| A pixel dichotomy coupled linear kernel-driven model for estimating fractional vegetation cover in arid areas from high-spatial-resolution images | |
| X. Ma; J. Ding; T. Wang; L. Lu; H. Sun; F. Zhang; X. Cheng; I. Nurmemet | |
| 2023-06-23 | |
| Source Publication | IEEE Transactions on Geoscience and Remote Sensing Impact Factor & Quartile |
| ISSN | 1558-0644 |
| Volume | PPIssue:99Pages:1-1 |
| Abstract | With the increased use of high-spatial-resolution (HSR) images for vegetation monitoring in arid areas, more details of the low vegetation coverage and interference from the land “background” are captured in the corresponding images. From computational time and accuracy, the multi-angle method (MAM) in the pixel dichotomy model is a potential algorithm to apply in arid areas, but MAM needs the multi-angle vegetation index (VI) as the driver parameters. However, most HSR images are obtained in nadir mode, and the multi-angle information of reflectance is difficult to obtain, which limits the estimation of multi-angle VI from HSR images. To address this issue, this study used a “graphical method” to modify the radiation influence caused by the canopy structure and land “background.” We developed an inversion method of the linear kernel-driven model (KDM) and designed a random sampling method to estimate multi-angle VI from HSR images. Then, we proposed a new pixel dichotomy coupled linear KDM (PDKDM), validated using simulated, field-measured, and reference data. The results showed that the FVC in arid areas estimated by PDKDM was highly consistent with “true” data, with root-mean-square error (RMSE) < 0.062, RMSE < 1.125, and RMSE < 0.027 for comparison with simulated, field-measured and reference data, respectively. PDKDM addressed the issue with the previous MAMs to estimate FVC from HSR images in arid areas. This study provides a useful algorithm with high computational efficiency for producing HSR FVCs in arid areas. |
| Keyword | Fractional vegetation cover of the arid areas Multi-angle method High-spatial-resolution image Modified linear pixel dichotomy model Linear kernel-driven model |
| Publisher | IEEE |
| DOI | 10.1109/TGRS.2023.3289093 |
| Indexed By | IEEE |
| Language | 英语 |
| EI Accession Number | 20232714334901 |
| EI Keywords | Pixels |
| EI Classification Number | 405.3 Surveying ; 443 Meteorology ; 444 Water Resources ; 922.2 Mathematical Statistics |
| Original Document Type | Article in Press |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/529082 |
| Collection | 兰州大学 |
| Affiliation | 1.College of Geography and Remote sensing Sciences, Xinjiang key Laboratory of Oasis Ecology, Postdoctoral Mobile Station, Xingjiang University, Urumqi, China 2.College of Geography and Remote sensing Sciences, Xinjiang key Laboratory of Oasis Ecology, Xingjiang University, Urumqi, China 3.Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, The Netherlands 4.College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China 5.College of Geography and Remote sensing Sciences, Xinjiang key Laboratory of Oasis Ecology, Xingjiang University, Urumqi, China 6.College of Geography and Remote sensing Sciences, Xinjiang key Laboratory of Oasis Ecology, Xingjiang University, Urumqi, China 7.Polar Science Center, Office of Scientific Research and Development, School of Geospatial Engineering and Science, Sun Yat-sen Universty, Zhuhai, China 8.College of Geography and Remote sensing Sciences, Xinjiang key Laboratory of Oasis Ecology, Xingjiang University, Urumqi, China |
| Recommended Citation GB/T 7714 | X. Ma,J. Ding,T. Wang,et al. A pixel dichotomy coupled linear kernel-driven model for estimating fractional vegetation cover in arid areas from high-spatial-resolution images[J]. IEEE Transactions on Geoscience and Remote Sensing,2023,PP(99):1-1. |
| APA | X. Ma.,J. Ding.,T. Wang.,L. Lu.,H. Sun.,...&I. Nurmemet.(2023).A pixel dichotomy coupled linear kernel-driven model for estimating fractional vegetation cover in arid areas from high-spatial-resolution images.IEEE Transactions on Geoscience and Remote Sensing,PP(99),1-1. |
| MLA | X. Ma,et al."A pixel dichotomy coupled linear kernel-driven model for estimating fractional vegetation cover in arid areas from high-spatial-resolution images".IEEE Transactions on Geoscience and Remote Sensing PP.99(2023):1-1. |
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