Landsat 8 Collection 2 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation. Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series processing analysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data that have well-characterized radiometry and are inter-calibrated across the different Landsat sensors. The georegistration of Tier 1 scenes will be consistent and within prescribed tolerances [<=12 m root mean square error (RMSE)]. All Tier 1 Landsat data can be considered consistent and inter-calibrated (regardless of sensor) across the full collection. See more information in the USGS docs. The T1_RT collection contains both Tier 1 and Real-Time (RT) assets. Newly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed upon downlink but use predicted ephemeris, initial bumper mode parameters, or initial TIRS line of sight model parameters. The data is placed in the Real-Time tier and made available for immediate download. Once the data have been reprocessed with definitive ephemeris, updated bumper mode parameters and refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier. The transition delay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.

Réflectance au niveau de l'atmosphère (TOA) calibrée de la collection 2 de Landsat 8, niveau 1 Les coefficients de calibrage sont extraits des métadonnées de l'image. Pour en savoir plus sur le calcul de la TOA, consultez Chander et al. (2009). Les scènes Landsat dont la qualité des données est la plus élevée sont placées dans la catégorie 1 et sont considérées comme adaptées à l'analyse de traitement des séries temporelles. …

CANUE staff developed annual estimates of maximum mean warm-season land surface temperature (LST) recorded by LandSat 8 at 30m resolution. To reduce the effect of missing data/cloud cover/shadows, the highest mean warm-season value reported over three years was retained - for example, the data for 2021 represent the maximum of the mean land surface temperature at a pixel location between April 1st and September 30th in 2019, 2020 and 2021. Land surface temperature was calculated in Google Earth Engine, using a public algorithm (see supplementary documentation). In general, annual mean LST may not reflect ambient air temperatures experienced by individuals at any given time, but does identify areas that are hotter during the day and therefore more likely to radiate excess heat at night - both factors that contribute to heat islands within urban areas.

Valeurs DN de niveau 1 de la collection 2 de Landsat 8, représentant la radiance au niveau du capteur, mise à l'échelle et calibrée. Les scènes Landsat dont la qualité des données est la plus élevée sont placées dans le niveau 1 et sont considérées comme adaptées à l'analyse du traitement des séries temporelles. Le niveau 1 inclut les données traitées au niveau 1 Precision Terrain (L1TP) dont la radiométrie est bien caractérisée et qui sont intercalibrées entre les différents capteurs Landsat. La géorégulation des scènes de niveau 1 sera cohérente et respectera les tolérances prescrites (<=12 m de racine carrée de l'erreur quadratique moyenne (RMSE)). Toutes les données Landsat de niveau 1 peuvent être considérées comme cohérentes et intercalibrées (quel que soit le capteur) pour l'ensemble de la collection. Pour en savoir plus, consultez la documentation de l'USGS.

Overview

This dataset includes the images (NIR-R-G bands for Landsat-8 or NICFI PlanetScope), auxiliary data (infrared, NCEP, forest gain, OpenStreetMap, SRTM, GFW), and data about forest loss (Global Forest Change) used to train, validate and test a model to classify direct deforestation drivers in Cameroon. The creation of this dataset follows the same structure as: Labelled dataset to classify direct deforestation drivers in Cameroon but with a different set of bands.

For more details about how this dataset has been created and can be used, please refer to our paper and code: https://github.com/aedebus/Cam-ForestNet. The paper, describing the generation of RGB images, can be found here: https://www.nature.com/articles/s41597-024-03384-z.

Citation: Debus, A. et al. A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon. Sci Data 11, 564 (2024).

Description of the files and images

• 'my_examples_landsat_nir.zip': Landsat-8 images (courtesy of the U.S. Geological Survey), auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon. This dataset contains 332x 332 pixels NIR-R-G calibrated top-of-atmosphere (TOA) reflectance with a 30 m resolution (less than 20% cloud cover)
• 'my_examples_landsat_sr_nir.zip': Same as above, but with surface reflectance (SR) instead of TOA
• 'my_examples_planet_nir.zip': NICFI PlanetScope images (catalog owner: Planet), auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon. This dataset contains 332x 332 pixels monthly NIR-R-G composite with a 4.77 m resolution
• 'my_examples_planet_nir_biannual.zip': Same as above, but with biannual composites instead of monthly composites

Details about the auxiliary data

• Forest gain from GFC: 30-m resolution, yearly data for 2000-2021, downloaded via Google Earth Engine
• Near infrared, shortwave infrared 1 and 2 bands from Landsat-8 TOA: 30-m resolution, data every 16 days for 2013-2023, downloaded via Google Earth Engine and selected using the same process as for Landsat-8 RGB images
• From NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products: surface level albedo and volumetric soil moisture content (depths: 0.1 m, 0.4 m, 1.0 m, 2.0m) in 0.01%; radiative fluxes (clear-sky longwave flux downward and upward, clear-sky solar flux downward and upward, direct evaporation from bare soil, longwave and shortwave radiation flux downward and upward, latent, ground and sensible heat net flux), potential evaporation rate, and sublimation in W/m²; humidity (specific, maximum specific, minimum specific) in 10-4 kg/kg; ground level precipitation in 0.1 mm; air pressure at surface level in 10 Pa; wind level (u and v component) in 0.01 m/s, water runoff at surface level in 232.01 kg/ m²; temperature in K: 22264-m resolution, available four times a day for 2011-2023, downloaded directly from the NOAA website and selected the mean of the monthly mean over 5 years before the forest loss event, the monthly maximum over 5 years before the forest loss event, and the monthly minimum over 5 years before the forest loss event for each parameter
• Closest street and closest city from OpenStreetMap in km: directly downloaded with the Nominatim API
• Altitude in m, slope and aspect in 0.01° from Shuttle Radar Topography Mission (SRTM): 30-m resolution, measured for 2000, downloaded via Google Earth Engine
• Presence of peat from GFW: 232-m resolution, measured for 2017, directly downloaded on the GFW website

Details about Global Forest Change

For each image, there is a corresponding 'forest_loss_region' .pkl file delimiting a forest loss region polygon from Global Forest Change (GFC). GFC consists of annual maps of forest cover loss with a 30-m resolution.

License

The NICFI PlanetScope images fall under the same license as the NICFI data program license agreement (data in 'my_examples_planet_nir.zip', 'my_examples_planet_nir_biannual.zip': subfolders '[coordinates]'>'images'>'visible').

OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF) (data in all 'my_examples' folders: subfolders '[coordinates]'>'auxiliary'>'closest_city.json'/'closest_street.json'). The documentation is licensed under the Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0).

The rest of the data is under a Creative Commons Attribution 4.0 International License. The data has been transformed following the code that can be found via this link: https://github.com/aedebus/Cam-ForestNet (in 'prepare_files').

Valeurs DN des données de la collection 2 de Landsat 8 de niveau 1 et en temps réel, représentant la radiance au niveau du capteur, étalonnée et calibrée. Les scènes Landsat dont la qualité des données est la plus élevée sont placées dans le niveau 1 et sont considérées comme adaptées à l'analyse du traitement des séries temporelles. Le niveau 1 inclut les données traitées au niveau 1 Precision Terrain (L1TP) dont la radiométrie est bien caractérisée et qui sont intercalibrées entre les différents capteurs Landsat. La géorégulation des scènes de niveau 1 sera cohérente et respectera les tolérances prescrites (<=12 m de racine carrée de l'erreur quadratique moyenne (RMSE)). Toutes les données Landsat de niveau 1 peuvent être considérées comme cohérentes et intercalibrées (quel que soit le capteur) pour l'ensemble de la collection. Pour en savoir plus, consultez la documentation de l'USGS. La collection T1_RT contient à la fois des composants de niveau 1 et des composants en temps réel. Les données Landsat 7 ETM+ et Landsat 8 OLI/TIRS nouvellement acquises sont traitées lors de la liaison descendante, mais utilisent des éphémérides prévues, des paramètres de mode pare-chocs initiaux ou des paramètres de modèle de ligne de visée TIRS initiaux. Les données sont placées dans le niveau "Temps réel" et sont disponibles en téléchargement immédiat. Une fois les données réexécutées avec des éphémérides définitives, des paramètres de mode de protection mis à jour et des paramètres TIRS affinés, les produits sont transférés vers le niveau 1 ou le niveau 2 et supprimés du niveau en temps réel. Le délai de transition entre le temps réel et le niveau 1 ou 2 est compris entre 14 et 26 jours.

Overview

This dataset includes the images (visible bands for Landsat-8 or NICFI PlanetScope), auxiliary data (infrared, NCEP, forest gain, OpenStreetMap, SRTM, GFW), and data about forest loss (Global Forest Change) used to train, validate and test a model to classify direct deforestation drivers in Cameroon.

Description of the files

• 'my_examples_landsat_final_detailed.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes: ‘Oil palm plantation’, ‘Timber plantation’, ‘Fruit plantation (e.g. banana)’, ‘Rubber plantation’, ‘Other large-scale plantation (e.g. tea, sugarcane)’, ‘Grassland/Shrubland’, ‘Small-scale oil palm plantation’, ‘Small-scale maize plantation’, ‘Other small-scale agriculture’, ‘Mining’, ‘Selective logging’, ‘Infrastructure’, ‘Wildfire’, ‘Hunting’, ‘Other’)
• 'my_examples_planet_final_detailed.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes)
• 'my_examples_landsat_final.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes: 'Plantation', 'Grassland/Shrubland', 'Smallholder agriculture', 'Other')
• 'my_examples_planet_final.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes)
• 'my_examples_landsat_detailed_timeseries.zip': Landsat-8 images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis
• 'my_examples_planet_detailed_timeseries.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis

• ‘labels.zip’: in csv files, the labels for each image in each folder described above (image identified by folder and coordinates or ‘path’) and matches the format of the csv files used as inputs to train, validate and test our classification model

  For ‘labels.zip’, we have subfolders for Landsat and PlanetScope. Then, for each type of imagery, we have subfolders for ‘detailed’, ‘groups’ and ‘time series’ which correspond to the different ‘my_examples’ folders listed above.

  For each folder, subfolders named with the coordinates of the centre of the images contain each:
  • A folder ‘images’, with a sub-folder ‘visible’ containing the PNG RGB image; and a sub-folder ‘infrared’ containing the infrared bands in a NPY file.
  • A folder ‘auxiliary’ with topographic and forest gain information in a NPY format, OpenStreetMap and peat data in a JSON format, and a sub-folder ‘ncep’ containing all data from NCEP in a NPY format.
  • The forest loss pickle file delimiting the area of forest loss.

Details about the images

• For Landsat-8 data (courtesy of the U.S. Geological Survey), this dataset contains 332x 332 pixels RGB calibrated top-of-atmosphere (TOA) reflectance images pan-sharpened to a 15 m resolution (less than 20% cloud cover)

• For NICFI PlanetScope data (catalog owner: Planet), this dataset contains 332x 332 pixels monthly RGB composite with a 4.77 m resolution

Details about the auxiliary data

• Forest gain from GFC: 30-m resolution, yearly data for 2000-2021, downloaded via Google Earth Engine
• Near infrared, shortwave infrared 1 and 2 bands from Landsat-8 TOA: 30-m resolution, data every 16 days for 2013-2023, downloaded via Google Earth Engine and selected using the same process as for Landsat-8 RGB images
• From NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products: surface level albedo and volumetric soil moisture content (depths: 0.1 m, 0.4 m, 1.0 m, 2.0m) in 0.01%; radiative fluxes (clear-sky longwave flux downward and upward, clear-sky solar flux downward and upward, direct evaporation from bare soil, longwave and shortwave radiation flux downward and upward, latent, ground and sensible heat net flux), potential evaporation rate, and sublimation in W/m²; humidity (specific, maximum specific, minimum specific) in 10-4 kg/kg; ground level precipitation in 0.1 mm; air pressure at surface level in 10 Pa; wind level (u and v component) in 0.01 m/s, water runoff at surface level in 232.01 kg/ m²; temperature in K: 22264-m resolution, available four times a day for 2011-2023, downloaded directly from the NOAA website and selected the mean of the monthly mean over 5 years before the forest loss event, the monthly maximum over 5 years before the forest loss event, and the monthly minimum over 5 years before the forest loss event for each parameter
• Closest street and closest city from OpenStreetMap in km: directly downloaded with the Nominatim API
• Altitude in m, slope and aspect in 0.01° from Shuttle Radar Topography Mission (SRTM): 30-m resolution, measured for 2000, downloaded via Google Earth Engine
• Presence of peat from GFW: 232-m resolution, measured for 2017, directly downloaded on the GFW website

Details about Global Forest Change

For each image, there is a corresponding 'forest_loss_region' .pkl file delimiting a forest loss region polygon from Global Forest Change (GFC). GFC consists of annual maps of forest cover loss with a 30-m resolution.

License

The NICFI PlanetScope images fall under the same license as the NICFI data program license agreement (data in 'my_examples_planet_final.zip', 'my_examples_planet_final_detailed.zip', 'my_examples_planet_detailed_timeseries.zip': subfolders '[coordinates]'>'images'>'visible').

OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF) (data in all 'my_examples' folders: subfolders '[coordinates]'>'auxiliary'>'closest_city.json'/'closest_street.json'). The documentation is licensed under the Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0).

The rest of the data is under a Creative Commons Attribution 4.0 International License. The data has been transformed following the code that can be found via this link: https://github.com/aedebus/Cam-ForestNet (in 'prepare_files').

PROCESSING STEPS:Cuyahoga County Planning Commission:Extracted Band 3: Difference from County Average LST of 92.4℉Rounded to nearest degree differenceCustom symbology appliedCleveland State University:Robert Moore, M.S. Candidate, Cleveland State University, Department of Biological, Geological and Environmental SciencesRaster CRS: EPSG:4326Raster Width: 2214Raster Height: 1324Number of Bands: 3Data Type: float64NoData Value: NonePixel Size: 30mData Sources:Landsat - USGS Landsat 8 Level 2, Collection 2, Tier 1 - https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2ST_B10 is band utilized to calculate LSTMODIS - MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km - https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1LST_Day_1km is band utilized to calculate LSTData Processing: Satellite scenes were compiled and averaged for summer months May 1 - September 30 for a 3-year period 2021-2023.Scenes with greater than 20% cloud cover were omitted from analysis using the CFMASK algorithm for Landsat scenes.MODIS scenes all met “clear-sky criteria” built into the MODIS algorithm.We utilized MODIS LST data to address missing pixels within Landsat data for Cuyahoga County.Workflow: The following Python data packages were utilized: Rasterio, NumPy, and matplotlib (package documentation below). Statistical Linear Regression was conducted between Landsat and MODIS LST values (94% R2 value) to calculate predicted Landsat LST values from MODIS values. Then using a mask, missing pixels values are replaced with their corresponding predicted Landsat LST values. 1.26% of land area was missing in Landsat data and replaced using this method.https://pypi.org/project/rasterio/https://pypi.org/project/numpy/https://pypi.org/project/matplotlib/ Each band holds LST values. Celsius (Band 1), Fahrenheit (Band 2), and Urban Heat Island Severity or the difference between the Observed LST (in ℉) and the County Average LST of 92.4℉ (Band 3)Coverage: Cuyahoga CountyUpdate Frequency: As new data becomes availableLast Update: August, 2024

El Niño Enhances Snowline Rise on the Quelccaya Ice Cap, Peru (in-review)

Kara A. Lamantia, Laura J. Larocca, Lonnie G. Thompson, Bryan Mark

Exported results from automated snow cover area detection on the Quelccaya Ice Cap (QIC). Further calculated results are detailed in the supplementary documentation in the draft manuscript. See READ ME.txt file for details

Sample Code available for Landsat 8 imagery here at the following URL: https://code.earthengine.google.com/cfcbd0780ff3f09b0698035cd6dd678a

Das Harmonized Landsat Sentinel-2 (HLS)-Projekt bietet konsistente Daten zur Oberflächenreflexion vom Operational Land Imager (OLI) an Bord des gemeinsamen NASA/USGS-Satelliten Landsat 8 und vom Multi-Spectral Instrument (MSI) an Bord der Copernicus Sentinel-2A-Satelliten Europas. Die kombinierte Messung ermöglicht alle zwei bis drei Tage globale Beobachtungen der Erde mit einer räumlichen Auflösung von 30 Metern.