Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

Cellular automata (CA) is a powerful tool for modeling the evolution of macroscopic scale phenomena as it couples time, space, and variables together while remaining in a simplified form. However, such application has remained challenging in forest insect epidemics due to the highly dynamic nature of insect behavior. Recent advances in temporal trajectory-based image analysis offer an alternative way to obtain high-frequency model calibration data. In this study, we propose an insect-CA modeling framework that integrates cellular automata, remote sensing, and Geographic Information System to understand the insect ecological processes, and tested it with measured data of mountain pine beetle (MPB) in the Rocky Mountains. The overall accuracy of the predicted MPB mortality pattern in the test years ranged from 88% to 94%, which illuminates its effectiveness in modeling forest insect dynamics. We further conducted sensitivity analysis to examine responses of model performance to various parameter settings. In our case, the ensemble random forest algorithm outperforms the traditional linear regression in constructing the suitability surface. Small neighborhood size is more effective in simulating the MPB movement behavior, indicating that short-distance is the dominating dispersal mode of MPB. The introduction of a stochastic perturbation component did not improve the model performance after testing a broad range of randomness degree, reflecting a relative compact dispersal pattern rather than isolated outbreaks. We conclude that CA with remote sensing observation is useful for landscape insect movement analyses;however, consideration of several key parameters is critical in the modeling process and should be more thoroughly investigated in future work

Thermal Imaging of Canals for Remote Detection of Leaks: Evaluation in the United Irrigation District

This report summarizes our initial analysis of the potential of thermal imaging for detecting leaking canals and pipelines. Thermal imagery (video format) was obtained during a fly over of a portion of the main canal of United Irrigation District. The video was processed to produce individual images, and 45 potential sites were identified as having possible canal leakage problems (see Appendix I for all 45 thermal images). District Management System Team personnel traveled to 11 of the 45 sites to determine if canal leakage was actually occurring. Of the 11 sites, 10 had leakage problems. Thus, thermal image analysis had a success rate of 91% for leak detection. Two sites had leaks classified as “severe” by the DMS Team. This report also provides a detailed analysis of 4 sites, 3 with leaks and 1 without. For each site, photographs are included showing the source of the leak and/or condition of the canal segment. A literature review of thermal imagery for leak detection is included in Appendix II. Our findings and recommendations are as following: 1. thermal imaging is a promising technique for evaluation of canal conditions and leak detection; 2. the district provide should provide personnel to help the DMS Team verify the remaining 34 sites; and 3. the district should consider correcting the problems identified at sites 7 and 8

Multispectral imaging systems for airborne remote sensing to support agricultural production management

This study investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production. The three systems have been used in agricultural studies. They range from low-cost to relatively high-cost, manually operated to automated, multispectral composite imaging with a single camera and integrated imaging with custom-mounting of separate cameras. Practical issues regarding use of the imaging systems were described and discussed. The low-cost system, due to band saturation, slow imaging speed and poor image quality, is more preferable to slower moving platforms that can fly close to the ground, such as unmanned autonomous helicopters, but not recommended for low or high altitude aerial remote sensing on fixed-wing aircraft. With the restriction on payload unmanned autonomous helicopters are not recommended for high-cost systems because they are typically heavy and difficult to mount. The system with intermediate cost works well for low altitude aerial remote sensing on fixed-wing aircraft with field shapefile-based global positioning triggering. This system also works well for high altitude aerial remote sensing on fixed-wing aircraft with global positioning triggering or manually operated. The custom-built system is recommended for high altitude aerial remote sensing on fixed-wing aircraft with waypoint global positioning triggering or manually operated. Keywords: airborne remote sensing, multispectral imaging, agricultural production managemen

Noise-Resistant Spectral Features for Retrieving Foliar Chemical Parameters

Foliar chemical constituents are important indicators for understanding vegetation growing status and ecosystem functionality. Provided the noncontact and nondestructive traits, the hyperspectral analysis is a superior and efficient method for deriving these parameters. In practice, thespectral noise issue significantly impacts the performance of the hyperspectral retrieving system. To systematically investigate this issue, by introducing varying levels of noise to spectral signals, an assessment on noiseresistant capability of spectral features and models for retrieving concentrations of chlorophyll, carotenoids, and leaf water content was conducted. Given the continuous waveletanalysis (CWA) showed superior performance in extracting critical information associating plants biophysical and biochemical status in recent years, both wavelet features (WFs) and some conventional features (CFs) were chosen for the test. Two datasets including a leaf optical properties experiment dataset (n = 330), and a corn leaf spectral experiment dataset (n = 213) were used for analysis and modeling. The results suggested that the WFs had stronger correlations with all leaf chemical parameters than the CFs. According to an evaluation by decay rate of retrieving error that indicates noise-resistant capability, both WFs and CFs exhibited strong resistance to spectral noise. Particularly for WFs, the noise-resistant capability is relevant to the scale of the features. Based on the identified spectral features, both univariate and multivariate retrieving models were established and achieved satisfactory accuracies. Synthesizing the retrieving accuracy, noise resistivity, and model’s complexity, the optimal univariate WF-models were recommended in practice for retrieving leaf chemical parameters

Current status and future directions of precision aerial application for site-specific crop management in the USA

The first variable-rate aerial application system was developed about a decade ago in the USA and since then, aerial application has benefitted from these technologies. Many areas of the United States rely on readily available agricultural airplanes or helicopters for pest management, and variable-rate aerial application provides a solution for applying field inputs such as cotton growth regulators, defoliants, and insecticides. In the context of aerial application, variable-rate control can simply mean terminating spray over field areas that do not require inputs, terminating spray near pre-defined buffer areas determined by Global Positioning, or applying multiple rates to meet the variable needs of the crop. Prescription maps for aerial application are developed using remote sensing, Global Positioning, and Geographic Information System technologies. Precision agriculture technology has the potential to benefit the agricultural aviation industry by saving operators and farmers time and money

Development and prospect of unmanned aerial vehicle technologies for agricultural production management

Unmanned aerial vehicles have been developed and applied to support agricultural production management. Compared with piloted aircraft, an Unmanned Aerial Vehicle (UAV) can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision. They can also “fill in the gap” in locations where fixed winged or rotary winged aircraft are not readily available. In agriculture, UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials. Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management. Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well. This paper presents an overview of research involving the development of UAV technology for agricultural production management. Technologies, systems and methods are examined and studied. The limitations of current UAVs for agricultural production management are discussed, as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management

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

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed