A detailed list of the components available on Orbify related to environmental monitoring.

Component Type: Chart

Data Type: Numerical Weather Prediction

Accuracy: Medium

Spatial Resolution: 27830 m

Temporal Resolution: 1 day

Description: The chart represents the average humidity levels over a specified region within a defined time period. The data is derived from the NOAA/GFS0P25 collection, specifically focusing on the relative humidity at 2 meters above ground.

Component Type: Stat

Data Type: Numerical Weather Prediction

Accuracy: Medium

Spatial Resolution: 13 km

Temporal Resolution: 1 day

Description: The Global Forecast Model (GFS) by NOAA is a valuable resource for obtaining specific humidity data at a height of 2 meters above ground level. This model is freely available to the public and is designed to provide accurate and up-to-date meteorological data on a global scale. In addition to specific humidity, the GFS model also provides a wide range of other meteorological parameters that can be useful for weather forecasting and climate analysis.

These parameters include temperature, wind speed and direction, precipitation, cloud cover, and atmospheric pressure, among others. The GFS model is based on advanced numerical weather prediction techniques and incorporates data from a variety of sources, including ground-based weather stations, satellites, and ocean buoys. This allows it to provide highly detailed and accurate forecasts for locations all around the world, with a particular focus on regions that are prone to extreme weather events.

Overall, the specific humidity data provided by the GFS model can be a valuable tool for a range of applications, including agriculture, energy production, and transportation planning. By providing detailed and accurate meteorological data on a global scale, the GFS model is helping to improve our understanding of weather and climate patterns, and to prepare for the impacts of climate change.

Component Type: Layer

Temporal Resolution: monthly

Description: The FireCCI51 is a globally available dataset that provides monthly burned area information at an approximate spatial resolution of 250 meters. This dataset is derived from the surface reflectance in the Near Infrared (NIR) band captured by the MODIS instrument onboard the Terra satellite. Additionally, it utilizes active fire data from both the Terra and Aqua satellites. The dataset shows a visual representation of the burned area and the total hectares of land burned.

Component Type: Chart

Temporal Resolution: quarterly

Description: This algorithm returns the near-surface concentration of chlorophyll-a (chlor_a) in mg m-3, calculated using empirical relationships derived from in situ measurements of chlor_a and remote sensing reflectances (Rrs). The implementation is contingent on the availability of three or more sensor bands spanning the 440 - 670 nm spectral regime. The algorithm is applicable to all current ocean color sensors.

The chlor_a product is included as part of the standard Level-2 OC product suite and the Level-3 CHL product suite. The current implementation for the standard chlorophyll product (chlor_a), as applied in version R2022 of NASA's multi-mission ocean color processing, is a blend between the updated OC3/OC4 (OCx) band ratio algorithm (O'Reilly and Werdell 2019) and the color index (CI) of Hu et al. (2019). The combined algorithm and theoretical basis is described in Hu et al. (2019).

Component Type: Layer

Temporal Resolution: quarterly

Description: This algorithm returns the near-surface concentration of chlorophyll-a (chlor_a) in mg m-3, calculated using empirical relationships derived from in situ measurements of chlor_a and remote sensing reflectances (Rrs). The implementation is contingent on the availability of three or more sensor bands spanning the 440 - 670 nm spectral regime. The algorithm is applicable to all current ocean color sensors.

The chlor_a product is included as part of the standard Level-2 OC product suite and the Level-3 CHL product suite. The current implementation for the standard chlorophyll product (chlor_a), as applied in version R2022 of NASA's multi-mission ocean color processing, is a blend between the updated OC3/OC4 (OCx) band ratio algorithm (O'Reilly and Werdell 2019) and the color index (CI) of Hu et al. (2019). The combined algorithm and theoretical basis is described in Hu et al. (2019).
​

Component Type: Layer

Data Type: Other

Accuracy: Medium

Spatial Resolution: 1 km

Description: The Earth Engine's version of the FIRMS dataset provides a rasterized representation of the LANCE fire detection product. This dataset showcases near real-time active fire locations derived from the MODIS MOD14/MYD14 Fire and Thermal Anomalies product. Each fire location indicates the center of a 1km pixel identified by the system as having one or more fires. The dataset identifies and visualizes fire occurrences within a specified region and date range, returning the visual representation of the fires along with the total area affected in hectares.

Component Type: Risk

Data Type: Optical, Radar

Accuracy: Medium

Spatial Resolution: 5 m

Description: The dataset represents flood risks for a given region and time range, derived from multiple Earth Engine sources, including precipitation, population, elevation, slope, and other parameters. It provides a visual categorisation of areas into low, medium, and high flood risk regions.

Component Type: Layer

Data Type: Other

Accuracy: High

Spatial Resolution: 0.0025°

Temporal Resolution: 2001-2019

Description: Forest carbon emissions refer to the release of greenhouse gases due to significant forest disturbances that took place in each year within the period from 2001 to 2021. These emissions are measured in megagrams of CO2 emissions per hectare. They encompass all relevant carbon pools within the ecosystem, including aboveground biomass, belowground biomass, dead wood, litter, and soil, and involve greenhouse gases such as CO2, CH4, and N2O.

The estimation of emissions for each geographical pixel adheres to the guidelines established by the IPCC for national greenhouse gas inventories and is contingent upon the occurrence of stand-replacing disturbances, as indicated in the annual tree cover loss data provided by Hansen et al. in 2013. The quantity of carbon emitted from each pixel is determined by referencing carbon densities in the year 2000, with adjustments made for carbon accumulation between 2000 and the year when the disturbance occurred.

It's important to note that these emissions are gross estimates and do not account for carbon removals resulting from subsequent forest regrowth after the clearing event. Instead, the removal of carbon due to regrowth is considered in a separate companion layer, denoted as the forest carbon removals layer. The proportion of carbon emitted from each pixel during a disturbance event, known as the emission factor, is influenced by various factors.

These factors encompass the direct cause of the disturbance, whether there was any observed fire activity in the year of the disturbance or the year preceding it, whether the disturbance took place on peatlands, and other relevant considerations. It should be noted that all emissions are assumed to occur in the year when the disturbance event took place, and it is possible to attribute emissions to specific years using the Hansen tree cover loss data.

Component Type: Layer

Data Type: Other

Accuracy: High

Spatial Resolution: 0.0025°

Temporal Resolution: 2001-2021

Description: The net forest carbon flux signifies the overall carbon exchange between forests and the atmosphere over the period spanning from 2001 to 2021. It is computed by determining the equilibrium between the carbon emissions released by forests and the carbon absorbed or sequestered by forests during this modeling period, measured in megagrams of CO2 emissions per hectare.

To calculate the net carbon flux, the average annual gross emissions are subtracted from the average annual gross removals for each modeled pixel. Negative values indicate instances where forests acted as carbon sinks, absorbing more carbon than they emitted, while positive values denote scenarios where forests acted as carbon sources, emitting more carbon than they absorbed between 2001 and 2021.

The computation of these net fluxes adheres to the guidelines outlined by the IPCC (Intergovernmental Panel on Climate Change) for national greenhouse gas inventories. This calculation is performed for each pixel where forests were present in 2000 or were established between 2000 and 2012, based on the Global Forest Change tree cover change data provided by Hansen et al. in 2013.

Component Type: Stat

Accuracy: High

Spatial Resolution: 0.0025°

Description: The net forest carbon flux signifies the overall carbon exchange between forests and the atmosphere over the period spanning from 2001 to 2021. It is computed by determining the equilibrium between the carbon emissions released by forests and the carbon absorbed or sequestered by forests during this modeling period, measured in megagrams of CO2 emissions per hectare. To calculate the net carbon flux, the average annual gross emissions are subtracted from the average annual gross removals for each modeled pixel.

Negative values indicate instances where forests acted as carbon sinks, absorbing more carbon than they emitted, while positive values denote scenarios where forests acted as carbon sources, emitting more carbon than they absorbed between 2001 and 2021. The computation of these net fluxes adheres to the guidelines outlined by the IPCC (Intergovernmental Panel on Climate Change) for national greenhouse gas inventories.

This calculation is performed for each pixel where forests were present in 2000 or were established between 2000 and 2012, based on the Global Forest Change tree cover change data provided by Hansen et al. in 2013.

Component Type: Layer

Accuracy: High

Spatial Resolution: 0.0025°

Temporal Resolution: 2001-2019

Description: Forest carbon removals from the atmosphere, which we refer to as sequestration, represent the total carbon captured in megagrams of CO2 per hectare through the growth of both established and newly regenerating forests throughout the modeling period spanning from 2001 to 2021. These removals encompass the accumulation of carbon in both aboveground and belowground live tree biomass.

In accordance with IPCC Tier 1 assumptions for forests that remain as forests, no removals are assumed for dead wood, litter, and soil carbon pools. The calculation of carbon removals for each geographical pixel adheres to the IPCC Guidelines for national greenhouse gas inventories. This calculation applies to areas where forests were present in the year 2000 or were established between 2000 and 2012, as indicated by the tree cover loss data provided by Hansen et al. in 2013.

The amount of carbon removed by each pixel is determined based on various factors, including the type of forest (e.g., mangrove, plantation), the ecozone (e.g., humid Neotropics), the age of the forest (e.g., primary, old secondary), and the number of years over which carbon removal has occurred.

This layer represents the cumulative removals throughout the modeling period, which spans from 2001 to 2021. To obtain the annual average removal rate for the modeling duration, this cumulative value must be divided by 21, as removal rates cannot be allocated to individual years within the model.

Component Type: Chart

Data Type: Other

Accuracy: Medium

Spatial Resolution: 10 x 10 m

Temporal Resolution: Annual (2001-2021)

The dataset represents the tree canopy height across Europe from 2001 to 2021 using multidecadal spectral data from the Landsat archive and calibration data from Airborne Laser Scanning (ALS) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidars. Annual tree canopy height was modeled using regression tree ensembles and integrated with annual tree canopy removal maps to produce a harmonised tree height map time series.

Component Type: Layer

Spatial Resolution: 0.00025 degree per pixel

The dataset represents the tree canopy height across Europe from 2001 to 2021 using multidecadal spectral data from the Landsat archive and calibration data from Airborne Laser Scanning (ALS) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidars. Annual tree canopy height was modeled using regression tree ensembles and integrated with annual tree canopy removal maps to produce a harmonised tree height map time series.

Component Type: Stat

The dataset represents the tree canopy height across Europe from 2001 to 2021 using multidecadal spectral data from the Landsat archive and calibration data from Airborne Laser Scanning (ALS) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidars. Annual tree canopy height was modeled using regression tree ensembles and integrated with annual tree canopy removal maps to produce a harmonised tree height map time series.

Component Type: Chart

Data Type: Other

Accuracy: Medium

Spatial Resolution: 27830 m

Description: The chart represents the average humidity levels over a specified region within a defined time period. The data is derived from the NOAA/GFS0P25 collection, specifically focusing on the relative humidity at 2 meters above ground.

Component Type: Layer

Data Type: Other

Description: Indigenous territories mapped by Native Land, showcasing historical and ancestral regions of indigenous communities.

Component Type: Layer

Description: The product measures sea surface temperature with an update latency of 3-4 days. It's part of the GCOM-C project, which aims for continuous global observation to understand radiation budget fluctuations and the carbon cycle. This data aids in making accurate future temperature projections. By collaborating with research institutions that use climate numerical models, GCOM-C enhances the accuracy of temperature rise predictions. The SGLI sensor, mounted on GCOM-C, succeeds the Global Imager from ADEOS-II and observes radiation across 19 channels, ranging from near-ultraviolet to thermal infrared. With its wide observation span, it allows for global observation roughly every two days around mid-latitude near Japan. Compared to similar sensors, SGLI boasts higher resolution and features both polarized and multi-angle observation capabilities.

Component Type: Chart

Description: Suspended Matter Concentration (gm−3) using the algorithm of Nechad et al. (2010) re- calibrated by Bouchra Nechad in September 2016, specifically for Landsat 8 and Sentinel-2.

Component Type: Layer

Description: Suspended Matter Concentration (gm−3) using the algorithm of Nechad et al. (2010) re- calibrated by Bouchra Nechad in September 2016, specifically for Landsat 8 and Sentinel-2.

Component Type: Layer

Description: Vegetation Condition Index (VCI) is a drought monitoring index that takes the previous year’s minimum and maximum NDVI values at a particular area and compares it against the current NDVI value to identify if the area is experiencing drought.

Component Type: Layer

Description: The dataset provides a binary mask that identifies water bodies within a specified region based on the NDWI (Normalized Difference Water Index) derived from the Sentinel-2 Surface Reflectance collection.

Component Type: Layer

Description: The water quality was assessed with Normalised Difference Chlorophyll Index (NDCI), that acts as a measure for the amount of chlorophyll on the surface of a waterbody.

Component Type: Layer

Description: The salinity value is obtained from Sentinel-2A image processing, using the salinity estimator algorithm's calculation called Cilamaya Algorithm.

​