scholarly journals Alfred Colpaert; Satellite data and environmental GIS, from remotely sensed data to geographical information

Rangifer ◽  
1999 ◽  
Vol 19 (1) ◽  
pp. 51
Author(s):  
Editor Editor
Keyword(s):  
Satellite Data ◽  
Remotely Sensed ◽  
Geographical Information ◽  
Remotely Sensed Data

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 Crop evapotranspiration estimation using remote sensing and the existing network of meteorological stations in Cyprus

2011 ◽  
Vol 30 ◽  
pp. 39-44 ◽  
Author(s):  
G. Papadavid ◽  
D. Hadjimitsis ◽  
S. Michaelides ◽  
A. Nisantzi
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Irrigation Management ◽  
Reference Method ◽  
Geographical Information ◽  
Crop Evapotranspiration ◽  
Remotely Sensed Data ◽  
Water Losses ◽  
Evapotranspiration Estimation ◽  
Agricultural Areas

Abstract. Cyprus is frequently confronted with severe droughts and the need for accurate and systematic data on crop evapotranspiration (ETc) is essential for decision making, regarding water irrigation management and scheduling. The aim of this paper is to highlight how data from meteorological stations in Cyprus can be used for monitoring and determining the country's irrigation demands. This paper shows how daily ETc can be estimated using FAO Penman-Monteith method adapted to satellite data and auxiliary meteorological parameters. This method is widely used in many countries for estimating crop evapotranspiration using auxiliary meteorological data (maximum and minimum temperatures, relative humidity, wind speed) as inputs. Two case studies were selected in order to determine evapotranspiration using meteorological and low resolution satellite data (MODIS – TERRA) and to compare it with the results of the reference method (FAO-56) which estimates the reference evapotranspiration (ETo) by using only meteorological data. The first approach corresponds to the FAO Penman-Monteith method adapted for using both meteorological and remotely sensed data. Furthermore, main automatic meteorological stations in Cyprus were mapped using Geographical Information System (GIS). All the agricultural areas of the island were categorized according to the nearest meteorological station which is considered as "representative" of the area. Thiessen polygons methodology was used for this purpose. The intended goal was to illustrate what can happen to a crop, in terms of water requirements, if meteorological data are retrieved from other than the representative stations. The use of inaccurate data can result in low yields or excessive irrigation which both lead to profit reduction. The results have shown that if inappropriate meteorological data are utilized, then deviations from correct ETc might be obtained, leading to water losses or crop water stress.

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.

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