to areas experiencing, or expecting, high rates of socio-economic change. A suitable approach to address these changes is scenario development; it offers exploring possible futures and the corresponding environmental consequences, and aids decision-making, as it enables to analyse possible options. Scenarios provide a creative methodology to depict possible futures, resulting from existing decisions, based on different assumptions of future socio-economic development. They have been used in various disciplines and on various scales, such as flood risk and soil erosion. Several studies have simulated future scenarios of land use/cover changes at a very high success rate, however usually these approaches are tailor made for specific case study areas and fit to available data. This study presents a multi-step scenario generation framework, which can be transferable to other local scale case study areas, taking into account the case study specific consequences of land use/cover changes. Through the use of experts' and decision-makers' knowledge, we aimed to develop a framework with the following characteristics: (1) it enables development of scenarios that are plausible, (2) it can overcome data inaccessibility, (3) it can address intangible and external driving forces of land use/cover change, and (4) it ensures transferability to other local scale case study areas with different land use/cover change processes and consequences. To achieve this, a set of different methods is applied

Monitoring Snow and Land Ice Using Satellite data in the GMES Project CryoLand

NASA Astrophysics Data System (ADS)

Bippus, Gabriele; Nagler, Thomas

2013-04-01

The main objectives of the project "CryoLand - GMES Service Snow and Land Ice" are to develop, implement and validate services for snow, glaciers and lake and river ice products as a Downstream Service within the Global Monitoring for Environment and Security (GMES) program of the European Commission. CryoLand exploits Earth Observation data from current optical and microwave sensors and of the upcoming GMES Sentinel satellite family. The project prepares also the basis for the cryospheric component of the GMES Land Monitoring services. The CryoLand project team consists of 10 partner organisations from Austria, Finland, Norway, Sweden, Switzerland and Romania and is funded by the 7th Framework Program of the European Commission. The CryoLand baseline products for snow include fractional snow extent from optical satellite data, the extent of melting snow from SAR data, and coarse resolution snow water equivalent maps from passive microwave data. Experimental products include maps of snow surface wetness and temperature. The products range from large scale coverage at medium resolution to regional products with high resolution, in order to address a wide user community. Medium resolution optical data (e.g. MODIS, in the near future Sentinel-3) and SAR (ENVISAT ASAR, in the near future Sentinel-1) are the main sources of EO data for generating large scale products in near real time. For generation of regional products high resolution satellite data are used. Glacier products are based on high resolution optical (e.g. SPOT-5, in the near future Sentinel-2) and SAR (TerraSAR-X, in the near future Sentinel-1) data and include glacier outlines, mapping of glacier facies, glacier lakes and ice velocity. The glacier products are generated on users demand. Current test areas are located in the Alps, Norway, Greenland and the Himalayan Mountains. The lake and river ice products include ice extent and its temporal changes and snow extent on ice. The algorithms for these

Spatial resolution requirements for urban land cover mapping from space

NASA Technical Reports Server (NTRS)

Todd, William J.; Wrigley, Robert C.

1986-01-01

Very low resolution (VLR) satellite data (Advanced Very High Resolution Radiometer, DMSP Operational Linescan System), low resolution (LR) data (Landsat MSS), medium resolution (MR) data (Landsat TM), and high resolution (HR) satellite data (Spot HRV, Large Format Camera) were evaluated and compared for interpretability at differing spatial resolutions. VLR data (500 m - 1.0 km) is useful for Level 1 (urban/rural distinction) mapping at 1:1,000,000 scale. Feature tone/color is utilized to distinguish generalized urban land cover using LR data (80 m) for 1:250,000 scale mapping. Advancing to MR data (30 m) and 1:100,000 scale mapping, confidence in land cover mapping is greatly increased, owing to the element of texture/pattern which is now evident in the imagery. Shape and shadow contribute to detailed Level II/III urban land use mapping possible if the interpreter can use HR (10-15 m) satellite data; mapping scales can be 1:25,000 - 1:50,000.

NASA Satellite Image of Japan Captured March 11, 2011

NASA Image and Video Library

2017-12-08

NASA's Aqua satellite passed over Japan one hour and 41 minutes before the quake hit. At the time Aqua passed overhead, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument captured a visible of Japan covered by clouds. The image was taken at 0405 UTC on March 11 (1:05 p.m. local time Japan /11:05 p.m. EST March 10). The quake hit at 2:46 p.m. local time/Japan. Satellite: Aqua Credit: NASA/GSFC/Aqua NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Join us on Facebook

Land-use and Land-cover Change from 1974 to 2008 around Mobile Bay

NASA Technical Reports Server (NTRS)

Ellis, Jean; Spruce, Joseph; Smoot, James; Hilbert, Kent; Swann, Roberta

2008-01-01

This project is a Gulf of Mexico Application Pilot in which NASA Stennis Space Center (SSC) is working within a regional collaboration network of the Gulf of Mexico Alliance. NASA researchers, with support from the NASA SSC Applied Science Program Steering Committee, employed multi-temporal Landsat data to assess land-use and land-cover (LULC) changes in the coastal counties of Mobile and Baldwin, AL, between 1974 and 2008. A multi-decadal time-series, coastal LULC product unique to NASA SSC was produced. The geographic extent and nature of change was quantified for the open water, barren, upland herbaceous, non-woody wetland, upland forest, woody wetland, and urban landscapes. The National Oceanic and Atmospheric Administration (NOAA) National Coastal Development Data Center (NCDDC) will assist with the transition of the final product to the operational end user, which primarily is the Mobile Bay National Estuary Program (MBNEP). We found substantial LULC change over the 34-year study period, much more than is evident when the change occurring in the last years. Between 1974 and 2008, the upland forest landscape lost almost 6% of the total acreage, while urban land cover increased by slightly more than 3%. With exception to open water, upland forest is the dominant landscape, accounting for about 25-30% of the total area.

NASA IceBridge: Scientific Insights from Airborne Surveys of the Polar Sea Ice Covers

NASA Astrophysics Data System (ADS)

Richter-Menge, J.; Farrell, S. L.

2015-12-01

The NASA Operation IceBridge (OIB) airborne sea ice surveys are designed to continue a valuable series of sea ice thickness measurements by bridging the gap between NASA's Ice, Cloud and Land Elevation Satellite (ICESat), which operated from 2003 to 2009, and ICESat-2, which is scheduled for launch in 2017. Initiated in 2009, OIB has conducted campaigns over the western Arctic Ocean (March/April) and Southern Oceans (October/November) on an annual basis when the thickness of sea ice cover is nearing its maximum. More recently, a series of Arctic surveys have also collected observations in the late summer, at the end of the melt season. The Airborne Topographic Mapper (ATM) laser altimeter is one of OIB's primary sensors, in combination with the Digital Mapping System digital camera, a Ku-band radar altimeter, a frequency-modulated continuous-wave (FMCW) snow radar, and a KT-19 infrared radiation pyrometer. Data from the campaigns are available to the research community at: http://nsidc.org/data/icebridge/. This presentation will summarize the spatial and temporal extent of the OIB campaigns and their complementary role in linking in situ and satellite measurements, advancing observations of sea ice processes across all length scales. Key scientific insights gained on the state of the sea ice cover will be highlighted, including snow depth, ice thickness, surface roughness and morphology, and melt pond evolution.

Object-based land cover classification based on fusion of multifrequency SAR data and THAICHOTE optical imagery

NASA Astrophysics Data System (ADS)

Sukawattanavijit, Chanika; Srestasathiern, Panu

2017-10-01

Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC classification applying remotely sensed data is a technique popularly employed on a global and local dimension particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN) classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data fusion provides higher land cover classification accuracy than per-pixel data fusion.

Advances Made in the Next Generation of Satellite Networks

NASA Technical Reports Server (NTRS)

Bhasin, Kul B.

1999-01-01

Because of the unique networking characteristics of communications satellites, global satellite networks are moving to the forefront in enhancing national and global information infrastructures. Simultaneously, broadband data services, which are emerging as the major market driver for future satellite and terrestrial networks, are being widely acknowledged as the foundation for an efficient global information infrastructure. In the past 2 years, various task forces and working groups around the globe have identified pivotal topics and key issues to address if we are to realize such networks in a timely fashion. In response, industry, government, and academia undertook efforts to address these topics and issues. A workshop was organized to provide a forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. The Satellite Networks: Architectures, Applications, and Technologies Workshop was hosted by the Space Communication Program at the NASA Lewis Research Center in Cleveland, Ohio. Nearly 300 executives and technical experts from academia, industry, and government, representing the United States and eight other countries, attended the event (June 2 to 4, 1998). The program included seven panels and invited sessions and nine breakout sessions in which 42 speakers presented on technical topics. The proceedings covers a wide range of topics: access technology and protocols, architectures and network simulations, asynchronous transfer mode (ATM) over satellite networks, Internet over satellite networks, interoperability experiments and applications, multicasting, NASA interoperability experiment programs, NASA mission applications, and Transmission Control Protocol/Internet Protocol (TCP/IP) over satellite: issues, relevance, and experience.

High Resolution Land Surface Modeling with the next generation Land Data Assimilation Systems

NASA Astrophysics Data System (ADS)

Kumar, S. V.; Eylander, J.; Peters-Lidard, C.

2005-12-01

Knowledge of land surface processes is important to many real-world applications such as agricultural production, water resources management, and flood predication. The Air Force Weather Agency (AFWA) has provided the USDA and other customers global soil moisture and temperature data for the past 30 years using the agrometeorological data assimilation model (now called AGRMET), merging atmospheric data. Further, accurate initialization of land surface conditions has been shown to greatly influence and improve weather forecast model and seasonal-to-interannual climate predictions. The AFWA AGRMET model exploits real time precipitation observations and analyses, global forecast model and satellite data to generate global estimates of soil moisture, soil temperature and other land surface states at 48km spatial resolution. However, to truly address the land surface initialization and climate prediction problem, and to mitigate the errors introduced by the differences in spatial scales of models, representations of land surface conditions need to be developed at the same fine scales such as that of cloud resolving models. NASA's Goddard Space Flight Center has developed an offline land data assimilation system known as the Land Information System (LIS) capable of modeling land atmosphere interactions at spatial resolutions as fine as 1km. LIS provides a software architecture that integrates the use of the state of the art land surface models, data assimilation techniques, and high performance computing and data management tools. LIS also employs many high resolution surface parameters such as the NASA Earth Observing System (EOS)-era products. In this study we describe the development of a next generation high resolution land surface modeling and data assimilation system, combining the capabilities of LIS and AGRMET. We investigate the influence of high resolution land surface data and observations on the land surface conditions by comparing with the operational AGRMET

Estimation of late twentieth century land-cover change in California

USGS Publications Warehouse

Sleeter, Benjamin M.; Wilson, Tamara S.; Soulard, Christopher E.; Liu, Jinxun

2011-01-01

We present the first comprehensive multi-temporal analysis of land-cover change for California across its major ecological regions and primary land-cover types. Recently completed satellite-based estimates of land-cover and land-use change information for large portions of the United States allow for consistent measurement and comparison across heterogeneous landscapes. Landsat data were employed within a pure-panel stratified one-stage cluster sample to estimate and characterize land-cover change for 1973–2000. Results indicate anthropogenic and natural disturbances, such as forest cutting and fire, were the dominant changes, followed by large fluctuations between agriculture and rangelands. Contrary to common perception, agriculture remained relatively stable over the 27-year period with an estimated loss of 1.0% of agricultural land. The largest net declines occurred in the grasslands/shrubs class at 5,131 km2 and forest class at 4,722 km2. Developed lands increased by 37.6%, composing an estimated 4.2% of the state’s land cover by 2000.

The NASA land processes program - Status and future directions

NASA Technical Reports Server (NTRS)

Murphy, R. E.

1984-01-01

For most of the past decade, NASA focused its efforts on the immediate exploitation of space-based sensors in earth-oriented programs. After an assessment of the current situation with respect to the conducted programs, NASA has restructured its earth-oriented programs to concentrate on the scientific use of its satellites while other agencies and private enterprise have assumed responsibility for programs of interest to them. In making this change of direction, NASA has conducted a series of studies to obtain information as a basis for its planning activities regarding future programs. Attention is given to a plan for Land Global Habitability, the development of a basic structure for the land program, a program plan for global biology, and a study on the role of biochemical cycles. The three major facets of the land processes program are discussed along with some examples of current work.

Analysis On Land Cover In Municipality Of Malang With Landsat 8 Image Through Unsupervised Classification

NASA Astrophysics Data System (ADS)

Nahari, R. V.; Alfita, R.

2018-01-01

Remote sensing technology has been widely used in the geographic information system in order to obtain data more quickly, accurately and affordably. One of the advantages of using remote sensing imagery (satellite imagery) is to analyze land cover and land use. Satellite image data used in this study were images from the Landsat 8 satellite combined with the data from the Municipality of Malang government. The satellite image was taken in July 2016. Furthermore, the method used in this study was unsupervised classification. Based on the analysis towards the satellite images and field observations, 29% of the land in the Municipality of Malang was plantation, 22% of the area was rice field, 12% was residential area, 10% was land with shrubs, and the remaining 2% was water (lake/reservoir). The shortcoming of the methods was 25% of the land in the area was unidentified because it was covered by cloud. It is expected that future researchers involve cloud removal processing to minimize unidentified area.

The Land Cover Dynamics and Conversion of Agricultural Land in Northwestern Bangladesh, 1973-2003.

NASA Astrophysics Data System (ADS)

Pervez, M.; Seelan, S. K.; Rundquist, B. C.

2006-05-01

The importance of land cover information describing the nature and extent of land resources and changes over time is increasing; this is especially true in Bangladesh, where land cover is changing rapidly. This paper presents research into the land cover dynamics of northwestern Bangladesh for the period 1973-2003 using Landsat satellite images in combination with field survey data collected in January and February 2005. Land cover maps were produced for eight different years during the study period with an average 73 percent overall classification accuracy. The classification results and post-classification change analysis showed that agriculture is the dominant land cover (occupying 74.5 percent of the study area) and is being reduced at a rate of about 3,000 ha per year. In addition, 6.7 percent of the agricultural land is vulnerable to temporary water logging annually. Despite this loss of agricultural land, irrigated agriculture increased substantially until 2000, but has since declined because of diminishing water availability and uncontrolled extraction of groundwater driven by population pressures and the extended need for food. A good agreement (r = 0.73) was found between increases in irrigated land and the depletion of the shallow groundwater table, a factor affecting widely practiced small-scale irrigation in northwestern Bangladesh. Results quantified the land cover change patterns and the stresses placed on natural resources; additionally, they demonstrated an accurate and economical means to map and analyze changes in land cover over time at a regional scale, which can assist decision makers in land and natural resources management decisions.

Impacts of Land Cover Changes on Climate over China

NASA Astrophysics Data System (ADS)

Chen, L.; Frauenfeld, O. W.

2014-12-01

Land cover changes can influence regional climate through modifying the surface energy balance and water fluxes, and can also affect climate at large scales via changes in atmospheric general circulation. With rapid population growth and economic development, China has experienced significant land cover changes, such as deforestation, grassland degradation, and farmland expansion. In this study, the Community Earth System Model (CESM) is used to investigate the climate impacts of anthropogenic land cover changes over China. To isolate the climatic effects of land cover change, we focus on the CAM and CLM models, with prescribed climatological sea surface temperature and sea ice cover. Two experiments were performed, one with current vegetation and the other with potential vegetation. Current vegetation conditions were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations, and potential vegetation over China was obtained from Ramankutty and Foley's global potential vegetation dataset. Impacts of land cover changes on surface air temperature and precipitation are assessed based on the difference of the two experiments. Results suggest that land cover changes have a cold-season cooling effect in a large region of China, but a warming effect in summer. These temperature changes can be reconciled with albedo forcing and evapotranspiration. Moreover, impacts on atmospheric circulation and the Asian Monsoon is also discussed.

NASA's mobile satellite development program

NASA Technical Reports Server (NTRS)

Rafferty, William; Dessouky, Khaled; Sue, Miles

1988-01-01

A Mobile Satellite System (MSS) will provide data and voice communications over a vast geographical area to a large population of mobile users. A technical overview is given of the extensive research and development studies and development performed under NASA's mobile satellite program (MSAT-X) in support of the introduction of a U.S. MSS. The critical technologies necessary to enable such a system are emphasized: vehicle antennas, modulation and coding, speech coders, networking and propagation characterization. Also proposed is a first, and future generation MSS architecture based upon realized ground segment equipment and advanced space segment studies.

Regional Climate Modeling over the Marmara Region, Turkey, with Improved Land Cover Data

NASA Astrophysics Data System (ADS)

Sertel, E.; Robock, A.

2007-12-01

Land surface controls the partitioning of available energy at the surface between sensible and latent heat,and controls partitioning of available water between evaporation and runoff. Current land cover data available within the regional climate models such as Regional Atmospheric Modeling System (RAMS), the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) was obtained from 1- km Advanced Very High Resolution Radiometer satellite images spanning April 1992 through March 1993 with an unsupervised classification technique. These data are not up-to-date and are not accurate for all regions and some land cover types such as urban areas. Here we introduce new, up-to-date and accurate land cover data for the Marmara Region, Turkey derived from Landsat Enhanced Thematic Mapper images into the WRF regional climate model. We used several image processing techniques to create accurate land cover data from Landsat images obtained between 2001 and 2005. First, all images were atmospherically and radiometrically corrected to minimize contamination effects of atmospheric particles and systematic errors. Then, geometric correction was performed for each image to eliminate geometric distortions and define images in a common coordinate system. Finally, unsupervised and supervised classification techniques were utilized to form the most accurate land cover data yet for the study area. Accuracy assessments of the classifications were performed using error matrix and kappa statistics to find the best classification results. Maximum likelihood classification method gave the most accurate results over the study area. We compared the new land cover data with the default WRF land cover data. WRF land cover data cannot represent urban areas in the cities of Istanbul, Izmit, and Bursa. As an example, both original satellite images and new land cover data showed the expansion of urban areas into the Istanbul metropolitan area, but in the WRF

Trends in NASA communication satellites.

NASA Technical Reports Server (NTRS)

Sivo, J. N.; Robbins, W. H.; Stretchberry, D. M.

1972-01-01

Discussion of the potential applications of satellite communications technology in meeting the national needs in education, health care, culture, and data transfer techniques. Experiments with the NASA ATS 1, 3 and 5 spacecraft, which are conducted in an attempt to satisfy such needs, are reviewed. The future needs are also considered, covering the requirements of multiple region coverage, communications between regions, large numbers of ground terminals, multichannel capability and high quality TV pictures. The ATS F and CTS spacecraft are expected to be available in the near future to expand experiments in this field.

Monitoring urban land cover with the use of satellite remote sensing techniques as a means of flood risk assessment in Cyprus

NASA Astrophysics Data System (ADS)

Alexakis, Dimitris; Hadjimitsis, Diofantos; Agapiou, Athos; Themistocleous, Kyriacos; Retalis, Adrianos

2011-11-01

The increase of flood inundation occuring in different regions all over the world have enhanced the need for effective flood risk management. As floods frequency is increasing with a steady rate due to ever increasing human activities on physical floodplains there is a respectively increasing of financial destructive impact of floods. A flood can be determined as a mass of water that produces runoff on land that is not normally covered by water. However, earth observation techniques such as satellite remote sensing can contribute toward a more efficient flood risk mapping according to EU Directives of 2007/60. This study strives to highlight the need of digital mapping of urban sprawl in a catchment area in Cyprus and the assessment of its contribution to flood risk. The Yialias river (Nicosia, Cyprus) was selected as case study where devastating flash floods events took place at 2003 and 2009. In order to search the diachronic land cover regime of the study area multi-temporal satellite imagery was processed and analyzed (e.g Landsat TMETM+, Aster). The land cover regime was examined in detail by using sophisticated post-processing classification algorithms such as Maximum Likelihood, Parallelepiped Algorithm, Minimum Distance, Spectral Angle and Isodata. Texture features were calculated using the Grey Level Co-Occurence Matrix. In addition three classification techniques were compared : multispectral classification, texture based classification and a combination of both. The classification products were compared and evaluated for their accuracy. Moreover, a knowledge-rule method is proposed based on spectral, texture and shape features in order to create efficient land use and land cover maps of the study area. Morphometric parameters such as stream frequency, drainage density and elongation ratio were calculated in order to extract the basic watershed characteristics. In terms of the impacts of land use/cover on flooding, GIS and Fragstats tool were used to

Land Cover and Seasonality Effects on Biomass Burning Emissions and Air Quality Impacts Observed from Satellites

NASA Astrophysics Data System (ADS)

Zoogman, P.; Hoffman, A.; Gonzalez Abad, G.; Miller, C. E.; Nowlan, C. R.; Huang, G.; Liu, X.; Chance, K.

2016-12-01

Trace gas emissions from biomass burning can vary greatly both regionally and from event to event, but our current scientific understanding is unable to fully explain this variability. The large uncertainty in ozone formation resulting from fire emissions has posed a great challenge for assessing fire impacts on air quality and atmospheric composition. Satellite observations from OMI offer a powerful tool to observe biomass burning events by providing observations globally over a range of environmental conditions that effect emissions of NOx, formaldehyde, and glyoxal. We have investigated the seasonal relationship of biomass burning enhancements of these trace gases derived from OMI observations over tropical South America, Africa, and Indonesia. Land cover type (also derived from satellite observations) has a significant impact on formaldehyde and glyoxal enhancements from fire activity. We have found that the chemical ratio between formaldehyde and glyoxal is dependent on the burned land type and will present our current hypotheses for the spatial variation of this ratio in the tropics. Furthermore, in individual case studies we will investigate how these chemical ratios can inform our knowledge of the secondary formation of ozone, particularly during exceptional pollution events.

Time series change detection: Algorithms for land cover change

NASA Astrophysics Data System (ADS)

Boriah, Shyam

The climate and earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, climate and ecosystem related observations from remote sensors on satellites, as well as outputs of climate or earth system models from large-scale computational platforms, provide terabytes of temporal, spatial and spatio-temporal data. These massive and information-rich datasets offer huge potential for advancing the science of land cover change, climate change and anthropogenic impacts. One important area where remote sensing data can play a key role is in the study of land cover change. Specifically, the conversion of natural land cover into humandominated cover types continues to be a change of global proportions with many unknown environmental consequences. In addition, being able to assess the carbon risk of changes in forest cover is of critical importance for both economic and scientific reasons. In fact, changes in forests account for as much as 20% of the greenhouse gas emissions in the atmosphere, an amount second only to fossil fuel emissions. Thus, there is a need in the earth science domain to systematically study land cover change in order to understand its impact on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Land cover conversions include tree harvests in forested regions, urbanization, and agricultural intensification in former woodland and natural grassland areas. These types of conversions also have significant public policy implications due to issues such as water supply management and atmospheric CO2 output. In spite of the importance of this problem and the considerable advances made over the last few years in high-resolution satellite data, data mining, and online mapping tools and services, end users still lack practical tools to help them manage and transform this data into actionable knowledge of changes in forest ecosystems that

Informing future NRT satellite distribution capabilities: Lessons learned from NASA's Land Atmosphere NRT capability for EOS (LANCE)

NASA Astrophysics Data System (ADS)

Davies, D.; Murphy, K. J.; Michael, K.

2013-12-01

NASA's Land Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE) provides data and imagery from Terra, Aqua and Aura satellites in less than 3 hours from satellite observation, to meet the needs of the near real-time (NRT) applications community. This article describes the architecture of the LANCE and outlines the modifications made to achieve the 3-hour latency requirement with a view to informing future NRT satellite distribution capabilities. It also describes how latency is determined. LANCE is a distributed system that builds on the existing EOS Data and Information System (EOSDIS) capabilities. To achieve the NRT latency requirement, many components of the EOS satellite operations, ground and science processing systems have been made more efficient without compromising the quality of science data processing. The EOS Data and Operations System (EDOS) processes the NRT stream with higher priority than the science data stream in order to minimize latency. In addition to expediting transfer times, the key difference between the NRT Level 0 products and those for standard science processing is the data used to determine the precise location and tilt of the satellite. Standard products use definitive geo-location (attitude and ephemeris) data provided daily, whereas NRT products use predicted geo-location provided by the instrument Global Positioning System (GPS) or approximation of navigational data (depending on platform). Level 0 data are processed in to higher-level products at designated Science Investigator-led Processing Systems (SIPS). The processes used by LANCE have been streamlined and adapted to work with datasets as soon as they are downlinked from satellites or transmitted from ground stations. Level 2 products that require ancillary data have modified production rules to relax the requirements for ancillary data so reducing processing times. Looking to the future, experience gained from LANCE can provide valuable lessons on

Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia-Sudan)

NASA Astrophysics Data System (ADS)

Hassaballah, Khalid; Mohamed, Yasir; Uhlenbrook, Stefan; Biro, Khalid

2017-10-01

Understanding the land use and land cover changes (LULCCs) and their implication on surface hydrology of the Dinder and Rahad basins (D&R, approximately 77 504 km2) is vital for the management and utilization of water resources in the basins. Although there are many studies on LULCC in the Blue Nile Basin, specific studies on LULCC in the D&R are still missing. Hence, its impact on streamflow is unknown. The objective of this paper is to understand the LULCC in the Dinder and Rahad and its implications on streamflow response using satellite data and hydrological modelling. The hydrological model has been derived by different sets of land use and land cover maps from 1972, 1986, 1998 and 2011. Catchment topography, land cover and soil maps are derived from satellite images and serve to estimate model parameters. Results of LULCC detection between 1972 and 2011 indicate a significant decrease in woodland and an increase in cropland. Woodland decreased from 42 to 14 % and from 35 to 14 % for Dinder and Rahad, respectively. Cropland increased from 14 to 47 % and from 18 to 68 % in Dinder and Rahad, respectively. The model results indicate that streamflow is affected by LULCC in both the Dinder and the Rahad rivers. The effect of LULCC on streamflow is significant during 1986 and 2011. This could be attributed to the severe drought during the mid-1980s and the recent large expansion in cropland.

Topographic Maps: Rediscovering an Accessible Data Source for Land Cover Change Research

ERIC Educational Resources Information Center

McChesney, Ron; McSweeney, Kendra

2005-01-01

Given some limitations of satellite imagery for the study of land cover change, we draw attention here to a robust and often overlooked data source for use in student research: USGS topographic maps. Topographic maps offer an inexpensive, rapid, and accessible means for students to analyze land cover change over large areas. We demonstrate our…

OVERVIEW OF US NATIONAL LAND-COVER MAPPING PROGRAM

EPA Science Inventory

Because of escalating costs amid growing needs for large-scale, satellite-based landscape information, a group of US federal agencies agreed to pool resources and operate as a consortium to acquire the necessary data land-cover mapping of the nation . The consortium was initiated...

Landsat—Earth observation satellites

USGS Publications Warehouse

,

2015-11-25

Since 1972, Landsat satellites have continuously acquired space-based images of the Earth’s land surface, providing data that serve as valuable resources for land use/land change research. The data are useful to a number of applications including forestry, agriculture, geology, regional planning, and education. Landsat is a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). NASA develops remote sensing instruments and the spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and data distribution. The result of this program is an unprecedented continuing record of natural and human-induced changes on the global landscape.

LandEx - Fast, FOSS-Based Application for Query and Retrieval of Land Cover Patterns

NASA Astrophysics Data System (ADS)

Netzel, P.; Stepinski, T.

2012-12-01

The amount of satellite-based spatial data is continuously increasing making a development of efficient data search tools a priority. The bulk of existing research on searching satellite-gathered data concentrates on images and is based on the concept of Content-Based Image Retrieval (CBIR); however, available solutions are not efficient and robust enough to be put to use as deployable web-based search tools. Here we report on development of a practical, deployable tool that searches classified, rather than raw image. LandEx (Landscape Explorer) is a GeoWeb-based tool for Content-Based Pattern Retrieval (CBPR) contained within the National Land Cover Dataset 2006 (NLCD2006). The USGS-developed NLCD2006 is derived from Landsat multispectral images; it covers the entire conterminous U.S. with the resolution of 30 meters/pixel and it depicts 16 land cover classes. The size of NLCD2006 is about 10 Gpixels (161,000 x 100,000 pixels). LandEx is a multi-tier GeoWeb application based on Open Source Software. Main components are: GeoExt/OpenLayers (user interface), GeoServer (OGC WMS, WCS and WPS server), and GRASS (calculation engine). LandEx performs search using query-by-example approach: user selects a reference scene (exhibiting a chosen pattern of land cover classes) and the tool produces, in real time, a map indicating a degree of similarity between the reference pattern and all local patterns across the U.S. Scene pattern is encapsulated by a 2D histogram of classes and sizes of single-class clumps. Pattern similarity is based on the notion of mutual information. The resultant similarity map can be viewed and navigated in a web browser, or it can download as a GeoTiff file for more in-depth analysis. The LandEx is available at http://sil.uc.edu

Integrated Modeling of Aerosol, Cloud, Precipitation and Land Processes at Satellite-Resolved Scales

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa; Tao, Wei-Kuo; Chin, Mian; Braun, Scott; Case, Jonathan; Hou, Arthur; Kumar, Anil; Kumar, Sujay; Lau, William; Matsui, Toshihisa;

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application.

PubMed

Maxwell, Susan K

2010-12-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. Copyright © 2010. Published by Elsevier Ltd.

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application

PubMed Central

Maxwell, Susan K.

2010-01-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. PMID:21135917

The Land Use and Land Cover Dichotomy: A Comparison of Two Land Classification Systems in Support of Urban Earth Science Applications

NASA Technical Reports Server (NTRS)

McAllister, William K.

2003-01-01

One is likely to read the terms 'land use' and 'land cover' in the same sentence, yet these concepts have different origins and different applications. Land cover is typically analyzed by earth scientists working with remotely sensed images. Land use is typically studied by urban planners who must prescribe solutions that could prevent future problems. This apparent dichotomy has led to different classification systems for land-based data. The works of earth scientists and urban planning practitioners are beginning to come together in the field of spatial analysis and in their common use of new spatial analysis technology. In this context, the technology can stimulate a common 'language' that allows a broader sharing of ideas. The increasing amount of land use and land cover change challenges the various efforts to classify in ways that are efficient, effective, and agreeable to all groups of users. If land cover and land uses can be identified by remote methods using aerial photography and satellites, then these ways are more efficient than field surveys of the same area. New technology, such as high-resolution satellite sensors, and new methods, such as more refined algorithms for image interpretation, are providing refined data to better identify the actual cover and apparent use of land, thus effectiveness is improved. However, the closer together and the more vertical the land uses are, the more difficult the task of identification is, and the greater is the need to supplement remotely sensed data with field study (in situ). Thus, a number of land classification methods were developed in order to organize the greatly expanding volume of data on land characteristics in ways useful to different groups. This paper distinguishes two land based classification systems, one developed primarily for remotely sensed data, and the other, a more comprehensive system requiring in situ collection methods. The intent is to look at how the two systems developed and how they

Land change monitoring, assessment, and projection (LCMAP) revolutionizes land cover and land change research

USGS Publications Warehouse

Young, Steven

2017-05-02

When nature and humanity change Earth’s landscapes - through flood or fire, public policy, natural resources management, or economic development - the results are often dramatic and lasting.Wildfires can reshape ecosystems. Hurricanes with names like Sandy or Katrina will howl for days while altering the landscape for years. One growing season in the evolution of drought-resistant genetics can transform semiarid landscapes into farm fields.In the past, valuable land cover maps created for understanding the effects of those events - whether changes in wildlife habitat, water-quality impacts, or the role land use and land cover play in affecting weather and climate - came out at best every 5 to 7 years. Those high quality, high resolution maps were good, but users always craved more: even higher quality data, additional land cover and land change variables, more detailed legends, and most importantly, more frequent land change information.Now a bold new initiative called Land Change Monitoring, Assessment, and Projection (LCMAP) promises to fulfill that demand.Developed at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, LCMAP provides definitive, timely information on how, why, and where the planet is changing. LCMAP’s continuous monitoring process can detect changes as they happen every day that Landsat satellites acquire clear observations. The result will be to place near real-time information in the hands of land and resource managers who need to understand the effects these changes have on landscapes.

Satellite Picture Shows the Snow-covered U.S. Deep Freeze

NASA Image and Video Library

2017-12-08

NOAA's GOES-East satellite provided a look at the frigid eastern two-thirds of the U.S. on Jan. 7, 2015, that shows a blanket of northern snow, lake-effect snow from the Great Lakes and clouds behind the Arctic cold front. A visible picture captured at 1600 UTC (11 a.m. EST) showed the effects of the latest Arctic outbreak. The cold front that brought the Arctic air has moved as far south as Florida, and stretches back over the Gulf of Mexico and just west of Texas today. The image shows clouds behind the frontal boundary stretching from the Carolinas west over the Heartland. Farther north, a wide band of fallen snow covers the ground from New England west to Montana, with rivers appearing like veins. The GOES-East satellite image also shows wind-whipped lake-effect snows off the Great Lakes, blowing to the southeast. Meanwhile, Florida, the nation's warm spot appeared almost cloud-free. To create the image, NASA/NOAA's GOES Project used cloud data from NOAA's GOES-East satellite and overlaid it on a true-color image of land and ocean created by data from the Moderate Resolution Imaging Spectroradiometer, or MODIS, instrument that flies aboard NASA's Aqua and Terra satellites. Together, those data created the entire picture of the Arctic outbreak. The forecast from NOAA's National Weather Service Weather Prediction Center (WPC) calls for more snow along the Appalachian Mountains from Tennessee north to upstate New York. Snow is also expected to fall from New England west to Montana, and in eastern New Mexico and the Colorado Rockies. The WPC summary for Jan. 7 noted: Bitter cold will be felt from the western High Plains to the Mid-Atlantic and Northeast U.S. for the next few days. Widespread subzero overnight lows are forecast for the Dakotas, Upper Midwest, Great Lakes, and interior New England. Wind Chill Advisories and Warnings are in effect for many of these areas. GOES-East provides visible and infrared images over the eastern U.S. and the Atlantic Ocean from

Land cover mapping in Latvia using hyperspectral airborne and simulated Sentinel-2 data

NASA Astrophysics Data System (ADS)

Jakovels, Dainis; Filipovs, Jevgenijs; Brauns, Agris; Taskovs, Juris; Erins, Gatis

2016-08-01

Land cover mapping in Latvia is performed as part of the Corine Land Cover (CLC) initiative every six years. The advantage of CLC is the creation of a standardized nomenclature and mapping protocol comparable across all European countries, thereby making it a valuable information source at the European level. However, low spatial resolution and accuracy, infrequent updates and expensive manual production has limited its use at the national level. As of now, there is no remote sensing based high resolution land cover and land use services designed specifically for Latvia which would account for the country's natural and land use specifics and end-user interests. The European Space Agency launched the Sentinel-2 satellite in 2015 aiming to provide continuity of free high resolution multispectral satellite data thereby presenting an opportunity to develop and adapted land cover and land use algorithm which accounts for national enduser needs. In this study, land cover mapping scheme according to national end-user needs was developed and tested in two pilot territories (Cesis and Burtnieki). Hyperspectral airborne data covering spectral range 400-2500 nm was acquired in summer 2015 using Airborne Surveillance and Environmental Monitoring System (ARSENAL). The gathered data was tested for land cover classification of seven general classes (urban/artificial, bare, forest, shrubland, agricultural/grassland, wetlands, water) and sub-classes specific for Latvia as well as simulation of Sentinel-2 satellite data. Hyperspectral data sets consist of 122 spectral bands in visible to near infrared spectral range (356-950 nm) and 100 bands in short wave infrared (950-2500 nm). Classification of land cover was tested separately for each sensor data and fused cross-sensor data. The best overall classification accuracy 84.2% and satisfactory classification accuracy (more than 80%) for 9 of 13 classes was obtained using Support Vector Machine (SVM) classifier with 109 band

Mapping dominant annual land cover from 2009 to 2013 across Victoria, Australia using satellite imagery

PubMed Central

Sheffield, Kathryn; Morse-McNabb, Elizabeth; Clark, Rob; Robson, Susan; Lewis, Hayden

2015-01-01

There is a demand for regularly updated, broad-scale, accurate land cover information in Victoria from multiple stakeholders. This paper documents the methods used to generate an annual dominant land cover (DLC) map for Victoria, Australia from 2009 to 2013. Vegetation phenology parameters derived from an annual time series of the Moderate Resolution Imaging Spectroradiometer Vegetation Indices 16-day 250 m (MOD13Q1) product were used to generate annual DLC maps, using a three-tiered hierarchical classification scheme. Classification accuracy at the broadest (primary) class level was over 91% for all years, while it ranged from 72 to 81% at the secondary class level. The most detailed class level (tertiary) had accuracy levels ranging from 61 to 68%. The approach used was able to accommodate variable climatic conditions, which had substantial impacts on vegetation growth patterns and agricultural production across the state between both regions and years. The production of an annual dataset with complete spatial coverage for Victoria provides a reliable base data set with an accuracy that is fit-for-purpose for many applications. PMID:26602009

Land-Cover Change in the East Central Texas Plains, 1973-2000

USGS Publications Warehouse

Karstensen, Krista A.

2009-01-01

Project Background: The Geographic Analysis and Monitoring (GAM) Program of the U.S. Geological Survey (USGS) Land Cover Trends project is focused on understanding the rates, trends, causes, and consequences of contemporary U.S. land-use and land-cover change. The objectives of the study are to: (1) develop a comprehensive methodology for using sampling and change analysis techniques and Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) data for measuring regional land-cover change across the United States, (2) characterize the types, rates and temporal variability of change for a 30-year period, (3) document regional driving forces and consequences of change, and (4) prepare a national synthesis of land-cover change (Loveland and others, 1999). Using the 1999 Environmental Protection Agency (EPA) Level III ecoregions derived from Omernik (1987) as the geographic framework, geospatial data collected between 1973 and 2000 were processed and analyzed to characterize ecosystem responses to land-use changes. The 27-year study period was divided into five temporal periods: 1973-1980, 1980-1986, 1986-1992, 1992-2000, and 1973-2000. General land-cover classes such as water, developed, grassland/shrubland, and agriculture for these periods were interpreted from Landsat MSS, TM, and Enhanced Thematic Mapper Plus imagery to categorize land-cover change and evaluate using a modified Anderson Land-Use Land-Cover Classification System for image interpretation. The interpretation of these land-cover classes complement the program objective of looking at land-use change with cover serving as a surrogate for land use. The land-cover change rates are estimated using a stratified, random sampling of 10-kilometer (km) by 10-km blocks allocated within each ecoregion. For each sample block, satellite images are used to interpret land-cover change for the five time periods previously mentioned. Additionally, historical aerial photographs from similar timeframes and other

NASA's MODIS/VIIRS Land Surface Temperature and Emissivity Products: Asssessment of Accuracy, Continuity and Science Uses

NASA Astrophysics Data System (ADS)

Hulley, G. C.; Malakar, N.; Islam, T.

2017-12-01

Land Surface Temperature and Emissivity (LST&E) are an important Earth System Data Record (ESDR) and Environmental Climate Variable (ECV) defined by NASA and GCOS respectively. LST&E data are key variables used in land cover/land use change studies, in surface energy balance and atmospheric water vapor retrieval models and retrievals, and in climate research. LST&E products are currently produced on a routine basis using data from the MODIS instruments on the NASA EOS platforms and by the VIIRS instrument on the Suomi-NPP platform that serves as a bridge between NASA EOS and the next-generation JPSS platforms. Two new NASA LST&E products for MODIS (MxD21) and VIIRS (VNP21) are being produced during 2017 using a new approach that addresses discrepancies in accuracy and consistency between the current suite of split-window based LST products. The new approach uses a Temperature Emissivity Separation (TES) algorithm, originally developed for the ASTER instrument, to physically retrieve both LST and spectral emissivity consistently for both sensors with high accuracy and well defined uncertainties. This study provides a rigorous assessment of accuracy of the MxD21/VNP21 products using temperature- and radiance-based validation strategies and demonstrates continuity between the products using collocated matchups over CONUS. We will further demonstrate potential science use of the new products with studies related to heat waves, monitoring snow melt dynamics, and land cover/land use change.

Characterizing bidirectional reflectance and spectral albedo of various land cover types in Midwest using GeoTASO Summer-2014 campaign

NASA Astrophysics Data System (ADS)

Wulamu, A.; Fishman, J.; Maimaitiyiming, M.; Leitch, J. W.; Zoogman, P.; Liu, X.; Chance, K.; Marshall, B.

2015-12-01

Understanding the bi-directional reflectance function (BRDF) and spectral albedo of various land-cover types is critical for retrieval of trace gas measurements from planned geostationary satellites such as the Tropospheric Emissions: Monitoring of Pollution (TEMPO). Radiant energy, which will be measured by these instruments at the top of atmosphere (TOA) at unprecedented spectral resolution, is strongly influenced by how this energy is reflected by the underlying surface. Thus, it is critical that we understand this phenomenon at comparable wavelength resolution. As part of the NASA ESTO-funded Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) development project, we carried out synchronous field and airborne data collection campaigns in the St Louis Metro region in Summer 2014. We collected spectral reflectance data of various land cover types on the ground within hours of a GeoTASO overpass using a field-based hyperspectral spectroradiometer (model PSR3500 from Spectral Evolution). Field measurements collecting in-situ spectral albedo and bidirectional reflectance factors were also obtained in July and August of 2015. In this study, we present our preliminary findings from in-situ and airborne GeoTASO derived spectral albedo and BRDF characteristics of major land cover types at TEMPO spectral profiles, which are necessary for the accurate retrieval of tropospheric trace gases and aerosols. First, a spectral database of various targets (e.g., plants, soils, rocks, man-made objects and water) was developed using field measurements. Next, the GeoTASO airborne data were corrected using MODTRAN and field measurements to derive spectral albedo and BRDF. High spatial resolution land-cover types were extracted using satellite images (e.g., Landsat, WorldView, IKONOS, etc.) at resolutions from 2 m - 30 m. Lastly, spectral albedo/BRDFs corresponding to various land cover types were analyzed using both field and GeoTASO measurements.

Effect of landslides on the structural characteristics of land-cover based on complex networks

NASA Astrophysics Data System (ADS)

He, Jing; Tang, Chuan; Liu, Gang; Li, Weile

2017-09-01

Landslides have been widely studied by geologists. However, previous studies mainly focused on the formation of landslides and never considered the effect of landslides on the structural characteristics of land-cover. Here we define the modeling of the graph topology for the land-cover, using the satellite images of the earth’s surface before and after the earthquake. We find that the land-cover network satisfies the power-law distribution, whether the land-cover contains landslides or not. However, landslides may change some parameters or measures of the structural characteristics of land-cover. The results show that the linear coefficient, modularity and area distribution are all changed after the occurence of landslides, which means the structural characteristics of the land-cover are changed.

On land-use modeling: A treatise of satellite imagery data and misclassification error

NASA Astrophysics Data System (ADS)

Sandler, Austin M.

Recent availability of satellite-based land-use data sets, including data sets with contiguous spatial coverage over large areas, relatively long temporal coverage, and fine-scale land cover classifications, is providing new opportunities for land-use research. However, care must be used when working with these datasets due to misclassification error, which causes inconsistent parameter estimates in the discrete choice models typically used to model land-use. I therefore adapt the empirical correction methods developed for other contexts (e.g., epidemiology) so that they can be applied to land-use modeling. I then use a Monte Carlo simulation, and an empirical application using actual satellite imagery data from the Northern Great Plains, to compare the results of a traditional model ignoring misclassification to those from models accounting for misclassification. Results from both the simulation and application indicate that ignoring misclassification will lead to biased results. Even seemingly insignificant levels of misclassification error (e.g., 1%) result in biased parameter estimates, which alter marginal effects enough to affect policy inference. At the levels of misclassification typical in current satellite imagery datasets (e.g., as high as 35%), ignoring misclassification can lead to systematically erroneous land-use probabilities and substantially biased marginal effects. The correction methods I propose, however, generate consistent parameter estimates and therefore consistent estimates of marginal effects and predicted land-use probabilities.

Satellite assessment of increasing tree cover 1982-2016

NASA Astrophysics Data System (ADS)

Song, X. P.; Hansen, M.

2017-12-01

The Earth's vegetation has undergone dramatic changes as we enter the Anthropocene. Recent studies have quantified global forest cover dynamics and resulting biogeochemical and biophysical impacts to the climate for the post-2000 time period. However, long-term gradual changes in undisturbed forests are less well quantified. We mapped annual tree cover using satellite data and quantified tree cover change during 1982-2016. The dataset was produced by combining optical observations from multiple satellite sensors, including the Advanced Very High Resolution Radiometer, the Moderate Resolution Imaging Spectroradiometer, the Landsat Enhanced Thematic Mapper Plus and various very high spatial resolution sensors. Contrary to current understanding of forest area change, global tree cover increased by 7%. The overall net gain in tree cover is a result of net loss in the tropics overweighed by net gain in the subtropical, temperate and boreal zones. All mountain systems, regardless of climate domain, experienced increases in tree cover. Regional patterns of tree cover gain including eastern United States, eastern Europe and southern China, indicate profound influences of socioeconomic, political or land management changes in shaping long-term environmental change. Results provide the first comprehensive record of global tree cover dynamics over the past four decades and may be used to reduce uncertainties in the quantification of the global carbon cycle.

Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-12-09

We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

Use of NASA Satellite Data in Aiding Mississippi Barrier Island Restoration Projects

NASA Technical Reports Server (NTRS)

Giardino, Marco; Spruce, Joseph; Kalcic, Maria; Fletcher, Rose

2009-01-01

data and products to support these federally funded efforts on multiple fronts. Landsat and ASTER data is being analyzed to assess changes in barrier island land cover over the last 35 years. ASTER, SRTM, and EAARL terrain products and other NASA airborne imagery are being applied in assessing changes in barrier island geomorphology and geospatial extent. MODIS data is being examined as a tool for sediment transport modeling by supplying geospatial data that quantifies in-water sediment concentrations. MODIS satellite data is being assessed for monitoring changes in the spatial extent of individual barrier islands. Results thus far indicate that NASA data products are useful in assessing barrier island conditions and changes. This value is enhanced with additional historical geospatial data, commercial high resolution satellite data, other non-NASA aerial imagery, and field survey data. The project s products are relevant to the Gulf of Mexico Alliance priority issues, including coastal habitat conservation, restoration and coastal community resilience. Such products will be available to state and federal agencies involved with coastal restoration. Potential end-users of these products include the National Park Service, U.S. Geological Survey, U.S. Army Corps of Engineers, Environmental Protection Agency, Mississippi Department of Environmental Quality, and Mississippi Department of Marine Resources.

On the Role of Urban and Vegetative Land Cover in the Identification of Tornado Damage Using Dual-Resolution Multispectral Satellite Imagery

NASA Astrophysics Data System (ADS)

Kingfield, D.; de Beurs, K.

2014-12-01

It has been demonstrated through various case studies that multispectral satellite imagery can be utilized in the identification of damage caused by a tornado through the change detection process. This process involves the difference in returned surface reflectance between two images and is often summarized through a variety of ratio-based vegetation indices (VIs). Land cover type plays a large contributing role in the change detection process as the reflectance properties of vegetation can vary based on several factors (e.g. species, greenness, density). Consequently, this provides the possibility for a variable magnitude of loss, making certain land cover regimes less reliable in the damage identification process. Furthermore, the tradeoff between sensor resolution and orbital return period may also play a role in the ability to detect catastrophic loss. Moderate resolution imagery (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS)) provides relatively coarse surface detail with a higher update rate which could hinder the identification of small regions that underwent a dynamic change. Alternatively, imagery with higher spatial resolution (e.g. Landsat) have a longer temporal return period between successive images which could result in natural recovery underestimating the absolute magnitude of damage incurred. This study evaluates the role of land cover type and sensor resolution on four high-end (EF3+) tornado events occurring in four different land cover groups (agriculture, forest, grassland, urban) in the spring season. The closest successive clear images from both Landsat 5 and MODIS are quality controlled for each case. Transacts of surface reflectance across a homogenous land cover type both inside and outside the damage swath are extracted. These metrics are synthesized through the calculation of six different VIs to rank the calculated change metrics by land cover type, sensor resolution and VI.

Land-Cover Trends of the Sierra Nevada Ecoregion, 1973-2000

USGS Publications Warehouse

Raumann, Christian G.; Soulard, Christopher E.

2007-01-01

The U.S. Geological Survey has developed and is implementing the Land Cover Trends project to estimate and describe the temporal and spatial distribution and variability of contemporary land-use and land-cover change in the United States. As part of the Land Cover Trends project, the purpose of this study was to assess land-use/land-cover change in the Sierra Nevada ecoregion for the period 1973 to 2000 using a probability sampling technique and satellite imagery. We randomly selected 36 100-km2 sample blocks to derive thematic images of land-use/land-cover for five dates of Landsat imagery (1973, 1980, 1986, 1992, 2000). We visually interpreted as many as 11 land-use/land-cover classes using a 60-meter minimum mapping unit from the five dates of imagery yielding four periods for analysis. Change-detection results from post-classification comparison of our mapped data showed that landscape disturbance from fire was the dominant change from 1973-2000. The second most-common change was forest disturbance resulting from harvest of timber resources by way of clear-cutting. The rates of forest regeneration from temporary fire and harvest disturbances coincided with the rates of disturbance from the previous period. Relatively minor landscape changes were caused by new development and reservoir drawdown. Multiple linear regression analysis suggests that land ownership and the proportion of forest and developed cover types were significant determinants of the likelihood of direct human-induced change occurring in sampling units. Driving forces of change include land ownership, land management such as fire suppression policy, and demand for natural resources.

NASA's Impacts Towards Improving International Water Management Using Satellites

NASA Astrophysics Data System (ADS)

Toll, D. L.; Doorn, B.; Searby, N. D.; Entin, J. K.; Lawford, R. G.; Mohr, K. I.; Lee, C. M.

2013-12-01

Key objectives of the NASA's Water Resources and Capacity Building Programs are to discover and demonstrate innovative uses and practical benefits of NASA's advanced system technologies for improved water management. This presentation will emphasize NASA's water research, applications, and capacity building activities using satellites and models to contribute to water issues including water availability, transboundary water, flooding and droughts to international partners, particularly developing countries. NASA's free and open exchange of Earth data observations and products helps engage and improve integrated observation networks and enables national and multi-national regional water cycle research and applications that are especially useful in data sparse regions of most developing countries. NASA satellite and modeling products provide a huge volume of valuable data extending back over 50 years across a broad range of spatial (local to global) and temporal (hourly to decadal) scales and include many products that are available in near real time (see earthdata.nasa.gov). To further accomplish these objectives NASA works to actively partner with public and private groups (e.g. federal agencies, universities, NGO's, and industry) in the U.S. and internationally to ensure the broadest use of its satellites and related information and products and to collaborate with regional end users who know the regions and their needs best. The event will help demonstrate the strong partnering and the use of satellite data to provide synoptic and repetitive spatial coverage helping water managers' deal with complex issues. This presentation will outline and describe NASA's international water related research, applications and capacity building programs' efforts to address developing countries critical water challenges in Asia, African and Latin America. This will specifically highlight impacts and case studies from NASA's programs in Water Resources (e.g., drought, snow

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Chun; Rottensteiner, Franz; Heipke, Christian

2018-04-01

Land cover describes the physical material of the earth's surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

Spatio - Temporal Variation of Aerosol and its relation to vegetation cover over mega-city New Delhi

NASA Astrophysics Data System (ADS)

Pandey, Alok; Pravesh Kumar, Ram; Berwal, Shivesh; Kumar, Krishan; Kumar, Ritesh

2016-07-01

MODerate resolution Imaging Spectro-radiometer (MODIS) on board NASA's Terra and Aqua satellite Level 2 Aerosol optical depth (AOD) data is used for aerosol study and LISS III sensor on board Indian Remote Sensing (IRS) Satellite procured from the National Remote Sensing Center (NRSC), Hyderabad, India was used for vegetation cover estimation over New Delhi and its surrounding regions. Lowest AOD was found in the spring and winter season where highest AOD in summer months. Different dates representing different seasons LISS III imageries were used for generation of land-cover maps for vegetation study. The land cover maps reveal that most of the surrounding areas of Delhi are covered with vegetation in the month of March. By the month of May-June herbs are cut or dry from most of the region surrounding Delhi and the land cover in the surrounding areas changes to bare soil. During the rainy season (July to September) the vegetation cover over Delhi and the surrounding areas increases significantly. In November - December there is dispersed vegetation cover over New Delhi and its surrounding regions depending upon the age of the newly sown crop and ornamental plants. We found that, there is statistically significant negative correlation between AOD and Vegetation in every season over New Delhi.

Metropolitan land cover inventory using multiseasonal Landsat data

USGS Publications Warehouse

Todd, William J.; Hill, R.N.; Henry, C.C.; Lake, B.L.

1978-01-01

As a part of the Pacific Northwest Land Resources Inventory Demonstration Project (PNLRIDP), planners from State, regional, and local agencies in Oregon are working with scientists from the EROS Data Center (USGS), Ames Research Center (NASA), and the Jet Propulsion Laboratory (California Institute of Technology) to obtain practical training and experience in the analysis of remotely sensed data collected from air and spacecraft. A 4,000 km2 area centered on metropolitan Portland was chosen as the demonstration site, and a four-date Landsat temporal overlay was created which contained January, April, July, and October data collected in 1973. Digital multispectral analysis of single dates and two-date combinations revealed that the spring-summer and summer-fall combinations were the most satisfactory for land cover inventory. Residential, commercial and industrial, improved open space, water, forested, and agriculture land cover categories were obtained consistently in the majority of classification iterations. Census tract and traffic zone boundaries were digitized and registered with the Landsat data to facilitate integration of the land cover information with socioeconomic and environmental data already available to Oregon planners.

NASA satellite to track North Pole expedition

NASA Technical Reports Server (NTRS)

1978-01-01

The proposed expedition of a lone explorer and the use of Nimbus 6 (NASA meteorological research satellite) to track his journey is reported. The journey is scheduled to start March 4, 1978, and will cover a distance of 6.000 Km (3,728 miles) from northern Canada to the North Pole and return, traveling the length of Greenland's isolated interior. The mode of transportation for the explorer will be by dog sled. Instrumentation and tracking techniques are discussed.

Accuracy Assessment of Satellite Derived Forest Cover Products in South and Southeast Asia

NASA Astrophysics Data System (ADS)

Gilani, H.; Xu, X.; Jain, A. K.

2017-12-01

South and Southeast Asia (SSEA) region occupies 16 % of worlds land area. It is home to over 50% of the world's population. The SSEA's countries are experiencing significant land-use and land-cover changes (LULCCs), primarily in agriculture, forest, and urban land. For this study, we compiled four existing global forest cover maps for year 2010 by Gong et al.(2015), Hansen et al. (2013), Sexton et al.(2013) and Shimada et al. (2014), which were all medium resolution (≤30 m) products based on Landsat and/or PALSAR satellite images. To evaluate the accuracy of these forest products, we used three types of information: (1) ground measurements, (2) high resolution satellite images and (3) forest cover maps produced at the national scale. The stratified random sampling technique was used to select a set of validation data points from the ground and high-resolution satellite images. Then the confusion matrix method was used to assess and rank the accuracy of the forest cover products for the entire SSEA region. We analyzed the spatial consistency of different forest cover maps, and further evaluated the consistency with terrain characteristics. Our study suggests that global forest cover mapping algorithms are trained and tested using limited ground measurement data. We found significant uncertainties in mountainous areas due to the topographical shadow effect and the dense tree canopies effects. The findings of this study will facilitate to improve our understanding of the forest cover dynamics and their impacts on the quantities and pathways of terrestrial carbon and nitrogen fluxes. Gong, P., et al. (2012). "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data." International Journal of Remote Sensing 34(7): 2607-2654. Hansen, M. C., et al. (2013). "High-Resolution Global Maps of 21st-Century Forest Cover Change." Science 342(6160): 850-853. Sexton, J. O., et al. (2013). "Global, 30-m resolution

Global land cover mapping and characterization: present situation and future research priorities

USGS Publications Warehouse

Giri, Chandra

2005-01-01

The availability and accessibility of global land cover data sets plays an important role in many global change studies. The importance of such science‐based information is also reflected in a number of international, regional, and national projects and programs. Recent developments in earth observing satellite technology, information technology, computer hardware and software, and infrastructure development have helped developed better quality land cover data sets. As a result, such data sets are increasingly becoming available, the user‐base is ever widening, application areas have been expanding, and the potential of many other applications are enormous. Yet, we are far from producing high quality global land cover data sets. This paper examines the progress in the development of digital global land cover data, their availability, and current applications. Problems and opportunities are also explained. The overview sets the stage for identifying future research priorities needed for operational land cover assessment and monitoring.

Linking local knowledge and satellite-derived land-use/land-cover change information in Krabi province, Thailand

NASA Astrophysics Data System (ADS)

Peneva-Reed, Elitsa I.

This study was designed to gain an understanding of the linkage between development and sustainability of mangrove forest conversion in three coastal communities in Thailand. It presents a methodology that could potentially aid coastal communities in determining sustainable land use conversion approaches by considering the viewpoint of villagers. A remote sensing analysis of Landsat satellite images from 1989, 2001 and 2007 showed the results of a moderate, but sustained shrimp farming industry that only partially exploited mangrove forests. The three villages experienced a range of changes in mangrove forest area. The villagers' perceptions (collected through field surveys) did not match the results from the remote sensing analysis and varied significantly. A logit multiple regression model was utilized to study the factors influencing whether villagers' estimates agreed or disagreed with the remote sensing analysis. Results showed that the only variables statistically significant at the 0.10 level were age, occupation, and proximity to the mangrove resource. There is a widespread belief that one of the main negative effects of the development of shrimp farms is the pollution of water and, as a consequence, the reduction of wild catch. In this study, a majority of fishing households reported a reduction in wild catch, with nearly all attributing it to shrimp farms. A relatively small number of households noted positive effects from shrimp farming and listed these as an increase of income as a result of working at shrimp farms. The most common negative effects identified by the locals were water pollution, followed by a decrease in wild catch, and an increase in the number of mosquitoes. Although shrimp farm developers promised many benefits from this enterprise, very few were realized by the villagers. Integrating information from household surveys with data on land-cover change derived from remote sensing improves our understanding of the causes and processes of

Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

NASA Astrophysics Data System (ADS)

Şekertekin, A.; Kutoglu, Ş. H.; Kaya, S.; Marangoz, A. M.

2015-12-01

Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

Using NASA Earth Observing Satellites and Statistical Model Analysis to Monitor Vegetation and Habitat Rehabilitation in Southwest Virginia's Reclaimed Mine Lands

NASA Astrophysics Data System (ADS)

Tate, Z.; Dusenge, D.; Elliot, T. S.; Hafashimana, P.; Medley, S.; Porter, R. P.; Rajappan, R.; Rodriguez, P.; Spangler, J.; Swaminathan, R. S.; VanGundy, R. D.

2014-12-01

The majority of the population in southwest Virginia depends economically on coal mining. In 2011, coal mining generated $2,000,000 in tax revenue to Wise County alone. However, surface mining completely removes land cover and leaves the land exposed to erosion. The destruction of the forest cover directly impacts local species, as some are displaced and others perish in the mining process. Even though surface mining has a negative impact on the environment, land reclamation efforts are in place to either restore mined areas to their natural vegetated state or to transform these areas for economic purposes. This project aimed to monitor the progress of land reclamation and the effect on the return of local species. By incorporating NASA Earth observations, such as Landsat 8 Operational Land Imager (OLI) and Landsat 5 Thematic Mapper (TM), re-vegetation process in reclamation sites was estimated through a Time series analysis using the Normalized Difference Vegetation Index (NDVI). A continuous source of cloud free images was accomplished by utilizing the Spatial and Temporal Adaptive Reflectance Fusion Model (STAR-FM). This model developed synthetic Landsat imagery by integrating the high-frequency temporal information from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and high-resolution spatial information from Landsat sensors In addition, the Maximum Entropy Modeling (MaxENT), an eco-niche model was used to estimate the adaptation of animal species to the newly formed habitats. By combining factors such as land type, precipitation from Tropical Rainfall Measuring Mission (TRMM), and slope from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the MaxENT model produced a statistical analysis on the probability of species habitat. Altogether, the project compiled the ecological information which can be used to identify suitable habitats for local species in reclaimed mined areas.

Analyzing vegetation dynamics of land systems with satellite data

USGS Publications Warehouse

Eidenshink, Jeffery C.; Haas, Robert H.

1992-01-01

Large area assessment of vegetation conditions is a major requirement for understanding the impact of weather on food, fiber, and forage production. The distribution of vegetation is largely associated with climate, terrain characteristics, and human activity. The interpretation of vegetation dynamics from satellite data can be improved by stratifying the land surface into ecoregions. The Soil Conservation Service, U.S. Department of Agriculture, has developed a system for mapping major land resource areas (MLRA) that groups land areas in the United States on the basis of climate, physiography, land use, and land cover characteristics.In 1989, the U.S. Geological Survey used National Oceanic and Atmospheric Administration weather satellite data to conduct a biweekly assessment of vegetation conditions in 17 western states. Advanced Very High Resolution Radiometer data were acquired daily, and were geographically registered, and the normalized difference vegetation index (NDVI) was computed for the Western United States during the 1989 growing season. Fifteen biweekly NDVI data sets were used to evaluate MLRA's as an appropriate stratification for monitoring and interpreting vegetation conditions in the study area.The results demonstrate the feasibility of using MLRA's to stratify areas for monitoring phenological development and vegetation condition assessment within the growing season. Assessments of the NDVI at biweekly intervals are adequate for monitoring seasonal growth patterns on MLRA's where rangelands, forests, or cultivated agriculture are the primary resource type. Descriptive statistics are indicators of the uniformity or diversity of land use and land cover within an MLRA. Growing season profiles of the NDVI are characterized by the seasonal effects of climate on various land use and land cover classes.

Satellite applications to electric-utility communications needs. [land mobile satellite service

NASA Technical Reports Server (NTRS)

Horstein, M.; Barnett, R.

1981-01-01

Significant changes in the Nation's electric power systems are expected to result from the integration of new technology, possible during the next decade. Digital communications for monitor and control, exclusive of protective relaying, are expected to double or triple current traffic. A nationwide estimate of 13 Mb/s traffic is projected. Of this total, 8 Mb/s is attributed to the bulk-power system as it is now being operated (4 Mb/s). This traffic could be accommodated by current communications satellites using 3- to 4.5-m-diameter ground terminals costing $35,000 to $70,000 each. The remaining 5-Mb/s traffic is attributed to new technology concepts integrated into the distribution system. Such traffic is not compatible with current satellite technology because it requires small, low-cost ground terminals. Therefore, a high effective isotropic radiated power satellite, such as the one being planned by NASA for the Land Mobile Satellite Service, is required.

The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.

SAMPLE SELECTION OF MRLC'S NLCD LAND COVER DATA FOR THEMATIC ACCURACY ASSESSMENT

EPA Science Inventory

The Multi-Resolution Land Characteristics (MRLC) consortium was formed in the early 1990s to cost- effectively acquire Landsat TM satellite data for the conterminous United States. One of the MRLC's objectives was to develop national land-cover data (NLCD) for the conterminous Un...

Development of a 30 m Spatial Resolution Land Cover of Canada: Contribution to the Harmonized North America Land Cover Dataset

NASA Astrophysics Data System (ADS)

Pouliot, D.; Latifovic, R.; Olthof, I.

2017-12-01

Land cover is needed for a large range of environmental applications regarding climate impacts and adaption, emergency response, wildlife habitat, air quality, water yield, etc. In Canada a 2008 user survey revealed that the most practical scale for provision of land cover data is 30 m, nationwide, with an update frequency of five years (Ball, 2008). In response to this need the Canada Centre for Remote Sensing has generated a 30 m land cover of Canada for the base year 2010 as part of a planned series of maps at the recommended five year update frequency. This land cover is the Canadian contribution to the North American Land Change Monitoring System initiative, which seeks to provide harmonized land cover across Canada, the United States, and Mexico. The methodology developed in this research utilized a combination of unsupervised and machine learning techniques to map land cover, blend results between mapping units, locally optimize results, and process some thematic attributes with specific features sets. Accuracy assessment with available field data shows it was on average 75% for the five study areas assessed. In this presentation an overview of the unique processing aspects, example results, and initial accuracy assessment will be discussed.

Using Satellite Data and Land Surface Models to Monitor and Forecast Drought Conditions in Africa and Middle East

NASA Astrophysics Data System (ADS)

Arsenault, K. R.; Shukla, S.; Getirana, A.; Peters-Lidard, C. D.; Kumar, S.; McNally, A.; Zaitchik, B. F.; Badr, H. S.; Funk, C. C.; Koster, R. D.; Narapusetty, B.; Jung, H. C.; Roningen, J. M.

2017-12-01

Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. In addition, these regions typically have sparse ground-based data networks, where sometimes remotely sensed observations may be the only data available. Long-term satellite records can help with determining historic and current drought conditions. In recent years, several new satellites have come on-line that monitor different hydrological variables, including soil moisture and terrestrial water storage. Though these recent data records may be considered too short for the use in identifying major droughts, they do provide additional information that can better characterize where water deficits may occur. We utilize recent satellite data records of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and the European Space Agency's Advanced Scatterometer (ASCAT) soil moisture retrievals. Combining these records with land surface models (LSMs), NASA's Catchment and the Noah Multi-Physics (MP), is aimed at improving the land model states and initialization for seasonal drought forecasts. The LSMs' total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which can provide an additional means of validation against in situ streamflow data. The NASA Land Information System (LIS) software framework drives the LSMs and HyMAP and also supports the capability to assimilate these satellite retrievals, such as soil moisture and TWS. The LSMs are driven for 30+ years with NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS/UCSB Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset. The seasonal water deficit forecasts are generated using downscaled and bias-corrected versions of NASA's Goddard Earth Observing System Model (GEOS-5), and NOAA's Climate Forecast System (CFSv2) forecasts

Analysis of economic values of land use and land cover changes in crisis territories by satellite data: models of socio-economy and population dynamics in war

NASA Astrophysics Data System (ADS)

Kostyuchenko, Yuriy V.; Yuschenko, Maxim; Movchan, Dmytro; Kopachevsky, Ivan

2017-10-01

Problem of remote sensing data harnessing for decision making in conflict territories is considered. Approach for analysis of socio-economic and demographic parameters with a limited set of data and deep uncertainty is described. Number of interlinked techniques to estimate a population and economy in crisis territories are proposed. Stochastic method to assessment of population dynamics using multi-source data using remote sensing data is proposed. Adaptive Markov's chain based method to study of land-use changes using satellite data is proposed. Proposed approach is applied to analysis of socio-economic situation in Donbas (East Ukraine) territory of conflict in 2014-2015. Land-use and landcover patterns for different periods were analyzed using the Landsat and MODIS data . The land-use classification scheme includes the following categories: (1) urban or built-up land, (2) barren land, (3) cropland, (4) horticulture farms, (5) livestock farms, (6) forest, and (7) water. It was demonstrated, that during the period 2014-2015 was not detected drastic changes in land-use structure of study area. Heterogeneously distributed decreasing of horticulture farms (4-6%), livestock farms (5-6%), croplands (3-4%), and increasing of barren land (6-7%) have been observed. Way to analyze land-cover productivity variations using satellite data is proposed. Algorithm is based on analysis of time-series of NDVI and NDWI distributions. Drastic changes of crop area and its productivity were detected. Set of indirect indicators, such as night light intensity, is also considered. Using the approach proposed, using the data utilized, the local and regional GDP, local population, and its dynamics are estimated.

Alternative method to validate the seasonal land cover regions of the conterminous United States

Treesearch

Zhiliang Zhu; Donald O. Ohlen; Raymond L. Czaplewski; Robert E. Burgan

1996-01-01

An accuracy assessment method involving double sampling and the multivariate composite estimator has been used to validate the prototype seasonal land cover characteristics database of the conterminous United States. The database consists of 159 land cover classes, classified using time series of 1990 1-km satellite data and augmented with ancillary data including...

Validation of Land Cover Maps Utilizing Astronaut Acquired Imagery

NASA Technical Reports Server (NTRS)

Estes, John E.; Gebelein, Jennifer

1999-01-01

This report is produced in accordance with the requirements outlined in the NASA Research Grant NAG9-1032 titled "Validation of Land Cover Maps Utilizing Astronaut Acquired Imagery". This grant funds the Remote Sensing Research Unit of the University of California, Santa Barbara. This document summarizes the research progress and accomplishments to date and describes current on-going research activities. Even though this grant has technically expired, in a contractual sense, work continues on this project. Therefore, this summary will include all work done through and 5 May 1999. The principal goal of this effort is to test the accuracy of a sub-regional portion of an AVHRR-based land cover product. Land cover mapped to three different classification systems, in the southwestern United States, have been subjected to two specific accuracy assessments. One assessment utilizing astronaut acquired photography, and a second assessment employing Landsat Thematic Mapper imagery, augmented in some cases, high aerial photography. Validation of these three land cover products has proceeded using a stratified sampling methodology. We believe this research will provide an important initial test of the potential use of imagery acquired from Shuttle and ultimately the International Space Station (ISS) for the operational validation of the Moderate Resolution Imaging Spectrometer (MODIS) land cover products.

A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data

USGS Publications Warehouse

Stibig, H.-J.; Belward, A.S.; Roy, P.S.; Rosalina-Wasrin, U.; Agrawal, S.; Joshi, P.K.; ,; Beuchle, R.; Fritz, S.; Mubareka, S.; Giri, C.

2007-01-01

Aim  Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project.Location  The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east.Methods  The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’.Results  The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics.Main conclusions  The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub

Fusion of Modis and Palsar Principal Component Images Through Curvelet Transform for Land Cover Classification

NASA Astrophysics Data System (ADS)

Singh, Dharmendra; Kumar, Harish

Earth observation satellites provide data that covers different portions of the electromagnetic spectrum at different spatial and spectral resolutions. The increasing availability of information products generated from satellite images are extending the ability to understand the patterns and dynamics of the earth resource systems at all scales of inquiry. In which one of the most important application is the generation of land cover classification from satellite images for understanding the actual status of various land cover classes. The prospect for the use of satel-lite images in land cover classification is an extremely promising one. The quality of satellite images available for land-use mapping is improving rapidly by development of advanced sensor technology. Particularly noteworthy in this regard is the improved spatial and spectral reso-lution of the images captured by new satellite sensors like MODIS, ASTER, Landsat 7, and SPOT 5. For the full exploitation of increasingly sophisticated multisource data, fusion tech-niques are being developed. Fused images may enhance the interpretation capabilities. The images used for fusion have different temporal, and spatial resolution. Therefore, the fused image provides a more complete view of the observed objects. It is one of the main aim of image fusion to integrate different data in order to obtain more information that can be de-rived from each of the single sensor data alone. A good example of this is the fusion of images acquired by different sensors having a different spatial resolution and of different spectral res-olution. Researchers are applying the fusion technique since from three decades and propose various useful methods and techniques. The importance of high-quality synthesis of spectral information is well suited and implemented for land cover classification. More recently, an underlying multiresolution analysis employing the discrete wavelet transform has been used in image fusion. It was found

Using NASA Remote Sensing Data to Reduce Uncertainty of Land-use Transitions in Global Carbon-Climate Models

NASA Astrophysics Data System (ADS)

Chini, L. P.; Hurtt, G. C.; Frolking, S. E.; Sahajpal, R.; Potapov, P.; Hansen, M.; Fisk, J.

2016-12-01

For the 5th IPCC Assessment almost all Earth System Models (ESMs) incorporated new gridded products of land-use and land-use change that were harmonized to ensure a continuous transition from historical to future data in a consistent format for all models. However, these Land-Use Harmonization (LUH) data products are estimates, constrained with data where available, and with modeling assumptions, and the remaining challenge is to quantify, and reduce, the uncertainty in these products. At the same time, satellite remote sensing of the terrestrial biosphere has also evolved. Global-scale land cover extent and change monitoring is now possible given systematically acquired earth observation data sets, advanced characterization algorithms and data intensive computing capabilities. Here we consider: how can satellite remote sensing products be used to generate (and reduce uncertainty in) new gridded maps of land-use transitions for use in coupled carbon-climate simulations? As part of the international effort to develop the next generation of land-use datasets (LUH2), new NASA remote-sensing-based maps of global forest extent and change (Hansen et al. 2013) were used as both an added constraint and diagnostic in the LUH process. Harmonizing this remote sensing data with the LUH data was a major computational challenge involving 143 billion 30m Landsat pixels, and the simulation of over 20 billion LUH unknowns. Our approach involved first harmonizing the definitions of forest loss between the observed and simulated data for the years 2000-2012. Next, new spatial patterns of historical wood harvest were calculated to match the observed forest loss transitions while simultaneously meeting all other constraints of the model, and ensuring consistency throughout the historical time-period. After reconciling definitions and developing new wood harvest patterns the LUH2 global forest loss for the period 2000-2012 was reduced from over 8.3 million km2 to 1.78 million km2

Survey methods for assessing land cover map accuracy

USGS Publications Warehouse

Nusser, S.M.; Klaas, E.E.

2003-01-01

The increasing availability of digital photographic materials has fueled efforts by agencies and organizations to generate land cover maps for states, regions, and the United States as a whole. Regardless of the information sources and classification methods used, land cover maps are subject to numerous sources of error. In order to understand the quality of the information contained in these maps, it is desirable to generate statistically valid estimates of accuracy rates describing misclassification errors. We explored a full sample survey framework for creating accuracy assessment study designs that balance statistical and operational considerations in relation to study objectives for a regional assessment of GAP land cover maps. We focused not only on appropriate sample designs and estimation approaches, but on aspects of the data collection process, such as gaining cooperation of land owners and using pixel clusters as an observation unit. The approach was tested in a pilot study to assess the accuracy of Iowa GAP land cover maps. A stratified two-stage cluster sampling design addressed sample size requirements for land covers and the need for geographic spread while minimizing operational effort. Recruitment methods used for private land owners yielded high response rates, minimizing a source of nonresponse error. Collecting data for a 9-pixel cluster centered on the sampled pixel was simple to implement, and provided better information on rarer vegetation classes as well as substantial gains in precision relative to observing data at a single-pixel.

Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960-2005.

PubMed

Dewan, Ashraf M; Yamaguchi, Yasushi

2009-03-01

This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images such as SPOT, IRS, IKONOS and field data. The overall accuracy of land cover change maps, generated from Landsat and IRS-1D data, ranged from 85% to 90%. The analysis indicated that the urban expansion of Dhaka Metropolitan resulted in the considerable reduction of wetlands, cultivated land, vegetation and water bodies. The maps showed that between 1960 and 2005 built-up areas increased approximately 15,924 ha, while agricultural land decreased 7,614 ha, vegetation decreased 2,336 ha, wetland/lowland decreased 6,385 ha, and water bodies decreased about 864 ha. The amount of urban land increased from 11% (in 1960) to 344% in 2005. Similarly, the growth of landfill/bare soils category was about 256% in the same period. Much of the city's rapid growth in population has been accommodated in informal settlements with little attempt being made to limit the risk of environmental impairments. The study quantified the patterns of land use/cover change for the last 45 years for Dhaka Metropolitan that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.

Watershed-scale land-use mapping with satellite imagery

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing data has many advantages compared with other data sources, such as field methods and aerial photography, for land cover classification. In particular,it is useful in evaluating temporal and spatial effects. In addition, remote sensing can offer a cost-effective means of prov...

Land-use and land-cover change in montane mainland southeast Asia.

PubMed

Fox, Jefferson; Vogler, John B

2005-09-01

This paper summarizes land-cover and land-use change at eight sites in Thailand, Yunnan (China), Vietnam, Cambodia, and Laos over the last 50 years. Project methodology included incorporating information collected from a combination of semiformal, key informant, and formal household interviews with the development of spatial databases based on aerial photographs, satellite images, topographic maps, and GPS data. Results suggest that land use (e.g. swidden cultivation) and land cover (e.g. secondary vegetation) have remained stable and the minor amount of land-use change that has occurred has been a change from swidden to monocultural cash crops. Results suggest that two forces will increasingly determine land-use systems in this region. First, national land tenure policies-the nationalization of forest lands and efforts to increase control over upland resources by central governments-will provide a push factor making it increasingly difficult for farmers to maintain their traditional swidden land-use practices. Second, market pressures-the commercialization of subsistence resources and the substitution of commercial crops for subsistence crops-will provide a pull factor encouraging farmers to engage in new and different forms of commercial agriculture. These results appear to be robust as they come from eight studies conducted over the last decade. But important questions remain in terms of what research protocols are needed, if any, when linking social science data with remotely sensed data for understanding human-environment interactions.

The NASA Earth Science Program and Small Satellites

NASA Technical Reports Server (NTRS)

Neeck, Steven P.

2015-01-01

Earth's changing environment impacts every aspect of life on our planet and climate change has profound implications on society. Studying Earth as a single complex system is essential to understanding the causes and consequences of climate change and other global environmental concerns. NASA's Earth Science Division (ESD) shapes an interdisciplinary view of Earth, exploring interactions among the atmosphere, oceans, ice sheets, land surface interior, and life itself. This enables scientists to measure global and climate changes and to inform decisions by Government, other organizations, and people in the United States and around the world. The data collected and results generated are accessible to other agencies and organizations to improve the products and services they provide, including air quality indices, disaster prediction and response, agricultural yield projections, and aviation safety. ESD's Flight Program provides the spacebased observing systems and supporting infrastructure for mission operations and scientific data processing and distribution that support NASA's Earth science research and modeling activities. The Flight Program currently has 21 operating Earth observing space missions, including the recently launched Global Precipitation Measurement (GPM) mission, the Orbiting Carbon Observatory-2 (OCO-2), the Soil Moisture Active Passive (SMAP) mission, and the International Space Station (ISS) RapidSCAT and Cloud-Aerosol Transport System (CATS) instruments. The ESD has 22 more missions and instruments planned for launch over the next decade. These include first and second tier missions from the 2007 Earth Science Decadal Survey, Climate Continuity missions to assure availability of key climate data sets, and small-sized competitively selected orbital missions and instrument missions of opportunity belonging to the Earth Venture (EV) Program. Small satellites (500 kg or less) are critical contributors to these current and future satellite missions

Computer implemented land cover classification using LANDSAT MSS digital data: A cooperative research project between the National Park Service and NASA. 3: Vegetation and other land cover analysis of Shenandoah National Park

NASA Technical Reports Server (NTRS)

Cibula, W. G.

1981-01-01

Four LANDSAT frames, each corresponding to one of the four seasons were spectrally classified and processed using NASA-developed computer programs. One data set was selected or two or more data sets were marged to improve surface cover classifications. Selected areas representing each spectral class were chosen and transferred to USGS 1:62,500 topographic maps for field use. Ground truth data were gathered to verify the accuracy of the classifications. Acreages were computed for each of the land cover types. The application of elevational data to seasonal LANDSAT frames resulted in the separation of high elevation meadows (both with and without recently emergent perennial vegetation) as well as areas in oak forests which have an evergreen understory as opposed to other areas which do not.

Next-Generation Satellite Precipitation Products for Understanding Global and Regional Water Variability

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2011-01-01

A major challenge in understanding the space-time variability of continental water fluxes is the lack of accurate precipitation estimates over complex terrains. While satellite precipitation observations can be used to complement ground-based data to obtain improved estimates, space-based and ground-based estimates come with their own sets of uncertainties, which must be understood and characterized. Quantitative estimation of uncertainties in these products also provides a necessary foundation for merging satellite and ground-based precipitation measurements within a rigorous statistical framework. Global Precipitation Measurement (GPM) is an international satellite mission that will provide next-generation global precipitation data products for research and applications. It consists of a constellation of microwave sensors provided by NASA, JAXA, CNES, ISRO, EUMETSAT, DOD, NOAA, NPP, and JPSS. At the heart of the mission is the GPM Core Observatory provided by NASA and JAXA to be launched in 2013. The GPM Core, which will carry the first space-borne dual-frequency radar and a state-of-the-art multi-frequency radiometer, is designed to set new reference standards for precipitation measurements from space, which can then be used to unify and refine precipitation retrievals from all constellation sensors. The next-generation constellation-based satellite precipitation estimates will be characterized by intercalibrated radiometric measurements and physical-based retrievals using a common observation-derived hydrometeor database. For pre-launch algorithm development and post-launch product evaluation, NASA supports an extensive ground validation (GV) program in cooperation with domestic and international partners to improve (1) physics of remote-sensing algorithms through a series of focused field campaigns, (2) characterization of uncertainties in satellite and ground-based precipitation products over selected GV testbeds, and (3) modeling of atmospheric processes and

Satellite Snow-Cover Mapping: A Brief Review

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.

1995-01-01

Satellite snow mapping has been accomplished since 1966, initially using data from the reflective part of the electromagnetic spectrum, and now also employing data from the microwave part of the spectrum. Visible and near-infrared sensors can provide excellent spatial resolution from space enabling detailed snow mapping. When digital elevation models are also used, snow mapping can provide realistic measurements of snow extent even in mountainous areas. Passive-microwave satellite data permit global snow cover to be mapped on a near-daily basis and estimates of snow depth to be made, but with relatively poor spatial resolution (approximately 25 km). Dense forest cover limits both techniques and optical remote sensing is limited further by cloudcover conditions. Satellite remote sensing of snow cover with imaging radars is still in the early stages of research, but shows promise at least for mapping wet or melting snow using C-band (5.3 GHz) synthetic aperture radar (SAR) data. Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning with the launch of the first EOS platform in 1998. Digital maps will be produced that will provide daily, and maximum weekly global snow, sea ice and lake ice cover at 1-km spatial resolution. Statistics will be generated on the extent and persistence of snow or ice cover in each pixel for each weekly map, cloudcover permitting. It will also be possible to generate snow- and ice-cover maps using MODIS data at 250- and 500-m resolution, and to study and map snow and ice characteristics such as albedo. been under development. Passive-microwave data offer the potential for determining not only snow cover, but snow water equivalent, depth and wetness under all sky conditions. A number of algorithms have been developed to utilize passive-microwave brightness temperatures to provide information on snow cover and water equivalent. The variability of vegetative Algorithms are being developed to map global snow

Analysis of global land surface albedo climatology and spatial-temporal variation during 1981-2010 from multiple satellite products

NASA Astrophysics Data System (ADS)

He, Tao; Liang, Shunlin; Song, Dan-Xia

2014-09-01

For several decades, long-term time series data sets of multiple global land surface albedo products have been generated from satellite observations. These data sets have been used as one of the key variables in climate change studies. This study aims to assess the surface albedo climatology and to analyze long-term albedo changes, from nine satellite-based data sets for the period 1981-2010, on a global basis. Results show that climatological surface albedo data sets derived from satellite observations can be used to validate, calibrate, and further improve surface albedo simulations and parameterizations in current climate models. However, the albedo products derived from the International Satellite Cloud Climatology Project and the Global Energy and Water Exchanges Project have large seasonal biases. At latitudes higher than 50°, the maximal difference in winter zonal albedo ranges from 0.1 to 0.4 among the nine satellite data sets. Satellite-based albedo data sets agree relatively well during the summer at high latitudes, with a standard deviation of 0.04 for the 70°-80° zone in both hemispheres. The fine-resolution (0.05°) data sets agree well with each other for all the land cover types in middle to low latitudes; however, large spread was identified for their albedos at middle to high latitudes over land covers with mixed snow and sparse vegetation. By analyzing the time series of satellite-based albedo products over the past three decades, albedo of the Northern Hemisphere was found to be decreasing in July, likely due to the shrinking snow cover. Meanwhile, albedo in January was found to be increasing, likely because of the expansion of snow cover in northern winter. However, to improve the albedo estimation at high latitudes, and ultimately the climate models used for long-term climate change studies, a still better understanding of differences between satellite-based albedo data sets is required.

Landsat: a global land imaging program

USGS Publications Warehouse

Byrnes, Raymond A.

2012-01-01

Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs across four decades. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. In practice, NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.

How To Cover NASA's Chandra X-ray Observatory

NASA Astrophysics Data System (ADS)

1999-07-01

NASA's newest space telescope, the Chandra X-ray Observatory, is scheduled for launch not earlier than July 20, 1999, aboard Space Shuttle mission STS-93. The world's most powerful X-ray observatory, Chandra will join the Hubble Space Telescope and NASA's other great observatories in an unprecedented study of our universe. With its capability to "see" an otherwise invisible but violent, vibrant and ever-changing universe, Chandra will provide insights into the universe's structure and evolution. The following information is designed to assist news media representatives cover launch and activation of the Chandra X-ray Observatory. Covering from the Chandra Control Center NASA will establish a news center at the Chandra X-ray Observatory Operations Control Center in Cambridge, Mass., during the critical period of launch and early activation. The news center will be open from approximately two days prior to launch until the observatory is established in its operating orbit approximately 11 days after launch. The telephone numbers for the news center are: (617) 496-4454 (617) 496-4462 (617) 496-4484 The news center will be staffed around the clock during the Shuttle mission by media relations officers knowledgeable about the Chandra mission and its status. Media covering from the news center will be provided work space and have opportunities for face-to-face interviews with Chandra management, control team members and Chandra scientists. They will be able to participate in daily Chandra status briefings and have access to a special control room viewing area. Additionally, media covering from Cambridge will receive periodic status reports on Chandra and the STS-93 mission, and will be able to participate in interactive televised briefings on the STS-93 mission originating from other NASA centers. While advance accreditation is not required, media interested in covering Chandra from the Operations Control Center should contact Dave Drachlis by telephone at (256) 544

Development of an Independent Global Land Cover Validation Dataset

NASA Astrophysics Data System (ADS)

Sulla-Menashe, D. J.; Olofsson, P.; Woodcock, C. E.; Holden, C.; Metcalfe, M.; Friedl, M. A.; Stehman, S. V.; Herold, M.; Giri, C.

2012-12-01

Accurate information related to the global distribution and dynamics in global land cover is critical for a large number of global change science questions. A growing number of land cover products have been produced at regional to global scales, but the uncertainty in these products and the relative strengths and weaknesses among available products are poorly characterized. To address this limitation we are compiling a database of high spatial resolution imagery to support international land cover validation studies. Validation sites were selected based on a probability sample, and may therefore be used to estimate statistically defensible accuracy statistics and associated standard errors. Validation site locations were identified using a stratified random design based on 21 strata derived from an intersection of Koppen climate classes and a population density layer. In this way, the two major sources of global variation in land cover (climate and human activity) are explicitly included in the stratification scheme. At each site we are acquiring high spatial resolution (< 1-m) satellite imagery for 5-km x 5-km blocks. The response design uses an object-oriented hierarchical legend that is compatible with the UN FAO Land Cover Classification System. Using this response design, we are classifying each site using a semi-automated algorithm that blends image segmentation with a supervised RandomForest classification algorithm. In the long run, the validation site database is designed to support international efforts to validate land cover products. To illustrate, we use the site database to validate the MODIS Collection 4 Land Cover product, providing a prototype for validating the VIIRS Surface Type Intermediate Product scheduled to start operational production early in 2013. As part of our analysis we evaluate sources of error in coarse resolution products including semantic issues related to the class definitions, mixed pixels, and poor spectral separation between

A new phase for NASA's communications satellite program

NASA Technical Reports Server (NTRS)

Dement, D. K.

1980-01-01

NASA's research in communications satellite technology is discussed, including orbit-efficient techniques and applications by the commercial sector. Attention is given to expanding the capacities of the C-band (6-4 GHz) and the Ku-band (14-11 GHz), opening the Ka-band (30/20 GHz), broadly applied 're-use' of the spectrum, and developing multibeam spacecraft antennas with on-board switching. Increasing wideband services in video, high-speed data, and voice trunking is considered, as are narrow-band systems that may be used for data collection or public safety, with possible expansion to a thin-route satellite system. In particular, communication for medical, disaster, or search-and-rescue emergencies may be met by the integration of a satellite service with land mobile communications via terrestrial radio links. Also considered is a large geostationary platform providing electrical power, thermal rejection, and orbital station-keeping for many communications payloads.

NASA Data for Water Resources Applications

NASA Technical Reports Server (NTRS)

Toll, David; Houser, Paul; Arsenault, Kristi; Entin, Jared

2004-01-01

Water Management Applications is one of twelve elements in the Earth Science Enterprise National Applications Program. NASA Goddard Space Flight Center is supporting the Applications Program through partnering with other organizations to use NASA project results, such as from satellite instruments and Earth system models to enhance the organizations critical needs. The focus thus far has been: 1) estimating water storage including snowpack and soil moisture, 2) modeling and predicting water fluxes such as evapotranspiration (ET), precipitation and river runoff, and 3) remote sensing of water quality, including both point source (e.g., turbidity and productivity) and non-point source (e.g., land cover conversion such as forest to agriculture yielding higher nutrient runoff). The objectives of the partnering cover three steps of: 1) Evaluation, 2) Verification and Validation, and 3) Benchmark Report. We are working with the U.S. federal agencies including the Environmental Protection Agency (EPA), the Bureau of Reclamation (USBR) and the Department of Agriculture (USDA). We are using several of their Decision Support Systems (DSS) tools. This includes the DSS support tools BASINS used by EPA, Riverware and AWARDS ET ToolBox by USBR and SWAT by USDA and EPA. Regional application sites using NASA data across the US. are currently being eliminated for the DSS tools. The current NASA data emphasized thus far are from the Land Data Assimilation Systems WAS) and MODIS satellite products. We are currently in the first two steps of evaluation and verification validation. Water Management Applications is one of twelve elements in the Earth Science Enterprise s National Applications Program. NASA Goddard Space Flight Center is supporting the Applications Program through partnering with other organizations to use NASA project results, such as from satellite instruments and Earth system models to enhance the organizations critical needs. The focus thus far has been: 1

Distributed Modelling of Stormflow Generation: Assessing the Effect of Ground Cover

NASA Astrophysics Data System (ADS)

Jarihani, B.; Sidle, R. C.; Roth, C. H.; Bartley, R.; Wilkinson, S. N.

2017-12-01

Understanding the effects of grazing management and land cover changes on surface hydrology is important for water resources and land management. A distributed hydrological modelling platform, wflow, (that was developed as part of Deltares's OpenStreams project) is used to assess the effect of land management practices on runoff generation processes. The model was applied to Weany Creek, a small catchment (13.6 km2) of the Burdekin Basin, North Australia, which is being studied to understand sources of sediment and nutrients to the Great Barrier Reef. Satellite and drone-based ground cover data, high resolution topography from LiDAR, soil properties, and distributed rainfall data were used to parameterise the model. Wflow was used to predict total runoff, peak runoff, time of rise, and lag time for several events of varying magnitudes and antecedent moisture conditions. A nested approach was employed to calibrate the model by using recorded flow hydrographs at three scales: (1) a hillslope sub-catchment: (2) a gullied sub-catchment; and the 13.6 km2 catchment outlet. Model performance was evaluated by comparing observed and predicted stormflow hydrograph attributes using the Nash Sutcliffe efficiency metric. By using a nested approach, spatiotemporal patterns of overland flow occurrence across the catchment can also be evaluated. The results show that a process-based distributed model can be calibrated to simulate spatial and temporal patterns of runoff generation processes, to help identify dominant processes which may be addressed by land management to improve rainfall retention. The model will be used to assess the effects of ground cover changes due to management practices in grazed lands on storm runoff.

GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia.

PubMed

Nigatu Wondrade; Dick, Øystein B; Tveite, Havard

2014-03-01

Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources. Land cover changes in Lake Hawassa Watershed, Southern Ethiopia, were investigated using Landsat MSS image data of 1973, and Landsat TM images of 1985, 1995, and 2011, covering a period of nearly four decades. Each image was partitioned in a GIS environment, and classified using an unsupervised algorithm followed by a supervised classification method. A hybrid approach was employed in order to reduce spectral confusion due to high variability of land cover. Classification of satellite image data was performed integrating field data, aerial photographs, topographical maps, medium resolution satellite image (SPOT 20 m), and visual image interpretation. The image data were classified into nine land cover types: water, built-up, cropland, woody vegetation, forest, grassland, swamp, bare land, and scrub. The overall accuracy of the LULC maps ranged from 82.5 to 85.0 %. The achieved accuracies were reasonable, and the observed classification errors were attributable to coarse spatial resolution and pixels containing a mixture of cover types. Land cover change statistics were extracted and tabulated using the ERDAS Imagine software. The results indicated an increase in built-up area, cropland, and bare land areas, and a reduction in the six other land cover classes. Predominant land cover is cropland changing from 43.6 % in 1973 to 56.4 % in 2011. A significant portion of land cover was converted into cropland. Woody vegetation and forest cover which occupied 21.0 and 10.3 % in 1973, respectively, diminished to 13.6 and 5.6 % in 2011. The change in water body was very

Land Use and Land Cover Change

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

Brown, Daniel; Polsky, Colin; Bolstad, Paul V.

2014-05-01

A contribution to the 3rd National Climate Assessment report, discussing the following key messages: 1. Choices about land-use and land-cover patterns have affected and will continue to affect how vulnerable or resilient human communities and ecosystems are to the effects of climate change. 2. Land-use and land-cover changes affect local, regional, and global climate processes. 3. Individuals, organizations, and governments have the capacity to make land-use decisions to adapt to the effects of climate change. 4. Choices about land use and land management provide a means of reducing atmospheric greenhouse gas levels.

Urban Land Cover/use Change Detection Using High Resolution SPOT 5 and SPOT 6 Images and Urban Atlas Nomenclature

NASA Astrophysics Data System (ADS)

Akay, S. S.; Sertel, E.

2016-06-01

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was

Next generation satellite communications networks

NASA Astrophysics Data System (ADS)

Garland, P. J.; Osborne, F. J.; Streibl, I.

The paper introduces two potential uses for new space hardware to permit enhanced levels of signal handling and switching in satellite communication service for Canada. One application involves increased private-sector services in the Ku band; the second supports new personal/mobile services by employing higher levels of handling and switching in the Ka band. First-generation satellite regeneration and switching experiments involving the NASA/ACTS spacecraft are described, where the Ka band and switching satellite network problems are emphasized. Second-generation satellite development is outlined based on demand trends for more packet-based switching, low-cost earth stations, and closed user groups. A demonstration mission for new Ka- and Ku-band technologies is proposed, including the payload configuration. The half ANIK E payload is shown to meet the demonstration objectives, and projected to maintain a fully operational payload for at least 10 years.

The impact of urban morphology and land cover on the sensible heat flux retrieved by satellite and in-situ observations

NASA Astrophysics Data System (ADS)

Gawuc, L.; Łobocki, L.; Kaminski, J. W.

2017-12-01

Land surface temperature (LST) is a key parameter in various applications for urban environments research. However, remotely-sensed radiative surface temperature is not equivalent to kinetic nor aerodynamic surface temperature (Becker and Li, 1995; Norman and Becker, 1995). Thermal satellite observations of urban areas are also prone to angular anisotropy which is directly connected with the urban structure and relative sun-satellite position (Hu et al., 2016). Sensible heat flux (Qh) is the main component of surface energy balance in urban areas. Retrieval of Qh, requires observations of, among others, a temperature gradient. The lower level of temperature measurement is commonly replaced by remotely-sensed radiative surface temperature (Chrysoulakis, 2003; Voogt and Grimmond, 2000; Xu et al., 2008). However, such replacement requires accounting for the differences between aerodynamic and radiative surface temperature (Chehbouni et al., 1996; Sun and Mahrt, 1995). Moreover, it is important to avoid micro-scale processes, which play a major role in the roughness sublayer. This is due to the fact that Monin-Obukhov similarity theory is valid only in dynamic sublayer. We will present results of the analyses of the impact of urban morphology and land cover on the seasonal changes of sensible heat flux (Qh). Qh will be retrieved by two approaches. First will be based on satellite observations of radiative surface temperature and second will be based on in-situ observations of kinetic road temperature. Both approaches will utilize wind velocity, and air temperature observed in-situ. We will utilize time series of MODIS LST observations for the period of 2005-2014 as well as simultaneous in-situ observations collected by road weather network (9 stations). Ground stations are located across the city of Warsaw, outside the city centre in low-rise urban structure. We will account for differences in urban morphology and land cover in the proximity of ground stations. We will

Remote Sensing Via Satellite: The Canadian Experience

ERIC Educational Resources Information Center

Classen, Hans George

1974-01-01

Describes the joint effort of Canada and NASA in monitoring the Canadian environment using remote-sensing techniques. The project involves the Earth Resources Technology Satellite and has been used to observe seasonal changes, extent of snow cover, crop growth, sea ice, and land use patterns. (GS)

Building a Continental Scale Land Cover Monitoring Framework for Australia

NASA Astrophysics Data System (ADS)

Thankappan, Medhavy; Lymburner, Leo; Tan, Peter; McIntyre, Alexis; Curnow, Steven; Lewis, Adam

2012-04-01

validation of land cover products. Among the upcoming missions, the Global Monitoring for Environment and Security (GMES) Sentinel-2 satellites are seen as an important source of optical data for updating land cover information in Australia. This paper outlines the DLCD development, key applications that inform nationally significant issues, further work on updating the DLCD that would enable transition to a national land cover monitoring framework, challenges and approaches to delivering land cover information at higher spatial resolutions on a continental scale, and the potential value of data from the Sentinel-2 mission in supporting land cover monitoring in Australia and globally.

Applications of the U.S. Geological Survey's global land cover product

USGS Publications Warehouse

Reed, B.

1997-01-01

The U.S. Geological Survey (USGS), in partnership with several international agencies and universities, has produced a global land cover characteristics database. The land cover data were created using multitemporal analysis of advanced very high resolution radiometer satellite images in conjunction with other existing geographic data. A translation table permits the conversion of the land cover classes into several conventional land cover schemes that are used by ecosystem modelers, climate modelers, land management agencies, and other user groups. The alternative classification schemes include Global Ecosystems, the Biosphere Atmosphere Transfer Scheme, the Simple Biosphere, the USGS Anderson Level 2, and the International Geosphere Biosphere Programme. The distribution system for these data is through the World Wide Web (the web site address is: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html) or by magnetic media upon special request The availability of the data over the World Wide Web, in conjunction with the flexible database structure, allows easy data access to a wide range of users. The web site contains a user registration form that allows analysis of the diverse applications of large-area land cover data. Currently, applications are divided among mapping (20 percent), conservation (30 percent), and modeling (35 percent).

Land Use and Change

NASA Technical Reports Server (NTRS)

Irwin, Daniel E.

2004-01-01

The overall purpose of this training session is to familiarize Central American project cooperators with the remote sensing and image processing research that is being conducted by the NASA research team and to acquaint them with the data products being produced in the areas of Land Cover and Land Use Change and carbon modeling under the NASA SERVIR project. The training session, therefore, will be both informative and practical in nature. Specifically, the course will focus on the physics of remote sensing, various satellite and airborne sensors (Landsat, MODIS, IKONOS, Star-3i), processing techniques, and commercial off the shelf image processing software.

Land cover characterization and land surface parameterization research

USGS Publications Warehouse

Steyaert, Louis T.; Loveland, Thomas R.; Parton, William J.

1997-01-01

The understanding of land surface processes and their parameterization in atmospheric, hydrologic, and ecosystem models has been a dominant research theme over the past decade. For example, many studies have demonstrated the key role of land cover characteristics as controlling factors in determining land surface processes, such as the exchange of water, energy, carbon, and trace gases between the land surface and the lower atmosphere. The requirements for multiresolution land cover characteristics data to support coupled-systems modeling have also been well documented, including the need for data on land cover type, land use, and many seasonally variable land cover characteristics, such as albedo, leaf area index, canopy conductance, surface roughness, and net primary productivity. Recently, the developers of land data have worked more closely with the land surface process modelers in these efforts.

A systems approach to the commercialization of space communications technology - The NASA/JPL Mobile Satellite Program

NASA Technical Reports Server (NTRS)

Weber, William J., III; Gray, Valerie W.; Jackson, Byron; Steele, Laura C.

1991-01-01

This paper discusss the systems approach taken by NASA and the Jet Propulsion Laboratory in the commercialization of land-mobile satellite services (LMSS) in the United States. As the lead center for NASA's Mobile Satellite Program, JPL was involved in identifying and addressing many of the key barriers to commercialization of mobile satellite communications, including technical, economic, regulatory and institutional risks, or uncertainties. The systems engineering approach described here was used to mitigate these risks. The result was the development and implementation of the JPL Mobile Satellite Experiment Project. This Project included not only technology development, but also studies to support NASA in the definition of the regulatory, market, and investment environments within which LMSS would evolve and eventually operate, as well as initiatives to mitigate their associated commercialization risks. The end result of these government-led endeavors was the acceleration of the introduction of commercial mobile satellite services, both nationally and internationally.

Visions of our Planet's Atmosphere, Land and Oceans: NASA/NOAA E-Theater 2003

NASA Technical Reports Server (NTRS)

Hasler, Fritz

2003-01-01

The NASA/NOAA Electronic Theater presents Earth science observations from space in a spectacular way. Fly in from outer space to the conference location as well as the site of the 2002 Olympic Winter Games using data from NASA satellites and the IKONOS 'Spy Satellite". See HDTV movie Destination Earth 2002 incorporating the Olympic Zooms, NBC footage of the 2002 Olympics, the shuttle, & the best NASA/NOAA Earth science visualizations. See the latest US and international global satellite weather movies including hurricanes, typhoons & "tornadoes". See the latest visualizations from NASA/NOAA and International remote sensing missions like Terra, Aqua, GOES, GMS, SeaWiFS, & Landsat. Feel the pulse of OUT planet. See how land vegetation, ocean plankton, clouds and temperatures respond to the sun & seasons. See vortexes and currents in the global oceans that bring up the nutrients to feed tiny algae and draw the fish, whales and fisherman. See the how the ocean blooms in response to these currents and El Nino/La Nina climate changes. See the city lights, fishing fleets, gas flares and bio-mass burning of the Earth at night observed by the "night-vision" DMSP satellite. The presentation will be made using the latest HDTV and video projection technology by: Dr. Fritz Hasler NASA/Goddard Space Flight Center.

Visions of our Planet's Atmosphere, Land and Oceans: NASA/NOAA E-Theater 2003

NASA Technical Reports Server (NTRS)

Hasler, Fritz

2003-01-01

The NASA/NOAA Electronic Theater presents Earth science observations from space in a spectacular way. Fly in from outer space to the conference location as well as the site of the 2002 Olympic Winter Games using data from NASA satellites and the IKONOS "Spy Satellite". See HDTV movie Destination Earth 2002 incorporating the Olympic Zooms, NBC footage of the 2002 Olympics, the shuttle, & the best NASA/NOAA Earth science visualizations. See the latest US and international global satellite weather movies including hurricanes, typhoons & "tornadoes". See the latest visualizations from NASA/NOAA and International remote sensing missions like Terra, Aqua, GOES, GMS, SeaWiFS, & Landsat. Feel the pulse of our planet. See how land vegetation, ocean plankton, clouds and temperatures respond to the sun & seasons. See vortexes and currents in the global oceans that bring up the nutrients to feed tiny algae and draw the fish, whales and fisherman. See the how the ocean blooms in response to these currents and El Nino/La Nina climate changes. See the city lights, fishing fleets, gas flares and bio-mass burning of the Earth at night observed by the "night-vision" DMSP satellite. The presentation will be made using the latest HDTV and video projection technology by: Dr. Fritz Hasler NASA/Goddard Space Flight Center

Visions of our Planet's Atmosphere, Land and Oceans: NASA/NOAA E-Theater 2003

NASA Technical Reports Server (NTRS)

Hasler, Fritz

2003-01-01

The NASA/NOAA Electronic Theater presents Earth science observations from space in a spectacular way. Fly in from outer space to the conference location as well as the site of the 2002 Olympic Winter Games using data from NASA satellites and the IKONOS "Spy Satellite". See HDTV movie Destination Earth 2002 incorporating the Olympic Zooms, NBC footage of the 2002 Olympics, the shuttle, & the best NASA/NOAA Earth science visualizations. See the latest US and international global satellite weather movies including hurricanes, typhoons & "tornadoes". See the latest visualizations from NASA/NOAA and International remote sensing missions like Terra, Aqua, GOES, GMS , SeaWiFS, & Landsat. Feel the pulse of our planet. See how land vegetation, ocean plankton, clouds and temperatures respond to the sun & seasons. See vortexes and currents in the global oceans that bring up the nutrients to feed tiny algae and draw the fish, whales and fisherman. See the how the ocean blooms in response to these currents and El Nino/La Nina climate changes. See the city lights, fishing fleets, gas flares and bio-mass burning of the Earth at night observed by the the "night-vision" DMSP satellite. The presentation will be made using the latest HDTV and video projection technology by: Dr. Fritz Hasler NASA/Goddard Space Flight Center

Land-cover change detection

USGS Publications Warehouse

Chen, Xuexia; Giri, Chandra; Vogelmann, James

2012-01-01

Land cover is the biophysical material on the surface of the earth. Land-cover types include grass, shrubs, trees, barren, water, and man-made features. Land cover changes continuously.  The rate of change can be either dramatic and abrupt, such as the changes caused by logging, hurricanes and fire, or subtle and gradual, such as regeneration of forests and damage caused by insects (Verbesselt et al., 2001).  Previous studies have shown that land cover has changed dramatically during the past sevearal centuries and that these changes have severely affected our ecosystems (Foody, 2010; Lambin et al., 2001). Lambin and Strahlers (1994b) summarized five types of cause for land-cover changes: (1) long-term natural changes in climate conditions, (2) geomorphological and ecological processes, (3) human-induced alterations of vegetation cover and landscapes, (4) interannual climate variability, and (5) human-induced greenhouse effect.  Tools and techniques are needed to detect, describe, and predict these changes to facilitate sustainable management of natural resources.

Updates of Land Surface and Air Quality Products in NASA MAIRS and NEESPI Data Portals

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina

2010-01-01

Following successful support of the Northern Eurasia Earth Sciences Partner Initiative (NEESPI) project with NASA satellite remote sensing data, from Spring 2009 the NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has been working on collecting more satellite and model data to support the Monsoon Asia Integrated Regional Study (MAIRS) project. The established data management and service infrastructure developed for NEESPI has been used and improved for MAIRS support.Data search, subsetting, and download functions are available through a single system. A customized Giovanni system has been created for MAIRS.The Web-based on line data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) allows scientists to explore, quickly analyze, and download data easily without learning the original data structure and format. Giovanni MAIRS includes satellite observations from multiple sensors and model output from the NASA Global Land Data Assimilation System (GLDAS), and from the NASA atmospheric reanalysis project, MERRA. Currently, we are working on processing and integrating higher resolution land data in to Giovanni, such as vegetation index, land surface temperature, and active fire at 5km or 1km from the standard MODIS products. For data that are not archived at the GESDISC,a product metadata portal is under development to serve as a gateway for providing product level information and data access links, which include both satellite, model products and ground-based measurements information collected from MAIRS scientists.Due to the large overlap of geographic coverage and many similar scientific interests of NEESPI and MAIRS, these data and tools will serve both projects.

Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

NASA Astrophysics Data System (ADS)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.

Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

USGS Publications Warehouse

Selkowitz, D.J.; Stehman, S.V.

2011-01-01

The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft, as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches. ?? 2011.

Landsat: A Global Land-Imaging Project

USGS Publications Warehouse

Headley, Rachel

2010-01-01

Across nearly four decades since 1972, Landsat satellites continuously have acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space; consequently, NASA develops remote-sensing instruments and spacecraft, then launches and validates the satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.

Landsat: A global land-imaging mission

USGS Publications Warehouse

,

2012-01-01

Across four decades since 1972, Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. NASA develops remote-sensing instruments and spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and distribution. The result of this program is a long-term record of natural and human induced changes on the global landscape.

Advance Inspection of NASA Next Mars Landing Site

NASA Image and Video Library

2017-03-29

This map shows footprints of images taken from Mars orbit by the High Resolution Imaging Science Experiment (HiRISE) camera as part of advance analysis of the area where NASA's InSight mission will land in 2018. The final planned image of the set is targeted to fill in the yellow-outlined rectangle on March 30, 2017. HiRISE is one of six science instruments on NASA's Mars Reconnaissance Orbiter, which reached Mars in 2006 and surpassed 50,000 orbits on March 27, 2017. The map covers an area about 100 miles (160 kilometers) across. HiRISE has been used since 2006 to inspect dozens of candidate landing sites on Mars, including the sites where the Phoenix and Curiosity missions landed in 2008 and 2012. The site selected for InSight's Nov. 26, 2018, landing is on a flat plain in the Elysium Planitia region of Mars, between 4 and 5 degrees north of the equator. HiRISE images are detailed enough to reveal individual boulders big enough to be a landing hazard. The March 30 observation that completes the planned advance imaging of this landing area brings the number of HiRISE images of the area to 73. Some are pairs covering the same ground. Overlapping observations provide stereoscopic, 3-D information for evaluating characteristics such as slopes. On this map, coverage by stereo pairs is coded in pale blue, compared to the gray-green of single HiRISE image footprints. The ellipses on the map are about 81 miles (130 kilometers) west-to-east by about 17 miles (27 kilometers) north-to-south. InSight has about 99 percent odds of landing within the ellipse for which it is targeted. The three ellipses indicate landing expectations for three of the possible InSight launch dates: white outline for launch at the start of the launch period, on May 5, 2018; blue for launch on May 26, 2018; orange for launch on June 8, 2018. InSight -- an acronym for "Interior Exploration using Seismic Investigations, Geodesy and Heat Transport" -- will study the deep interior of Mars to improve

Completion of the National Land Cover Database (NLCD) 1992-2001 Land Cover Change Retrofit Product

EPA Science Inventory

The Multi-Resolution Land Characteristics Consortium has supported the development of two national digital land cover products: the National Land Cover Dataset (NLCD) 1992 and National Land Cover Database (NLCD) 2001. Substantial differences in imagery, legends, and methods betwe...

The role of ERTS in the establishment and of a nationwide land cover information system

NASA Technical Reports Server (NTRS)

Abram, P.; Tullos, J.

1974-01-01

The economic potential of utilizing an ERTS type satellite in the development, updating, and maintenance of a nation-wide land cover information system in the post-1977 time frame was examined. Several alternative acquisition systems were evaluated for land cover data acquisition, processing, and interpretation costs in order to determine, on a total life cycle cost basis, under which conditions of user demand (i.e., area of coverage, frequency of coverage, timeliness of information, and level of information detail) an ERTS type satellite would be cost effective, and what the annual cost savings benefits would be. It was concluded that a three satellite system with high and low altitude aircraft and ground survey team utilizing automatic interpretation and classification techniques is an economically sound proposal.

Commentary: A cautionary tale regarding use of the National Land Cover Dataset 1992

USGS Publications Warehouse

Thogmartin, Wayne E.; Gallant, Alisa L.; Knutson, Melinda G.; Fox, Timothy J.; Suarez, Manuel J.

2004-01-01

Digital land-cover data are among the most popular data sources used in ecological research and natural resource management. However, processes for accurate land-cover classification over large regions are still evolving. We identified inconsistencies in the National Land Cover Dataset 1992, the most current and available representation of land cover for the conterminous United States. We also report means to address these inconsistencies in a bird-habitat model. We used a Geographic Information System (GIS) to position a regular grid (or lattice) over the upper midwestern United States and summarized the proportion of individual land covers in each cell within the lattice. These proportions were then mapped back onto the lattice, and the resultant lattice was compared to satellite paths, state borders, and regional map classification units. We observed mapping inconsistencies at the borders between mapping regions, states, and Thematic Mapper (TM) mapping paths in the upper midwestern United States, particularly related to grass I and-herbaceous, emergent-herbaceous wetland, and small-grain land covers. We attributed these discrepancies to differences in image dates between mapping regions, suboptimal image dates for distinguishing certain land-cover types, lack of suitable ancillary data for improving discrimination for rare land covers, and possibly differences among image interpreters. To overcome these inconsistencies for the purpose of modeling regional populations of birds, we combined grassland-herbaceous and pasture-hay land-cover classes and excluded the use of emergent-herbaceous and small-grain land covers. We recommend that users of digital land-cover data conduct similar assessments for other regions before using these data for habitat evaluation. Further, caution is advised in using these data in the analysis of regional land-cover change because it is not likely that future digital land-cover maps will repeat the same problems, thus resulting in

Towards a better understanding of flood generation and surface water inundation mechanisms using NASA remote sensing data products

NASA Astrophysics Data System (ADS)

Lucey, J.; Reager, J. T., II; Lopez, S. R.

2017-12-01

Floods annually cause several weather-related fatalities and financial losses. According to NOAA and FEMA, there were 43 deaths and 18 billion dollars paid out in flood insurance policies during 2005. The goal of this work is to improve flood prediction and flood risk assessment by creating a general model of predictability of extreme runoff generation using various NASA products. Using satellite-based flood inundation observations, we can relate surface water formation processes to changes in other hydrological variables, such as precipitation, storage and soil moisture, and understand how runoff generation response to these forcings is modulated by local topography and land cover. Since it is known that a flood event would cause an abnormal increase in surface water, we examine these underlying physical relationships in comparison with the Dartmouth Flood Observatory archive of historic flood events globally. Using ground water storage observations (GRACE), precipitation (TRMM or GPCP), land use (MODIS), elevation (SRTM) and surface inundation levels (SWAMPS), an assessment of geological and climate conditions can be performed for any location around the world. This project utilizes multiple linear regression analysis evaluating the relationship between surface water inundation, total water storage anomalies and precipitation values, grouped by average slope or land use, to determine their statistical relationships and influences on inundation data. This research demonstrates the potential benefits of using global data products for early flood prediction and will improve our understanding of runoff generation processes.

Impacts of Myanmar's Democratic Transition on its Land Cover Dynamics.

NASA Astrophysics Data System (ADS)

Biswas, S.

2016-12-01

Recently Myanmar transitioned from a closed economy, military government to market based economy and democracy. The impacts of the political and economic transition on its land cover can be described by characterizing the land cover dynamics during the transition period. Preliminary stratified sampling of forest conversions revealed that most changes from forest to non-forest are due to establishment of rubber plantations. Agricultural concessions are granted by the government to develop the agriculture sector and rubber is the most common plantation crop in Southern Myanmar. This study establishes a method to map and quantify the extent and age of rubber plantations in Thaton district of Myanmar using satellite remote sensing, GIS and ground data. The resultant rubber maps can be used to inform policy on land use planning, agriculture, forest and sustainable development.

Land-cover change research at the U.S. Geological Survey-assessing our nation's dynamic land surface

USGS Publications Warehouse

Wilson, Tamara S.

2011-01-01

The U.S. Geological Survey (USGS) recently completed an unprecedented, 27-year assessment of land-use and land-cover change for the conterminous United States. For the period 1973 to 2000, scientists generated estimates of change in major types of land use and land cover, such as development, mining, agriculture, forest, grasslands, and wetlands. To help provide the insight that our Nation will need to make land-use decisions in coming decades, the historical trends data is now being used by the USGS to help model potential future land use/land cover under different scenarios, including climate, environmental, economic, population, public policy, and technological change.

Satellite time synchronization of a NASA network.

NASA Technical Reports Server (NTRS)

Laios, S. C.

1972-01-01

A satellite time synchronization technique has been used for synchronization of remotely separated clocks during the past several years. The NASA network has been successfully synchronized to an accuracy of tens of microseconds via the NASA Geodetic Earth Orbiting Satellite GEOS-11. The results indicate that a polar orbit satellite having an onboard clock can effectively be used to synchronize clocks on a global basis.

A land use and land cover classification system for use with remote sensor data

USGS Publications Warehouse

Anderson, James R.; Hardy, Ernest E.; Roach, John T.; Witmer, Richard E.

1976-01-01

The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The proposed system uses the features of existing widely used classification systems that are amenable to data derived from remote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in U.S. Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.

Assessment of Consistencies and Uncertainties between the NASA MODIS and VIIRS Snow-Cover Maps

NASA Astrophysics Data System (ADS)

Hall, D. K.; Riggs, G. A., Jr.; DiGirolamo, N. E.; Roman, M. O.

2017-12-01

Snow cover has great climatic and economic importance in part due to its high albedo and low thermal conductivity and large areal extent in the Northern Hemisphere winter, and its role as a freshwater source for about one-sixth of the world's population. The Rutgers University Global Snow Lab's 50-year climate-data record (CDR) of Northern Hemisphere snow cover is invaluable for climate studies, but, at 25-km resolution, the spatial resolution is too coarse to provide accurate snow information at the basin scale. Since 2000, global snow-cover maps have been produced from the MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites at 500-m resolution, and from the Suomi-National Polar Program (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) since 2011 at 375-m resolution. Development of a moderate-resolution (375 - 500 m) earth system data record (ESDR) that utilizes both MODIS and VIIRS snow maps is underway. There is a 6-year overlap between the data records. In late 2017 the second in a series of VIIRS sensors will be launched on the Joint Polar Satellite System-1 (JPSS-1), with the JPSS-2 satellite scheduled for launch in 2021, providing the potential to extend NASA's snow-cover ESDR for decades into the future and to create a CDR. Therefore it is important to investigate the continuity between the MODIS and VIIRS NASA snow-cover data products and evaluate whether there are any inconsistencies and biases that would affect their value as CDR. Time series of daily normalized-difference snow index (NDSI) Terra and Aqua MODIS Collection 6 (C6) and NASA VIIRS Collection 1 (C1) snow-cover tile maps (MOD10A1 and VNP10A1) are studied for North America to identify NDSI differences and possible biases between the datasets. Developing a CDR using the MODIS and VIIRS records is challenging. Though the instruments and orbits are similar, differences in bands, viewing geometry, spatial resolution, and cloud- and snow-mapping algorithms

Modelling the angular effects on satellite retrieved LST at global scale using a land surface classification

NASA Astrophysics Data System (ADS)

Ermida, Sofia; DaCamara, Carlos C.; Trigo, Isabel F.; Pires, Ana C.; Ghent, Darren

2017-04-01

Land Surface Temperature (LST) is a key climatological variable and a diagnostic parameter of land surface conditions. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Although LST estimation from remote sensing instruments operating in the Infrared (IR) is widely used and has been performed for nearly 3 decades, there is still a list of open issues. One of these is the LST dependence on viewing and illumination geometry. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. The model is calibrated using LST data as provided by a wide range of sensors to optimize spatial coverage, namely: 1) a LEO sensor - the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and 2) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the obtained model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA Technical Reports Server (NTRS)

Santanello, Joseph

2011-01-01

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

NASA's aviation safety - meteorology research programs

NASA Technical Reports Server (NTRS)

Winblade, R. L.

1983-01-01

The areas covering the meteorological hazards program are: severe storms and the hazards to flight generated by severe storms; clear air turbulence; icing; warm fog dissipation; and landing systems. Remote sensing of ozone by satellites, and the use of satellites as data relays is also discussed.

An integrated voice and data multiple-access scheme for a land-mobile satellite system

NASA Technical Reports Server (NTRS)

Li, V. O. K.; Yan, T.-Y.

1984-01-01

An analytical study is performed of the satellite requirements for a land mobile satellite system (LMSS). The spacecraft (MSAT-X) would be in GEO and would be compatible with multiple access by mobile radios and antennas and fixed stations. The FCC has received a petition from NASA to reserve the 821-825 and 866-870 MHz frequencies for the LMSS, while communications with fixed earth stations would be in the Ku band. MSAT-X transponders would alter the frequencies of signal and do no processing in the original configuration considered. Channel use would be governed by an integrated demand-assigned, multiple access protocol, which would divide channels into reservation and information channels, governed by a network management center. Further analyses will cover tradeoffs between data and voice users, probability of blocking, and the performance impacts of on-board switching and variable bandwidth assignment. Initial calculations indicate that a large traffic volume can be handled with acceptable delays and voice blocking probabilities.

An integrated voice and data multiple-access scheme for a land-mobile satellite system

NASA Astrophysics Data System (ADS)

Li, V. O. K.; Yan, T.-Y.

1984-11-01

An analytical study is performed of the satellite requirements for a land mobile satellite system (LMSS). The spacecraft (MSAT-X) would be in GEO and would be compatible with multiple access by mobile radios and antennas and fixed stations. The FCC has received a petition from NASA to reserve the 821-825 and 866-870 MHz frequencies for the LMSS, while communications with fixed earth stations would be in the Ku band. MSAT-X transponders would alter the frequencies of signal and do no processing in the original configuration considered. Channel use would be governed by an integrated demand-assigned, multiple access protocol, which would divide channels into reservation and information channels, governed by a network management center. Further analyses will cover tradeoffs between data and voice users, probability of blocking, and the performance impacts of on-board switching and variable bandwidth assignment. Initial calculations indicate that a large traffic volume can be handled with acceptable delays and voice blocking probabilities.

Spatial Relationships between Biomass Burning and Land Use / Land Cover Dynamics in Northern Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Ellison, L.; Ichoku, C. M.

2016-12-01

Biomass burning (BB) is an extensive and persistent phenomenon across the world, and is a result of either natural (via lightning strikes) or anthropogenic processes, depending on the location. In Northern Sub-Saharan Africa (NSSA), where access to affordable modern farming equipment is extremely limited, agricultural practices dominate and BB is completely anthropogenic for all practical purposes, resulting in NSSA consistently contributing 15-20% of the total global annual emission of particulate matter from fires, according to estimates from version 1.0 of the Fire Energetics and Emissions Research BB emissions inventory (FEERv1.0, http://feer.gsfc.nasa.gov/data/emissions/). The FEERv1.0 algorithm uses a land cover type (LCT) product at either 0.5° or 0.1° resolutions for the conversion of total particulate matter estimates to various other smoke constituents. Due to the fact that fires are closely associated with land cover types, it became apparent that a fire-prone land cover type product at those spatial resolutions were needed, resulting in the FEERv1 BB-LCT product (http://feer.gsfc.nasa.gov/data/landcover/). In version 2 of the product, it was found that 6% of all grid cells with partial or full land cover in the original 0.5° LCT product is reclassified when considering BB practices. In NSSA, we see that the differences fall mainly along the borders between major regions of different LCT. Roughly speaking, fires along the cropland/savanna and savanna/forest borders in NSSA are mostly from from savanna burning. An in-depth analysis of the spatial extent and variability of fires and land cover in NSSA reveals that within the last one-and-a-half decades, the maximum fire activity occurred in the 2006/07 fire season and has been decreasing ever since. Interestingly, despite this decrease in fire activity, we observe a continuing increase in land cover conversion to cropland over the same time period at a rate of 0.3%/yr, which is equal to ≈37,500 km2/yr

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE): Changing patterns in the use of NRT satellite imagery

NASA Astrophysics Data System (ADS)

Davies, D.; Michael, K.; Schmaltz, J. E.; Harrison, S.; Ding, F.; Durbin, P. B.; Boller, R. A.; Cechini, M. F.; Rinsland, P. L.; Ye, G.; Mauoka, E.

2015-12-01

NASA's Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE) provides data and imagery approximately 3 hours from satellite observation, to monitor natural events globally and to meet the needs of the near real-time (NRT) applications community. This article describes LANCE, and how the use of NRT data and imagery has evolved. Since 2010 there has been a four-fold increase in both the volume of data and the number of files downloaded. Over the last year there has been a marked shift in the way in which users are accessing NRT imagery; users are gravitating towards Worldview and the Global Imagery Browse Services (GIBS) and away from MODIS Rapid Response, in part due to the increased exposure through social media. In turn this is leading to a broader range of users viewing NASA NRT imagery. This article also describes new, and planned, product enhancements to LANCE. Over the last year, LANCE has expanded to support NRT products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Multi-angle Imaging SpectroRadiometer (MISR). LANCE elements are also planning to ingest and process NRT data from the Visible Infrared Imager Radiometer Suite (VIIRS), and the advanced Ozone Mapping and Profiler Suite (OMPS) instruments onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite in the near future.

Propagation considerations in land mobile satellite transmission

NASA Technical Reports Server (NTRS)

Vogel, W. J.; Smith, E. K.

1985-01-01

It appears likely that the Land Mobile Satellite Services (LMSS) will be authorized by the FCC for operation in the 800 to 900 MHz (UHF) and possibly near 1500 MHz (L-band). Propagation problems are clearly an important factor in the effectiveness of this service, but useful measurements are few, and produced contradictory interpretations. A first order overview of existing measurements is presented with particular attention to the first two NASA balloon to mobile vehicle propagation experiments. Some physical insight into the interpretation of propagation effects in LMSS transmissions is provided.

NASA Ice, Cloud and land Elevation Satellite-2 Applications - Advancing Dialogue for More Effective Decisions and Societal benefits

NASA Astrophysics Data System (ADS)

Delgado Arias, S.; Brown, M. E.; Escobar, V. M.; Jasinski, M. F.; Neumann, T.

2016-12-01

Since 2012, the NASA Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) Applications Program has worked to understand how future mission observations can be effectively used to inform operational sea ice forecasting for Arctic shipping, global flood risk monitoring, fire fuel mapping, and other applications. The ICESat-2 Applications Program has implemented various engagement and outreach activities, as well as an Early Adopter program, to facilitate dialogue between potential users, project scientists, science definition team members, NASA Headquarters and the mission's data distribution center. This dialogue clarifies how ICESat-2's science data can be integrated, improved or leveraged to advance science objectives aligned with or beyond those of the mission, and in support of a range of decisions and actions of benefit to communities across the globe. In this presentation, we will present an overview of the Program initiatives and highlight the research-to-applications chains that mission Early Adopters are helping build for ICESat-2. With a total of 19 Early Adopters and more than 400 people engaged as part of the applications community, ICESat-2 has positioned itself to ensure applications where its observations are used to meet the needs of decision makers, policy makers and managers at different scales. For more information visit: http://icesat-2.gsfc.nasa.gov/applications

Rapid prototyping of soil moisture estimates using the NASA Land Information System

NASA Astrophysics Data System (ADS)

Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.

2007-12-01

The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.

Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images.

PubMed

Quirós, Elia; Felicísimo, Angel M; Cuartero, Aurora

2009-01-01

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

EL68D Wasteway Watershed Land-Cover Generation

USGS Publications Warehouse

Ruhl, Sheila; Usery, E. Lynn; Finn, Michael P.

2007-01-01

Classification of land cover from Landsat Enhanced Thematic Mapper Plus (ETM+) for the EL68D Wasteway Watershed in the State of Washington is documented. The procedures for classification include use of two ETM+ scenes in a simultaneous unsupervised classification process supported by extensive field data collection using Global Positioning System receivers and digital photos. The procedure resulted in a detailed classification at the individual crop species level.

Completion of the National Land Cover Database (NLCD) 1992–2001 Land Cover Change Retrofit product

USGS Publications Warehouse

Fry, J.A.; Coan, Michael; Homer, Collin G.; Meyer, Debra K.; Wickham, J.D.

2009-01-01

The Multi-Resolution Land Characteristics Consortium has supported the development of two national digital land cover products: the National Land Cover Dataset (NLCD) 1992 and National Land Cover Database (NLCD) 2001. Substantial differences in imagery, legends, and methods between these two land cover products must be overcome in order to support direct comparison. The NLCD 1992-2001 Land Cover Change Retrofit product was developed to provide more accurate and useful land cover change data than would be possible by direct comparison of NLCD 1992 and NLCD 2001. For the change analysis method to be both national in scale and timely, implementation required production across many Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) path/rows simultaneously. To meet these requirements, a hybrid change analysis process was developed to incorporate both post-classification comparison and specialized ratio differencing change analysis techniques. At a resolution of 30 meters, the completed NLCD 1992-2001 Land Cover Change Retrofit product contains unchanged pixels from the NLCD 2001 land cover dataset that have been cross-walked to a modified Anderson Level I class code, and changed pixels labeled with a 'from-to' class code. Analysis of the results for the conterminous United States indicated that about 3 percent of the land cover dataset changed between 1992 and 2001.

Lunar Landing Testing at NASA Langley

NASA Image and Video Library

1965-06-18

Lunar Landing Testing at NASA Langley. Lunar Landing Testing at NASA Langley. A simulated environment that contributed in a significant way to the success of Apollo project was the Lunar Landing Research Facility, an imposing 250 foot high, 400 foot long gantry structure that became operational in 1965. Published in the book "Space Flight Revolution" NASA SP-4308 pg. 376

Automatic mapping of event landslides at basin scale in Taiwan using a Montecarlo approach and synthetic land cover fingerprints

NASA Astrophysics Data System (ADS)

Mondini, Alessandro C.; Chang, Kang-Tsung; Chiang, Shou-Hao; Schlögel, Romy; Notarnicola, Claudia; Saito, Hitoshi

2017-12-01

We propose a framework to systematically generate event landslide inventory maps from satellite images in southern Taiwan, where landslides are frequent and abundant. The spectral information is used to assess the pixel land cover class membership probability through a Maximum Likelihood classifier trained with randomly generated synthetic land cover spectral fingerprints, which are obtained from an independent training images dataset. Pixels are classified as landslides when the calculated landslide class membership probability, weighted by a susceptibility model, is higher than membership probabilities of other classes. We generated synthetic fingerprints from two FORMOSAT-2 images acquired in 2009 and tested the procedure on two other images, one in 2005 and the other in 2009. We also obtained two landslide maps through manual interpretation. The agreement between the two sets of inventories is given by the Cohen's k coefficients of 0.62 and 0.64, respectively. This procedure can now classify a new FORMOSAT-2 image automatically facilitating the production of landslide inventory maps.

Mapping land cover through time with the Rapid Land Cover Mapper—Documentation and user manual

USGS Publications Warehouse

Cotillon, Suzanne E.; Mathis, Melissa L.

2017-02-15

The Rapid Land Cover Mapper is an Esri ArcGIS® Desktop add-in, which was created as an alternative to automated or semiautomated mapping methods. Based on a manual photo interpretation technique, the tool facilitates mapping over large areas and through time, and produces time-series raster maps and associated statistics that characterize the changing landscapes. The Rapid Land Cover Mapper add-in can be used with any imagery source to map various themes (for instance, land cover, soils, or forest) at any chosen mapping resolution. The user manual contains all essential information for the user to make full use of the Rapid Land Cover Mapper add-in. This manual includes a description of the add-in functions and capabilities, and step-by-step procedures for using the add-in. The Rapid Land Cover Mapper add-in was successfully used by the U.S. Geological Survey West Africa Land Use Dynamics team to accurately map land use and land cover in 17 West African countries through time (1975, 2000, and 2013).

Impact of Northern California Fires Seen in New NASA Satellite Image

NASA Image and Video Library

2017-10-23

As firefighters continue to work toward full containment of the rash of wildfires burning in Northern California, a new image from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on NASA's Terra satellite shows the growing fire scar on the landscape. In this ASTER image, acquired Oct. 21, 2017, vegetation is red, while burned areas appear dark gray. The image covers an area of 38 by 39 miles (60.5 by 63 kilometers) and is located near 38.5 degrees north, 122.4 degrees west. https://photojournal.jpl.nasa.gov/catalog/PIA22049

Land Cover Characterization Program

USGS Publications Warehouse

,

1997-01-01

(2) identify sources, develop procedures, and organize partners to deliver data and information to meet user requirements. The LCCP builds on the heritage and success of previous USGS land use and land cover programs and projects. It will be compatible with current concepts of government operations, the changing needs of the land use and land cover data users, and the technological tools with which the data are applied.

Collision Avoidance: Coordination of Predicted Conjunctions between NASA Satellites and Satellites of other Countries

NASA Astrophysics Data System (ADS)

Kelly, A.; Watson, W.

2014-09-01

This paper describes one of the challenges facing the flight operations teams of the International Earth Observing constellation satellites at the 705 km orbit, including NASAs satellites. The NASA Earth Science Mission Operations (ESMO) Project has been dealing with predicted conjunctions (close approach) between operational/non-operational space objects and the satellites in the International Earth observing constellations for several years. Constellation satellites include: NASAs Earth Observing System (EOS) Terra, Aqua, and Aura, CloudSat, the joint NASA/CNES CALIPSO mission, Earth Observing 1 (EO-1), the Japan Aerospace and Exploration Agency (JAXA) Global Change Observation Mission-Water 1 (GCOM-W1) mission, the United States Geological Survey (USGS) Landsat 7 and Landsat 8, and until 2013, Argentinas SAC-C mission and the CNES PARASOL mission. The NASA Conjunction Analysis and Risk Assessment (CARA) team provides daily reports to the ESMO Project regarding any high interest close approach events (HIEs) involving the constellation satellites. The daily CARA reports provide risk assessment results that help the operations teams to determine if there is a need to perform a risk mitigation action. If the conjuncting space object is an operational satellite that is capable of maneuvering, the affected satellite team needs to coordinate their action plan with the owner operator of the conjuncting satellite. It is absolutely critical for the two teams to communicate as soon as possible. The goal is to minimize the collision risk; this can happen if both satellite operators do not coordinate their maneuver plans. The constellation teams have established guidelines for coordinating HIEs. This coordination process has worked successfully for several years for satellites that are operated by other organizations in the United States and by NASAs international partners, all with whom NASA has a cooperative agreement. However, the situation is different for HIEs with

Open Source GIS Connectors to NASA GES DISC Satellite Data

NASA Technical Reports Server (NTRS)

Kempler, Steve; Pham, Long; Yang, Wenli

2014-01-01

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) houses a suite of high spatiotemporal resolution GIS data including satellite-derived and modeled precipitation, air quality, and land surface parameter data. The data are valuable to various GIS research and applications at regional, continental, and global scales. On the other hand, many GIS users, especially those from the ArcGIS community, have difficulties in obtaining, importing, and using our data due to factors such as the variety of data products, the complexity of satellite remote sensing data, and the data encoding formats. We introduce a simple open source ArcGIS data connector that significantly simplifies the access and use of GES DISC data in ArcGIS.

Vegetation Analysis and Land Use Land Cover Classification of Forest in Uttara Kannada District India Through Geo-Informatics Approach

NASA Astrophysics Data System (ADS)

Koppad, A. G.; Janagoudar, B. S.

2017-05-01

The study was conducted in Uttara Kannada districts during the year 2012-2014. The study area lies between 13.92° N to 15.52° N latitude and 74.08° E to 75.09° E longitude with an area of 10,215 km2. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32 % (6468.70 sq km) followed by agriculture 12.88 % (1315.31 sq. km), sparse forest 10.59 % (1081.37 sq. km), open land 6.09 % (622.37 sq. km), horticulture plantation and least was forest plantation (1.07 %). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non-vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.

Integrating NASA Satellite Data Into USDA World Agricultural Outlook Board Decision Making Environment To Improve Agricultural Estimates

NASA Technical Reports Server (NTRS)

Teng, William; Shannon, Harlan; deJeu, Richard; Kempler, Steve

2012-01-01

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. The goal of the current project is to improve WAOB estimates by integrating NASA satellite precipitation and soil moisture observations into WAOB's decision making environment. Precipitation (Level 3 gridded) is from the TRMM Multi-satellite Precipitation Analysis (TMPA). Soil moisture (Level 2 swath and Level 3 gridded) is generated by the Land Parameter Retrieval Model (LPRM) and operationally produced by the NASA Goddard Earth Sciences Data and Information Services Center (GBS DISC). A root zone soil moisture (RZSM) product is also generated, via assimilation of the Level 3 LPRM data by a land surface model (part of a related project). Data services to be available for these products include GeoTIFF, GDS (GrADS Data Server), WMS (Web Map Service), WCS (Web Coverage Service), and NASA Giovanni. Project benchmarking is based on retrospective analyses of WAOB analog year comparisons. The latter are between a given year and historical years with similar weather patterns and estimated crop yields. An analog index (AI) was developed to introduce a more rigorous, statistical approach for identifying analog years. Results thus far show that crop yield estimates derived from TMPA precipitation data are closer to measured yields than are estimates derived from surface-based precipitation measurements. Work is continuing to include LPRM surface soil moisture data and model-assimilated RZSM.

High resolution satellite remote sensing used in a stratified random sampling scheme to quantify the constituent land cover components of the shifting cultivation mosaic of the Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Molinario, G.; Hansen, M.; Potapov, P.

2016-12-01

High resolution satellite imagery obtained from the National Geospatial Intelligence Agency through NASA was used to photo-interpret sample areas within the DRC. The area sampled is a stratifcation of the forest cover loss from circa 2014 that either occurred completely within the previosly mapped homogenous area of the Rural Complex, at it's interface with primary forest, or in isolated forest perforations. Previous research resulted in a map of these areas that contextualizes forest loss depending on where it occurs and with what spatial density, leading to a better understading of the real impacts on forest degradation of livelihood shifting cultivation. The stratified random sampling approach of these areas allows the characterization of the constituent land cover types within these areas, and their variability throughout the DRC. Shifting cultivation has a variable forest degradation footprint in the DRC depending on many factors that drive it, but it's role in forest degradation and deforestation had been disputed, leading us to investigate and quantify the clearing and reuse rates within the strata throughout the country.

Impact of land cover and land use change on runoff characteristics.

PubMed

Sajikumar, N; Remya, R S

2015-09-15

Change in Land Cover and Land Use (LCLU) influences the runoff characteristics of a drainage basin to a large extent, which in turn, affects the surface and groundwater availability of the area, and hence leads to further change in LCLU. This forms a vicious circle. Hence it becomes essential to assess the effect of change in LCLU on the runoff characteristics of a region in general and of small watershed levels (sub-basin levels) in particular. Such an analysis can effectively be carried out by using watershed simulation models with integrated GIS frame work. SWAT (Soil and Water Analysis Tool) model, being one of the versatile watershed simulation models, is found to be suitable for this purpose as many GIS integration modules are available for this model (e.g. ArcSWAT, MWSWAT). Watershed simulation using SWAT requires the land use and land cover data, soil data and many other features. With the availability of repository of satellite imageries, both from Indian and foreign sources, it becomes possible to use the concurrent local land use and land cover data, thereby enabling more accurate modelling of small watersheds. Such availability will also enable us to assess the effect of LCLU on runoff characteristics and their reverse impact. The current study assesses the effect of land use and land cover on the runoff characteristics of two watersheds in Kerala, India. It also assesses how the change in land use and land cover in the last few decades affected the runoff characteristics of these watersheds. It is seen that the reduction in the forest area amounts to 60% and 32% in the analysed watersheds. However, the changes in the surface runoff for these watersheds are not comparable with the changes in the forest area but are within 20%. Similarly the maximum (peak) value of runoff has increased by an amount of 15% only. The lesser (aforementioned) effect than expected might be due to the fact that forest has been converted to agricultural purpose with major

Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data.

PubMed

Shang, Huazhe; Letu, Husi; Nakajima, Takashi Y; Wang, Ziming; Ma, Run; Wang, Tianxing; Lei, Yonghui; Ji, Dabin; Li, Shenshen; Shi, Jiancheng

2018-01-18

Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements. For AHI and MODIS, the false alarm rate of AHI and MODIS in cloud identification over the TP was 7.51% and 1.94%, respectively, and the cloud hit rate was 73.55% and 80.15%, respectively. Using hourly cloud-cover data from the Himawari-8 satellites, we found that at the monthly scale, the diurnal cycle in cloud cover over the TP tends to increase throughout the day, with the minimum and maximum cloud fractions occurring at 10:00 a.m. and 18:00 p.m. local time. Due to the limited time resolution of polar-orbiting satellites, the underestimation of MODIS daytime average cloud cover is approximately 4.00% at the annual scale, with larger biases during the spring (5.40%) and winter (5.90%).

Land cover trends dataset, 1973-2000

USGS Publications Warehouse

Soulard, Christopher E.; Acevedo, William; Auch, Roger F.; Sohl, Terry L.; Drummond, Mark A.; Sleeter, Benjamin M.; Sorenson, Daniel G.; Kambly, Steven; Wilson, Tamara S.; Taylor, Janis L.; Sayler, Kristi L.; Stier, Michael P.; Barnes, Christopher A.; Methven, Steven C.; Loveland, Thomas R.; Headley, Rachel; Brooks, Mark S.

2014-01-01

The U.S. Geological Survey Land Cover Trends Project is releasing a 1973–2000 time-series land-use/land-cover dataset for the conterminous United States. The dataset contains 5 dates of land-use/land-cover data for 2,688 sample blocks randomly selected within 84 ecological regions. The nominal dates of the land-use/land-cover maps are 1973, 1980, 1986, 1992, and 2000. The land-use/land-cover maps were classified manually from Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus imagery using a modified Anderson Level I classification scheme. The resulting land-use/land-cover data has a 60-meter resolution and the projection is set to Albers Equal-Area Conic, North American Datum of 1983. The files are labeled using a standard file naming convention that contains the number of the ecoregion, sample block, and Landsat year. The downloadable files are organized by ecoregion, and are available in the ERDAS IMAGINETM (.img) raster file format.

The Significance of Land Cover Delineation on Soil Erosion Assessment.

PubMed

Efthimiou, Nikolaos; Psomiadis, Emmanouil

2018-04-25

The study aims to evaluate the significance of land cover delineation on soil erosion assessment. To that end, RUSLE (Revised Universal Soil Loss Equation) was implemented at the Upper Acheloos River catchment, Western Central Greece, annually and multi-annually for the period 1965-92. The model estimates soil erosion as the linear product of six factors (R, K, LS, C, and P) considering the catchment's climatic, pedological, topographic, land cover, and anthropogenic characteristics, respectively. The C factor was estimated using six alternative land use delineations of different resolution, namely the CORINE Land Cover (CLC) project (2000, 2012 versions) (1:100,000), a land use map conducted by the Greek National Agricultural Research Foundation (NAGREF) (1:20,000), a land use map conducted by the Greek Payment and Control Agency for Guidance and Guarantee Community Aid (PCAGGCA) (1:5,000), and the Landsat 8 16-day Normalized Difference Vegetation Index (NDVI) dataset (30 m/pixel) (two approximations) based on remote sensing data (satellite image acquired on 07/09/2016) (1:40,000). Since all other factors remain unchanged per each RUSLE application, the differences among the yielded results are attributed to the C factor (thus the land cover pattern) variations. Validation was made considering the convergence between simulated (modeled) and observed sediment yield. The latter was estimated based on field measurements conducted by the Greek PPC (Public Power Corporation). The model performed best at both time scales using the Landsat 8 (Eq. 13) dataset, characterized by a detailed resolution and a satisfactory categorization, allowing the identification of the most susceptible to erosion areas.

Customizing NASA's Earth Science Research Products for addressing MENA Water Challenges

NASA Technical Reports Server (NTRS)

Habib, Shahid

2012-01-01

As projected by IPCC 2007 report, by the end of this century the Middle East North Mrica (MENA) region is projected to experience an increase of 3 C to 5 C rise in mean temperatures and a 20% decline in precipitation. This poses a serious problem for this geographic zone especially when majority of the hydrological consumption is for the agriculture sector and the remaining amount is for domestic consumption. In late 2011, the World Bank, USAID and NASA have joined hands to establishing integrated, modem, up to date NASA developed capabilities for various countries in the MENA region for addressing water resource issues and adapting to climate change impacts for improved decision making for societal benefits. The main focus of this undertaking is to address the most pressing societal issues which can be modeled and solved by utilizing NASA Earth Science remote sensing data products and hydrological models. The remote sensing data from space is one of the best ways to study such complex issues and further feed into the decision support systems. NASA's fleet of Earth Observing satellites offer a great vantage point from space to look at the globe and provide vital signs necessary to maintain healthy and sustainable ecosystem. NASA has over fifteen satellites and thirty instruments operating on these space borne platforms and generating over 2000 different science products on a daily basis. Some of these products are soil moisture, global precipitation, aerosols, cloud cover, normalized difference vegetation index, land cover/use, ocean altimetry, ocean salinity, sea surface winds, sea surface temperature, ozone and atmospheric gasses, ice and snow measurements, and many more. All of the data products, models and research results are distributed via the Internet freely through out the world. This project will utilize several NASA models such as global Land Data Assimilation System (LDAS) to generate hydrological states and fluxes in near real time. These LDAS products

Proceedings of the Twentieth NASA Propagation Experimenters Meeting (NAPEX XX) and the Advanced Communications Technology Satellite (ACTS) Propagation Studies Miniworkshop

NASA Technical Reports Server (NTRS)

Golshan, Nassar (Editor)

1996-01-01

The NASA Propagation Experimenters (NAPEX) Meeting and associated Advanced Communications Technology Satellite (ACTS) Propagation Studies Miniworkshop convene yearly to discuss studies supported by the NASA Propagation Program. Representatives from the satellite communications (satcom)industry, academia, and government with an interest in space-ground radio wave propagation have peer discussion of work in progress, disseminate propagation results, and interact with the satcom industry. NAPEX XX, in Fairbanks, Alaska, June 4-5, 1996, had three sessions: (1) "ACTS Propagation Study: Background, Objectives, and Outcomes," covered results from thirteen station-years of Ka-band experiments; (2) "Propagation Studies for Mobile and Personal Satellite Applications," provided the latest developments in measurement, modeling, and dissemination of propagation phenomena of interest to the mobile, personal, and aeronautical satcom industry; and (3)"Propagation Research Topics," covered a range of topics including space/ground optical propagation experiments, propagation databases, the NASA Propagation Web Site, and revision plans for the NASA propagation effects handbooks. The ACTS Miniworkshop, June 6, 1996, covered ACTS status, engineering support for ACTS propagation terminals, and the ACTS Propagation Data Center. A plenary session made specific recommendations for the future direction of the program.

Communications: Mosquito Habitats, Land Use, and Malaria Risk in Belize from Satellite Imagery

NASA Technical Reports Server (NTRS)

Pope, Kevin; Masuoka, Penny; Rejmankova, Eliska; Grieco, John; Johnson, Sarah; Roberts, Donald

2004-01-01

Satellite imagery of northern Belize is used to examine the distribution of land use and breeding habitats of the malaria vector the Anopheles mosquito. A land cover classification based on multispectral SPOT and multitemporal Radarsat images identified eleven land cover classes, including agricultural, forest, and marsh types. Two of the land cover types, Typha domingensis marsh and flooded forest, are Anopheles vestitipennis larval habitats, and one, Eleocharis spp. marsh, is the larval habitat for Anopheles albimanus. Geographic Information Systems (GIS) analyses of land cover demonstrate that the amount of Typha domingensis in a marsh is positively correlated with the amount of agricultural land in the adjacent upland, and negatively correlated with the amount of adjacent forest. This finding is consistent with the hypothesis that nutrient (phosphorus) runoff from agricultural lands is causing an expansion of Typha domingensis in northern Belize. Thus, land use induced expansion of Anopheles vestitipennis larval habitat is potentially increasing malaria risk in Belize, and in other regions where Anopheles vestitipennis is a major malaria vector.

Global Assessment of Land Surface Temperature From Geostationary Satellites and Model Estimates

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Q.; Minnis, P.; daSilva, A. M., Jr.; Palikonda, R.; Yost, C. R.

2012-01-01

Land surface (or 'skin') temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research we compare two global and independent data sets: (i) LST retrievals from five geostationary satellites generated at the NASA Langley Research Center (LaRC) and (ii) LST estimates from the quasi-operational NASA GEOS-5 global modeling and assimilation system. The objective is to thoroughly understand both data sets and their systematic differences in preparation for the assimilation of the LaRC LST retrievals into GEOS-5. As expected, mean differences (MD) and root-mean-square differences (RMSD) between modeled and retrieved LST vary tremendously by region and time of day. Typical (absolute) MD values range from 1-3 K in Northern Hemisphere mid-latitude regions to near 10 K in regions where modeled clouds are unrealistic, for example in north-eastern Argentina, Uruguay, Paraguay, and southern Brazil. Typically, model estimates of LST are higher than satellite retrievals during the night and lower during the day. RMSD values range from 1-3 K during the night to 2-5 K during the day, but are larger over the 50-120 W longitude band where the LST retrievals are derived from the FY2E platform

West Africa land use and land cover time series

USGS Publications Warehouse

Cotillon, Suzanne E.

2017-02-16

Started in 1999, the West Africa Land Use Dynamics project represents an effort to map land use and land cover, characterize the trends in time and space, and understand their effects on the environment across West Africa. The outcome of the West Africa Land Use Dynamics project is the production of a three-time period (1975, 2000, and 2013) land use and land cover dataset for the Sub-Saharan region of West Africa, including the Cabo Verde archipelago. The West Africa Land Use Land Cover Time Series dataset offers a unique basis for characterizing and analyzing land changes across the region, systematically and at an unprecedented level of detail.

Integration between terrestrial-based and satellite-based land mobile communications systems

NASA Technical Reports Server (NTRS)

Arcidiancono, Antonio

1990-01-01

A survey is given of several approaches to improving the performance and marketability of mobile satellite systems (MSS). The provision of voice/data services in the future regional European Land Mobile Satellite System (LMSS), network integration between the Digital Cellular Mobile System (GSM) and LMSS, the identification of critical areas for the implementation of integrated GSM/LMSS areas, space segment scenarios, LMSS for digital trunked private mobile radio (PMR) services, and code division multiple access (CDMA) techniques for a terrestrial/satellite system are covered.

High Performance Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System at NASA/GSFC

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Kumar, S. V.; Santanello, J. A.; Tian, Y.; Rodell, M.; Mocko, D.; Reichle, R.

2008-12-01

The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters-Lidard et al., 2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. The LIS software was the co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts - North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell et al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of these systems, now use specific configurations of the LIS software in their current implementations. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through 'plugins'. In addition to these capabilities, LIS has also been demonstrated for parameter estimation (Peters-Lidard et al., 2008; Santanello et al., 2007) and data assimilation (Kumar et al., 2008). Examples and case studies

China's land cover and land use change from 1700 to 2005: Estimations from high-resolution satellite data and historical archives

NASA Astrophysics Data System (ADS)

Liu, Mingliang; Tian, Hanqin

2010-09-01

One of the major limitations in assessing the impacts of human activities on global biogeochemical cycles and climate is a shortage of reliable data on historical land cover and land use change (LCLUC). China had extreme discrepancies in estimating contemporary and historical patterns of LCLUC over the last 3 centuries because of its geographical complexity, long history of land use, and limited national surveys. This study aims to characterize the spatial and temporal patterns of China's LCLUC during 1700-2005 by reconstructing historical gridded data sets from high-resolution satellite data and long-term historical survey data. During this 300 year period, the major characteristics of LCLUC in China have been shrinking forest (decreased by 22%) and expanding cropland (increased by 42%) and urban areas (including urban and rural settlements, factories, quarries, mining, and other built-up land). New cropland areas have come almost equally from both forested and nonforested land. This study also revealed that substantial conversion between forest and woodland can be attributed to forest harvest, forest regeneration, and land degradation. During 1980-2005, LCLUC was characterized by shrinking cropland, expanding urban and forest areas, and large decadal variations on a national level. LCLUC in China showed significant spatial variations during different time periods, which were caused by spatial heterogeneity in vegetation, soils, and climate and regional imbalance in economy development. During 1700-2005, forests shrunk rapidly while croplands expanded in the northeast and southwest of China. During 1980-2005, we found a serious loss of cropland and urban sprawl in the eastern plain, north, and southeast regions of China and a large increase in forested area in the southeast and southwest regions. The reconstructed LCLUC data sets from this study could be used to assess the impacts of land use change on biogeochemical cycles, the water cycle, and the regional

Analysis of Summer Thunderstorms in Central Alabama Using the NASA Land Information System

NASA Technical Reports Server (NTRS)

James, Robert; Case, Jonathan; Molthan, Andrew; Jedloved, Gary

2010-01-01

Forecasters have difficulty predicting "random" afternoon thunderstorms during the summer months. Differences in soil characteristics could be a contributing factor for storms. The NASA Land Information System (LIS) may assist forecasters in predicting summer convection by identifying boundaries in land characteristics. This project identified case dates during the summer of 2009 by analyzing synoptic weather maps, radar, and satellite data to look for weak atmospheric forcing and disorganized convective development. Boundaries in land characteristics that may have lead to convective initiation in central Alabama were then identified using LIS.

National land-cover pattern data

Treesearch

Kurt H. Riitters; James D. Wickham; James E. Vogelmann; K. Bruce Jones

2000-01-01

Land cover and its spatial patterns are key ingredients in ecological studies that consider large regions and the impacts of human activities. Because humanity is a principal driver of land-cover change over large regions (Turner et al. 1990), land-cover data provide direct measures of human activity, and both direct and indirect measures of ecological conditions...

Monitoring land use/cover changes on the Romanian Black Sea Coast

NASA Astrophysics Data System (ADS)

Zoran, L. F. V.; Dida, A. I.; Zoran, M. A.

2014-10-01

Remotely sensed satellite data are critical to understanding the coastal zones' physical and social systems interaction, complementing ground based methods and providing accurate wide range, objective and comparable, at widely-varying scales, synoptically data. For some environmental agreements remote sensing may provide the only viable means of compliance verification because the phenomena are monitored occurs over large and inaccessible geographic areas. The main aim of this paper was the assessment of coastal zone land cover/use changes based on fusion technique of satellite remote sensing imagery. The evaluation of coastal zone landscapes was based upon different sub-functions which refer to landscape features such as water, soil, land-use, buildings, groundwater, biotope types. A newly proposed sub-pixel mapping algorithm was applied to a set of multispectral and multitemporal satellite data for Danube Delta, Constantza and Black Sea coastal zone areas in Romania. A land cover classification and subsequent environmental quality analysis for change detection was done based on Landsat TM , Landsat ETM, QuickBird satellite images over 1990 to 2013 period of time. Spectral signatures of different terrain features have been used to separate and classify surface units of coastal zone and sub-coastal zone area.The change in the position of the coastline in Constantza area was examined in relation with the urban expansion. A distinction was made between landfill/sedimentation processes on the one hand and dredging/erosion processes on the other. We considered the Romanian Black Sea coastal zone dynamics in connection with the spatio-temporal variation of physical and biogeochemical processes and their influences on the environmental state in the near-shore area.

Mapping Land Cover and Land Use Changes in the Congo Basin Forests with Optical Satellite Remote Sensing: a Pilot Project Exploring Methodologies that Improve Spatial Resolution and Map Accuracy

NASA Astrophysics Data System (ADS)

Molinario, G.; Baraldi, A.; Altstatt, A. L.; Nackoney, J.

2011-12-01

-sharpened Landsat imagery with 15m resolution and Very High Resolution imagery from different sensors, obtained from the Department of Defense database that was recently opened to NASA and its Earth Observation partners. Particular emphasis is placed on the detection of agricultural fields and their expansion in primary forests or intensification in secondary forests and fallow fields, as this is the primary driver of deforestation in this area. Fields in this area area also of very small size and irregular shapes, often partly obscured by neighboring forest canopy, hence the technical challenge of correctly detecting them and tracking them through time. Finally, the potential for use of this methodology in other regions where information on land cover changes is needed for land use sustainability planning, is also addressed.

Regional land cover characterization using Landsat thematic mapper data and ancillary data sources

USGS Publications Warehouse

Vogelmann, James E.; Sohl, Terry L.; Campbell, P.V.; Shaw, D.M.; ,

1998-01-01

As part of the activities of the Multi-Resolution Land Characteristics (MRLC) Interagency Consortium, an intermediate-scale land cover data set is being generated for the conterminous United States. This effort is being conducted on a region-by-region basis using U.S. Standard Federal Regions. To date, land cover data sets have been generated for Federal Regions 3 (Pennsylvania, West Virginia, Virginia, Maryland, and Delaware) and 2 (New York and New Jersey). Classification work is currently under way in Federal Region 4 (the southeastern United States), and land cover mapping activities have been started in Federal Regions 5 (the Great Lakes region) and 1 (New England). It is anticipated that a land cover data set for the conterminous United States will be completed by the end of 1999. A standard land cover classification legend is used, which is analogous to and compatible with other classification schemes. The primary MRLC regional classification scheme contains 23 land cover classes.The primary source of data for the project is the Landsat thematic mapper (TM) sensor. For each region, TM scenes representing both leaf-on and leaf-off conditions are acquired, preprocessed, and georeferenced to MRLC specifications. Mosaicked data are clustered using unsupervised classification, and individual clusters are labeled using aerial photographs. Individual clusters that represent more than one land cover unit are split using spatial modeling with multiple ancillary spatial data layers (most notably, digital elevation model, population, land use and land cover, and wetlands information). This approach yields regional land cover information suitable for a wide array of applications, including landscape metric analyses, land management, land cover change studies, and nutrient and pesticide runoff modeling.

Using land-cover data to understand effects of agricultural and urban development on regional water quality

USGS Publications Warehouse

Karstensen, Krista A.; Warner, Kelly L.

2010-01-01

The Land-Cover Trends project is a collaborative effort between the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS), the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA) to understand the rates, trends, causes, and consequences of contemporary land-use and land-cover change in the United States. The data produced from this research can lead to an enriched understanding of the drivers of future landuse change, effects on environmental systems, and any associated feedbacks. USGS scientists are using the EPA Level III ecoregions as the geographic framework to process geospatial data collected between 1973 and 2000 to characterize ecosystem responses to land-use changes. General land-cover classes for these periods were interpreted from Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus imagery to categorize and evaluate land-cover change using a modified Anderson Land-Use/Land-Cover Classification System for image interpretation.

Climate and air quality impacts of altered BVOC fluxes from land cover change in Southeast Asia 1990 - 2010

NASA Astrophysics Data System (ADS)

Harper, Kandice; Yue, Xu; Unger, Nadine

2016-04-01

Large-scale transformation of the natural rainforests of Southeast Asia in recent decades, driven primarily by logging and agroforestry activities, including rapid expansion of plantations of high-isoprene-emitting oil palm (Elaeis guineensis) trees at the expense of comparatively low-emitting natural dipterocarp rainforests, may have altered the prevailing regime of biogenic volatile organic compound (BVOC) fluxes from this tropical region. Chemical processing of isoprene in the atmosphere impacts the magnitude and distribution of several short-lived climate forcers, including ozone and secondary organic aerosols. Consequently, modification of the fluxes of isoprene and other BVOCs from vegetation serves as a mechanism by which tropical land cover change impacts both air quality and climate. We apply satellite-derived snapshots of land cover for the period 1990 - 2010 to the NASA ModelE2-Yale Interactive Terrestrial Biosphere (ModelE2-YIBs) global carbon-chemistry-climate model to quantify the impact of Southeast Asian land cover change on atmospheric chemical composition and climate driven by changes in isoprene emission. NASA ModelE2-YIBs features a fully interactive land carbon cycle and includes a BVOC emission algorithm which energetically couples isoprene production to photosynthesis. The time-slice simulations are nudged with large-scale winds from the GMAO reanalysis dataset and are forced with monthly anthropogenic and biomass burning reactive air pollution emissions from the MACCity emissions inventory. Relative to the year 1990, regional isoprene emissions in 2010 increased by 2.6 TgC/yr from the expansion of Southeast Asian oil palm plantations and decreased by 0.7 TgC/yr from the loss of regional dipterocarp rainforest. Considering only the impact of land-cover-change-induced isoprene emission changes in Southeast Asia over this period, we calculate a spatially heterogeneous impact on regional seasonal surface-level ozone concentrations (minimum: -1

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

NASA Astrophysics Data System (ADS)

Hagensieker, Ron; Roscher, Ribana; Rosentreter, Johannes; Jakimow, Benjamin; Waske, Björn

2017-12-01

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.

NASA Launches NOAA Weather Satellite to Improve Forecasts

NASA Image and Video Library

2017-11-18

Early on the morning of Saturday, Nov. 18, NASA successfully launched for the National Oceanic and Atmospheric Administration (NOAA) the first in a series of four advanced polar-orbiting satellites, equipped with next-generation technology and designed to improve the accuracy of U.S. weather forecasts out to seven days. The Joint Polar Satellite System-1 (JPSS-1) lifted off on a United Launch Alliance Delta II rocket from Vandenberg Air Force Base on California’s central coast. JPSS-1 data will improve weather forecasting and help agencies involved with post-storm recovery by visualizing storm damage and the geographic extent of power outages.

Evolving land cover classification algorithms for multispectral and multitemporal imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Theiler, James P.; Bloch, Jeffrey J.; Harvey, Neal R.; Perkins, Simon J.; Szymanski, John J.; Young, Aaron C.

2002-01-01

The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

NASA Astrophysics Data System (ADS)

Di Vittorio, A. V.; Mao, J.; Shi, X.; Chini, L.; Hurtt, G.; Collins, W. D.

2018-01-01

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850-2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

DOE PAGES

Di Vittorio, A. V.; Mao, J.; Shi, X.; ...

2018-01-03

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. In this paper, we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO 2 in 2004, and generates carbon uncertainty that is equivalentmore » to 80% of the net effects of CO 2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. Finally, we conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.« less

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

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

Di Vittorio, A. V.; Mao, J.; Shi, X.

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. In this paper, we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO 2 in 2004, and generates carbon uncertainty that is equivalentmore » to 80% of the net effects of CO 2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. Finally, we conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.« less

Sensitivity of selected landscape pattern metrics to land-cover misclassification and differences in land-cover composition

Treesearch

James D. Wickham; Robert V. O' Neill; Kurt H. Riitters; Timothy G. Wade; K. Bruce Jones

1997-01-01

Calculation of landscape metrics from land-cover data is becoming increasingly common. Some studies have shown that these measurements are sensitive to differences in land-cover composition, but none are known to have tested also their a sensitivity to land-cover misclassification. An error simulation model was written to test the sensitivity of selected land-scape...

Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions

USGS Publications Warehouse

Xian, George

2008-01-01

By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.

Investigating the variation of terrestrial water storage under changing climate and land cover

NASA Astrophysics Data System (ADS)

Fang, Y.; Niu, G. Y.; Zhang, X.; Troch, P. A. A.

2015-12-01

Terrestrial water storage (TWS) consists of groundwater, soil moisture, snow and ice, lakes and rivers and water contained in biomass. The water storage, especially the subsurface storage, is an essential property of the catchment, which controls climate, hydrological and biogeochemical processes at different scales. During the past decades, climate and land cover change has been proved to exert significant influences on hydrological processes which in turn alters the TWS variation. In order to better understand the interaction and feedback mechanism between TWS and earth system, it is necessary to quantify the effects of climate and land cover change on TWS variation. Direct estimation of total TWS has been made possible by the Gravity Recovery And Climate Experiment (GRACE) satellites that measures the earth gravity field. At present, few efforts were made to explicitly investigate the TWS variation under changing climate and land cover. GRACE data has its own limitations. One is its temporal coverage is short, it's only available since 2002, which is not sufficient to reflect the trend due to climate and land cover change. The other reason is that it cannot distinguish different components contributing to TWS. The limitation of TWS observation data can be overcame by numerical models developed to reproduce or to predict different earth system processes. After calibration and validation, with limited observations, these models can be trusted to extend our knowledge to where observations are not available both in time and space. In this study, based on Noah-MP LSM and satellite and ground data, we aim to: (1) Investigate the variation of total TWS as well as its components over Upper Colorado River Basin from 1990 to 2014. (2) Identify the major factors that control the TWS variation. (3) Quantify how the changing climate and land cover affect TWS variation in the same period.

Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000

PubMed Central

Bodart, Catherine; Brink, Andreas B; Donnay, François; Lupi, Andrea; Mayaux, Philippe; Achard, Frédéric

2013-01-01

Aim This study provides regional estimates of forest cover in dry African ecoregions and the changes in forest cover that occurred there between 1990 and 2000, using a systematic sample of medium-resolution satellite imagery which was processed consistently across the continent. Location The study area corresponds to the dry forests and woodlands of Africa between the humid forests and the semi-arid regions. This area covers the Sudanian and Zambezian ecoregions. Methods A systematic sample of 1600 Landsat satellite imagery subsets, each 20 km × 20 km in size, were analysed for two reference years: 1990 and 2000. At each sample site and for both years, dense tree cover, open tree cover, other wooded land and other vegetation cover were identified from the analysis of satellite imagery, which comprised multidate segmentation and automatic classification steps followed by visual control by national forestry experts. Results Land cover and land-cover changes were estimated at continental and ecoregion scales and compared with existing pan-continental, regional and local studies. The overall accuracy of our land-cover maps was estimated at 87%. Between 1990 and 2000, 3.3 million hectares (Mha) of dense tree cover, 5.8 Mha of open tree cover and 8.9 Mha of other wooded land were lost, with a further 3.9 Mha degraded from dense to open tree cover. These results are substantially lower than the 34 Mha of forest loss reported in the FAO's 2010 Global Forest Resources Assessment for the same period and area. Main conclusions Our method generates the first consistent and robust estimates of forest cover and change in dry Africa with known statistical precision at continental and ecoregion scales. These results reduce the uncertainty regarding vegetation cover and its dynamics in these previously poorly studied ecosystems and provide crucial information for both science and environmental policies. PMID:23935237

The effects of war on land-use/land-cover change: An analysis of Landsat imagery for northeast Bosnia

NASA Astrophysics Data System (ADS)

Witmer, Frank D. W.

The use of satellite technology by military planners has a relatively long history as a tool of warfare, but little research has used satellite technology to study the effects of war. This research addresses this gap by applying satellite remote sensing imagery to study the effects of war on land-use/land-cover change in northeast Bosnia. The war in Bosnia, 1992-1995, resulted in over 100,000 deaths, many more wounded, and the mass displacement of nearly half the population of 4.2 million. When combined with the destruction of much of the transportation infrastructure and housing stock, widespread mine placement, and loss of agricultural machinery, the impacts to both the people and land were dramatic. Though the most severe war impacts are visible at local scales (e.g. destroyed buildings), this study focuses on impacts to agricultural land, a larger scale visible to satellite sensors. Multispectral Landsat Thematic Mapper (TM) data (30m pixels) from before and during the war in addition to recent imagery from 2004/05 were used to detect abandoned agricultural land. The satellite images were co-registered to enable a perpixel analysis of changes based on the statistical properties of the pixels using multiple change detection methods. Ground reference data were collected in May of 2006 at survey sites selected using a stratified random sampling approach based on the derived map of abandoned agricultural land. Fine resolution (60cm) Quickbird imagery was also used to verify the accuracy of the classification. The remote sensing analysis results were then used to test two hypotheses related to war outcomes: (a) land abandonment is due to wartime minefields and (b) land abandonment is greater in pre-war Croat areas and areas where ethnic cleansing was heaviest. The effects of minefields on land abandonment was first tested in a geographic information system (GIS), and then by using multiple regression models that account for spatial autocorrelation among observations

Multi-Sensor Approach to Mapping Snow Cover Using Data From NASA's EOS Aqua and Terra Spacecraft

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.

2003-12-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. For the period 1978 to 2002, we have shown earlier that both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are, depending on season, less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Snow mapping using optical data is based on the magnitude of the surface reflectance while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Our previous work has defined the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and it is clear that a blended product is optimal. We present a multi-sensor approach to snow mapping based both on historical data as well as data from current NASA EOS sensors. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets. An example of validation at the brightness temperature level is provided through the comparison of AMSR-E with data from the well-calibrated heritage SSM/I sensor over a large homogeneous snow-covered surface (Dome C, Antarctica). Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current blended product is being developed in the 25 km EASE-Grid while the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km

Mobile satellite service communications tests using a NASA satellite

NASA Technical Reports Server (NTRS)

Chambers, Katherine H.; Koschmeder, Louis A.; Hollansworth, James E.; ONeill, Jack; Jones, Robert E.; Gibbons, Richard C.

1995-01-01

Emerging applications of commercial mobile satellite communications include satellite delivery of compact disc (CD) quality radio to car drivers who can select their favorite programming as they drive any distance; transmission of current air traffic data to aircraft; and handheld communication of data and images from any remote corner of the world. Experiments with the enabling technologies and tests and demonstrations of these concepts are being conducted before the first satellite is launched by utilizing an existing NASA spacecraft.

Intercomparison of Satellite-Derived Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Tait, Andrew B.; Foster, James L.; Chang, Alfred T. C.; Allen, Milan

1999-01-01

In anticipation of the launch of the Earth Observing System (EOS) Terra, and the PM-1 spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study area acquired from different sensors using different snow- mapping algorithms. Four locations are studied: 1) southern Saskatchewan; 2) a part of New England (New Hampshire, Vermont and Massachusetts) and eastern New York; 3) central Idaho and western Montana; and 4) parts of North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm used on Landsat Thematic Mapper (TM) scenes of each study area at 30-m and when the TM data were degraded to 1 -km resolution. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1 -km resolution snow maps were also used, as were snow maps derived from 1/2 deg. x 1/2 deg. resolution Special Sensor Microwave Imager (SSM/1) data. A land-cover map derived from the International Geosphere-Biosphere Program (IGBP) land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g., tree canopies, branches and trunks) are seen in the TM data, but not in the coarser-resolution maps. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30-m resolution TM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Comparisons show that the percent change in amount of snow cover relative to the 3 0-m resolution TM maps is lowest using the TM I -km resolution maps, ranging from 0 to 40

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.; Roman, Miguel O.

2017-01-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data

Assessing uncertainties in land cover projections.

PubMed

Alexander, Peter; Prestele, Reinhard; Verburg, Peter H; Arneth, Almut; Baranzelli, Claudia; Batista E Silva, Filipe; Brown, Calum; Butler, Adam; Calvin, Katherine; Dendoncker, Nicolas; Doelman, Jonathan C; Dunford, Robert; Engström, Kerstin; Eitelberg, David; Fujimori, Shinichiro; Harrison, Paula A; Hasegawa, Tomoko; Havlik, Petr; Holzhauer, Sascha; Humpenöder, Florian; Jacobs-Crisioni, Chris; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Lavalle, Carlo; Lenton, Tim; Liu, Jiayi; Meiyappan, Prasanth; Popp, Alexander; Powell, Tom; Sands, Ronald D; Schaldach, Rüdiger; Stehfest, Elke; Steinbuks, Jevgenijs; Tabeau, Andrzej; van Meijl, Hans; Wise, Marshall A; Rounsevell, Mark D A

2017-02-01

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover. © 2016 John Wiley & Sons Ltd.

Impact of landuse/land cover change on run-off in the catchment of a hydro power project

NASA Astrophysics Data System (ADS)

Khare, Deepak; Patra, Diptendu; Mondal, Arun; Kundu, Sananda

2017-05-01

The landuse/land cover change and rainfall have a significant influence on the hydrological response of the river basins. The run-off characteristics are changing naturally due to reduction of initial abstraction that increases the run-off volume. Therefore, it is necessary to quantify the changes in the run-off characteristics of a catchment under the influence of changed landuse/land cover. Soil conservation service model has been used in the present study to analyse the impact of various landuse/land cover (past, present and future time period) change in the run-off characteristics of a part of Narmada basin at the gauge discharge site of Mandaleswar in Madhya Pradesh, India. Calculated run-off has been compared with the observed run-off data for the study. The landuse/land cover maps of 1990, 2000 and 2009 have been prepared by digital classification method with proper accuracy using satellite imageries. The impact of the run-off change on hydro power potential has been assessed in the study along with the estimation of the future changes in hydro power potential. Five types of conditions (+10, +5 %, average, -5, -10 % of average rainfall) have been applied with 90 and 75 % dependability status. The generated energy will be less in 90 % dependable flow in respect to the 75 % dependable flow. This work will be helpful for future planning related to establishment of hydropower setup.

Changing landscape in the Three Gorges Reservoir Area of Yangtze River from 1977 to 2005: Land use/land cover, vegetation cover changes estimated using multi-source satellite data

NASA Astrophysics Data System (ADS)

Zhang, Jixian; Zhengjun, Liu; Xiaoxia, Sun

2009-12-01

The eco-environment in the Three Gorges Reservoir Area (TGRA) in China has received much attention due to the construction of the Three Gorges Hydropower Station. Land use/land cover changes (LUCC) are a major cause of ecological environmental changes. In this paper, the spatial landscape dynamics from 1978 to 2005 in this area are monitored and recent changes are analyzed, using the Landsat TM (MSS) images of 1978, 1988, 1995, 2000 and 2005. Vegetation cover fractions for a vegetation cover analysis are retrieved from MODIS/Terra imagery from 2000 to 2006, being the period before and after the rising water level of the reservoir. Several analytical indices have been used to analyze spatial and temporal changes. Results indicate that cropland, woodland, and grassland areas reduced continuously over the past 30 years, while river and built-up area increased by 2.79% and 4.45% from 2000 to 2005, respectively. The built-up area increased at the cost of decreased cropland, woodland and grassland. The vegetation cover fraction increased slightly. We conclude that significant changes in land use/land cover have occurred in the Three Gorges Reservoir Area. The main cause is a continuous economic and urban/rural development, followed by environmental management policies after construction of the Three Gorges Dam.

Land Cover Change Community-based Processing and Analysis System (LC-ComPS): Lessons Learned from Technology Infusion

NASA Astrophysics Data System (ADS)

Masek, J.; Rao, A.; Gao, F.; Davis, P.; Jackson, G.; Huang, C.; Weinstein, B.

2008-12-01

The Land Cover Change Community-based Processing and Analysis System (LC-ComPS) combines grid technology, existing science modules, and dynamic workflows to enable users to complete advanced land data processing on data available from local and distributed archives. Changes in land cover represent a direct link between human activities and the global environment, and in turn affect Earth's climate. Thus characterizing land cover change has become a major goal for Earth observation science. Many science algorithms exist to generate new products (e.g., surface reflectance, change detection) used to study land cover change. The overall objective of the LC-ComPS is to release a set of tools and services to the land science community that can be implemented as a flexible LC-ComPS to produce surface reflectance and land-cover change information with ground resolution on the order of Landsat-class instruments. This package includes software modules for pre-processing Landsat-type satellite imagery (calibration, atmospheric correction, orthorectification, precision registration, BRDF correction) for performing land-cover change analysis and includes pre-built workflow chains to automatically generate surface reflectance and land-cover change products based on user input. In order to meet the project objectives, the team created the infrastructure (i.e., client-server system with graphical and machine interfaces) to expand the use of these existing science algorithm capabilities in a community with distributed, large data archives and processing centers. Because of the distributed nature of the user community, grid technology was chosen to unite the dispersed community resources. At that time, grid computing was not used consistently and operationally within the Earth science research community. Therefore, there was a learning curve to configure and implement the underlying public key infrastructure (PKI) interfaces, required for the user authentication, secure file

The Water Cycle from Space: Use of Satellite Data in Land Surface Hydrology and Water Resource Management

NASA Technical Reports Server (NTRS)

Laymon, Charles; Blankenship, Clay; Khan, Maudood; Limaye, Ashutosh; Hornbuckle, Brian; Rowlandson, Tracy

2010-01-01

This slide presentation reviews how our understanding of the water cycle is enhanced by our use of satellite data, and how this informs land surface hydrology and water resource management. It reviews how NASA's current and future satellite missions will provide Earth system data of unprecedented breadth, accuracy and utility for hydrologic analysis.

Land-cover trends in the Mojave basin and range ecoregion

USGS Publications Warehouse

Sleeter, Benjamin M.; Raumann, Christian G.

2006-01-01

The U.S. Geological Survey's Land-Cover Trends Project aims to estimate the rates of contemporary land-cover change within the conterminous United States between 1972 and 2000. A random sampling approach was used to select a representative sample of 10-km by 10-km sample blocks and to estimate change within +/- 1 percent at an 85-percent confidence interval. Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus data were used, and each 60-m pixel was assigned to one of 11 distinct land-cover classes based upon a modified Anderson classification system. Upon completion of land-cover change mapping for five dates, land-cover change statistics were generated and analyzed. This paper presents estimates for the Mojave Basin and Range ecoregion located in the southwestern United States. Our research suggests land-cover change within the Mojave to be relatively rare and highly localized. The primary shift in land cover is unidirectional, with natural desert grass/shrubland being converted to development. We estimate that more than 1,300 km2 have been converted since 1973 and that the conversion is being largely driven by economic and recreational opportunities provided by the Mojave ecoregion. The time interval with the highest rate of change was 1986 to 1992, in which the rate was 0.21 percent (321.9 km2) per year total change.

A data mining approach for sharpening satellite thermal imagery over land

USDA-ARS?s Scientific Manuscript database

Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes which are at significant...

The Use of NASA near Real-time and Archived Satellite Data to Support Disaster Assessment

NASA Technical Reports Server (NTRS)

McGrath, Kevin M.; Molthan, Andrew; Burks, Jason

2014-01-01

With support from a NASA's Applied Sciences Program, The Short-term Prediction Research and Transition (SPoRT) Center has explored a variety of techniques for utilizing archived and near real-time NASA satellite data to support disaster assessment activities. MODIS data from the NASA Land Atmosphere Near Real-time Capability for EOS currently provides true color and other imagery for assessment and potential applications including, but not limited to, flooding, fires, and tornadoes. In May 2013, the SPoRT Center developed unique power outage composites using the VIIRS Day/Night Band to represent the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF-5 tornado that occurred on May 20. Pre-event imagery provided by the NASA funded Web-Enabled Landsat Data project offer a basis of comparison for monitoring post-disaster recovery efforts. Techniques have also been developed to generate products from higher resolution imagery from the recently available International Space Station SERVIR Environmental Research and Visualization System instrument. Of paramount importance is to deliver these products to end users expeditiously and in formats compatible with Decision Support Systems (DSS). Delivery techniques include a Tile Map Service (TMS) and a Web Mapping Service (WMS). These mechanisms allow easy integration of satellite products into DSS's, including the National Weather Service's Damage Assessment Toolkit for use by personnel conducting damage surveys. This poster will present an overview of the developed techniques and products and compare the strengths and weaknesses of the TMS and WMS.

NASA's Next Generation Space Geodesy Program

NASA Technical Reports Server (NTRS)

Pearlman, M. R.; Frey, H. V.; Gross, R. S.; Lemoine, F. G.; Long, J. L.; Ma, C.; McGarry J. F.; Merkowitz, S. M.; Noll, C. E.; Pavilis, E. C.;

NASA's Next Generation Space Geodesy Program

NASA Technical Reports Server (NTRS)

Merkowitz, S. M.; Desai, S. D.; Gross, R. S.; Hillard, L. M.; Lemoine, F. G.; Long, J. L.; Ma, C.; McGarry, J. F.; Murphy, D.; Noll, C. E.;

Spatial and temporal land cover changes in Terminos Lagoon Reserve, Mexico.

PubMed

Soto-Galera, Ernesto; Piera, Jaume; López, Pilar

2010-06-01

Terminos Lagoon ecosystem is the largest fluvial-lagoon estuarine system in the country and one of the most important reserves of coastal flora and fauna in Mexico. Since the seventies, part of the main infrastructure for country's oil extraction is located in this area. Its high biodiversity has motivated different type of studies including deforestation processes and land use planning. In this work we used satellite image analysis to determine land cover changes in the area from 1974 to 2001. Our results indicate that tropical forest and mangroves presented the most extensive losses in its coverage. In contrast, urban areas and induced grassland increased considerably. In 2001 more than half of the ecosystem area showed changes from its original land cover, and a third part of it was deteriorated. The main causes of deforestation were both the increase in grassland and the growth of urban areas. However, deforestation was attenuated by natural reforestation and plant canopy recovery. We conclude that the introduction of cattle and urban development were the main causes for the land cover changes; however, the oil industry activity located in the ecosystem, has promoted indirectly to urban growth and rancher boom.

Access NASA Satellite Global Precipitation Data Visualization on YouTube

NASA Astrophysics Data System (ADS)

Liu, Z.; Su, J.; Acker, J. G.; Huffman, G. J.; Vollmer, B.; Wei, J.; Meyer, D. J.

2017-12-01

Since the satellite era began, NASA has collected a large volume of Earth science observations for research and applications around the world. Satellite data at 12 NASA data centers can also be used for STEM activities such as disaster events, climate change, etc. However, accessing satellite data can be a daunting task for non-professional users such as teachers and students because of unfamiliarity of terminology, disciplines, data formats, data structures, computing resources, processing software, programing languages, etc. Over the years, many efforts have been developed to improve satellite data access, but barriers still exist for non-professionals. In this presentation, we will present our latest activity that uses the popular online video sharing web site, YouTube, to access visualization of global precipitation datasets at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). With YouTube, users can access and visualize a large volume of satellite data without necessity to learn new software or download data. The dataset in this activity is the 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis (TMPA). The video consists of over 50,000 data files collected since 1998 onwards, covering a zone between 50°N-S. The YouTube video will last 36 minutes for the entire dataset record (over 19 years). Since the time stamp is on each frame of the video, users can begin at any time by dragging the time progress bar. This precipitation animation will allow viewing precipitation events and processes (e.g., hurricanes, fronts, atmospheric rivers, etc.) on a global scale. The next plan is to develop a similar animation for the GPM (Global Precipitation Measurement) Integrated Multi-satellitE Retrievals for GPM (IMERG). The IMERG provides precipitation on a near-global (60°N-S) coverage at half-hourly time interval, showing more details on precipitation processes and development, compared to the 3

The long-term Global LAnd Surface Satellite (GLASS) product suite and applications

NASA Astrophysics Data System (ADS)

Liang, S.

2015-12-01

Our Earth's environment is experiencing rapid changes due to natural variability and human activities. To monitor, understand and predict environment changes to meet the economic, social and environmental needs, use of long-term high-quality satellite data products is critical. The Global LAnd Surface Satellite (GLASS) product suite, generated at Beijing Normal University, currently includes 12 products, including leaf area index (LAI), broadband shortwave albedo, broadband longwave emissivity, downwelling shortwave radiation and photosynthetically active radiation, land surface skin temperature, longwave net radiation, daytime all-wave net radiation, fraction of absorbed photosynetically active radiation absorbed by green vegetation (FAPAR), fraction of green vegetation coverage, gross primary productivity (GPP), and evapotranspiration (ET). Most products span from 1981-2014. The algorithms for producing these products have been published in the top remote sensing related journals and books. More and more applications have being reported in the scientific literature. The GLASS products are freely available at the Center for Global Change Data Processing and Analysis of Beijing Normal University (http://www.bnu-datacenter.com/), and the University of Maryland Global Land Cover Facility (http://glcf.umd.edu). After briefly introducing the basic characteristics of GLASS products, we will present some applications on the long-term environmental changes detected from GLASS products at both global and local scales. Detailed analysis of regional hotspots, such as Greenland, Tibetan plateau, and northern China, will be emphasized, where environmental changes have been mainly associated with climate warming, drought, land-atmosphere interactions, and human activities.

Implications of sea level rise scenarios on land use /land cover classes of the coastal zones of Cochin, India.

PubMed

Mani Murali, R; Dinesh Kumar, P K

2015-01-15

Physical responses of the coastal zones in the vicinity of Cochin, India due to sea level rise are investigated based on analysis of inundation scenarios. Quantification of potential habitat loss was made by merging the Land use/Land cover (LU/LC) prepared from the satellite imagery with the digital elevation model. Scenarios were generated for two different rates of sea level rise and responses of changes occurred were made to ascertain the vulnerability and loss in extent. LU/LC classes overlaid on 1 m and 2 m elevation showed that it was mostly covered by vegetation areas followed by water and urban zones. For the sea level rise scenarios of 1 m and 2 m, the total inundation zones were estimated to be 169.11 km(2) and 598.83 km(2) respectively using Geographic Information System (GIS). The losses of urban areas were estimated at 43 km(2) and 187 km(2) for the 1 m and 2 m sea level rise respectively which is alarming information for the most densely populated state of India. Quantitative comparison of other LU/LC classes showed significant changes under each of the inundation scenarios. The results obtained conclusively point that sea level rise scenarios will bring profound effects on the land use and land cover classes as well as on coastal landforms in the study region. Coastal inundation would leave ocean front and inland properties vulnerable. Increase in these water levels would alter the coastal drainage gradients. Reduction in these gradients would increase flooding attributable to rainstorms which could promote salt water intrusion into coastal aquifers and force water tables to rise. Changes in the coastal landforms associated with inundation generate concern in the background that the coastal region may continue to remain vulnerable in the coming decades due to population growth and development pressures. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling Land Use/Cover Changes in an African Rural Landscape

NASA Astrophysics Data System (ADS)

Kamusoko, C.; Aniya, M.

2006-12-01

Land use/cover changes are analyzed in the Bindura district of Zimbabwe, Africa through the integration of data from a time series of Landsat imagery (1973, 1989 and 2000), a household survey and GIS coverages. We employed a hybrid supervised/unsupervised classification approach to generate land use/cover maps from which landscape metrics were calculated. Population and other household variables were derived from a sample of surveyed villages, while road accessibility and slope were obtained from topographic maps and digital elevation model, respectively. Markov-cellular automata modeling approach that incorporates Markov chain analysis, cellular automata and multi-criteria evaluation (MCE) / multi-objective allocation (MOLA) procedures was used to simulate land use/cover changes. A GIS-based MCE technique computed transition potential maps, whereas transition areas were derived from the 1973-2000 land use/cover maps using the Markov chain analysis. A 5 x 5 cellular automata filter was used to develop a spatially explicit contiguity- weighting factor to change the cells based on its previous state and those of its neighbors, while MOLA resolved land use/cover class allocation conflicts. The kappa index of agreement was used for model validation. Observed trends in land use/cover changes indicate that deforestation and the encroachment of cultivation in woodland areas is a continuous trend in the study area. This suggests that economic activities driven by agricultural expansion were the main causes of landscape fragmentation, leading to landscape degradation. Rigorous calibration of transition potential maps done by a MCE algorithm and Markovian transition probabilities produced accurate inputs for the simulation of land use/cover changes. Overall standard kappa index of agreement ranged from 0.73 to 0.83, which is sufficient for simulating land use/cover changes in the study area. Land use/cover simulations under the 1989 and 2000 scenario indicated further

Utility of Satellite Remote Sensing for Land-Atmosphere Coupling and Drought Metrics

NASA Technical Reports Server (NTRS)

Roundy, Joshua K.; Santanello, Joseph A.

2017-01-01

Feedbacks between the land and the atmosphere can play an important role in the water cycle and a number of studies have quantified Land-Atmosphere (L-A) interactions and feedbacks through observations and prediction models. Due to the complex nature of L-A interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in-situ data and reanalysis products. NASA's AQUA satellite and retrievals of soil moisture and lower tropospheric temperature and humidity properties are used as input. Overall, the AQUA-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in-situ and reanalysis products. In addition, this work represents the first time that in-situ observations were utilized for the coupling classification and CDI. The combination of in-situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

Potential reciprocal effect between land use / land cover change and climate change

NASA Astrophysics Data System (ADS)

Daham, Afrah; Han, Dawei; Rico-Ramirez, Miguel

2016-04-01

Land use/land cover (LULC) activity influences climate change and one way to explore climate change is to analyse the change in LULC patterns. Modelling the Spatio-temporal pattern of LULC change requires the use of satellite remote sensing data and aerial photographs with different pre-processing steps. The aim of this research is to analyse the reciprocal effects of LUCC (Land Use and Cover Change) and the climate change on each other in the study area which covers part of Bristol, South Gloucestershire, Bath and Somerset in England for the period (1975-2015). LUCC is assessed using remote sensing data. Three sets of remotely sensed data, LanSAT-1 Multispectral Scanner (MSS) data obtained in (1975 and 1976), LanSAT-5 Thematic Mapper (TM) data obtained in (1984 and 1997), and LandSAT-7 Enhanced Thematic Mapper Plus (ETM+) acquired in (2003 and 2015), with a time span of forty years were used in the study. One of the most common problems in the satellite images is the presence of cloud covers. In this study, the cloud cover problem is handled using a novel algorithm, which is capable of reducing the cloud coverage in the classified images significantly. This study also examines a suite of possible photogrammetry techniques applicable to detect the change in LULC. At the moment photogrammertic techniques are used to derive the ground truth for supervised classification from the high resolution aerial photos which were provided by Ordnance Survey (contract number: 240215) and global mapper for the years in (2001 and 2014). After obtaining the classified images almost free of clouds, accuracy assessment is implemented with the derived classified images using confusion matrix at some ground truth points. Eight classes (Improved grassland, Built up areas and gardens, Arable and horticulture, Broad-leaved / mixed woodland, Coniferous woodland, Oceanic seas, Standing open water and reservoir, and Mountain; heath; bog) have been classified in the chosen study area. Also

NASA launches carbon dioxide research satellite

NASA Astrophysics Data System (ADS)

Wendel, JoAnna

2014-07-01

Last week NASA launched a new satellite to study atmospheric carbon dioxide (CO2). Once in orbit, the Orbiting Carbon Observatory-2 (OCO-2) satellite, launched from Vandenberg Air Force Base in California, will take more than 100,000 individual measurements of atmospheric CO2 per day.

Characterizing human-environment interactions in the Galapagos Islands: A case study of land use/land cover dynamics in Isabela Island

NASA Astrophysics Data System (ADS)

McCleary, Amy L.

This dissertation examines contemporary land use and land cover (LULC) change in the communities and protected areas of Isabela Island to provide insights into human-environment interactions in the Galapagos Islands of Ecuador. The growing human presence in Galapagos over the last four decades has been accompanied by significant changes in LULC on inhabited islands in the archipelago. Local stakeholders and decision-makers have recently called for a more integrative approach to understanding interactions between people and the environment in the archipelago. This study is guided by two complementary bodies of work situated within the human-environment tradition of Geography---land change science and landscape ecology. First, support Vector Machine (SVM) and Object Based Image Analysis (OBIA) classifiers are evaluated for mapping LULC from high spatial resolution satellite images. The results show that thematic LULC classifications produced by OBIA are more accurate overall than those generated by SVM. However, important tradeoffs exist between improvements in classification accuracy and processing requirements. The composition and spatial configuration of LULC change are then mapped and quantified from a time series of QuickBird and WorldView-2 satellite images from 2003 to 2010. The pattern metric and change detection analyses reveal that land use change is extensive within the communities due to the expansion and consolidation of built-up areas, and fragmentation of and declines in agriculture. The Galapagos National Park is primarily transformed by exotic plant invasion, forests expansion, and shrinking coastal lagoons. Patterns of agricultural land abandonment, plant invasion, and forest expansion over the same period are described from pattern metric and overlay analyses. Potential drivers of these LULC transitions are identified from logistic regression models, descriptive statistics of agricultural surveys and population censuses, and interviews with

Human well-being and land cover types in the southeastern U.S.A.

Treesearch

B. Gyawali; R. Fraser; J. Schelhas; Y. Wang; W. Tadesse; J. Bukenya

2009-01-01

The west-central region of Alabama is rich in natural resources.  Yet changes in land use seem unrelated to improvements in human well-being.  Satellite imagery and U.S. census data for 1980 and 2000 were analyzed to test whether changes in land cover were related to changes in a human well-being index-of income, employment and education at the Cenus Block Group (CBG)...

IMPLEMENTATION STRATEGY FOR PRODUCTION OF NATIONAL LAND-COVER DATA (NLCD) FROM THE LANDSAT 7 THEMATIC MAPPER SATELLITE

EPA Science Inventory

As environmental programs within and outside the federal government continue to move away from point-based studies to larger and larger spatial (not cartographic) scale, the need for land-cover and other geographic data have become ineluctable. The national land-cover mapping pr...

National Land Cover Database 2001 (NLCD01)

USGS Publications Warehouse

LaMotte, Andrew E.

2016-01-01

This 30-meter data set represents land use and land cover for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System (see http://water.usgs.gov/GIS/browse/nlcd01-partition.jpg). The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004), (see: http://www.mrlc.gov/mrlc2k.asp). The NLCD 2001 was created by partitioning the United States into mapping zones. A total of 68 mapping zones (see http://water.usgs.gov/GIS/browse/nlcd01-mappingzones.jpg), were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

Viirs Land Science Investigator-Led Processing System

NASA Astrophysics Data System (ADS)

Devadiga, S.; Mauoka, E.; Roman, M. O.; Wolfe, R. E.; Kalb, V.; Davidson, C. C.; Ye, G.

2015-12-01

The objective of the NASA's Suomi National Polar Orbiting Partnership (S-NPP) Land Science Investigator-led Processing System (Land SIPS), housed at the NASA Goddard Space Flight Center (GSFC), is to produce high quality land products from the Visible Infrared Imaging Radiometer Suite (VIIRS) to extend the Earth System Data Records (ESDRs) developed from NASA's heritage Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the EOS Terra and Aqua satellites. In this paper we will present the functional description and capabilities of the S-NPP Land SIPS, including system development phases and production schedules, timeline for processing, and delivery of land science products based on coordination with the S-NPP Land science team members. The Land SIPS processing stream is expected to be operational by December 2016, generating land products either using the NASA science team delivered algorithms, or the "best-of" science algorithms currently in operation at NASA's Land Product Evaluation and Algorithm Testing Element (PEATE). In addition to generating the standard land science products through processing of the NASA's VIIRS Level 0 data record, the Land SIPS processing system is also used to produce a suite of near-real time products for NASA's application community. Land SIPS will also deliver the standard products, ancillary data sets, software and supporting documentation (ATBDs) to the assigned Distributed Active Archive Centers (DAACs) for archival and distribution. Quality assessment and validation will be an integral part of the Land SIPS processing system; the former being performed at Land Data Operational Product Evaluation (LDOPE) facility, while the latter under the auspices of the CEOS Working Group on Calibration & Validation (WGCV) Land Product Validation (LPV) Subgroup; adopting the best-practices and tools used to assess the quality of heritage EOS-MODIS products generated at the MODIS Adaptive Processing

Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data.

PubMed

Fallati, Luca; Savini, Alessandra; Sterlacchini, Simone; Galli, Paolo

2017-08-01

The Maldives islands in recent decades have experienced dramatic land-use change. Uninhabited islands were turned into new resort islands; evergreen tropical forests were cut, to be replaced by fields and new built-up areas. All these changes happened without a proper monitoring and urban planning strategy from the Maldivian government due to the lack of national land-use and land-cover (LULC) data. This study aimed to realize the first land-use map of the entire Maldives archipelago and to detect land-use and land-cover change (LULCC) using high-resolution satellite images and socioeconomic data. Due to the peculiar geographic and environmental features of the archipelago, the land-use map was obtained by visual interpretation and manual digitization of land-use patches. The images used, dated 2011, were obtained from Digital Globe's WorldView 1 and WorldView 2 satellites. Nine land-use classes and 18 subclasses were identified and mapped. During a field survey, ground control points were collected to test the geographic and thematic accuracy of the land-use map. The final product's overall accuracy was 85%. Once the accuracy of the map had been checked, LULCC maps were created using images from the early 2000s derived from Google Earth historical imagery. Post-classification comparison of the classified maps showed that growth of built-up and agricultural areas resulted in decreases in forest land and shrubland. The LULCC maps also revealed an increase in land reclamation inside lagoons near inhabited islands, resulting in environmental impacts on fragile reef habitat. The LULC map of the Republic of the Maldives produced in this study can be used by government authorities to make sustainable land-use planning decisions and to provide better management of land use and land cover.

Forest Cover Mapping in Iskandar Malaysia Using Satellite Data

NASA Astrophysics Data System (ADS)

Kanniah, K. D.; Mohd Najib, N. E.; Vu, T. T.

2016-09-01

Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

Extraction of land cover change information from ENVISAT-ASAR data in Chengdu Plain

NASA Astrophysics Data System (ADS)

Xu, Wenbo; Fan, Jinlong; Huang, Jianxi; Tian, Yichen; Zhang, Yong

2006-10-01

Land cover data are essential to most global change research objectives, including the assessment of current environmental conditions and the simulation of future environmental scenarios that ultimately lead to public policy development. Chinese Academy of Sciences generated a nationwide land cover database in order to carry out the quantification and spatial characterization of land use/cover changes (LUCC) in 1990s. In order to improve the reliability of the database, we will update the database anytime. But it is difficult to obtain remote sensing data to extract land cover change information in large-scale. It is hard to acquire optical remote sensing data in Chengdu plain, so the objective of this research was to evaluate multitemporal ENVISAT advanced synthetic aperture radar (ASAR) data for extracting land cover change information. Based on the fieldwork and the nationwide 1:100000 land cover database, the paper assesses several land cover changes in Chengdu plain, for example: crop to buildings, forest to buildings, and forest to bare land. The results show that ENVISAT ASAR data have great potential for the applications of extracting land cover change information.