Souleymane Fall

Impacts of land use/land cover change on climate and future research priorities.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Documentation of Uncertainties and Biases Associated with Surface Temperature Measurement Sites for Climate Change Assessment

The objective of this research is to determine whether poorly sited long-term surface temperature monitoring sites have been adjusted in order to provide spatially representative independent data for use in regional and global surface temperature analyses. We present detailed analyses that demonstrate the lack of independence of the poorly sited data when they are adjusted using the homogenization procedures employed in past studies, as well as discuss the uncertainties associated with undocumented station moves. We use simulation and mathematics to determine the effect of trend on station adjustments and the associated effect of trend in the reference series on the trend of the adjusted station. We also compare data before and after adjustment to the reanalysis data, and we discuss the effect of land use changes on the uncertainty of measurement. A major conclusion of our analysis is that there are large uncertainties associated with the surface temperature trends from the poorly sited stations. Moreover...

Analysis of Mean Climate Conditions in Senegal (1971–98)

Abstract This paper presents a GIS-based analysis of climate variability over Senegal, West Africa. It responds to the need for developing a climate atlas that uses local observations instead of gridded global analyses. Monthly readings of observed rainfall (20 stations) and mean temperature (12 stations) were compiled, digitized, and quality assured for a period from 1971 to 1998. The monthly, seasonal, and annual temperature and precipitation distributions were mapped and analyzed using ArcGIS Spatial Analyst. A north–south gradient in rainfall and an east–west gradient in temperature variations were observed. June exhibits the greatest variability for both quantity of rainfall and number of rainy days, especially in the western and northern parts of the country. Trends in precipitation and temperature were studied using a linear regression analysis and interpolation maps. Air temperature showed a positive and significant warming trend throughout the country, except in the southeast. A significant corre...

Distribution of Landscape Types in the Global Historical Climatology Network

Abstract The Global Historical Climate Network version 2 (GHCNv.2) surface temperature dataset is widely used for reconstructions such as the global average surface temperature (GAST) anomaly. Because land use and land cover (LULC) affect temperatures, it is important to examine the spatial distribution and the LULC representation of GHCNv.2 stations. Here, nightlight imagery, two LULC datasets, and a population and cropland historical reconstruction are used to estimate the present and historical worldwide occurrence of LULC types and the number of GHCNv.2 stations within each. Results show that the GHCNv.2 station locations are biased toward urban and cropland (>50% stations versus 18.4% of the world’s land) and past century reclaimed cropland areas (35% stations versus 3.4% land). However, widely occurring LULC such as open shrubland, bare, snow/ice, and evergreen broadleaf forests are underrepresented (14% stations versus 48.1% land), as well as nonurban areas that have remained uncultivated in the pa...

Adopting Multisensor Remote Sensing Datasets and Coupled Models for Disaster Management

An application and a process involving integration of dynamic models for a data-rich environment, incorporating a multi sensor dataset is discussed. The potential utility of such data fusion for different phases of disaster management: vulnerability assessment, early warning systems, disaster mitigation, response, damage assessment and recovery are delineated. Case studies are drawn from disaster scenarios for flooding, drought management, and heavy rains in India. Applicability of the technology and processes, with potentially different sources of data, is described. Solutions to several technological challenges to handle large data sets using distributed cluster technology and data visualization, using high-resolution large display systems, are presented. Taking an example of the July 26, 2005 heavy rain events in Mumbai, India, which caused flooding, and resulted in over 400 deaths and nearly a billion US economic losses, the ability of multiple models to study the predictability, variability and use of model – satellite data fusion for severe weather and disaster mitigation, as well as response needs is discussed. A case for multisensory satellite datasets and the use of upcoming technologies, including handheld computers and cell phones in facilitating early warning, evacuation and emergency intervention is addressed. A case is made for a technological and educational infrastructure development that can benefit from remote sensing centric models with different complexity and a community cyberinfrastructure for multidata access for disaster management.

Remote Sensing, Public Health & Disaster Mitigation

The authors review advances in applications for geotechnologies, specifically earth-observing satellite remote sensing, geo-positioning (i.e. USA’s Global Positioning System (GPS), Russia’s Global’naya Navigatsionnaya Sputnikovaya Sistema (GLONASS), Europe’s Galileo and China’s Beidou/Compass) and selected geo-spatial modeling software for public health and disaster management applications, with an emphasis on environmental health and environmental sustainability. Specific applications addressed include the use of remote sensing for infectious disease vector habitat identification and ecologically sustainable disease vector population mitigation, as well as the integration of GPS into mobile CD4 testing devices for HIV/AIDS. Public domain software models described include the Spatio-Temporal Epidemiological Modeler (STEM) and the Hydrologic Engineering River Analysis System (HEC-RAS) for flood modeling. Examples of regional, national and global real-time data acquisition and near-real-time data product development and distribution for time-critical events are offered, specifically through the Purdue Terrestrial Observatory (PTO), the United States Geological Survey (USGS) supported AmericaView and the International Charter – Space & Major Disasters.

Reply to comment by David E. Parker et al. on ''Unresolved issues with the assessment of multidecadal global land surface temperature trends''

[1] Pielke et al. [2007a] identified a variety of problems affecting the accuracy or appropriate level of confidence of the global historical land surface temperature data set, as applied to estimates of global temperature trends, and called for several measures to be taken to improve this network for this purpose. Parker et al. [2009], while acknowledging the importance of making improvements to the network and its data, take issue with two particular aspects of our analysis. We are grateful for the opportunity to engage in further discussion regarding these important issues.

Overcoming Bandwidth and Satellite Communications Limitations to Accelerate Applications of Remote Sensing and High Performance Computing for Sustainable African Development: Contributions from Egypt, Nigeria and South Africa

Sixty-two years after the first photos of earth taken by a camera aboard a V-2 rocket in October, 1946, and forty-eight years since the first CORONA satellite images were captured, the status of remote sensing research and applications within the African continent has made dramatic progress. Many African countries now have remote sensing research centers, within government agencies, research institutes and universities. Some countries in Africa currently have earth observing and/or telecommunications satellites in orbit and/or have such assets in various planning stages. The authors document such progress, in addition to the constraints to further applications of remote sensing for sustainable development in Africa, with special reference to data distribution constraints. Moreover, the authors address the urgency for bandwidth improvements within the African continent, so as to enable sustainable development initiatives to benefit from advances in high performance computing, required for ab initio near-real-time analysis of satellite-data. Such capabilities, it is argued, are propaedeutic for time-critical initiatives, such as vulnerability assessment, disaster preparedness and mitigation, emergency response, humanitarian assistance and post-calamity reconstruction, associated with a wide array of biogenic and anthropogenic disasters. Case studies of advances in infrastructure for satellite remote sensing and high performance computing, with implications for sustainable development in Africa, are provided from Egypt, Nigeria and South Africa.

Estimation of Soil Moisture Percentage Using LANDSAT-based Moisture Stress Index

The global agronomy community needs quick and frequent information on soil moisture variability and spatial trends in order to maximize crop production to meet growing food demands in a changing climate. However, in situ soil moisture measurement is expensive and labor intensive. Remote sensing based biophysical and predictive regression modeling approach have the potential for efficiently estimating soil moisture content over large areas. The study investigates the use of Moisture Stress Index (MSI) to estimate soil moisture variability in Alabama. In situ data were obtained from Soil Climate Analysis Network (SCAN) sites in Alabama and MSI developed from LANDSAT 8 OLI and LANDSAT 5 TM data. Pearson product moment correlation analysis showed that MSI strongly correlates with 16-day average growing season soil moisture measurements, with negative correlations of -0.519, -0.482 and -0.895 at 5, 10, and 20 cm soil depths respectively. The correlations of MSI and growing season moisture were low at sites where soil moisture was extremely low (<-0.3 at all depths). Simple linear regression model constructed for soil moisture at 20 cm depth (R²=0.79, p<0.05) correlated well with MSI values and was successfully used to estimate soil moisture percentage within a standard error of ± 3. Resulting MSI products were used to successfully produce the spatial distribution of soil moisture percentage at 20 cm depth. The study concludes that MSI is a good indicator of soil moisture conditions, and could be efficiently utilized in areas where in situ soil moisture data are unavailable.

Deployment of real-time satellite remote sensing infrastructure to support disaster mitigation: A NATO Science for Peace collaboration project with Research Universities in Turkey, Egypt and the USA

The authors delineate the specific roles of the research partner institutions from Turkey, Egypt and the USA, in planning and implementing the North Atlantic Treaty Organization (NATO) Science for Peace sponsored Kamal Ewida Earth Observatory (KEEO), a network of real-time satellite remote sensing ground stations, being established over the next three years in Egypt, with a tracking station for polar orbiting satellites at Cairo University, and a networked geostationary receiving station for the European Space Agencys Meteosat being deployed at Al Azhar University. The primary objective of the project is to facilitate early warning and mitigation of a wide range of biogenic and anthropogenic disasters. The project will also address mitigation of epidemics and epizootics, through identification and monitoring of infectious disease vector and reservoir habitat. Some examples of common concern among participating countries are climate change and its impacts, the land use problems in agriculture, air pollution problems in major cities such as Cairo and Istanbul, recent epidemics such as the bird flu, swine flu and oil spills along the seashores. Archival and real-time remote sensing and generation of near-real-time spatial data products, utilizing high performance computing clusters, are planned throughout the life cycle of disaster management, including vulnerability assessment, infrastructure safeguards, early warning, emergency response, humanitarian relief, as well as post-disaster damage assessment, reconstruction and societal recovery.