Towards understanding the environmental and climatic changes and its contribution to the spread of wildfires in Ghana using remote sensing tools and machine learning (Google Earth Engine)  

作　　者：Kueshi Sémanou Dahan Raymond Abudu Kasei Rikiatu Husseini Mohammed Y.Said Md.Mijanur Rahman 

机构地区：[1]Department of Environment and Sustainability Sciences,University for Development Studies,Tamale,Ghana [2]West African Centre for Water,Irrigation and Sustainable Agriculture(WACWISA),University for Development Studies,Tamale,Ghana [3]Department of Environment,Water and Waste Engineering,University for Development Studies,Tamale,Ghana [4]Department of Forestry and Forest Resources Management,Faculty of Natural Resources and Environment,University for Development Studies,Tamale,Ghana [5]Center for Sustainable Drylands Ecosystems and Societies,University of Nairobi,Nairobi,Kenya [6]Founder and CEO Study Hacks(Institute of GIS&Remote Sensing),Department of Geography&Environment,Jagannah University,Dhaka,Bangladesh

出　　处：《International Journal of Digital Earth》2023年第1期1300-1331,共32页国际数字地球学报（英文）

摘　　要：Data processing and climate characterisation to study its impact is becoming difficult due to insufficient and unavailable data,especially in developing countries.Understanding climate’s impact on burnt areas in Ghana(Guinea-savannah(GSZ)and Forest-savannah Mosaic zones(FSZ))leads us to opt for machine learning.Through Google Earth Engine(GEE),rainfall(PR),maximum temperature(Tmax),minimum temperature(Tmin),average temperature(Tmean),Palmer Drought Severity Index(PDSI),relative humidity(RH),wind speed(WS),soil moisture(SM),actual evapotranspiration(ETA)and reference evapotranspiration(ETR)have been acquired through CHIRPS(Climate Hazards group Infrared Precipitation with Stations),FLDAS dataset(Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System)and TerraClimate platform from 1991 to 2021.The objective is to analyse the link and the contribution of climatic and environmental parameters on wildfire spread in GSZ and FSZ in Ghana.Variables were analysed(area burnt and the number of activefires)through Spearman correlation and the cross-correlation function(CCF)(2001 to 2021).The tests(Mann-Kendall and Sens’s slope trend test,Pettitt test and the Lee and Heghinian test)showed the overall decrease in rainfall and increase in temperature respectively(-0.1 mm;+0.8℃)in GSZ and(-0.9 mm;+0.3℃)in FSZ.In terms of impact,PR,ETR,FDI,Tmean,Tmax,Tmin,RH,ETA and SM contribute tofire spread.Through the codes developed,researchers and decision-makers could update them at different times easily to monitor climate variability and its impact onfires.

关 键 词：Climate change Google Earth Engine mitigation machine learning WILDFIRE Ghana 

分 类 号：P46[天文地球—大气科学及气象学]