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Eucalyptus plantations play an important role in the timber supply and global warming mitigation around the world. Forest age is a critical factor for evaluating and modeling forest structure (e.g., diameter at breast height (DBH), height (H), aboveground carbon stocks (ACS)) and their dynamics. Recently, the spatial distribution of forest age at different scales based on time series remote sensing data has been widely investigated. However, it is unclear whether such data can effectively support the simulation and assessment of forest structure, especially in fast-growing plantation forests. In this study, the physiological principles in predicting growth (3-PG) model was firstly optimized and calibrated using survey and UAV lidar data at the sample plot (SP) scale, and was then applied at the forest sub-compartment (FSC) scale by designing different simulation scenarios driven by different forest age data sources and adjustments. The sensitivity of the simulated forest structure parameters to forest age was assessed at the SP and FSC levels. The results show that both the survey forest age data and the remote-sensing-derived forest age data could accurately estimate the DBH, H, and ACS of eucalyptus plantations with the coefficients of determination (R2) ranging from 0.87 to 0.94, and the relative root mean square error (RRMSE) below 20% at SP level. At the FSC level, the simulation results based on remotely sensed forest age data are significantly better than FSC forest age data from surveys by forestry bureaus, with R2 of ACS 0.7, RMSE 9.12 Mg/ha, and RRMSE 28.24%. The results of the sensitivity analysis show that the DBH, H, and ACS show different degrees of variation under different adjusted forest ages at SP and FSC level. The maximum difference in ACS is 82.91% at the SP scale if the forest age decreases 12 months and 41.23% at the FSC scale if the forest age increases 12 months. This study provides an important reference for future studies using forest age data obtained by remote sensing to drive the forest carbon model in a large spatial scale. [ABSTRACT FROM AUTHOR]

Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land cover and vegetation growth of coastal areas. However, rarely has research investigated these frequently changed coastal areas. In this study, Landsat Normalized Difference Vegetation Index time series (1984-2016) data and the Breaks For Additive Seasonal and Trend algorithm were used to detect the intensity and dates of abrupt changes in a typical coastal area-Hangzhou Bay, China. The prior and posterior land cover categories of each change were classified using phenology information through a Random Forest model. The impacts of land cover change on vegetation greenness trends of the inland and reclaimed areas were analyzed through distinguishing gradual and abrupt changes. The results showed that the intensity and date of land cover change were detected successfully with overall accuracies of 88.7% and 86.1%, respectively. The continuous land cover dynamics were retrieved accurately with an overall accuracy of 91.0% for ten land cover classifications. Coastal reclamation did not alleviate local cropland occupation, but prompted the vegetation greenness of the reclaimed area. Most of the inland area showed a browning trend. The main contributors to the greenness and browning trends were also quantified. These findings will help the natural resource management community generate better understanding of coastal reclamation and make better management decisions. [ABSTRACT FROM AUTHOR]

• Historical disturbances were detected based on time series remote sensing data. • Negative abrupt vegetation change accounted for 78.9% of the disturbed area. • The gradual vegetation change of undisturbed area mainly showed greenness trend. • Spatial clusters of total vegetation change accounted for 40.9% of the study area. • Spatial patterns of vegetation change identified key areas to reduce fragmentation. Measures to characterize the spatial patterns of vegetation change can provide important information for understanding and assessing habitat fragmentation and its causes. This study developed a new framework to assess fragmentation by integrating the spatial patterns of vegetation coverage and its change across the giant panda habitat ecosystem of China. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, we detected the historical vegetation disturbances and used "abrupt," "gradual," and "total" to characterize the vegetation change processes from 2000 to 2017. The spatial patterns of vegetation coverage and its change were described by spatial clusters and outliers, and the framework was established to find the potential area for reduction of habitat fragmentation. The results indicated that 1.9% of the study area experienced disturbances during 2000–2017. Most of the disturbed area (78.9%) experienced negative abrupt vegetation change, and the undisturbed area mainly showed an increase in vegetation (85.8%). The spatial clusters of high and low total changes accounted for 22.7% and 18.2% of the study area, respectively. The high change clusters were primarily along the northern and southeastern borders of the giant panda habitat, while the low change clusters spread over the central and southern areas. Considering the spatial patterns of both vegetation coverage and its change, 12.7% of the study area, located mainly in southern Minshan and eastern Qionglaishan, needs careful management to reduce habitat fragmentation. This study provides new insight to understand habitat fragmentation in terms of spatial and temporal characteristics of vegetation status and change, and will benefit future habitat management efforts. [ABSTRACT FROM AUTHOR]

Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of different datasets (e.g., Landsat Thematic Mapper (TM), ALOS PALSAR L-band data, and their combinations) and modeling algorithms (e.g., artificial neural network (ANN), support vector regression (SVR), Random Forest (RF), k-nearest neighbor (kNN), and linear regression (LR)) for AGB estimation in a subtropical region under non-stratification and stratification of forest types. The results show the following: (1) Landsat TM imagery provides more accurate AGB estimates (root mean squared error (RMSE) values in 27.7–29.3 Mg/ha) than ALOS PALSAR (RMSE values in 30.3–33.7 Mg/ha). The combination of TM and PALSAR data has similar performance for ANN and SVR, worse performance for RF and KNN, and slightly improved performance for LR. (2) Overestimation for small AGB values and underestimation for large AGB values are major problems when using the optical (e.g., Landsat) or radar (e.g., ALOS PALSAR) data. (3) LR is still an important tool for AGB modeling, especially for the AGB range of 40–120 Mg/ha. Machine learning algorithms have limited effects on improving AGB estimation overall, but ANN can improve AGB modeling when AGB values are greater than 120 Mg/ha. (4) Forest type and AGB range are important factors that influence AGB modeling performance. (5) Stratification based on forest types improved AGB estimation, especially when AGB was greater than 160 Mg/ha, using the LR approach. This research provides new insight for remote sensing-based AGB modeling for the subtropical forest ecosystem through a comprehensive analysis of different source data, modeling algorithms, and forest types. It is critical to develop an optimal AGB modeling procedure, including the collection of a sufficient number of sample plots, extraction of suitable variables and modeling algorithms, and evaluation of the AGB estimates. [ABSTRACT FROM AUTHOR]