1 edition of Coarse resolution remote sensing of the Sahelian environemnt found in the catalog.
Coarse resolution remote sensing of the Sahelian environemnt
Reprinted from the International Journal of Remote Sensing, Vol.12 no.6 June 1991.
|Statement||edited by S.D. Prince and C.O. Justice.|
|Series||International Journal of Remote Sensing -- Vol.12(6)|
|Contributions||Prince, S. D., Justice, Chris.|
|The Physical Object|
|Number of Pages||1421|
3 Brief History of Remote Sensing (1) The invention of photography ’s Photography from balloons Theory of electromagnetic energy by J. C. Maxwell Photography from airplanes ’s World War I: aerial reconnaissance ’s Development and applications of aerial photography and photogrammetry ’s Development of radar in Germany, USA, and UK. Coarse Resolution Remote Sensing of the Sahelian Environment. Special Edition of the International Journal of Remote Sensing, v. 12, No. 6. (this special edition presents a collection of research papers on satellite monitoring of African rangelands productivity). Justice C.O. 1 (Editor),
The presentation is about the basics of Remote Sensing. The presentation talks about its need and who uses Remote sensing. The process of remote sensing, its principles, platforms and sensors are discussed. The four types of resolutions- Spatial, . Remote Sensing of Environment (5): – Google Scholar Anderson, ES, Thompson, JA, Crouse, DA, Austin, RE () Horizontal resolution and data density effects on remotely sensed LIDAR-based by:
Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling Assefa M. Melesse 1,*, Qihao Weng 2, Prasad bail 3 and Gabriel B. Senay 4 1 Department of Environmental Studies, Florida International University 2 Center for Urban and Environmental Change, Department of Geography, Indiana State UniversityCited by: People and Pixels in the Sahel: A Study Linking Coarse-Resolution Remote Sensing Observations to Land Users’ Perceptions of Their Changing Environment in Senegal. Ecology and Society 19 (3): [Google Scholar] Hulme M Climatic Perspectives on Sahelian Desiccation: – Global Environmental Change 11 (1): 19– [Google Scholar]Cited by: 2.
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IJRS () Coarse resolution remote sensing of the Sahelian Environment: Special Issue of the International Journal of Remote Sens 6, June Google Scholar Milford JR Dugdale G () Estimation of rainfall using geostationary satellite : J. Williams. Satellite remote sensing, in particular the analysis of coarse resolution time series of vegetation indices, has played an important role in challenging earlier assumptions of widespread The Map Is not the Territory: How Satellite Remote Sensing and Ground Evidence Have Re-shaped the Image of Sahelian Desertification | SpringerLinkCited by: 6.
Justice, C & Tucker III, C'Coarse spatial resolution optical sensors', in The sage handbook of remote sensing, SAGE Publications Ltd, London, pp.viewed 28 Maydoi: /n Justice, Christopher Owen and Compton James Tucker III.
"Coarse Spatial Resolution Optical Sensors." The SAGE Handbook of Remote. to land users, flies under the radar of coarse-resolution remote sensing data because it is not necessarily associated with a decrease in biomass but rather with undesired changes in.
People and pix els in the Sahel: a study linking coarse-resolution remote sensing observations to land users’ per ceptions of their changing environment in Senegal.
Ecology and Society 19 (3): Using coarse remote sensing radar observations to control the trajectory of a simple Sahelian land surface model January Remote Sensing of Environment 94(2) Written by world renowned scientists, this book provides an excellent overview of a wide array of methods and techniques for the processing and analysis of multitemporal remotely sensed images.
These methods and techniques include change detection, multitemporal data fusion, coarse-resolution. Due to the coarse spatial resolution (> m) of synoptic sensors, remote sensing monitors seasonal dynamics of the vegetated land surface that often includes multiple types of vegetation mixed with other scene objects, such as soil, water, and human by: Remote sensing reflectance (R rs) classification of coastal waters is a useful tool to monitor environmental processes and manage marine environmental resources.
This study presents classification work for data sets that were collected in the Yellow Sea during six cruises (spring and autumn, ; summer and winter, /; and spring and. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements April Remote Sensing of Environment 80(1) ().
A model of regional primary production for use with coarse resolution satellite data. International Journal of Remote Sensing: Vol. 12, No. 6, pp. Cited by: 2.
The Sudano-Sahelian zone (SSZ) – environmental characteristics and implications for remote sensing. The SSZ consists of two roughly parallel ecological regions and includes areas of 17 countries (Le Houéron,Nicholson, ; Fig.
The Sahel is located on the fringes of the Sahara desert and covers the area that receives between and mm mean annual rainfall, while the Sudanian Cited by: New concepts and tools are needed to efficiently combine long-term (coarse resolution) data sets with these novel high-resolution observations, such as data mining, data fusion, and data-assimilation of remote sensing observations into environmental models and monitoring networks, to explore the capabilities for climate change research to its.
Use of remote sensing data in a GIS environment for water resources management GERT A. SCHULTZ Institute of Hydrology, Water Resources and Environmental Technology, Ruhr University Bochum, D Bochum, Germany Abstract While in the field of hydrology remote sensing techniques are often applied and well accepted there is still a deficit in File Size: KB.
Remote Sensing of Forest Environments: Concepts and Case Studies is designed to meet the needs of a professional audience composed of both practitioners and researchers. This book is also suitable as a secondary text for graduate-level students in Forestry, Environmental Science, Geography, Engineering, and Computer Science.
Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery Article (PDF Available) in Plant Ecology (2) August with Reads. Mapping fragmented agricultural systems in the Sudano-Sahelian environments of Africa using random forest and ensemble metrics of coarse resolution MODIS imagery.
system and the 30 remote-sensing. 'A magnificent achievement. A who's who of contemporary remote sensing have produced an engaging, wide-ranging and scholarly review of the field in just one volume' - Professor Paul Curran, Vice-Chancellor, Bournemouth UniversityRemote Sensing acquires and interprets small or large-scale data about the Earth from a distance.
Using a wide range of spatial, spectral, temporal, and 5/5(1). Remote Sensing of Environment - An Interdisciplinary Journal. Remote Sensing of Environment (RSE) serves the Earth observation community with the publication of results on the theory, science, applications, and technology of remote sensing studies.
Thoroughly interdisciplinary, RSE publishes on terrestrial, oceanic and atmospheric sensing. Analysis of the vegetation trends using low resolution remote sensing data in Burkina Faso (–) for the monitoring of desertification.
Radiometric Resolution Every time an image is acquired by a sensor, its sensitivity to the magnitude of the electromagnetic energy determines the radiometric resolution. The finer the radiometric resolution of a sensor, the more sensitive it is to detecting small differences in reflected or emitted energy.
Remote Sensing of Forest Environments: Concepts and Case Studies is designed to meet the needs of a professional audience composed of both practitioners and researchers.
This book is also suitable as a secondary text for graduate-level students in Forestry, Environmental Science, Geography, Engineering, and Computer Science.CiteScore: ℹ CiteScore: CiteScore measures the average citations received per document published in this title.
CiteScore values are based on citation counts in a given year (e.g. ) to documents published in three previous calendar years (e.g. – 14), divided by the number of documents in these three previous years (e.g.