A detailed list of the components available on Orbify related to Land Use monitoring.
ESRI Land cover changes
Component Type: Layer
Data Type: Optical
Accuracy: High
Spatial Resolution: 10 x 10 m
Description: Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2021. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These datasets produced by Impact Observatory and licensed by Esri were fetched from Microsoft Planetary Computer's data catalog & storage.
Indigenous territories
Component Type: Layer
Data Type: Other
Description: Indigenous territories mapped by Native Land, showcasing historical and ancestral regions of indigenous communities.
Land Cover Brazil (MAPBIOMAS)
Component Type: Layer
Data Type: Other
Accuracy: High
Spatial Resolution: 10 m
Temporal Resolution: 2016 – 2022
Description: The MapBiomas 10 meters BETA Collection includes land use and land cover (LULC) annual maps from 2016 to 2022 (the period available for Sentinel-2 satellite images).
Land Use Categories (Dynamic World)
Component Type: Chart
Data Type: Optical
Accuracy: High
Spatial Resolution: 10 m
Description: A pie-chart representing the land use categories within a specific project area, derived from the Dynamic World's 10-meter near-real-time Land Use/Land Cover (LULC) dataset. Dynamic World utilizes high-resolution imagery primarily from the Copernicus Sentinel-2 mission. These satellites capture images of Earth's surface with a spatial resolution of 10 meters. To classify the land cover, Dynamic World employs advanced machine learning algorithms. These algorithms are trained on large sets of labeled satellite imagery and can accurately distinguish between different types of land cover, such as water, forest, agriculture, urban areas, and more.
Land Use Categories (Dynamic World)
Component Type: Layer
Data Type: Optical
Accuracy: High
Spatial Resolution: 10 m
Description: Dynamic world land use categories within a specific project area, derived from the Dynamic World's 10-meter near-real-time Land Use/Land Cover (LULC) dataset. Dynamic World utilizes high-resolution imagery primarily from the Copernicus Sentinel-2 mission. These satellites capture images of Earth's surface with a spatial resolution of 10 meters. To classify the land cover, Dynamic World employs advanced machine learning algorithms. These algorithms are trained on large sets of labeled satellite imagery and can accurately distinguish between different types of land cover, such as water, forest, agriculture, urban areas, and more.
Land Use Category Changes (Dynamic World)
Component Type: Layer
Data Type: Optical
Accuracy: Medium
Spatial Resolution: 10 m
Description: Dynamic world land use categories for the period 2016, to the user input end-date year within a specific project area. Land-use classifications are derived from the Dynamic World's 10-meter near-real-time Land Use/Land Cover (LULC) dataset. Dynamic World utilizes high-resolution imagery primarily from the Copernicus Sentinel-2 mission. These satellites capture images of Earth's surface with a spatial resolution of 10 meters. To classify the land cover, Dynamic World employs advanced machine learning algorithms. These algorithms are trained on large sets of labeled satellite imagery and can accurately distinguish between different types of land cover, such as water, forest, agriculture, urban areas, and more.
Oil palm plantations
Component Type: Chart
Data Type: Optical, Radar
Accuracy: Very High
Spatial Resolution: 10 x 10 m
Description: The chart displays the classification percentages for oil palm plantations in 2019, including categories like industrial closed-canopy oil palm plantations, smallholder closed-canopy oil palm plantations and other land covers and/or uses that are not closed-canopy oil palm. It covers areas where oil palm plantations were detected. The mapping results are generated by a convolutional neural network using composite images from the first and second Sentinel satellites over six-month intervals.
Conclusion
For more information, check the complete list of components available on Orbify.