cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17026/dans-zrx-jyjt
title: Improved analysis of solar signals for differential reflectivity monitoring

description: This dataset contains the underlying data that are used in the figures of the article 'Improved analysis of solar signals for differential reflectivity monitoring' published in Atmospheric Measurement Techniques. The Finnish Meteorological Institute (FMI) operates a network of 10 C-band Doppler weather radars, of which nine radars are polarimetric. Every 15 min the radars perform a 30 12-elevation volume scan between 0.3 and 45◦ elevations, where 6 elevations up to 9◦ are scanned in single–PRF with 570 Hz, and then 6 elevations starting from 2 up to 45◦ in dual–PRF. Every 5 min the first 6 of these 12 elevations are repeated. The dataset contain analyses of so-called 'sunhits' that are detected in the polar volume data produced by the Finnish radars. Datasets are ascii text files where each line represents a datapoint, the content is described in separate 'Readme' files. The data files are best understood when they are read alongside the accompanying article by Huuskonen, Kurri, and Holleman entitled "Improved analysis of solar signals for differential reflectivity monitoring" in Atmos. Meas. Tech., 9, 3183-3192, 2016 (doi:10.5194/amt-9-3183-2016).
DOI: 10.17026/dans-27m-9mkm
title: The influence of meteorology and air quality on parkrun athletic performance Meteorology, air quality and parkrun

description: Despite increased awareness of climate change and urban air pollution, little research has been performed to examine the influence of meteorology and air quality on athletic performance of the general public and recreational exercisers. This project focuses on the potential impacts on the general public. With the rise of parkrun events (timed 5 km runs) across the UK and worldwide concerns regarding public health in relation to both air quality and activity levels, the potential influence of air quality and meteorology on what is viewed as a ‘healthy’ activity has been investigated. A weekly dataset of parkrun participants at fifteen events, located in London UK, from 2011-2016 alongside local meteorological and air quality data has been analysed.
DOI: 10.17026/dans-xhv-s75j
title: Copulas for integrating weather and land information in space and time

description: Environmental processes are driven by weather, land, and water variables and their interactions that change continuously in space and time. A complete process description considers both spatio-temporal dependencies and associations between those variables. Describing the dependencies is challenging because natural phenomena are often observed at a discrete set of locations and times. This research focuses on weather data provided by ECMWF reanalysis that are being used increasingly for those process descriptions. Major dilemmas locally are that observations are sparse, and the use of ECMWF reanalysis data is prone to uncertainty because of the coarse spatial resolution and systematic bias. The complete study of dependencies will also lead to an increase in the number of the involved variables. To address these problems, this research demonstrates the potentials of copulas. It uses two datasets: daily mean air temperature collected from weather stations in the Qazvin Plain, Iran, and daily air temperature and precipitation retrieved from weather stations in the Netherlands. The European Centre for Medium-range Weather Forecasts (ECMWF) provided gridded weather data.
DOI: 10.17026/dans-z2m-44e4
title: Uncertainty in the modeling of spatial big data on a pattern of bushfires holes

description: The datasets used are in public access. Here is the URL for the database of forest fire footprints: https://www.ffm.vic.gov.au/The Landsat images are also in public access: https://earthexplorer.usgs.gov/.The code for the analysis is attached (Bush_fires_v2_2.R). The results can be reproduced using the code.The figures for the publication were created using the other R code attached to this email.
DOI: 10.17026/dans-2ca-sn23
title: Scaling of Remote Sensing Information for Orchard Management

description: Precision orchard management as a specific form of precision agriculture aims at supporting decision makers and farm managers by providing strategies to optimize crop production. Multiple information sources are used. In this thesis, the use of remote sensing images is explored for that purpose. In the past, an orchard was the smallest management scale to deal with it, whereas nowadays it concerns individual trees and leaves. This research explored downscaling methods for satellite images, bridging the gap between the tree patterns and detailed geographical information of trees on the ground. It focused on using both coarse and very high resolution satellite images with the aim of providing meaningful information at different level of scales.
DOI: 10.17026/dans-233-s8t8
title: Tree species classification using CIR and LiDAR data

description: Data used in the published paper "Improving LiDAR-based tree species mapping using multi-temporal aerial colour-infrared orthophotos"
DOI: 10.17026/dans-xcg-h326
title: Generation of seed electrons by extensive air showers, and the lighting inception problem including narrow bipolar events

description: In our article "Generation of seed electrons by extensive air showers ..." by Rutjes, Ebert, Buitink, Scholten, Trinh,published in J. Geophys. Res. - Atmospheres, 2019,DOI: 10.1029/2018JD029040, we simulate 5811 extensive air showers with the CORSIKA simulation package. The data file deposited here contains the output of these simulations that are further described and evaluated in our article.
DOI: 10.17026/dans-27g-urfx
title: Retrieving the 3d distribution of leaves in forests using LiDAR

description: 1) the digital elevation model of the 4 transects generated from leaf off lidar point cloud in the study area of Bavarian Forest National Park2) the leaf area index of the 4 transects generated from 2017 summer leaf on lidar point cloud in the study area of Bavarian Forest National Park
DOI: 10.17026/dans-zs4-veur
title: Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia.

description: Satellite rainfall products have different performances in different geographic regions under different physical and climatological conditions. In this study, the objective was to select the most reliable and accurate satellite rainfall products for specific, environmental conditions of Bali Island. The performances of four spatio-temporal satellite rainfall products, i.e., CMORPH25, CMORPH8, TRMM, and PERSIANN, were evaluated at the island, zonation (applying elevation and climatology as constraints), and pixel scales, using (i) descriptive statistics and (ii) categorical statistics, including bias decomposition. The results showed that all the satellite products had low accuracy because of spatial scale effect, daily resolution and the island complexity. That accuracy was relatively lower in (i) dry seasons and dry climatic zones than in wet seasons and wet climatic zones; (ii) pixels jointly covered by sea and mountainous land than in pixels covered by land or by sea only; and (iii) topographically diverse than uniform terrains. CMORPH25, CMORPH8, and TRMM underestimated and PERSIANN overestimated rainfall when comparing them to gauged rain. The CMORPH25 had relatively the best performance and the PERSIANN had the worst performance in the Bali Island. The CMORPH25 had the lowest statistical errors, the lowest miss, and the highest hit rainfall events; it also had the lowest miss rainfall bias and was relatively the most accurate in detecting, frequent in Bali, ? 20 mm day?1 rain events. Lastly, the CMORPH25 coarse grid better represented rainfall events from coastal to inlands areas than other satellite products, including finer grid CMORPH8.
DOI: 10.17026/dans-xrq-3t93
title: A voxel matching method for effective leaf area index estimation in temperate deciduous forests from leaf-on and leaf-off airborne LiDAR data

description: The quantification of leaf area index (LAI) is essential for modeling the interaction between atmosphere and biosphere. The airborne LiDAR has emerged as an effective tool for mapping plant area index (PAI) in a landscape consisting of both woody and leaf materials. However, the discrimination between woody and leaf materials and the estimation of effective LAI (eLAI) have, to date, rarely been studied at landscape scale. We applied a voxel matching algorithm to estimate eLAI of deciduous forests using simulated and field LiDAR data under leaf-on and leaf-off conditions. We classified LiDAR points as either a leaf or a woody hit on leaf-on LiDAR data by matching the point with leaf-off data. We compared the eLAI result of our voxel matching algorithm against the subtraction method, where the leaf-off effective woody area index (eWAI) is subtracted from the effective leaf-on PAI (ePAI). Our results, which were validated against terrestrial LiDAR derived eLAI, showed that the voxel matching method, with an optimal voxel size of 0.1 m, produced an unbiased estimation of terrestrial LiDAR derived eLAI with an R2 of 0.70 and an RMSE of 0.41 (RRMSE: 20.1 %).The Airborn_LiDAR_2016.xlsx data contain the estimation of PAI, LAI and WAI derived from airborne LiDAR using voxelization method. The Terrestrial_LiDAR_2017.xlsx data contain the estimation of PAI, LAI and WAI derived from terrestrial LiDAR for the validation of Airborn LiDAR.
DOI: 10.17026/dans-z7j-g3w3
title: Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data

description: First responders and recovery planners need accurate and quickly derived information about the status of buildings as well as newly built ones to both help victims and to make decisions for reconstruction processes after a disaster. Deep learning and, in particular, convolutional neural network (CNN)-based approaches have recently become state-of-the-art methods to extract information from remote sensing images, in particular for image-based structural damage assessment. However, they are predominantly based on manually extracted training samples. In the present study, we use pre-disaster OpenStreetMap building data to automatically generate training samples to train the proposed deep learning approach after the co-registration of the map and the satellite images. The proposed deep learning framework is based on the U-net design with residual connections, which has been shown to be an effective method to increase the efficiency of CNN-based models. The ResUnet is followed by a Conditional Random Field (CRF) implementation to further refine the results. Experimental analysis was carried out on selected very high resolution (VHR) satellite images representing various scenarios after the 2013 Super Typhoon Haiyan in both the damage and the recovery phases in Tacloban, the Philippines. The results show the robustness of the proposed ResUnet-CRF framework in updating the building map after a disaster for both damage and recovery situations by producing an overall F1-score of 84.2%.
DOI: 10.34740/kaggle/dsv/7146100
title: Lottery Mega Millions Winning Numbers
DOI: 10.17026/dans-zue-zz2y
title: Melanoma ML

description: CSV files (1-188).csv represent indexed images (quantization depth: 2^8) transformed from RGB (quantization depth: 2^24) dermoscopy images (digital camera: Minolta Dimage Z5 equipped with an epiluminescence lense with white halogen lighting; the working resolution is 2272x1704).File labels.csv includes class labels (1=melanoma, 0=displastic nevus) from a hist-pat examination.Forthcoming publication: The corresponding paper to this dataset will be available soon.
DOI: 10.17026/dans-zfh-qrxu
title: Study of volcanic activity at different time scales using hypertemporal land surface temperature data

description: The objective of the study was to detect volcanic activity at different timescales using geostationary satellite derived Land Surface Temperature data. LST data of the second-generation European Meteorological Satellites, were used to study (a) a paroxysmal, 1 day long eruption of Mount Etna (Italy); (b) a prolonged, 6 month period of effusive and lateral lava ﬂows of the Nyamuragira volcano (Democratic Republic of Congo); and (c) intermittent activity in the permanent lava lake of Nyiragongo (Democratic Republic of Congo) over a period of 2 years (2011–2012). The results of the analysis were compared with published ground-based observations and satellite-based alert systems. The same analysis is also applied on mid-infrared and long-infrared brightness temperatures for the case of Etna, to compare findings and explore possibilities of synergistic application of different data
DOI: 10.17026/dans-xem-fra9
title: Estimating Tree Heights Using Multibaseline PolInSAR Data With Compensation for Temporal Decorrelation, Case Study: AfriSAR Campaign Data

description: The project studied using of multi-baseline SAR data for estimating tree heights with compensation of temporal decorrelation and new structure function.Estimating Tree Heights Using Multi-baseline PolInSAR Data With Compensation for Temporal Decorrelation, Case Study: AfriSAR Campaign Data Ghasemi, N., Tolpekin, V. A. & Stein, A., 1 Oct 2018, In : IEEE Journal of selected topics in applied earth observations and remote sensing. 11, 10, p. 3464 - 3477 14 p., 8477041.https://ieeexplore.ieee.org/document/8477041This paper presents a multibaseline method to increase the accuracy of height estimation when using SAR tomographic data. It is based upon mitigating the temporal decorrelation induced by wind. The Fourier-Legendre function of different orders was fitted to each pixel as the structure function in the PCT model. It was combined with the motion standard deviation function from the random-motion-over ground (RMoG) model. L-band multibaseline data are used that were acquired during the AfriSAR campaign over La Lope National Park in Gabon with a height range between 0 and 60 m that has an average of 30 m and standard deviation of 15 m. The results were compared with those from the regular PCT model using the root mean square error (RMSE). Histograms were compared to the one obtained from Lidar height map. The average RMSE was equal to 7.5 m for the regular PCT model and to 5.6 m for the modified PCT model. We concluded that the accuracy of tree height estimation increased after modeling of temporal decorrelation. This is of value for future satellite missions that would collect tomographic data over forest areas.
DOI: 10.17026/dans-xx4-328k
title: Bestand Bodemgebruik - BBG 2008 - BBG Zuid 2010 - BBG Mutatiereeks 1996-2008

description: Het Bestand Bodemgebruik bevat digitale geometrie van het bodemgebruik in Nederland.Sinds 1989 publiceert het CBS om de drie tot vier jaar de digitale geometrie van de begrenzingen van het bodemgebruik in Nederland in het Bestand Bodemgebruik. Deze afbakening geeft inzicht in de verspreiding van verschillende vormen van ruimtegebruik binnen Nederland. Voorbeelden van het bodemgebruik zijn verkeersterreinen, bebouwing, recreatieterreinen, natuurterreinen, binnenwater en buitenwater.De digitale geometrie is een basisbestand bij het afleiden van de oppervlakte- en dichtheidsstatistieken voor verschillende regionale indelingen. Met ingang van de publicatie van de digitale geometrie in het Bestand Bodemgebruik 2000 zijn de begrenzingen gebaseerd op de Top10 kaart. Dit is de digitale topografische kaart op een schaal van 1:10.000 van het Kadaster.Het bestand BBG 2010-Zuid heeft alleen betrekking op de volgende zuidelijke provincies van Nederland: Limburg, Noord-Brabant, Zeeland, Zuid-Holland, Utrecht.Met de BBG mutatiebestanden 1996-2008 is en vergelijking van het bodemgebruik in de tijd mogelijk.DANS en het Kadaster werken samen met betrekking tot het duurzaam archiveren en beschikbaarstellen van topografische en kadastrale kaarten voor studiedoeleinden en voor wetenschappelijk onderzoek.Het betreft de volgende topografische bestanden: TOP50Vector, TOP250Raster, TOP50Raster, TOP25Raster, de Kadastrale Kaart, TOP10NL, TOP50Vector, TOP50NL, en TOP10Vector.Zie ook het overzicht van de Kadasterbestanden die via DANS beschikbaar zijn.De meest actuele versies van de Kadasterbestanden kunnen direct gedownload worden via de website van Publieke Dienstverlening op de Kaart – PDOK.In het informatieblad vindt u meer informatie over toegang tot het Bestand Bodemgebruik.
DOI: 10.17026/dans-22n-w7s8
title: Smart Indoor Models in 3D (SIMs3D)

description: This data is part of the TTW Maps4Society project Smart 3D Indoor Models (SIMs3D) to support crisis management in large public buildings (13742), which are (partly) financed by the Netherlands Organization for Scientific Research (NWO).
DOI: 10.17026/dans-x68-s6qv
title: Multitemporal image analysis for monitoring fuzzy shorelines

description: The dataset present remote sensing images that were used to extract shoreline information in the northern part of Central Java, Indonesia. In each file, the data are divided into date of acquisition. These remote sensing images are available for the various years start from 1988 up to 2017 as explain in the related publications.
DOI: 10.17026/dans-zre-d9q4
title: Optical properties of turbid coastal water and their remote sensing retrievals based on radiative transfer approaches

description: The submitted data is for the purpose of reproducing the research results obtained in Xiaolong Yu's Ph.D. dissertation. The dataset including the raw measurements that support the dissertation. All data were collected in the Yangtze estuary to investigate the optical properties in such region. Data are collected by Prof. Fang Shen's group of East China Normal University and the copyright of all submitted dataset is owned by East China Normal University.
DOI: 10.17026/dans-z4g-eh9r
title: The use of bivariate copulas for bias correction of reanalysis air temperature data

description: Air temperature data retrieved from global atmospheric models may show a systematic bias with respect to measurements from weather stations. This is a common concern in local climate studies. The current study presents two methods based upon copulas and Conditional Probability (CP) to predict bias-corrected mean air temperature in a data-scarce environment: CP-I offers a single conditional probability as a predictor, CP-II is a pixel-wise version of CP-I and offers spatially varying predictors. The methods were applied on daily air temperature in the Qazvin Plain, Iran that were collected at 24 weather stations and 150 ECMWF ERA-interim grid cells from a single month (June) over 11 years. We compared the methods with the commonly applied conditional expectation and conditional median methods.
DOI: 10.17026/dans-xd3-rp95
title: Bestand Bodemgebruik - BBG Mutatiereeks 1996-2010

description: Iedere twee à drie jaar publiceert het CBS een bestand van het bodemgebruik (BBG), de meest recente nu is van het verslagjaar 2010. De bestanden van het BBG zijn samengevoegd in één GIS-bestand. Dit zijn de bestanden geweest van 1996, 2000, 2003, 2006, 2008 en 2010. Die van 2003, 2006, 2008 en 2010 zijn gearchiveerd in EASY.Bij iedere productie verbetert het CBS ook gebieden waarvan is geconstateerd dat die de vorige keer niet correct geïnterpreteerd waren. Deze veranderingen worden aangemerkt als 'correctie', ten onderscheid van echte 'wijzigingen'.Bij het vergelijken van het bodemgebruik in de tijd over meerdere jaren zorgen deze correcties voor wijzigingen in het bodemgebruik die niet overeenkomen met echte wijzigingen in het terrein. Om te komen tot BBG-bestanden die wél geschikt zijn voor vergelijkingen over langere periodes, waarin alle wijzigingen en correcties op juiste wijze zijn verwerkt, is door het CBS een methode ontwikkeld. Het resultaat hiervan vindt u in deze dataset. Het benadert de werkelijkheid beter dan de losse bestanden van de verschillende verslagjaren.Om inzicht te geven in de gebruikte methode bevat het hierin geplaatste Documentatierapport Dans-Easy: Methodebeschrijving Mutatiereeks BBG 1996-2010 een samenvatting van de bewerking van de GIS-bestanden.Met dit bestand zijn ontwikkelingen in de tijd beter te volgen. De onterechte effecten van correcties zijn eruit gehaald en de correcties zijn toegepast op alle verslagjaren waar deze betrekking op hebben. Op microniveau kan het bestand er nog steeds rafelig uitzien.DANS en het Kadaster werken samen met betrekking tot het duurzaam archiveren en beschikbaarstellen van topografische en kadastrale kaarten voor studiedoeleinden en voor wetenschappelijk onderzoek.Het betreft de volgende topografische bestanden: TOP50Vector, TOP250Raster, TOP50Raster, TOP25Raster, de Kadastrale Kaart, TOP10NL, TOP50Vector, TOP50NL, en TOP10Vector.Zie ook het overzicht van de Kadasterbestanden die via DANS beschikbaar zijn.De meest actuele versies van de Kadasterbestanden kunnen direct gedownload worden via de website van Publieke Dienstverlening op de Kaart - PDOK.In het informatieblad vindt u meer informatie over toegang tot het Bestand Bodemgebruik.
DOI: 10.17026/dans-xea-j4wd
title: Flexible strategies for coping with rainfall variability : seasonal adjustments in cropped area in the Ganges basin

description: One of the main manifestations of climate change is expected to be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in cropproduction, including both variations in crop yield and cropped area. The Ganges basin is used as a case study.First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved.From the statistics, we found that in the period 1999-2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions.
DOI: 10.17026/dans-zw5-yw9b
title: Surface energy balance (SEBS) based daily global land evapotranspiration

description: This ET(SEBS) daily data provide estimation of global evapotranspiration values in a 0.05 X 0.05 degree latitude and longitude grid (3600 rows and 7200 columns). MODIS satellite remote sensing data (albedo, emissivity, land surface temperature, LAI, NDVI) and ERA5 meteorological data have been used in this ET calculation based on the SEBS algorithm. The dataset covers the time period 2018. More dataset will be produced and posted later.