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At least 217 records · Page 12

Iona Ecological Conservation: Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan civil war in 2002, human and livestock populations have increased exponentially within Iona National Park. An ongoing drought since 2017 has brought these people and livestock into increasing competition with local wildlife for resources – highlighting a conservation challenge that will become more entrenched as the effects of anthropogenic climate change increase. In 2019, African Parks began co-managing Iona National Park in Angola with the Angolan government, hoping to enact scientifically grounded management strategies to meet this challenge. To accomplish this, African Parks needed contemporary and historic information on the spatial distribution of landcover types within Iona and adjacent areas. We constructed and applied a Random Forest classifier in Google Earth Engine to multispectral imagery gathered from Landsat 5, 7, 8 and Sentinel-1 and 2 to meet this need. Using the classifier, we generated a time-series of land cover maps between 1990–2023, from which landscape metrics and change detection analysis were calculated to show how certain habitats and formations had changed over time. The resulting maps have producer and user’s accuracies above 87% and show four broad landcover regions within the study area. Notably, we observed a decrease in the park’s diversity as per the Shannon Diversity Index – an index that considers the richness of classes, as well the evenness of their distribution. A lack of arid specific land cover indices and ground-truthed training data from earlier years limited the accuracy and resolution of our landcover maps. However, this project still demonstrates that Earth observations can be used to form the basis of conservation policy in arid environments, where ground-truth data may be difficult to obtain or non-existent.

remote sensing↗

Hybrid predictive modeling approach simulated evapotranspiration and ecosystem respiration data

This dataset includes measured data used for developing hybrid-predictive-modeling (HPM) approach and simulated evapotranspiration and ecosystem respiration data across several Fluxnet sites, SNOTEL sites and East River locations (Chen et al., 2020 in review). Fluxnet sites considered in this study include: CA-OAS, CA-OBS, US-NR1, US-TON, US-VAR, US-SRM, US-WHS, US-WKG. Snotel sites Butte (ER-BT), Porphyry Creek (ER-PK) and Schofield Pass (ER-SP) are included as well 16 east river locations with different vegetation types denoted by evergreen forests (EF), deciduous forests (DF), riparian shrublands (RS) and meadow grassland (MS). This data package includes: 1) A summary spread sheet of the sites and acronyms for data variables. 2) Processed Fluxnet datasets that are obtained from Fluxnet.fluxdata.org with HPM estimated evapotranspiration (ET) and ecosystem respiration (RECO). Trained models and metrics for parameters for the Fluxnet sites were added in an update on November 2, 2020; 3) Processed SNOTEL datasets obtained from www.nrcs.usda.gov/snow/ and HPM estimated ET and RECO; 4) Weather data at east river locations obtained from DAYMET (daymet.ornl.gov) as well as HPM estimated ET and RECO. NDVI datasets are obtained from Landsat 5, 7 and 8 on Google Earth Engine platform. Detailed QA/QC and sampling methods of measured data are available at the original data source.

54 ENVIRONMENTAL SCIENCES↗

Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

54 ENVIRONMENTAL SCIENCES↗

Multispectral remote observations of hydrologic features on the North Slope of Alaska

Visible and near-infrared satellite data and active and passive microwave aircraft data are used to analyze some hydrologic features in Arctic Alaska. The following features have been studied: the small thaw lakes on the Arctic Coastal Plain (oriented lakes), Chandalar Lake in the Brooks Range, several North Slope rivers, surface water on the tundra, and snowcover on the North Slope and in the Brooks Range. Passive microwave brightness temperatures (T sub b) as seen on Electrically Scanned Microwave Radiometer (ESMR) imagery are shown to increase with increasing ice thickness on all of the lakes studied. Aufeis, an important hydrologic parameter in the Arctic, is observable in the Sagavanirktok River channel on April ESMR imagery. LANDSAT imagery with better (80 m) resolution is useful for measuring aufeis extent using band 5 imagery obtained just after snowmelt in June. It is shown that the extent of aufeis (as measured on LANDSAT imagery) varies with meteorological conditions and, therefore, may be a useful indicator of annual climate fluctuations on the North Slope. Snow and ice breakup has been traced from the Brooks Range Mountains to the Arctic Ocean Coast using LANDSAT band 7 imagery in May when melting begins in the mountains.

Hall, D. K.↗

An investigation into the utilization of HCMM thermal data for the discrimination of volcanic and Eolian geological units

The nature of the HCMM data set and the general geologic application of thermal inertia imaging using HCMM data were studied. The CCT's of five sites of interest were obtained and displayed. The upgrading of the image display system was investigated. Fragment/block size distributions in various terrain types wre characterized with emphasis on volcanic terrain. The SEASAT L-band radar images of volcanic landforms at Newberry Volcano, Oregon, were analyzed and compared to imagery from LANDSAT band 7.

Head, J. W., III↗

Geological-structural interpretation using products of remote sensing in the region of Carrancas, Minas Gerais, Brazil

The efficiency of some criteria developed for the utilization of small scale and low resolution remote sensing products to map geological and structural features was demonstrated. Those criteria were adapted from the Logical Method of Photointerpretation which consists of textural qualitative analysis of landforms and drainage net patterns. LANDSAT images of channel 5 and 7, 4 LANDSAT-RBV scenes, and 1 radar mosiac were utilized. The region of study is characterized by supracrustal metassediments (quartzites and micaschist) folded according to a "zig-zag" pattern and gnaissic basement. Lithological-structural definition was considered outstanding when compared to data acquired during field work, bibliographic data and geologic maps acquired in larger scales.

Parada, N. D. J.↗

Shuttle radar images for geologic mapping in tropical rainforest

Images of forested low-relief terrain in the Amazon basin of Brazil, obtained with airborne imaging radar in the Radambrasil project, are compared with SIR-A and Landsat MSS band-7 images to evaluate their usefulness in constructing geologic maps. Sample images are shown, and it is found that Radam images are more useful in distinguishing drainage patterns and mapping the region distribution of stream channels due to their relatively low depression angles (less than 25 deg as opposed to 43-37 deg for SIR-A), but that SIR-A images give superior discrimination of alluvial forest, where trees stand in water, due to the higher reflectivity of branches and water at the SIR-A wavelength (23.5 cm as opposed to 3 cm for Radam). Alluvial forest is also identified by Landsat band 7.

Ford, J. P.↗

Applications of wavelet-based compression to multidimensional Earth science data

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

Bradley, Jonathan N.↗

Possible Ancient Anthrosols Near Lost City of Ubar Site in Oman

During mapping for the Wadi al Jubal Archaeological Project in Yemen, USGS Geologists Overstreet and Grolier mapped "anthrosols of pre-Islaniic age" east of the Marib dam site (15D 24M N, 45D 18M E). These soils were the result of agriculture supported by irrigation enabled by water impounded by the dam, areas which were abandoned after dam failure. During analysis of Landsat Thematic Mapper satellite images of Yemen and Oman for the Mahra Archaeological Project, we noted that these anthrosols had a distinctive image expression. Based on other remote sensing research and laboratory spectroscopy, we think that the distinctive image signature is due to low reflectivity in Landsat band 7 resulting from relatively high concentrations of gypsum in the anthrosols. Many anthrosol sites were noted, most, but not all, of them previously documented. Undocumented possible anthrosol sites include an area east of Shisr in Oman, the archaeological site discovered by us to be responsible for some features of the "Lost City of Ubar" legends. Included in legendary accounts of the Ubar region are reports of fertile oases, and "areas that have known the plow". Based on demonstrated reliability of aspects of carefully interpreted legendary accounts, we postulate that we may have located the area of desert agriculture that may have existed to support the frankincense caravansary of Ubar. The possible anthrosol area is located at approximately 18D 10M N, 53D 54M E, and will be the subject of study in a future expedition.

Blom, Ronald G.↗

Everglades Ecological Forecasting II: Utilizing NASA Earth Observations to Enhance the Capabilities of Everglades National Park to Monitor & Predict Mangrove Extent to Aid Current Restoration Efforts

Mangroves act as a transition zone between fresh and salt water habitats by filtering and indicating salinity levels along the coast of the Florida Everglades. However, dredging and canals built in the early 1900s depleted the Everglades of much of its freshwater resources. In an attempt to assist in maintaining the health of threatened habitats, efforts have been made within Everglades National Park to rebalance the ecosystem and adhere to sustainably managing mangrove forests. The Everglades Ecological Forecasting II team utilized Google Earth Engine API and satellite imagery from Landsat 5, 7, and 8 to continuously create land-change maps over a 25 year period, and to allow park officials to continue producing maps in the future. In order to make the process replicable for project partners at Everglades National Park, the team was able to conduct a supervised classification approach to display mangrove regions in 1995, 2000, 2005, 2010 and 2015. As freshwater was depleted, mangroves encroached further inland and freshwater marshes declined. The current extent map, along with transition maps helped create forecasting models that show mangrove encroachment further inland in the year 2030 as well. This project highlights the changes to the Everglade habitats in relation to a changing climate and hydrological changes throughout the park.

Kirk, Donnie↗

An integrated software system for geometric correction of LANDSAT MSS imagery

A system for geometrically correcting LANDSAT MSS imagery includes all phases of processing, from receiving a raw computer compatible tape (CCT) to the generation of a corrected CCT (or UTM mosaic). The system comprises modules for: (1) control of the processing flow; (2) calculation of satellite ephemeris and attitude parameters, (3) generation of uncorrected files from raw CCT data; (4) creation, management and maintenance of a ground control point library; (5) determination of the image correction equations, using attitude and ephemeris parameters and existing ground control points; (6) generation of corrected LANDSAT file, using the equations determined beforehand; (7) union of LANDSAT scenes to produce and UTM mosaic; and (8) generation of output tape, in super-structure format.

Parada, N. D. J.↗

Atmospheric effects on radiation reflected from soil and vegetation as measured by orbital sensors using various scanning directions

Ground-measured spectral reflectance data for Avondale loam and drought-stressed and unstressed wheat were converted into digital counts for spectral bands 5 and 7 of the Landsat Multispectral Scanner System (MSS). For dry loam, the differences between ratios of MSS bands 7-5 as determined from space and from ground level measurements were 2.3 percent for clear and 5.6 percent for turbid atmospheric conditions. By contrast, for wet loam the differences were 10.4 and 29.5 percent. It is found that atmospheric conditions may cause a delay of from 3 to 7 days in the discrimination between drought-stressed and unstressed wheat. For oblique angle observations the atmospheric modification of ground-measured reflectances increased with angle at a greater rate in the 0/180 deg azimuth than in the 90/270 deg azimuth. Implications of this result are discussed for oblique angle Systeme Probatoire d'Observation de la Terre (SPOT), Mapsat, future multispectral linear array system imagery, and wide-angle imagery collected from scanners in high-altitude aircraft.

Slater, P. N.↗

Imaging Systems Provide Maps for U.S. Soldiers

Spanning nearly four decades, the remarkable Landsat program has continuously provided data about the Earth s surface, including detailed maps of vegetation, land use, forest extent and health, surface water, population distribution, as well as how these features have changed over time. Managed by NASA and the U.S. Geological Survey, Landsat s series of satellites obtain data through passive remote sensing, or the use of sensors to read the energy reflected or emitted from the Earth s surface. After the data from the sensors is processed and analyzed, it can be applied to create information-rich images of the planet. While the Landsat program has launched seven satellites since 1972, only Landsat 5 and 7 are currently operating. The next spacecraft in line to ensure continuity of data for years to come is the Landsat Data Continuity Mission (LDCM). Planned for launch in 2012, LDCM will take measurements of the Earth in visible, nearinfrared, shortwave infrared, and thermal infrared bands. In addition to widespread use for land use planning and monitoring on local to regional scales, support for disaster response and evaluations, as well as water use monitoring, LDCM measurements will directly serve NASA s research in the areas of climate, the carbon cycle, ecosystems, the water cycle, biogeochemistry, and Earth s surface and interior.

Source record↗

Finding the lost river gas field - Lineament density analysis in hydrocarbon exploration

A comparative trial analysis of lineament density in 1:500,000 Landsat and 1:48,000 aerial Landsat-simulator images of a gas-field region in West Virginia is presented. The high-contrast, band 4, 5, 7 color composite Landsat image was interpreted independently by two analysts. The slightly different lineament maps were evaluated in terms of lineament-density variation in 10 x 10-km areas, and the resulting areal-variation maps are found to be statistically equivalent. High lineament density is shown to be associated with productive gas wells. Comparison of ground-based substructural contour maps and lineament-density analyses of the Landsat-simulator images reveals good correlation between density maxima or isopleths and substructural features associated with hydrocarbon formations. A hydrocarbon-exploration strategy using both Landsat and aerial images is proposed.

Lang, H. R.↗

Monitoring the Mesoamerican Biological Corridor: A NASA/CCAD Cooperative Research Project

To foster scientific cooperation under a Memorandum of Understanding between NASA and the Central American countries, the research project developed regional databases to monitor forest condition and environmental change throughout the region. Of particular interest is the Mesoamerican Biological Corridor (MBC), a chain of protected areas and proposed conservation areas that will link segments of natural habitats in Central America from the borders of northern Columbia to southern Mexico. The first and second year of the project focused on the development of regional satellite databases (JERS-IC, MODIS, and Landsat-TM), training of Central American cooperators and forest cover and change analysis. The three regional satellite mosaics were developed and distributed on CD-ROM to cooperators and regional outlets. Four regional remote sensing training courses were conducted in 3 countries including participants from all 7 Central American countries and Mexico. In year 3, regional forest change assessment in reference to Mesoamerican Biological Corridor was completed and land cover maps (from Landsat TM) were developed for 7 Landsat scenes and accuracy assessed. These maps are being used to support validation of MODIS forest/non forest maps and to examine forest fragmentation and forest cover change in selected study sites. A no-cost time extension (2003-2004) allowed the completion of an M.S. thesis by a Costa Rican student and preparation of manuscripts for future submission to peer-reviewed outlets. Proposals initiated at the end of the project have generated external funding from the U.S. Forest Service (to U. Maine), NASA-ESSF (Oregon State U.) and from USAID and EPA (to NASA-MSFC-GHCC) to test MODIS capabilities to detect forest change; conduct literature review on biomass estimation and carbon stocks and develop a regional remote sensing monitoring center in Central America. The success of the project has led to continued cooperation between NASA, other federal agencies, and scientists from all seven Central American Countries (see SERVIR web site for this ongoing work - servir.nsstc.nasa.gov).

Sever, Thomas↗