GIScience & Remote Sensing (Dec 2023)
Spatiotemporal patterns of planted forests on the Loess Plateau between 1986 and 2021 based on Landsat NDVI time-series analysis
Abstract
Long-term and large scale spatiotemporal patterns of planted forests are essential for evaluating local plantation effectiveness and to promote sustainable afforestation. Satellite remote sensing data provide broad spatial coverage and increasingly long-term and large scale records for spatiotemporal analysis of planted forests. In this study, we developed a dynamic and conversion extraction (DCE) framework by combining the similarity of Landsat normalized difference vegetation index (NDVI) time-series with a change detection algorithm. This framework allows the analysis of spatiotemporal dynamics of planted forests. The proposed framework can be used to extract the planting year and conversion patterns of planted forests by performing the Mann-Kendall (MK) statistic test on the inter-annual Landsat NDVI time-series and measuring the similarity of the intra-annual time-series, respectively. Therefore, it can be applied to addressing the key questions on where, when, and how planted forests conversions have occurred. Took the Loess Plateau in China as an example, we applied the DCE framework to illustrate how planted forests have changed over time and space and have been converted from other land cover types from 1986 to 2021. The producer and user accuracies for the mapping of planted forests were 88.22% and 87.11% in 2021, respectively, and the accuracy of identifying planted forests conversions was 80.25%. The results showed that the newly planted forests experienced four phases: a slow rise from 1986 to 2000, a sharp increase during 2001–2004, a fluctuating pattern from 2005 to 2013, and a reduction until 2020, resulting in a total planted forest area of 7.13 Mha in 2021. In addition, 83.51% of the planted forest pixels underwent conversion from grasslands (58.19% or 4.15 Mha) or croplands (25.32% or 1.81 Mha) during 1986–2021. These results indicate the capability of the DCE framework to capture essential information (planting year and conversion patterns) to support the spatiotemporal analysis of planted forests at large scales.
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