Laju Gandharum, Heri Sadmono, D. B. Sencaki, A. Eugenie, Hari Prayogi, I. F. Cahyaningtiyas
{"title":"高光谱航空数据在印尼热带泥炭沼泽森林树种识别中的应用","authors":"Laju Gandharum, Heri Sadmono, D. B. Sencaki, A. Eugenie, Hari Prayogi, I. F. Cahyaningtiyas","doi":"10.1109/AGERS56232.2022.10093651","DOIUrl":null,"url":null,"abstract":"Hyperspectral remote sensing imaging, like HyMAP, offers extremely precise spectrum data. Therefore, by using a spectral angle mapper (SAM) technique, HyMap was ideal for differentiating tree species in remote places like tropical peat swamp forests in Indonesia. The results showed tree species of Bangka, Gercinia, and Balau were mapped more dominantly than others. At a threshold of 0.2 radians, these three species, in that order, dominated 56.69%, 29.18%, and 4.44% of the study area. The percentage of unclassified pixels was decreased by 3.72% by raising the threshold (from 0.2 to 0.3 radians).","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Hyperspectral Airborne Data for Discriminating Tree Species in Tropical Peat Swamp Forest, Indonesia\",\"authors\":\"Laju Gandharum, Heri Sadmono, D. B. Sencaki, A. Eugenie, Hari Prayogi, I. F. Cahyaningtiyas\",\"doi\":\"10.1109/AGERS56232.2022.10093651\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hyperspectral remote sensing imaging, like HyMAP, offers extremely precise spectrum data. Therefore, by using a spectral angle mapper (SAM) technique, HyMap was ideal for differentiating tree species in remote places like tropical peat swamp forests in Indonesia. The results showed tree species of Bangka, Gercinia, and Balau were mapped more dominantly than others. At a threshold of 0.2 radians, these three species, in that order, dominated 56.69%, 29.18%, and 4.44% of the study area. The percentage of unclassified pixels was decreased by 3.72% by raising the threshold (from 0.2 to 0.3 radians).\",\"PeriodicalId\":370213,\"journal\":{\"name\":\"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AGERS56232.2022.10093651\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AGERS56232.2022.10093651","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Hyperspectral Airborne Data for Discriminating Tree Species in Tropical Peat Swamp Forest, Indonesia
Hyperspectral remote sensing imaging, like HyMAP, offers extremely precise spectrum data. Therefore, by using a spectral angle mapper (SAM) technique, HyMap was ideal for differentiating tree species in remote places like tropical peat swamp forests in Indonesia. The results showed tree species of Bangka, Gercinia, and Balau were mapped more dominantly than others. At a threshold of 0.2 radians, these three species, in that order, dominated 56.69%, 29.18%, and 4.44% of the study area. The percentage of unclassified pixels was decreased by 3.72% by raising the threshold (from 0.2 to 0.3 radians).