Kernel Density Estimation Approach Research Articles

Reliability analysis of digital reactor protection systems in floating nuclear power plants

Abstract This paper presents a reliability model for digital reactor protection systems (RPSs) in floating nuclear power plants (FNPPs) that accounts for both the internal characteristics of RPS and the external environment. The internal characteristics of RPS include independent failures and common-cause failures (CCFs) of components, repair behavior, and actuation logic degradation. For the external environment, we incorporated a parts-pressure method and used the environmental factors to describe the impact of marine environment at component level. Detailed Monte Carlo simulation (MCS) algorithm was proposed to solve the reliability models with different environmental factors, and the results showed that the maximum value of the environmental factor was 3.2 under the requirements that the probability for RPS failing to generate the trip signal does not exceed 1 × 10−5 and the spurious trip frequency does not exceed one time per year. Reliability indexes, such as failure probability and spurious trip frequency, were also derived. The 90 % confidence intervals of these two indexes were further calculated in the uncertainty analysis by using the kernel density estimation (KDE) approach.

Large-scale Structures in COSMOS2020: Evolution of Star Formation Activity in Different Environments at 0.4 < z < 4

To study the role of environment in galaxy evolution, we reconstruct the underlying density field of galaxies based on COSMOS2020 (The Farmer catalog) and provide the density catalog for a magnitude-limited (K s < 24.5) sample of ∼210,000 galaxies at 0.4 < z < 5 within the COSMOS field. The environmental densities are calculated using a weighted kernel density estimation approach with the choice of a von Mises–Fisher kernel, an analog of the Gaussian kernel for periodic data. Additionally, we make corrections for the edge effect and masked regions in the field. We utilize physical properties extracted by LePhare to investigate the connection between star formation activity and the environmental density of galaxies in six mass-complete subsamples at different cosmic epochs within 0.4 < z < 4. Our findings confirm a strong anticorrelation between star formation rate (SFR)/specific SFR (sSFR) and environmental density out to z ∼ 1.1. At 1.1 < z < 2, there is no significant correlation between SFR/sSFR and density. At 2 < z < 4, we observe a reversal of the SFR/sSFR–density relation such that both SFR and sSFR increase by a factor of ∼10 with increasing density contrast, δ, from −0.4 to 5. This observed reversal at higher redshifts supports the scenario where an increased availability of gas supply, along with tidal interactions and a generally higher star formation efficiency in dense environments, could potentially enhance star formation activity in galaxies located in rich environments at z > 2.

Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics.

Noise removal is a critical stage in the preprocessing of point clouds, exerting a significant impact on subsequent processes such as point cloud classification, segmentation, feature extraction, and 3D reconstruction. The exploration of methods capable of adapting to and effectively handling the noise in point clouds from real-world outdoor scenes remains an open and practically significant issue. Addressing this issue, this study proposes an adaptive kernel approach based on local density and global statistics (AKA-LDGS). This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and noise points according to the spatial function relationship, which varies with the distance from the points to the center of the LiDAR. The probability density function (PDF) for real points is constructed using a multivariate Gaussian distribution, while the PDF for noise points is established using a data-driven, non-parametric adaptive kernel density estimation (KDE) approach. Experimental results demonstrate that this method can effectively remove noise from point clouds in real-world outdoor scenes while maintaining the overall structural features of the point cloud.

An integrated system to significant wave height prediction: Combining feature engineering, multi-criteria decision making, and hybrid kernel density estimation

Accurate prediction of significant wave height is paramount for the effective design, operation, and maintenance of wave energy converters. However, current research falls short in achieving precise and stable point predictions, along with comprehensive uncertainty analysis of significant wave height. To address this gap, this study presents a comprehensive significant wave height combined prediction system. This integrated system encompasses outlier detection utilizing Autoencoders, sophisticated feature engineering, a multi-criteria decision-based model selection methodology, a multi-objective homogeneous nuclear molecular optimization algorithm, and a hybrid kernel density estimation technique. To tackle the critical issue of model selection within ensemble prediction, we introduce a multi-criteria compromise solution ranking algorithm known as VIKOR for the selection of sub-models. Additionally, a novel multi-objective homogeneous nuclear molecular optimization algorithm is proposed, which incorporates joint opposing selection and an elite retention strategy to effectively manage multiple objectives simultaneously, yielding Pareto optimal solutions for combining weights. Furthermore, a hybrid kernel density estimation approach is developed, surpassing previous methods reliant on a single kernel function and fixed bandwidth, thereby achieving a more precise fit to the distribution of wave height data. The effectiveness of the proposed model is rigorously evaluated using wave height datasets from three distinct locations. The experimental results convincingly demonstrate that the significant wave height combined prediction system outperforms existing solutions, excelling in both point and interval predictions of significant wave height.

A Formation Mechanism of Spatial Distribution Pattern of Industrial Clusters under Flow Space

This study focuses on analyzing the spatial distribution pattern and formation mechanisms of urban industrial clusters and aims to address the mismatch between industrial clusters and resource distribution. Firstly, the spatial distribution pattern of industrial clusters is analyzed using the kernel density estimation approach. Subsequently, a multi-layered model of interactive driving factors is constructed to analyze the functional types within the multi-layered network space. Lastly, a spatial weighted regression analysis model, considering the intensity of flow space, is developed to explore the intrinsic formation mechanisms of industrial agglomeration. The experimental results indicate the following: (1) There is a trend of industrial agglomeration in the Yangtze River Delta region, primarily concentrated in cities such as Shanghai, Nanjing, Hefei, Jinhua, and Taizhou. (2) The impact of spatial interaction factors on industrial agglomeration development is significant, and the analysis of interaction networks reflects the strength of interactive influencing factors to a certain extent. (3) The regression analysis model, which incorporates interactive information considering flow space intensity, better aligns with the study of the actual mechanisms behind industrial agglomeration in physical space.

Reworking wild bootstrap‐based inference for clustered errors

AbstractCluster‐robust inference is increasingly common in empirical research. With few clusters, inference is often conducted using the wild cluster bootstrap. With conventional bootstrap weights the set of valid ‐values can create ambiguities in inference. I consider several modifications to the bootstrap procedure to resolve these ambiguities. Monte Carlo simulations provide evidence that both a new 6‐point bootstrap weight distribution and a kernel density estimation approach improve the reliability of inference. A brief empirical example highlights the implications of these findings.

Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection

Cancer is a highly heterogeneous condition that can occur almost anywhere in the human body. [&lt;sup&gt;18&lt;/sup&gt;F]fluorodeoxyglucose Positron Emission Tomography (&lt;sup&gt;18&lt;/sup&gt;F-FDG PET) is a imaging modality commonly used to detect cancer due to its high sensitivity and clear visualisation of the pattern of metabolic activity. Nonetheless, as cancer is highly heterogeneous, it is challenging to train general-purpose discriminative cancer detection models, with data availability and disease complexity often cited as a limiting factor. Unsupervised learning methods, more specifically anomaly detection models, have been suggested as a putative solution. These models learn a healthy representation of tissue and detect cancer by predicting deviations from the healthy norm, which requires models capable of accurately learning long-range interactions between organs, their imaging patterns, and other abstract features with high levels of expressivity. Such characteristics are suitably satisfied by transformers, which have been shown to generate state-of-the-art results in unsupervised anomaly detection by training on normal data. This work expands upon such approaches by introducing multi-modal conditioning of the transformer via cross-attention i.e. supplying anatomical reference information from paired CT images to aid the PET anomaly detection task. Furthermore, we show the importance and impact of codebook sizing within a Vector Quantized Variational Autoencoder, on the ability of the transformer network to fulfill the task of anomaly detection. Using 294 whole-body PET/CT samples containing various cancer types, we show that our anomaly detection method is robust and capable of achieving accurate cancer localization results even in cases where normal training data is unavailable. In addition, we show the efficacy of this approach on out-of-sample data showcasing the generalizability of this approach even with limited training data. Lastly, we propose to combine model uncertainty with a new kernel density estimation approach, and show that it provides clinically and statistically significant improvements in accuracy and robustness, when compared to the classic residual-based anomaly maps. Overall, a superior performance is demonstrated against leading state-of-the-art alternatives, drawing attention to the potential of these approaches.

Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach.

The current methods of classifying plant disease images are mainly affected by the training phase and the characteristics of the target dataset. Collecting plant samples during different leaf life cycle infection stages is time-consuming. However, these samples may have multiple symptoms that share the same features but with different densities. The manual labelling of such samples demands exhaustive labour work that may contain errors and corrupt the training phase. Furthermore, the labelling and the annotation consider the dominant disease and neglect the minor disease, leading to misclassification. This paper proposes a fully automated leaf disease diagnosis framework that extracts the region of interest based on a modified colour process, according to which syndrome is self-clustered using an extended Gaussian kernel density estimation and the probability of the nearest shared neighbourhood. Each group of symptoms is presented to the classifier independently. The objective is to cluster symptoms using a nonparametric method, decrease the classification error, and reduce the need for a large-scale dataset to train the classifier. To evaluate the efficiency of the proposed framework, coffee leaf datasets were selected to assess the framework performance due to a wide variety of feature demonstrations at different levels of infections. Several kernels with their appropriate bandwidth selector were compared. The best probabilities were achieved by the proposed extended Gaussian kernel, which connects the neighbouring lesions in one symptom cluster, where there is no need for any influencing set that guides toward the correct cluster. Clusters are presented with an equal priority to a ResNet50 classifier, so misclassification is reduced with an accuracy of up to 98%.

Identification of Seismo‐Volcanic Regimes at Whakaari/White Island (New Zealand) Via Systematic Tuning of an Unsupervised Classifier

AbstractWe present an algorithm based on Self‐Organizing Maps (SOM) and k‐means clustering to recognize patterns in a continuous 12.5‐year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano‐seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre‐eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes.

KDE-Based Rainfall Event Separation and Characterization

Rainfall event separation is mainly based on the selection of the minimum inter-event time (MIET). The traditional approach to determining a suitable MIET for estimating the probability density functions is often using the frequency histograms. However, this approach cannot avoid arbitrariness and subjectivity in selecting the histogram parameters. To overcome the above limitations, this study proposes a kernel density estimation (KDE) approach for rainfall event separation and characterization at any specific site where the exponential distributions are suitable for characterizing the rainfall event statistics. Using the standardized procedure provided taking into account the Poisson and Kolmogorov–Smirnov (K-S) statistical tests, the optimal pair of the MIET and rainfall event volume threshold can be determined. Two climatically different cities, Hangzhou and Jinan of China, applying the proposed approach are selected for demonstration purposes. The results show that the optimal MIETs determined are 12 h for Hangzhou and 10 h for Jinan while the optimal event volume threshold values are 3 mm for both Hangzhou and Jinan. The KDE-based approach can facilitate the rainfall statistical representation of the analytical probabilistic models of urban drainage/stormwater control facilities.

Convolutions of totally positive distributions with applications to kernel density estimation

In this work we study the estimation of the density of a totally positive random vector. Total positivity of the distribution of a random vector implies a strong form of positive dependence between its coordinates and, in particular, it implies positive association. Since estimating a totally positive density is a non-parametric problem, we take on a (modified) kernel density estimation approach. Our main result is that the sum of scaled standard Gaussian bumps centered at a min-max closed set provably yields a totally positive distribution. Hence, our strategy for producing a totally positive estimator is to form the min-max closure of the set of samples, and output a sum of Gaussian bumps centered at the points in this set. We can frame this sum as a convolution between the uniform distribution on a min-max closed set and a scaled standard Gaussian. We further conjecture that convolving any totally positive density with a standard Gaussian remains totally positive.

Toward robust process monitoring of complex process industries based on denoising sparse auto-encoder

Robust industrial process monitoring is crucial to ensure production safety and product quality stability. However, the process monitoring of high-dimensional, nonlinear, complex industrial processes is still challenging due to their inherent complexities, such as multi-phase, multi-field, and tight coupling of multiple sub-processes. In this article, a simple yet robust nonlinear process monitoring scheme based on a kind of denoising sparse auto-encoder (DSAE) is proposed. Specifically, a novel hybrid auto-encoder, namely DSAE, is established, to address the strong redundancy, nonlinearity and noise interference in process variables by integrating a sparse auto-encoder (SAE) and a denoising auto-encoder (DAE). Successively, an online process monitoring model is established by introducing two new process monitoring statistics computed on the DSAE-based feature representation space and residual space, respectively. Moreover, the Kernel density estimation (KDE) approach is adopted to determine the corresponding control limits of the monitoring statistics, which can avoid the conventional empirical assumption of F or Chi-square distribution on monitoring statistics. Extensive confirmative and comparative experiments conducted for the fault monitoring of a nonlinear numerical simulation system, the benchmark Tennessee Eastman process (TEP) and ventilation exhaust fans (VEFs) in the continuous casting process (CCP) from a top steel plant in China show that the proposed method is promising. Specifically, the proposed method performs favorably against the representative process monitoring methods, and it has especially a stronger robustness against noise and uncertain interference in nonlinear process systems.

Assessment of various seismic fragility analysis approaches for structures excited by non-stationary stochastic ground motions

A probabilistic assessment is performed using different seismic fragility analysis approaches for structures under non-stationary stochastic ground motions. Four commonly used probabilistic seismic fragility analysis (PSFA) approaches are adopted, which are least squares regression (LSR), maximum likelihood estimation (MLE), kernel density estimation (KDE) and Monte Carlo simulation (MCS). The principles of the four PSFA approaches are first introduced, and the theories of non-stationary stochastic acceleration time series are introduced in light of the spectral representation of random functions. Then an analytical procedure of different PSFA approaches is constructed for structures under non-stationary stochastic motions. After that a case study is carried out to analyze the results of the four PSFA approaches. The demand distributions of univariate random variable under certain intensity measure levels are discussed, and multiple fragility curves under different limit states for all the approaches are compared. In general, the MCS approach is set as the benchmark to verify the effectiveness of other approaches, but it is computationally consuming. The non-parametric KDE approach is recommended to estimate the univariate distribution under specified IM levels, because it can reflect the real distribution characteristics with combined efficiency and accuracy. For the seismic fragility under different limit states, the LSR approach is suitable for efficient data processing and general trend assessment, at the expense of accuracy. The MLE approach indicates a better applicability under a smaller response threshold and slighter damage conditions, while the KDE approach shows a better applicability under a larger response threshold and severer damage conditions. The paper compares the accuracy and applicability of various PSFA approaches under multiple conditions, and provides a basis to link probabilistic hazard and risk analyses for performance assessment in the future research.

An efficient QR-BiMGM model for probabilistic PV power forecasting

The inherent volatility and randomness of PV output has posed a severe danger to the safe and stable functioning of power systems in recent years, as grid-connected PV capacity has increased. Short-term probabilistic PV power predictions can provide power system decision-makers with more detailed information. A novel probabilistic PV power forecast model based on the fuzzy C-mean clustering algorithm and the QR-BiMGM model is proposed in this research. The MGM (Minimal Gated Memory) model reduces training time while enhancing prediction accuracy by simplifying the neuron structure. The model’s data mining capabilities is further enhanced by the bidirectional propagation structure. To provide probabilistic prediction results, the Quantile Regression (QR) model is employed to integrate the BiMGM model. Under diverse weather conditions, a comparative analysis using actual measurement data with four benchmark models shows that the proposed model has higher prediction performance and good generalization potential. The QR-BiMGM model’s mean integrated interval evaluation metric WC is 31.38 percent lower than QR-MGM, 50.21 percent lower than QR-LSTM, 44.85 percent lower than QR-BiLSTM, and 44.83 percent lower than QR-GRU, according to simulation results. The mean probabilistic prediction evaluation metric CRPS of the QR-BiMGM model is 3.5 percent lower than that of QR-MGM. 3.6 percent lower than QR-MGM, 20.91 percent lower than QR-LSTM, 17.56 percent lower than QR-BiLSTM, and 20.36 percent lower than QR-GRU. In addition, the probability density function based on the conditional quantile of PV power is estimated using a modified kernel density estimation approach.

A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth.

Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure 3 days prior to delivery. The newly developed methods are available in the R package KDExp.

A Kernel Density Estimation Approach and Statistical Generalized Additive Model of Western North Pacific Typhoon Activities

This paper presents a development of a statistical model of typhoon genesis, tracks based on kernel density estimation and a generalized additive model (GAM). Modeling of typhoon activity is ultimately beneficial to the people living in coastal zones, insurance/re-insurance companies, policy, planning and decision departments. A 50-year record (1972–2021) of typhoon track observations from the International Best Track Archive Climate Stewardship have been used to observe the distribution of typhoon genesis by kernel density estimation. The tracks are simulated through the development of a GAM. It reproduces the observation well. A distance calculation approach between observed and simulated tracks’ landfall have been used to validate the model and the model shows a very good skill (approximately 75%).

Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling

Uncertainties associated with power generation from wind energy systems and Photovoltaic (PV) power systems present a major challenge for power system planners and operators. To account for such uncertainties, probabilistic models and probability density estimations for wind speed and solar irradiance, and their corresponding wind and PV power are highly required for long-term (multi-year) power system planning, expansion, and dispatching tools. In this article, a novel Kernel Density Estimator (KDE) model using unbiased cross-validation method for bandwidth selection is proposed for the estimation of both wind speed and solar irradiance probability densities. The estimation performance of the proposed model is assessed against the traditional parametric models (Weibull and Rayleigh distributions for wind speed, and Beta distribution for solar irradiance), and the traditional nonparametric KDE approach employing a rule-of-thumb method for bandwidth selection. The performance accuracy of all models is tested using the coefficient of determination R2, two error metrics (Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)), in addition to the Kolmogorov–Smirnov (K–S) test that was used to assess the goodness-of-fit. The proposed approach achieved the highest percentage improvements for R2 (24% and 23%), and the lowest MAE (66% and 36%) and RMSE (63% and 25%) metrics over the popular parametric distributions for wind speed and solar irradiance, respectively, in addition to the K–S test p-values indicating a clear evidence of a good fit. Results confirm the accuracy and robustness of the probability density estimates for wind speed and solar irradiance produced by the proposed model.

The correlation of quadrupole transition rates of deformed nuclei by non-parametric approach

In this study, we tried to investigate the possible correlation of inter and intra-band quadrupole transition probabilities of rotational bands. To this aim, all the available experimental values of quadrupole transition rates between different levels of the ground band together quadrupole transitions of gamma and beta bands, 2γ+→0g+ and 2β+→0g+, of even-even prolate nuclei in the 150<A<250 mass region are analyzed. We extended the kernel density estimation approach as a non-parametric estimation formalism for this analysis and compared its predictions with the results of the commonly used parametric technique with emphasis on Porter-Thomas distribution. The results show that the statistical correlation is dominant for such inter-band transitions between different levels of the ground band but for intra-band transitions which originated from the beta band, deviation from correlated behavior is obvious. Also, the comparison of the statistical behavior of 2+→0+ transitions of different rotational bands, suggests the most GOE-like behavior for the quadrupole transitions between the levels of the ground band.

Pupil’s Fraction Learning based on Board Game Playingn of Confidence Intervals (MICIH) Kernel Density Estimation Approach

Primary school pupils have good mathematics learning achievements but lack interest and attitude towards mathematics learning in Taiwan. Therefore, it is necessary and feasible to study how to use game-based learning in mathematics. In this research, the board games are adopted and designed by the team members, and then are integrated into the learning of mathematics fractions. The pre-test and post-test are designed to analyze the effectiveness of pupils using board games for learning and the formation of fraction conceptions, supplemented by interviews to understand pupils’ interest and attitudes changes in learning. Research has found that pupils who only use board games for learning can recognize and read unit fractions, which can achieve the expected learning goals and enhance pupils’ interest and attitude towards mathematics learning. Finally, the limitations of this research study and the directions for future research are also proposed. Keywords: board game, fraction learning, game-based learning, teaching aids

珠江口伶仃洋中华白海豚栖息地利用对海岸线等变迁的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 珠江口伶仃洋中华白海豚栖息地利用对海岸线等变迁的响应 DOI: 10.5846/stxb202101070066 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国水产科学研究院南海水产研究所中央级公益性科研院所基本科研业务费专项资金资助（2017YB20）；广东省科技计划项目（2019B121201001）；农业农村部财政项目"中华白海豚保护行动计划" Long-term changes in habitat use of Indo-Pacific humpback dolphins (Sousa chinensis) in response to anthropogenic coastline shift in Lingding Bay of Pearl River Estuary, China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:沿岸鲸豚类栖息地易受人类活动的干扰，导致其分布和核心栖息地发生变化。珠江口-漠阳江口中华白海豚种群是目前所知全球范围内最大的种群，其中伶仃洋水域是其重要的栖息地。近年来，珠江口伶仃洋周边城市发展带来的人类活动增加，白海豚的生存压力日益增大，分析伶仃洋中华白海豚对栖息地环境变化的响应，研究对应的保护策略显得非常迫切。以多源陆地资源卫星Landsat为数据源，通过影像分析近43年珠江口伶仃洋围填海造成的海域流失，结合近20年来采用截线抽样法收集的海豚观测数据，运用含障碍核插值（Kernel interpolation with barriers）方法，分析白海豚的分布及核心栖息地的变化。结果显示：1986-2015年期间，研究区域内流失的海域面积为344.08km2；目击分布离人工海岸线的平均距离大于自然海岸线的平均距离，目击分布到自然海岸线和人工海岸线的平均距离均在减小，表明过去20年白海豚的栖息地使用选择发生了一些变化，被迫适应人类活动的干扰；1997-2016年白海豚的分布范围呈现先增加后减小，白海豚栖息地使用的重心偏向伶仃洋东部水域，核心栖息地趋向主航道和无人海岛附近水域萎缩，可能是海豚因海域食物资源减少而迫不得已的选择。不同时期，珠江口中华白海豚国家级自然保护区所覆盖的核心栖息地比例呈递减趋势，占比由79.9%下降到49.4%，当前有必要对保护区范围和功能区作出一些优化调整，以适应栖息地使用的变化格局。 Abstract:Cetacean living coastal habitat often suffer from anthropogenic disturbance, which would change their original distribution and core habitat use. Pearl River Estuary-Moyang River Estuary (PRE-MRE) population is the largest known (putative) population of Indo-Pacific humpback dolphin in the world. Lingding Bay is a critical habitat of PRE-MRE population, where human activities have become frequently in recent years and dolphin survival pressure is increasing. Analysis the response of Indo-Pacific humpback dolphin to environment change and making corresponding conservation strategy are urgent. This research mainly discussed coastal waters loss process in the past 43 years from sea reclamation in the Lingding Bay using the multi-source Landsat imageries. To examine the change of distribution and core habitat of dolphins, a new kernel density estimation approach that accounted for physical barriers to movements was applied with sighting records from line-transect surveys during past 20 years. Our results showed that about 344.08 km2 waters became unavailable from 1986 to 2015; average proximity of sighting sites to artificial shoreline was farther than to natural shoreline and average proximity of sighting sites to artificial shoreline and natural shoreline decreased, which indicated that dolphin habitat use changed and dolphin had to adapt anthropogenic disturbance; habitat use of dolphin focused on eastern Lingding Bay waters and distribution range reached peak in 2005-2006, but fell sharply in 2015-2016; dolphin's core habitat approaching main channels and around undeveloped islands might be the only option result from resource depletion. The proportion of core habitat showed a decreasing trend in the Chinese White Dolphin National Nature Reserve of Guangdong Pearl River Estuary, which decreased from 79.9% to 49.4% in different period. We recommend that the government should adjust current protected range to fulfill the change pattern from habitat use. 参考文献 相似文献 引证文献