Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data

Wenjuan Shen,Mingshi Li,Anshi Wei,Chengquan Huang

doi:10.3390/rs8070595

Abstract

Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery (1986–2011) and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007–2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R2 of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones.

Highlights

Forest biomass is a key biophysical parameter used for evaluating and modeling terrestrial carbon stocks and dynamics and in supporting forest disturbance responses to climate change modeling [1]under rising global temperatures [2]
The PercentIncMSE and IncNodePurity estimated from random forest (RF) out of bag’ (OOB) data were used to rank all of the predictor variables by their capacity to predict aboveground biomass (AGB)
The progressive removal of the least important predictor variables generally resulted in reduced root mean square error (RMSE) for the OOB data and the model with the lowest RMSE (2 t/pixel) and 13 predictor variables were selected for mapping AGB

Summary

Introduction

Forest biomass is a key biophysical parameter used for evaluating and modeling terrestrial carbon stocks and dynamics and in supporting forest disturbance responses to climate change modeling [1]under rising global temperatures [2]. Traditional and frequently used approaches for estimating the spatial distribution of aboveground biomass (AGB) are through field plot measurements [3] or calculation using allometric regression equations or biomass expansion factors [4]. Allometric equations use forest structures like diameter at breast height (DBH) to obtain accurate AGB, but such details are only available in rich forest inventory data [5]. Both of these techniques are not well-suited for large area AGB spatial distribution measurements when used individually. The National Forest Inventory (NFI) provides highly detailed information about forest vegetation composition and structure, from which plot-based estimates of forest conditions can be calculated [8]. China’s NFI is constructed based on 5-year inventory periods including forest type, area, volume, growth, cutting and changes [9,10]

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Discussion

Conclusion