A neural network model for classification of coastal wetlands vegetation structure with Moderate Resolution Imaging Spectro-radiometer (MODIS) data

Mapping coastal marshes is an important component in the management of coastal environments. Classification of marshes using remote sensing data has traditionally been performed by employing either parametric supervised classification algorithms or unsupervised classification algorithms. The implementation of these conversional classification methods is based on the underlying distributions concerning the probability density functions (PDF). Neural networks provide a practical approach to this classification because they are essentially non-parametric data transformations that are not restricted by any underlying assumptions. The major objective of this study was to evaluate the ability of neural networks using Moderate Resolution Imaging Spectro-radiometer (MODIS) data to classify coastal marshes based on the phenelogical stages of plants. The first stage of the study was to develop a neural network model. The analysis has shown that six day images with eight input variables each are required to perform the classification. The variables are: MODIS bands - the near infrared and the near infrared composite bands, biophysical variables – the leaf area index (LAI) and the fraction of photosynthetically active radiation (fPAR). Other variables are vegetation indices – the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the wetness index (WI), and, the day time land surface temperature. The near infrared and the wetness index were found to be the strongest predictor variables in the classification. Six hidden neurons and one output neuron were required in the neural network model for the output of six classes. The second stage of the dissertation was the model application. Images from four years: 2001, 2002, 2003, and 2004 were classified using the model. Accuracy assessment of the classification indicated that neural network techniques using MODIS data could achieve an accuracy of over 80% (at 0.95 confidence level). Using the classified images change detection was performed to determine the loss and gain of four marsh types; saline marsh, brackish marsh, intermediate marsh, and, fresh water marsh found in the south eastern coastal areas of Louisiana. The greatest gain was in the intermediate marsh, 3.0% of the study area, and the greatest loss was in the saline marsh, 3.8% of the study area

Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning.

Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from facilitating the prediction of the pixel values for probability assessment, the proposed model can also help urban-planners and disaster managers make appropriate decisions regarding future plans and earthquake management

Assessing Environmental Issues in Upland Game Birds

Wildlife management is essentially the balance between maintenance of habitat and control of population density. To demonstrate the application of multivariate techniques for habitat assessment, I evaluated 4 contemporary classification schemes for use as experimental units for mourning dove (Zenaida macroura) research in Texas. I conducted a generalized canonical discriminant analysis (CDA) for each classification scheme using 25 habitat variables obtained adjacent to each of the 133 U.S. Fish and Wildlife Services call-count survey routes within Texas. Classification results from each CDA were used to generate a confusion matrix for each classification scheme (i.e., overall accuracy, average accuracy, and expected agreement). Because classification schemes differed in the number of categories, the Kappa Coefficient of Agreement was used to account for the proportion of agreement due to chance. The Kappa estimates were higher for the Gould (0.760) and Omernik (0.700) classification schemes, than for the Fenneman (0.618) or George (0.673) classification schemes, indicating the newer classification schemes provide a more accurate partitioning of multidimensional habitat space, and are therefore better suited for use as experimental units for mourning dove research in Texas. To demonstrate the impact of human land use on wildlife habitat, I evaluated the spatial-temporal effects of habitat loss and anthropogenic land use on grassland birds from 1993–2012. I used 8 habitat metrics corresponding to the U.S. Census of Agriculture data for Texas during this period, and northern bobwhite (Colinus virginianus) abundance estimates from the Breeding Bird Survey and Texas Parks and Wildlife Department as the proxy grassland bird species. The redundancy analysis indicated that economic, agricultural, and land use metrics accounted for 74.5% of the total variance in bobwhite relative abundance during the period (Radj ² = 60.8%, P < 0.0016), and most anthropogenic land trend variables (e.g., Population Density, Market Value, Production Value) were inversely proportional to quail relative abundance. The canonical discriminant analysis indicated that economic, agricultural, and land use metrics explained 88.6% of the variability among ecoregions (P < 0.0002) and 99.5% of the variability among years (P < 0.0167). These results indicate that land values (market value and production value per hectare) and human population density may signal the onset of anthropogenic land conversion, and might be used to predict future changes that will impact grassland bird species and other natural resources. Finally, to demonstrate the feasibility of combining scientific and citizen-science data to obtain a regional estimate of grassland bird abundance, I obtained congruent estimates of northern bobwhite (Colinus virginianus) abundance using a double-sampling paradigm. Spring cock call-counts were conducted on 12 ranches within the Rolling Plains of Texas during 2012–2014. This sampling effort collected calls and distances at each point, yielding 1,022 total counts, detected 36,415 calls, 4,647 birds, and obtained 4,627 distances. Data were analyzed using program DISTANCE to generate local and regional estimates of quail density for each year, and to calibrate density estimates with birds heard using a double-sampling paradigm. My results demonstrated that it is economically feasible and logistically pragmatic to calibrate metrics obtained through citizen-science efforts (call-counts; relative abundance) with results obtained by more intensive scientific methods (distance sampling; density estimates). Collectively, these results illustrate that it is within the microcosm of single-species management that we test the limits of our ecological knowledge and understanding

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen