| Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance | |
Yin, Gaofei1 ; Li, Ainong1; Wu, Chaoyang2; Wang, Jiyan3; Xie, Qiaoyun4; Zhang, Zhengjian1; Nan, Xi1; Jin, Huaan1; Bian, Jinhu1; Lei, Guangbin1
| |
| 2018 | |
| Source Publication | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| EISSN | 2220-9964 |
| Volume | 7Issue:7Pages:14 |
| Subtype | Article |
| Abstract | The spatially explicit aboveground biomass (AGB) generated through upscaling field measurements is critical for carbon cycle simulation and optimized management of grasslands. However, the spatial gaps that exist in the optical remote sensing data, underutilization of the multispectral data cube and unavailability of uncertainty information hinder the generation of seamless and accurate AGB maps. This study proposes a novel framework to address the above challenges. The proposed framework filled the spatial gaps in the remote sensing data via the consistent adjustment of the climatology to actual observations (CACAO) method. Gaussian process regression (GPR) was used to fully exploit the multispectral data cube and generated the pixelwise uncertainty concurrent with the AGB estimation. A case study in a 100 km x 100 km area located in the Zoige Plateau, China was used to evaluate this framework. The results show that the CACAO method can fill almost all of the gaps, accounting for 93.1% of the study area, with satisfactory accuracy. The generated AGB map from the GPR was characterized by a relatively high accuracy (R-2 = 0.64, RMSE = 48.13 g/m(2)) compared to vegetation index-derived ones, and was accompanied by a corresponding uncertainty map that provides a new source of information on the credibility of each pixel. This study demonstrates the potential of the joint use of gap-filling and machine-learning methods to generate spatially explicit AGB. |
| Keyword | aboveground biomass (AGB) uncertainty consistent adjustment of the climatology to actual observations (CACAO) Gaussian process regression (GPR) |
| DOI | 10.3390/ijgi7070242 |
| Indexed By | SCI |
| WOS Keyword | LEAF-AREA INDEX ; REMOTE-SENSING DATA ; TIME-SERIES ; SURFACE REFLECTANCE ; VEGETATION INDEX ; RETRIEVAL ; MODIS ; LAI ; CHINA ; MODEL |
| Language | 英语 |
| Quartile | 4区 |
| Funding Project | National Natural Science Foundation of China[41571373] ; National Natural Science Foundation of China[41601403] ; National Natural Science Foundation of China[41631180] ; National Natural Science Foundation of China[41531174] ; GF6 Project[30-Y20A03-90030-17/18] ; National Key Research and Development Program of China[2016YFA0600103] ; Youth Talent Team Program of the Institute of Mountain Hazards and Environment, CAS[SDSQB-2015-02] |
| TOP | 否 |
| WOS Research Area | Physical Geography ; Remote Sensing |
| WOS Subject | Geography, Physical ; Remote Sensing |
| WOS ID | WOS:000445150900009 |
| Funding Organization | National Natural Science Foundation of China ; GF6 Project ; National Key Research and Development Program of China ; Youth Talent Team Program of the Institute of Mountain Hazards and Environment, CAS |
| Publisher | MDPI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imde.ac.cn/handle/131551/23882 |
| Collection | 数字山地与遥感应用中心 |
| Corresponding Author | Li, Ainong |
| Affiliation | 1.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Southwest Petr Univ, Sch Civil Engn & Architecture, Chengdu 610500, Sichuan, Peoples R China 4.Univ Technol Sydney, C3, Sydney, NSW 2007, Australia |
| First Author Affilication | 中国科学院水利部成都山地灾害与环境研究所 |
| Corresponding Author Affilication | 中国科学院水利部成都山地灾害与环境研究所 |
| Recommended Citation GB/T 7714 | Yin, Gaofei,Li, Ainong,Wu, Chaoyang,et al. Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2018,7(7):14. |
| APA | Yin, Gaofei.,Li, Ainong.,Wu, Chaoyang.,Wang, Jiyan.,Xie, Qiaoyun.,...&Lei, Guangbin.(2018).Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,7(7),14. |
| MLA | Yin, Gaofei,et al."Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 7.7(2018):14. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Seamless Upscaling o（2835KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment