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First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam.

ABSTRACT: Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km²) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km²). The spatial-temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. The explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals.

SUBMITTER: Phan DC

PROVIDER: S-EPMC8113344 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Research on spatial non-stationarity of land-cover classification accuracy has been ongoing for over two decades with most of the work focusing on single date maps. We extend the understanding of thematic map accuracy spatial patterns by: (1) quantifying spatial patterns of map-reference agreement for class-specific land-cover change rather than class-specific land cover for both omission and commission expressions of map error; (2) reporting goodness-of-fit estimates for the empirical models, which have been lacking in previous assessments, and; (3) using the empirical model results to map the locations of the relative likelihoods of map-reference agreement for specific land-cover change classes. We evaluated 10 map-based explanatory variables in single and multivariable logistic regression models to predict the likelihood of agreement between map and reference land-cover change (2001-2011) labels using the National Land Cover Database (NLCD) 2011 land cover and accuracy data. Logistic models for omission error had better goodness-of-fit estimates than models for commission error. For the omission error models, the explanatory variable, density of the mapped class-specific change in the immediate neighbourhood surrounding the sample pixel, produced the best model fit results (Tjur coefficient of discrimination, D, ranged from 0.59 to 0.98) compared to multivariable models and all other single explanatory variable models. Maps of the predicted likelihood of map-reference agreement produced from the best fitting omission error models provide a spatially explicit description of spatial variation of classification uncertainty at both local and regional scales. Application of the models indicated higher likelihoods of agreement (>50%) comprised a greater proportion of the land-cover change class area than the proportion of the land-cover change class with lower likelihoods of agreement. NLCD users can apply reported equations to map land-cover change uncertainty.

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First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam.

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