Field spectroscopy data from non-arable, grass-dominated objects in an intensively used agricultural landscape in East Anglia, UK.

Remote sensing of vegetation provides important information for ecological applications and environmental assessments. The association between vegetation composition and structure with its spectral signal can most fully be assessed with hyperspectral data. Particularly field spectroscopy data can improve such understanding as the spectral data can be linked with the vegetation under consideration without the geographic registration uncertainties of aerial or satellite imagery. The data provided in this article contain field spectroscopy measurements from non-arable, grass-dominated objects on four farms in an intensively used agricultural landscape in the South-East of the UK. Detailed data on the plant species composition of the objects are also supplied with this article to support further analysis. Reuse potential includes linking the vegetation data with the spectral response using spectral unmixing techniques to map certain plant species or including the field spectroscopy data in a larger study with data from a wider area. This data article is related to the paper 'Classifying grass-dominated habitats from remotely sensed data: the influence of spectral resolution, acquisition time and the vegetation classification system on accuracy and thematic resolution' (Bradter et al., 2019) in which the ability to classify the recorded vegetation from the field spectroscopy data was analysed.

Classifying grass-dominated habitats from remotely sensed data: The influence of spectral resolution, acquisition time and the vegetation classification system on accuracy and thematic resolution.

Detailed maps of vegetation facilitate spatial conservation planning. Such information can be difficult to map from remotely sensed data with the detail (thematic resolution) required for ecological applications. For grass-dominated habitats in the South-East of the UK, it was evaluated which of the following choices improved classification accuracies at various thematic resolutions: 1) Hyperspectral data versus data with a reduced spectral resolution of eight and 13 bands, which were simulated from the hyperspectral data. 2) A vegetation classification system using a detailed description of vegetation (sub)-communities (the British National Vegetation Classification, NVC) versus clustering based on the dominant plant species (Dom-Species). 3) The month of imagery acquisition. Hyperspectral data produced the highest accuracies for vegetation away from edges using the NVC (84-87%). Simulated 13-band data performed also well (83-86% accuracy). Simulated 8-band data performed poorer at finer thematic resolutions (77-78% accuracy), but produced accuracies similar to those from simulated 13-band or hyperspectral data for coarser thematic resolutions (82-86%). Grouping vegetation by NVC (84-87% accuracy for hyperspectral data) usually achieved higher accuracies compared to Dom-Species (81-84% for hyperspectral data). Highest discrimination rates were achieved around the time vegetation was fully developed. The results suggest that using a detailed description of vegetation (sub)-communities instead of one based on the dominating species can result in more accurate mapping. The NVC may reflect differences in site conditions in addition to differences in the composition of dominant species, which may benefit vegetation classification. The results also suggest that using hyperspectral data or the 13-band multispectral data can help to achieve the fine thematic resolutions that are often required in ecological applications. Accurate vegetation maps with a high thematic resolution can benefit a range of applications, such as species and habitat conservation.

Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing.

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

MUC2 is a molecular marker for pseudomyxoma peritonei.

Pseudomyxoma peritonei, a syndrome first described by Rokitansky in 1842, is an enigmatic, often fatal intra-abdominal disease characterized by dissecting gelatinous ascites and multifocal peritoneal epithelial implants secreting copious globules of extracellular mucin. Although much past interest in the syndrome has focused on the question of whether the disease arises from primary appendiceal or ovarian mucinous tumors of varying malignant potential, the accumulation of extracellular mucin with its resulting obstruction of abdominal viscera and adhesion formation is one major cause of this disease's morbidity and mortality irrespective of the origin or transformed status of the epithelium secreting it. Because of this and because of the recent discovery and cloning of a series of specific mucin genes responsible for mucin secretion and extracellular deposition, we decided to analyze cases of pseudomyxoma peritonei with specific mucin cDNAs and corresponding antibodies to identify a characteristic marker for this disease which ultimately might be targeted therapeutically. Our study specifically investigated MUC2 and MUC5AC because these two mucins possessed the physicochemical property of being gel-forming, a property exhibited by pseudomyxoma peritonei grossly. Expression of MUC2 and MUC5AC in pseudomyxoma peritonei and in accompanying and non-accompanying appendiceal and ovarian mucinous neoplasms were analyzed by in situ hybridization, immunocytochemistry and digital image analysis. A striking overexpression of both MUC2 and MUC5AC was observed in nearly all cases of pseudomyxoma peritonei of unknown and appendiceal origin. In these cases, however, MUC2 gene expression was more prominent. The mucin:cell ratio averaged 10 to 1 in these cases. The primary ovarian mucinous tumors, some of which exhibited pseudomyxoma ovarii and/or peritoneal implants but not classic pseudomyxoma peritonei, in contrast, expressed only MUC5AC and gave rise to implants where the mucin:cell ratio averaged only 1 to 1. MUC2 overexpression then supported an intestinal rather than ovarian origin for true pseudomyxoma peritonei, irrespective of whether an appendiceal primary was documented. In all cases studied, the fidelity of MUC2 and MUC5AC expression held irrespective of the degree of malignant transformation which was present. MUC2 is therefore a reliable molecular marker for pseudomyxoma peritonei.

Pseudomyxoma peritonei is a disease of MUC2-expressing goblet cells.

Pseudomyxoma peritonei, a syndrome first described by Karl F. Rokitansky in 1842, is an enigmatic, often fatal intra-abdominal disease characterized by dissecting gelatinous ascites and multifocal peritoneal epithelial implants secreting copious globules of extracellular mucin. Although past interest in the syndrome has focused on the questions of the site of origin (appendix versus ovary), mechanisms of peritoneal spread (multicentricity, redistribution phenomenon, or metastasis), and the degree of malignant transformation present (adenoma, borderline tumor, or carcinoma), another important question is the mechanism behind the accumulation of extracellular mucin, the real cause of the disease's morbidity and mortality irrespective of the site of origin, mechanism of peritoneal spread, or transformed status of its epithelium. Taking advantage of the recently cloned human mucin genes, we decided to investigate this question. Our studies revealed that pseudomyxoma peritonei is a disease of MUC2-expressing goblet cells. These cells also express MUC5AC but the latter mucin is not specific for pseudomyxoma peritonei. MUC2 expression accounts for the voluminous deposits of extracellular mucin (mucin:cell ratios exceeding 10:1) and distinguishes pseudomyxoma peritonei secondarily involving the ovary from primary ovarian mucinous tumors with peritoneal implants. Because mucinous tumors of the appendix similarly express MUC2, the MUC2 expression profile also supports an appendiceal rather than ovarian origin for pseudomyxoma peritonei. Increased steady-state mRNA is observed in pooled cases of pseudomyxoma peritonei but does not occur on the basis of gene rearrangement or gene amplification. Primary epithelial cell cultures obtained from pseudomyxoma peritonei express MUC2 whose levels can be epigenetically regulated. These lines up-regulate MUC2 expression in response to both methylation inhibition by 5-azacytidine and exposure to Pseudomonas aeruginosa lipopolysaccharide, both of whose effects can be suppressed by genistein pretreatment. Both immunocytochemical as well as in situ hybridization studies with ancillary digital image analysis reveal that MUC2 expression in cases of pseudomyxoma peritonei is independent of the degrees of malignant transformation that are present and, in fact, reflects the constitutive levels of expression observed in normal goblet cells of the appendix. Extracellular mucin accumulates dramatically in pseudomyxoma peritonei because the number of MUC2-secreting cells dramatically increase and because this MUC2 has no place to drain. These studies suggest that pseudomyxoma peritonei should be regarded as a disease of MUC2-expressing goblet cells whose MUC2 expression might be susceptible to pharmacological targeting.