Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted Copulas
With model-assisted (MA) estimation, remote sensing (RS) has provided auxiliary modeling data to enhance precision in estimators of forest parameters for continuous forest monitoring as mandated by various official reporting instruments. However, model-assisted estimation is largely reliant on a sam...
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25000615 |
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| author | Xinjie Cheng Zhengyang Hou Annika Kangas Jean-Pierre Renaud Hao Tang Weisheng Zeng Qing Xu |
| author_facet | Xinjie Cheng Zhengyang Hou Annika Kangas Jean-Pierre Renaud Hao Tang Weisheng Zeng Qing Xu |
| author_sort | Xinjie Cheng |
| collection | DOAJ |
| description | With model-assisted (MA) estimation, remote sensing (RS) has provided auxiliary modeling data to enhance precision in estimators of forest parameters for continuous forest monitoring as mandated by various official reporting instruments. However, model-assisted estimation is largely reliant on a sample resulting from costly field surveys to meet the precision standard mandated by these instruments. While a large sample is more likely to represent the population in question and ensure meeting the prescribed precision, it is crucial to reduce costs by finding a balance between precision and sample size. Consequently, this study aims to (1) develop and demonstrate estimation using Copulas modeling; (2) propose a sample size optimization procedure for MA estimators in the context of continuous forest monitoring; and (3) compare survey precisions of the estimators using Copulas and Weighted Least Squares regression (WLS) as a function of sample sizes. Four main conclusions are relevant: for both Burkina Faso (BF) and Genhe (GH) study area, (1) Copulas outperforms WLS in modeling and prediction, both in terms of mean values and maximum/minimum values; (2) Copulas consistently demonstrates superior performance and precision across varying sample sizes compared to the WLS with MA estimators; (3) a straightforward sample size optimization approach reveals that variance estimates of Copulas remain lower than those of WLS as the sample size decreases in monitoring surveys; (4) Copulas requires about 20% smaller sample size than WLS does when achieving a specified precision, suggesting enhanced efficiency. Overall, Copulas appears promising to satisfy the precision, cost-efficiency, and flexibility requirements of monitoring surveys, particularly in situations involving small sample sizes. |
| format | Article |
| id | doaj-art-71e46342e8514094b037b14183e42ad6 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-71e46342e8514094b037b14183e42ad62025-01-26T05:03:36ZengElsevierEcological Indicators1470-160X2025-02-01171113132Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted CopulasXinjie Cheng0Zhengyang Hou1Annika Kangas2Jean-Pierre Renaud3Hao Tang4Weisheng Zeng5Qing Xu6The Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China; Ecological Observation and Research Station of Heilongjiang Sanjiang Plain Wetlands, National Forestry and Grassland Administration, Shuangyashan 518000, ChinaThe Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China; Ecological Observation and Research Station of Heilongjiang Sanjiang Plain Wetlands, National Forestry and Grassland Administration, Shuangyashan 518000, China; Corresponding author.Natural Resources Institute Finland (Luke), Bioeconomy and Environment Unit, Joensuu, FinlandOffice National des Forêts, Département Recherche Développement Innovation, 5 rue Girardet, 54052 Nancy, France; Laboratoire d’inventaire forestier, ENSG, IGN, 14 rue Girardet, 54000 Nancy, FranceDepartment of Geography, National University of Singapore, SingaporeAcademy of Forest and Grassland Inventory and Planning, National Forest and Grassland Administration, Beijing 100714, ChinaKey Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, International Center for Bamboo and Rattan, Beijing 100102, ChinaWith model-assisted (MA) estimation, remote sensing (RS) has provided auxiliary modeling data to enhance precision in estimators of forest parameters for continuous forest monitoring as mandated by various official reporting instruments. However, model-assisted estimation is largely reliant on a sample resulting from costly field surveys to meet the precision standard mandated by these instruments. While a large sample is more likely to represent the population in question and ensure meeting the prescribed precision, it is crucial to reduce costs by finding a balance between precision and sample size. Consequently, this study aims to (1) develop and demonstrate estimation using Copulas modeling; (2) propose a sample size optimization procedure for MA estimators in the context of continuous forest monitoring; and (3) compare survey precisions of the estimators using Copulas and Weighted Least Squares regression (WLS) as a function of sample sizes. Four main conclusions are relevant: for both Burkina Faso (BF) and Genhe (GH) study area, (1) Copulas outperforms WLS in modeling and prediction, both in terms of mean values and maximum/minimum values; (2) Copulas consistently demonstrates superior performance and precision across varying sample sizes compared to the WLS with MA estimators; (3) a straightforward sample size optimization approach reveals that variance estimates of Copulas remain lower than those of WLS as the sample size decreases in monitoring surveys; (4) Copulas requires about 20% smaller sample size than WLS does when achieving a specified precision, suggesting enhanced efficiency. Overall, Copulas appears promising to satisfy the precision, cost-efficiency, and flexibility requirements of monitoring surveys, particularly in situations involving small sample sizes.http://www.sciencedirect.com/science/article/pii/S1470160X25000615Survey samplingMachine learningModel-assisted estimatorsCopulasSample size optimization |
| spellingShingle | Xinjie Cheng Zhengyang Hou Annika Kangas Jean-Pierre Renaud Hao Tang Weisheng Zeng Qing Xu Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted Copulas Ecological Indicators Survey sampling Machine learning Model-assisted estimators Copulas Sample size optimization |
| title | Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted Copulas |
| title_full | Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted Copulas |
| title_fullStr | Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted Copulas |
| title_full_unstemmed | Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted Copulas |
| title_short | Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted Copulas |
| title_sort | alleviating small sample problem in continuous forest monitoring with remote sensing assisted copulas |
| topic | Survey sampling Machine learning Model-assisted estimators Copulas Sample size optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25000615 |
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