Landsat Thematic Mapper scenes

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Open-File Report By Daniel H. Knepper, Jr. U.S. Department of the Interior U.S. Geological Survey

Open-File Report By Daniel H. Knepper, Jr. U.S. Department of the Interior U.S. Geological Survey

As part of the Central Colorado Mineral Resource Assessment Project, the digital image data for four Landsat Thematic Mapper scenes covering central Colorado between Wyoming and New Mexico were acquired and band ratios were calculated after masking pixels dominated by vegetation, snow, and terrain shadows. Ratio values were visually enhanced by contrast stretching, revealing only those areas with strong responses (high ratio values). A color-ratio composite mosaic was prepared for the four scenes so that the distribution of potentially hydrothermally altered rocks could be visually evaluated. To provide a more useful input to a Geographic Information System-based mineral resource assessment, the information contained in the color-ratio composite raster image mosaic was converted to vector- based polygons after thresholding to isolate the strongest ratio responses and spatial filtering to reduce vector complexity and isolate the largest occurrences of potentially hydrothermally altered rocks.
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Estimation of surface energy fluxes under complex terrain of Mt  Qomolangma over the Tibetan Plateau

Estimation of surface energy fluxes under complex terrain of Mt Qomolangma over the Tibetan Plateau

Abstract. Surface solar radiation is an important parame- ter in surface energy balance models and in estimation of evapotranspiration. This study developed a DEM based ra- diation model to estimate instantaneous clear sky solar ra- diation for surface energy balance system to obtain accurate energy absorbed by the mountain surface. Efforts to improve spatial accuracy of satellite based surface energy budget in mountainous regions were made in this work. Based on eight scenes of Landsat TM/ETM+ (Thematic Mapper/Enhanced Thematic Mapper+) data and observations around the Qo- molangma region of the Tibetan Plateau, the topographi- cal enhanced surface energy balance system (TESEBS) was tested for deriving net radiation, ground heat flux, sensible heat flux and latent heat flux distributions over the heteroge- neous land surface. The land surface energy fluxes over the study area showed a wide range in accordance with the sur- face features and their thermodynamic states. The model was validated by observations at QOMS/CAS site in the research area with a reasonable accuracy. The mean bias of net radia- tion, sensible heat flux, ground heat flux and latent heat flux is lower than 23.6 W m −2 . The surface solar radiation esti- mated by the DEM based radiation model developed by this study has a mean bias as low as − 9.6 W m −2 . TESEBS has a decreased mean bias of about 5.9 W m −2 and 3.4 W m −2 for sensible heat and latent heat flux, respectively, compared to the Surface Energy Balance System (SEBS).
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Coarse Resolution Image Analysis and Cleaning UpCloud Radiation

Coarse Resolution Image Analysis and Cleaning UpCloud Radiation

A method of sub-pixel land cover estimation including an algorithm for minimizing missing data due to cloud cover was proposed for the purpose of evaluating and monitoring the environment of wide areas. A pair of Landsat Thematic Mapper (TM) scenes over coincident multitemporal National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) time-series of directional hemispherical reflectance were used to develop a fine-scale land cover map using either eight or three categories and to estimate the endmembers of the AVHRR image using a positive constrained linear least-squares method. Furthermore, three approaches were evaluated for compositing sub-pixel estimates over cloudy areas in the AVHRR image. Finally, from validation tests made for unmixing and compositing methods, the results suggest that these methods may be generally useful for comparing multispectral images in space and time.
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Coherent Noise Removal In Dark Pixels (Case Study: Images Acquired By Landsat 5 Thematic Mapper)

Coherent Noise Removal In Dark Pixels (Case Study: Images Acquired By Landsat 5 Thematic Mapper)

Thematic mapper sensor, abbreviated as TM, is a multispectral sensor mounted on Landsat 4 and 5. Imaging technique in this sensor is as whisk broom [6]. Thematic Mapper is a sensor with high spectral and spatial resolution. This feature of TM leads to the reduced signal- to-noise ratio. This ratio is particularly very low in images acquired from dark scenes such as water surfaces and subsequently leads to reduced data quality. On the other hand, one of the most widely used atmospheric corrections methods is using dark pixels. In this method, the acquired values in pixels where very low reflectance is expected are identified as atmospheric effects and are used for atmospheric corrections throughout the image. A kind of
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Overcoming limitations with Landsat imagery for mapping of peat swamp forests in Sundaland

Overcoming limitations with Landsat imagery for mapping of peat swamp forests in Sundaland

Abstract: Landsat can be used to map tropical forest cover at 15–60 m resolution, which is helpful for detecting small but important perturbations in increasingly fragmented forests. However, among the remaining Landsat satellites, Landsat-5 no longer has global coverage and, since 2003, a mechanical fault in the Scan-Line Corrector (SLC-Off) of the Landsat-7 satellite resulted in a 22–25% data loss in each image. Such issues challenge the use of Landsat for wall-to-wall mapping of tropical forests, and encourage the use of alternative, spatially coarser imagery such as MODIS. Here, we describe and test an alternative method of post-classification compositing of Landsat images for mapping over 20.5 million hectares of peat swamp forest in the biodiversity hotspot of Sundaland. In order to reduce missing data to levels comparable to those prior to the SLC-Off error, we found that, for a combination of Landsat-5 images and SLC-off Landsat-7 images used to create a 2005 composite, 86% of the 58 scenes required one or two images, while 14% required three or more images. For a 2010 composite made using only SLC-Off Landsat-7 images, 64% of the scenes required one or two images and 36% required four or more images.
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Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data

Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data

Input data from periodic or annual products, such as MOD44A and MOD44B, limit the frequency of deforestation monitoring. The starting and ending dates for annual or periodic data products can also greatly impact the ability to detect deforestation. For example, if deforestation occurs near the end of the processing period, the change may not be detected until the following product cycle. The regional applicability of these data products is constrained by two elements of a global product algorithm. First, global products are not optimized for any particu- lar region, but are generalized for a wide range of conditions. Second, these algorithms were designed to map forest conversion and forest cover without ad- ditional data sources, such as the forest/nonforest mask applied in this study. VCC data had very low commission errors, accurately identifying very large clearings but missing a substantial percentage of clearings of all sizes. Although the MOD44B percent of tree-cover data were created at 500-m resolution, VCF data performed comparably to other techniques. This performance suggests that a 250-m VCF product could be highly accurate for annual deforestation monitoring. In addition, a VCF percent of the tree-cover product could be used as an initial cloud-free forest mask in the Brazilian Amazon or in other deforestation- monitoring efforts. Using the VCF product as a forest/nonforest mask would enable the extension of the simple methods presented in this study in other regions. Deforestation detection accuracy was especially sensitive to the degree of forest fragmentation in each test scene. In highly fragmented forest conditions such as scene 223/66, VCF, soil fraction, and EVI inputs constructed from 500-m data generated a high number of edge-effect commission errors. In scenes with more continuous forest cover, edge-effect errors as a result of 500-m source data or 250-m MODIS resolution more generally were less problematic. With only two MODIS bands at 250-m resolution, future subpixel spectral unmixing at 250-m resolution may need to focus on red and NIR unmixing algorithms that have been applied to data from other sensors (Hall et al. 1995).
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Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying aMaximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia

Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying aMaximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia

Accurate, detailed and recent Information about land cover/use is important and much more needed for different aspects of sustainable development and environmental management. As remote sensing datasets are becomes one of the most important and effective tools to generate such information, this study aimed to generating land cover map for sub area in Al-Ahasaa Oasis, Saudi Arabia, by using and classifying a subset of Landsat-ETM+ image of the selected study area, as bases and required input for future studies and researches. Different image preprocessing techniques in addition to a will-known and widely used classification method (i.e., Maximum Likelihood classifier) were applied. To be reliable with the final product, accuracy assessment was carried out with 89% agreement and accepted according to the applied method. Different land cover classes were found in the study area, which includes (Sand dunes, Water bodies, Sabakha, Bare soil, Urban, and Agricultural lands). The study also revealed that the dominant land cover class is sand dunes with approximately ± 70% in area. The study strongly indicated that the area has long been affected by sand movement. Finally, the study suggested that, further researches with more advanced methods rather than traditional methods are needed in the future to support the findings of this study, with high degree of accuracy.
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Lithological mapping of ophiolite complex with emphasis on chromite and magnesite exploration using remote sensing techniques

Lithological mapping of ophiolite complex with emphasis on chromite and magnesite exploration using remote sensing techniques

Ophiolites complex present an excellent opportunity for studying oceanic crust and can be the best candidate for mapping complex lithology using remote sensing satellite data. In addition, these rock formations are significant for exploration mineral resources, mainly for chromite and magnasite ore deposits. The current improvement o f multi-spectral remote sensing devices, like ASTER and Landsat TM sensor, potentially suggest to geologists and mining engineers to employ remote sensing methods to reduce the cost and time-consuming for regional geological mapping and new source mineral exploration. Prior studies, which used traditional methods, are confronted with the follow problems in the study area:
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Structural And Lithological Reconnaissance Studies Of Part Of North Central Nigeria Using Landsat Enhanced Thematic Mapper Plus [Etm+] And Shuttle Radar Topography Mission [Srtm] Satellite Imageries

Structural And Lithological Reconnaissance Studies Of Part Of North Central Nigeria Using Landsat Enhanced Thematic Mapper Plus [Etm+] And Shuttle Radar Topography Mission [Srtm] Satellite Imageries

The use of satellite imageries in geological studies dates back to the launch of the first and most popular Earth observation Satellite programme: The Landsat series of Earth observatory Satellites from Landsat-1 to Landsat -8. These satellite imageries gives geologic studies a regional perspective so that the Structure and Lithology that characterize an area can be easily accessed and determined. Multispectral imagery gives information on the lithology, Radar imagery gives sufficient information on the structural disposition of the lithology, topography and relief. A synergistic combination between multispectral and Radar imagery is most suited for geologic studies and allied applications. Detailed Geological mapping carried out around part of North Central Nigeria is that of [1], who mapped the Kusheriki zone and concluded that the migmatite-gniess, schist belt and the older granites originated amid a solitary orogenic occasion. [2] and [3] carried out geochronological work on the gneissic complex and concluded that the Nigerian basement is polycyclic. [4] and [5] confirmed the presence of migmatite-gneiss of different ages with a structural discontinuity between the migmatite and the schist belt called Zungeru Mylonites.
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A Study of techniques to improve the effective resolution of thematic mapper thermal infrared imagery

A Study of techniques to improve the effective resolution of thematic mapper thermal infrared imagery

output was image exhibited an apparent bleeding of thermal due to rooftop heat loss that diffused beyond the Salvaggio and 3.1.2.7 Schott, approach developed This expanded upon Warnick M[r]

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Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

For processing satellite data and database building, the Gauss–Krueger coordinate system was selected. The false color composite imagery of Landsat TM data (8 August 1998 and 6 August 2009) covering the study area was produced in the ArcGIS environment (Fig. 3). Land use classes were se- lected in the false color composite imagery for supervised classification of image. Different land use types were clas- sified using maximum likelihood classification in ArcGIS environment (Fig. 4). Following land classification was ac- cepted using the Landsat TM data: asphalt road, park and green spaces, waste land and bare soil, fallow land, residen- tial (urban), commercial and industrial (Mallick et al., 2008). Classification was performed on Landsat TM for spectral reparability of the land use classes existing in the study area. Finally, the relationship between land use classes, surface temperature and NDVI in Yazd was analyzed in detail. 2.3 Calibration and validation of Landsat TM LST
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Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries

Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries

This study explained the use of some data input combination to map mangrove land cover, derived from Landsat 5 TM, ALOS PALSAR, and spectral information implemented by RF algorithm and then compared to ML algorithm. Various accuracy results were obtained from each layer input combination. The best result of RF algorithm was achieved by all layer input combination. Meanwhile, the best result for ML algorithm was obtained by combination of Landsat 5 TM and ALOS PALSAR. The RF Algorithm could map mangrove land cover better and could also reduce noise existence in classification result compared to ML algorithm. Adding total layer input in RF algorithm tended to increase the classification result, but not in ML algorithm.
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Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with landsat thematic mapper

Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with landsat thematic mapper

To assess burn severity in the field, field data were collected in September 2008, i.e. one year post-fire. The field data consist of 160 GeoCBI (Geo Composite Burn Index) plots. The GeoCBI is a modified version of the Composite Burn Index (CBI) (De Santis and Chuvieco 2009). The (Geo)CBI is an operational tool used in conjunction with the Landsat dNBR approach to assess burn severity in the field (Key and Benson 2005). The GeoCBI divides the ecosystem into five different strata, one for the substrates and four vegetation layers. These strata are: (i) substrates, (ii) herbs, low shrubs and trees less than 1 m, (iii) tall shrubs and trees of 1 to 5 m, (iv) intermediate trees of 5 to 20 m and (v) large trees higher than 20 m. In the field form, 20 different factors can be rated (e.g. soil and rock cover/colour change, % LAI change, char height) (see table 1) but only those factors present and reliably rateable, are considered. The rates are given on a continuous scale between 0 and 3 and the resulting factor ratings are averaged per stratum. Based on these stratum averages, the GeoCBI is calculated in proportion to their corresponding fraction of cover, resulting in a weighted average between zero and three that expresses burn severity.
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Merging panchromatic multispectral images for enhanced image analysis

Merging panchromatic multispectral images for enhanced image analysis

List of Tables Table 1-1 A Comparison of Multispectral Scanners MSS and Thematic Mapper TM Table 3-1 1Scene Acquisition Table 3-2 Scene Statistics Table 3-3 Transformation Coefficients T[r]

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A Practical Study of the Role of the Cinematographer

A Practical Study of the Role of the Cinematographer

Preproduction was in full swing. My crew of fourteen inexperienced students was quickly starting to resemble a team of professionals. I had been working with Tony, approving his color choices in costuming and set dressing. Kevin and I were discussing furniture styles and props. I spent several afternoons with Phil generating sketches of storyboards for the shoot. I tried to convince Phil of the importance of coverage; most of our scenes were being covered in one dolly shot or one master. Phil thought these could work, I somewhat agreed, but I thought it would greatly limit him later in the editing room. Nevertheless, we reached a compromise at the end of our session and I committed to drawing the storyboards myself.
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Statistical Analysis and Parameter Selection for Mapper

Statistical Analysis and Parameter Selection for Mapper

per. In this article, the authors provide a way to go from the input space to the Mapper using small perturbations. We build on this precise relation between the input space and its Mapper to show that the Mapper is itself a measurable construction. In Carri` ere and Oudot (2017b), the authors also show that the topological structure of the Mapper can actually be predicted from the cover I by looking at appropriate signatures that take the form of extended persistence diagrams. In this article, we use this observation, together with an approximation inequality, to show that the Mapper, computed with a specific set of parameters, is actually an optimal estimator of its continuous analogue, the so-called Reeb graph. Moreover, these specific parameters act as natural candidates to obtain a reliable Mapper with no artifacts.
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Hadoop and Big Data. Keijo Heljanko. Department of Information and Computer Science School of Science Aalto University

Hadoop and Big Data. Keijo Heljanko. Department of Information and Computer Science School of Science Aalto University

phase starts where M × R file transfers are used to send all of the mapper outputs to the reducer handling each key partition. After reducer receives the input files, reducer sorts (and groups) the (key, value) pairs by the key 6. User defined Reduce functions iterate over the (key,

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Elucidating Ubiquitin Chain Formation Catalyzed by E2 Enzymes through use of a Di Ubiquitin Probe

Elucidating Ubiquitin Chain Formation Catalyzed by E2 Enzymes through use of a Di Ubiquitin Probe

With regard to product features, screenplays with more characters, higher percentages of outdoor scenes, and more dialogic interactions have a significant impact on box office ROI. In contrast, the effect of faster scene pace is negative, and dialogic interaction between minor characters does not contribute to films’ performance. Unlike the results of the selection model, the quadratic terms of the product and emotional features are not significant in the main model. It is possible that there is no tipping point observed in our data set of greenlighted screenplays, however, that does not mean that inverted-U relationships do not exist. It is also possible that the screenplay experts may intuitively know how to select scripts without too many key features. We account for the potential expert intuition by handling the endogeneity issues.
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Comparison of pixel -based and artificial neural networks classification methods for detecting forest cover changes in Malaysia

Comparison of pixel -based and artificial neural networks classification methods for detecting forest cover changes in Malaysia

This study used two classification methods, namely Artificial Neural Networks (ANN) and Maximum likelihood (ML) to classify different land use and land cover types in the state o f Johor. The highest classification accuracy was obtained by ANN, and the Landsat images classified using this method was used to detect the change notably in forest cover between 2000 and 2009. It was found that during a period o f 10 years Johor lost approximately 28% o f forested areas. It is suggested that the forested areas must be monitored on a continuous manner to detect any illegal deforestation. Also, the state government should make all forested areas as protected forest in order to prevent further loss o f this valuable natural resource in the state.
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Radiometric correction of Landsat-8 and Sentinel-2A scenes using drone imagery in synergy with field spectroradiometry

Radiometric correction of Landsat-8 and Sentinel-2A scenes using drone imagery in synergy with field spectroradiometry

To maximize efforts in terms of scientific, social, and economic benefits, spatial agencies advance together through initiatives as the Global Earth Observation System of Systems (GEOSS) [5]. In GEOSS, the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), among others, collaborate in the acquisition of high spatial resolution (SR) multispectral imagery, with the Landsat Data Continuity Mission (LDCM) [6] and the Copernicus Sentinel-2 (S2) [7] programs, respectively. The synergistic use of different satellite missions implies that images must have similar characteristics (spatial, spectral, radiometric, etc.). In fact, the design of several missions in recent years has gone in this direction, such as the aforementioned LCDM and Copernicus, currently with two Landsat (Landsat-7 (L7) and Landsat-8 (L8)) and two Sentinel-2 (Sentinel-2A (S2A) and Sentinel-2B (S2B)) satellites in orbit. The Sentinel constellation was designed to support the Landsat Program and provide continuity to the SPOT program [8] by embedding sensors with similar spatial and spectral resolution features. The temporal orbit cycle of Sentinel-2 platforms was specifically thought to maximize combined consecutive observations with Landsat [9], thereby increasing their joint monitoring possibilities.
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