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iSDAsoil: soil sand content (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil sand content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_sand_tot_psa_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil sand content mean value,</li> <li>sol_sand_tot_psa_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil sand content model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: sand_tot_psa R-square: 0.736 Fitted values sd: 22.8 RMSE: 13.7 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -80.626 -5.321 0.221 6.071 88.686 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.687471 24.022001 0.278 0.780714 regr.ranger 1.060521 0.003503 302.742 < 2e-16 *** regr.xgboost -0.018718 0.004910 -3.812 0.000138 *** regr.cubist 0.031749 0.003922 8.096 5.73e-16 *** regr.nnet -0.161127 0.422746 -0.381 0.703098 regr.cvglmnet -0.028217 0.004462 -6.323 2.57e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 13.65 on 122261 degrees of freedom Multiple R-squared: 0.736, Adjusted R-squared: 0.736 F-statistic: 6.818e+04 on 5 and 122261 DF, p-value: < 2.2e-16</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil total organic Nitrogen for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil total organic Nitrogen (N) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.n_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total N mean value,</li> <li>sol_log.n_tot_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total N model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.n_tot_ncs R-square: 0.732 Fitted values sd: 0.326 RMSE: 0.197 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.87298 -0.09584 -0.00985 0.07613 3.14728 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.267429 0.493235 0.542 0.588 regr.ranger 1.128208 0.005766 195.669 < 2e-16 *** regr.xgboost -0.048780 0.006108 -7.987 1.4e-15 *** regr.cubist 0.143954 0.004424 32.539 < 2e-16 *** regr.nnet -0.482261 0.797938 -0.604 0.546 regr.cvglmnet -0.170889 0.004955 -34.489 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1972 on 99249 degrees of freedom Multiple R-squared: 0.7319, Adjusted R-squared: 0.7319 F-statistic: 5.419e+04 on 5 and 99249 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = expm1( y / 100 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil silt content (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil silt content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_silt_tot_psa_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil silt content mean value,</li> <li>sol_silt_tot_psa_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil silt content model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: silt_tot_psa R-square: 0.64 Fitted values sd: 11.9 RMSE: 8.92 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -63.746 -3.631 -0.526 2.630 72.486 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -35.876865 36.887592 -0.973 0.331 regr.ranger 0.948111 0.003874 244.733 < 2e-16 *** regr.xgboost 0.062717 0.005506 11.391 < 2e-16 *** regr.cubist 0.025705 0.004747 5.415 6.14e-08 *** regr.nnet 1.902142 1.968248 0.966 0.334 regr.cvglmnet -0.028579 0.005799 -4.928 8.32e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 8.915 on 122223 degrees of freedom Multiple R-squared: 0.6399, Adjusted R-squared: 0.6399 F-statistic: 4.344e+04 on 5 and 122223 DF, p-value: < 2.2e-16</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
L4D - Probability map of giant trees occurrence (> 70 m) in the Brazilian Amazon
<p>The probability of giant trees occurrence (> 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). </p>
Land Use and Land Cover 2019 of Ribaue Mountains (Mount Ribaue and Mount M'paluwe) in Mozambique
<p>This repository includes the land use and land cover map of the Ribaue Mountains and surroundings (Ribaue district, Nampula province, Mozambique), using remote sensing.</p> <p>The Ribaue massif is a series of granite inselbergs in northern Mozambique near the town of Ribaue in Nampula Province. The main area of the massif is made up of the Serra de Ribaue to the west and the Serra de M'paluwe to the east. The inselbergs rise from a relatively flat landscape at ca 500-600 m altitude up to 1675 m on Monte M'paluwe. They form part of a belt of granite rock outcrops, inselbergs and mountains, running NE-SW across Nampula and Zambezia provinces and including Mt Inago (1804 m) and Mt Namuli (2419 m) to the southwest of the Ribaue massif.</p> <p>This belt is considered as a center of endemism. Overall the site supports 15 nationally endemic plant taxa (plants that only occur in Mozambique), 11 near-endemics (plants that are restricted to Mozambique and neighbouring countries) and 10 taxa that are threatened with extinction on the Global IUCN Red List. Steeply sloping granite rock outcrops, mid-altitude moist forest and miombo woodland are the dominant habitat types at the Ribaue massif. The site also includes smaller areas of gallery forest, marsh, seasonal stream gullies, seepage on granite rock, and shaded granite cliffs.</p> <p>The methodology used in this study is based on a classical approach of remote sensing: satellite image collection (cloud-free and shadow free Sentinel 2, 10 m resolution, two season), identification of land use typology (based on field campains), delineation of training plots, supervised classification of land use using a machine learning algorithm (Random Forest) and finally, calculation of land occupation statistics.</p> <p>The methodology and statistics are presented in the report included in the repository.</p>
MFS-M-00313 Air temperature at 2m, raised bog-hollow, DS18B20 (temperature logger)
<p>Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger in APIK complex), 2018, 30 min frequency, N60.89494 E68.66999, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00006 Air temperature measured at 2m, raised bog-ryam, ROTRONIC HygroClip S3
<p>Air temperature at 2m measured in a raised bog ecosystem (treed bog community) by ROTRONIC HygroClip S3 (temperature logger), 2010-continues, 30 min frequency, several gaps in data, N60.89164 E68.67690, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00004 Air temperature at 2m, raised bog-hollow, DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem (sphagnum lawn community) by DS18B20 (temperature logger), 2012-continues, 30 and 60 min frequency, several gaps in data, N60.89494 E68.68176, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00007 Air temperature measured at 2m, raised bog-lawn, ROTRONIC HygroClip S3
<p>Air temperature at 2m measured in a raised bog ecosystem (treed bog community) by ROTRONIC HygroClip S3 (temperature logger), 2010-continues, 30 min frequency, several gaps in data, N60.89164 E68.67690, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00303 Air temperature at +2 m, raised bog-ridge, Baro-Diver (DI 500)
<p>Air temperature at 2m measured in a raised bog ecosystem (ridge) by Baro-Diver (DI 500), 2006-2016 (with several breaks) as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00003 Air temperature at 2m, raised bog-ryam, DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger), 2012-2018, 30 and 60 min frequency, several gaps in data, N60.89471 E68.68176, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00216 Air temperature at 2m, raised bog (hollow in complex bog), DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem (sphagnum lawn) by DS18B20 (temperature logger), 2016, 30 min frequency, N60.89325 E68.68311, OTC-wet experimental site, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00368 Air temperature at 2m, raised eriophorum-sphagnum bog, DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem (eriophorum-Sphagnum bog) by DS18B20 (temperature logger), 2018-2020, 30 min frequency, N60.89439 E68.68113, OTC-dry experimental site,<br> as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00157 Snow precipitation measured manually (Tretyakov rain gauge), raised bog (ridge-hollow complex), 2010-present
<p>A raine gauge or Russian hydro-meteorological network standard (Tretyakov' construction) was installed as part of manual meteorological station in Mukhrino research polygon in 2010. The bucket installed at 2m height and protected by wind shield. Since 2014 only winter measurements were collected to supplement rain precipitation measurements measured in summer by automatic rain gauges (see also MFS-M-00154, MFS-M-00307, MFS-M-00308 for summer measurements in the same location).</p>
MFS-M-00308 Rain precipitation measured in raised bog (ridge-hollow complex), HOBO rain gauge RG3-M, 2017-present
<p>A raine gauge HOBO RG3-M was installed at the Mukhrino field station reseach polygon in the raised bog, treed bog community (ryam) in 2017, installation on the ground level (to prevent wind turbulence), measurement frequency - by event. This series is duplicated by MFS-M-00154 and MFS-M-00307 to cover variability and in case of breakdown. See also MFS-M-157 for winter (snow) precipitation at the same site measured manually.</p>
MFS-M-00154 Rain precipitation measured in raised bog (ridge-hollow compleх), HOBO rain gauge RG3-M, 2008-2019
<p>A raine gauge HOBO RG3-M was installed at the Mukhrino field station reseach polygon in the raised bog (ridge-hollow complex) in 2008, installation on the ground level (to prevent wind turbulence), measurement frequency - by event. This series was stopped by 2019 and replaced by another in close proximity (50 m), see MFS-M-00308 for precipitation after 2019.</p>
MFS-M-00156 Rain precipitation measured nearby Shapsha field station (in the village), HOBO rain gauge RG3-M, 2016-present
<p>A raine gauge HOBO RG3-M was installed in Shapsha village in an open space (Shapsha field station) as part of macrofungi long-term plots monitoring in Shapsha vicinity. Installation on the ground level to prevent wind turbulence.</p>
MFS-M-00155 Rain precipitation measured in coniferous forest, HOBO rain gauge RG3-M, 2015
<p>A raine gauge HOBO RG3-M was installed at the Mukhrino field station research polygon, in coniferous mixed forest to measure precipitation under tree canopy. But this installation worked only for one year (2015), after this was stopped.</p>
MFS-M-00307 Rain precipitation measured in raised bog (ryam), HOBO rain gauge RG3-M, 2017-present
<p>A raine gauge HOBO RG3-M was installed at the Mukhrino field station reseach polygon in the raised bog, treed bog community (ryam) in 2017, installation on the ground level (to prevent wind turbulence), measurement frequency - by event. This series is duplicated by MFS-M-00154 and MFS-M-00308 to cover variability and in case of breakdown. See also MFS-M-157 for winter (snow) precipitation at the same site measured manually.</p>
COADS_45(M)-8
Diagnostic biface collected in Union Township, Ross County, Ohio. Material: Flint Ridge Uploaded by: Kendall Brophy Suggested Data Citation: Nolan, Kevin C., Eric Olson, Sidney Travis, Michael Shott, and Kendall Brophy, 2017. COADS_45-8, 3D Model .ply file. Central Ohio Archaeological Digitization Survey, Department of Anthropology, University of Akron and Applied Anthropology Laboratories, Department of Anthropology, Ball State University. Source: Objaverse 1.0 / Sketchfab
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