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In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is an important soil property relating to soil formation, structure, and water-holding capacity. Remote sensing has been used for predicting SOC but remains a challenge due to the complex and indirect relationship between SOC and remote sensing variables. In this research we explored the approaches of predicting SOC distribution in a hickory plantation region using random forest (RF) and geographically weighted regression (GWR) based on Landsat and ancillary data, analyzed SOC spatial distribution and dynamic change between 2008 and 2013 through a thresholding approach, and examined major factors that resulted in SOC degradation in the young and mature hickory plantations using a logistic regression. The results showed that RF outperformed GWR in the prediction of SOC and provided stable and reliable SOC predictions with root mean squared errors of 4.6 g kg −1 in 2008 and 4.4 g kg −1 in 2013. A large area of hickory plantation was experiencing SOC decrease. The analysis of major factors causing SOC degradation indicated that steep slope and high proportion of silt component in soil resulted in SOC decrease in the young hickory plantations, and high elevation, high proportion of silt component in the soil, and the increase of soil fraction in the ground cover led to SOC decrease in the mature hickory plantations. This research provides valuable approaches to spatially predict SOC and identify major factors driving SOC degradation, which will be useful for adopting better measures to improve management of hickory plantations. [ABSTRACT FROM AUTHOR]