Marsh Classes Research Articles

Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine

Wetlands provide many benefits, such as water storage, flood control, transformation and retention of chemicals, and habitat for many species of plants and animals. The ongoing degradation of wetlands in the Great Lakes basin has been caused by a number of factors, including climate change, urbanization, and agriculture. Mapping and monitoring wetlands across such large spatial and temporal scales have proved challenging; however, recent advancements in the accessibility and processing efficiency of remotely sensed imagery have facilitated these applications. In this study, the historical Landsat archive was first employed in Google Earth Engine (GEE) to classify wetlands (i.e., Bog, Fen, Swamp, Marsh) and non-wetlands (i.e., Open Water, Barren, Forest, Grassland/Shrubland, Cropland) throughout the entire Great Lakes basin over the past four decades. To this end, an object-based supervised Random Forest (RF) model was developed. All of the produced wetland maps had overall accuracies exceeding 84%, indicating the high capability of the developed classification model for wetland mapping. Changes in wetlands were subsequently assessed for 17 time intervals. It was observed that approximately 16% of the study area has changed since 1984, with the highest increase occurring in the Cropland class and the highest decrease occurring in the Forest and Marsh classes. Forest mostly transitioned to Fen, but was also observed to transition to Cropland, Marsh, and Swamp. A considerable amount of the Marsh class was also converted into Cropland.

Evaluation of BioPlex 2200 tTG-IgA Diagnostic Performance for Serology-Based Diagnosis of Celiac Disease.

Forgoing biopsy for the diagnosis of celiac disease in children with tissue transglutaminase immunoglobulin A (tTG-IgA) levels greater than or equal to 10 times the upper limit of normal (≥10×ULN) has been advocated by the European Society of Paediatric Gastroenterology, Hepatology and Nutrition. Our retrospective study tested the specificity and positive predictive value (PPV) of the BioPlex 2200 assay (Bio-Rad Laboratories) in diagnosing celiac disease at the ≥10×ULN tTG-IgA threshold, which is ≥150 U/mL (negative <15 U/mL). We used the tTG-IgA and duodenal biopsy results within 6 months following tTG-IgA measurements from 542 patients who had any number of duodenal biopsy fragments, of whom 165 patients had 5 or more tissue fragments. Sensitivity and specificity of the test were calculated using histology as the gold standard for Marsh class 2 and above. For histopathologic findings in the duodenum with Marsh 2 and higher, the specificity and PPV of the BioPlex 2200 at ≥10×ULN tTG-IgA were 99.5% and 95.4% using all biopsies and 97.9% and 94.9% for biopsies with 5 or more tissue fragments. Should clinical considerations preclude endoscopy, the BioPlex 2200 assay at ≥10×ULN TTG-IgA could be considered highly suggestive of disease.

Improvements in multi-metric index development using a whole-index approach

Abstract Multi-metric indices (MMIs) are the most common tool used to assess ecological integrity. Traditionally, only the metrics with the highest correlation to disturbance are incorporated. The rationale being that combining the metrics with the strongest individual relationships to disturbance should yield an MMI with the strongest possible relationship to disturbance. An alternative to this traditional metric-based method was proposed, which we term the index-based approach. Under this method, thousands of MMIs are generated through the random selection of candidate metrics and then a model competition framework is adopted to select the best performing MMI. An index-based approach may prove more sensitive to disturbance because even metrics weakly related to disturbance on their own may nonetheless contribute unique and valuable information to the whole MMI. Our goal was to compare the traditional metric-based method with the novel index-based approach to MMI development and validation to ascertain which generates a superior MMI for vegetation-based biomonitoring in prairie pothole wetlands. We sampled vegetation from 72 marshes from across prairie Alberta between 2014 and 2015. These ranged from temporarily- to permanently-ponded marsh classes and spanned a gradient in anthropogenic disturbance. Vegetation surveys yielded 732 initial metrics associated with abundance, richness and traits, 88 of which were reasonably sensitive to disturbance (p

Типология и синтаксономический состав территориальных единиц растительности: новый подход на примере изучения арктических маршей

Типология и синтаксономический состав территориальных единиц растительности: новый подход на примере изучения арктических маршей

Does National Wetland Inventory class consistently identify vegetation and edaphic differences in Oregon tidal wetlands?

Accurately mapping, modeling, and managing the diversity of wetlands present in estuaries often relies on habitat classification systems that consistently identify differences in biotic structure or other ecosystem characteristics between classes. We used field data from four Oregon estuaries to test for differences in vegetation structure and edaphic characteristics among three tidal emergent marsh classes derived from National Wetlands Inventory (NWI) data: low marsh, high marsh, and palustrine tidal marsh. Independently of NWI class, we also evaluated the number and types of plant assemblages present and how edaphic variables, non-native plant cover, and plant species richness varied among them. Pore water salinity varied most strongly across marsh classes, with sediment carbon and nitrogen content, grain size and marsh surface elevation showing smaller differences. Cover of common vascular plant species differed between marsh classes and overall vegetation composition was somewhat distinct among marsh types. High marsh had the largest species pools. However, plot-level plant diversity was similar among marsh classes. Non-native species cover was highest in palustrine and high marshes. The marshes in the study contained a large number of plant assemblages with most occurring across more than one marsh class. The more common assemblages occurred along a continuum of tidal elevation, soil salinity, and edaphic characteristics, with varying plant richness and non-native cover. Our data suggest that NWI classes are useful for differentiating several general features of Oregon tidal marsh structure, but that more detailed information on plant assemblages found within those wetland classes would allow more precise characterization of additional wetland features such as edaphic conditions and plant diversity.

Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

Wetlands are important ecosystems around the world, although they are degraded due both to anthropogenic and natural process. Newfoundland is among the richest Canadian province in terms of different wetland classes. Herbaceous wetlands cover extensive areas of the Avalon Peninsula, which are the habitat of a number of animal and plant species. In this study, a novel hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a sub-region located in the north eastern portion of the Avalon Peninsula. Particularly, multi-polarization and multi-frequency SAR data, including X-band TerraSAR-X single polarized (HH), L-band ALOS-2 dual polarized (HH/HV), and C-band RADARSAT-2 fully polarized images, were applied in different classification levels. First, a SAR backscatter analysis of different land cover types was performed by training data and used in Level-I classification to separate water from non-water classes. This was followed by Level-II classification, wherein the water class was further divided into shallow- and deep-water classes, and the non-water class was partitioned into herbaceous and non-herbaceous classes. In Level-III classification, the herbaceous class was further divided into bog, fen, and marsh classes, while the non-herbaceous class was subsequently partitioned into urban, upland, and swamp classes. In Level-II and -III classifications, different polarimetric decomposition approaches, including Cloude-Pottier, Freeman-Durden, Yamaguchi decompositions, and Kennaugh matrix elements were extracted to aid the RF classifier. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. The importance of input features was also determined using the variable importance obtained by RF. It was found that the Kennaugh matrix elements, Yamaguchi, and Freeman-Durden decompositions were the most important parameters for wetland classification in this study. Using this new hierarchical RF classification approach, an overall accuracy of up to 94% was obtained for classifying different land cover types in the study area.

Clinicopathological Study of Seronegative Celiac Disease in Adults in Pakistan: A Pilot Study.

BACKGROUND Celiac disease (CD) is usually missed, if the serology is negative. We aimed to evaluate the clinicopathological characteristics of seronegative CD (SNCD) and its response to gluten-free diet (GFD) in adult patients. METHODS This observational study was carried out at the Department of Hepatogastroenterology, Sindh Institute of Urology and Transplantation, Karachi, Pakistan from 2009 to 2015. All consecutive adult patients (≥17 years) with features of marked villous atrophy (Marsh class≥III) on duodenal biopsy, negative tissue transglutaminase IgA and IgG antibodies (anti-tTg IgA and IgG) and human leukocyte antigen (HLA) DQ2 or DQ8 serotypes were studied. Clinical characteristics, laboratory parameters, and response to GFD were analyzed by SPSS software version 20. Median and interquartile range (IQR) were used for summarizing quantitative data. Frequency (percentages) was used for qualitative data. RESULTS A total of 12 patients with median age of 31.5 years (IQR: 19.75-46.75 years), of whom five (41.6%) were men were studied. The presenting complaints were: weight loss in 11 (91.6%) and abdominal pain in 9 (75%) patients. Anemia was observed in 10 (83.3%) patients with median hemoglobin of 9.5 g/dL (IQR: 6.3-13.25 g/dL). Median alanine transaminase (ALT) was 21 U/L (IQR: 13–27 U/L) and median albumin was 3 g/dL (IQR: 2.4-3.6 g/dL). Anti-tTg IgA and IgG were negative in all patients. HLA DQ serotyping showed homozygous DQ2 and DQ8 in four and one patients, respectively; while heterozygous DQ2 and DQ8 in five and two patients, respectively. All patients were advised to receive GFD. Nine (75%) patients showed complete clinical response. Two patients were non-compliant and one with non-alcoholic fatty liver disease (NAFLD)-related cirrhosis had partial clinical response. Out of the nine responders, two patients showed response within 6 months while the remaining showed improvement over a year period. CONCLUSION The diagnosis of SNCD is rewarding as it responds favorably to GFD in most patients. HLA serology provides an important tool for diagnosis of this entity.

Diagnostic Yield of Isolated Deamidated Gliadin Peptide Antibody Elevation for Celiac Disease.

Serologic testing for celiac disease includes tissue transglutaminase and endomysial antibodies. In addition to these tools, assays for deamidated gliadin peptide antibodies have been shown to have sensitivity and specificity that are comparable to tissue transglutaminase testing, and are increasingly being used for celiac disease testing. The goal of this study is to evaluate the utility of deamidated gliadin peptide (DGP) testing in the setting of a negative tissue transglutaminase (TTG) IgA test. We reviewed the records of all patients seen at two U.S. celiac disease referral centers and identified those who had an elevated DGP IgA and/or IgG in the setting of a negative TTG IgA. Of these patients, those who underwent duodenal biopsy while on a gluten-containing diet were included. Patients with prior biopsy-proven celiac disease or prior TTG IgA positivity were excluded. The results of the biopsy were used as the gold standard for celiac disease diagnosis, and patients with villous atrophy (Marsh class 3) on duodenal biopsy were considered to have celiac disease. Between the two institutions, 84 patients were identified with negative TTG IgA and positive DGP IgA or IgG who also had duodenal biopsies performed while maintaining a gluten-containing diet. Of these patients, 13 patients (15.5%; 95% CI 8.5-25.0%) were found to have celiac disease on duodenal biopsy. DGP antibody testing can identify cases of celiac disease in TTG-negative individuals, although the low positive predictive value suggests that the yield may be low.

Plasma Inflammatory Cytokines as Predictor Markers for Severity of Histological Variations of Intestine among Celiac Patients

Background: Celiac is a chronic hereditary intestinal disorder caused by autoimmune stimulation and injury of intestinal mucosa and different studies have reported an increase of inflammatory cytokines production in these patients. This study aimed to investigate the serum level of inflammatory cytokines and their correlations with the severity of histological variations among celiac patients. Material and methods: By a cross sectional study all celiac patients attending to gastrointestinal clinics in Ilam city during Oct. 2011- MAR. 2013 were investigated. Demographic data was collected using a validate questionnaire and patient's interview and each patient was taken a blood sample for laboratory investigation of tTG-IgA antibody via ELISA test and the serum level of hs- CRP, IL-6 and TNF-a was measured. All patients underwent endoscopic evaluation and biopsy samples were evaluated according to Marsh classification and correlation between severity of histological variations and serum levels of inflammatory cytokines was measured. Results: Totally 274 patients including 90 male and 184 female with a mean age of 28.8±14.4 years were evaluated. The mean serum levels of hs-CRP, IL-6 and TNF-a were 7.8±3.1 mg/l, 2.6±0.9 pg/ml and 6.9±4.6 pg/ml respectively (p=0.599, p=0.629 and p= 0.651). Hs-CRP showed a significant relationship with the severity of mucosal damage upon class I of Marsh classification (p=0.01); however, IL-6 showed a significant level among patients with Marsh I, II and IIIC (p=0.000, p=0.02 and p=0.000) but not in those with Marsh class IIIA and IIIB. Conclusion: There was a positive relationship between mean serum levels of IL-6 and TNF-a with increasing the Marsh classification from class I to class IIIC and the highest rate of IL-6 and TNF-a was shown in Marsh IIIC but the serum level of hs-CRP was not increased with Marsh class increasing. It was concluded that as the Marsh classification increased from class I to class IIIC, and/or the severity of pathologic variations was increased, the mean serum levels of inflammatory markers were increased too.

An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery

Mapping wetlands across both natural and human-altered landscapes is important for the management of these ecosystems. Though they are considered important landscape elements providing both ecological and socioeconomic benefits, accurate wetland inventories do not exist in many areas. In this study, a multi-scale geographic object-based image analysis (GEOBIA) approach was employed to segment three high spatial resolution images acquired over landscapes of varying heterogeneity due to human-disturbance to determine the robustness of this method to changing scene variability. Multispectral layers, a digital elevation layer, normalized-difference vegetation index (NDVI) layer, and a first-order texture layer were used to segment images across three segmentation scales with a focus on accurate delineation of wetland boundaries and wetland components. Each ancillary input layer contributed to improving segmentation at different scales. Wetlands were classified using a nearest neighbor approach across a relatively undisturbed park site and an agricultural site using GeoEye1 imagery, and an urban site using WorldView2 data. Successful wetland classification was achieved across all study sites with an accuracy above 80%, though results suggest that overall a higher degree of landscape heterogeneity may negatively affect both segmentation and classification. The agricultural site suffered from the greatest amount of over and under segmentation, and lowest map accuracy (kappa: 0.78) which was partially attributed to confusion among a greater proportion of mixed vegetated classes from both wetlands and uplands. Accuracy of individual wetland classes based on the Canadian Wetland Classification system varied between each site, with kappa values ranging from 0.64 for the swamp class and 0.89 for the marsh class. This research developed a unique approach to mapping wetlands of various degrees of disturbance using GEOBIA, which can be applied to study other wetlands of similar settings.

Schooling in Capitalism: Navigating the Bleak Pathways of Structural Fate

AbstractIn this review essay Kevin Murray and Dan Liston examine three texts in what this symposium has deemed the recent resurgence in neo‐Marxist accounts of schooling: David Blacker's The Falling Rate of Learning and the Neoliberal Endgame, Mike Cole's Marxism and Educational Theory, and John Marsh's Class Dismissed. Murray and Liston argue that Blacker, Cole, and Marsh provide a much‐needed structural delineation of schooling in capitalist society. All three works have substantial merit and are in need of minor adjustments. Blacker's vision is telling and chilling but may need some fine‐tuning; Cole's defense of Marxism is commendable but tends toward the disembodied abstract; and Marsh's text capably highlights educationists' blinders but seems to miss elements of a critical structural account. While recognizing real limitations on transformative education under capitalism, Murray and Liston nevertheless elaborate a variation of the radical humanist project. It is their skeletal attempt at an educationally and morally defensible reparative theory.

Mapping the Contours of Neoliberal Educational Restructuring: A Review of Recent Neo‐Marxist Studies of Education and Racial Capitalist Considerations

AbstractIn this article Clayton Pierce reviews three books representative of the recent neo‐Marxist literature on education: David Blacker's The Falling Rate of Learning and the Neoliberal Endgame, John Marsh's Class Dismissed: Why We Cannot Teach or Learn Our Way out of Inequality, and Pauline Lipman's The New Political Economy of Urban Education: Neoliberalism, Race, and the Right to the City. His analysis of these books focuses on how each author remains consistent or advances traditional Marxist interpretations of the role of education in capitalist society. In addition, he puts the arguments of each author into conversation with W. E. B. Du Bois's analysis of schooling in a racial capitalist society — what he called caste education — as a way to generate discussion around some of the inherent limitations of Marxist studies of education. Here, Pierce is particularly concerned with the ability of neo‐Marxist analyses of the neoliberal restructuring of education to articulate how white supremacy is preserved even in revolutionary critiques of capitalist schooling.

Patterns of Distribution and Environmental Correlates of Macroalgal Assemblages and Sediment Chlorophyll A in Oregon Tidal Wetlands.

Algae have important functional roles in estuarine wetlands. We quantified differences in macroalgal abundance, composition and diversity, and sediment chl a and pheophytin a (pheo a) among three National Wetlands Inventory (NWI) emergent marsh classes in four Oregon estuaries spanning a range of riverine to marine dominance. We also assessed the strength of macroalgal-vascular plant associations and the degree to which environmental variables correlated with algal community metrics in marsh and woody wetlands. The frequency of occurrence of most macroalgal genera, total benthic macroalgal cover, macroalgal diversity, and sediment chl a content were several times higher in low emergent marsh than in high marsh or palustrine tidal marsh. Conversely, pheo a: chl a ratios were highest in high and palustrine marsh. Attached macroalgae (Fucus and Vaucheria) were strongly associated with plants common at lower tidal elevations such as Sarcocornia perennis and Jaumea carnosa; Ulva (an unattached alga) was not strongly associated with any common low marsh plants. In structural equation models, intertidal elevation was the most influential predictor of macroalgal cover and richness and chl a; light availability and soil salinity played secondary roles. Although common taxa such as Ulva spp. occurred across a broad range of salinities, wetlands with oligohaline soils (salinity<5) had the lowest macroalgal diversity and lower sediment chl a. These types of baseline data on algal distributions are critical for evaluating the structural and functional impacts of future changes to coastal estuaries including sea-level rise (SLR), altered salinity dynamics, and habitat modification.

Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects

This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes in a salt marsh. Three classification issues were investigated in the context of segmentation scale: (1) a comparison of single- and multi-scale GEOBIA using spectral bands, (2) the relative benefit of incorporating texture derived from the grey-level co-occurrence matrix (GLCM) in classifying the salt marsh features in single- and multi-scale GEOBIA and (3) the effect of quantization level of GLCM texture in the context of multi-scale GEOBIA. The single-scale GEOBIA experiments indicated that the optimal segmentation was both class and scale dependent. Therefore, the single-scale approach produced an only moderately accurate classification for all marsh classes. A multi-scale approach, however, facilitated the use of multiple scales that allowed the delineation of individual classes with increased between-class and reduced within-class spectral variation. With only spectral bands used, the multi-scale approach outperformed the single-scale GEOBIA with an overall accuracy of 82% vs. 76% (Kappa of 0.71 vs. 0.62). The study demonstrates the potential importance of ancillary data, GLCM texture, to compensate for limited between-class spectral discrimination. For example, gains in classification accuracies ranged from 3% to 12% when the GLCM mean texture was included in the multi-scale GEOBIA. The multi-scale classification overall accuracy varied with quantization level of the GLCM texture matrix. A quantization level of 2 reduced misclassifications of channel and bare mud and generated a statistically higher classification than higher quantization levels. Overall, the multi-scale GEOBIA produced the highest classification accuracy. The multi-scale GEOBIA is expected to be a useful methodology for creating a seamless spatial database of marsh landscape features to be used for further geographic information system (GIS) analyses.

Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data

Large area land-cover mapping involving large volumes of data is becoming more common in remote sensing applications. Thus, there is a pressing need for increased automation in the land-cover mapping process. The main objective of this research was to compare the performance of three machine learning algorithms (MLAS) for mapping wetlands in the Sanjiang Plain combined Landsat TM imagery with ancillary geographical data. Three MLAS included random forest (RF), classification and regression tree (CART), and maximum likelihood classification (MLC). Comparisons were based on several criteria: overall accuracy, sensitivity to data set size, and noise. Our results indicated that first, the random forest and CART approach can achieve substantial improvements in accuracy over the traditional MLC method. Random forest produced the highest overall accuracy (91.3 percent) the kappa coefficient 0.8943, with marsh class accuracies ranging from 77.4 percent to 90.0 percent. Secondly, the random forest method was least sensitive to reduction in training sample size, and it was most resistant to the presence of noise compared to CART and MLC. The comparison between three MLAS revealed that the random forest approach was most resistant to training data deficiencies while improved land-cover map accuracy in marsh area.

Historical vegetation reconstruction of a degraded sub-arctic coastal marsh using Landsat imagery and ancillary data

In recent decades, increasing numbers of Lesser Snow Geese have severely damaged the sub-arctic coastal marshes on which they feed in summer. Because the geese are an economic, aesthetic and visible species, plans for population control via increased harvest are controversial, and accurate vegetation maps are needed both for management decision-making and for public education. Documentation of the extent, pattern, and timing of habitat damage over large spatial and temporal scales requires reconstruction of the previously-existing vegetation. Satellite imagery is increasingly used for mapping and monitoring vegetation change over large areas, and archives of Landsat imagery provide consistent, spatially-extensive historical data. Previously-existing vegetation at La Pérouse Bay, Manitoba, was mapped using unsupervised classification of a 1984 Landsat TM image and ancillary information. Using a commercial clustering algorithm, sixty-three spectral clusters were produced and subsequently identified using a combination of historical and current aerial photos, current vegetation data, and knowledge of local vegetation trajectories in time. These initial clusters were then aggregated into 14 classes, resulting in a detailed map of vegetation as it was before degradation by the geese. The map was compared to earlier results documenting change in the study area. Vegetation degradation between 1973 and 1993 occurred earliest and most extensively in the salt marsh and other classes containing salt marsh vegetation. Map accuracy was assessed with current vegetation data collected independently at a number of precisely located points.

Classifying coastal resources by integrating optical and radar imagery and color infrared photography

A progressive classification of a marsh and forest system using Landsat Thematic Mapper (TM), color infrared (CIR) photograph, and ERS-1 synthetic aperture radar (SAR) data improved classification accuracy when compared to classification using solely TM reflective band data. The classification resulted in a detailed identification of differences within a nearly monotypic black needlerush marsh. Accuracy percentages of these classes were surprisingly high given the complexities of classification. The detailed classification resulted in a more accurate portrayal of the marsh transgressive sequence than was obtainable with TM data alone. Individual sensor contribution to the improved classification was compared to that using only the six reflective TM bands. Individually, the green reflective CIR and SAR data identified broad categories of water, marsh, and forest. In combination with TM, SAR and the green CIR band each improved overall accuracy by about 3% and 15% respectively. The SAR data improved the TM classification accuracy mostly in the marsh classes. The green CIR data also improved the marsh classification accuracy and accuracies in some water classes. The final combination of all sensor data improved almost all class accuracies from 2% to 70% with an overall improvement of about 20% over TM data alone. Not only was the identification of vegetation types improved, but the spatial detail of the classification approached 10 m in some areas.