To reveal the climate response patterns of radial growth of Cunninghamia lanceolata (Lamb.) Hook. plantations under different competition intensities under the background of climate change, and to provide a reference for formulating targeted survival strategies for Chinese fir stands with varying competition levels.

Taking Jiangle state-owned forest farm in Fujian Province as the research area, 23 standard sampling plots were set up, and tree-ring cores were collected from 171 Cunninghamia lanceolata individuals. Meanwhile, diameter at breast height, tree height and crown width of each tree were measured to calculate the comprehensive competition index (CCI). All sampled trees were classified into three groups by threshold values: low competition group (CCI<1.76, n=45), medium competition group (1.76≤CCI≤3.38, n=82) and high competition group (CCI>3.38, n=44), and standardized tree-ring chronologies were developed for each group. Pearson correlation and partial correlation analyses were adopted to explore the effects of meteorological factors on radial growth characteristics of Chinese fir under different competition intensities. Climate variables with severe multicollinearity (VIF>5) were eliminated via stepwise multiple regression, and climate response models for radial growth of Cunninghamia lanceolata under various competition gradients were finally established.

1) Correlation analysis indicated that the radial growth of Chinese fir in the high-competition group exhibited the strongest sensitivity to meteorological factors, showing highly significant correlations with precipitation of the previous October and the standardized precipitation evapotranspiration index (SPEI) in March of the current year. The radial growth of trees in the medium-competition group was mainly correlated with relative humidity in January and temperature from March to April of the current year, while radial growth in the low-competition group had significant associations with temperatures during the non-growing season and early growing season; 2) The adjusted coefficients of determination of the stepwise multiple regression models for the high-, medium- and low-competition groups were 0.772, 0.518 and 0.363, respectively, all reaching significant levels. The radial growth of Chinese fir under high competition was markedly affected by non-growing season precipitation as well as minimum and maximum temperatures during the growing season. Early growing season temperature dominated the growth variations of trees in the medium-competition group, whereas non-growing season precipitation and early growing season minimum temperature exerted dominant impacts on radial growth in the low-competition group.

Meteorological factors including temperature, precipitation and relative humidity are core drivers regulating the radial growth of Cunninghamia lanceolata. Stand competition intensity can significantly alter the response magnitude of Chinese fir to climate fluctuations. Differentiated tending and management schemes should be implemented for Cunninghamia lanceolata plantations according to stand competition levels.

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.

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.

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.

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.