Remote event detection and tracking using multiple heterogeneous satellite data fusion

2009 ◽  
Author(s):  
Ashit Talukder ◽  
Shen-Shyang Ho
Keyword(s):  
Data Fusion ◽  
Satellite Data ◽  
Event Detection ◽  
Detection And Tracking ◽  
Remote Event

scholarly journals Wildfire Detection and Tracking over Greece Using MSG‑SEVIRI Satellite Data

2011 ◽  
Vol 3 (3) ◽  
pp. 524-538 ◽  
Author(s):  
Nicolaos I. Sifakis ◽  
Christos Iossifidis ◽  
Charalabos Kontoes ◽  
Iphigenia Keramitsoglou
Keyword(s):  
Satellite Data ◽  
Detection And Tracking

scholarly journals Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain

2015 ◽  
Vol 151 ◽  
pp. 416-426 ◽  
Author(s):  
Carolina Doña ◽  
Ni-Bin Chang ◽  
Vicente Caselles ◽  
Juan M. Sánchez ◽  
Antonio Camacho ◽  
...  
Keyword(s):  
Water Quality ◽  
Data Fusion ◽  
Satellite Data ◽  
Lake Water ◽  
Lake Water Quality ◽  
Water Quality Status ◽  
Quality Status

scholarly journals Evapotranspiration Partitioning at Field Scales Using TSEB and Multi-Satellite Data Fusion in The Middle Reaches of Heihe River Basin, Northwest China

2020 ◽  
Vol 12 (19) ◽  
pp. 3223
Author(s):  
Yan Li ◽  
Chunlin Huang ◽  
William P. Kustas ◽  
Hector Nieto ◽  
Liang Sun ◽  
...  
Keyword(s):  
Data Fusion ◽  
River Basin ◽  
Satellite Data ◽  
Temporal Dynamics ◽  
Surface Flux ◽  
Northwest China ◽  
Heihe River Basin ◽  
Heihe River ◽  
Field Scale ◽  
Natural Ecosystems

Daily evapotranspiration (ET) and its components of evaporation (E) and transpiration (T) at field scale are often required for improving agricultural water management and maintaining ecosystem health, especially in semiarid and arid regions. In this study, multi-year daily ET, E, and T at a spatial resolution of 100 m in the middle reaches of Heihe River Basin were computed based on an ET partitioning method developed by combing remote sensing-based ET model and multi-satellite data fusion methodology. Evaluations using flux tower measurements over irrigated cropland and natural desert sites indicate that this method can provide reliable estimates of surface flux partitioning and daily ET. Modeled daily ET yielded root mean square error (RMSE) values of 0.85 mm for cropland site and 0.84 mm for desert site, respectively. The E and T partitioning capabilities of this proposed method was further assessed by using ratios E/ET and T/ET derived from isotopic technology at the irrigated cropland site. Results show that apart from early in the growing season when the actual E was reduced by plastic film mulching, the modeled E/ET and T/ET agree well with observations in terms of both magnitude and temporal dynamics. The multi-year seasonal patterns of modeled ET, E, and T at field scale from this ET partitioning method shows reasonable seasonal variation and spatial variability, which can be used for monitoring plant water consumption in both agricultural and natural ecosystems.

scholarly journals Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques

2020 ◽  
Vol 12 (3) ◽  
pp. 584
Author(s):  
José Manuel Delgado Blasco ◽  
Fabio Cian ◽  
Ramon F. Hanssen ◽  
Gert Verstraeten
Keyword(s):  
Land Cover ◽  
Data Fusion ◽  
Satellite Data ◽  
Rural Areas ◽  
European Space Agency ◽  
Optical Remote Sensing ◽  
Joint Research ◽  
Human Environment ◽  
Multi Temporal ◽  
Agricultural Areas

Population growth in rural areas of Egypt is rapidly transforming the landscape. New cities are appearing in desert areas while existing cities and villages within the Nile floodplain are growing and pushing agricultural areas into the desert. To enable control and planning of the urban transformation, these rapid changes need to be mapped with high precision and frequency. Urban detection in rural areas in optical remote sensing is problematic when urban structures are built using the same materials as their surroundings. To overcome this limitation, we propose a multi-temporal classification approach based on satellite data fusion and artificial neural networks. We applied the proposed methodology to data of the Egyptian regions of El-Minya and part of Asyut governorates collected from 1998 until 2015. The produced multi-temporal land cover maps capture the evolution of the area and improve the urban detection of the European Space Agency (ESA) Climate Change Initiative Sentinel-2 Prototype Land Cover 20 m map of Africa and the Global Human Settlements Layer from the Joint Research Center (JRC). The extension of urban and agricultural areas increased over 65 km2 and 200 km2, respectively, during the entire period, with an accelerated increase analysed during the last period (2010–2015). Finally, we identified the trends in urban population density as well as the relationship between farmed and built-up land.

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