New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)

The United States Geological Survey has published "An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems" in Global Ecology and Conservation Journal. This work was produced by a team led by Roger Sayre, Ph.D., Senior Scientist for Ecosytems at the USGS Land Change Science Program with the support from The Nature Conservancy and Esri. We described this work using two introduction story maps, Introduction to World Ecosystems Map and Introduction to World Climate Regions Map. This story map is an introduction for World Climate Regions Map. You can have more information by accessing the published paper and you can access the dataset by downloading the pro package.

The Crop Monitor sub-national crop calendars are based on a combination of the best available data from different international agencies, national ministries, and expert knowledge from country partners. For the Crop Monitor for AMIS sub-national calendars, the USDA Major World Crop Areas and Climatic Profiles was used as a base to create the crop calendars at the national level. The Crop Monitor for Early Warning sub-national calendars used FAO GIEWS, FEWS NET, and national ministries as a base to create the crop calendars at the national level. Then for both Crop Monitors, each country was broken down into sub-national regions through the combination of agro-ecological zones, administrative level 1 FAO GAUL units, production statistics, and national partner inputs. The sub-national regions are each an aggregation of administrative FAO GAUL level 1 units or in some cases the administrative level 1 units themselves.

This data set consists of 83 digital maps that were produced by the Food and Agriculture Organization of the United Nations (FAO) for the World Bank as part of a Global Farming Systems Study. The maps are distributed through the FAO-UN GeoNetwork Portal to Spatial Data and Information.

As part of the World Bank's review of its rural development strategy, the Bank sought the assistance of FAO in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and a global overview. The global overview, which synthesizes the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. The global document is supplemented by two case study reports of development issues of importance to farming systems globally.

The six regions studied include:

East Asia Pacific East Europe and Central Asia Latin America and Caribbean Middle East and North Africa South Asia Sub-Saharan Africa

Map coverages for each region include the following:

Average precipitation Average temperature Elevation Irrigation intensity Land cover Length of growing period Livestock stocking density Major environmental constraints Major farming systems NOAA Satellite imagery (shaded relief imagery and ocean floor bathymetry) Permanent crop and arable land Rural population Slope Total population

The map coverages were prepared by FAO based on the following data sources:

Doll, P. and Siebert, S. 1999. A Digital Global Map of Irrigated Areas, Report No A9901, Centre for Environmental Systems Research, University of Kassel, Kassel, Germany.

Environmental Systems Research Institute (ESRI) Data and Maps 1999, Volume 1. World Worldsat Color Shaded Relief Image. Based on 1996 NOAA weather satellite images, with enhanced shaded relief imagery and ocean floor relief data (bathymetry) to provide a land and undersea topographic view. ESRI, Redlands, California, USA.

Food and Agriculture Organization of the United Nations (FAO), Land and Water Development Division (AGL) with the collaboration of the International Institute for Applied Systems Analysis (IIASA). 2000. Global Agro-Ecological Zones Study. FAO, Rome, Italy.

Gomes, R. 1999. Major Environmental Constraints for Agricultural Production Project. Based on FAOCLIM database, ARTEMIS NDVI imagery, and soil and terrain data provided by Soil Resources Management and Conservation Service. FAO-GIS. Food and Agriculture Organization of the United Nations (FAO), Environment and Natural Resources Service, Rome, Italy.

Leemans, R. and Cramer, W. 1991. The IIASA Database for Mean Monthly Values of Temperature, Precipitation and Cloudiness on a Global Terrestrial Grid. Research Report RR-91-18. November 1991. International Institute of Applied Systems Analyses, Laxenburg, pp. 61.

Oak Ridge National Laboratory, LandScan Global Population 1998 Database. Oak Ridge National Laboratory (ORNL), Oak Ridge, Tennessee, USA.

Slingenbergh, J. Livestock Distribution, Production and Diseases: Towards a Global Livestock Atlas. Food and Agriculture Organization of the United Nations (FAO), AGAH, Rome, Italy. (aka Global Livestock Production and Health Atlas (GLiPHA))

U.S. Geological Survey, EROS Data Center. 1996. GTOPO30 Digital Data Set. EDC, Sioux Falls, South Dakota, USA.

Use this country model layer when performing analysis within a single country. This layer displays a single global land cover map that is modeled by country for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

This Web Map is a subset of Above and below ground biomass carbon (tonnes/ha)This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.This layer supports analysis but, if needed, a direct download of the data can be accessed here.The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see table 1 for further information on datasets selected).DatasetScopeYearResolutionDefinitionSantoro et al. 2018Global2010100 mAbove-ground woody biomass for trees that are >10 cm diameter-at-breast-height, masked to Landsat-derived canopy cover for 2010; biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.Xia et al. 2014Global1982-20068 kmAbove-ground grassland biomass.Bouvet et al. 2018Africa201025 mAbove-ground woodland and savannah biomass; low woody biomass areas, which therefore exclude dense forests and deserts.Spawn et al. 2017Global2010300 mSynthetic, global above- and below-ground biomass maps that combine recently released satellite-based data of standing forest biomass with novel estimates for non-forest biomass stocks.After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above- and below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.This dataset has not been validated.

This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.This layer supports analysis but, if needed, a direct download of the data can be accessed here.

The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.

Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see table 1 for further information on datasets selected).

Dataset

Scope

Year

Resolution

Definition

Santoro et al. 2018

Global

2010

100 m

Above-ground woody biomass for trees that are >10 cm diameter-at-breast-height, masked to Landsat-derived canopy cover for 2010; biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.

Xia et al. 2014

Global

1982-2006

8 km

Above-ground grassland biomass.

Bouvet et al. 2018

Africa

2010

25 m

Above-ground woodland and savannah biomass; low woody biomass areas, which therefore exclude dense forests and deserts.

Spawn et al. 2017

Global

2010

300 m

Synthetic, global above- and below-ground biomass maps that combine recently released satellite-based data of standing forest biomass with novel estimates for non-forest biomass stocks.

After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.

Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above- and below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.This dataset has not been validated.

Climate plays a major role in determining the distribution of plants and animals. Bioclimatology, the study of climate as it affects and is affected by living organisms, is key to understanding the patterns of forests and deserts on the landscape, where productive agricultural lands may be found, and how changes in the climate will affect rare species. This layer is part of the Ecophysiographic Project and is one of the four input layers used to create the World Ecological Land Units Map. This layer provides access to a 250m cell-sized raster with a bioclimatic stratification. The source dataset was a 30-arcsecond resolution raster (equivalent to 0.86 km2 at the equator or about a 920m pixel size). The layer has the following attributes: Temperature Description - Seven classes based on the number of growing degree days (the monthly mean temperature multiplied by number of days in the month summed for all months). The 1950 to 2000 monthly average temperature was used to calculate growing degree days. Values in this field and associated number of growing degree days are: Temperature DescriptionGrowing Degree DaysVery Hot9,000 – 13,500Hot7,000 – 9,000Warm4,500 – 7,000Cool2,500 – 4,500Cold1,000 – 2,500Very Cold300 – 1,000Arctic0 - 300 Aridity Description - Six classes based on an index of aridity calculated by dividing precipitation by evapotranspiration. Precipitation and evapotranspiration are average values from 1950 to 2000. Aridity DescriptionAridity IndexVery Wet1.5 – 70Wet1.0 – 1.5Moist0.6 – 1.0Semi-dry0.3 – 0.6Dry0.1 – 0.3Very Dry0.01 – 0.1 Bioclimate Class - a 2-part description that combines the value of the Temperature Description field and the Aridity Description field. The alias for this field is ELU Bioclimate Reclass. This layer was created by modifying the dataset documented in the publication: Metzger and others. 2012. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. A service is available providing access to the data table associated with this layer. The data table services can be used by developers to quickly and efficiently query the data and to create custom applications. For more information see the World Ecophysiographic Tables. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The existence, sources, distribution, circulation, and physicochemical nature of macroscale oceanic water bodies have long been a focus of oceanographic inquiry. Building on that work, this paper describes an objectively derived and globally comprehensive set of 37 distinct volumetric region units, called ecological marine units (EMUs). They are constructed on a regularly spaced ocean point-mesh grid, from sea surface to seafloor, and attributed with data from the 2013 World Ocean Atlas version 2. The point attribute data are the means of the decadal averages from a 57-year climatology of six physical and chemical environment parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate). The database includes over 52 million points that depict the global ocean in x, y, and z dimensions. The point data were statistically clustered to define the 37 EMUs, which represent physically and chemically distinct water volumes based on spatial variation in the six marine environmental characteristics used. The aspatial clustering to produce the 37 EMUs did not include point location or depth as a determinant, yet strong geographic and vertical separation was observed. Twenty-two of the 37 EMUs are globally or regionally extensive, and account for 99% of the ocean volume, while the remaining 15 are smaller and shallower, and occur around coastal features. We assessed the vertical distribution of EMUs in the water column and placed them into classical depth zones representingepipelagic (0 m to 200 m), mesopelagic (200 m to 1,000 m), bathypelagic (1,000 m to 4,000 m) and abyssopelagic (>4,000 m) layers. The mapping and characterization of the EMUs represent a new spatial framework for organizing and understanding the physical, chemical, and ultimately biological properties and processes of oceanic water bodies. The EMUs are an initial objective partitioning of the ocean using longterm historical average data, and could be extended in the future by adding new classification variables and by introducing functionality to develop time-specific EMU distribution maps. The EMUs are an open-access resource, and as both a standardized geographic framework and a baseline physicochemical characterization of the oceanicenvironment, they are intended to be useful for disturbance assessments, ecosystem accounting exercises, conservation priority setting, and marine protected area network design, along with other research and management applications.

Here we present the first-available global dataset that quantifies human alterations in 15 million sq km floodplains along the world’s 520 major river basins. We developed these data using a comprehensive 27-year (1992-2019) analysis of remotely sensed land use change at 250-m resolution. This new dataset reveals that the world has lost ~600,000 sq km floodplains in 27 years (1992-2019), moving from natural forest, grassland, and wetland conditions to 460,000 sq km of new agricultural and 140,000 sq km of new developed areas.

To ensure the maximum reuse of this dataset, we also developed three web-based semi-automatic programming tools partly supported with data-driven tutorials and step-by-step audiovisual instructions.

(1) Floodplain Mapping Tool - Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1xQlARZXKPexmDInYV-EMoJ-HZxmFL-eW?usp=sharing - A tutorial developed and published through an online data-driven geoscience education platform: https://serc.carleton.edu/hydromodules/steps/246320.html - A YouTube video with step-by-step instructions: https://youtu.be/TgMbkJdALig

(2) Land Use Change Tool - Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1vmIaUCkL66CoTv4rNRIWpJXYXp4TlAKd?usp=sharing - A tutorial developed and published through an online data-driven geoscience education platform: https://serc.carleton.edu/hydromodules/steps/241489.html - A YouTube video with step-by-step instructions: https://youtu.be/wH0gif_y15A

(3) Human Alteration Tool - Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1r2zNJNpd3aWSuDV2Kc792qSEjvDbFtBy?usp=sharing

Note, the floodplain dataset used in this analysis (GFPLAIN250m; Nardi et al., 2019) does not cover deserts and ice-covered regions. Hence, places like northern Africa, Persian Gulf, Tibetan plateau, and the region above 60 degrees north latitude are not included in this analysis.

This global floodplain alteration dataset is built off our recent work published in the Nature Scientific Data: Rajib et al. (2021). The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset. https://doi.org/10.1038/s41597-021-01048-w

Carbon stocks in mangroves in the Zambezi River Delta of Mozambique (East Africa) were inventoried using a stratified random sampling approach from 2012 to 2016. A total 52 plots containing 287 subplots were objectively distributed using a GIS based spatial decision support system (SDSS) to represent the characteristics of mangroves and the operating constraints within the Delta area. The inventory was designed to provide estimates of above- and below-ground carbon stocks for the entire Delta. Data include species, height and diameter at breast height for overstory, understory and dead trees, mass of woody debris, litter, and ground vegetation. Data to estimate soil carbon and nitrogen content to 2 meters depth are also included.Mangroves are recognized for their numerous ecosystem services and functions that are critical to environmental health and human wellbeing in the regions where they occur and beyond. Although mangroves comprise only 0.7% of the world’s tropical forests, they have been shown to contain globally-significant carbon pools, storing up to five times more carbon than typical upland tropical forests per area. As a result, there is interest in considering mangroves in national climate adaptation and mitigation strategies, yet little research related to climate change and carbon sequestration has been completed in mangroves, especially in Africa. WWF-Mozambique’s project, Sustainable Finance for the Protected Areas System of Mozambique recommended development of a pilot project for carbon sequestration in the mangrove forests of the Zambezi Delta, Mozambique, with aims to conserve mangroves in the Zambezi River Delta through reforestation mechanisms and implementation of sustainable use and management activities with local stakeholders, financed sustainably through carbon markets. The USFS project, The Zambezi River Delta Mangrove Carbon Project: A Pilot Baseline Assessment for REDD+ Reporting and Monitoring is a foundational scientific component of the larger WWF effort, designed to quantify the mangrove carbon stocks to contribute to Mozambique’s REDD+ National Program by building in-country technical capacity and measuring the soil and vegetation carbon pools in the intact mangrove stands within the Zambezi River Delta.This project was also used to assess the distribution of mangroves and change in distribution over time. The results of these analyses are available in Shapiro et al. 2015. Lidar data were also collected as part of this project for a portion of the Zambezi River Delta; that data along with remote sensing derived canopy height data are available online (Lagomasino et al. 2016).

Original metadata date was 10/03/2017. On 03/25/2019 we added a file containing the coordinates of the carbon inventory plots and dominant species found on each plot, in addition to minor metadata updates.

The Food and Agriculture Organization of the United Nations (FAO) and its partners -- the United Nations Environment Programme (UNEP), the International Soil Reference and Information Centre (ISRIC), the International Institute for Applied Systems Analysis (IIASA), the European Soil Bureau (ESB), and national soil institutes -- are working towards a common World Soil and Terrain Resources [SOTER (SO for soils, TER for terrain)] Map covering the globe at 1:5 million scale. The main objectives of this work are: (1) to provide sound soil and terrain resources information for use in global and continental models to simulate food production potentials, climatic change, river flow simulation, livestock distribution, research priorities, land constraints, and (very) general land management advice; (2) to provide an educational tool to the soil science community; and (3) to provide harmonized norms for soil mapping, soil classification, soil analysis and interpretation of soil resources information.

SOTER is a methodology for storing and handling soils and terrain data. The SOTER program provides an orderly arrangement of natural resource data in such a way that these data can be readily accessed, combined, and analyzed from the point of view of potential use and production, in relation to food requirements, environmental impact, and conservation. Fundamental in the SOTER approach is the mapping of areas with a distinctive, often repetitive pattern of landform, morphology, slope, parent material, and soils at 1:1 million scale (SOTER units). Each SOTER unit is linked through a geographic information system (GIS) with a computerized database containing, in theory, all available attributes on topography, landform and terrain, soils, climate, vegetation, and land use. In this way, each type of information or each combination of attributes can be displayed spatially as a separate layer or overlay or in tabular form. The methodology is explained in the SOTER Procedures Manual (ftp://ftp.fao.org/agl/agll/docs/sotereng.pdf).

The SOTER concept was originally developed for application at country (national) scale and national SOTER maps have been prepared, with ISRIC's assistance, for Uruguay (1:1 M), Kenya (1:1 M), Hungary (1:500,000), and Jordan and Syria (1:500,000).

Several continental SOTER databases have been prepared and are available on CD-ROMs from FAO. These include SOTER databases for northeastern Africa, Latin America and the Caribbean, north and central Eurasia, and central and eastern Europe. [Note: Separate metadata reports for the continental SOTER databases have been registered in the ORNL DAAC Mercury Metadata Database for Global and Regional Environmental Data.] SOTER Europe and SOTER Southern Africa are in preparation.

The different SOTER puzzle-pieces will eventually be correlated and integrated into a unique 1:5 million scale WORLD SOTER, using whatever best soil profile information is available, resulting in a significant update of the global soil and terrain information source. In the interim, FAO, ISRIC, and the European Commission Joint Research Centre have recently updated the 1990 FAO Map of World Soil Resources (1:25 Million Scale) that is generalized from the FAO/Unesco Soil Map of the World (1:5 Million Scale) (FAO, 1971-1981). The Map of World Soil Resources has been redrawn under the SOTER Programme to incorporate the findings of all regional SOTER projects and additional data provided by the United States and Canada. In addition, the FAO legend has been replaced by the World Reference Base for Soil Resources (WRB) as a classification system, and the updated map also contains topographical information. The updated Map of World Soil Resources (January 2003) can be viewed and downloaded at [http://www.fao.org/ag/agl/agll/wrb/wrbmaps/htm/soilres.htm].

This data set contains landcover imagery from Tropical Resources and Environment monitoring by Satellite (TREES) of the Central African Region. This project, which is being jointly conducted by the Commission of the European Communities (CEC) and the European Space Agency (ESA).

 Geospatial Reference Information:

 Geographic Coordinates Units decimal degrees
 Horizontal Datum Name North American Datum of 1927
 Ellipsoid Name Clark 1866

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010. This layer supports analysis but, if needed, a direct download of the data can be accessed here.

The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.

Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see table 1 for further information on datasets selected).

Dataset

Scope

Year

Resolution

Definition

Santoro et al. 2018

Global

2010

100 m

Above-ground woody biomass for trees that are >10 cm diameter-at-breast-height, masked to Landsat-derived canopy cover for 2010; biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.

Xia et al. 2014

Global

1982-2006

8 km

Above-ground grassland biomass.

Bouvet et al. 2018

Africa

2010

25 m

Above-ground woodland and savannah biomass; low woody biomass areas, which therefore exclude dense forests and deserts.

Spawn et al. 2017

Global

2010

300 m

Synthetic, global above- and below-ground biomass maps that combine recently released satellite-based data of standing forest biomass with novel estimates for non-forest biomass stocks.

After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.

Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above- and below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.

Use this country model layer when performing analysis within a single country. This layer displays predictions within each country of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create these predictions.

Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture

Use this regional model layer when performing analysis within a single continent. This layer displays predictions within each continent of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create these predictions.Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 - GlobalLand Cover Vulnerability to Change 2050 - RegionalLand Cover Vulnerability to Change 2050 - CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture