Availability of Remotely Sensed Data and Information from the U.S. National Oceanic and Atmospheric Administration’s Satellite Data Services Division

1986 ◽  
pp. 137-162
Author(s):  
Bruce H. Needham
Keyword(s):  
Satellite Data ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Data Services ◽  
The U.S

Remotely Sensed Wind, Wave, and Sea Level for European Sea Climatology

2002 ◽  
Author(s):  
Abderrahim Bentamy ◽  
Hafedh Hajji ◽  
Carlos Guedes Soares
Keyword(s):  
Sea Level ◽  
Satellite Data ◽  
Wind Wave ◽  
Remotely Sensed ◽  
Wind Data ◽  
Remotely Sensed Data ◽  
Level Data ◽  
Wind Sea ◽  
Initial Results ◽  
And Sea Level

This paper provides an overview of the analysis of remotely sensed data that has been performed within the scope of a project aiming at obtaining a 40-year hindcast of wind, sea level and wave climatology for the European waters. The satellite data, including wind, wave and sea-level data, are collected for the same areas and are calibrated with available and validated measurements. It will be used to be compared with the hindcast results, so as to yield some uncertainty measures related to the data. This paper describes the type of data that will be used and presents the initial results, which concern mainly remote sensed wind data.

Advance in Monitoring Forest Fire in China Based on Multi-Satellite Data

2012 ◽  
Vol 518-523 ◽  
pp. 5668-5672 ◽  
Author(s):  
Jia Hua Zhang ◽  
Feng Mei Yao
Keyword(s):  
Forest Fire ◽  
Satellite Data ◽  
Hot Spots ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Burned Areas ◽  
Noaa Avhrr

The advance in monitoring forest fire in China based on multi-Satellite data were discussed in the paper. Since the 1980s in China, the satellite remotely-sensed data have been acquired, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for monitoring forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots.

scholarly journals Mapping alien and indigenous vegetation in the KwaZulu-Natal Sandstone Sourveld using remotely sensed data

Bothalia ◽  
2016 ◽  
Vol 46 (2) ◽  
Author(s):  
John Odindi ◽  
Onisimo Mutanga ◽  
Mathieu Rouget ◽  
Nomcebo Hlanguza
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Learning Algorithm ◽  
Remotely Sensed ◽  
Woody Vegetation ◽  
Machine Learning Algorithm ◽  
Grassland Ecosystem ◽  
Remotely Sensed Data ◽  
Kwazulu Natal ◽  
New Generation

Background: The indigenous KwaZulu-Natal Sandstone Sourveld (KZN SS) grassland is highly endemic and species-rich, yet critically endangered and poorly conserved. Ecological threats to this grassland ecosystem are exacerbated by encroachment of woody plants, with severe negative environmental and economic consequences. Hence, there is an increasing need to reliably determine the extent of encroached or invaded areas to design optimal mitigation measures. Because of inherent limitations that characterise traditional approaches like field surveys and aerial photography, adoption of remotely sensed data offer reliable and timely mapping of landscape processes.Objectives: We sought to map the distribution of woody vegetation within the KZN SS using remote sensing approaches.Method: New generation RapidEye imagery, characterised by strategically positioned bands, and the advanced machine learning algorithm Random Forest (RF) were used to determine the distribution and composition of alien and indigenous woody vegetation within the KZN SS.Results: Results show that alien and indigenous encroachment and invasion could be mapped with over 86% accuracy whilst the dominant indigenous and alien tree species could be mapped with over 74% accuracy. These results highlight the potential of new generation RapidEye satellite data in combination with advanced machine learning technique in predicting the distribution of alien and indigenous woody cover within a grassland ecosystem. The successful discrimination of the two classes and the species within the classes can be attributed to the additional strategically positioned bands, particularly the red-edge in the new generation RapidEye image.Conclusion: Results underscore the potential of new generation RapidEye satellite data with strategically positioned bands and an advanced machine learning algorithm in predicting the distribution of woody cover in a grassland ecosystem.

A contribution towards simplifying area-wide tsetse surveys using medium resolution meteorological satellite data

2001 ◽  
Vol 91 (5) ◽  
pp. 333-346 ◽  
Author(s):  
G. Hendrickx ◽  
A. Napala ◽  
J.H.W. Slingenbergh ◽  
R. De Deken ◽  
D.J. Rogers
Keyword(s):  
Satellite Imagery ◽  
Satellite Data ◽  
Field Data ◽  
Spatial Epidemiology ◽  
Remotely Sensed ◽  
Optimal Number ◽  
Tsetse Fly ◽  
Training Data ◽  
Remotely Sensed Data ◽  
Distribution Limits

AbstractA raster or grid-based Geographic Information System with data on tsetse, trypanosomiasis, animal production, agriculture and land use has recently been developed in Togo. The area-wide sampling of tsetse fly, aided by satellite imagery, is the subject of two separate papers. This paper on a first paper, published in this journal, describing the generation of digital tsetse distribution and abundance maps and how these accord with the local climatic and agro-ecological setting. Such maps when combined with data on the disease, the hosts and their owners, should contribute the knowledge of the spatial epidemiology of trypanosomiasis and assist planning of integrated control operations. Here we address the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. Different discriminant models have been applied using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration (NOAA) and Meteosat platforms. The results confirm the potential of satellite data application and multivariate for the prediction of the tsetse distribution and abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen and/or substitute one another. The analysis shows how the strategic incorporation of satellite imagery may minimize field of data. Field surveys may be modified and conducted in two stages, first concentrating on the expected fly distribution limits and thereafter on fly abundance. The study also shows that when applying satellite data, care should be taken in selecting the optimal number of predictor because this number varies with the amount of training data for predicting abundance and on the homogeneity of the distribution limits for predicting fly presence. Finally, it is suggested that in addition to the use of contemporary training data and predictor variables, training predicted data sets should refer to the same eco-geographic zone.

Automated Target Detection and Grouping From Remotely Sensed Data

2006 ◽  
Author(s):  
Brian Bonnlander ◽  
Clark Glymour ◽  
Choh M. Teng
Keyword(s):  
Target Detection ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Automated Target Detection
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