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Background: This study aims to conduct a scientometric analysis and thematically-based narrative review of a century of Taraxacum officinale research (TOR), uncovering patterns, trends, themes, and advancements in the field to provide insights for future investigations. The study followed PRISMA guidelines and utilized the Scopus database with MeSH terms for bibliographic data retrieval. Scientometric mapping employed VOSviewer and R-package-based Bibliometrix, while extracted themes were reviewed narratively. A detailed analysis of TOR was achieved by including only original studies. Results: The findings include the extensive duration of TOR since 1908 and its significant growth, particularly in the last two decades. China emerges as the most productive country, but the United States leads in recognizable and collaborative TOR. The thematic map displays dynamic and diverse themes, with a rich knowledge structure revealed through the analysis of term co-occurrence. The year 2016 represents a turning point in the thematic map, marked by numerical growth and thematic bifurcation. The study extracted several main research topics within the field of TOR, including germination, antioxidant activity, bioherbicide, oxidative stress, Taraxacum kok-saghyz, and heavy metals. These topics represent key areas of investigation and provide insights into the diverse aspects of research surrounding T. officinale. Additionally, emerging topics in TOR encompass toxicity, metabolomics, dandelion extract, and diabetes mellitus. Conclusions: The study consolidated knowledge, highlighted research gaps, and provided directions for future investigations on TOR. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify the most significant studies and authors, as well as outline the distribution and effects of publications in the field of Asset-Liability Management (ALM) between 1983 and 2023, using bibliometric analysis. The data was extracted from the Scopus database for the period 1983-2023. A total of 488 documents were derived using VOSviewer and Biblioshiny software. The results show that the most productive journal is Insurance: Mathematics and Economics, the most cited author is Li D, and the United States is the top country with the highest level of collaboration in ALM research. Moreover, to know the current trend, a thematic map of keywords is analyzed using Biblioshiny R package and the co-occurrence of keywords using VOSviewer software. The study found that asset-liability management is the trending topic among researchers in 2023. [ABSTRACT FROM AUTHOR]

The main objective of this work was to carry out an analysis of the scientific production on the theme "Personal relationships and work overload", through the use of bibliometric tools. A search of the Scopus abstract database has been performed, retrieving all documents published from 1974 to 2023. Descriptive analyses, correlation, thematic maps, and cocitation networks were carried out, which were developed with the use of statistical programs such as Rstudio and Excel. The results found indicate that 83% were journal articles, the most productive year at the level of academic works was 2022 with 209 documents, in terms of production by country, the United States was the one that contributed the most with 1452, the authors with the highest h index were Cooper C.L. and De White H. with a score of 7 and finally the most frequent terms and words were Workload, stress, Bournout, mental workload and work engagement, just to mention the most relevant. [ABSTRACT FROM AUTHOR]

Transparent, consistent, and statistically reliable land use/ land cover area estimates are needed to assess land use change and greenhouse gas emissions associated with biofuel production and other land uses that are influenced by policy. As relevant studies have increased rapidly during past decades, the methods used to combine data extracted from land use land cover (LULC) surveys and remote sensing-based products and track or report sources of uncertainty vary notably. This paper reviews six data sources that are most commonly used to investigate LULC and change in the contiguous U.S. by highlighting the main characteristics, strengths and weaknesses and considering how uncertainty is assessed by the June Area Survey (JAS), the Census of Agriculture (COA), the Farm Survey Agency (FSA) acreage, the National Resources Inventory (NRI), the National Wetlands Inventory (NWI), and the Forest Inventory and Analysis (FIA); and two remote sensing-based data products, the Cropland Data Layer (CDL) and the National Land Cover Database (NLCD). The summary and conclusion identify important research gaps or challenges limiting current land use/land cover and change studies (e.g., lack of high-quality reference data and uncertainty quantification, etc.) and opportunities and emerging techniques (data fusion and machine learning) that will improve reliability of land use/land cover assessments and associated policies. Blended approaches that marry high quality ground truth data that are more finely resolved than data supplied by government surveys with multitemporal imagery are needed track use of non-agricultural lands vulnerable to agricultural expansion. These considerations are notably important as the U.S. considers the renewal and possibly revision of its Renewable Fuel Standard, which includes provisions that require monitoring of agricultural land expansion. • Producers and consumers of land use change information rely on key data sources that are being continually updated and improved. • Strengths, weaknesses and data gaps associated with key survey and remote sensing products are reviewed. • Trust in land use and biofuels policies that are reliant on accurate land use allocation will increase with transparency and consistency in methods used. [ABSTRACT FROM AUTHOR]

Forests are susceptible to sudden disturbances, particularly those induced by wind, leading potentially to important ecological changes and economic losses. The extensive multidisciplinary literature on this topic poses challenges for traditional literature reviews. Employing quantitative bibliometric analysis, our study provides a comprehensive overview of the evolving landscape of forest wind disturbance research. A systematic data collection process from the Web of Science Core Collection identified 839 relevant publications. Results reveal a consistent growth in publications, with a notable spike corresponding to Hurricane Hugo. Most contributions originate from the United States, showcasing leadership in collaboration both nationally and institutionally. Co-citation analysis identifies three overarching research themes: factors influencing wind disturbance, the impacts of wind disturbance, and an integrated exploration of factors and impacts. Thematic map analysis unveils that key topics include windthrow, tree motion during windstorms, European forests, wind damage risk estimation, hurricane impacts on forests, and long-term impacts of wind disturbances. Thematic evolution underscores the dynamic nature of the research themes, with hurricanes and mortality emerging as foundational, while forest structure, tree stability, and Picea abies exhibit evolving complexities. The study also identifies prospective research directions, emphasizing hurricanes, forest structure, climate change, and typhoon-related impacts. This research contributes to a nuanced understanding of the dynamic forest wind disturbance landscape, providing a foundation for future research and enabling better-informed and more effective management decisions. • Employed the bibliometric analysis to provide an overview of the evolving landscape of forest wind disturbance research. • Identified the United States as the primary contributor, highlighting significant national and institutional. • Highlighted key areas such as windthrow, tree motion, European forests, and long-term impacts of wind disturbances. • Outlined future research directions emphasizing hurricanes, forest structure, climate change, and typhoon impacts. [ABSTRACT FROM AUTHOR]

The article discusses map-based mortgage risk-rating techniques used by the U.S. Federal Housing Administration (FHA) in the 1930s. Particular focus is given to attitudes towards these techniques and policies within the FHA itself. It is suggested that while historians have criticized these techniques as discriminatory, FHA officials believed that they were developing an objective decision-making aid and computational tool. Particular focus is given to the development of the fields of real estate, social science, and urban science. Topics discussed include mortgage underwriting, appraiser Frederick Babcock, human ecology, and quantitative data.

The prevalence of wheat streak mosaic, caused by Wheat streak mosaic virus, was assessed using Landsat 5 Thematic Mapper (TM) images in two counties of the Texas Panhandle during the 2005-2006 and 20072008 crop years. In both crop years, wheat streak mosaic was widely distributed in the counties studied. Healthy and diseased wheat were separated on the images using the maximum likelihood classifier. The overall classification accuracies were between 89.47 and 99.07% for disease detection when compared to "ground truth" field observations. Omission errors (i.e., pixels incorrectly excluded from a particular class and assigned to other classes) varied between 0 and 12.50%. Commission errors (i.e., pixels incorrectly assigned to a particular class that actually belong to other classes) ranged from 0 to 23.8 1%. There were substantial differences between planted wheat acreage reported by the United States Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) and that detected by image analyses. However, harvested wheat acreage reported by USDANASS and that detected by image classifications were closely matched. These results indicate that the TM image can be used to accurately detect and quantify incidence of wheat streak mosaic over large areas. This method appears to be one of the best currently available for identification and mapping disease incidence over large and remote areas by offering a repeatable, inexpensive, and synoptic strategy during the course of a growing season. [ABSTRACT FROM AUTHOR]

Abstract: The land-cover thematic accuracy of NLCD 2001 was assessed from a probability-sample of 15,000pixels. Nationwide, NLCD 2001 overall Anderson Level II and Level I accuracies were 78.7% and 85.3%, respectively. By comparison, overall accuracies at Level II and Level I for the NLCD 1992 were 58% and 80%. Forest and cropland were two classes showing substantial improvements in accuracy in NLCD 2001 relative to NLCD 1992. NLCD 2001 forest and cropland user''s accuracies were 87% and 82%, respectively, compared to 80% and 43% for NLCD 1992. Accuracy results are reported for 10 geographic regions of the United States, with regional overall accuracies ranging from 68% to 86% for Level II and from 79% to 91% at Level I. Geographic variation in class-specific accuracy was strongly associated with the phenomenon that regionally more abundant land-cover classes had higher accuracy. Accuracy estimates based on several definitions of agreement are reported to provide an indication of the potential impact of reference data error on accuracy. Drawing on our experience from two NLCD national accuracy assessments, we discuss the use of designs incorporating auxiliary data to more seamlessly quantify reference data quality as a means to further advance thematic map accuracy assessment. [Copyright &y& Elsevier]

A comparison of land-cover maps, emphasizing row crop agriculture, resulting from independent classifications of coincident Landsat-5 Thematic Mapper (TM) and Resourcesat-1 Advanced Wide Field Sensor (AwiFs) imagery is presented. Three agriculturally intensive study areas within the midsection of the United States were analyzed during the peak of their growing season. For each region the data were collected within the same hour during August 2005. Identical decision tree style classification methodologies relying on ground truth from the June Agricultural Survey were applied to the image pairs for each of the three cases. The direct comparison of mapping accuracy results show, on average, the TM output to perform slightly better than that of the complimentary AWIFS. It is concluded AWIFS is a valid alternative to TM for classifying cultivated agriculture in areas with reasonably large field sizes. Furthermore, AWIFS offers increased benefits due to larger swath widths and shorter revisit frequencies. [ABSTRACT FROM AUTHOR]

The U.S. Landsat satellite series provide the longest dedicated land remote sensing data record with a balance between requirements for localized high spatial resolution studies and global monitoring. As with any other optical wavelength satellite sensor, cloud contamination greatly compromises image usability for land surface studies. Additionally, selective scene acquisition due to payload, ground station and mission cost constraints further reduces Landsat image availability. Since the 1999 launch of the Landsat Enhanced Thematic Mapper Plus (ETM+) a Long-term Acquisition Plan (LTAP) has been used to anticipate user requests with the goal of annually refreshing a global daytime archive of cloud-free ETM+ data. This research evaluates the availability of cloud-free Landsat ETM+ data over the conterminous U.S. and globally using 3 years of ETM+ cloud fraction metadata archived by the U.S. Landsat project. Landsat application requirements including obtaining at least one cloud-free observation in a year, a season, and two different seasons, or at least a pair of cloud-free observations occurring no more than 16, 32, 48, 64, and 80 days apart within a year and season are considered. Probabilistic analyses indicate that over the conterminous U.S., land applications requiring at least one cloud-free observation in a year, a season, two different seasons, or at least two cloud-free observations occurring within any period of the year, are on average largely unaffected by cloud cover, except for certain Winter applications and cloudy scenes near the U.S.–Canada border and the Great Lakes. Cloud becomes a constraint when at least two cloud-free observations are required from the same season over the conterminous U.S., especially when the separation between observations is restricted to short time intervals. Global applications requiring at least one cloud-free observation in a season, in two different seasons, and applications requiring at least two cloud-free observations in a year, are all severely affected by cloud and data availability constraints; and globally it is generally not practical to consider land applications that require at least two cloud-free observations in any season. Globally, only land applications requiring at least one cloud-free observation per year are largely unaffected by cloud cover and the reduced global ETM+ data availability. These results are specific only to the U.S. Landsat ETM+ archive; they suggest the need for an increased global Landsat acquisition rate for the current and future Landsat missions and/or the development of new approaches to mitigating cloud contamination in the U.S. global Landsat ETM+ archive. [Copyright &y& Elsevier]

Time series of snow covered area (SCA) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper (ETM+) were merged with a spatially explicit snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km2). A linear optimization scheme was used to derive SCA estimates that preserve the statistical moments of the higher spatial resolution (i.e. 30 m) ETM+ data and resolve the superior temporal signal (i.e. ∼daily) of the MODIS data. It was found that merging the two SCA products led to an 8% decrease and an 18% increase in the basinwide SWE in 2001 and 2002, respectively, compared to the SWE estimated from ETM+ only. Relative to SWE simulations using only ETM+ data, the hybrid SCA estimates reduced the mean absolute SWE error by 17 and 84% in 2001 and 2002, respectively; errors were determined using intensive snow survey data and two separate methods of scaling snow survey field measurements of SWE to the 1-km model pixel resolution. SWE bias for both years was reduced by 49% and skewness was reduced from −0.78 to 0.49. These results indicate that the hybrid SWE was closer to being an unbiased estimate of the measured SWE and errors were distributed more normally. The accuracy of the SCA estimates is likely dependent on the vegetation fraction. [Copyright &y& Elsevier]

The concern about water quality in inland water bodies such as lakes and reservoirs has been increasing. Owing to the complexity associated with field collection of water quality samples and subsequent laboratory analyses, scientists and researchers have employed remote sensing techniques for water quality information retrieval. Due to the limitations of linear regression methods, many researchers have employed the artificial neural network (ANN) technique to decorrelate satellite data in order to assess water quality. In this paper, we propose a method that establishes the output sensitivity toward changes in the individual input reflectance channels while modeling water quality from remote sensing data collected by Landsat thematic mapper (TM). From the sensitivity, a hypothesis about the importance of each band can be made and used as a guideline to select appropriate input variables (band combination) for ANN models based on the principle of parsimony for water quality retrieval. The approach is illustrated through a case study of Beaver Reservoir in Arkansas, USA. The results of the case study are highly promising and validate the input selection procedure outlined in this paper. The results indicate that this approach could significantly reduce the effort and computational time required to develop an ANN water quality model. [ABSTRACT FROM AUTHOR]

Daily for the United States, the National Oceanic and Atmospheric Administration (NOAA), Coast Watch Program produces multiple regional daytime images and sea surface temperature (SST) images derived from Advanced Very High Resolution Radiometry (AVHRR). Images have 1.1 km pixel resolution at nadir, <1 pixel systematic error (rotation, scale, distortion), but up to 9 pixels translation (offset) error. New fully-automated registration to a reference map corrects translation error to mean radial error <1 pixel suitable for full pixel resolution application in inshore waters and large estuaries. The approach converts AVHBR Channel 1 and 2 data to a land and water thematic image, finds the offset to maximize classification accuracy, and repositions the AVHHH and SST images. Registration was robust to cloud cover and missing data and was more reliable than manual registration for cloudy images. Developed for the southeast region of the U.S., the approach is transportable to other regions. [ABSTRACT FROM AUTHOR]

This paper examines characteristics of urban land-use and land-cover (LULC) classes using spectral mixture analysis (SMA), and develops a conceptual model for characterizing urban LULC patterns. A Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City was used in this research and a minimum noise fraction (MNF) transform was employed to convert the ETM+ image into principal components. Five image endmembers (shade, green vegetation, impervious surface, dry soil, and dark soil) were selected, and an unconstrained least-squares solution was used to un-mix the MNF components into fraction images. Different combina- tions of three or four endmembers were evaluated. The best fraction images were chosen to classify LULC classes based on a hybrid procedure that combined maximum-likelihood and decision-tree algorithms. The results indicate that the SMA- based approach significantly improved classification accuracy as compared to the maximum-likelihood classifier. The fraction images were found to be effective for characterizing the urban landscape patterns. [ABSTRACT FROM AUTHOR]

The MultiResolution Land Characteristics (MRLC) consortium sponsored production of the National Land Cover Data (NLCD) for the conterminous United States, using Landsat imagery collected on a target year of 1992 (1992 NLCD). Here we report the thematic accuracy of the 1992 NLCD for the six western mapping regions. Reference data were collected in each region for a probability sample of pixels stratified by map land-cover class. Results are reported for each of the six mapping regions with agreement defined as a match between the primary or alternate reference land-cover label and a mode class of the mapped 3×3 block of pixels centered on the sample pixel. Overall accuracy at Anderson Level II was low and variable across the regions, ranging from 38% for the Midwest to 70% for the Southwest. Overall accuracy at Anderson Level I was higher and more consistent across the regions, ranging from 82% to 85% for five of the six regions, but only 74% for the South-central region. [Copyright &y& Elsevier]

To conserve the Earth''s most extraordinary expressions of temperate biodiversity in the Pacific Northwest (PNW), USA, the mapping of late seral (old and mature) conifer forests plays a critical role. For this paper, we define old conifer forests as >150 years and mature conifer forest between 50 and 150 years. We offer a new Optimal Iterative Unsupervised Classification (OIUC) procedure for mapping late seral conifer forests over an eight-ecoregion area. The key steps of the OIUC classification were: (1) fully using the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) 15 m panchromatic channel merged with other 30 m bands 3 and 5 to make a pan-sharpened false color composite for high resolution image interpretation; (2) splitting the ETM+ scene by ecologically distinct areas, or ecoregions, to create relatively homologous images for classification; (3) using a procedure similar to cluster busting where multiple iterative manipulation of the ISODATA clusters was employed; and (4) edge matching of sub-scenes to form ecoregions, then later merged together to form a map for the entire PNW. Supporting data and information included ancillary spatial GIS data layers, aerial photos, Digital Ortho Quad images (DOQs), field investigations, and previously reported forest age results. Classification accuracy was assessed using 2081 stratified random locations on 105 individual DOQs covering the entire region. Approximately 4.7 million ha (∼19%) of the PNW was classified as old conifer forest (>150 years). Another 4.8 million ha (∼19%) was classified as mature conifer forest (50–150 years). Over 9.48 million ha (∼38%) of the PNW was late seral conifer forest. The extent of late seral forests (old and mature conifer cover classes) varied greatly between the eight ecoregions. The Central and Southern Cascades and Klamath–Siskiyou ecoregions contained the highest amount of late seral forest in the region. The results showed high accuracy of the late seral forest classification for the PNW with an overall accuracy of 90.72% and KAPPA test K value 0.8534. Producer''s (Omission) accuracy for old and mature forests were 91.36% and 80.40%, User''s (Commission) accuracy were 89.42% and 80.59%, respectively. Accuracy levels differed for the different ecoregions examined. In general, mature conifer forests exhibited higher levels of confusion than did old conifer forests, due to the spectral influences of high-density young conifer stands and terrain shadow effects. The results fill an important data gap needed for ongoing conservation planning purposes throughout the region. We found that for relatively large geographic areas the OIUC method is an efficient and cost-effective alternative that yields high quality results. [Copyright &y& Elsevier]

In response to Hurricane Floyd, the Tar River crested at a record height of 4.30 m above the flood stage at the river gauge station of Greenville (North Carolina, USA) on 21 September 1999. This resulted in a massive flooding in the area. To delineate the maximum flood extent, an area of 238.4 km2 along the Tar/Pamlico River, North Carolina, and within the overlapped area of Landsat 7 Thematic Mapper (TM) path 14/row 35 and path 15/row 35 scenes was studied. Three TM datasets of 28 July 1999 (path 15/row 35), 23 September 1999 (path 14/row 35) and 30 September 1999 (path 15/row 35) were analysed as pre-flood data, near peak data, and nine days after the peak data, respectively. The 23 and 30 September flood extent maps were derived by change detection and then verified by 85 nonflooded and flooded sites within the study area. The overall accuracies at the sites were between 82.5–99.3% on both inundation extent maps. Although the recorded river surface level fell 2.62 m from 23 to 30 September at the river gauge station of Greenville, comparison of the two flood extent maps on a pixel-by-pixel basis showed an agreement of 90.7% in terms of regular river channels and waterbodies, flooded areas and nonflooded areas. The 30 September map captured over 90% of the flood extent as identified on the 23 September map. These results suggest that it is possible to use remotely sensed data acquired days after a river's crest to capture most of the maximum extent of a flood occurring on a coastal floodplain, and should somewhat reduce the requirement to have concurrently remotely sensed data in mapping a flood extent on a coastal floodplain. [ABSTRACT FROM AUTHOR]

The accuracy of the 1992 National Land-Cover Data (NLCD) map is assessed via a probability sampling design incorporating three levels of stratification and two stages of selection. Agreement between the map and reference land-cover labels is defined as a match between the primary or alternate reference label determined for a sample pixel and a mode class of the mapped 3×3 block of pixels centered on the sample pixel. Results are reported for each of the four regions comprising the eastern United States for both Anderson Level I and II classifications. Overall accuracies for Levels I and II are 80% and 46% for New England, 82% and 62% for New York/New Jersey (NY/NJ), 70% and 43% for the Mid-Atlantic, and 83% and 66% for the Southeast. [Copyright &y& Elsevier]

Abstract: We assembled a time series of 20 Landsat thematic mapper images from 1982 to 1996 for Key Largo, Florida, to ascertain whether satellite imagery can detect temporal changes in coral reef communities. Selected reef and control areas were examined for changes in brightness, spectral reflectance, band ratios, spatial texture, and temporal texture ( pixel-to-pixel change over time). We compared the data to known changes in the reef ecosystem of Carysfort Reef and terrestrial sample sites. Changes in image brightness and spectral-band ratios were suggestive of shifts from coral- to algal-dominated community structure, but the trends were not statistically significant. The spatial heterogeneity of the reef community decreased in the early 1980s at scales consistent with known ecological changes to the coral community on Carysfort Reef. An analysis of pixel-scale variation through time, termed temporal texture, revealed that the shallow reef areas are the most variable in regions of the reef that have experienced significant ecological decline. Thus, the process of reef degradation, which alters both the spatial patterning and variability of pixel brightness, can be identified in unclassified thematic mapper images. [ABSTRACT FROM AUTHOR]

Landsat Thematic Mapper (TM) and SPOT panchromatic data were analysed to determine the lateral variation of the sedimentary and volcanic formations within and surrounding the Petrified Forest National Park in northeastern Arizona. Detailed geological mapping of portions of Petrified Forest National Park allowed for a correlation between TM images and the geological formations that aided in identifying these formations in unmapped areas outside and inside the park. A variety of image processing techniques, including band ratios, RGB composite images, selective principal component analysis and Brovey transformations were used in distinguishing these geological formations. Newly determined regions of the Lower and Upper parts of the Petrified Forest Member of the Chinle Formation were identified south of Pilot Rock and east of the Blue Mesa region. A final spectral reflectance-based geological map created by a maximum likelihood supervised classification algorithm provides a first-order approximation to the location of numerous sedimentary and volcanic units within and surrounding Petrified Forest National Park. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop and evaluate a multi-spectral vegetation index for quantifying relative amounts of hardwood and conifer cover from Thematic Mapper (TM) imagery. We focused on closed canopy forests in the Oregon Coast Range, where hardwood, conifer, and mixed stand conditions are prevalent. An approach based on the Gramm-Schmidt orthogonalization process was used to derive three different hardwood-conifer mixture indices (HCMIs). Using correlation and regression analyses, the capacity of these indices to predict closed canopy hardwood percentage was compared with three other groups of spectral variables: (1) the untransformed TM reflectance bands, (2) the Tasseled Cap indices of brightness, greenness, and wetness, and (3) the first three principal components of closed canopy forest reflectance. Results show that while similar amounts of information were explained by HCMI, TM band, Tasseled Cap, and principal component models, only predictions derived from the HCMI1 and HCMI2 variables were unbiased with respect to topographic effects. [ABSTRACT FROM AUTHOR]

Focuses on a study which examined the effectiveness of using the Thematic Mapper-based logistic regression models to distinguish conventional and conservation tillage practices in the Lower Minnesota River watershed. Materials and methods; Results and discussion.

Projection pursuit (PP) and principal component analysis (PCA) projections derived from Landsat Thematic Mapper (TM) imagery of central Colorado were compared. While PCA is a simple subset of the general class of PP algorithms, it cannot distinguish Gaussian from non-Gaussian distributions, since it maximizes projected variance. PP algorithms, which maximize higher-order statistics, can be used to find skew or multi-modal projections in order to reveal underlying class structure. These data projections have greater fidelity to underlying land-cover distributions. On sequestered test data, PP projections improved separation of individual categories from a few percent to as much as 24%. PP performance exceeded that of PCA for all but one of the 14 land-cover categories. [ABSTRACT FROM AUTHOR]

Traditionally, the classes in thematic maps have been treated as crisp sets, using classical set theory. In this formulation, map classes are assumed to be mutually exclusive and exhaustive. This approach limits the ability of thematic maps to represent the continuum of variation found in most landscapes. Substitution of fuzzy sets allows more flexibility for treatment of map classes in the areas of accuracy assessment and area estimation. Accuracy assessment methods based on fuzzy sets allow consideration of the magnitude of errors and assessment of the frequency of ambiguity in map classes. An example of an accuracy assessment from a vegetation map of the Plumas National Forest illustrates the implementation of these methods. Area estimation based on fuzzy sets and using accuracy assessment data allows estimation of the area of classes as a function of levels of class membership. The fuzzy area estimation methods are an extension of previous methods presented by Card (1982). One interesting result is that the sum of the areas of the classes in a map need not be unity. This approach allows a wider range of queries within a GIS. [ABSTRACT FROM AUTHOR]

Fire disturbance in boreal forests can release carbon to the atmosphere stored in both the aboveground vegetation and the organic soil layer. Estimating pyrogenic emissions of carbon released during biomass burning in these forests is useful for understanding and estimating global carbon budgets. In this work, we have developed a method to estimate carbon efflux for the burned black spruce in an Alaskan forest by combining information derived from Landsat Thematic Mapper (TM) data and field measurements. We have used the spatial and spectral information of TM data to identify and measure two important factors: pre-burn black spruce density and burn severity. Field measurements provided estimates of aboveground and ground layer carbon per unit area for the pre-burn Landsat spectral classes, and percentage of carbon consumed for the post-burn Landsat spectral classes. Carbon release estimates for the burned black spruce were computed using field data and the co-occurrence of the pre-burn and post-burn spectral classes. The estimated carbon released was 39.9tha-1, which is 57% greater than an estimate computed using AVHRR data and estimates of pre-burn biomass and carbon fractions consumed that were not site specific or spatially varying. We conclude that the spectral bands and spatial resolution of Landsat TM data provide the potential for improved estimates of pyrogenic carbon efflux relative to the coarser spectral and spatial resolution of other multispectral sensors. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing or fracking is becoming common throughout the United States as the demand for oil increases. However, fracking is a controversial and hot topic due to potential environmental and human health concerns. One concern is the impact that fracking sites have on motor vehicle crashes due to the increased traffic and population in local communities. This project aimed to determine if fracking sites are associated with an increase in motor vehicle crashes in Texas. Due to increased traffi c requirements to build and maintain fracking sites, the project hypothesized that counties with increased fracking site growth would also experience an increase in the reported number of motor vehicle crashes. A thematic map was produced with publicly available data to outline the location and changes in motor vehicle crashes compared to the fracking site growth by county in Texas (n=254). The thematic map showed a spatial association between fracking growth and growth of motor vehicle accidents; this association has a correlation coefficient of r=0.34. This project found areas that had increasing numbers of fracking sites, typically also had an increased number in the total number of motor vehicle crashes. [ABSTRACT FROM AUTHOR]

Alluvial geomorphic surfaces in the southern Whipple Mountains (south-eastern California) were mapped using Landsat Thematic Mapper (TM) data. Surfaces can readily be classified as young, intermediate or old in terms of stages of development. Spectral and textural information contained in these data are interpretable in terms of source rock composition, degree of desert pavement formation and drainage patterns, which are among the most important criteria used in field-based classification. Thus, classification and mapping of geomorphic surfaces based on image analysis is directly comparable to maps produced from field-based observations, potentially providing information about climate and neotectonics. [ABSTRACT FROM AUTHOR]

Double sampling was used to provide a cost efficient estimate of the accuracy of a Landsat Thematic Mapper (TM) classification map of a scene located in the Rocky Mountain National Park, Colorado. In the first phase, 200 sample points were randomly selected to assess the accuracy between Landsat TM data and aerial photography. The overall accuracy and Kappa statistic were 49.5 per cent and 32.5 per cent, respectively. In the second phase, 25 sample points identified in the first phase were selected using stratified random sampling and located in the field. This information was used to correct for misclassification errors associated with the first phase samples. The overall accuracy and Kappa statistic increased to 59.6 per cent and 45.6 per cent, respectively. [ABSTRACT FROM AUTHOR]

Many organizations require accurate intermediate-scale landcover information for many applications, including modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land-use planning, and policy development. While many techniques have been successfully used to classify land cover in relatively small regions, there are substantial obstacles in applying these methods to large, multiscene regions. The purpose of this study was to generate and evaluate u large region land-cover classification product using u multiple-layer land-characteristics database approach. To derive land-cover information, mosaicked Landsat thematic mapper (TM) scenes were analyzed in conjunction with digital elevation data (and derived slope, aspect, and shaded relief), population census information, Defense Meteorological Satellite Program city lights data, prior land-use and landcover data, digital line graph data, and National Wetlands Inventory data. Both leaf-on and leaf-off TM data sets were analyzed. The study urea was U.S. Federal Region III, which includes the states of Pennsylvania, Virginia, Maryland, Delaware, and West Virginia. The general procedure involved (1) generating mosaics of multiple scenes of leaves-on TM data using histogram equalization methods; (2) clustering mosaics into 100 spectral classes using unsupervised classification; (3) interpreting and labeling spectral classes into approximately 15 landcover categories (analogous to Anderson Level 1 and 2 classes) using aerial photographs; (4) developing decision-making rules and models using from one to several ancillary data layers to resolve confusion in spectral classes that represented two or more targeted land-cover categories; and (5) incorporating data from other sources (for example, leaf-off TM data and National Wetlands Inventory data) to yield a final land-cover product. Although standard accuracy assessments were not done, a series of consistency checks using available... [ABSTRACT FROM AUTHOR]

Line intercept sampling was used to determine if boundaries between desert scrub vegetation stands corresponded with boundaries between regions in an image derived from a segmentation algorithm applied to Thematic Mapper (TM) data for the Anza-Borrego Desert State Park, California. An image segmentation algorithm developed by Woodcock and Harward (1992) was applied to images comprising TM bands 3, 4, and 5, from April 1987, principal components images bused on April 1987 and June 1990 imagery, and each with texture added. The Global Positioning System (GPS) was used to determine coordinates of both physiographic (land-form) and vegetation boundaries in the field us they intersected line transects. These boundary locutions were then registered to the segmented images. Image region boundaries that fell within ε tolerances (spatial error bounds) of surveyed boundaries were considered accurate. Image region boundaries showed less than 10 percent omission error but about 50 percent commission error when compared with the true locations of vegetation and physiographic boundaries. The use of image principal components and texture in the segmentations did not produce anticipated increases in the correspondence between field-mapped and image-region vegetation boundaries, although there is some suggestion that multidate principal components may be sensitive to vegetation boundaries, and texture to physiographic boundaries. [ABSTRACT FROM AUTHOR]

A map that shows the availability of physics education in U.S. high schools in 2013 is presented.