A detailed list of the components available on Orbify for vegetation monitoring.

Component Type: Layer

Data Type: Radar, Other

Accuracy: High

Spatial Resolution: 100m

Temporal Resolution: 2018-2020

Description: Above ground biomass (AGB, unit: tons/ha i.e., Mg/ha). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Data is expressed as per-pixel estimates of above-ground biomass, uncertainty expressed as the standard deviation in Mg/ha (raster dataset).

This dataset comprises estimates of forest above-ground biomass for the year 2020. This dataset is derived from a combination of Earth observation data, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources.

The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions.

Cautions: Users should take care to inspect the error layer, and be mindful that AGB values at forest edges or in heavily fragmented forest may appear smaller than expected.

Component Type: Stat

Data Type: Radar, Other

Accuracy: High

Temporal Resolution: 1 year

Description: Above ground biomass (AGB, unit: tons/ha i.e., Mg/ha). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020.

They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. Maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019).

The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. 2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset).

Component Type: Layer

Data Type: Radar, Other

Accuracy: High

Spatial Resolution: 100m

Temporal Resolution: 2018-2020

Description: Per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha.

Above ground biomass (AGB, unit: tons/ha i.e., Mg/ha). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Data is expressed as per-pixel estimates of above-ground biomass, uncertainty expressed as the standard deviation in Mg/ha (raster dataset)

This dataset comprises estimates of forest above-ground biomass for the year 2020. This dataset is derived from a combination of Earth observation data, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.

The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions.

Component Type: Layer

Data Type: Optical

Accuracy: Medium

Spatial Resolution: 5 km

Description: The dataset function evaluates drought risks for a specified region over a defined date range. It leverages data from the Sentinel-2 satellite, which is used to calculate the Vegetation Condition Index (VCI). This index serves as an indicator of vegetation health by comparing the current NDVI value of an area to its historical minimum and maximum NDVI values, helping to identify drought conditions.

The function also incorporates precipitation and temperature data in its analysis. Current precipitation levels are juxtaposed against historical data to gauge the severity of the drought conditions. Similarly, temperature data is evaluated to refine the drought risk assessment further.

Another significant aspect of the assessment is the evaluation of the proximity of areas to populated cities. The function considers regions closer to densely populated areas to be at a higher risk during droughts due to the potential strain on water resources. Additionally, the function examines the proximity of areas to major roads and rivers. Regions farther from significant water sources or main roads may face elevated risk levels during droughts because of limited water accessibility and potential challenges in relief operations.

Based on these combined datasets and indices, areas are then classified into three risk categories: low, medium, or high.

Component Type: Stat

Data Type: Optical

Accuracy: High

Spatial Resolution: 10 x 10 m

Description: This tool provides an analysis of changes in crop cover between the user input start and end dates utilising the Dynamic World's 10-meter near-real-time Land Use/Land Cover dataset.

The Dynamic World dataset underpinning this tool employs high-resolution satellite imagery to map land cover changes at a 10-meter spatial resolution.

Component Type: Chart

Data Type: Optical

Accuracy: High

Spatial Resolution: 10 x 10 m

Description: The EVI Anomalies dataset offers an in-depth analysis of vegetation health over a specified region, leveraging the Enhanced Vegetation Index derived from Sentinel-2 satellite imagery. This dataset highlights anomalies by contrasting current vegetation metrics against historical patterns.

Such deviations provide vital clues about regions experiencing unusual vegetation behavior, potentially signaling ecological stress, drought conditions, or other environmental anomalies. The dataset's visual layers, complemented by a legend, assist in interpreting and categorizing the health of vegetation into distinct levels, from bare areas to high vegetation concentrations.

Component Type: Layer

Data Type: Optical

Accuracy: High

Spatial Resolution: 10 m

Description: The EVI Anomalies dataset offers an in-depth analysis of vegetation health over a specified region, leveraging the Enhanced Vegetation Index derived from Sentinel-2 satellite imagery. This dataset highlights anomalies by contrasting current vegetation metrics against historical patterns.

Such deviations provide vital clues about regions experiencing unusual vegetation behavior, potentially signaling ecological stress, drought conditions, or other environmental anomalies. The dataset's visual layers, complemented by a legend, assist in interpreting and categorizing the health of vegetation into distinct levels, from bare areas to high vegetation concentrations.

Component Type: Layer

Data Type: Optical

Accuracy: High

Spatial Resolution: 10 m

Description: The EVI Median Layer offers a similar perspective but uses the Enhanced Vegetation Index for its computation. EVI is a more refined measure, especially in high biomass regions, and it factors in near-infrared, red, and blue light reflections from vegetation.

Component Type: Chart

Data Type: Optical

Accuracy: High

Spatial Resolution: 10 x 10 m

Temporal Resolution: Annual

Description: NDVI, a key vegetation index, serves as a critical indicator of the health and vitality of plant life. This component systematically processes Sentinel-2 data on an annual basis, enabling the creation of detailed NDVI layers that represent the vegetative conditions across the targeted region.

Component Type: Layer

Data Type: Optical

Accuracy: High

Spatial Resolution: 10 x 10 m

Temporal Resolution: Annual

Description: NDVI, a key vegetation index, serves as a critical indicator of the health and vitality of plant life. This component systematically processes Sentinel-2 data on an annual basis, enabling the creation of detailed NDVI layers that represent the vegetative conditions across the targeted region.

Component Type: Chart

Data Type: Optical

Accuracy: High

Spatial Resolution: 10 x 10 m

Description: The NDVI Anomalies dataset offers an in-depth analysis of vegetation health over a specified region, leveraging the Normalized difference vegetation index derived from Sentinel-2 satellite imagery. This dataset highlights anomalies by contrasting current vegetation metrics against historical patterns.

Such deviations provide vital clues about regions experiencing unusual vegetation behavior, potentially signaling ecological stress, drought conditions, or other environmental anomalies. The dataset's visual layers, complemented by a legend, assist in interpreting and categorizing the health of vegetation into distinct levels, from bare areas to high vegetation concentrations.

Component Type: Layer

Data Type: Optical

Accuracy: High

Spatial Resolution: 10m

Description: The NDVI Median Layer provides a snapshot of median vegetation health over a defined period. NDVI is derived from the difference between near-infrared and red light reflection from plants.

For more information, check the complete list of components available on Orbify.