Geostatistical Estimation of Water Quality using River and Flow Covariance Models

Assessing water quality along rivers is vital for watershed management and to protect the public health. Monitoring water quality at every river mile is logistically impractical and prohibitively expensive. Geostatistical estimation offers a cost effective alternative that can be rapidly implemented to statistically model spatially dependent water quality parameters using the available monitoring data. Geostatistical modeling requires a covariance model to describe the variability and autocorrelation of the water quality along rivers. Three main classes of covariance models, namely the Euclidean, river, and flow-weighted covariance models, are commonly used in geostatistical water quality estimation. In the first study we use a river covariance model to successfully characterize the space/time variability of chloride, an emerging contaminant, along rivers in Maryland. This method leads to a 24% reduction in mean square estimation error compared to the Euclidean method. In the next two studies we use the flow-weighted covariance for the estimation of fecal coliform (FC), and Dissolved Organic Carbon (DOC), respectively. Surprisingly, very few geostatistical water quality studies have successfully implemented the flow-weighted covariance model and improved estimation accuracy. To address this critical gap, we introduce the first implementation of a flow weighted covariance model that uses gradual flow, and we then use this model in a novel hybrid Euclidean/Gradual-flow covariance model to estimate FC in the Haw and Deep rivers in North Carolina, and DOC in three sub-basins in Maryland. Our novel hybrid Euclidean/Gradual-flow covariance model captures variability coming from both terrestrial sources and hydrological transport, and it leads to a 12% and 15% reduction in mean square error for FC and DOC, respectively, compared to the traditional Euclidean covariance. This novel hybrid covariance model is widely applicable to any other study area and to other water quality parameters.Doctor of Philosoph

Cluster and Principle Component Analysis of Soybean Grown at Various Row Spacings, Planting Dates and Plant Populations

Increased light interception (LI), along with concomitant increases in crop growth rate (CGR), is the main factor explaining how cultural factors such as row spacing, plant population, and planting date affect soybean yield. Leaf area index (LAI), LI, and CGR are interrelated in a “virtuous spiral” where increased LAI leads to greater LI resulting in a higher CGR and more total dry matter per area (TDM). This increases LAI, thus accelerating the entire physiological process to a higher level. A greater understanding of this complex growth dynamic process could be achieved through use of cluster analysis and principle components analysis (PCA). Cluster analysis involves grouping of similar objects in such way that objects in same cluster are similar to each other and dissimilar to objects in other cluster. PCA is a technique used to reduce a large set of variables to a few meaningful ones. Seasonal relative leaf area index (RLAI), relative light interception (RLI), and relative total dry matter (RTDM) response curves were determined from the data by a stepwise regression analysis in which these parameters were regressed against relative days after emergence (RDAE). Greatest levels of RLAI, RLI and RTDM were observed in soybean planted early on narrow row spacings and recorded greater plant population. In contrast, lower levels of these parameters occurred on plants with wide row spacings at late planting dates. For farmers, these results are useful in terms of adopting certain cultural practices which can help in the management of stress in soybean

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery