Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks

Abstract

China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40 years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32 years and a determination coefficient (R (2)) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R (2) values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360 %. For the persistent forest area since 1974, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 0.98 t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03 t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.

Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison

“…The stratification of forest types based on tree species and environmental variables can provide the potential of improving the estimation of forest AGB [22,32,33]. Moreover, incorporating the tree age into the estimation models can also lead to an improvement of the AGB/carbon sequestration estimates and can reduce the overestimation and underestimation [64][65][66][67][68].…”

“…Lefsky et al [66] mapped the forest stand age using the time series of Landsat images, then estimated the AGB of young stands using Lidar data in western Oregon, USA, and found that the estimation models were appropriate for the forests with the classes of ages 14.5 to 20.5 years but resulted in overestimations for the smaller age classes. Based on the years in which the trees were planted, Liu et al [67] also obtained and added the age variable into their linear regression models to estimate the AGB of plantations using Landsat images. The studies indicate that taking tree ages into account would obtain a considerable improvement in estimating AGB.…”

Distinct Carbon and Nitrogen Metabolism of Two Contrasting Poplar Species in Response to Different N Supply Levels

“…China is the country possessing the largest artificial forest in the world [1]. Considering the timber production and environmental adaptability, plantations in North China mainly rely on poplars.…”

Management intensification maintains wood production over multiple harvests in tropical Eucalyptus plantations

“…To answer these questions, we build on decades of studies using time series of vegetation indices derived from satellite imagery to model primary productivity (Tucker 1979, Paruelo et al 1997, tree growth (Wang et al 2004), and plantation wood production (le Maire et al 2011, Liu et al 2014, Dube et al 2015. We also use time series of satellite observations to map plantations on the landscape (Dong et al 2013, Qiao et al 2016 and to identify harvest and subsequent forest regrowth (DeVries et al 2015).…”