Contributors: Raúl Roberto poppiel, Jose Alexandre Dematte, Marilusa Pinto Coelho Lacerda, José Lucas Safanelli, Rodnei Rizzo, Manuel Pereira de Oliveira junior, Jean Jesus Novais
... Maps of clay, silt and sand contents (g kg-1) predicted at 0-20 cm, 20-60 cm and 60-100 cm depths intervals (3D) obtained by random forest regression in Google Earth Engine. Gridded soil information covers Midwest Brazil, from 12° S to 20° S and from 45° W to 54° W, and is available with 250m resolution. The maps were cross-validated and had Coefficient of Determination ranging from 0.64 to 0.85 at all depth intervals.
Contributors: Qiankun Liu, Jingang Jiang, Changwei Jing, Zhong Liu, Jiaguo Qi
... In this paper, a new, alternative, multi-scale, multi-pollution source waste load allocation (WLA) system was developed, with a goal to produce optimal, fair quota allocations at multiple scales. The new WLA system integrates multi-constrained environmental Gini coefficients (EGCs) and Delphi-analytic hierarchy process (Delphi-AHP) optimization models to achieve the stated goal. This dataset consists of the raw data and the source code of models (The multi-constrained environmental Gini coefficients and Delphi-analytic hierarchy process optimization models). The source code of the multi-constrained EGCs and Delphi-AHP models was used to run the program in MATLAB environment to allocate waste load reduction quotas at both the regional scale and the site-specific scale with multiple pollution sources. The raw data mainly consists of the following two parts: (1) The shp files of various geographic information data, which was used to depicture the administrative divisions, pollution source distribution, geographical characteristics and patterns of Xian-jiang watershed; (2) The basic data includes the statistical yearbook data of villages and towns in Ningbo city, the various indicator data using to calculate the weights at criteria level and decision-making level, the contribution coefficients, and the EGC values of the three pollutants. On the basis of these data, a new, alternative, multi-scale, multi-sector optimal WLA framework was developed. The new scheme provides decision-makers critical information (i.e., the best compromise solutions of WLA) and practical guidance as they address the related water pollution control. The results, in comparison with existing practices by the local governments, suggested that the pollution discharge quota at regional scale is much fairer than the existing WLA and, even have some environmental economic benefits at pollutant source scale after optimal WLA. Some important conclusions had been found: 1) Reductions and proportions of pollutants at regional scale are significantly associated with the region’s actual socioeconomic development modes. 2）There are certain characteristics that high-reduced pollution sources tend to share (which are listed in the article). The sources with the above features should be the top priorities in the reduction of removals. 3）Most previous studies reported primarily on the WLA of removals among point sources pollution. Conversely, we found that the industrial pollution source should be the last option for reduction from an environmental-economic benefit perspective. Instead, the often overlooked types, such as agricultural non-point source and domestic sources, deserve more attention, especially in extensive rural areas.
Contributors: Shannon Burnett
... Sedimentary data for Nullarbor Etched dunes paper
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Contributors: Lina Garcia, Jean Diaz, Humberto Loaiza-Correa, Andrés Restrepo
... Thermal and visible aerial images, with their metadata, captured using the UAV Matrice 100 equiped with a visible camera (Zenmuse X3) and a thermal camera (Zenmuse XT) of a planar scene at Universidad del Valle, Cali, Colombia. The thermal images were captured using the Zenmuse XT camera on board of a Matrice 100 UAV. Altitude = 100.4 m, Speed = 6.4 m/s and Overlap ratio = 90%. The visible images were captured using the Zenmuse X3 camera on board of a Matrice 100 UAV. Altitude = 80.9 m, Speed = 6.4 m/s and Overlap ratio = 80%. Also, a georeferenced orthoimage was generated using the captured visible images and the Agisoft Metashape software. In addition, homography matrices were obtained from a small number of correspondences manually selected between the orthoimage and every thermal and visible image. File "ORTHOUV2018.TIFF" = The georeferenced orthoimage. Folder "THERMAL" - Files "LWIRXX" = Thermal aerial images. - Files "HORTHOtoLWIRXX" = The homographies between the orthoimage and the thermal aerial images. - Files "LWIR.kml" = The geospatial data related to the thermal images. Folder "VISIBLE" - Files "VSXX" = Visible aerial images. - Files "HORTHOtoVSXX" = The homographies between the orthoimage and the visible aerial images. - Files "VS.kml" = The geospatial data related to the visible images.
Contributors: Tongtong Wang, Yuankun Luo, Zhilin Tao, Weijie Chen, Xin Gu
... The zip file contains project files, screenshots of research results, chart data, experimental data, simulation data, and grid independence verification data.
Contributors: Olanrewaju Lawal
... Exposure capture factors which could be manifested in the magnitude and intensity of long-term changes in climate (Intergovernmental Panel on Climate Change, 2007) and in this context factors with impact on agricultural production. Temperature and rainfall were used to capture the extent to which Maize is exposed to climate change. Data was sourced from the Centre for Environmental Data Analysis (CRU TS release 4), with data extracted for 1941 - 2015. The data were processed within R (Version 3.4.2), within this environment the mean (temperature and rainfall) for northern and southern parts of the country were computed. The growing season for Maize in the north spans from May to September while in the south it starts from March and ends in August (FAO, 2018). Furthermore, long (1941 – 2015) and short (1961 – 2015) term averages for the respective growing season were computed for each of the regions. Following the computation of the long and short-term averages, exposure was computed as the ratio of the long-term to the short-term averages. With exposure index for rainfall and temperature computed separately, the two were added to get the combined exposure index. A high value indicates high exposure to climate variability. In this dataset, the exposure index is presented in raster format (Geotiff) to allow for easy processing across GIS software. In addition, the boundaries of the northern and the southern regions were also included as shapefiles.
Contributors: Lukas Graf, Levente Papp
... This dataset provides sample data demonstrating the capacities of the OBIA4RTM tool. OBIA4RTM combines radiative transfer modelling (RTM) of vegetation with object-based image analysis (OBIA). Its main purpose is to provide vegetation parameters such as Leaf Area Index (LAI) or leaf Chlorophyll a+b content (CAB) on a per-object rather than per pixel base. In this dataset, the OBIA4RTM tool was applied to two Sentinel-2 scenes covering an agricultural area in Southern Germany. Field parcels were used as image objects that were delineated from high-resolution ortho-photography and classified into vegetated and non-vegetated parcels using a Support Vector Machine trained on manually selected samples. For each of the two scenes - dating back on the 6th and 18th of July 2017 - the canopy RTM ProSAIL was run in forward mode and the synthetic spectra stored in a Lookup-Table (LUT). For parameter retrieval, the 5 closest matches between spectra in the LUT and a given observed satellite spectrum averaged per parcel were used. Matches were found in terms of the lowest Root Mean Squared Error (RMSE). The utilized vegetation parameterisation is provided additionally. The results include the Leaf Area Index (LAI), the Chlorophyll a+b content (CAB) of leaves and the fraction of brown leaves (Cbrown). In addition, the retrieval error in terms of RMSE is provided together with the average of the 5 best matching synthetic spectra in the LUT to a given object-based spectrum. This allows for evaluating the quality of the inversion results and enables user to further improve the results by applying a more appropiate vegetation parameterisation. The structure of the dataset (see below) is straightforward: - The "Field Parcels" folder contains an ESRI shapefile with the field parcels as well as the classification results for the two image acquisition dates - The "ProSAIL Parametersisation" directory provides the vegetation parameters used to run the ProSAIL model. - The actual results are stored as ESRI-shapefiles in "Retrieved Vegetation Parameters" folder containing the LAI, CAB, Fraction of brown leaves and the RMSE as well as inverted Sentinel-2 spectra - "Sentinel-2 data" contains the utilized Sentinel-2 data as GeoTiff clipped to the study area in Level-2A This information should allow for reproducing the results using the freely available base version of OBIA4RTM (for research and education) or within other software packages. All geodata is projected in UTM-Zone 32N, WGS-84.
Data for: Legacy of a Pleistocene bacterial community: Patterns in community dynamics through changing ecosystems.
Contributors: Senthil Kumar Sadasivam, Anbarasu Kumaresan, Sivakumar Krishnan, Bhavatharini Shanmuganathan, Manoj Kumar Jaiswal, SHAN P THOMAS
... The dataset contains supplementary data files for the manuscript titled "Legacy of a Pleistocene bacterial community: Patterns in community dynamics through changing ecosystems."
Abaqus Code for a Residual Control Staggered Solution Scheme for the Phase-Field Modeling of Brittle Fracture
Contributors: Karlo Seleš
... Abaqus UEL and UMAT subroutines for the phase-field modeling of brittle fracture. The code consists of the 3-layered system of user elements and user material subroutine producing a staggered algorithm with a residual norm based stopping criterion. The elements are 4-node full integration 2D and 8-node full integration 3D linear elements. The implementation files (source code and input files) for some examples published in the associated journal article are given. The files contain detailed explanations and instructions for users. This is an updated version of the dataset. See more info in Version_3-ChangeLog.txt For additional information, suggestions or comments, please contacts us at email@example.com
Contributors: Chunli Dai
... These data include the 2-m resolution coastline polylines (54_06_2_2_coast_v1.0), 2-m resolution water probability map (54_06_2_2_prob_v1.0), as well as the map of the total count of repeat images (54_06_2_2_nov_v1.0) for a 50 km by 50 km tile as shown in Fig. 9 in Dai et al. 2019.