This dataset provides seven Sentinel-1 Synthetic Aperture Radar (SAR) scenes covering a portion of the Madeira River in Brazil from Porto Velho to Abuna. Each scene has been pre-processed for the use of detecting artisanal and small-scale mining (ASM) riverine dredges. The scenes were collected from the ASF search vertex (https://search.asf.alaska.edu/#/) as L1 Detected High-Res Dual-Pol (GRD-HD) products. They were acquired using a C-band SAR with the Interferometric Wide Swath (IW) mode, VV and VH polarizations, and a temporal resolution of 6 days. Each SAR acquisition covers a 250 km swath; however, these have been clipped to the study area. They have a 20 m x 22 m (ground range x azimuth) resolution, a 10 m x 10 m pixel spacing, and an Equivalent Number of Looks (ENL) of 4.4. The data is collected in ascending and descending passes; although, only descending passes were available in the study area. Four of the dates (20190604, 20190616, 20190628, and 20190710) required two scenes to be mosaicked together after pre-processing in order to cover the study area. The SAR data is pre-processed using the Sentinel Applications Platform (SNAP) software by following the steps: apply orbit file, thermal noise removal, border noise removal, calibration, and range doppler terrain correction. The commonly applied speckle filtering step is not applied as it reduces small spatial structures in the data, impacting the potential detection of the potentially small features produced by backscatter from ASM riverine dredges. Each scene contains the VH polarization in band 1 and the VV polarization in band 2, and should be displayed using the stretch type: standard deviation.

The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement in the radar line-of-sight. The DISP products enable the detection of anthropogenic and natural surface changes, including subsidence, tectonic deformation, and landslides.

The OPERA DISP suite comprises complementary datasets derived from Sentinel-1 and NISAR inputs, designated as DISP-S1 and DISP-NI, respectively. Each product, created per acquisition, adheres to a consistent structure, HDF5 file format, file-naming convention, and a 30 m spatial posting. This collection specifically includes DISP-S1 products, derived from Sentinel-1 data.

DISP-S1 products provide spatial coverage across North America, encompassing the United States, U.S. territories within 200 km of the U.S. border, Canada, and mainland countries from the southern U.S. border to Panama. These products are generated from Sentinel-1 Interferometric Wide (IW) swath mode acquisitions starting in mid-2016.

The OPERA DISP-S1 product contains modified Copernicus Sentinel data (2016-2025).

This is a supporting dataset for Tang et al. (2023), "Oblique blind faulting underneath the Luzon volcanic arc during the 2022 Mw 7.0 Abra earthquake, the Philippines". The original and downsampled line-of-sight displacements for modeling are presented in this repository. The coseismic interferogram using the synthetic aperture radar images from Copernicus Sentinel-1A descending track 32 on 21 July and 2 August, 2022 (6 days before and after the mainshock). The flight direction is ~N190° with a westward look angle ranging from 36° to 45°. Details of processing and downsampling schemes can be found in the paper. The Sentinel-1 images were processed by European Space Agency (ESA) and downloaded from Alaska Satellite Facility (ASF) Data Search Vertex (https://search.asf.alaska.edu/).

AIRSAR topographic SAR digital elevation model CTIF product

ASF's SAR Radiometric Terrain Corrected (RTC) dataset gives terrestrial world-wide coverage; excluding Antarctica, Greenland, Iceland, and Asia north of 60 degrees latitude. Users can analyze radiometry with the effects of topography removed. Topography can be analyzed with the effects of layover and shadow corrected. This SAR product works well with optical Landsat 8.

ALOS PALSAR Level 1.1

Population Density : This vector dataset provides the population density by commune in Cambodia, as provided by Cambodian Demographic Census 2008 (Ministry of Planning, National Institute of Statistics). Dataset were provided to Open Development Cambodia (ODC) in vector format by Save Cambodia's Wildlife's Atlas Working Group.Urban Density in Cambodia (2011) : This vector dataset provides the urban density in Cambodia, as given by the United Nations Population Fund (UNFPA). Dataset were provided to Open Development Cambodia (ODC) by Save Cambodia's Wildlife's Atlas Working Group.Population Projections for 2030 in Cambodia (2010) : This dataset provides projected population of 2030, projected annual growth rate in each province in Cambodia, given by National Institute of Statistics and the United Nations. Data were provided to Open Development Cambodia (ODC) in vector format by Save Cambodia's Wildlife's Atlas Working Group.River networks of Cambodia : Vector polyline data of river networks in Cambodia. Attributes include: name of river, name of basin, name of sub-basin, Strahler number.Canals in Cambodia (2008) : This dataset is included geographical locations of canals and types of canal such as earthen, levee and masonry. The data is released by Department of Geography of Ministry of Land Management, Urban Planning, and Construction of Cambodia, and then it is contributed by Office for the Coordination of Humanitarian Affairs (OCHA) and shared on Humanitarian Data Exchange (HDX). ODC's map and data team has collected the data from HDX website in Shapefile format and re-published it on ODC's website.Special economic zone in Cambodia (2006-2019) : This dataset describes the information of special economic zone (SEZ) in Cambodia from 2006 to 2019. The total number of 42 SEZ is recorded. The data was collected from many sources by ODC’s mappers such as the royal gazette of Cambodia's government, and reports of the governmental ministries in hard and soft copies of pdf format. Geographic data is encoded in the WGS 84, Zone 48 North coordinate reference system.Road and railway networks in Cambodia (2012- 2019) : Road networks are produced by Open Street Map. ODC's map and data team extracted the data in vector format. Moreover, the polyline data of railway​ given by Save Cambodia's Wildlife's Atlas Working Group in Cambodia for two statuses such as existing, proposed new lines in Cambodia.Forest cover in Cambodia (2015-2018) : This forest cover is extracted from the Forest Monitoring System (https://rlcms-servir.adpc.net/en/forest-monitor/) which is developed by SERVIR-Mekong and the Global Land Analysis and Discovery Lab (GLAD) from University of Maryland. The definition of forest for this dataset is the tree canopy greater than 10% with height more than 5 meters.Schools in flood-prone area 2013 (information 2012-2014) : This dataset is created by clipping between Cambodia flood-prone areas in 2013 dataset and Basic information of school dataset to identify schools are under the flood extend in 2013. The basic information of school contains the spatial location of school, the attribute information in 2014, and total enrollment in 2012.Basic map of Cambodia (2014) : These datasets contain three different types of administrative boundary levels: provincial, district and commune which were contributed by Office for the Coordination of Humanitarian Affairs (OCHA) to Humanitarian Data Exchange (HDX). The datasets were obtained from the Department of Geography of Ministry of Land Management, Urban Planning and Construction (MLMUPC) in 2008 and then unofficially updated in 2014 by referring to Sub-decrees on administrative modifications. Most Recent Changes: New province added (Tbong Khmum), with underlying districts and communes.Land cover in Cambodia (2012- 2016) : The land cover is extracted from the Regional Land Cover Monitoring System (https://rlcms-servir.adpc.net/en/landcover/) which is developed by SERVIR-Mekong. The primitives are calculated from remote sensing indices which were made from yearly Landsat surface reflectance composites. The training data were collected by combining field information with high-resolution satellite imagery.Cropland in Cambodia : This dataset contains information of cropland and location of croplands in Cambodia which was downloaded from World Food Programme GeoNode (WFPGeoNode) using data in 2013 from​ the Department of Land and Geography of the Ministry of Land Management, Urban Planning and Construction.Community Fisheries Map for Cambodia (2011) : This dataset provides 2011 geographic boundaries, size and the number of villages covered by each community fishery for which coordinates are available in Cambodia, as given by the Fisheries Administration. For those community fisheries sites without coordinates, locations are given as the center points of communes and metrics are taken from the Commune Database of 2011. Geographic data is encoded in the WGS 84 coordinate reference system. Data were provided to ODC in vector format by Save Cambodia's Wildlife's Atlas Working Group.Digital Elevation Model (DEM 12.5 m) in 2010 : This raster dataset provides the Digital Elevation Model in the world. Dataset were provided to ASF Data Search Vertex by EarthData. This dataset has high resolution terrain at 12.5 meter. Alaska Satellite Facility (ASF) : making remote-sensing data accessible. ASF operates the NASA archive of synthetic aperture radar (SAR) data from a variety of satellites and aircraft, providing these data and associated specialty support services to researchers in support of NASA’s Earth Science Data and Information System (ESDIS) project.Function Area : This dataset are produced by Open Street Map. The data extracted the data in vector format (point feature).Tourism area (Museum, Attraction) : This dataset are produced by Open Street Map. The data extracted the data in vector format (point feature).Entity : Royal Government of Cambodia, Ministry of Planning, National Institute of Statistics; Cambodian Demographic Census 2008. Phnom Penh, 2008; Save Cambodia's Wildlife; In Atlas of Cambodia: maps on socio-economic development and environment;Time period : 2006-2018Frequency of update : Always up-to-dateGeo-coverage() : NationalGeo-coverage: National() : Cambodia

The OPERA Level 4 Troposphere Zenith Radar Delays (TROPO-ZENITH) products are radar sensor-agnostic, one-way troposphere zenith-integrated delays, including both wet and hydrostatic components, at various height levels.

Tropospheric delay in satellite radar measurements is primarily influenced by atmospheric temperature, water vapor, and pressure, which are correlated with topography. It is characterized as the integral of air refractivity from the surface up to top of the atmosphere at approximately 80 km altitude. Refractivity consists of wet and hydrostatic components that vary in space and time, driven by atmospheric pressure, temperature, and water vapor partial pressure. While hydrostatic delay is mainly governed by atmospheric pressure and temperature , wet delay is predominantly affected by water vapor content, i.e water pressure normalized by the temperature. Global estimates of these atmospheric parameters are available from Numerical Weather Prediction models, such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Model high-resolution 15-day forecast (HRES) dataset, which provides the data used to compute the TROPO-ZENITH product.

The TROPO-ZENITH product contains one-way zenith-integrated tropospheric delays, which must be intersected with topography elevation, projected to an imaging path and scaled to the radar wavelength to apply the two-way tropospheric correction for radar propagation.

Understanding species distribution and habitat preferences is crucial for effective conservation strategies. However, the lack of information about population responses to environmental change at different scales hinders effective conservation measures. In this study, we estimate the potential and realized distribution of Atractus lasallei, a semi-fossorial snake endemic to the northwestern region of Colombia. We modelled the potential distribution of A. lasallei based on ecological niche theory (using maxent), and habitat use was characterized while accounting for imperfect detection using a single-season occupancy model. Our results suggest that A. lasallei selects areas characterized by slopes below 10°, with high average annual precipitation (>2500mm/year) and herbaceous and shrubby vegetation. Its potential distribution encompasses the northern Central Cordillera and two smaller centers along the Western Cordillera, but its habitat is heavily fragmented within this potential distribution. When the two models are combined, the species’ realized distribution sums up to 935 km2, highlighting its vulnerability. We recommend approaches that focus on variability at different spatio-temporal scales to better comprehend the variables that affect species’ ranges and identify threats to vulnerable species. Prompt actions are needed to protect herbaceous and shrub vegetation in this region, highly demanded for agriculture and cattle grazing. Methods Ecological Niche Model and Potential Distribution Presence data were acquired from three sources: 1) specimens from biological collections, obtained from the Global Biodiversity Information Facility (accessed 22 March 2022) [35] and most of them revised in situ in the following collections: MHUA-Museo de Herpetología Universidad de Antioquia (Curator: J.M. Daza-Rojas), CSJ-h-Museo de Ciencias Naturales de La Salle (D.Z. Urrego), CBUCES-D-Colecciones Biológicas Universidad CES (J.C. Duque); 2) iNaturalist records obtained directly from them (accessed March-May 2022; we did not use iNaturalist records from GBIF) by searching Atractus records in the northwest of Colombia that included pictures that allowed verification through morphology (coloration patterns, and scale counts when possible), and 3) individuals encountered during the field phase for occupancy models. Identification of individuals was based on the original species description and taxonomic revisions of the genus [28, 33]. Further, a geographical filter was applied to presence records that were within 1 km of each other to reduce spatial autocorrelation [36, 37]. We used the final database to delimit the species accessible area or M [38] based on the intersection between the minimum convex polygon generated with 50 km buffers around each presence record using QGIS (v.3.10) [39], and the biogeographic regions of the northern Central Cordillera and the Western Cordillera of Colombia 40. The environmental variables for the niche model included topographic variables, atmospheric climate variables including temperature corrected to ground level [41] and soil variables 42. Climate variables represent long-term averages (1980-2010 in the case of atmospheric variables, and 2000-2020 in the case of ground-level temperature; see S1). These variables were selected based on previous research findings regarding the distribution of semi-fossorial reptiles [25, 43-45]. All variables were used in the models at a spatial resolution of 1 km. Variables with finer resolutions were resampled using the bilinear method, with the R-package “raster” v.3.5 [46] in R v.4.2.1 [47]. Subsequently, a Spearman correlation test (S2) was conducted to select non-correlated variables (< 0.8), using R-package “corrplot” v.0.92 [48]. Finally, two sets of variables were created, one that included two ground-level temperature variables estimated at five centimeters above the ground (S1) [41], and the second included the same two variables but measured at atmospheric level [49]. Models were calibrated with each data set independently, ensuring all variables used were not correlated. The ecological niche model was generated using the maximum entropy algorithm [15] through the R-package “kuenm” [50]. This methodology allows the evaluation of different sets of environmental variables (set 1 and set 2, S1) and various model parameterizations to ultimately identify the best model according to a set of criteria. We allowed the regularization parameter to vary from 0.1 (very strict in relation to observed values) to 5 (more flexible in relation to observed values), where 1 is the default value. We also evaluated across linear (l), quadratic (q), and linear-quadratic (lq) responses. The models were trained using a 20% random partition of the occurrence data for model evaluation. The evaluation criteria included omission rate (<5%), partial receiver operating characteristic (partial ROC), area under the curve (AUC ratio>1), and the Akaike Information Criterion corrected for sample size (AICc) [51]. In case several models achieved the evaluation criteria, we performed a consensus model using the median of the selected models. Finally, to obtain the geographic projection, a cutoff threshold was applied using the 10 percentile training presence criteria from the best model(s) to generate a presence/absence map. Occupancy Models To identify fine-scale factors influencing the occupancy of A. lasallei within its known distribution area, single-season occupancy models were employed [24]. The sampling design followed the recommendations of a previous study for semi-fossorial species [52], wherein 30 linear transects of 100 m x 2 m were established within the sampling area, spaced at a minimum of 200 m apart to ensure independence of detection histories across sites (Fig 1). Each transect was equipped with nine artificial cover objects (three roof tiles, three boards, and three plastic sheets), which were installed a minimum of two months prior to sampling for the organisms to habituate to their presence (S3). The transects were surveyed between October 2021 and January 2022 to ensure consistent occupancy status during the sampling period (closed-site assumption) between 8 AM and 4 PM. Surveys involved searching beneath leaf litter and under cover objects (both artificial and natural). Each transect was surveyed a minimum of four times, with visits spaced at least two weeks apart to satisfy the assumption of temporal independence. Animals were photographed and examined in the field to ensure correct identification (Approval Act No. 138, February 9, 2021, granted by the Committee on Ethics for Animal Experimentation, Universidad de Antioquia). Occupancy models were constructed using the R-package “unmarked” (v.1.2.5) [53] implemented in the R software. All covariates were standardized (mean=0, units in standard deviations) prior to modelling. To identify the best models, we first established the best detection model assuming constant occupancy, and then we used this detection model in all occupancy models [54]. To model detectability, we included as covariates, the number of cover objects, both natural and artificial (N_obj); vegetation height (Veg_H) [55]; soil moisture (Soil_moisture); and soil temperature (T_ground), both measured using a HOBO proV2 datalogger beneath a roof tile or under the object where an individual of the species was located at the time of each visit. As covariates for occupancy, we used vegetation height (Veg_H) [55]; terrain slope (Slope); topographic convergence (Con); compound topographic index (CTI) [56]; annual mean soil temperature (Tprom), maximum temperature of the warmest month (Tmax), and minimum temperature of the coldest month (Tmin)[41]; depth of leaf litter (Leaf_Dep) and depth of the 0 horizon (Hori0), both measured in the field using a soil auger; euclidean distance to the nearest house (D_house), nearest forest (D_forest), and nearest water body (D_water). These distances were estimated in QGIS [39], identifying the nearest houses and forests to the centroid of each transect using satellite imagery from GoogleEarth (https://www.google.com/intl/es/earth/). To calculate the distance to water bodies, it was necessary to construct a detailed hydrographic network for the area using a 12.5 m resolution DEM obtained from Alaska vertex (https://search.asf.alaska.edu/), utilizing the hydrology toolbox in ArcGIS Pro (v.2.7) [57]. A total of 87 biologically plausible and simple models were evaluated, each including one or two variables (S4), 20 of the models were for the detection component with constant occupancy, and the remaining models were for the occupancy component. Finally, to evaluate model fit to our data, we performed a parametric bootstrap test on the chosen model, using the parboot function of R package “unmarked” v.1.4.1 [53]. This test generates multiple sets of data iteratively from the best model and then compares these sets with the detection histories obtained in the field. A chi-squared test was employed to evaluate the null hypothesis that the observations are consistent with the proposed model. Integration of models To estimate the species’ realized distribution area [58], we used the binary (presence-absence) geographic projection from the consensus niche model to identify the areas where the macro conditions were suitable and applied the best occupancy model within those areas at a higher spatial resolution (0.00025° 27 meters). Finally, the resulting map was transformed into a binary outcome using a threshold of 0.78, based on the Q3 (third quartile) of the occupancy distribution values of that map; this threshold corresponds to 4 m of vegetation height according to the best occupancy model (Fig 2), which is biologically justified if we consider that all presence records obtained in the field phase were found in places with vegetation below 4 m. References 15.

SMAP Level 1B Sigma Naught Low Res Product Metadata Version 2