This study explored spatiotemporal patterns in movement, diet, and baseline phycotoxin concentrations in immature bull sharks (Carcharhinus leucas) of the Indian River Lagoon (IRL), an estuary of national significance that has been considerably impacted by multiple toxic harmful algal blooms (HABs). Long-term spatial use of the system was assessed for 29 acoustically tagged sharks over a 4 year period (2017–2020). Tissue samples for diet and toxin analysis were collected from a separate cohort of 50 individuals between 2018 and 2020. UPLC-MS/MS was used to screen tissues for 14 algal toxins. Young bull sharks were found to be mainly piscivorous and displayed high residency to the IRL as well as to specific regions of the IRL, with small activity spaces. Multiple phycotoxins were detected in screened tissues, indicating that young bull sharks in the IRL may be compromised by trophic transfer of HABs while they reside in this important nursery. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Diploma thesis deals with the land cover classification in Sidama region of Ethiopia and 2 kebeles, Chancho and Dangora Morocho. High resolution Sentinel-2 and very high resolution PlanetScope satellite images are used. The development of the classification algorithm is done in the Google Earth Engine cloud based environment. Ten combinations of the 4 most important parameters of the Random Forest classification method are tested. The defined legend contains 8 land cover classes, namely built-up, crops, grassland/pasture, forest, scrubland, bareland, wetland and water body. The training dataset is collected in the field during the fall 2020. The classification results of the two data types at two scales are compared. The highest overall accuracy for land cover classification of Sidama region came out to be 84.1% and kappa index of 0.797, with Random Forest method parameters of 100 trees, 4 spectral bands entering each tree, value of 1 for leaf population and 40% of training data used for each tree. For the land cover classification of Chancho and Dangora Morocho kebele with the same method settings, the overall accuracy came out to be 66.00 and 73.73% and kappa index of 0.545 and 0.601. For the classification of Chancho kebele, a different combination of parameters (80, 3, 1, 0.4) worked out better...

Introduction: Sentinel lymph node biopsy is an established method used to investigate the riskof lymphatic metastasis especially in breast cancer and melanoma patients. SPECT/ CT isconsidered to be an advantageous method in mapping of sentinel nodes. Aim: The aim of this systematic literature review was to investigate the clinical value ofSPECT/ CT in the detection of sentinel lymph nodes in breast cancer patients. Method: Using specific search terms the database PubMed was used to find studies of potentialrelevance for this systematic review. Criteria for inclusions and exclusion were decided todetermine article relevance. Eligibility of articles was determined according to these criteriawhich lead to the selection of the specific articles included in this study. Results: Eleven studies were included in this systematic review. Seven out of 8 studies foundhigher identification rates of sentinel lymph nodes with SPECT/ CT in comparison to planarlymphoscintigraphy. SPECT/ CT could detect additional lymph nodes in 9 out of 9 studies.SPECT/ CT detected additional extra-axillary lymph nodes in 6 out of 7 studies. SPECT/ CTdetected lymph nodes in 9 out of 9 studies where planar lymphoscintigraphy was negative.Information from additional SPECT/ CT lead to changes in surgical treatment plan in 4 out of4 studies. None of the included studies contained information about change in oncologicaltreatment plan due to findings on SPECT/ CT. Conclusion: SPECT/ CT is an imaging technique with much potential as it seems to allow amore accurate SLN mapping and more precise anatomical localization of SLN in breast cancerpatients, specifically in certain clinical situations. Despite this however, the impact of SLNmapping through SPECT/ CT on patient prognosis remains uncertain.

Thesis (M.Sc. (Geography)) -- University of Limpopo, 2020 / Wetland vegetation provides a variety of goods and services such as carbon sequestration, flood control, climate regulation, filtering contamination, improve and maintain water quality, ecological functioning. However, changes in land cover and uses, overgrazing and environmental changes have resulted in the transformation of the wetland ecosystem. So far, a lot of focus has been biased towards large wetlands neglecting wetlands at a local scale. Smaller wetlands continue to receive massive degradation by the surrounding communities.Therefore, this study seeks to assess and map wetland vegetation as an indicator of ecological productivity on a small scale. The Sentinel-2 MSI image was used to map wetland plant species diversity and above-ground biomass (AGB). Four key diversity indices; the Shannon Wiener (H), Simpson (D), Pielou (J), and Species richness (S) were used to measure species diversity. A multilinear regression technique was applied to establish the relationship between remotely sensed data and diversity indices and AGB. The results indicated that Simpson (D) has a high relationship with combined vegetation indices and spectral band, yielding the highest accuracy when compared to other diversity indices. For example, an R² of 0.75, and the RMSE of 0.08 and AIC of -191.6 were observed. Further, vegetation AGB was estimated with high accuracy of an R² of 0.65, the RMSE 29.02, and AIC of 280.21. These results indicate that Maungani wetland has high species abundance largely dominated by one species (Cyperus latifidius) and highly productive. The findings of this work underscore the relevance of remotely sensed to estimate and monitor wetland plant species diversity with high accuracy.

Příloha k disertační práci: Abstrakt v AJ (Mgr. Josef Laštovička) Abstract This dissertation thesis deals with the study of forest ecosystems in the central Europe with the time series of multispectral optical satellite data. These forest ecosystems have been influenced by biotic and abiotic disturbances for the last decade. The time series of the satellite data with high spatial resolution allow the detection and analysis of forest disturbances. This thesis is mainly focused primally on free available Landsat and Sentinel-2 data, these two data types were compared. From methods, the difference time series analyses / algorithms were used. The whole thesis can be divided into two main parts. The first one analyses usability of classifiers for detection of forest ecosystems with per-pixel and sub-pixel methods. Specifically, the Neural Network, the Support Vector Machine and the Maximum Likelihood per-pixel classifiers were used and compared for different types of data (for data with high spatial resolution - Landsat or Sentinel-2; very high spatial resolution - WorldView-2) and for classification of protected forest areas. The Support Vector Machine were selected as the most suitable method for forest classifications (with most accurate outputs) from the list of selected per-pixel classifiers. Also, Spectral...

The literature review of the thesis deals with issues related to precision agriculture and remote sensing. The practical part evaluates the correlation of selected vegetation indices and absolute yields of individual crops on selected fields. Subsequently, it is evaluated how the NDVI correlation values and yields between individual fields differ. The last point was mentioned the evaluation of spatial variability of NDVI values and yields within the selected period, crops and land. The method was divided into several steps. The first step was to create a geodatabase of satellite images from the Sentinel 2A platform and yield data for 2017. The calculation of the vegetation indices followed by the creation of a point layer representing the values of the vegetation indices and the yield. The last step was focused on statistical evaluation and creation of map data. The results show that data indicate a high correlation rate at the end of May and during June. NDVI correlation value and yield are different for individual fields. Due to the used satellite data, spatial yield variability can be determined for individual agricultural crops and land.

Healthy forests and soils are crucial for the very existence of mankind as they provide food, clean water and air, shade and protection against floods and storms. With their photosynthetic carbon storage ability, they mitigate climate change and fertilise and stabilise soils. Unfortunately, deforestation and the loss of fertile soils are the bleak reality and among the world’s most pressing challenges. Over the past decades Kenya has faced severe deforestation, but efforts are being undertaken to reverse deforestation, revegetate degraded land and combat erosion. Satellite remote sensing technology becomes increasingly useful for vegetation monitoring as the data quality improves and the costs decrease. This thesis explores the potential of free open access Sentinel-2 data for vegetation monitoring through Random Forest land cover classification and post-classification change detection on Rusinga Island, Kenya. Different single-date and multi-temporal predictor datasets differentiating respectively between five and four classes were examined to develop the most suitable model. The classification achieved acceptable results when assessed on an independent test dataset (overall accuracy of 90.06% with five classes and 96.89% with four classes), which should however be confirmed on the ground and could potentially be improved with better reference data. In this study, change detection could only be analysed over a time frame of two years, which is too short to produce meaningful results. Nevertheless, the method was proven conceptually and could be applied in the future to monitor land cover changes on Rusinga Island.

Semi-natural grasslands are amongst the most biodiverse ecosystems in Europe, though their importance they are experiencing a declining trend. To monitor and assess the health of these ecosystems is generally costly, personnel demanding and time-consuming. With satellite imagery and machine learning becoming more accessible, this can offer a cheap and effective way to gain ecological information about semi-natural grasslands.This thesis explores the possibilities to predict plant species richness in semi-natural grasslands with high resolution satellite imagery through machine learning. Five different machine learning models were employed with various subsets of spectral- and geographical features to see how they performed and why. The study area was in southern Sweden with satellite and survey data from the summer of 2019.Geographical features were the features that influenced the machine learning models most. This can be explained by the geographical spread of the semi-natural grasslands, as well as difficulties in finding correlations in the relatively noisy satellite data. The most important spectral features were found in the red edge- and the short-wave infrared spectrums. These spectrums represent leaf chlorophyll content and water content in vegetation, respectively. The most accurate machine learning model was Random Forest when it was trained using with all the spectral- and geographical features. The other models; Logistic Regression, Support Vector Machine, Voting Classifier and Neural Network, showed general inabilities to interpret feature subsets containing the spectral data.This thesis shows that with deeper knowledge about the satellite-biodiversity relationship and how to apply it with machine learning have the possibilities of cheaper, more efficient and standardized monitoring of ecologically valuable areas such as semi-natural grasslands.

As technology advances, the possibility of using satellite data and observations to aid inagricultural activities comes closer to reality. Swedish farmers can apply for subsidies for their land in which crop management and animal grazing occurs, and every year thousands of manual follow-up checks are conducted by Svenska Jordbruksverket (Swedish Board of Agriculture) to validate the farmers’ claims to financial aid. RISE (Research Institutes of Sweden) is currently researching a replacement for the manual follow-up checks using an automated process with optical satellite observations from primarily the ESA-made satellite constellation Sentinel-2, and secondarily the radar observations of the Sentinel-1 constellation. The optical observations from Sentinel-2 are greatly hindered by the presence of weather on the Earth’s atmosphere and lack of sunlight, but the radar-based observations of Sentinel-1 are able to penetrate any weather conditions entirely independently from sunlight. By using the optical index NDVI (Normalized Difference Vegetation Index) which is strongly correlated with plant chlorophyll, and the radar index RVI (Radar Vegetation Index), classifications on animal grazing activities are sought to be made. Dynamic Time Warping and hierarchical clustering are used to analyse and attempt to make classifications on the two selected datasets of sizes 959 and 20 fields. Five experiments were conducted to analyse the observational data from mainly Sentinel-2, but also Sentinel-1. The results were inconclusive and were unable to perform successful classifications primarily on the 959 fields large dataset. An indication is given in one of the experiments, performed on the smaller dataset of 20 fields, that classification is indeed possible by using mean valued NDVI time series. However, it is difficult to draw conclusions due to the small size of the 20 fields large dataset. To validate any possible methods classification a larger dataset must be used.

Objective: Background: Rare disease An epithelial inclusion cyst within a lymph node denotes a heterotopic phenomenon. Nodal epithelial inclusion cysts have been reported in a variety of anatomical locations including pelvic, abdominal, mediastinal, and axillary regions. While nodal melanocytic nevus (also known as nevus cell aggregates) is the most common heterotopic phenomena involving the axillary lymph nodes, the presence of benign epithelial inclusion cysts in axillary lymph nodes is a rare but well-reported finding. Such documentation is in part due to assessment of sentinel lymph nodes in breast cancer becoming standard of care. These epithelial inclusion cysts offer a diagnostic pitfall in evaluation of sentinel lymph node in the setting of breast carcinoma. They also complicate assessment of sentinel lymph node during intraoperative frozen sections analysis. Case Report: We report a case of co-existent of benign squamous-type and glandular-type epithelial inclusions cysts in 2 sentinel lymph nodes in a patient with grade III invasive ductal carcinoma involving the left breast. There have been at least 4 cases reported in literature in which benign epithelial inclusion cysts in sentinel lymph nodes were first mistakenly diagnosed as metastatic carcinoma both during intraoperative frozen section analysis and during review of permanent sections. The missed diagnosis could potentially occur intraoperatively during frozen section sentinel lymph node analysis secondarily due to lack of availability of the primary tumor for comparison and inability to use immunohistochemical stains. Conclusions: Pathologists should be aware of this pitfall especially in frozen section analysis of sentinel lymph node to avoid misdiagnosis and its associated potential grave consequences.