Explore the vast range of titles available.

Spatial data infrastructures (SDIs) are becoming more mature worldwide. However, despite this growing maturity, longitudinal research on the governance of SDIs is rare. The current research examines the governance history of two SDIs in the Netherlands and Flanders (Belgium). Both represent decades-long undertakings to create a large-scale base map. During these processes, SDI governance changed, often quite radically. We analyse written accounts from geo-information industry magazines to determine if the SDI governance of these two base maps can be considered adaptive. We conclude that SDI governance was adaptive, as it changed considerably during the evolution of the two SDIs. However, we also find that most governance models did not hold up very long, as they were either not meeting their goals, were not satisfying all stakeholders or were not in alignment with new visions and ideas. In recent years, the policy instruments governing these base maps became increasingly diverse. In particular, more hierarchical instruments were introduced. Indeed, governance scholars increasingly agree that governance can better respond to changes when a broader mix of policy instruments is applied. Alas, this does not make SDI governance any less complex.

Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species' distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.

One of the most frequently used approaches to represent collaborative mapping are probabilistic occupancy grid maps. These maps can be exchanged and integrated among robots to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such map fusion requires solving the unknown initial correspondence problem. This article presents an effective feature-based map fusion approach that includes processing the spatial occupancy probabilities and detecting features based on locally adaptive nonlinear diffusion filtering. We also present a procedure to verify and accept the correct transformation to avoid ambiguous map merging. Further, a global grid fusion strategy based on the Bayesian inference, which is independent of the order of merging, is also provided. It is shown that the presented method is suitable for identifying geometrically consistent features across various mapping conditions, such as low overlapping and different grid resolutions. We also present the results based on hierarchical map fusion to merge six individual maps at once in order to constrict a consistent global map for SLAM.

There exists a demand for effective land administration systems that can support the protection of unrecorded land rights, thereby assisting to reduce poverty and support national development—in alignment with target 1.4 of UN Sustainable Development Goals (SDGs). It is estimated that only 30% of the world’s population has documented land rights recorded within a formal land administration system. In response, we developed, adapted, applied, and tested innovative remote sensing methodologies to support land rights mapping, including (1) a unique ontological analysis approach using smart sketch maps (SmartSkeMa); (2) unmanned aerial vehicle application (UAV); and (3) automatic boundary extraction (ABE) techniques, based on the acquired UAV images. To assess the applicability of the remote sensing methodologies several aspects were studied: (1) user needs, (2) the proposed methodologies responses to those needs, and (3) examine broader governance implications related to scaling the suggested approaches. The case location of Kajiado, Kenya is selected. A combination of quantitative and qualitative results resulted from fieldwork and workshops, taking into account both social and technical aspects. The results show that SmartSkeMa was potentially a versatile and community-responsive land data acquisition tool requiring little expertise to be used, UAVs were identified as having a high potential for creating up-to-date base maps able to support the current land administration system, and automatic boundary extraction is an effective method to demarcate physical and visible boundaries compared to traditional methodologies and manual delineation for land tenure mapping activities.

Hillslope position has long been important in soil geomorphology. At the scale of county‐level soil maps, more soil boundaries are based on topography than any other soil‐forming factor. However, the inability to accurately delineate topographic breaks across hillslopes—either due to lack of sufficient topographic resolution or the proper technology to develop and/or model them—hinders soil mapping efforts. In this research, we developed a decision tree model for classifying hillslope position, which was calibrated and validated using the observations of soil scientists in the field. Different decision tree structures were tested with classification breaks based on calibration groups' mean midpoints, median midpoints, and fuzzy membership. The final model objectively and quantitatively classifies the five major hillslope positions and performs well on different landscapes, making it suitable for efficient application to large areal extents. The resulting maps of hillslope position represent base maps that can be used to (i) improve research on toposequences by providing explicit definitions of each hillslope element's location, (ii) facilitate the disaggregation of soil map unit complexes, and (iii) identify map unit inclusions that occur due to subtle topographic variation. Base maps developed by the model can also help identify areas of possible inaccuracies in soil maps, especially where soil boundaries cross topographic breaks. Predictions from the model enable the mapper to better place soil map unit boundaries at locations where defendable landscape breaks exist.

Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments.

For the problem where the existing hyperspectral unmixing methods do not take full advantage of the correlations and differences between all these bands, resulting in affecting the final unmixing results, we design an enhanced 2DTV (E-2DTV) regularization term and suggest a blind hyperspectral unmixing method with the E-2DTV regularization term (E-gTVMBO), which adds E-2DTV regularization to the previous blind hyperspectral unmixing based on g-TV model. The E-2DTV regularization term is based on the gradient mapping of all bands of HSI, and the sparsity is calculated on the basis of the subspace, rather than applying sparsity to the gradient map itself, which can utilize the correlations and differences between all bands naturally. The experimental results prove the superiority of the E-gTVMBO method from both qualitative and quantitative perspectives. The research results can be applied to land cover classification, mineral analysis, and other fields.

The purpose of multisource map super-resolution is to reconstruct high-resolution maps based on low-resolution maps, which is valuable for content-based map tasks such as map recognition and classification. However, there is no specific super-resolution method for maps, and the existing image super-resolution methods often suffer from missing details when reconstructing maps. We propose a map super-resolution (mapSR) model that fuses local and global features for super-resolution reconstruction of low-resolution maps. Specifically, the proposed model consists of three main modules: a shallow feature extraction module, a deep feature fusion module, and a map reconstruction module. First, the shallow feature extraction module initially extracts the image features and embeds the images with appropriate dimensions. The deep feature fusion module uses Transformer and Convolutional Neural Network (CNN) to focus on extracting global and local features, respectively, and fuses them by weighted summation. Finally, the map reconstruction module uses upsampling methods to reconstruct the map features into the high-resolution map. We constructed a high-resolution map dataset for training and validating the map super-resolution model. Compared with other models, the proposed method achieved the best results in map super-resolution.

Aim: Integration of macroecology and palaeoecology is an important trend in understanding rapidly changing marine ecosystems. However, the spatial mismatch between these two data types has led to difficulties in interpretation, particularly for short-lived phytoplankton and their microfossils. Fronts are narrow transition zones between distinct water masses and play an essential role in partitioning phytoplankton assemblages in the ocean. Whether they also delimit microfossil assemblages deposited at the sea floor is unclear. We examined the correlation between quasi-stationary mesoscale fronts and the spatial distribution of microfossils (diatoms, dinoflagellates and silicoflagellates) in the Bohai, Yellow and East China Seas, to establish a causal link between microfossil assemblages and the factors controlling pelagic species assemblages on continental shelves. Location: China. Time period: 2003–2015. Major taxa studied: Phytoplankton. Methods: Front locations were determined using gradient analysis of monthly satellite sea surface temperature (SST) for 2003–2015. Microfossil assemblages were classified using two-way indicator species analysis of the relative abundance of 345 species collected from surface sediments at 126 sites. The relationships between frontal patterns and microfossil assemblages were evaluated by superimposing maps of front location, microfossil distribution and environmental features in the main water masses and by canonical correspondence analysis. Results: Ten major fronts and four primary microfossil assemblages were identified. Analyses of the spatial patterns of fronts, microfossil assemblages, SST, salinity and nutrients revealed that the fronts partitioned the microfossils into assemblage types corresponding to the physicochemical features of the water masses. Main conclusions: Microfossil species assemblages and their indicator species are separated by mesoscale fronts and are correlated with water properties. Producing base maps of microfossil assemblages in relationship to SST fronts enables examination of the importance of quasi-stationary mesoscale fronts in constructing microfossil patterns on continental shelves. The results displayed potential for the interpretation sediment core data and their integration with the macroecological context.

Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.