LEADER 01830nam 2200469 450 001 9910794288803321 005 202101560622.0 010 $a1-910443-19-0 035 $a(CKB)4100000011351832 035 $a(MiAaPQ)EBC6265604 035 $a(BSAVA)BSAVA 10443194 035 $a(EXLCZ)994100000011351832 100 $a20191130h20192019 uy 0 101 0 $aeng 135 $aur|n| ||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBSAVA Manual of Backyard Poultry Medicine and Surgery$b[electronic resource] /$fGuyPoland and Aidan Raftery 205 $a1st Edition 210 $aQuedgeley, Gloucester, England :$cBritish Small Animal Veterinary Association,$d2019. 215 $a1 online resource (378 pages) 225 1 $aBSAVA Manual Series 311 $a1-905319-43-6 320 $aIncludes bibliographic references. 330 3 $aThis manual provides practical and high quality information to help veterinarians provide the level of care to backyard birds that an increasingly wide client base expects and deserves. The manual covers chickens, turkeys, ducks, geese, guinea fowl, ornamental pheasants and peafowl. 410 0$aBSAVA manual series. 606 $aPoultry$xDiseases$vHandbooks, manuals, etc 606 $aPoultry$xSurgery$vHandbooks, manuals, etc 606 $aAvian medicine$vHandbooks, manuals, etc 615 0$aPoultry$xDiseases 615 0$aPoultry$xSurgery 615 0$aAvian medicine 676 $a636.5089 700 $aPoland$b Guy$01497816 701 $aRaftery$b Aidan$01338524 712 02$aBritish Small Animal Veterinary Association, 801 0$bCaUkQuBSA 906 $aBOOK 912 $a9910794288803321 996 $aBSAVA Manual of Backyard Poultry Medicine and Surgery$93723079 997 $aUNINA LEADER 04802nam 2201201z- 450 001 9910595070103321 005 20220916 035 $a(CKB)5680000000080829 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92046 035 $a(oapen)doab92046 035 $a(oapen)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 $aBasel$d2022 215 $a1 online resource (314 p.) 311 08$a3-0365-4308-2 311 08$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 $aGeography$2bicssc 606 $aResearch and information: general$2bicssc 610 $aABI 610 $aaerial image 610 $aagriculture 610 $aautomatic landslide detection 610 $aBangladesh 610 $aBRT 610 $aCART 610 $aclimate change 610 $aclimate migrants 610 $aconvolutional neural networks 610 $adamage assessment 610 $adeep learning 610 $aDhaka 610 $adigital elevation model 610 $adisaster impact 610 $adrought 610 $aearthquake 610 $aensemble models 610 $aflash flood 610 $aflood mapping 610 $aflooding 610 $aforest ecosystems 610 $ageohydrological model 610 $agroundwater 610 $aice jam 610 $aice storm 610 $aInSAR 610 $aInSAR time series 610 $aK-Nearest Neighbor 610 $aland subsidence and rebound 610 $aland use and land cover 610 $alandslide 610 $alandslide deformation 610 $alandslide susceptibility 610 $alandslides 610 $alogistic regression 610 $amachine learning models 610 $amodified frequency ratio 610 $aMODIS 610 $amonitoring and prediction 610 $aMulti-Layer Perceptron 610 $anaive Bayes tree 610 $anatural hazards 610 $aNDVI 610 $anight-time light data 610 $aNUAE 610 $aOBIA 610 $aordinal regression 610 $aPBA 610 $aperi-urbanization 610 $apost-disaster recovery 610 $arandom forest 610 $aRandom Forest 610 $arandom forests 610 $arapid mapping 610 $aremote sensing 610 $areservoir water level 610 $aSentinel-1 610 $asequential estimation 610 $asnowmelt 610 $asupervised classification 610 $aSupport Vector Machine 610 $aThree Gorges Reservoir area (China) 610 $auncertainty 610 $aurban growth boundary demarcation 610 $avalidation 610 $aVIIRS 615 7$aGeography 615 7$aResearch and information: general 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