ASSESSING THE IMPACT OF BEET WEBWORM MOTHS ON SUNFLOWER FIELDS USING MULTITEMPORAL SENTINEL-2 SATELLITE IMAGERY AND VEGETATION INDICES

Remote sensing technology plays a crucial role in detecting and monitoring environmental issues, offering the ability to monitor large areas, diagnose problems early, and facilitate accurate interventions. By integrating in-situ data with qualitative measurements obtained from satellite images, comprehensive insights can be obtained, and statistical inferences can be established. This study focuses on analyzing the damages caused by beet webworm moths (Loxostege sticticalis) in sunflower fields located in the Ortaca neighborhood of Tekirdağ province in Thrace region, utilizing Sentinel-2 satellite images and in-situ data collected from the sunflower fields in Ortaca. The relationship between different spectral indices, such as the Enhanced Vegetation Index, Chlorophyll Index Green, and spectral transformation techniques like Tasseled Cap Greenness, derived from Sentinel-2 satellite images, and the observed damage rates in various sunflower fields' in-situ data was investigated. The results revealed a negative correlation between the variables, highlighting EVI as the most effective indicator of damage among the plant indices. Leveraging these findings, a damage map was generated using EVI, enabling visual interpretation of the damage status in other sunflower fields within the study area. These findings offer valuable insights into the impact of pests on sunflower crops, despite the accuracy evaluation results falling below the desired level, with an overall accuracy of 75% and a Kappa accuracy of 65%, attributed to the limited availability of in-situ data

Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators

Optimal and suboptimal decentralized estimators in wireless sensor networks (WSNs) over orthogonal multiple-access fading channels are studied in this paper. Considering multiple-bit quantization before digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel state information (CSI). When training symbols are available, we derive a MLE that is a special case of the MLE with unknown CSI. It implicitly uses the training symbols to estimate the channel coefficients and exploits the estimated CSI in an optimal way. To reduce the computational complexity, we propose suboptimal estimators. These estimators exploit both signal and data level redundant information to improve the estimation performance. The proposed MLEs reduce to traditional fusion based or diversity based estimators when communications or observations are perfect. By introducing a general message function, the proposed estimators can be applied when various analog or digital transmission schemes are used. The simulations show that the estimators using digital communications with multiple-bit quantization outperform the estimator using analog-and-forwarding transmission in fading channels. When considering the total bandwidth and energy constraints, the MLE using multiple-bit quantization is superior to that using binary quantization at medium and high observation signal-to-noise ratio levels

Cooperative spectrum sensing with noisy hard decision transmissions

Most critical component of the cognitive radio paradigm is spectrum sensing and accordingly, detection of primary users. Recently proposed cooperative spectrum sensing methods do not consider errors occurring during the transmission of local cognitive radio decisions to the cognitive base station. However, perfect communication is clearly not the case in realistic cooperative spectrum sensing scenarios and might lead to misleading performance result interpretations. In this paper, we extend the simple cooperative spectrum sensing communication model to admit transmission imperfections. Specifically, we consider the case where the local hard cognitive radio decisions that are based on any local detection scheme are corrupted by additive noise during transmission from cognitive radios to cognitive base station. Utilizing this extended cooperative spectrum sensing model, we present the complex optimal and a practical and effective suboptimal detector that is capable of operating with any local cognitive radio detection scheme: Two-step detector. We present simulation results investigating the performance of the proposed detectors and the effect of parameters of interest such as number of cognitive radios and signal to noise ratio. Moreover, we provide comparisons between non-cooperative and cooperative spectrum sensing performances revealing some cases where non-cooperative scheme is more effective

Cooperative spectrum sensing over imperfect channels

Cognitive radio has emerged as an innovative approach able to cope with the spectral limitations. Cognitive radio networks rely on detecting whether a particular segment of the radio spectrum is currently in use and to exploit the temporarily unused spectrum rapidly without interfering with the transmissions of other users. Thus, one of the most important and critical components of the cognitive radio is spectrum sensing and accordingly, detection of primary users. Recently proposed cooperative spectrum sensing methods do not consider errors occurring during the transmission of local cognitive radio decisions to the cognitive base station. However, perfect communication is clearly not the case in realistic cooperative spectrum sensing scenarios and might lead to misleading performance result interpretations. In this paper, we extend the cooperative spectrum sensing model to admit transmission imperfections and propose two simple, hardware-friendly and effective primary user detectors operating on noisy local cognitive radio decisions. We present simulation results investigating the performance loss incurred by considering realistic channels with varying signalto-noise ratio values. Moreover, we compare the two proposed cooperative spectrum sensing algorithms in various practical scenarios

Time divisional and time-frequency divisional cooperative spectrum sensing

In this paper we present a time divisional and a time-frequency divisional cooperative spectrum sensing technique suitable for cognitive radio (CR) networks. The two methods are well suited for very high bandwidth CR networks, such as UWB networks, where the individual nodes need to scan a wide range of spectrum which is a time consuming process. With the time divisional and the time-frequency divisional cooperative spectrum sensing approaches the nodes share the spectrum sensing functions cooperatively, coordinating in time and frequency, covering the total frequency band and also in near-continuous time. In this paper we present the corresponding algorithms and techniques for the two cooperative spectrum sensing approaches, analyze their performances, and compare the advantages and disadvantages with each other. We also present simulation results to verify the performance improvements in terms of probability of miss detection and the probability of false alarm for detecting the PU. Results show that the the proposed methods are best suited for detecting the PUs having low spectral occupancy statistics who occupy the spectrum very seldom

Spectrum sensing for cognitive radios with transmission statistics: Considering linear frequency sweeping

The spectrum sensing performance of Cognitive Radios (CRs) considering noisy signal measurements and the time domain transmission statistics of the Primary User (PU) is considered in this paper. When the spectrum is linearly swept in the frequency domain continuously to detect the presence of the PU the time-domain statistics of the PU plays an important role in the detection performance. This is true especially when the PU's bandwidth is much smaller than the CR's scanning frequency range. We model the transmission statistics that is the temporal characteristics of the PU as a Poisson arrival process with a random occupancy time. The spectrum sensing performance at the CR node is then theoretically analyzed based on noisy envelope detection together with the time domain spectral occupancy statistics. The miss detection and false alarm probabilities are derived from the considered spectral occupancy model and the noise model, and we present simulation results to verify our theoretical analysis. We also study the minimum required sensing time for the wideband CR to reliably detect the narrowband PU with a given confidence level considering its temporal characteristics

Cooperative shared spectrum sensing for dynamic cognitive radio network

Cooperative spectrum sensing for cognitive radio networks is recently being studied to simultaneously minimize uncertainty in primary user detection and solve hidden terminal problem. Sensing wideband spectrum is another challenging task for a single cognitive radio due to large sensing time required. In this paper, we introduce a technique to tackle both wideband and cooperative spectrum sensing tasks. We divide the wideband spectrum into several subbands. Then a group of cognitive radios is assigned for sensing of a particular narrow subband. A cognitive base station is used for collecting the results and making the final decision over the full spectrum. Our proposed algorithm minimizes time and amount of energy spent for wideband spectrum scanning by a cognitive radio, and effectively detects the primary users in the wideband spectrum thanks to cooperative shared spectrum sensing

Bayesian tracking in cooperative localization for cognitive radio networks

In this paper we consider cooperative localization and tracking of primary users (PU) in a cognitive radio network using Bayesian techniques. We use particle filtering methods to track the location of a PU in the network using cooperative localization techniques and present some results for noisy measurements. The cognitive radio (CR) nodes estimate the information related to the geographical position of the PU based on existing location identification and localization techniques and forward the noisy information to a cognitive radio base station (CRB), which then fuses the information to estimate the position of the PU in the network in order to perform a radio scene analysis. We propose a particle filtering approach that is suitable for tracking Gaussian and non-Gaussian noisy signals at the CRB to estimate the position of a PU, two importance-functions relative to the particle filtering algorithm are also presented. Simulations are performed on the proposed tracking algorithm and the results are presented in terms of the mean squared error of the positional estimates

Bayesian tracking in cooperative localization for cognitive radio networks

In this paper we consider cooperative localization and tracking of primary users (PU) in a cognitive radio network using Bayesian techniques. We use particle filtering methods to track the location of a PU in the network using cooperative localization techniques and present some results for noisy measurements. The cognitive radio (CR) nodes estimate the information related to the geographical position of the PU based on existing location identification and localization techniques and forward the noisy information to a cognitive radio base station (CRB), which then fuses the information to estimate the position of the PU in the network in order to perform a radio scene analysis. We propose a particle filtering approach that is suitable for tracking Gaussian and non-Gaussian noisy signals at the CRB to estimate the position of a PU, two importance-functions relative to the particle filtering algorithm are also presented. Simulations are performed on the proposed tracking algorithm and the results are presented in terms of the mean squared error of the positional estimates