High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland
Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/17/4266 |
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| author | Guangrui Zhong Jianjun Chen Renjie Huang Shuhua Yi Yu Qin Haotian You Xiaowen Han Guoqing Zhou |
| author_facet | Guangrui Zhong Jianjun Chen Renjie Huang Shuhua Yi Yu Qin Haotian You Xiaowen Han Guoqing Zhou |
| author_sort | Guangrui Zhong |
| collection | DOAJ |
| description | Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R<sup>2</sup> = 0.967, RMSE = 0.045) > Random Forest (RF) model (R<sup>2</sup> = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R<sup>2</sup> = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R<sup>2</sup> = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments. |
| first_indexed | 2024-03-10T23:14:34Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:14:34Z |
| publishDate | 2023-08-01 |
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| series | Remote Sensing |
| spelling | doaj.art-9ca7d9fcfd974b0bb6389e073848e8b02023-11-19T08:46:47ZengMDPI AGRemote Sensing2072-42922023-08-011517426610.3390/rs15174266High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine GrasslandGuangrui Zhong0Jianjun Chen1Renjie Huang2Shuhua Yi3Yu Qin4Haotian You5Xiaowen Han6Guoqing Zhou7College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaSchool of Geographic Sciences, Nantong University, Nantong 226007, ChinaState Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaFractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R<sup>2</sup> = 0.967, RMSE = 0.045) > Random Forest (RF) model (R<sup>2</sup> = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R<sup>2</sup> = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R<sup>2</sup> = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.https://www.mdpi.com/2072-4292/15/17/4266fractional vegetation coverage (FVC)multidimensional feature datasetmachine learningSource Region of the Yellow River (SRYR)unmanned aerial vehicle (UAV)spatiotemporal variation characteristics |
| spellingShingle | Guangrui Zhong Jianjun Chen Renjie Huang Shuhua Yi Yu Qin Haotian You Xiaowen Han Guoqing Zhou High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland Remote Sensing fractional vegetation coverage (FVC) multidimensional feature dataset machine learning Source Region of the Yellow River (SRYR) unmanned aerial vehicle (UAV) spatiotemporal variation characteristics |
| title | High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland |
| title_full | High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland |
| title_fullStr | High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland |
| title_full_unstemmed | High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland |
| title_short | High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland |
| title_sort | high spatial resolution fractional vegetation coverage inversion based on uav and sentinel 2 data a case study of alpine grassland |
| topic | fractional vegetation coverage (FVC) multidimensional feature dataset machine learning Source Region of the Yellow River (SRYR) unmanned aerial vehicle (UAV) spatiotemporal variation characteristics |
| url | https://www.mdpi.com/2072-4292/15/17/4266 |
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