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opencc-by-4.0Sep 2022View details →

HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru

The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.

dryad40/100

spectre: An R package to estimate spatially-explicit community composition using sparse data

An understanding of how biodiversity is distributed across space is key to much of ecology and conservation. Many predictive modelling approaches have been developed to estimate the distribution of biodiversity over various spatial scales. Community modelling techniques may offer many benefits over single-species modelling. However, techniques capable of estimating precise species makeups of communities are highly data intensive and thus often limited in their applicability. Here we present an R package, spectre, which can predict regional community composition at a fine spatial resolution using only sparsely sampled biological data. The package can predict the presence and absence of all species in an area, both known and unknown, at the sample site scale. Underlying the spectre package is a min-conflicts optimisation algorithm that predicts species' presences and absences throughout an area using estimates of α-, β-, and γ-diversity. We demonstrate the utility of the spectre package using a spatially-explicit simulated ecosystem to assess the accuracy of the package's results. spectre offers a simple-to-use tool with which to accurately predict community compositions across varying scales, facilitating further research and knowledge acquisition into this fundamental aspect of ecology.

opencc-zeroOct 2022View details →

opencc-by-4.0Mar 2022View details →

A framework for spatial map generation using acoustic echoes for robotic platforms

<div> <div> <div> <div> <div> <div> In this work, we present a framework for constructing a spatial map of an indoor environment using the concept of echolocation. More specifically, we propose a non-linear least squares (NLS) estimator which is combined with a spatial filtering technique, e.g., beamforming, to estimate both the time-of-arrival (TOA) and direction-of-arrival (DOA) of the acoustic echoes. The proposed framework is complemented with an echo detector to classify a spurious estimate and an acoustic reflector, i.e., a wall. Based on these estimators, we propose two algorithms that complement existing range sensors and aid robotic platforms in acoustic reflector localization and mapping: single-channel localization and mapping (ScLAM) and a multi-channel localization and mapping (McLAM). Compared to commonly used sensors, such as lidar, cameras and ultrasonic sensors, our proposed model-based approach can detect transparent surfaces that are typically found in an office environment and could work in audible frequency ranges. A proof-of-concept robotic platform was built to test our algorithms. According to our evaluation, both qualitative and quantitative experiments reveal that the proposed methods can detect an acoustic reflector up to a distance of 1.5 m at a signal-to-diffuse-noise ratio (SDNR) of 0 dB in a simulated environment and 10 dB in a real environment with an accuracy of 80%. </div> </div> </div> </div> </div> </div>

Fig. 5 in Using spatial indicators to investigate fish spawning strategies from ichthyoplankton surveys: A case study on co-occurring pelagic species from the North-East Aegean Sea Abstract

Fig. 5: Distribution and abundance maps of eggs during May (A, B) and July 2010 (C-E), in the NE Aegean Sea. The major patches are indicated by different colours, while the size of the circles is scaled by the maximum abundance per species and period (Table 2). The number of each patch indicates the ranking according to the abundance of individuals in each patch. The small black cross symbols show the centres of gravity (CG) of each major patch (i.e. those patches having more than 10% of the overall abundance; Table 3). The large cross indicates the CG of the population. The length of the cross axes indicate the isotropy of the egg distribution. The 200m isobaths are also shown (dark contour line).

Fig. 4 in Using spatial indicators to investigate fish spawning strategies from ichthyoplankton surveys: A case study on co-occurring pelagic species from the North-East Aegean Sea Abstract

Fig. 4: Box-and-whisker plots for temperature (oC; at 10 m depth), salinity (at 10 m depth), log-transformed integrated fluorescence (μg l-1) and log-transformed mesozooplankton biomass (mg m-2) between three groups of sampling stations: L – Lemnos plateau, O – offshore pelagic area north of Lemnos island, and T- Thracian Sea shelf. Boxes indicate median and interquartile ranges, whiskers delineate full ranges. F-values are provided for comparisons of the parameters between the three groups (L, O, T) and within each season. For comparisons that did not meet the assumptions of the analysis of variance, the Kruskal-Wallis statistic H is provided. Asterisks indicate significant differences: *p <0.05, **p <0.01, ***p <0.001. Post-hoc multiple comparisons were performed with a Student-Newman-Keul's test, where statistically significant differences among groups are indicated by letters a, b, c on the left side of each box. Groups with the same letter do not differ significantly.

Fig. 3 in Using spatial indicators to investigate fish spawning strategies from ichthyoplankton surveys: A case study on co-occurring pelagic species from the North-East Aegean Sea Abstract

Fig. 3: Contour maps of the vertical distribution of temperature (oC; left column), salinity (middle column) and fluorescence (μg Chlα l-1; right column) during May 2010 along transect A (A-C) and transect B (D-F), and during July 2010 along transects A (H-J) and B (K-M). Y-axis: depth of the water column; X-axis: distance (nmi) from the northern sampling station (0 nmi) to the southern station. The position (distance, nmi) of the sampling stations along the transects are shown in white, dashed lines on top of the temperature contour maps.

Fig. 2 in Using spatial indicators to investigate fish spawning strategies from ichthyoplankton surveys: A case study on co-occurring pelagic species from the North-East Aegean Sea Abstract

Fig. 2: Temperature (oC, 10 m depth; A, E), salinity (10 m; B, F), integrated fluorescence (μg Chl-a l-1, 0-100 m; C, G) and mesozooplankton biomass (mg m-2; D, H) during May (A-D) and July 2010 (E-H) in the study area.

Fig. 1 in Using spatial indicators to investigate fish spawning strategies from ichthyoplankton surveys: A case study on co-occurring pelagic species from the North-East Aegean Sea Abstract

Fig. 1: Ichthyoplankton (shown as open red circles) and CTD sampling stations (shown as filled red circles) during May and July 2010. The isobaths of 100 and 200 m are shown (light and dark blue lines, respectively). Black arrows indicate the main circulation pattern in the area: LIS - Lemnos-Imvros stream, SG - Samothraki gyre (Somarakis et al., 2002). The sampling stations of transects A and B (grey lines) show the vertical structure of the water column in Figure 3.

Fig. 3 in Spatial, seasonal and ontogenetic changes in food resource use by a piscivore fish in two Pantanal lagoons, Brazil

Fig. 3. Diet composition of Plagioscion ternetzi by size class in Sinhá Mariana lagoon (a) and in Chacororé lagoon (b) (EIG: Eigenmannia spp.; PIM: Pimelodella spp.; ROE: Roeboides spp.; PCU: Psectrogaster curviventris; CDO: Curimatella dorsalis; BRA: Brachyhypopomus spp.; SMA: Synbranchus marmoratus; TAR: Tetragonopterus argenteus; HOR: Hemiodus orthonops; LOR: Loricariichthys spp.; SBR: Schizodon borellii; AST: Astyanax spp.; SMG: Serrasalmus marginatus; LEP: Leporinus spp.; OF: other fish; SH: shrimp; INS: insect; FR: fish remains).

Fig. 2 in Spatial, seasonal and ontogenetic changes in food resource use by a piscivore fish in two Pantanal lagoons, Brazil

Fig. 2. Diet composition of Plagioscion ternetzi in the lagoons Sinhá Mariana (a) and Chacororé (b), during the flood and dry periods.

Fig. 4 in Spatial, seasonal and ontogenetic changes in food resource use by a piscivore fish in two Pantanal lagoons, Brazil

Fig. 4. Species abundance curve of fish species at Sinhá Mariana lagoon (a = flood period and b = dry period) and Chacororé lagoon (c = flood period and d = dry period). Species within the rectangle correspond to the ten most abundant in the lagoon, in order of importance; the species out of the rectangle correspond to the most consumed by Plagioscion ternetzi.

Fig. 1 in Spatial, seasonal and ontogenetic changes in food resource use by a piscivore fish in two Pantanal lagoons, Brazil

Fig. 1. Location of the study area in the system of Chacororé-Sinhá and Mariana lagoons, Pantanal Matogrossense, Mato Grosso State, Brazil.

opencc-by-4.0Jun 2013View details →

Fig. 2 in Relationships between morphology, diet and spatial distribution: testing the effects of intra and interspecific morphological variations on the patterns of resource use in two Neotropical Cichlids

Fig. 2. Head of Satanoperca pappaterra (a) and Crenicichla britskii (b), showing differences in the mouth protrusion.

opencc-by-sa-4.0Jul 2024View details →

Supplementary Datasets and Movies for the Paper "Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change"

Supplementary Datasets and Movies for the Paper  Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change  by Anthony Lomax DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2404.05437</a>   Movie S1 Animation of the 2018, Mw 7.1 Anchorage, Alaska sequence and background seismicity 2014-2022. Relocated seismicity shown for: 2014 – 2018 mainshock (light blue), 2018 mainshock – 1 month after mainshock (green), 1 month after mainshock through 2022 (light orange); large black dot indicates the Mw 7.1 mainshock hypocenter. See figure caption in main paper for more details. Movie S2 Animation of seismicity-stress, 3D finite-faulting potential slip results the 2018 Mw 7.1 Anchorage, Alaska earthquake sequence. The high-potential portion of the seismicity-stress finite-faulting field is shown in red for west-dipping reciever faults inferred from the first 1 day of aftershocks (blue dots) after the 2018 mainshock (large black dot). See figure caption in main paper for more details.   CSV (.csv) and NLL-Hypocenter (.hyp) format catalogs of NLL-SSST-coherence relocations used in this study: Parkfield_2022_NLL-SSST-coherence_20231201A.csv Parkfield_2022_NLL-SSST-coherence_20231201A.hyp AntelopeValley_2021_NLL-SSST-coherence_20231223A.csv AntelopeValley_2021_NLL-SSST-coherence_20231223A.hyp Anchorage_2018_NLL-SSST-coherence_20231125A.csv Anchorage_2018_NLL-SSST-coherence_20231125A.hyp

opencc-by-4.0Apr 2019View details →

Hyperparameter tuning and performance assessment of statistical and machine-learning models using spatial data.

This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data". The code (including figures, appendices and the manuscript) is packed in pathogen-modeling-3.zip or can be found directly in the <a href="https://github.com/pat-s/pathogen-modeling">Github repository</a>. <ul> <li>Publication figures: analysis/paper/submission/3/latex-source-files/</li> <li>Appendices: analysis/paper/submission/3/</li> </ul> This RC represents a static snapshot at the time of submission. The Github repository will receive changes after the publication was published. Data sources <ul> <li>Atlas Climatico: <a href="http://opengis.uab.es/wms/iberia/index.htm">http://opengis.uab.es/wms/iberia/index.htm</a></li> <li>DEM: ftp://ftp.geo.euskadi.eus/lidar/MDE_LIDAR_2016_ETRS89/</li> <li>Lithology: <a href="http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home">http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home</a></li> <li>pH: <a href="https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0">https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0</a></li> <li>soil: <a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a></li> </ul> Licenses All files are shared via the given license with the exception of "soil.tif" which is shared via the ODbL license.

opencc-by-4.0Apr 2022View details →

Figure 2 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil

Figure 2 Monthly mean relative abundance of medically relevant sand fly species. Please note the scale difference in the Y axis. Paracambi, RJ, Brazil, 1992-1994.

opencc-by-4.0Aug 2024View details →

Spatial Feature Engineering Dataset for Forest Aboveground Biomass Estimation Using Landsat Imagery

Study Area: The dataset covers forested regions in Oregon, Washington, Idaho, and eastern Montana, characterized by diverse climatic conditions due to orographic effects. The forests in the Coast Range and western slopes of the Cascades, with high precipitation (800-3000 mm annually), contrast with the drier forests in Idaho and Montana, which receive over 400 mm annually. The dataset includes highly productive Douglas-fir and western hemlock forests, with aboveground biomass (AGB) densities exceeding 1200 Mg ha⁻¹, as well as fire-adapted lodgepole and ponderosa pine forests in the rainshadow regions. LiDAR AGB Estimates: The dataset includes 176 lidar-derived AGB maps from 2002 to 2016, covering various regions in Oregon, Washington, Idaho, and Montana. A Random Forest (RF) model was used to estimate AGB at a 30m² resolution, utilizing lidar height features, DEM features, and climate data. Non-forested areas and buildings were masked using binary forest cover maps from the LCMS dataset and the Microsoft Building Footprints dataset. Reference Dataset: A composited AGB map, derived from the 176 lidar maps, was created to develop Landsat-based AGB models, covering 9,361,622 ha of forested land. The AGB layer was stratified into 30 bins, and training, development, and testing sets were constructed for model validation. The dataset includes 7500 test samples and 300,000 training and development samples, with a 500m buffer around test set locations to prevent spatial autocorrelation. Landsat Satellite Imagery: Landsat imagery from 1990 to 2022 was utilized, with three time series derived: all scenes, scenes from May to November, and annual medoid composites. The imagery was processed using the Google Earth Engine (GEE) platform, focusing on periods of maximum phenological activity. Feature Engineering: Extensive feature engineering was performed, generating spectral, spatial, temporal, and topographic features from Landsat imagery and DEM data. Features were extracted over the reference AGB map's domain, synchronized with the lidar acquisition dates. <ul> <li>LandTrendr Fitted Imagery: Spectral features were derived from LandTrendr-fitted imagery, smoothing variations in the time series.</li> <li>LandTrendr Disturbance and Recovery Features: Temporal features were derived from LandTrendr models, characterizing disturbance and recovery events.</li> <li>CCDC Disturbance and Recovery Features: CCDC algorithm-derived features characterized disturbances and recovery using harmonic models.</li> <li>Buffer Features: Local variations were captured using buffer statistics around each pixel.</li> <li>GLCM Features: GLCM texture features summarized the joint distribution of gray-tone values.</li> <li>Edge Detectors: Various edge detection operators captured spatial derivatives and edges.</li> <li>Morphological Operations: Morphological features were derived using multi-channel image processing techniques.</li> <li>Neighborhood Vectorization: Direct vectorization of satellite measurements in pixel neighborhoods.</li> <li>Neighborhood Similarity: Similarity features characterized the relationship between pixel neighborhoods and their centroids.</li> <li>Topographic Features: Topography was characterized using elevation, slope, aspect embeddings, and hillshade layers from the NED DEM.</li> </ul> This comprehensive dataset enables robust analysis of AGB models and their performance across diverse forested landscapes in the Pacific Northwest

opencc-by-4.0Aug 2024View details →

Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")

This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (https://doi.org/10.1016/j.jhydrol.2024.131930). Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.

opencc-by-4.0Sep 2018View details →

Fig. 1 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore

Fig. 1. Map showing the location of the Central Catchment Nature Reserve on mainland Singapore. All 27 camera points are indicated with a red circle. Black squares indicate the three camera points added to the 1 km2 grid. The six cage traps are marked with a blue cross. Dotted circles indicate areas the last remaining patches of primary forest in Singapore.