Measurement Sampling Points Research Articles

Anthropogenic activities have accelerated the restoration of carbon sequestration services in the upper Yellow River

Carbon sequestration services stemming from ecosystems facilitate the absorption of CO2 and mitigation of greenhouse effects. Thus, investigating the spatiotemporal changes of carbon sequestration services and their response patterns to human activities is essential in relation to achieving the strategic carbon peak and carbon neutrality (“double carbon”) goal in a region. In this study, the spatiotemporal carbon sequestration patterns in the upper reaches of the Yellow River from 1985 to 2020 were assessed based on measured sample points and spatial modeling combined with multi-source remote sensing data. Specifically, the impacts of human activities on the carbon sequestration services in the area were quantitatively analyzed. The results showed that, for the past 35 years, carbon sequestration in the upper reaches of the Yellow River ranged from 80.09 Tg to 98.48 Tg, with lower levels in the northeast and southwest, and higher ones in the northwest and southeast. From 1985 to 1998, carbon sequestration in the upper reaches of the Yellow River was mainly affected by the natural climate and showed a fluctuating upward trend. From 1998 to 2001, carbon sequestration declined sharply due to the influence of human activities and the natural climate, whereas it showed a significant increasing trend from 2001 to 2020, affected by the combined effects of ecological engineering and climate change. In 1998–2001, the degree of human influence was −5.92% to approximately −11.68%, and from 2001 to 2020, it was approximately 2.32% to 6.78%. This study shows that while human social development can negatively affect the carbon sequestration services of ecosystems, ecological engineering can accelerate its recovery, recovery trends and recovery endpoints are constrained by natural factors.

Distributed event-triggered fusion estimation for networked asynchronous multi-rate systems

This paper studies the event-triggered distributed fusion estimation problem for a class of networked asynchronous multi-rate systems with nonlinear disturbances and bounded noises. Firstly, the asynchronous multi-rate systems are transformed into synchronous ones at the measurement sampling points (MSPs) by iterating the state equation. This paper proposes an event-triggered strategy to alleviate the communication burden to meet the finite communication resources during information transmission. Since the reduction of information will inevitably reduce the estimation performance, a compensation mechanism is adopted to restructure the untransmitted measurement. Then, a local estimator at the MSP is designed by the compensation information, and the local estimation at the unsampled measurement point is obtained by the multi-step prediction. Furthermore, by constructing the upper bound of the fusion estimation error, a robust recursive optimization approach, which can deal with nonlinear disturbances and bounded noises, is proposed to determine the distributed fusion criterion. Meanwhile, the stability conditions are derived such that the square errors of the designed estimators are asymptotically bounded. Finally, a target tracking example is used to demonstrate the effectiveness of the proposed method.

An experimental evaluation of grouping definitions for moving entities

One important pattern analysis task for trajectory data is to find a group: a set of entities that travel together over a period of time. In this paper, we compare four definitions of groups by conducting extensive experiments using various data sets. The grouping definitions are different by one or more of three different characteristics: whether they use the measured sample points or continuous movement, how distance is used to decide if entities are in the same group, and whether the duration of the group is measured cumulatively or as one contiguous time interval. We are interested in the differences between the definitions and comparisons to human-annotated data, if available. We concentrate on pedestrian data and on different crowd densities. Furthermore, we analyze the robustness of the definitions with respect to their dependence on different sampling rates. We use two types of trajectory data sets: synthetic trajectories and real-life trajectories extracted from video surveillance. We present the results of the quantitative evaluations. For experiments with real-life trajectories, we augment them with a qualitative evaluation using videos that show groups in the trajectories with a color coding.

Applicability of Grassland Production Estimation Using Remote Sensing for the Mongolian Plateau by Comparing Typical Regions in China and Mongolia

Grasslands on the Mongolian Plateau are critical for supporting local sustainable development. Sufficient measured sample information is the basis of remote sensing modeling and estimation of grassland production. Limited by field inventory costs, it is difficult to collect sufficient and widely distributed samples in the Mongolian Plateau, especially in transboundary areas, which affects the results of grassland production estimation. Here, considering that the measured sample points are sparse, this study took Xilingol League of Inner Mongolia Autonomous Region in China and Dornogovi Province in Mongolia as the study areas, introduced multiple interpolation methods for interpolation experiments, established a statistical regression model based on the above measured and interpolated samples combined with the normalized differential vegetation index, and discussed the applicability of grassland production estimation. The comparison results revealed that the point estimation biased sample hospital-based area disease estimation method and radial basis function showed the best interpolation results for grassland production in Xilingol League and Dornogovi Province, respectively. The power function model was suitable for grassland production estimation in both regions. By inversion, we obtained annual grassland production for 2010–2021 and the uneven spatial distribution of grassland production in both regions. In these two regions, the spatial change in grassland production showed a decreasing trend from northeast to southwest, and the interannual change generally showed a dynamic upward trend. The growth rate of grassland output was faster in Xilingol League than in Dornogovi Province with similar physical geography and climate conditions, indicating that the animal husbandry regulation policies play important roles beyond the influence of climate change. The study recommended grassland estimation methods for an area with sparse samples and the results can be used to support decision making for sustainable animal husbandry and grassland succession management.

Estimation for Networked Random Sampling Systems With Packet Losses

The state estimation problem is investigated in this article for networked random sampling linear stochastic systems. In the system, the system state uniformly updates and the measurement is randomly sampled. Packet losses induced by unreliable networks from a controller to an actuator and from a sensor to an estimator under the TCP protocol are tackled by employing two independent Bernoulli distributed stochastic variables. A state space model (SSM) at successfully received measurement sampling (SRMS) points is developed under the condition of known sampling time. Using an innovation analysis approach, a recursive nonaugmented optimal estimator is proposed in the linear minimum variance (LMV) sense. It can obtain state estimates at state update (SU) points and SRMS points. In addition, for multisensor systems, a centralized fusion estimator by reordering measurement data from sensors and a suboptimal distributed covariance intersection fusion estimator are proposed, respectively. The effectiveness of the proposed algorithms is verified through an example.

Distributed Fusion Estimation for Multisensor Multirate Systems With Packet Dropout Compensations and Correlated Noises

This article investigates distributed fusion estimation problems for multisensor multirate (MSMR) stochastic systems with correlated noises (CNs) and packet dropouts (PDs). The state updates at the fast rate while sensors uniformly sample at positive integer multiples of the state updating period. Different sensors may have different sampling rates. The system noise and measurement noises are auto- and cross-correlated at the same instant. The phenomenon of PDs randomly occurs during data transmission from a sensor to a data processor through unreliable networks. A recent developed compensation strategy that a predictor of a lost packet is employed as a compensator is adopted to optimize the tracking process. First, an optimal linear local filter (LF) for each sensor at measurement sampling points (MSPs) is presented by using an innovation analysis approach. Then, a local estimator (LE) at state updating points (SUPs) is proposed by filtering or prediction based on the LF at MSPs. Furthermore, estimation error cross-covariance matrices (CCMs) between arbitrary two LEs at SUPs are deduced, which can recursively be calculated by three joint difference equations. Finally, a distributed fusion filter (DFF) weighted by matrices in the sense of linear unbiased minimum variance (LUMV) is addressed. Period steady-state (PSS) property of the LEs, CCMs, and DFF is proved. A simulation example verifies the effectiveness of algorithms.

Remote sensing retrieval for Chlorophyll-a and Suspended Matter Concentration of Longyangxia Reservoir based on landsat OLI data

In this study, the Longyangxia Reservoir was taken as the research area and the chlorophyll-a and suspended matter concentration data of 20 measured sample points and the simultaneous landsat8 remote sensing image data were used. Firstly, remote sensing images were preprocessed, and then 15 sample data were randomly selected from 20 data points. Through correlation analysis of the 15 sample data and landsat OLI data, bands sensitive to chlorophyll-a and solid suspended matter concentration were found. Using the detected sensitive bands, an inversion model of the relevant concentrations of chlorophyll-a and solid suspended matter was constructed. Finally, the remaining 5 sample data are used to calculate the relevant precision index. By using the calculated determination coefficient, root mean square error and overall relative error, the precision of the inversion model is comprehensively evaluated, so as to find a universal model. Through the established inversion formula, ENVI/IDL and ArcGIS software were used to complete the mapping of chlorophyll-a and suspended matter concentration of Longyangxia Reservoir in 2013 and 2018, providing a reference for the comprehensive treatment of Longyangxia Reservoir.

Sparkpr: An Efficient Parallel Inversion of Forest Canopy Closure

Forest canopy closure is an important parameter to study forest ecosystem and understand the status of forest resources. With the development of remote sensing big data, the amount of remote sensing data has increased sharply, which makes the existing serial processing of remote sensing data face severe challenges.In order to satisfy the requirements of efficient remote sensing data processing, Spark open source framework is applied to the parallel processing of remote sensing images, and a parallel forest canopy density inversion algorithm based on Spark is proposed. We call this algorithm Sparkpr. Based on the GF-1 remote sensing images and 80 actual measured sample points obtained by Maoershan Laoshan Experimental Forest Farm of Northeast Forestry University in 2016. In this paper, a multi-element linear regression algorithm is used to carry out parallel inversion of the forest canopy density in the Laoshan Experimental Forest Farm of the Maoershan. The comparison experiment between single machine mode and spark standalone and spark on yarn mode is carried out. The experimental results show that the serial and parallel inversion results of forest depression density based on the model are consistent, and the parallel inversion results are accurate and credible. With the increase of computing nodes, the efficiency of parallel inversion is also improving.

Distributed Fusion Estimator for Multisensor Multirate Systems With Correlated Noises

A distributed fusion estimation algorithm is studied for multisensor multirate systems with correlated noises, where the state update rate is positive integer multiples of the measurement sampling rates and different sensors sample uniformly with different sampling rates. The measurement noises from different sensors are correlated with each other and are also correlated with the process noise. First, the state space model is established at the measurement sampling points (MSPs). Then, the optimal local filter at the MSPs and optimal local estimators (LEs) at the state update points are presented by an innovation analysis approach, respectively. Moreover, the cross-covariance matrices of estimation errors between any two LEs are derived, which involves three jointly recursive difference equations. At last, a distributed fusion estimator is proposed by applying the matrix-weighted fusion estimation algorithm in the linear minimum variance sense. The stability of the proposed algorithms is analyzed. Simulation results illustrate the effectiveness of the algorithms.

A Solution to Estimation Fusion for Multirate Measurements with Delays

This paper presents the distributed estimation fusion algorithms for a class of multirate multisensor systems with measurement delays. Different sensors uniformly sample measurements with different sampling rates and time delays. First, a new state-space model at the measurement sampling points is constructed and the original time delayed system is transformed to a delay-free one with correlated noises in limited time intervals to obtain the real-time estimation. Based on the new state-space model, the optimal local filter at the measurement sampling points is obtained for each single-sensor subsystem, and then the local estimator at the state update points is derived by using the predictor based on the filter at the measurement sampling points. Then, the estimation error cross-covariance matrices between any two local estimators are derived. Finally, the real-time distributed fusion estimator at the state update points is obtained based on the optimal fusion criterion weighed by matrices in the linear unbiased minimum variance sense. Besides, to avoid the correlation between system and measurement noises in the developed state-space model, a simple alternative but nonreal-time estimation fusion algorithm is also presented by employing dummy measurements. A numerical example is given to illustrate the effectiveness of the proposed algorithms.

State Estimation for a Class of Non-Uniform Sampling Systems with Missing Measurements.

This paper is concerned with the state estimation problem for a class of non-uniform sampling systems with missing measurements where the state is updated uniformly and the measurements are sampled randomly. A new state model is developed to depict the dynamics at the measurement sampling points within a state update period. A non-augmented state estimator dependent on the missing rate is presented by applying an innovation analysis approach. It can provide the state estimates at the state update points and at the measurement sampling points within a state update period. Compared with the augmented method, the proposed algorithm can reduce the computational burden with the increase of the number of measurement samples within a state update period. It can deal with the optimal estimation problem for single and multi-sensor systems in a unified way. To improve the reliability, a distributed suboptimal fusion estimator at the state update points is also given for multi-sensor systems by using the covariance intersection fusion algorithm. The simulation research verifies the effectiveness of the proposed algorithms.

Information Fusion Filter for Multi-rate Multi-sensor Systems

This paper is concerned with the information fusion filtering problem for a class of multi-rate multi-sensor systems, where the system is described at the highest sampling rate and different sensors may have different lower sampling rates. Firstly, the local filters (LFs) at state updating points are proposed by using the LFs at measurement sampling points. Then, the distributed suboptimal fusion filter is obtained by the wellknown covariance intersection fusion (CIF) algorithm. The filtering error variance matrices are derived to obtain the fusion weights. The computational cost is reduced since the cross-covariance matrices between any two local filters are avoided. Simulation example verifies the correctness and feasibility of the proposed algorithm.

Impacts of Climate Change on Regional Hydrological Regimes in the Spokane River Watershed

This study develops and implements a methodology to estimate the impacts of global climate change on regional hydrological regimes using ArcGIS Geostatistical Analyst. The model is easily used and can be expanded to different watersheds. The ArcGIS Geostatistical Analyst interface provides a comprehensive set of tools for creating surfaces from measured sample points compared with the previous method of using adjustable tension continuous curvature surface gridding. As a result, users can rapidly compare different interpolation techniques in order to obtain the best solution. Model results can subsequently be used in GIS models for visualization and analyses. The methodology was applied to the Spokane River Watershed. Results indicate that a 30% precipitation increase causes a 50% increase of streamflow when the temperature is normal compared to only a 20–30% increase in streamflow if the average annual air temperature is 1.5°C higher than normal. Conversely, a 20% precipitation decrease results in approx...

Geodesic Entropic Graphs for Dimension and Entropy Estimation in Manifold Learning

In the manifold learning problem, one seeks to discover a smooth low dimensional surface, i.e., a manifold embedded in a higher dimensional linear vector space, based on a set of measured sample points on the surface. In this paper, we consider the closely related problem of estimating the manifold's intrinsic dimension and the intrinsic entropy of the sample points. Specifically, we view the sample points as realizations of an unknown multivariate density supported on an unknown smooth manifold. We introduce a novel geometric approach based on entropic graph methods. Although the theory presented applies to this general class of graphs, we focus on the geodesic-minimal-spanning-tree (GMST) to obtaining asymptotically consistent estimates of the manifold dimension and the Re/spl acute/nyi /spl alpha/-entropy of the sample density on the manifold. The GMST approach is striking in its simplicity and does not require reconstruction of the manifold or estimation of the multivariate density of the samples. The GMST method simply constructs a minimal spanning tree (MST) sequence using a geodesic edge matrix and uses the overall lengths of the MSTs to simultaneously estimate manifold dimension and entropy. We illustrate the GMST approach on standard synthetic manifolds as well as on real data sets consisting of images of faces.