This dataset contains an archived copy of the Safari 2000 Project website as of October 2008. This archived website is provided for informational purposes only. No updates to the website and associated content have been made since January of 2008. The database that once provided content for this website was transitioned to text and is included herein. SAFARI 2000 was an international regional science initiative developed for southern Africa to explore, study and address linkages between land-atmosphere processes and the relationship of biogenic, pyrogenic or anthropogenic emissions and the consequences of their deposition to the functioning of the biogeophysical and biogeochemical systems of southern Africa. This initiative was built around a number of on-going, already funded activities by NASA, the international community and African nations in the southern African region.

This data set consists of a southern African subset of the University of Maryland (UMD) 1-degree Global Land Cover product in ASCII GRID and binary image formats. The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD. The product is based on Advanced Very High Resolution Radiometer (AVHRR) maximum monthly composites for 1987 of Normalized Difference Vegetation Index (NDVI) values at approximately 8-km resolution, averaged to one-by-one degree resolution. This coarse- resolution data set was used as the basis for a supervised classification of eleven cover types that broadly represent the major biomes of the world. Because of missing values at high latitudes, the Pathfinder AVHRR data set for 1987 for summer monthly NDVI and red reflectance values were used to distinguish the following cover types: tundra, high latitude deciduous forest and woodland, coniferous evergreen forest and woodland. The 1-degree global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. The data are available as a global coverage in both binary and ASCII format. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ) and in the readme file found along with the data [ ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data_1deg/comp/glcf1deg_readme.pdf].

In December 2024, Microsoft's Edge browser had a market share of 13.34 percent in the United States. Edge was first publicly released in July 2015, with the consumer release of Windows 10. However, Chrome held a majority of the market share, with almost 66 percent in the same month. What are web browsers? A web browser is a software application for visualizing websites, documents and data. The most popular current browsers are Google Chrome, Apple’s Safari, Microsoft Edge, and Firefox. Historically one of the large players in the segment, Internet Explorer has unfortunately lost its tight grip on the web browser market.As shown by the graph at hand, Google Chrome has been the most popular browser in the United States since December 2013. In other countries, Google Chrome has also taken up a dominating role. In the European browser market, Chrome and Safari have established strong market positions with 61 and 11.4 percent, respectively. On a worldwide scale, Chrome provided a share of around 64 percent in the global web browser market as of December 2021.

This data set consists of a southern African subset of the University of Maryland (UMD) 8-km Global Land Cover product in ASCII GRID and binary image files formats. Over the past several years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland (UMD) have employed the NASA/NOAA Pathfinder Land (PAL) data set with a spatial resolution of 8 km. This data set has a length of record of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, this data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project aim is to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data. The 8-km global land cover product was derived by testing several metrics that describe the temporal dynamics of vegetation over an annual cycle. These metrics were applied to 1984 PAL data at 8-km resolution to derive a global land cover classification product using a decision tree classifier. The final product contains 13 land cover classes. The original 8-km global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ). More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data_8km/comp/glcf8km_readme.pdf.

The Cloud and Aerosol Research Group (CARG) of the University of Washington participated in the SAFARI-2000 Dry Season Aircraft campaign with their Convair-580 research aircraft. This campaign covered five countries in southern Africa from 10 August through 18 September, 2000. Various types of measurements were obtained on the thirty-one research flights of the Convair-580 in SAFARI-2000, to study their relationships to simultaneous measurements from satellites (particularly Terra), other research aircraft, and SAFARI-2000 ground-based measurements and activities. The main goals of the University of Washington's Convair-580 research aircraft were to: * Measure the physical and chemical properties of aerosols and trace gases in ambient air, and from various sources, in southern Africa. * Obtain measurements on aerosols, trace gases, clouds, and surface properties for comparisons with simultaneous remote sensing measurements from the NASA ER-2 aircraft and Terra satellite and from SAFARI-2000 ground stations. * Carry out closure studies using in situ and remote sensing measurements made aboard the Convair-580. * Compare aerosol and trace gas measurements aloft at various locations in Southern Africa. * Measure the nature and concentrations of aerosols and trace gases, and their emission factors, in smoke from prescribed fires and non-prescribed fires of biomass in southern Africa. * Measure the spectral albedo and bidirectional reflection distribution function (BRDF) of various surfaces and clouds in southern Africa. * Measure the microstructures of clouds off the Atlantic Coast of southern Africa. * Investigate aerosol-cloud interactions. For a complete detailed guide to the extensive measurements obtained aboard the UW Convair-580 aircraft in support of SAFARI 2000, see the UW Technical Report for the SAFARI 2000 Project [PDF format]. The latest version of this document can be found at the UW SAFARI 2000 Web site [Internet Link], listed in the CARG Publications on SAFARI 2000 section.

This data set consists of a southern African subset of the University of Maryland (UMD) 1-degree Global Land Cover product in ASCII GRID and binary image formats. The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD. The product is based on Advanced Very High Resolution Radiometer (AVHRR) maximum monthly composites for 1987 of Normalized Difference Vegetation Index (NDVI) values at approximately 8-km resolution, averaged to one-by-one degree resolution. This coarse- resolution data set was used as the basis for a supervised classification of eleven cover types that broadly represent the major biomes of the world. Because of missing values at high latitudes, the Pathfinder AVHRR data set for 1987 for summer monthly NDVI and red reflectance values were used to distinguish the following cover types: tundra, high latitude deciduous forest and woodland, coniferous evergreen forest and woodland. The 1-degree global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. The data are available as a global coverage in both binary and ASCII format. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ) and in the readme file found along with the data [ ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data_1deg/comp/glcf1deg_readme.pdf].

AERONET (AErosol RObotic NETwork) is an optical ground-based aerosol monitoring network and data archive system. AERONET measurements of the column-integrated aerosol optical properties in the southern Africa region were made by sun-sky radiometers at several sites in August-September 2000 as a part of the SAFARI 2000 dry season aircraft campaign.AERONET is supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions. The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities. Data from this collaboration provides globally-distributed near-real-time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes.The AERONET (AErosol RObotic NETwork) program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and PHOTON and greatly expanded by AEROCAN (the Canadian sun-photometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of aerosol optical properties. The network imposes standardization of instruments, calibration, and processing. Data from this collaboration provides globally distributed observations of spectral aerosol optical depths, inversion products, and precipitable water in geographically diverse aerosol regimes. Three levels of data are available from the AERONET website: Level 1.0 (unscreened), Level 1.5 (cloud-screened), and Level 2.0 (Cloud-screened and quality-assured). (CAUTION: Data presented in the real time data version is unscreened and may not have final calibration reprocessing.) For each site there is a Principal Investigator (PI), the person responsible for deployment, maintenance and data collection. The PI is entitled to be informed of any use of that site data.NOTICE TO NON-AERONET INVESTIGATORS: To maintain the integrity of the data base and fairness to the individuals who have contributed, use of these data for publication requires an offer of authorship to the AERONET PI(s).

This data set consists of a southern African subset of the University of Maryland (UMD) 8-km Global Land Cover product in ASCII GRID and binary image files formats. Over the past several years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland (UMD) have employed the NASA/NOAA Pathfinder Land (PAL) data set with a spatial resolution of 8 km. This data set has a length of record of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, this data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project aim is to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data. The 8-km global land cover product was derived by testing several metrics that describe the temporal dynamics of vegetation over an annual cycle. These metrics were applied to 1984 PAL data at 8-km resolution to derive a global land cover classification product using a decision tree classifier. The final product contains 13 land cover classes. The original 8-km global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ). More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data_8….

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