FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes

Le statut de la nasalité en créole de Sainte-Lucie

Nous proposons dans ce travail que les voyelles nasales du créole de Sainte-Lucie sont en fait dérivées d’une même suite sous-jacente voyelle orale et consonne nasale adjointe (consonne sans position temporelle, mais associée à la rime). Cette représentation sous-jacente permet de rendre compte de façon claire de toutes les formes de surface attestées dans ce créole à base lexicale française, soit les voyelles orales suivies d’une consonne nasale, les voyelles nasales, les variations de la nasalité, les assimilations des consonnes occlusives sonores en position finale, les formes morphologiquement dérivées et les formes du déterminant postposé.This paper shows that nasal vowels in St. Lucia Creole are in fact derived from a single underlying sequence consisting of an oral vowel with an adjoined nasal consonant (a consonant which does not have a temporal position but which is associated to the rhyme). This underlying representation straightforwardly accounts for all the surface forms attested in this French-based Creole, including oral vowels followed by a nasal consonant, nasal vowels, variations in nasality, assimilation of final voiced stops, morphologically derived forms and the forms of the postposed determiner

Comparison of high-resolution NAIP and unmanned aerial vehicle (UAV) imagery for natural vegetation communities classification using machine learning approaches

To map and manage forest vegetation including wetland communities, remote sensing technology has been shown to be a valid and widely employed technology. In this paper, two ecologically different study areas were evaluated using free and widely available high-resolution multispectral National Agriculture Imagery Program (NAIP) and ultra-high-resolution multispectral unmanned aerial vehicle (UAV) imagery located in the Upper Great Lakes Laurentian Mixed Forest. Three different machine learning algorithms, random forest (RF), support vector machine (SVM), and averaged neural network (avNNet), were evaluated to classify complex natural habitat communities as defined by the Michigan Natural Features Inventory. Accurate training sets were developed using both spectral enhancement and transformation techniques, field collected data, soil data, texture, spectral indices, and expert knowledge. The utility of the various ancillary datasets significantly improved classification results. Using the RF classifier, overall accuracies (OA) between 83.8% and 87.7% with kappa (k) values between 0.79 and 0.85 for the NAIP imagery and between 87.3% and 93.7% OA with k values between 0.83 and 0.92 for the UAV dataset were achieved. Based on the results, we concluded RF to be a robust choice for classifying complex forest vegetation including surrounding wetland communities

Image Processing in Dense Forest Areas using Unmanned Aerial System (UAS)

Description: A detailed workflow using Structure from Motion (SfM) techniques for processing high-resolution Unmanned Aerial System (UAS) NIR and RGB imagery in a dense forest environment where obtaining control points is difficult due to limited access and safety issues. Abstract: Imagery collected via Unmanned Aerial System (UAS) platforms has become popular in recent years due to improvements in a Digital Single-Lens Reflex (DSLR) camera (centimeter and sub-centimeter), lower operation costs as compared to human piloted aircraft, and the ability to collect data over areas with limited ground access. Many different application (e.g., forestry, agriculture, geology, archaeology) are already using and utilizing the advantages of UAS data. Although, there are numerous UAS image processing workflows, for each application the approach can be different. In this study, we developed a processing workflow of UAS imagery collected in a dense forest (e.g., coniferous/deciduous forest and contiguous wetlands) area allowing users to process large datasets with acceptable mosaicking and georeferencing errors. Imagery was acquired with near-infrared (NIR) and red, green, blue (RGB) cameras with no ground control points. Image quality of two different UAS collection platforms were observed. Agisoft Metashape, a photogrammetric suite, which uses SfM (Structure from Motion) techniques, was used to process the imagery. The results showed that an UAS having a consumer grade Global Navigation Satellite System (GNSS) onboard had better image alignment than an UAS with lower quality GNSS

Fine-Scale Mapping of Natural Ecological Communities Using Machine Learning Approaches

Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizing multispectral high-resolution (60 cm) National Agriculture Imagery Program (NAIP) imagery data. For accurate training set delineation, NAIP imagery, soils data and spectral enhancement techniques such as principal component analysis (PCA) and independent component analysis (ICA) were integrated. The study evaluated the importance of biogeophysical parameters such as topography, soil characteristics and gray level co-occurrence matrix (GLCM) textures, together with the normalized difference vegetation index (NDVI) and NAIP water index (WINAIP) spectral indices, using the joint mutual information maximization (JMIM) feature selection method and various machine learning algorithms (MLAs) to accurately map the natural habitat communities. Individual habitat community classification user’s accuracies (UA) ranged from 60 to 100%. An overall accuracy (OA) of 79.45% (kappa coefficient (k): 0.75) with random forest (RF) and an OA of 75.85% (k: 0.70) with support vector machine (SVM) were achieved. The analysis showed that the use of the biogeophysical ancillary data layers was critical to improve interclass separation and classification accuracy. Utilizing widely available free high-resolution NAIP imagery coupled with an integrated classification approach using MLAs, fine-scale natural habitat communities were successfully delineated in a spatially and spectrally complex Laurentian Mixed Forest environment

Triple mesh technique in repair of recurrent lumbar incisional hernia

Lumbar hernias occur infrequently and can be congenital, primary (inferior or Petit type, and superior or Grynfeltt type), post-traumatic, or incisional. They are bounded by the 12th rib, the iliac crest, the erector spinae, and the external oblique muscle. Most postoperative incisional hernias occur in nephrectomy or aortic aneurysm repair incisions for which various surgical method in context of meshplasty are available. In this case 60 yr. male hypertensive patient presented to the outpatient clinic of institute with recurrent left side lumbar incisional hernia, patient was previously operated for left side nephrolithiasis 15 years back and onlay meshplasty 2 years back for incisional hernia. The patient was operated under high risk for recurrent incisional hernia repair by triple layered meshplasties in the same sitting. Lumbar incisional hernias are often diffuse with fascial defects that are usually hard to appreciate. Computed tomography scan is the diagnostic modality of choice with adjuvant clinical findings, which allows differentiating them from abdominal wall musculature denervation atrophy complicating flank incisions. Repairing these hernias is difficult due to the surrounding structures for which our surgical approach included a triple mesh repair consisting of underlay, inlay and onlay meshplasty thereby anticipating further such incidences of incisional hernia

Impedance Spectroscopic Investigation of the Degraded Dye-Sensitized Solar Cell due to Ageing

This paper investigates the effect of ageing on the performance of dye-sensitized solar cells (DSCs). The electrical characterization of fresh and degraded DSCs is done under AM1.5G spectrum and the current density-voltage (J-V) characteristics are analyzed. Short circuit current density (JSC) decreases significantly whereas a noticeable increase in open circuit voltage is observed. These results have been further investigated electroanalytically using electrochemical impedance spectroscopy (EIS). An increase in net resistance results in a lower JSC for the degraded DSC. This decrease in current is mainly due to degradation of TiO2-dye interface, which is observed from light and dark J-V characteristics and is further confirmed by EIS measurements. A reduction in the chemical capacitance of the degraded DSC is observed, which is responsible for the shifting of Fermi level with respect to conduction band edge that further results in an increase of open circuit voltage for the degraded DSC. It is also confirmed from EIS that the degradation leads to a better contact formation between the electrolyte and Pt electrode, which improves the fill factor of the DSC. But the recombination throughout the DSC is found to increase along with degradation. This study suggests that the DSC should be used under low illumination conditions and around room temperature for a longer life

Picturing One\u27s Self: Camera Use in Zoom Classes during the COVID-19 Pandemic

Starting from the spring of 2020, higher institutions in the US underwent a rapid shift from in-person classes to emergency remote education, in response to the COVID-19 outbreak. Under this circumstance, a variety of video conferencing tools (e.g., Zoom) have been adopted for distance education, which pose a set of new challenges arising from synchronous online classes. Among these, one significant issue was students\u27 unwillingness to open cameras, resulting in a lack of non-verbal cues that instructors could rely on to gauge students\u27 understanding and adjust their teachings. Towards addressing this issue, our qualitative study aims at investigating the rationales behind students\u27 camera avoidance. Through a series of semi-structured interviews on undergraduate students in the U.S, we identified prominent factors -- namely the class size, lecture style, level of interactivity and privacy concerns -- that influenced students\u27 motivation for opening their cameras. At the same time, we uncovered several difficulties, such as heightened self-awareness, feeling of minority and academic perspective, that discouraged students from opening camera, with more substantial impacts on international students. We conclude with actionable insights into the design of online classes, video-conferencing platforms and camera technology that can promote camera usage, thereby contributing to scalable and inclusive interventions for facilitating the transition into remote education

Human Tumor Targeted Cytotoxic Mast Cells for Cancer Immunotherapy

The diversity of autologous cells being used and investigated for cancer therapy continues to increase. Mast cells (MCs) are tissue cells that contain a unique set of anti-cancer mediators and are found in and around tumors. We sought to exploit the anti-tumor mediators in MC granules to selectively target them to tumor cells using tumor specific immunoglobin E (IgE) and controllably trigger release of anti-tumor mediators upon tumor cell engagement. We used a human HER2/neu-specific IgE to arm human MCs through the high affinity IgE receptor (FcεRI). The ability of MCs to bind to and induce apoptosis of HER2/neu-positive cancer cells in vitro and in vivo was assessed. The interactions between MCs and cancer cells were investigated in real time using confocal microscopy. The mechanism of action using cytotoxic MCs was examined using gene array profiling. Genetically manipulating autologous MC to assess the effects of MC-specific mediators have on apoptosis of tumor cells was developed using siRNA. We found that HER2/neu tumor-specific IgE-sensitized MCs bound, penetrated, and killed HER2/neu-positive tumor masses in vitro. Tunneling nanotubes formed between MCs and tumor cells are described that parallel tumor cell apoptosis. In solid tumor, human breast cancer (BC) xenograft mouse models, infusion of HER2/neu IgE-sensitized human MCs co-localized to BC cells, decreased tumor burden, and prolonged overall survival without indications of toxicity. Gene microarray of tumor cells suggests a dependence on TNF and TGFβ signaling pathways leading to apoptosis. Knocking down MC-released tryptase did not affect apoptosis of cancer cells. These studies suggest MCs can be polarized from Type I hypersensitivity-mediating cells to cytotoxic cells that selectively target tumor cells and specifically triggered to release anti-tumor mediators. A strategy to investigate which MC mediators are responsible for the observed tumor killing is described so that rational decisions can be made in the future when selecting which mediators to target for deletion or those that could further polarize them to cytotoxic MC by adding other known anti-tumor agents. Using autologous human MC may provide further options for cancer therapeutics that offers a unique anti-cancer mechanism of action using tumor targeted IgE’s