Optical remote sensing data have been considered to display signal saturation phenomena in regions of high aboveground biomass (AGB) and multi-storied forest canopies. However, some recent studies using texture indices derived from optical remote sensing data via the Fourier-based textural ordination (FOTO) approach have provided promising results without saturation problems for some tropical forests, which tend to underestimate AGB predictions. This study was applied to the temperate mixed forest of the Liangshui National Nature Reserve in Northeastern China and demonstrated the capability of FOTO texture indices to obtain a higher prediction quality of forest AGB. Based on high spatial resolution aerial photos (1.0 m spatial resolution) acquired in September 2009, the relationship between FOTO texture indices and field-derived biomass measurements was calibrated using a support vector regression (SVR) algorithm. Ten-fold cross-validation was used to construct a robust prediction model, which avoided the over-fitting problem. By further comparison the performance of the model estimates for greater coverage, the predicted results were compared with a reference biomass map derived from LiDAR metrics. This study showed that the FOTO indices accounted for 88.3% of the variance in ground-based AGB; the root mean square error (RMSE) was 34.35 t/ha, and RMSE normalized by the mean value of the estimates was 22.31%. This novel texture-based method has great potential for forest AGB estimation in other temperate regions.

The increasing availability of freely accessible remote sensing data has been crucial for improved global monitoring studies. Multisource image combination is a common approach for overcoming a major limitation associated with single-sensor data sources, which cannot provide adequate observations to fill data gaps arising from cloud contamination, shadows, and other atmospheric effects. In particular, image compositing is often used to generate clear view images over a large area. For example, the best available pixel (BAP) method has been proposed to construct clear view and spatially contiguous composites based on pixel-level quality rules. For any location with a bad observation, this method searches observations acquired in other dates and uses the one with the highest score to replace the contaminated observation. This, however, can lead to artificially large discontinuities along the edge of a filled area, which is typically caused by large phenological differences among the observations considered. To mitigate this issue, we developed a multifactor weighting (MFW) method for constructing clear view composites with a higher level of spatial continuity and radiometric consistency than those produced using the BAP method. Assessments over 4 study sites selected from different climate zones in China demonstrated that the composites produced using the MFW method were more consistent with reference images than those generated using the BAP method. Spectral agreements between MFW composites and the reference ( R = 0.78 to 0.95) were generally higher than the agreements between BAP composites and the reference ( R = 0.65 to 0.93). These results demonstrated that the proposed MFW method can provide a promising strategy for constructing clear view, seamless, and radiometrically consistent image composites for large-scale applications.

Accurately assessing biomass of temperate forests from remote sensing observation still remains a great challenge. High resolution optical imageries become more and more widely available from many kinds of space-borne and airborne sensors at reasonable price. These high resolution imageries contain ample texture indices which have already been used in remote sensing classification and forest structure parameter estimation. Fourier-based Textural Ordination (FOTO) method is one of recent technique developed texture indices, which combines Fast Fourier transform (FFT) and principle component analysis (PCA) then derives texture indices. These FOTO texture indices contain canopy grain information in high resolution optical image have successfully predicted forest biomass in some tropical area. Our research performed FOTO method in LiangShui National Nature Reserve, where is a typical representative of the broadleaf mixed coniferous temperate forests, located in Heilongjiang Province, northeast of China. The airborne optical image acquired in September 22th, 2009 were used. Our research showed that texture indices derived based on FOTO method had great potential for forest biomass estimation in temperate zone and the inversion results proved to be accurate predictions.