Spatial Variance Research Articles

Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China

AbstractSnow density is an essential property of snowpack. To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm−3) was generally greater than that in the stable (0.20 g cm−3) and accumulation periods (0.18 g cm−3), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p < 0.05) with elevation during the accumulation and stable periods. In addition to elevation, latitude and ground surface temperature also had critically impacted the spatial variability of snow density in the study area. In the current study, the machine learning models, especially RF, performed better than MLR for simulating snow density in the three periods. Based on the key environmental variables identified by the machine learning model and correlation analysis, this study also provides practical MLR equations to estimate the spatial variance of snow density during different snow periods in the middle Tianshan Mountains. This method can be used for regional snow mass and snow water equivalent prediction, leading to a better understanding of local snow resources.

Mesoscale eddies induce variability in the sea surface temperature gradient in the Kuroshio Extension

The Kuroshio Extension (KE) region is one of the most energetic regions in the global ocean where prominent mesoscale dynamics persistently occur. The spatial distribution and temporal evolution of the sea surface temperature (SST) gradient and mesoscale eddies in the KE are investigated. The SST gradient can be applied for identifying the fronts, and the SST gradient within two times the radii of the eddies is composited to quantify the impact of eddies on frontal activities. Depressed SST gradients are identified for eddies with both polarities, but prominent spatial variance in the SST gradient reveals that a large SST gradient is located to the north of anticyclones and along the south periphery for cyclones. The eddies are further separated into two groups depending on their location relative to the main path of the KE, as the background fields to the north and south of the KE are largely different. The spatial pattern, e.g., monopole and dipole features, and temporal variation in the SST gradient are fully studied over the lifespans of eddies. The results show that most eddies can significantly weaken the internal SST gradient and induce the horizontal redistribution of the SST gradient in surrounding regions. Cyclonic eddies north of the KE elevate the fronts along the periphery of eddies. The temporal variability in the SST gradient is prominent and largely varies for each group of eddies. This study offers quantitative analyses of the spatial and temporal relationships between eddies and fronts that are important for understanding the mesoscale dynamics in the world’s oceans.

Analyzing spatial variance of urban waterlogging disaster at multiple scales based on a hydrological and hydrodynamic model

Analyzing spatial variance of urban waterlogging disaster at multiple scales based on a hydrological and hydrodynamic model

Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy

The mountain pine (Pinus mugo ssp. Mugo Turra) is an important component of the alpine treeline ecotone and fulfills numerous ecosystem functions. To understand and quantify the impacts of increasing logging activities and climatic changes in the European Alps, accurate information on the occurrence and distribution of mountain pine stands is needed. While Earth observation provides up-to-date information on land cover, space-borne mapping of mountain pines is challenging as different coniferous species are spectrally similar, and small-structured patches may remain undetected due to the sensor’s spatial resolution. This study uses multi-temporal optical imagery from PlanetScope (3 m) and Sentinel-2 (10 m) and combines them with additional features (e.g., textural statistics (homogeneity, contrast, entropy, spatial mean and spatial variance) from gray level co-occurrence matrix (GLCM), topographic features (elevation, slope and aspect) and canopy height information) to overcome the present challenges in mapping mountain pine stands. Specifically, we assessed the influence of spatial resolution and feature space composition including the GLCM window size for textural features. The study site is covering the Sarntal Alps, Italy, a region known for large stands of mountain pine. Our results show that mountain pines can be accurately mapped (PlanetScope (90.96%) and Sentinel-2 (90.65%)) by combining all features. In general, Sentinel-2 can achieve comparable results to PlanetScope independent of the feature set composition, despite the lower spatial resolution. In particular, the inclusion of textural features improved the accuracy by +8% (PlanetScope) and +3% (Sentinel-2), whereas accuracy improvements of topographic features and canopy height were low. The derived map of mountain pines in the Sarntal Alps supports local forest management to monitor and assess recent and ongoing anthropogenic and climatic changes at the treeline. Furthermore, our study highlights the importance of freely available Sentinel-2 data and image-derived textural features to accurately map mountain pines in Alpine environments.

A Novel Frequency-Domain Focusing Method for Geosynchronous Low-Earth-Orbit Bistatic SAR in Sliding-Spotlight Mode

The low-earth-orbit synthetic aperture radar (SAR) can achieve enhanced remote-sensing capabilities by exploiting the large-scale and long-duration beam coverage of a geosynchronous (GEO) SAR illuminator. Different bistatic imaging modes can be implemented by the steering of an antenna beam onboard the LEO receiver, such as high-resolution sliding-spotlight mode. In this paper, the accurate focusing of GEO-LEO bistatic SAR (GEO-LEO BiSAR) in sliding-spotlight mode is investigated. First, the two major problems of the accurate bistatic range model, i.e., curved trajectory within long integration time and ‘stop-and-go’ assumption error, for sliding-spotlight GEO-LEO BiSAR are analyzed. Then, a novel bistatic range model based on equivalent circular orbit trajectory is proposed to accurately represent the range history of GEO-LEO BiSAR in sliding-spotlight mode. Based on the proposed range model, a frequency-domain imaging method is put forward. First, a modified two-step preprocessing method is implemented to remove the Doppler aliasing caused by azimuth variance of Doppler centroid and beam steering. Then, an azimuth trajectory scaling is formulated to remove the azimuth variance of motion parameters due to curved trajectory. A modified frequency-domain imaging method is derived to eliminate the 2-D spatial variance and achieve accurate focusing of the echo data. Finally, imaging results and analysis on both simulated data and real data from an equivalent BiSAR experiment validate the effectiveness of the proposed method.

Representative range of acoustic point source measurements in the Chukchi Sea

The use of stationary, active acoustics provides an effective approach to characterize and monitor temporal variability in the abundance and behavior of pelagic organisms, especially in seasonally ice-covered waters of high latitude marine ecosystems. However, point measurements from stationary echosounders are limited in their spatial coverage. A quantification of the spatial area represented by point measurements (i.e., representative range) is required to ensure effective biological characterization and monitoring. Here, concurrent mobile and stationary active acoustic data collected during summers of 2015 and 2017 are used to assess the representative range of fish and zooplankton density measurements from the Chukchi Ecosystem Observatory located at Hanna Shoal, Northeast Chukchi Sea. Six methods used to calculate representative ranges of backscatter means and variances resulted in representative ranges between approximately 0.3 and 86 km, depending on the year and calculation method. Such relatively large representative ranges reflect the tight bio-physical associations and large characteristic environmental length scales of the NE Chukchi Sea. Between years, up to 10-fold variations in representative ranges were attributed to interannual changes in water mass characteristics and associated species assemblages. Differences of 1–2 orders of magnitude in our calculated ranges among methods are attributed to differences in the rationale and associated assumptions of each approach. The choice of method and resulting representative range depends on monitoring goals: detection of change, mapping of spatial distributions, characterization of spatial variance, or interpolation of temporal variability over space. Our comparison of stationary acoustic to mobile surveys extends the understanding of spatiotemporal variability of marine organism distributions in the NE Chukchi Sea and informs cost-effective design of observing systems to monitor and predict impacts of environmental change.

Thermodynamic framework for variance-based non-local constitutive models

The present work deals with the development of a framework dedicated to the construction of constitutive models with non-local internal variables. Such internal variables allow considering the impact of microstructural changes on the current state of a material point. Non-locality is introduced by considering, not only the spatial average, but also the spatial variance of an internal variable in constitutive relations. The proposed framework relies on continuum thermodynamics to construct the set of constitutive equations. Such a framework allows including some information regarding the spatial distribution of internal variables when constructing non-local models for thermomechanical applications. In contrast with gradient-type models, this strategy does not require additional equilibrium equations and boundary conditions. For the purpose of illustration, some numerical examples are presented. According to the numerical results, the proposed framework can be used to circumvent the difficulties associated with excessive spatial localization or to consider size effects.

Statistical modeling and monitoring of image data in the presence of temporal and spatial correlations

In this paper, the regression-based approach was developed to monitor image data under two-scale analysis. In the first scale, wavelet transformation was used to extract the main features of geometric profile created from the images. The next scale was to monitor the small-scale components which could be expressed by correlation in error terms. To monitor correlation in error terms, one parametric and one non-parametric methods were developed. Parameters of the parametric model including spatial correlation coefficient and error term variance were estimated using Ordinary Least Squares (OLS) and Generalized Least Squares (GLS) estimators, respectively. In non-parametric method, no assumption was made about the structure of correlation in error terms. To extract useful information about the nature of correlation in error terms, Functional Principal Component Analysis (FPCA) was used. After extracting features for both scales, some appropriate test statistics were computed. Then, monitoring the process was performed by plotting these test statistics on corresponding control charts. Simulation and industrial case studies were also performed to evaluate the proposed method’s performance in detecting different shifts. The results indicated the proper performance of the proposed method in monitoring industrial processes to detect out-of-control conditions and identify the source of variability.

Figure-ground responsive fields of monkey V4 neurons estimated from natural image patches.

Neurons in visual area V4 modulate their responses depending on the figure-ground (FG) organization in natural images containing a variety of shapes and textures. To clarify whether the responses depend on the extents of the figure and ground regions in and around the classical receptive fields (CRFs) of the neurons, we estimated the spatial extent of local figure and ground regions that evoked FG-dependent responses (RF-FGs) in natural images and their variants. Specifically, we applied the framework of spike triggered averaging (STA) to the combinations of neural responses and human-marked segmentation images (FG labels) that represent the extents of the figure and ground regions in the corresponding natural image stimuli. FG labels were weighted by the spike counts in response to the corresponding stimuli and averaged over. The bias due to the nonuniformity of FG labels was compensated by subtracting the ensemble average of FG labels from the weighted average. Approximately 50% of the neurons showed effective RF-FGs, and a large number exhibited structures that were similar to those observed in virtual neurons with ideal FG-dependent responses. The structures of the RF-FGs exhibited a subregion responsive to a preferred side (figure or ground) around the CRF center and a subregion responsive to a non-preferred side in the surroundings. The extents of the subregions responsive to figure were smaller than those responsive to ground in agreement with the Gestalt rule. We also estimated RF-FG by an adaptive filtering (AF) method, which does not require spherical symmetry (whiteness) in stimuli. RF-FGs estimated by AF and STA exhibited similar structures, supporting the veridicality of the proposed STA. To estimate the contribution of nonlinear processing in addition to linear processing, we estimated nonlinear RF-FGs based on the framework of spike triggered covariance (STC). The analyses of the models based on STA and STC did not show inconsiderable contribution of nonlinearity, suggesting spatial variance of FG regions. The results lead to an understanding of the neural responses that underlie the segregation of figures and the construction of surfaces in intermediate-level visual areas.

Handling the MAUP: methods for identifying appropriate scales of aggregation based on measures on spatial and non-spatial variance

Abstract. The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determining appropriate scales of spatial aggregation. It examines a travel survey, undertaken in Ha Noi, Vietnam, that captures attitudes towards a potential ban of motorised transport in the city centre. The data are rich, capturing travel destinations, purposes, modes and frequencies, as well as respondent demographics (age, occupation, housing etc) including home locations. The dataset is highly dimensional, with a large n (26339 records) and a large m (142 fields). When the raw individual level data are used to analyse the factors associated with travel ban attitudes, the resultant models are weak and inconclusive - the data are too noisy. Aggregating the data can overcome this, but this raises the question of appropriate aggregation scales. This paper demonstrates how aggregation scales can be evaluated using a range of different metrics related to spatial and non-spatial variances. In so doing it demonstrates how the MAUP can be directly addressed in analyses of spatial data.

P.124 Subnuclear contact localization within the subthalamic nucleus in deep brain stimulation for Parkinson Disease

Background: Therapeutic response from subthalamic nucleus (STN) deep brain stimulation (DBS) for Parkinson disease (PD) has been associated with proximity to an ideal target, commonly in the dorsal sensorimotor STN. Automated registration and atlas-based segmentation has allowed for contact localization within STN subnuclei. We sought to apply these methods to characterize the spatial distribution of our active contact placements. Methods: We conducted a retrospective analysis of 55 patients who underwent bilateral STN DBS for PD. Post-operative CT/MRI scans were non-linearly registered into a standard space, and DBS-electrodes were localized using Lead-DBS. 3-dimensional meshes from a segmented atlas (Ewert 2017) were utilized. Analysis was performed in MATLAB R2019b. Results: Mean active contacts were within sensorimotor STN bilaterally, located posteroinferiorly compared to reported ideal targets. Centroids fell within (left/right): sensorimotor (46%/40%), associative (22%/22%), limbic (0%/2%) and outside STN (32%/36%). Principal components analysis demonstrated most spatial variance is explained by the first component (left 65.8%, right 61.9%). Conclusions: We obtained contact locations in relation to STN subnuclei, allowing for an anatomically guided approach to our analysis. 66% of the active contacts were located within the STN, and most of the spatial variation occurred along a single dimension. Future directions include utilizing subnuclei localizations to investigate clinical outcomes.

Evolution of an Amazonian Fish Is Driven by Allopatric Divergence Rather Than Ecological Divergence

Lowland central Amazonia is characterized by heterogeneous riverscapes dominated by two chemically divergent water types: black (ion-poor, rich in dissolved organic carbonate and acidic) and white (rich in nutrient and turbid) waters. Recent phylogeographic and genomic studies have associated the ecotone formed by these environments to an ecologically driven genetic divergence between fish present in both water types. With the objective of better understanding the evolutionary forces behind the central Amazonian teleostean diversification, we sampled 240 Mesonauta festivus from 12 sites on a wide area of the Amazonian basin. These sites included three confluences of black and white water environments to seek for repeated evidences of ecological divergence at the junction of these ecotones. Results obtained through our genetic assessment based on 41,268 single nucleotide polymorphism (SNPs) contrast with previous findings and support a low influence of diverging water physicochemical characteristics on the genetic structuration of M. festivus populations. Conversely, we detected patterns of isolation by downstream water current and evidence of past events of vicariance potentially linked to the Amazon River formation. Using a combination of population genetics, phylogeographic analysis and environmental association models, we decomposed the spatial and environmental genetic variances to assess which evolutionary forces shaped inter-population differences in M. festivus’ genome. Our sampling design, comprising three confluences of black and white water rivers, supports the main role of evolution by allopatry. While an ecologically driven evolution admittedly played a role in Amazonian fish diversification, we argue that neutral evolutionary processes explain most of the divergence between M. festivus populations.

MULTI-SPECTRAL EDGE DETECTION FOR ENHANCED EXTRACTION AND CLASSIFICATION OF HOMOGENEOUS REGIONS IN REMOTELY SENSED IMAGES

Abstract. Mediterranean environments are characterized by high spatial and temporal heterogeneity due to their climatological, lithological, soil and vegetation geo-diversity and their high population density which cause growing land-use transformations at the rural-urban fringe. Remote sensing mapping and monitoring land cover in these environments under such conditions is a challenging task. Instead of the common per pixel approach we suggest combining application of an object-oriented classification based on image objects separation through edge detection with unsupervised classification. The main elements of our methodology are: (1) separating image areas into vegetation/ non-vegetation regions utilizing NDVI threshold; (2) calculation of the spatial variance at different bands; (3) image objects extraction through enhancement of the differences between edge pixels and regions of homogeneity; (4) per-object classification for the homogenous areas; (5) overlaying large unclassified image areas by the results of ISODATA (Iterative Self-Organizing Data Analysis) unsupervised classification. Our methodology was applied on multi-spectral images acquired by the VENμS remote sensing system. The study area consists of a typical rural area in semi-arid climate regions undergoing increasing urbanization. Six test areas were selected representing different spatial combinations of natural/ planted forests, agriculture and built-up land-use/ land cover types. While bare fields were poorly classified, areas of low vegetation cover were classified with producer/user accuracies below 60%, built-up areas and roads, cultivated areas, shrublands and bata (dwarf-shrubs) and rocky areas gained good producer/ user classification accuracies.

Influence of Source Apportionment of PAHs Occurrence in Aquatic Suspended Particulate Matter at a Typical Post-Industrial City: A Case Study of Freiberger Mulde River

Polycyclic aromatic hydrocarbons (PAHs) have received extensive attention because of their widespread presence in various environmental media and their high environmental toxicity. Thus, figuring out the long-term variances of their occurrence and driving force in the environment is helpful for environmental pollution control. This study investigates the concentration levels, spatial variance, and source apportionment of PAHs in suspended particulate matter of Freiberger Mulde river, Germany. Results show that the concentrations of the 16 priority PAHs suggested by USEPA (Σ16PAHs) were in the range of 707.0–17,243.0 μg kg−1 with a mean value of 5258.0 ± 2569.2 μg kg−1 from 2002 to 2016. The relatively high average concentrations of Σ16PAHs were found in the midstream and upstream stations of the given river (7297.5 and 6096.9 μg kg−1 in Halsbrucke and Hilbersdorf, respectively). In addition, the annual average concentration of Σ16PAHs showed an obvious decreasing pattern with time. Positive Matrix Factorization (PMF) receptor model identified three potential sources: coke ovens (7.6–23.0%), vehicle emissions (35.9–47.7%), and coal and wood combustion (34.5–47.3%). The source intensity variation and wavelet coherence analysis indicated that the use of clean energy played a key role in reducing PAHs pollution levels in suspended sediments. The risk assessment of ecosystem and human health suggested that the Σ16PAHs in the given area posed a non-negligible threat to aquatic organisms and humans. The data provided herein could assist the subsequent management of PAHs in the aquatic environment.

Forest leaf litter nutrient discharge patterns in snowmelt surface runoff and watershed scale remote sensed simulation

The leaf litter decomposition is the important chain for the nutrient cycle in forest ecosystem, but its degradation dynamics and pulse discharge patterns in freeze-thawing watershed needed complete understanding. By integrating field observations and MODIS data, the temporal-spatial distributions of snow coverage and forest leaf litter biomass were analyzed. The critical period for snowmelt runoff under warming temperature and the relatively slow degradation patterns were identified. The on-site observations snowmelt runoff showed discharge concentration and fraction dynamics of typical forest leaf litter nutrients (carbon, nitrogen, and phosphorus) in thawing period. The snowmelt runoff flow and nutrient flux observed the linear regressions with the increased temperature from −8 °C to 6 °C (r2 = 0.443–0.987). The concentration of TOC, TN, and TP reached summit value around 50.0, 6.0, and 0.5 mg L−1 in the snowmelt runoff, respectively. The fraction analysis proved that the much high composition of dissolved organic fraction and the biggest organic phosphorus percentage was 94%. The comparison experiments of forest soil with or without leaf litter cover demonstrated that the leaf litter caused a lower discharge load in the snowmelt flow, and the leaf litter cover can decease the potential transport capability of the snowmelt runoff. Coupled with remote sensing data, the watershed leaf litter nutrient discharge model was developed with snowmelt hydrological process mode. The watershed averaged discharge of TOC, TN, and TP from deciduous broad-leaved forest leaf litter was around 851.99, 75.05, and 9.78 mg·m−2, respectively. The yearly simulation showed the spatial distribution variance of the nutrient discharge loads were held by different forest types, elevations, and slopes. The critical loss area identification provided new mitigations solution. The findings suggested that seasonal discharge of forest leaf litter nutrient in thawing period acted as a key contributor to watershed water pollution.

Temporal and Spatial Taylor's Law: Application to Japanese Subnational Mortality Rates

AbstractTaylor's law is a widely observed empirical pattern that relates the variances to the means of population densities. We present four extensions of the classical Taylor's law (TL): (1) a cubic extension of the linear TL describes the mean–variance relationship of human mortality at subnational levels well; (2) in a time series, long-run variance measures not only variance but also autocovariance, and it is a more suitable measure than variance alone to capture temporal/spatial correlation; (3) an extension of the classical equally weighted spatial variance takes account of synchrony and proximity; (4) robust linear regression estimators of TL parameters reduce vulnerability to outliers. Applying the proposed methods to age-specific Japanese subnational death rates from 1975 to 2018, we study temporal and spatial variations, compare different coefficient estimators, and interpret the implications. We apply a clustering algorithm to the estimated TL coefficients and find that cluster memberships are strongly related to prefectural gross domestic product. The time series of spatial TL coefficients has a decreasing trend that confirms the narrowing gap between rural and urban mortality in Japan.

Adrenergic stimulation amplifies the difference in beat-to-beat variability between the scar border zone and remote region

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): KU Leuven BOF-C1 “Blood pressure induced premature ventricular beats as triggers for ventricular arrhythmia in ischemic cardiomyopathy” Background Myocardial infarction (MI) results in a dense scar region surrounded by a heterogeneous region of fibrosis and remodeled myocytes called the border zone (BZ). Beta-adrenergic stimulation results in increased beat-to-beat variability of repolarization (BVR) which could increase spatial heterogeneity and arrhythmia vulnerability. Objective To examine the effect of adrenergic stimulation on the beat-to-beat variability in the BZ, compared to the remote region, using novel methodology for determining spatially dense activation-repolarization intervals. Methods Anterior-septal myocardial infarction (MI) was induced in 10 domestic pigs by 120-minute occlusion of the left anterior descending artery followed by reperfusion. Electro-anatomical mapping was performed after one month. The BZ was defined using contact mapping as the region with bipolar voltage between 0.5 and1.5mV. A non-contact recording of a 64-electrode array was translated to 2048 non-contact electrograms distributed over the LV (EnSite PrecisionTM, St. Jude/Abbott Medical). Electrophysiological recordings were made during baseline and during an isoproterenol (ISO) infusion (incremental doses of 0.01µg/kg until 0.04µg/kg). In each of the 2048 points non-contact electrograms over 25 consecutive beats were processed to determine the BVR using a custom-made algorithm, validated against monophasic action potential recordings. Results During baseline conditions the maximal BVR was increased in the BZ compared to the remote region (BZ: 3.28±0.90 ms vs remote: 2.61±0.67 ms, P=0.002). During ISO infusion the maximal BVR was also increased in the BZ (BZ: 3.55±0.74 ms vs remote: 2.21±0.60 ms, P<0.001). During baseline the BZ exhibited a larger spatial variance of BVR than the remote region (BZ: 0.20±0.11 ms2 vs remote: 0.087±0.055 ms2, P=0.002). During ISO infusion the spatial variance of BVR was larger in the BZ (BZ: 0.23±0.12 ms2 vs remote: 0.083±0.056 ms2, P=0.001). The maximal BVR was not significantly different during baseline and ISO in the BZ, nor the remote region (P>0.05). However, the difference of the maximal BVR between BZ and remote regions was significantly increased during ISO (baseline: 0.67±0.48 ms vs ISO: 1.34±0.49ms, P=0.001). Conclusion The MI BZ showed increased temporal heterogeneity in repolarization that could serve as functional substrate for re-entry. Adrenergic stimulation amplified this vulnerability by increasing the difference in maximal BVR between BZ and remote regions.

Spatial Patterns, Mechanisms, and Predictability of Barents Sea Ice Change

AbstractRecent Arctic winter sea ice loss has been most pronounced in the Barents Sea. Here we explore the spatial structure of Barents Sea ice change as observed over the last 40 years. The dominant mode of winter sea ice concentration interannual variability corresponds to areal change (explains 43% of spatial variance) and has a center of action in the northeastern Barents Sea where the temperate Atlantic inflow meets the wintertime sea ice. Sea ice area import and northerly wind also contribute to this “areal-change mode”; the area increases with more ice import and stronger winds from the north. The remaining 57% variance in sea ice, individually and combined, redistributes the sea ice without changing the total area. The two leading redistribution modes are a dipole of increase in sea ice concentration south of Svalbard with decrease southwest of Novaya Zemlya, and a tripole of increase in the central Barents Sea with decrease east of Svalbard and in the southeastern Barents Sea. Redistribution is mainly contributed by anomalous wind and sea ice area import. Basic predictability (i.e., the lagged response to observed drivers) is predominantly associated with the areal-change mode as influenced by temperature of the Atlantic inflow and sea ice import from the Arctic.

Land Use Spatial Optimization Using Accessibility Maps to Integrate Land Use and Transport in Urban Areas

The scarcity of urban land resources requires a well-organized spatial layout of land use to better accommodate human activities, however, as a widely accepted concept, the integration of land use and transport is not given due consideration in land use spatial optimization (LUSO). This paper aims to integrate land use and transport in LUSO to support urban land use planning. Maximizing accessibility fitness, which follows the underlying logic between land use types and transport characteristics, is introduced into multi-objective land use spatial optimization (MOLUSO) modelling to address transport considerations, together with widely-used objectives such as maximizing compactness, compatibility, and suitability. The transport characteristics, in this study, are identified by driving accessibility, cycling accessibility, and walking accessibility. Accessibility maps, which quantify and visualize the spatial variances in accessibility fitness for different land use types, are developed based on the empirical results of the relationship between land use types and transport characteristics for LUSO and addressing policy issues. The 4-objective LUSO model and a corresponding non-dominated sorting genetic algorithm (NSGA-II) based optimization method constitute a prototype decision support system (DSS) for urban land use planning. Decision-makers (e.g., planning departments) can choose an ideal solution to accommodate urban development needs from a set of Pareto-optimal alternatives generated by the DSS. The approaches to creating accessibility maps and MOLUSO modelling are demonstrated by the case study of Eindhoven, the Netherlands. This study advocates limited changes to the current land use pattern in urban planning, and the LUSO emphasizes urban renewal and upgrading rather than new town planning.

A Unified Algorithm for the Sliding Spotlight and TOPS Modes Data Processing in Bistatic Configuration of the Geostationary Transmitter with LEO Receivers

This paper deals with the imaging problem for sliding spotlight (SS) and terrain observation by progressive scan (TOPS) modes in bistatic configuration of the geostationary (GEO) transmitter with a low earth orbit satellite (LEO) receiver, named GTLR-BiSAR system. A unified imaging algorithm is proposed to process the GTLR-BiSAR data acquired in SS or TOPS modes. Our main contributions include four aspects. Firstly, the imaging geometry of this novel configuration is described in detail. Furthermore, the GTLR-BiSAR signal expressions were deduced in both time and frequency domains. These signal expressions provide great support for the design of processing the algorithm theoretically. Secondly, we present a unified deramping-based technique according to the special geometry of GTLR-BiSAR to overcome the azimuth spectrum aliasing phenomenon, which typically affects SS and TOPS data. Thirdly, the spatial variance of GTLR-BiSAR data were thoroughly analyzed based on the range-Doppler (RD) geolocation functions. On the basis of a former analysis, we put forward the azimuth variance correction strategy and modified the conventional chirp scaling function to solve the range variance problem. Finally, we completed the derivation of the two-dimensional spectrum after the range chirp scaling. On the basis of spectrum expressions, we compensated for the quadratic and residue phase, and the azimuth compression was completed by SPECAN operation. In addition, we provide a flow diagram to visually exhibit the processing procedures. At the end of this paper, the simulation and real data experiment results are presented to validate the effectiveness of the proposed algorithm.