LEADER 04764nam 2201177z- 450 001 9910595070103321 005 20231214133144.0 035 $a(CKB)5680000000080829 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92046 035 $a(EXLCZ)995680000000080829 100 $a20202209d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRemote Sensing of Natural Hazards 210 $aBasel$cMDPI Books$d2022 215 $a1 electronic resource (314 p.) 311 $a3-0365-4308-2 311 $a3-0365-4307-4 330 $aEach year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches. 606 $aResearch & information: general$2bicssc 606 $aGeography$2bicssc 610 $asequential estimation 610 $aInSAR time series 610 $agroundwater 610 $aland subsidence and rebound 610 $aearthquake 610 $arapid mapping 610 $adamage assessment 610 $adeep learning 610 $aconvolutional neural networks 610 $aordinal regression 610 $aaerial image 610 $alandslide 610 $amachine learning models 610 $aremote sensing 610 $aensemble models 610 $avalidation 610 $aice storm 610 $aforest ecosystems 610 $adisaster impact 610 $apost-disaster recovery 610 $aice jam 610 $asnowmelt 610 $aflood mapping 610 $amonitoring and prediction 610 $aVIIRS 610 $aABI 610 $aNUAE 610 $aflash flood 610 $aBRT 610 $aCART 610 $anaive Bayes tree 610 $ageohydrological model 610 $alandslide susceptibility 610 $aBangladesh 610 $adigital elevation model 610 $arandom forest 610 $amodified frequency ratio 610 $alogistic regression 610 $aautomatic landslide detection 610 $aOBIA 610 $aPBA 610 $arandom forests 610 $asupervised classification 610 $alandslides 610 $auncertainty 610 $aK-Nearest Neighbor 610 $aMulti-Layer Perceptron 610 $aRandom Forest 610 $aSupport Vector Machine 610 $aagriculture 610 $adrought 610 $aNDVI 610 $aMODIS 610 $alandslide deformation 610 $aInSAR 610 $areservoir water level 610 $aSentinel-1 610 $aThree Gorges Reservoir area (China) 610 $aperi-urbanization 610 $aurban growth boundary demarcation 610 $aclimate change 610 $aclimate migrants 610 $anatural hazards 610 $aflooding 610 $aland use and land cover 610 $anight-time light data 610 $aDhaka 615 7$aResearch & information: general 615 7$aGeography 700 $aAhmed$b Bayes$4edt$01332322 702 $aAlam$b Akhtar$4edt 702 $aAhmed$b Bayes$4oth 702 $aAlam$b Akhtar$4oth 906 $aBOOK 912 $a9910595070103321 996 $aRemote Sensing of Natural Hazards$93040829 997 $aUNINA