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Accurate spatial estimation of forest carbon stocks and their spatial uncertainties at local, regional, national, and global scales is a critical step in global carbon cycle modeling and management. This study aimed at enhancing the methods that are currently used in this area by combining plot data from the forest inventory and analysis program of the U.S. Forest Service and free landsat thematic mapper image data. Three mapping methods including linear regression, sequential Gaussian co-simulation, and block co-simulation algorithm were compared with respect to the accuracy of forest carbon stock estimates obtained for a study area in Southern Illinois, USA. The results indicated that although the linear regression resulted in smaller prediction errors than the sequential Gaussian co-simulation and the block co-simulation approaches, it also produced both negative and unreasonably large estimates, which is a serious drawback. Moreover, the sequential Gaussian co-simulation and the block co-simulation produced not only accurate carbon predictions, but also uncertainties for the local estimates. In addition, the block co-simulation approach scaled up both forest carbon stocks and the input uncertainties from finer to coarser spatial resolutions as is required for mapping forest carbon at national and global scales. Thus, the co-simulation and block co-simulation algorithms resolved an important current methodological challenge. [ABSTRACT FROM AUTHOR]

Moso bamboo is an evergreen plant that extensively distributes in subtropical regions. Comparing to other forest types, Moso bamboo forest has some unique characteristics: high growth rate, short harvesting rotation, and on/off-year phenomenon. Plant phenology plays an important role in regulating carbon sequestration of the bamboo forest ecosystem. However, it is a challenge task to capture the phenological features of Moso bamboo forests on a regional scale due to frequent change of canopy structures and lack of high spatiotemporal remotely sensed data. The Vegetation and Environment monitoring on a New Micro-Satellite (VENµS) data with high spatiotemporal resolution provide the potential to examine the seasonal change of Moso bamboo forests. This research employs the VENµS time-series data (from January 2018 to December 2019) to analyse the spectral characteristics of on-year/off-year Moso bamboo forests and other two evergreen forest types (i.e., broadleaf forest and coniferous forest). The optimal spectral ranges for examining the seasonal variation of bamboo forests were determined. Three red-edge-based vegetation indices were reconstructed using the Harmonic analysis of time series (Hants) and compared. Red-edge position index (REPI) was selected to identify different phenological periods of Moso bamboo forests and other evergreen forest types. The results show that the spectral range of 730–920 nm in the VENμS data is sensitive to seasonal variation of Moso bamboo forests. The REPI can more effectively identify the two-year growing cycle of the bamboo forests than other vegetation indices, especially the bamboo shoots period. The start of the growing season of the off-year bamboo forest is approximately 50 to 60 days earlier than on-year bamboo forest. The results provided time-series phenological datasets of on-year and off-year Moso bamboo forests, which is valuable for local governments to conduct better ecological management and decision-making. [ABSTRACT FROM AUTHOR]