Review of Remote Sensing Applications in Grassland Monitoring
The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This p...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/12/2903 |
| _version_ | 1797482672531963904 |
|---|---|
| author | Zhaobin Wang Yikun Ma Yaonan Zhang Jiali Shang |
| author_facet | Zhaobin Wang Yikun Ma Yaonan Zhang Jiali Shang |
| author_sort | Zhaobin Wang |
| collection | DOAJ |
| description | The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite. |
| first_indexed | 2024-03-09T22:35:50Z |
| format | Article |
| id | doaj.art-7aef84fbb9aa4cf69477707da219fd6d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T22:35:50Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7aef84fbb9aa4cf69477707da219fd6d2023-11-23T18:48:38ZengMDPI AGRemote Sensing2072-42922022-06-011412290310.3390/rs14122903Review of Remote Sensing Applications in Grassland MonitoringZhaobin Wang0Yikun Ma1Yaonan Zhang2Jiali Shang3School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaNational Cryosphere Desert Data Center, Lanzhou 730000, ChinaAgriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, CanadaThe application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.https://www.mdpi.com/2072-4292/14/12/2903grassland remote sensingparameter estimationland degradation monitoringgrassland usedisaster monitoringcarbon cycle |
| spellingShingle | Zhaobin Wang Yikun Ma Yaonan Zhang Jiali Shang Review of Remote Sensing Applications in Grassland Monitoring Remote Sensing grassland remote sensing parameter estimation land degradation monitoring grassland use disaster monitoring carbon cycle |
| title | Review of Remote Sensing Applications in Grassland Monitoring |
| title_full | Review of Remote Sensing Applications in Grassland Monitoring |
| title_fullStr | Review of Remote Sensing Applications in Grassland Monitoring |
| title_full_unstemmed | Review of Remote Sensing Applications in Grassland Monitoring |
| title_short | Review of Remote Sensing Applications in Grassland Monitoring |
| title_sort | review of remote sensing applications in grassland monitoring |
| topic | grassland remote sensing parameter estimation land degradation monitoring grassland use disaster monitoring carbon cycle |
| url | https://www.mdpi.com/2072-4292/14/12/2903 |
| work_keys_str_mv | AT zhaobinwang reviewofremotesensingapplicationsingrasslandmonitoring AT yikunma reviewofremotesensingapplicationsingrasslandmonitoring AT yaonanzhang reviewofremotesensingapplicationsingrasslandmonitoring AT jialishang reviewofremotesensingapplicationsingrasslandmonitoring |