Abstract
To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation.
Highlights
As an important characteristic of forest ecosystems, forest aboveground biomass (AGB) provides a basis for ecosystem and forestry research; AGB estimation further provides data critical to estimating the forest carbon sink [1,2]
Note: CFF, coniferous forest; BLF, broadleaf forest; MXF, mixed forest; bi_M, original band I of month M; DVI_Jan, difference vegetation index of January; SR_M, simple ratio of month M; PCA3_Otc, band 3 of principal component analysis in October; bi_XYjM, textural image developed from spectral band i with a window size of jxj pixels of month M using texture entropy (EN), angular second moment (SEM), variance (VA), correlation (COR), contrast (CON), mean (ME), dissimilarity (DI) or homogeneity (HO)
Study plots were classified according to forest types (CFF, BLF, MXF and total vegetation) and stepwise regression was used to select appropriate variables and effectively model AGB based on the seasonal images
Summary
As an important characteristic of forest ecosystems, forest aboveground biomass (AGB) provides a basis for ecosystem and forestry research; AGB estimation further provides data critical to estimating the forest carbon sink [1,2]. High precision AGB field measurement methodologies have involved extensive field surveys [3] These methods are time-consuming, laborious and destructive; in addition, they cannot be used to analyze the spatial distribution and dynamic change of AGB on a large scale [4]. Remote sensing data can be divided into two categories: Passive remote sensing (optical sensors, thermal and microwave) and active remote sensing (radar and light detection and ranging (LiDAR)) [5,6,7] Optical sensors such as Landsat, Systeme Probatoire d’Observation de la Terre (SPOT), the moderate-resolution imaging spectroradiometer (MODIS), QuickBird and the Advanced Very High-Resolution Radiometer (AVHRR) have been widely used for AGB estimation because of their coverage, repetitive observation and cost-effectiveness [6,8].
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