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Overview

21-30 of 102 datasets.

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» Land cover classification Santa Gracia - Chile created by: Alexander Klug (last update: 2022-09-21)

» High spatiotemporal resolution of Rainfall in Ecuador created by: Nazli Turini, Boris Thies, Jörg Bendix (last update: 2021-10-06)

» Climate station data from the Ghisonaccia LCRS station in Corsica 2017 - 2019 created by: Isabel Knerr (last update: 2021-08-24)

» Climate station data from the Ajaccio LCRS station in Corsica 2017 - 2019 created by: Isabel Knerr (last update: 2021-08-24)

» Predicted density and depth of holes created by burrowing animals across 8 hillsides along Chilean climate gradient created by: Paulina Grigusova (last update: 2021-07-14)

» High spatiotemporal resolution of Rainfall in Iran created by: Nazli Turini, Boris Thies, Jörg Bendix (last update: 2021-06-03)

Detailed view

Land cover classification Santa Gracia - Chile

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Dataset ID:	478
Last updated:	2022-09-21
Dataset creators:	Alexander Klug
Contact:	Maik Dobbermann Alexander Klug
Temporal coverage:	2019-06-01 00:00:00 - 2019-06-01 00:00:00 - time zone: Europe/Berlin
Geographic coverage:	Chile - Santa Gracia
Abstract:	Datum der Rohdaten unbekannt? Source code: https:... Abstract: Datum der Rohdaten unbekannt? Source code: https://gitlab.com/lcrsmarburg/remote_sensing_chile
Additional info:	Santa Gracia - SG Wert Klasse 99 No Data (re... Additional info: Santa Gracia - SG Wert Klasse 99 No Data (removed, couldnt handle N/A) Trees 12 Leaftree Lower Vegetation 21 Shrubs 22 Ground Vegetation 23 Grass 24 Cactus Isles 25 Cactus Ground 31 Bare Soil 32 Stones
Keywords:	\| remote sensing \| WorldView-2 \| remote sensed data \| Chile \| worldview \| land cover classification \|
Intellectual rights:	LCRS

Entity name:	sg_lnk
Entity type:	otherEntity
Attributes:	Land cover classes (land_cover_classes) (Unitless)

Entity name:	klassenschlssel
Entity type:	otherEntity

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High spatiotemporal resolution of Rainfall in Ecuador

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Dataset ID:	448
Last updated:	2021-10-06
Dataset creators:	Nazli Turini Boris Thies Jörg Bendix
Contact:	Nazli Turini
Temporal coverage:	2017-04-19 00:00:00 - 2018-04-19 00:00:00 - time zone: Europe/Berlin
Geographic coverage:	Ecuador
Abstract:	A new satellite-based algorithm for rainfall retri... Abstract: A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution for Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It was developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieval technique (multispectral) outperforms the IR-only IMERG rainfall product.
Additional info:	--- Additional info:
Keywords:	\| rainfall \| Geostationary satellites \| multispectrl satellite data \|
Intellectual rights:	Laboratory for climatology and Remote sensing

Entity name:	High spatiotemporal resolution of Rainfall in Ecuador
Entity type:	otherEntity

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Climate station data from the Ghisonaccia LCRS station in Corsica 2017 - 2019

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Dataset ID:	438
Last updated:	2021-08-24
Dataset creators:	Isabel Knerr
Contact:	Maik Dobbermann Isabel Knerr
Temporal coverage:	2017-05-01 00:00:00 - 2019-11-17 00:00:00 - time zone: Europe/Berlin
Geographic coverage:	Ghisonaccia climate station LCRS
Abstract:	--- Abstract:
Additional info:	Metek U-Sonic-3 station with additional Campbell s... Additional info: Metek U-Sonic-3 station with additional Campbell sensors
Keywords:	\| climate \| climate station \| corsica \|
DOI:	10.5678/lcrs/for816.dat.438
Intellectual rights:	LCRS

Entity name:	ghisonaccia.csv
Entity type:	dataTable
Attributes:	Date and Time (datetime) (Unitless) Battery voltage (BattV) [Volt] Wind vector x in m/s (windvector_x) [Meter per second] Wind vector y in m/s (windvector_y) [Meter per second] Wind vector z in m/s (windvector_z) [Meter per second] Air temperature (Ta) [Degrees Celsius] Standard deviation of wind vector x in m/s (windvector_x_sig) [Meter per second] Standard deviation of wind vector y in m/s (windvector_y_sig) [Meter per second] Standard deviation of wind vector z in m/s (windvector_z_sig) [Meter per second] Standard deviation of the temperature (Ta_sig) [Degrees Celsius] Covariance of X and Y wind vectors (windvector_xy_cov) [Square meter per square second] Covariance of X and Z wind vectors (windvector_xz_cov) [Square meter per square second] Covariance of X and T (windvector_xta_cov) [Millikelvin per second] Covariance of Y and Z wind vectors (windvector_yz_cov) [Square meter per square second] Covariance of Y and T (windvector_yta_cov) [Millikelvin per second] Covariance of Z and T (windvector_zta_cov) [Millikelvin per second] Standard deviation of the wind component parallel to the mean wind direction (psig) [Meter per second] Standard deviation of the wind component horizontal perpendicular to the mean wind direction (qsig) [Meter per second] Standard deviation of the wind component vertical perpendicular to the mean wind direction (rsig) [Meter per second] Longitudinal turbulence intensity (tp) [Unitless] Transversal turbulence intensity (tq) [Unitless] Vertical turbulence intensity (tr) [Unitless] Maximum wind gust in the averaging interval (mgust) [Meter per second] Standard deviation of horizontal wind direction (thsig) [Degree] Standard deviation of vertical wind direction (phsig) [Degree] Diffusion class according to KTA-1508 guidelines (DiffC) (Unitless) Shear stress velocity (ustar) [Meter per second] Characteristic temperature (tstar) [Degree Kelvin] Ground friction coefficient (cd) [Unitless] Monin-Obukhov stability parameter (mos) [One per meter] Vertical pulse current (mf) [Kilogram per meter second] Vertical heat flow (hf) [Watt per square meter] Net radiation balance (nrb) [Watt per square meter] Mean wind component in east-west direction (u) [Meter per second] Mean wind component in north-south direction (v) [Meter per second] Mean vertical wind component (w) [Meter per second] Wind speed (vel) [Meter per second] Wind direction (dir) [Degree] Mean horizontal wind speed scalar averaged (svel) [Meter per second] Mean horizontal wind direction, scalar averaged to geographic north (sdir) [Degree] air temperature in 2m (Ta_2m) [Degrees Celsius] Air humidity 2m (Huma_2m) [Percent] Volumetric water content (Vwc) [Count] Electric conductivity (EC) [Square meter per square meter] Soil temperature (Ts) [Degrees Celsius] Global radiation (2m) (gRad1) [Watt per square meter] precipitation (PCP) [Millimeter]

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Climate station data from the Ajaccio LCRS station in Corsica 2017 - 2019

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Dataset ID:	437
Last updated:	2021-08-24
Dataset creators:	Isabel Knerr
Contact:	Maik Dobbermann Isabel Knerr
Temporal coverage:	2017-05-01 00:00:00 - 2019-11-17 00:00:00 - time zone: Europe/Berlin
Geographic coverage:	Ajaccio climate station LCRS
Abstract:	--- Abstract:
Additional info:	Metek U-Sonic-3 station with additional Campbell s... Additional info: Metek U-Sonic-3 station with additional Campbell sensors
Keywords:	\| climate \| climate station \| corsica \|
DOI:	10.5678/lcrs/for816.dat.437
Intellectual rights:	LCRS

Entity name:	ajaccio.csv_out
Entity type:	dataTable
Attributes:	Battery voltage (BattV) [Volt] Wind vector x in m/s (windvector_x) [Meter per second] Wind vector y in m/s (windvector_y) [Meter per second] Wind vector z in m/s (windvector_z) [Meter per second] Air temperature (Ta) [Degrees Celsius] Standard deviation of wind vector x in m/s (windvector_x_sig) [Meter per second] Standard deviation of wind vector y in m/s (windvector_y_sig) [Meter per second] Standard deviation of wind vector z in m/s (windvector_z_sig) [Meter per second] Standard deviation of the temperature (Ta_sig) [Degrees Celsius] Covariance of X and Y wind vectors (windvector_xy_cov) [Square meter per square second] Covariance of X and Z wind vectors (windvector_xz_cov) [Square meter per square second] Covariance of X and T (windvector_xta_cov) [Millikelvin per second] Covariance of Y and Z wind vectors (windvector_yz_cov) [Square meter per square second] Covariance of Y and T (windvector_yta_cov) [Millikelvin per second] Covariance of Z and T (windvector_zta_cov) [Millikelvin per second] Standard deviation of the wind component parallel to the mean wind direction (psig) [Meter per second] Standard deviation of the wind component horizontal perpendicular to the mean wind direction (qsig) [Meter per second] Standard deviation of the wind component vertical perpendicular to the mean wind direction (rsig) [Meter per second] Longitudinal turbulence intensity (tp) [Unitless] Transversal turbulence intensity (tq) [Unitless] Vertical turbulence intensity (tr) [Unitless] Maximum wind gust in the averaging interval (mgust) [Meter per second] Standard deviation of horizontal wind direction (thsig) [Degree] Standard deviation of vertical wind direction (phsig) [Degree] Diffusion class according to KTA-1508 guidelines (DiffC) (Unitless) Shear stress velocity (ustar) [Meter per second] Characteristic temperature (tstar) [Degree Kelvin] Ground friction coefficient (cd) [Unitless] Monin-Obukhov stability parameter (mos) [One per meter] Vertical pulse current (mf) [Kilogram per meter second] Vertical heat flow (hf) [Watt per square meter] Net radiation balance (nrb) [Watt per square meter] Mean wind component in east-west direction (u) [Meter per second] Mean wind component in north-south direction (v) [Meter per second] Mean vertical wind component (w) [Meter per second] Wind speed (vel) [Meter per second] Wind direction (dir) [Degree] Mean horizontal wind speed scalar averaged (svel) [Meter per second] Mean horizontal wind direction, scalar averaged to geographic north (sdir) [Degree] air temperature in 2m (Ta_2m) [Degrees Celsius] Air humidity 2m (Huma_2m) [Percent] Volumetric water content (Vwc) [Count] Electric conductivity (EC) [Square meter per square meter] Soil temperature (Ts) [Degrees Celsius] Global radiation (2m) (gRad1) [Watt per square meter] precipitation (PCP) [Millimeter] Date and Time (datetime) (Unitless)

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Predicted density and depth of holes created by burrowing animals across 8 hillsides along Chilean climate gradient

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Dataset ID:	433
Last updated:	2021-07-14
Dataset creators:	Paulina Grigusova
Contact:	Paulina Grigusova
Temporal coverage:	2019-09-13 00:00:00 - 2019-11-13 00:00:00 - time zone: Europe/Berlin
Geographic coverage:	Chile
Abstract:	Burrowing animals are important ecosystem engineer... Abstract: Burrowing animals are important ecosystem engineers affecting soil properties, the redis-tribution of nutrients and soil carbon sequestration through their burrowing activity. The magnitude of these effects depends on the spatial density and depth of the burrows, but a method to derive this type of spatial explicit data of is still missing. Here, we test the poten-tial of consumer-oriented UAV RGB imagery to determine density and depth of holes cre-ated by burrowing animals at four study sites along a climate gradient in Chile by com-bining UAV data with empirical field plot observations and machine learning. We used the trained models for area wide predictions of density and depth of holes created by burrowing animals across 8 hillsides along a climate gradient in Chile.
Additional info:	Coordinates: Pda north facing: -70.61875999999 ... Additional info: Coordinates: Pda north facing: -70.61875999999 -25.97690569047 PdA south-facing: -70.61747800000 -25.98792750527 SG north-facing -71.169226580458 -29.758944299143 SG south-facing -71.167225099000 -29.764107785292 LC north-facing -71.09082499999 -32.94316192996 LC south-facing -71.09315231476 -32.93436314092 NA -73.01619835389 -37.80787731366
Keywords:	\| Burrowing animals \|
DOI:	10.5678/lcrs/dat.433
Intellectual rights:	Paulina Grigusova

Entity name:	prediction
Entity type:	otherEntity

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High spatiotemporal resolution of Rainfall in Iran

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Dataset ID:	430
Last updated:	2021-06-03
Dataset creators:	Nazli Turini Boris Thies Jörg Bendix
Contact:	Nazli Turini
Temporal coverage:	2017-02-01 10:00:00 - 2018-02-02 00:00:00 - time zone: Europe/London
Geographic coverage:	Iran
Abstract:	A new satellite-based technique for rainfall retri... Abstract: A new satellite-based technique for rainfall retrieval in high spatio-temporal resolution (3 km, 15 min) for Iran is presented. The algorithm is based on the infrared bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI). Random forest models using microwave-only rainfall information of the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) product as a reference were developed to (i) delineate the rainfall area and (ii) to assign the rainfall rate. The method was validated against independent microwave-only GPM IMERG rainfall data not used for model training. Additionally, the new technique was validated against completely independent gauge station data. The validation results show a promising performance of the new rainfall retrieval technique, especially when compared to the GPM IMERG IR-only rainfall product. The standard verification scored an average Heidke Skill Score of 0.4 for rain area delineation and an average R between 0.1 and 0.7 for rainfall rate assignment, indicating uncertainties for the Lut Desert area and regions with high altitude gradients
Additional info:	--- Additional info:
Keywords:	\| rainfall retrieval \| rainfall \| Rainfall rate \| satellite-based rainfall detection scheme \|
Intellectual rights:	LCRS

Entity name:	iran_1_hist_hydromet_tp_20170201_20180201
Entity info:	High Spatio-temporal resolution of rainfall in Iran
Entity type:	otherEntity

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