Mapping annual forest change due to afforestation in Guangdong Province of China using active and passive remote sensing data

dc.creatorShen, Wenjuan
dc.creatorLi, Mingshi
dc.creatorHuang, Chengquan
dc.creatorTao, Xin
dc.creatorLi, Shu (TTU)
dc.creatorWei, Anshi
dc.date.accessioned2023-03-31T15:52:35Z
dc.date.available2023-03-31T15:52:35Z
dc.date.issued2019
dc.description© 2019 by the authors. cc-by
dc.description.abstractAccurate acquisition of spatial distribution of afforestation in a large area is of great significance to contributing to the sustainable utilization of forest resources and the evaluation of the carbon accounting. Annual forest maps (1986-2016) of Guangdong, China were generated using time series Landsat images and PALSAR data. Initially, four PALSAR-based classifiers were used to classify land cover types. Then, the optimal mapping algorithm was determined. Next, an accurate identification of forest and non-forest was carried out by combining Landsat-based phenological variables and PALSAR-based land cover classifications. Finally, the spatio-temporal distribution of forest cover change due to afforestation was created and its forest biomass dynamics changes were detected. The results indicated that the overall accuracy of forest classification of the improved model based on the PALSAR-based stochastic gradient boosting (SGB) classification and the maximum value of normalized difference vegetation index (NDVI; SGB-NDVI) were approximately 75-85% in 2005, 2010, and 2016. Compared with the Japan Aerospace Exploration Agency (JAXA) PALSAR-forest/non-forest, the SGB-NDVI-based forest product showed great improvement, while the SGB-NDVI product was the same or slightly inferior to the Global Land Cover (GLC) and vegetation tracker change (VCT)-based land cover types, respectively. Although this combination of multiple sources contained some errors, the SGB-NDVI model effectively identified the distribution of forest cover changes by afforestation events. By integrating aboveground biomass dynamics (AGB) change with forest cover, the trend in afforestation area closely corresponded with the trend in forest AGB. This technique can provide an essential data baseline for carbon assessment in the planted forests of southern China.
dc.identifier.citationShen, W., Li, M., Huang, C., Tao, X., Li, S., & Wei, A.. 2019. Mapping annual forest change due to afforestation in Guangdong Province of China using active and passive remote sensing data. Remote Sensing, 11(5). https://doi.org/10.3390/rs11050490
dc.identifier.urihttps://doi.org/10.3390/rs11050490
dc.identifier.urihttps://hdl.handle.net/2346/92248
dc.language.isoeng
dc.subjectBiomass
dc.subjectData integration
dc.subjectForest change (afforestation)
dc.subjectLandsat
dc.subjectPALSAR
dc.subjectTime series
dc.titleMapping annual forest change due to afforestation in Guangdong Province of China using active and passive remote sensing data
dc.typeArticle

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