Petri Varvia , Svetlana Saarela , Matti Maltamo , Petteri Packalen , Terje Gobakken , Erik Næsset , Göran Ståhl , Lauri Korhonen
{"title":"利用分层混合推理从 ICESat-2 数据估算北方森林生物量","authors":"Petri Varvia , Svetlana Saarela , Matti Maltamo , Petteri Packalen , Terje Gobakken , Erik Næsset , Göran Ståhl , Lauri Korhonen","doi":"10.1016/j.rse.2024.114249","DOIUrl":null,"url":null,"abstract":"<div><p>The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides profile samples over the terrain. While primarily designed for snow and ice monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good spatial coverage of boreal forests.</p><p>The aim of this study is to evaluate the estimation of mean AGBD from ICESat-2 data using a hierarchical modeling approach combined with rigorous statistical inference. We propose a hierarchical hybrid inference approach for uncertainty quantification of the average AGBD of the area of interest estimated directly from a sample of ICESat-2 lidar profiles. Our approach models the errors coming from the multiple modeling steps, including the allometric models used for predicting tree-level AGB. For testing the procedure, we have data from two adjacent study sites, denoted Valtimo and Nurmes, of which Valtimo site is used for model training and Nurmes for validation.</p><p>The ICESat-2 estimated mean AGBD in the Nurmes validation area was 65.7 ± 1.9 Mg/ha (relative standard error of 2.9%). The local reference hierarchical model-based estimate obtained from wall-to-wall airborne lidar data was 63.9 ± 0.6 Mg/ha (relative standard error of 1.0%). The reference estimate was within the 95% confidence interval of the ICESat-2 hierarchical hybrid estimate. The small standard errors indicate that the proposed method is useful for AGBD assessment. However, some sources of error were not accounted for in the study and thus the real uncertainties are probably slightly larger than those reported.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1000,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425724002670/pdfft?md5=62dd6d1758a6f94aea59bc9a79594e10&pid=1-s2.0-S0034425724002670-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference\",\"authors\":\"Petri Varvia , Svetlana Saarela , Matti Maltamo , Petteri Packalen , Terje Gobakken , Erik Næsset , Göran Ståhl , Lauri Korhonen\",\"doi\":\"10.1016/j.rse.2024.114249\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides profile samples over the terrain. While primarily designed for snow and ice monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good spatial coverage of boreal forests.</p><p>The aim of this study is to evaluate the estimation of mean AGBD from ICESat-2 data using a hierarchical modeling approach combined with rigorous statistical inference. We propose a hierarchical hybrid inference approach for uncertainty quantification of the average AGBD of the area of interest estimated directly from a sample of ICESat-2 lidar profiles. Our approach models the errors coming from the multiple modeling steps, including the allometric models used for predicting tree-level AGB. For testing the procedure, we have data from two adjacent study sites, denoted Valtimo and Nurmes, of which Valtimo site is used for model training and Nurmes for validation.</p><p>The ICESat-2 estimated mean AGBD in the Nurmes validation area was 65.7 ± 1.9 Mg/ha (relative standard error of 2.9%). The local reference hierarchical model-based estimate obtained from wall-to-wall airborne lidar data was 63.9 ± 0.6 Mg/ha (relative standard error of 1.0%). The reference estimate was within the 95% confidence interval of the ICESat-2 hierarchical hybrid estimate. The small standard errors indicate that the proposed method is useful for AGBD assessment. However, some sources of error were not accounted for in the study and thus the real uncertainties are probably slightly larger than those reported.</p></div>\",\"PeriodicalId\":417,\"journal\":{\"name\":\"Remote Sensing of Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2024-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0034425724002670/pdfft?md5=62dd6d1758a6f94aea59bc9a79594e10&pid=1-s2.0-S0034425724002670-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing of Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0034425724002670\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425724002670","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference
The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides profile samples over the terrain. While primarily designed for snow and ice monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good spatial coverage of boreal forests.
The aim of this study is to evaluate the estimation of mean AGBD from ICESat-2 data using a hierarchical modeling approach combined with rigorous statistical inference. We propose a hierarchical hybrid inference approach for uncertainty quantification of the average AGBD of the area of interest estimated directly from a sample of ICESat-2 lidar profiles. Our approach models the errors coming from the multiple modeling steps, including the allometric models used for predicting tree-level AGB. For testing the procedure, we have data from two adjacent study sites, denoted Valtimo and Nurmes, of which Valtimo site is used for model training and Nurmes for validation.
The ICESat-2 estimated mean AGBD in the Nurmes validation area was 65.7 ± 1.9 Mg/ha (relative standard error of 2.9%). The local reference hierarchical model-based estimate obtained from wall-to-wall airborne lidar data was 63.9 ± 0.6 Mg/ha (relative standard error of 1.0%). The reference estimate was within the 95% confidence interval of the ICESat-2 hierarchical hybrid estimate. The small standard errors indicate that the proposed method is useful for AGBD assessment. However, some sources of error were not accounted for in the study and thus the real uncertainties are probably slightly larger than those reported.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.