Spatial Estimation Research Articles

Assessing relationships of cover crop biomass and nitrogen content to multispectral imagery

AbstractCover crops provide valuable roles in sustainable agriculture, provided they produce enough biomass. To accurately measure their services to field management, spatial estimates would be useful to producers. This study used multispectral drone imagery to produce maps of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and a digital surface model (DSM) of cover crop plots on sandy, Mid‐Atlantic soils. Cover crops included cereal rye (Secale cereale), mixtures of rye and crimson clover (Trifolium incarnatum), and mixtures of rye and hairy vetch (Vicia villosa). Their biomass was sampled in the spring of 2019, 2020, and 2021, dried, weighed, and analyzed for total nitrogen (N) content. Measurements of NDVI became saturated (i.e., reached a linear plateau) at 3.86 Mg biomass ha−1, NDRE at 5.72 Mg biomass ha−1, and the DSM at 5.11 Mg biomass ha−1. The measured N content became saturated at 80.9, 139.1, and 75 kg N ha−1 for NDVI, NDRE, and the DSM, respectively. Based on log transformations, NDVI was a stronger predictor of biomass and N, but not C:N. The NDRE was important for biomass, N, and C:N, while the DSM interactions with cover crop species helped predict both the N content and C:N of cover crop tissues. Accumulated growing degree days was important as an individual variable for biomass and N and as an interaction with cover crop species.

Real-time high-resolution modelling of grid carbon emissions intensity

Reducing greenhouse gas emissions in the energy industry is broadly acknowledged as important with numerous countries implementing targets to reduce emissions. Understanding the carbon emissions intensity of electricity grids is an important step in this process, and higher spatial and temporal granularity estimates are critical for delivering actionable insights. Existing emissions intensity models are limited in terms of their real-time availability and, in particular, their spatial granularity. Here we outline a method for estimating emissions intensity of large-scale interconnected electricity networks in real-time at substation-level resolution. This is realized by integrating nation-wide high-resolution electricity generation and demand data, regional population data, a detailed model of the electricity grid, and power flow simulation and flow tracing models. Results for Australia's National Electricity Market reveal that most capital city locations have higher emissions intensity than neighbouring regional areas, though the variation within a city can also be substantial. In general, emissions intensity is higher in areas closer to, or directly fed by emissions intensive coal and gas generators. These results have important implications for the design of energy efficiency incentives and demand response strategies, in the rollout of distributed renewable generation, and in carbon accounting.

Estimating urban land subsidence with satellite data using a spatially multiscale geographically weighted regression approach

Land subsidence has attracted widespread attention because of its potential hazards to sustainable urban development. The traditional methods for estimating land subsidence disregard the crucial aspect of altitude by analyzing spatial data solely on the 2D plane (longitude and latitude geographic coordinates). This research incorporates the altitude feature into the construction of the spatial matrix to develop a new method named spatially multiscale geographically weighted regression (SM-GWR) for a better estimation of land subsidence, enabling to determine the optimal bandwidth at 3D spatial scales. By adopting a 3D perspective, the new algorithm accurately fits settlement points based on spatial distances, addressing previous limitations of projecting points on a two-dimensional surface and ignoring height differences, thereby providing a more realistic depiction of spatial relationships. The satellite data is first used to monitor land subsidence through the small baseline subset synthetic aperture radar interferometry (SBAS-InSAR). Then, the SM-GWR model is developed to construct the 3D spatial weight matrix and update estimate parameters through back-fitting calibration. The proposed method's feasibility is confirmed through examples of land subsidence in China and Singapore. The case study results demonstrate that: (1) The SM-GWR model shows significant improvement in fitting land subsidence compared to the traditional method (R2 value of 0.908 and 0.934 in predicting annual subsidence and cumulative subsidence, respectively); (2) The contribution of the influencing factors is highly related to the nature properties and urban development of the city. The novelty of this study lies in the development of an SM-GWR model combined with satellite monitoring to achieve high-precision estimation of land subsidence, considering the 3D structure of spatial geographic data.

Spatial estimation of unidirectional wave evolution based on ensemble data assimilation

With the limitation of the high sensitivity of nonlinear models to initial conditions, the accurate estimation of wave spatial evolution is difficult to perform at a long distance. At this stage, a helpful approach is to improve the accuracy and robustness of the model through data assimilation technique. A robust data assimilation framework is developed by coupling ensemble Kalman filtering (EnKF) with the nonlinear wave model. The spatial evolution is obtained by numerically integrating the viscous modified Nonlinear Schrödinger (MNLS) equation. The performance of the EnKF-MNLS coupled framework is tested using synthetic data and laboratory measurements. The synthetic data is generated by the MNLS simulation superposing the Gaussian noise. In the synthetic cases, the estimated wave envelopes agree well with the clean solution. The results of laboratory experiments indicate that the EnKF-MNLS framework can improve the accuracy of wave forecasts compared to noised MNLS simulations. This study aims to enhance the noise resistance of the nonlinear wave model in spatial evolution and improve the accuracy of the model forecast.

High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China

Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R2 of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions.

TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.

Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications

Object recognition, localization, and tracking play a role of primordial importance in computer vision applications. However, it is still an extremely difficult task, particularly in scenarios where objects are attended to using fast-moving UAVs that need to robustly operate in real time. Typically the performance of these vision-based systems is affected by motion blur and geometric distortions, to name but two issues. Gimbal systems are thus essential to compensate for motion blur and ensure visual streams are stable. In this work, we investigate the advantages of active tracking approaches using a three-degrees-of-freedom (DoF) gimbal system mounted on UAVs. A method that utilizes joint movement and visual information for actively tracking spherical and planar objects in real time is proposed. Tracking methodologies are tested and evaluated in two different realistic Gazebo simulation environments: the first on 3D positional tracking (sphere) and the second on tracking of 6D poses (planar fiducial markers). We show that active object tracking is advantageous for UAV applications, first, by reducing motion blur, caused by fast camera motion and vibrations, and, second, by fixating the object of interest within the center of the field of view and thus reducing re-projection errors due to peripheral distortion. The results demonstrate significant object pose estimation accuracy improvements of active approaches when compared with traditional passive ones. More specifically, a set of experiments suggests that active gimbal tracking can increase the spatial estimation accuracy of known-size moving objects, under conditions of challenging motion patterns and in the presence of image distortion.

High‐Resolution Terrestrial Water Storage Estimates From GRACE and Land Surface Models

AbstractTerrestrial Water Storage (TWS) changes have been estimated at basin to continental scales from gravity variations using data from the Gravity Recovery and Climate Experiment (GRACE) satellites since 2002. The relatively low spatial resolution (∼300 km) of GRACE observations has been a main limitation in such studies. Various data processing strategies, including mascons, forward modeling, and constrained linear deconvolution (CLD), have been employed to address this limitation. Here we develop a revised CLD method to obtain a TWS estimate that combines GRACE observations with much higher spatial resolution land surface models. The revised CLD constrains model estimates to agree with GRACE TWS when smoothed. As an example, we apply the method to obtain a high spatial resolution TWS estimate in Australia. We assess the accuracy of the approach using synthetic GRACE data.

Neural-Network-Based DOA Estimation in the Presence of Non-Gaussian Interference

This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the interference is considered to be Gaussian-distributed and spatially white. However, in practice, this assumption is not guaranteed, which results in degraded DOA estimation performance. Maximum likelihood DOA estimation in the presence of non-Gaussian and spatially colored interference is computationally complex and not practical. Therefore, this work proposes a neural network (NN) based DOA estimation approach for spatial spectrum estimation in multi-source scenarios with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a-priori</i> unknown number of sources in the presence of non-Gaussian spatially-colored interference. The proposed approach utilizes a single NN instance for simultaneous source enumeration and DOA estimation. It is shown via simulations that the proposed approach significantly outperforms conventional and NN-based approaches in terms of probability of resolution, estimation accuracy, and source enumeration accuracy in conditions of low SIR, small sample support, and when the angular separation between the source DOAs and the spatially-colored interference is small.

Uncertainty and outliers in high‐resolution gridded precipitation products over eastern North America

AbstractSeveral observational precipitation products that provide high temporal (≤3 h) and spatial (≤0.25°) resolution gridded estimates are available, although no single product can be assumed worldwide to be closest to the (unknown) “reality.” Here, we propose and apply a methodology to quantify the uncertainty of a set of precipitation products and to identify, at individual grid points, the products that are likely wrong (i.e., outliers). The methodology is applied over eastern North America for the 2015–2019 period for eight high‐resolution gridded precipitation products: CMORPH, ERA5, GSMaP, IMERG, MSWEP, PERSIANN, STAGE IV and TMPA. Four difference metrics are used to quantify discrepancies in different aspects of the precipitation time series, such as the total accumulation, two characteristics of the intensity‐frequency distribution, and the timing of precipitating events. Large regional and seasonal variations in the observational uncertainty are found across the ensemble. The observational uncertainty is higher in Canada than in the United States, reflecting large differences in the density of precipitation gauge measurements. In northern midlatitudes, the uncertainty is highest in winter, demonstrating the difficulties of satellite retrieval algorithms in identifying precipitation in snow‐covered areas. In southern midlatitudes, the uncertainty is highest in summer, probably due to the more discontinuous nature of precipitation. While the best product cannot be identified due to the lack of an absolute reference, our study is able to identify products that are likely wrong and that should be excluded depending on the specific application.

Способ построения двухэлементной цифровой антенной решетки для оценки угловых координат радиосигналов со сверхразрешением с повышенной точностью на основе нижней границы Крамер-Рао

In the paper an effective method for optimal placement of directional antenna elements for digital spatial signal estimation is discussed. The digital antenna array evaluates spatial coordinates with the subsequent beamforming. However, as it is well known, the relative position of antenna elements, their number and the shape of the radiation pattern have a significant impact on the quality of the spatial pseudospectrum. The paper uses the previously obtained expression for estimating the Cramer-Rao lower bound to reduce the number of antenna elements without losing the accuracy of direction-of-arrival estimation of the signals. For example, a two-element array is obtained. The elements have a flat-top radiation pattern. It is shown that the accuracy of the MUSIC method in the obtained array is higher or comparable to the array composed of three elements.

The impact of acute asymmetric hearing loss on multisensory integration.

Humans have the remarkable ability to integrate information from different senses, which greatly facilitates the detection, localization and identification of events in the environment. About 466 million people worldwide suffer from hearing loss. Yet, the impact of hearing loss on how the senses work together is rarely investigated. Here, we investigate how a common sensory impairment, asymmetric conductive hearing loss (AHL), alters the way our senses interact by examining human orienting behaviour with normal hearing (NH) and acute AHL. This type of hearing loss disrupts auditory localization. We hypothesized that this creates a conflict between auditory and visual spatial estimates and alters how auditory and visual inputs are integrated to facilitate multisensory spatial perception. We analysed the spatial and temporal properties of saccades to auditory, visual and audiovisual stimuli before and after plugging the right ear of participants. Both spatial and temporal aspects of multisensory integration were affected by AHL. Compared with NH, AHL caused participants to make slow, inaccurate and unprecise saccades towards auditory targets. Surprisingly, increased weight on visual input resulted in accurate audiovisual localization with AHL. This came at a cost: saccade latencies for audiovisual targets increased significantly. The larger the auditory localization errors, the less participants were able to benefit from audiovisual integration in terms of saccade latency. Our results indicate that observers immediately change sensory weights to effectively deal with acute AHL and preserve audiovisual accuracy in a way that cannot be fully explained by statistical models of optimal cue integration.

Acoustic Scattering Characteristics and Geometric Parameter Prediction for Underwater Multiple Targets Arranged in a Linear Pattern

The construction of wind farm pilings, submarine pipelines, and underwater submarines involves multiple cylinders. However, there is currently a lack of economic research on predicting the mechanism and characteristics of mutual coupling of acoustic scattering from multiple cylindrical targets. This study investigates the mechanism and prediction method of acoustic scattering for the structural distribution characteristics of underwater multi-cylindrical targets. A model of a multi-cylindrical target’s two-dimensional acoustic field was established using the finite element method. Numerical calculations were then carried out to elucidate the scattering characteristics of the frequency–angle spectrum in far-field omnidirectional scattering. The simulation of echoes in the time domain explains how echoes propagate and interact with each other, and provides formulas for calculating interference and resonance frequencies. The frequency calculation formula extracts key features from the spectrum, providing a basis for predicting the characteristics of multi-cylindrical targets in terms of scale and spatial position. Measurement experiments were conducted on a double-cylindrical target in a water tank, and the theoretical calculations and experimental data were used to estimate the target’s radius and distance. The actual layout confirms the accuracy of the interference and resonance frequency prediction formulas. This study offers a valuable solution for refined feature extraction and spatial estimation of underwater targets.

Effect of mountainous rainfall on uncertainty in flood model parameter estimation

Abstract Explaining the significant variability of rainfall in orographically complex mountainous regions remains a challenging task even for modern raingauge networks. To address this issue, a real-time spatial rainfall field estimation model, called WREPN (WRF Rainfall-Elevation Parameterized Nowcasting), has been developed, incorporating the influence of mountain effect based on ground raingauge networks. In this study, we examined the effect of mountainous rainfall estimates on the uncertainty of flood model parameter estimation. As a comparison, an inverse distance weighting technique was applied to ground raingauge data to estimate the spatial rainfall field. To convert the spatial rainfall fields into flood volumes, we employed the ModClark model, a conceptual rainfall–runoff model with distributed rainfall input. Bayesian theory was applied for parameter estimation to incorporate uncertainty analysis. The ModClark model demonstrated good flood reproducibility regardless of the estimation method for spatial rainfall fields. Parameter estimation results indicated that the WREPN spatial rainfall field, which accounted for the influence of the mountain effect, led to lower curve numbers due to higher estimated rainfall compared to the IDW spatial rainfall field, while the concentration time and storage coefficient showed minimal differences.

That's not the Mona Lisa! How to interpret spatial capture-recapture density surface estimates.

Spatial capture-recapture methods are often used to produce density surfaces, and these surfaces are often misinterpreted. In particular, spatial change in density is confused with spatial change in uncertainty about density. We illustrate correct and incorrect inference visually by treating a grayscale image of the Mona Lisa as an activity center intensity or density surface and simulating spatial capture-recapture survey data from it. Inferences can be drawn about the intensity of the point process generating activity centers, and about the likely locations of activity centers associated with the capture histories obtained from a single survey of a single realization of this process. We show that treating probabilistic predictions of activity center locations as estimates of the intensity of the process results in invalid and misleading ecological inferences, and that predictions are highly dependent on where the detectors are placed and how much survey effort is used. Estimates of the activity center density surface should be obtained by estimating the intensity of a point process model for activity centers. Practitioners should state explicitly whether they are estimating the intensity or making predictions of activity center location, and predictions of activity center locations should not be confused with estimates of the intensity.

Geospatial analysis of Plasmodium falciparum serological indicators: school versus community sampling in a low-transmission malaria setting

BackgroundDue to low numbers of active infections and persons presenting to health facilities for malaria treatment, case-based surveillance is inefficient for understanding the remaining disease burden in low malaria transmission settings. Serological data through the detection of IgG antibodies from previous malaria parasite exposure can fill this gap by providing a nuanced picture of where sustained transmission remains. Study enrollment at sites of gathering provides a potential approach to spatially estimate malaria exposure and could preclude the need for more intensive community-based sampling.MethodsThis study compared spatial estimates of malaria exposure from cross-sectional school- and community-based sampling in Haiti. A total of 52,405 blood samples were collected from 2012 to 2017. Multiplex bead assays (MBAs) tested IgG against P. falciparum liver stage antigen-1 (LSA-1), apical membrane antigen 1 (AMA1), and merozoite surface protein 1 (MSP1). Predictive geospatial models of seropositivity adjusted for environmental covariates, and results were compared using correlations by coordinate points and communes across Haiti.ResultsConsistent directional associations were observed between seroprevalence and environmental covariates for elevation (negative), air temperature (negative), and travel time to urban centers (positive). Spearman’s rank correlation for predicted seroprevalence at coordinate points was lowest for LSA-1 (ρ = 0.10, 95% CI: 0.09–0.11), but improved for AMA1 (ρ = 0.36, 95% CI: 0.35–0.37) and MSP1 (ρ = 0.48, 95% CI: 0.47–0.49).ConclusionsIn settings approaching P. falciparum elimination, case-based prevalence data does not provide a resolution of ongoing malaria transmission in the population. Immunogenic antigen targets (e.g., AMA1, MSP1) that give higher population rates of seropositivity provide moderate correlation to gold standard community sampling designs and are a feasible approach to discern foci of residual P. falciparum transmission in an area.

Correction to: Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model

Correction to: Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model

Modeling and digital mapping of soil quality indicators in different land uses (a case study: Ravansar-Sanjabi Plain, Kermanshah).

Identifying the spatial variability of soil quality indices is necessary to manage soil resources on a watershed scale. This study aimed to identify suitable indices for soil quality assessment at the watershed scale using various soil characteristics and modeling approaches. Another objective was to map soil quality variability in a representative area in the Qarasu watershed in Kermanshah province, west of Iran. Latin hypercube sampling method using the auxiliary variables used to select 163 sampling points based on land use, soil, and topographical variability in an area of about 57 thousand hectares. In the field operations, soil profiles were described, and samples were taken from different soil profile horizons. Soil properties such as texture, pH, salinity, available water, equivalent calcium carbonate, saturation percentage, soil organic carbon, nitrogen, available phosphorous, potassium, Fe, Zn, Cu and Mn, and soil aggregate stability (mean weight diameter (MWD), geometric mean diametric (GMD), and stable aggregates larger than 0.25 mm (WAS)) measured in the laboratory. Soil quality indices (productivity index (PI), soil quality index (SQI) and reduced dimension soil quality index using principal component analysis (SQI-PCA)) were calculated for each point using the measured soil properties. Soil quality indices were simulated using modeling with the random forest and support vector machine methods and auxiliary variables. Results showed that the range of soil characteristics and integrated soil quality indices was very high across the study area. Soil organic carbon percent varied from about 0.19 to 8.44%. The range of changes in PI in the study area was more than SQI and SQI-PCA indices. Quantities of all soil quality indices were higher in forest and rangeland than in agricultural lands. The spatial estimation accuracy of the SQI-PCA was higher than other soil quality indices and converged well with land use changes compared to PI and SQI.

Mesoscopic mapping of hemodynamic responses and neuronal activity during pharmacologically induced interictal spikes in awake and anesthetized mice

Imaging hemodynamic responses to interictal spikes holds promise for presurgical epilepsy evaluations. Understanding the hemodynamic response function is crucial for accurate interpretation. Prior interictal neurovascular coupling data primarily come from anesthetized animals, impacting reliability. We simultaneously monitored calcium fluctuations in excitatory neurons, hemodynamics, and local field potentials (LFP) during bicuculline-induced interictal events in both isoflurane-anesthetized and awake mice. Isoflurane significantly affected LFP amplitude but had little impact on the amplitude and area of the calcium signal. Anesthesia also dramatically blunted the amplitude and latency of the hemodynamic response, although not its area of spread. Cerebral blood volume change provided the best spatial estimation of excitatory neuronal activity in both states. Targeted silencing of the thalamus in awake mice failed to recapitulate the impact of anesthesia on hemodynamic responses suggesting that isoflurane’s interruption of the thalamocortical loop did not contribute either to the dissociation between the LFP and the calcium signal nor to the alterations in interictal neurovascular coupling. The blood volume increase associated with interictal spikes represents a promising mapping signal in both the awake and anesthetized states.

Asymptotic spatial behavior for the heat equation on noncompact regions

AbstractWe consider the isotropic initial boundary value problem for the heat equation on open regions with noncompact boundary and construct differential inequalities for a generalized heat flow measure defined over a spherical cross section. Under suitable assumptions, integration of the differential inequality leads to spatial growth and decay rate estimates for mean‐square cross‐sectional measures of the time‐weighted temperature spatial gradient. The estimates are then used to obtain similar results for the time‐weighted temperature. In particular, when the base heat flow measure is positive, the time‐weighted temperature becomes pointwise unbounded at large spatial distance.