Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models
National-scale forest inventories such as the Forest Inventory and Analysis (FIA) program in the United States are designed to provide data and estimates that meet target precision at the national and state levels. However, such design-based direct estimates are often not accurate at a smaller geogr...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2407 |
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| author | Okikiola M. Alegbeleye Krishna P. Poudel Curtis VanderSchaaf Yun Yang |
| author_facet | Okikiola M. Alegbeleye Krishna P. Poudel Curtis VanderSchaaf Yun Yang |
| author_sort | Okikiola M. Alegbeleye |
| collection | DOAJ |
| description | National-scale forest inventories such as the Forest Inventory and Analysis (FIA) program in the United States are designed to provide data and estimates that meet target precision at the national and state levels. However, such design-based direct estimates are often not accurate at a smaller geographic scale due to the small sample size. Small area estimation (SAE) techniques provide precise estimates at small domains by borrowing strength from remotely sensed auxiliary information. This study combined the FIA direct estimates with gridded mean canopy heights derived from recently published Global Ecosystem Dynamics Investigation (GEDI) Level 3 data and Landsat data to improve county-level estimates of total and merchantable volume, aboveground biomass, and basal area in the states of Alabama and Mississippi, USA. Compared with the FIA direct estimates, the area-level SAE models reduced root mean square error for all variables of interest. The multi-state SAE models had a mean relative standard error of 0.67. In contrast, single-state models had relative standard errors of 0.54 and 0.59 for Alabama and Mississippi, respectively. Despite GEDI’s limited footprints, this study reveals its potential to reduce direct estimate errors at the sub-state level when combined with Landsat bands through the small area estimation technique. |
| format | Article |
| id | doaj-art-dd2d85e3c70e49cba5d35ddef070f84a |
| institution | Matheson Library |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-dd2d85e3c70e49cba5d35ddef070f84a2025-07-25T13:35:14ZengMDPI AGRemote Sensing2072-42922025-07-011714240710.3390/rs17142407Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot ModelsOkikiola M. Alegbeleye0Krishna P. Poudel1Curtis VanderSchaaf2Yun Yang3School of the Environment, Washington State University, Pullman, WA 99164, USADepartment of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, MS 39762, USADepartment of Forestry, Forest and Wildlife Research Center, Mississippi State University, Starkville, MS 39762, USACollege of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853, USANational-scale forest inventories such as the Forest Inventory and Analysis (FIA) program in the United States are designed to provide data and estimates that meet target precision at the national and state levels. However, such design-based direct estimates are often not accurate at a smaller geographic scale due to the small sample size. Small area estimation (SAE) techniques provide precise estimates at small domains by borrowing strength from remotely sensed auxiliary information. This study combined the FIA direct estimates with gridded mean canopy heights derived from recently published Global Ecosystem Dynamics Investigation (GEDI) Level 3 data and Landsat data to improve county-level estimates of total and merchantable volume, aboveground biomass, and basal area in the states of Alabama and Mississippi, USA. Compared with the FIA direct estimates, the area-level SAE models reduced root mean square error for all variables of interest. The multi-state SAE models had a mean relative standard error of 0.67. In contrast, single-state models had relative standard errors of 0.54 and 0.59 for Alabama and Mississippi, respectively. Despite GEDI’s limited footprints, this study reveals its potential to reduce direct estimate errors at the sub-state level when combined with Landsat bands through the small area estimation technique.https://www.mdpi.com/2072-4292/17/14/2407small area estimationFIAGEDIlandsatarea-level composite estimator |
| spellingShingle | Okikiola M. Alegbeleye Krishna P. Poudel Curtis VanderSchaaf Yun Yang Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models Remote Sensing small area estimation FIA GEDI landsat area-level composite estimator |
| title | Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models |
| title_full | Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models |
| title_fullStr | Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models |
| title_full_unstemmed | Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models |
| title_short | Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models |
| title_sort | improving the estimates of county level forest attributes using gedi and landsat derived auxiliary information in fay herriot models |
| topic | small area estimation FIA GEDI landsat area-level composite estimator |
| url | https://www.mdpi.com/2072-4292/17/14/2407 |
| work_keys_str_mv | AT okikiolamalegbeleye improvingtheestimatesofcountylevelforestattributesusinggediandlandsatderivedauxiliaryinformationinfayherriotmodels AT krishnappoudel improvingtheestimatesofcountylevelforestattributesusinggediandlandsatderivedauxiliaryinformationinfayherriotmodels AT curtisvanderschaaf improvingtheestimatesofcountylevelforestattributesusinggediandlandsatderivedauxiliaryinformationinfayherriotmodels AT yunyang improvingtheestimatesofcountylevelforestattributesusinggediandlandsatderivedauxiliaryinformationinfayherriotmodels |