Forest age inversion and dynamic monitoring based on Sentinel-1 and Sentinel-2 data fusion
[Objective]To fuse multi-source remote sensing data for inversion and dynamic monitoring of forest stand age on the Google Earth Engine(GEE)cloud platform with the help of its powerful computing and data storage capabilities.[Method]The land cover information was obtained through the fusion of Sentinel-1,Sentinel-2,and elevation data from 2017 to 2023 using the random forest(RF)classification method.Additionally,the distribution and area of forests were further extracted.Meanwhile,time series vegetation indices were constructed to accurately identify areas of forest change.Based on forest inventory data and the fusion of multi-source remote sensing data,three regression models were developed on GEE:RF,classification and regression trees(CART),and Gradient tree boosting(GTB).These models were applied to estimate the forest age in 2018 for different tree species groups,such as Chinese fir,masson pine,moso bamboo,hardwood,and other tree species groups.The estimated forest ages in 2017 and 2023 were also obtained to reveal the dynamic changes in forest age and age groups from 2017 to 2023.[Result]1)From 2017 to 2023,the overall change in forest area in the study area amounted to 113.93 km².During this period,forest reduction and regeneration coexisted,and the spatial distribution exhibited distinct regional variations.Specifically,forest area changes were more prominent in areas near urban centers and lower altitudes,with forest area reductions near urban areas often not recovering to a forested state;2)Among the three models constructed for five different tree species groups,the RF regression model produced the best results for forest age estimation.It achieved an average R2 of 0.845 and an average RMSE of 5.32 a,with bamboo forests exhibiting the highest accuracy(R2=0.863,RMSE=2.411 a);3)From 2017 to 2023,forests ages below 40 years in the study area decreased from 54.59%to 51.06%.The most significant age group change was observed in mature pine forests,with an increase of 38.88%in their area.[Conclusion]The fusion of multi-source remote sensing data for forest age estimation and dynamic monitoring on GEE holds significant application potential.The results of this study can serve as a reference and inspiration for utilizing cloud platforms and Sentinel series satellite data in the long-term forest age estimation and dynamic monitoring of forest resources.
data fusioninversion of remote sensingforest agedynamic monitoringSentinel-1Sentinel-2Google Earth Engine
陈馨、孙玉军、丁志丹
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北京林业大学 森林资源和环境管理国家林业和草原局重点开放实验室,北京 100083
数据融合 遥感反演 林龄 动态监测 Sentinel-1 Sentinel-2 Google Earth Engine
NETL
NSTL
万方数据
