A feasibility of six-hourly rainfall forecast over central India using model output and remote sensing data

2012 ◽  
Vol 2 (2) ◽  
pp. 138 ◽  
Author(s):  
Satya Prakash ◽  
C. Mahesh ◽  
Rakesh Mohan Gairola ◽  
Batjargal Buyantogtokh
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Central India ◽  
Model Output ◽  
Sensing Data ◽  
Rainfall Forecast ◽  
Hourly Rainfall

scholarly journals The Determination of Rainfall Threshold Triggering Landslides Using Remote Sensing

2021 ◽  
Vol 893 (1) ◽  
pp. 012011
Author(s):  
L Agustina ◽  
A Safril
Keyword(s):  
Remote Sensing ◽  
Rainfall Intensity ◽  
Nearest Neighbor ◽  
Remote Sensing Data ◽  
Rainfall Threshold ◽  
Rainfall Data ◽  
Bilinear Method ◽  
Sensing Data ◽  
Central Java ◽  
Hourly Rainfall

Abstract Landslide is one of the natural disasters that can cause a lot of loss, both material and fatalities. Banjarnegara Regency is one of Central Java Province regencies where landslides often occur due to the region's topography and high intensity rainfall.. Therefore, it is necessary to determine the threshold of rainfall that can trigger landslides to be used as an early warning for landslides. The rainfall data used for the threshold is daily and hourly rainfall intensity from remote sensing data that provides complete data but relatively rough resolution. So that remote sensing data need to be re-sampled. The remote sensing data used is CMORPH satellite data that has been re-sampled for detailing existing information of rainfall data. The resampling method used is the bilinear method and nearest neighbor by choosing between the two based on the highest correlation. Threshold calculation using Cumulative Threshold (CT) method resulted equation P3 = 7.0354 - 1.0195P15 and Intensity Duration (ID) method resulted equation I = 1.785D-0305. The peak rainfall intensity occurs at the threshold of 97-120 hours before a landslide occur.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

A procedure for integrated assessment of landscape fire risk using remote sensing data

2011 ◽  
Vol 17 (6) ◽  
pp. 30-44
Author(s):  
Yu.V. Kostyuchenko ◽  
◽  
M.V. Yushchenko ◽  
I.M. Kopachevskyi ◽  
S. Levynsky ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Integrated Assessment ◽  
Remote Sensing Data ◽  
Fire Risk ◽  
Sensing Data

Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data

2017 ◽  
Vol 6 (1) ◽  
pp. 2246-2252 ◽  
Author(s):  
Ajay Roy ◽  
◽  
Anjali Jivani ◽  
Bhuvan Parekh ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Landsat 7
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