Ethiopia biodiversity data

NDVI Anomaly is a geospatial metric used to measure how current vegetation conditions deviate from what is considered "normal" or average for a specific location and time period. It helps identify abnormal vegetation growth, whether better or worse than expected, and is especially valuable in agriculture, drought monitoring, food security, and environmental management. The dataset used for this analysis is the MOD13Q1 V6.1 product, which provides a Vegetation Index (VI) value on a per-pixel basis. For this study, we used MODIS data from July for the past three years, along with historical data spanning from 2010 to 2020.

These datasets provide Ethiopia’s Vegetation Health Index (VHI) values for the past five years, focusing specifically on the growing season from June to September. The Vegetation Health Index (VHI) is a satellite-derived indicator used to monitor the overall health and stress conditions of vegetation, particularly in relation to drought, heat, and moisture availability. It offers an integrated measure by combining data on vegetation greenness and thermal conditions. VHI is a vital remote sensing index for assessing vegetation condition, drought impacts, and agricultural stress. It combines two indices: the Vegetation Condition Index (VCI), which reflects vegetation greenness, biomass, and vigor; and the Temperature Condition Index (TCI), which indicates thermal conditions including heat and water stress. The long-term dataset analyzed for seasonal VHI spans from 2000 to the present, concentrating on the months of June, July, August, and September, corresponding to the Ethiopian growing season. These time series datasets highlight VHI values for the most recent five growing seasons, enabling direct comparison with current year conditions.

These datasets provide Ethiopia’s Vegetation Condition Index (VCI) values for the past five years, specifically covering the June to September growing season. The VCI is a satellite-derived indicator used to evaluate vegetation health and drought conditions. It distinguishes drought-related stress from other factors affecting vegetation growth. The VCI values range from 0 to 100, where 0% indicates the worst vegetation condition—approaching the historical minimum and signifying severe drought—and 100% indicates optimal conditions, near the historical maximum, reflecting healthy and lush vegetation. The VCI data is obtained from the MODIS satellite. The long-term dataset used for seasonal analysis spans from the year 2000 to the present, focusing on the months of June through September, aligning with Ethiopia’s main growing season. These time series datasets enable the comparison of current vegetation conditions with those of recent growing seasons, supporting drought monitoring and vegetation health assessments.

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Gridded datasets used in Jones et al. (2021) paper 'Agrobiodiversity Index scores show agrobiodiversity is underutilized in national food systems'. Details of how datasets were made and underlying sources are provided in Jones et al. (2021) Supplementary Information - with a resolution of 0.05* 0.05 pixels approximately 5000 metres and referenced 2022

This dataset provides a spatial mask of Ethiopia rangelands, identifying areas dominated by grasslands, shrublands, and sparse vegetation that are primarily used for grazing and pastoral livelihoods. The rangeland mask for Ethiopia delineates regions where pastoralism and agro-pastoralism are prevalent, helping distinguish these land systems from croplands, forests, or urban areas. The dataset is derived from a combination of remote sensing data and land cover classifications, ensuring consistency across ecological zones-

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

This dataset contains the Ethiopia Above Ground Biomass (AGDB)  Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group with a resolution of 0.05 pixels ,

The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009 to 2023). Pixel values indicate the percentage of good NDVI observations over the entire time period. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: 255 Unit: % Map code: L3-AWA-QUAL-NDVI-LT **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009-2019). Pixel values indicate the percentage of good NDVI observations over the entire time period. While the dekadal NDVI Quality layer shows the quality of the data for a specific time of year, the long-term NDVI quality layer has been added to give users a quick overview of the areas that tend to suffer more cloud cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The layer gives an indication of the quality of the NDVI input data. It shows whether a valid observation was available for that dekad, and if not, the length of the data gap since the nearest valid observation, in dekads. The data is provided from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Number of Dekads (D) Map code : L3-QUAL-NDVI-D.AWA **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The quality layer is produced during the compositing and smoothing of the NDVI. The quality index (QI) for every pixel in each dekad depicts the number of dekads since the last valid observation used for reconstruction. The fundamental idea is that the longer the gap, the less reliable the smoothing is. When a number of consecutive dekads did not have direct observations for calculating the NDVI, the quality layer pixel value of the consecutive dekadal NDVI quality layers will represent the cumulative number of dekads since a valid observation. The user can use consecutive NDVI Quality layers to calculate the length of the gap between valid observations. In the case that the pixel observation is usable and no reconstruction was needed, the QI is set to 0 (ideal situation). For the observations at the profile edges, it is assumed that the (a priori unknown) observations preceding the first valid observation and following the last valid observations are "good".\n\nThis quality layer depicts the quality for the NDVI, fAPAR, albedo and NPP, as all these data components rely on the same input, i.e. the spectral reflectance data. Furthermore, the length of the data gap is the same. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad. The data is provided from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: g/m² Scale Factor : 0.001 Map code : L3-NPP-D.AWA Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP: - Daily:incoming solar radiation and temperature data (Tmin/Tmax); - Dekadal:fAPAR and soil moisture stress; - Seasonal:Land Cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the NPP for that specific month. The data is provided from January 2009 to 2023. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: gC/m² Scale Factor : 0.001 Map code : L3-NPP-M.AWA Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP: - Daily:incoming solar radiation and temperature data (Tmin/Tmax); - Monthly:fAPAR and soil moisture stress; - Seasonal:Land Cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009 to date). Pixel values indicate the percentage of good NDVI observations over the entire time period. The data is provided from January 2009 to 2023. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: 255 Unit: % Map code: L3-KOG-QUAL-NDVI-LT **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009-2019). Pixel values indicate the percentage of good NDVI observations over the entire time period. While the dekadal NDVI Quality layer shows the quality of the data for a specific time of year, the long-term NDVI quality layer has been added to give users a quick overview of the areas that tend to suffer more cloud cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The layer gives an indication of the quality of the NDVI input data. It shows whether a valid observation was available for that dekad, and if not, the length of the data gap since the nearest valid observation, in dekads. The data is provided from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Number of Dekads (D) Map code : L3-QUAL-NDVI-D.KOG **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The quality layer is produced during the compositing and smoothing of the NDVI. The quality index (QI) for every pixel in each dekad depicts the number of dekads since the last valid observation used for reconstruction. The fundamental idea is that the longer the gap, the less reliable the smoothing is. When a number of consecutive dekads did not have direct observations for calculating the NDVI, the quality layer pixel value of the consecutive dekadal NDVI quality layers will represent the cumulative number of dekads since a valid observation. The user can use consecutive NDVI Quality layers to calculate the length of the gap between valid observations. In the case that the pixel observation is usable and no reconstruction was needed, the QI is set to 0 (ideal situation). For the observations at the profile edges, it is assumed that the (a priori unknown) observations preceding the first valid observation and following the last valid observations are "good".\n\nThis quality layer depicts the quality for the NDVI, fAPAR, albedo and NPP, as all these data components rely on the same input, i.e. the spectral reflectance data. Furthermore, the length of the data gap is the same. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the NPP for that specific month. The data is provided from January 2009 to 2023. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: gC/m² Scale Factor : 0.001 Map code : L3-NPP-M.KOG Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP: - Daily:incoming solar radiation and temperature data (Tmin/Tmax); - Monthly:fAPAR and soil moisture stress; - Seasonal:Land Cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage