Integrating volunteered smartphone data with multispectral remote sensing to estimate forest fuels

作     者：Colin J.Ferster Nicholas C.Coops 

作者机构：Department of Forest Resources ManagementUniversity of British ColumbiaVancouverBCCanada 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2016年第9卷第2期

页      面：171-196页

核心收录：

学科分类：08[工学] 0708[理学-地球物理学] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science and Engineering Research Council(NSERC)Discovery grant to Coops and a NSERC Engage to Ferster,Coops,and Valhalla under University of British Columbia ethics application H12-00257 

主　　题：citizen science public participation in scientific research mobile RapidEye red edge WUI 

摘      要：Volunteered data sources are readily available due to advances in electronic communications *** example,smartphones provide tools to collect ground-based observations over broad areas from a diverse set of data collectors,including people with,and without,extensive *** this study,volunteers used a smartphone application to collect ground-based *** structural components were then estimated over a broader area using high spatial resolution RapidEye remote sensing imagery(5 spectral bands 440–850 nm,5 m spatial resolution)and a digital elevation model following a three nearest neighbor approach(K-NN).Participants with professional forestry experience on average chose highpriority fuel load locations near buildings,while nonprofessional participants chose a broader range of conditions over a larger *** used together,the professional and nonprofessional observations provided a more complete assessment of forest conditions.A generalized framework is presented that utilizes K-NN imputation tools for estimating the distribution of forest fuels using remote sensing and topography variables,ensuring spatial representation,checking attribute accuracy,and evaluating predictor *** to integrate volunteered data from smartphone platforms with remote sensing may contribute toward more complete Earth observation for Digital Earth.