Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level

encephalitis in Florida

Background: Eastern Equine Encephalitis virus (EEEV) is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses GIS to examine spatial patterns of documented horse cases during 2005–2010 in order to understand the relationships between habitat and transmission intensity of EEEV in Florida. Methods: Cumulative incidence rates of EEE in horses were calculated for each county. Two cluster analyses were performed using density-based spatial clustering of applications with noise (DBSCAN). The first analysis was based on regional clustering while the second focused on local clustering. Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission. Results: The DBSCAN algorithm identified five distinct regional spatial clusters that contained 360 of the 438 horse cases. The local clustering resulted in 18 separate clusters containing 105 of the 438 cases. Both the compositional analysis and Euclidean distance analysis indicated that the top five habitats positively associated with horse cases were rural residential areas, crop and pastureland, upland hardwood forests, vegetated non-forested wetlands, an

A Wildlife Movement Approach to Optimally Locate Wildlife Crossing Structures

Transportation networks negatively impact wildlife populations by limiting the physical movement of the individual animal. In extreme cases road presence can lead to collisions between vehicles and animals, resulting in direct mortality if an animal attempts to cross the road. Crossing structures are one commonly used method for reducing wildlife–vehicle collisions. However, limited funding often reduces the amount of structures that may be constructed in practice. Therefore, areas that have the highest probability for animal interactions with roads should be targeted for locating new structures to provide the best possible outcome. This research uses a probabilistic time-geographic strategy coupled with a site selection phase handled by a classical optimization model to site wildlife crossing structures. To achieve optimal site selection, crossing locations are first identified where wildlife frequently cross roads, and then a maximum covering location problem is applied to these areas as demand nodes. The objective is to cover the largest area having the highest probability of interaction given a finite number of crossing structures available to be located. Coverage is defined in terms of fencing distance associated with a particular structure. The approach was demonstrated using Florida panther telemetry data identifying potential crossing structures across two counties in south Florida. The maximal covering location problem (MCLP) was solved for four coverage distances using radio telemetry tracking data, which captured frequent contact with roads. The results identify that the most effective coverage distance is 2000 m, which incrementally covers more total animal–road interaction probability than that of lower fencing distances in the case of the Florida panther. The results illustrate how this new time-geographic approach, combined with location modeling, measures animal–road interactions probabilistically for finding the optimum placement of wildlife crossing structures

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level

Comparison Between Wavelet Spectral Features and Conventional Spectral Features in Detecting Yellow Rust for Winter Wheat

Detection of yellow rust is of great importance in disease control and reducing the use of fungicide. Spectral analysis is an important method for disease detection in terms of remote sensing. In this study, an emerging spectral analysis method known as continuous wavelet analysis (CWA) was examined and compared with several conventional spectral features for the detection of yellow rust disease at a leaf level. The leaf spectral measurements were made by a spectroradiometer at both Zodaks 37 and 70 stages with a large sample size. The results showed that the wavelet features were able to capture the major spectral signatures of yellow rust, and exhibited considerable potential for disease detection at both growth stages. Both the accuracies of the univariate and multivariate models suggested that wavelet features outperformed conventional spectral features in quantifying disease severity at leaf level. Optimal accuracies returned a coefficient of determination (R2) of 0.81 and a root mean square error (RMSE) of 0.110 for pooled data at both stages. Furthermore, wavelet features showed a stronger response to the yellow rust at Zodaks 70 stage than at Zodaks 37 stage, indicating reliable estimation of disease severity can be made until the Zodaks 70 stage

Agent-Based Simulation of Muscovy Duck Movements Using Observed Habitat Transition and Distance Frequencies

This paper presents an agent based model simulating animal tracking datasets for individual animals based on observed habitat use characteristics, movement behaviours and environmental context. The model is presented as an alternative simulation methodology for movement trajectories for animal agents, useful in home range, habitat use and animal interaction studies. The model was implemented in NetLogo 5.1.0 using observed behavioural data for the Muscovy duck, obtained in a previous study. Four test scenarios were completed to evaluate the fidelity of model results to behavioural patterns observed in the field. Results suggest the model framework illustrated in this paper provides an effective alternative to traditional animal movement simulation methods such as correlated random walks

Voxel-Based Probabilistic Space-Time Prisms for Analysing Animal Movements and Habitat Use

Time-geographic analysis has been limited in the past by its capacity to model only potential locations for moving objects, without sufficiently evaluating which locations are more probable. This paper expands upon existing research in probabilistic time geography by accomplishing two main tasks. First, a new geocomputational approach is presented for generating probabilistic space-time prisms. Here, probabilistic space-time prisms are represented as three-dimensional rasters of volume elements, or voxels, that record the probability that an object was located at any location at any time. After describing the geocomputational approach, its utility is illustrated through a detailed analysis of tracking data collected for a Muscovy duck (Cairina mochata). Specifically, probabilistic space-time prisms are used to map the duck\u27s fine-scale movement patterns over five complete days of global positioning system (GPS)-tracking. Then, the space-time prisms are used in conjunction with a detailed habitat map of the study area in order to quantify the duck\u27s habitat usage over the course of each day. This application highlights the utility of probabilistic space-time prisms for understanding the movements and activities of animals at fine temporal and spatial scales

Testing Time-Geographic Density Estimation for Home Range Analysis Using an Agent-Based Model of Animal Movement

Time-geographic density estimation (TGDE) is a method of movement pattern analysis that generates a continuous intensity surface from a set of tracking data. TGDE has recently been proposed as a method of animal home range estimation, where the goal is to delineate the spatial extents that an animal occupies. This paper tests TGDE’s effectiveness as a home range estimator using simulated movement data. First, an agent-based model is used to simulate tracking data under 16 movement scenarios representing a variety of animal life history traits (habitat preferences, homing behaviour, mobility) and habitat configurations (levels of habitat fragmentation). Second, the accuracy of TGDE is evaluated for four temporal sampling frequencies using three adaptive velocity parameters for 30 sample data sets from each scenario. Third, TGDE accuracy is compared to two other common home range estimation methods, kernel density estimation (KDE) and characteristic hull polygons (CHP). The results demonstrate that TGDE is the most effective at estimating core areas, home ranges and total areas at high sampling frequencies, while CHP performs better at low sampling frequencies. KDE was ineffective across all scenarios explored

Forging a Bayesian link between habitat selection and avoidance behavior in a grassland grouse

Abstract Habitat selection is a basic aspect of the ecology of many species, yet often the term is conflated or confused with both habitat preference and habitat use. We argue that each term fits within a conceptual framework that can be viewed in Bayesian terms and demonstrate, using long-term data on occupancy patterns of a grassland grouse, how prior probability profiles can be estimated. We obtained estimates by specifically focusing on whether and to what extent the Lesser Prairie-Chicken (Tympanuchus pallidicinctus) avoids anthropogenic features such as roads, powerlines, petroleum wells, fences, and buildings, in two study areas, one with denser and one with sparser incidence of features. Grouse strongly avoided large features such as outbuildings and tended to avoid tall features such as powerlines; by contrast, grouse did not or only slightly avoided low, unobtrusive features such as fences. We further examined co-location of pairs of anthropogenic features and found that certain features were avoided so strongly that avoidance distance may be shorter for other features; that is, birds were “pushed toward” some features because they are “pushed away” from others. In each case, our approach points toward a means to incorporate avoidance behavior directly into analytic studies of habitat selection, in that data on use (the posterior, as it were) could be used to infer the selection process provided data on preference (the prior, as it were) could be obtained

Strategically Locating Wildlife Crossing Structures for Florida Panthers Using Maximal Covering Approaches

Crossing structures are an effective method for mitigating habitat fragmentation and reducing wildlife-vehicle collisions, although high construction costs limit the number that can be implemented in practice. Therefore, optimizing the placement of crossing structures in road networks is suggested as a strategic conservation planning method. This research explores two approaches for using the maximal covering location problem (MCLP) to determine optimal sites to install new wildlife crossing structures. The first approach is based on records of traffic mortality, while the second uses animal tracking data for the species of interest. The objective of the first is to cover the maximum number of collision sites, given a specified number of proposed structures to build, while the second covers as many animal tracking locations as possible under a similar scenario. These two approaches were used to locate potential wildlife crossing structures for endangered Florida panthers (Puma concolor coryi) in Collier, Lee, and Hendry Counties, Florida, a population whose survival is threatened by excessive traffic mortality. Historical traffic mortality records and an extensive radio-tracking dataset were used in the analyses. Although the two approaches largely select different sites for crossing structures, both models highlight key locations in the landscape where these structures can remedy traffic mortality and habitat fragmentation. These applications demonstrate how the MCLP can serve as a useful conservation planning tool when traffic mortality or animal tracking data are available to researchers