Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest China
Accurate identification of desert vegetation dynamics in arid regions is challenging because of its complex composition of grass species, obscure boundary with non-desert vegetation, and high sensitiveness to climatic variations. This study examined the ability of optimized multi-endmember spectral...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-02-01
|
| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2021.1883940 |
| _version_ | 1797679063325736960 |
|---|---|
| author | Yaoliang Chen Xiaotao Huang Jingfeng Huang Shanshan Liu Dengsheng Lu Shuai Zhao |
| author_facet | Yaoliang Chen Xiaotao Huang Jingfeng Huang Shanshan Liu Dengsheng Lu Shuai Zhao |
| author_sort | Yaoliang Chen |
| collection | DOAJ |
| description | Accurate identification of desert vegetation dynamics in arid regions is challenging because of its complex composition of grass species, obscure boundary with non-desert vegetation, and high sensitiveness to climatic variations. This study examined the ability of optimized multi-endmember spectral mixture analysis (MESMA) for monitoring desert vegetation degradation, recovery, and greening in a dryland basin of Northwest China using Landsat time series data from the 1990s to 2016. Eight modeled endmember fractions were generated using the best endmember model with the lowest fraction error and root mean square error (RMSE). Abundances of non-desert vegetation, desert vegetation, soil, and impervious surface areas were incorporated based on the eight original fractions and validated using high spatial resolution images. Finally, the post-classification comparison approach was used to detect desert vegetation degradation, recovery, and greening. Results show that: (1) More than 97% of the land pixels were modeled successfully into eight endmember fractions for each period with the mean RMSE less than 0.01. All four simulated abundances had high correlations (r = 0.89–0.96) with the corresponding reference data, indicating good performance of MESMA in this study; (2) Desert vegetation increased dramatically (772.68 km2) during the 26-year period. The major change was desert vegetation recovery with a total area of 10,705 km2, followed by degradation with a total area of 4,715 km2. The greening area was the smallest, covering only 1,509 km2; (3) Increased precipitation was the major contributor for desert vegetation greening in the west of upper region while decreased precipitation was the major contributor for the degradation in the west of lower region. Anthropogenic factors (e.g., improvement of irrigation, crop expansion) were major contributors for the change in desert vegetation in the middle region. This research demonstrates that MESMA is promising in detecting desert vegetation dynamics in semi-arid and arid regions. |
| first_indexed | 2024-03-11T23:08:58Z |
| format | Article |
| id | doaj.art-f3fa42b2daae466c908d07569cc6313c |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:58Z |
| publishDate | 2021-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-f3fa42b2daae466c908d07569cc6313c2023-09-21T12:34:16ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262021-02-0158230032110.1080/15481603.2021.18839401883940Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest ChinaYaoliang Chen0Xiaotao Huang1Jingfeng Huang2Shanshan Liu3Dengsheng Lu4Shuai Zhao5Fujian Normal UniversityChinese Academy of SciencesZhejiang UniversityFujian Normal UniversityFujian Normal UniversityFujian Normal UniversityAccurate identification of desert vegetation dynamics in arid regions is challenging because of its complex composition of grass species, obscure boundary with non-desert vegetation, and high sensitiveness to climatic variations. This study examined the ability of optimized multi-endmember spectral mixture analysis (MESMA) for monitoring desert vegetation degradation, recovery, and greening in a dryland basin of Northwest China using Landsat time series data from the 1990s to 2016. Eight modeled endmember fractions were generated using the best endmember model with the lowest fraction error and root mean square error (RMSE). Abundances of non-desert vegetation, desert vegetation, soil, and impervious surface areas were incorporated based on the eight original fractions and validated using high spatial resolution images. Finally, the post-classification comparison approach was used to detect desert vegetation degradation, recovery, and greening. Results show that: (1) More than 97% of the land pixels were modeled successfully into eight endmember fractions for each period with the mean RMSE less than 0.01. All four simulated abundances had high correlations (r = 0.89–0.96) with the corresponding reference data, indicating good performance of MESMA in this study; (2) Desert vegetation increased dramatically (772.68 km2) during the 26-year period. The major change was desert vegetation recovery with a total area of 10,705 km2, followed by degradation with a total area of 4,715 km2. The greening area was the smallest, covering only 1,509 km2; (3) Increased precipitation was the major contributor for desert vegetation greening in the west of upper region while decreased precipitation was the major contributor for the degradation in the west of lower region. Anthropogenic factors (e.g., improvement of irrigation, crop expansion) were major contributors for the change in desert vegetation in the middle region. This research demonstrates that MESMA is promising in detecting desert vegetation dynamics in semi-arid and arid regions.http://dx.doi.org/10.1080/15481603.2021.1883940arid vegetationdesertificationvegetation recoveryvegetation greeningspectral decomposinglandsat imagery |
| spellingShingle | Yaoliang Chen Xiaotao Huang Jingfeng Huang Shanshan Liu Dengsheng Lu Shuai Zhao Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest China GIScience & Remote Sensing arid vegetation desertification vegetation recovery vegetation greening spectral decomposing landsat imagery |
| title | Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest China |
| title_full | Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest China |
| title_fullStr | Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest China |
| title_full_unstemmed | Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest China |
| title_short | Fractional monitoring of desert vegetation degradation, recovery, and greening using optimized multi-endmembers spectral mixture analysis in a dryland basin of Northwest China |
| title_sort | fractional monitoring of desert vegetation degradation recovery and greening using optimized multi endmembers spectral mixture analysis in a dryland basin of northwest china |
| topic | arid vegetation desertification vegetation recovery vegetation greening spectral decomposing landsat imagery |
| url | http://dx.doi.org/10.1080/15481603.2021.1883940 |
| work_keys_str_mv | AT yaoliangchen fractionalmonitoringofdesertvegetationdegradationrecoveryandgreeningusingoptimizedmultiendmembersspectralmixtureanalysisinadrylandbasinofnorthwestchina AT xiaotaohuang fractionalmonitoringofdesertvegetationdegradationrecoveryandgreeningusingoptimizedmultiendmembersspectralmixtureanalysisinadrylandbasinofnorthwestchina AT jingfenghuang fractionalmonitoringofdesertvegetationdegradationrecoveryandgreeningusingoptimizedmultiendmembersspectralmixtureanalysisinadrylandbasinofnorthwestchina AT shanshanliu fractionalmonitoringofdesertvegetationdegradationrecoveryandgreeningusingoptimizedmultiendmembersspectralmixtureanalysisinadrylandbasinofnorthwestchina AT dengshenglu fractionalmonitoringofdesertvegetationdegradationrecoveryandgreeningusingoptimizedmultiendmembersspectralmixtureanalysisinadrylandbasinofnorthwestchina AT shuaizhao fractionalmonitoringofdesertvegetationdegradationrecoveryandgreeningusingoptimizedmultiendmembersspectralmixtureanalysisinadrylandbasinofnorthwestchina |