Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data
Using a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to dev...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2022.2163070 |
| _version_ | 1827811740088270848 |
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| author | Thuong V. Tran David Bruce Cho-Ying Huang Duy X. Tran Soe W. Myint Duy B. Nguyen |
| author_facet | Thuong V. Tran David Bruce Cho-Ying Huang Duy X. Tran Soe W. Myint Duy B. Nguyen |
| author_sort | Thuong V. Tran |
| collection | DOAJ |
| description | Using a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to develop an integrated Multivariate Drought Index (iMDI). The iMDI incorporates the vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI). The 54,474 km2 Vietnamese Central Highlands region, which has been significantly affected by drought severity for several decades, was selected as a test site to assess the feasibility of the iMDI. Spearman correlation between the iMDI and other commonly used spectral drought indices (i.e. the Drought Severity Index (DSI–12) and the annual Vegetation Health Index (VHI–12)) and ground-based drought indices (i.e. the Standardized Precipitation Index (SPI–12) and the Reconnaissance Drought Index (RDI–12)) was employed to evaluate performance of the proposed drought index. Pixel-based linear regression together with clustering models of the iMDI time-series was applied to characterize the spatiotemporal pattern of drought from 2001 to 2020. In addition, a persistent area of LULC types (i.e. forests, croplands, and shrubland) during the 2001–2020 period was used to understand drought variation in relation to LULC. Results suggested that the iMDI outperformed the other spectral drought indices (r > 0.6; p < 0.005). The analysis revealed an increase in drought risk in some provinces of the Central Highlands including Gia Lai, Kon Tum, and Dak Lak. It was also found that changes in LULC patterns could minimize (reforestation) or exacerbate (deforestation) the impacts of drought. Our study suggests that applying a multivariate drought index enables a better understanding of drought patterns at the local scale. This provides valuable information for the development of appropriate land and environmental management practices that can affect and mitigate climate change effects. |
| first_indexed | 2024-03-11T23:08:52Z |
| format | Article |
| id | doaj.art-e414dcf2359846ed8a0890561c88519a |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:52Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-e414dcf2359846ed8a0890561c88519a2023-09-21T12:43:09ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262023-12-0160110.1080/15481603.2022.21630702163070Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series dataThuong V. Tran0David Bruce1Cho-Ying Huang2Duy X. Tran3Soe W. Myint4Duy B. Nguyen5Monash UniversityFlinders UniversityNational Taiwan UniversityAgResearch LtdArizona State UniversityHanoi University of Mining and GeologyUsing a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to develop an integrated Multivariate Drought Index (iMDI). The iMDI incorporates the vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI). The 54,474 km2 Vietnamese Central Highlands region, which has been significantly affected by drought severity for several decades, was selected as a test site to assess the feasibility of the iMDI. Spearman correlation between the iMDI and other commonly used spectral drought indices (i.e. the Drought Severity Index (DSI–12) and the annual Vegetation Health Index (VHI–12)) and ground-based drought indices (i.e. the Standardized Precipitation Index (SPI–12) and the Reconnaissance Drought Index (RDI–12)) was employed to evaluate performance of the proposed drought index. Pixel-based linear regression together with clustering models of the iMDI time-series was applied to characterize the spatiotemporal pattern of drought from 2001 to 2020. In addition, a persistent area of LULC types (i.e. forests, croplands, and shrubland) during the 2001–2020 period was used to understand drought variation in relation to LULC. Results suggested that the iMDI outperformed the other spectral drought indices (r > 0.6; p < 0.005). The analysis revealed an increase in drought risk in some provinces of the Central Highlands including Gia Lai, Kon Tum, and Dak Lak. It was also found that changes in LULC patterns could minimize (reforestation) or exacerbate (deforestation) the impacts of drought. Our study suggests that applying a multivariate drought index enables a better understanding of drought patterns at the local scale. This provides valuable information for the development of appropriate land and environmental management practices that can affect and mitigate climate change effects.http://dx.doi.org/10.1080/15481603.2022.2163070evapotranspirationintegrated multivariate drought indexdrought severity indexvegetation health indexground standardized precipitation indexground reconnaissance drought index |
| spellingShingle | Thuong V. Tran David Bruce Cho-Ying Huang Duy X. Tran Soe W. Myint Duy B. Nguyen Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data GIScience & Remote Sensing evapotranspiration integrated multivariate drought index drought severity index vegetation health index ground standardized precipitation index ground reconnaissance drought index |
| title | Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data |
| title_full | Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data |
| title_fullStr | Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data |
| title_full_unstemmed | Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data |
| title_short | Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data |
| title_sort | decadal assessment of agricultural drought in the context of land use land cover change using modis multivariate spectral index time series data |
| topic | evapotranspiration integrated multivariate drought index drought severity index vegetation health index ground standardized precipitation index ground reconnaissance drought index |
| url | http://dx.doi.org/10.1080/15481603.2022.2163070 |
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