Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa
The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainf...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-01-01
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| Series: | Frontiers in Climate |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fclim.2021.589981/full |
| _version_ | 1818719350590275584 |
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| author | Shahriar Pervez Amy McNally Amy McNally Amy McNally Kristi Arsenault Kristi Arsenault Michael Budde James Rowland |
| author_facet | Shahriar Pervez Amy McNally Amy McNally Amy McNally Kristi Arsenault Kristi Arsenault Michael Budde James Rowland |
| author_sort | Shahriar Pervez |
| collection | DOAJ |
| description | The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration (ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world. |
| first_indexed | 2024-12-17T20:05:33Z |
| format | Article |
| id | doaj.art-a1d3d39779cc4f2d94355ff69d3efe42 |
| institution | Directory Open Access Journal |
| issn | 2624-9553 |
| language | English |
| last_indexed | 2024-12-17T20:05:33Z |
| publishDate | 2021-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Climate |
| spelling | doaj.art-a1d3d39779cc4f2d94355ff69d3efe422022-12-21T21:34:20ZengFrontiers Media S.A.Frontiers in Climate2624-95532021-01-01310.3389/fclim.2021.589981589981Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East AfricaShahriar Pervez0Amy McNally1Amy McNally2Amy McNally3Kristi Arsenault4Kristi Arsenault5Michael Budde6James Rowland7Arctic Slope Regional Corporation (ASRC) Federal Data Solutions, Contractor to U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD, United StatesUnited States Agency for International Development, Washington, DC, United StatesScience Application International Corporation, McLean, VA, United StatesHydrological Sciences Laboratory, National Aeronautics and Space Administration (NASA) Goddard Space Flight Center, Greenbelt, MD, United StatesScience Application International Corporation, McLean, VA, United StatesHydrological Sciences Laboratory, National Aeronautics and Space Administration (NASA) Goddard Space Flight Center, Greenbelt, MD, United StatesU.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD, United StatesU.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD, United StatesThe majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration (ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.https://www.frontiersin.org/articles/10.3389/fclim.2021.589981/fulltriple collocationEast Africavegetation monitoringevapotranspirationnormalized difference vegetation index |
| spellingShingle | Shahriar Pervez Amy McNally Amy McNally Amy McNally Kristi Arsenault Kristi Arsenault Michael Budde James Rowland Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa Frontiers in Climate triple collocation East Africa vegetation monitoring evapotranspiration normalized difference vegetation index |
| title | Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa |
| title_full | Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa |
| title_fullStr | Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa |
| title_full_unstemmed | Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa |
| title_short | Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa |
| title_sort | vegetation monitoring optimization with normalized difference vegetation index and evapotranspiration using remote sensing measurements and land surface models over east africa |
| topic | triple collocation East Africa vegetation monitoring evapotranspiration normalized difference vegetation index |
| url | https://www.frontiersin.org/articles/10.3389/fclim.2021.589981/full |
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