Forests in tropical and sub-tropical countries face severe pressures due to a combination of poverty and environment degradation. To be effective, measures to protect these forests must therefore consider both economic and ecological dimensions synergistically. The purpose of this paper was to synthesize our long-term work (1994–2015) on a Ginkgo (Ginkgo biloba L.) agroforestry system and demonstrate its potential for achieving both goals, and discuss its wider application in tropical and sub-tropical countries. The performance of various ecological, economic, and social indicators was compared among five Ginkgo agroforestry systems. Two additional indicators, Harmony Degree (HD) and Development Degree (DD), were also used to show the integrated performance of these indicators. Ginkgo-Wheat-Peanut (G+W+P) and Ginkgo-Rapeseed-Peanut (G+R+P) are the best systems when compared to pure and mixed Ginkgo plantations, or pure agricultural crops. Results demonstrate that it is possible to achieve both economic development and environmental protection through implementation of sustainable agroforestry systems in sub-tropical regions.

Terrestrial laser scanning technology has developed rapidly, and substantial data have been accumulated in dynamic forest monitoring. Point cloud data of standing trees not only provide tree parameters but also show three-dimensional tree structure. Selection of the key parameters from the point cloud data is a prerequisite for volume estimation of standing trees. Our study collected three phases of data over 5 years from Liriodendron chinense plantation forest. A series of the height-related characteristic parameters were extracted from the scanned points of each tree stems, including a proposed new parameter and the height cumulative percentage (Hz % ). The upper diameter accuracy obtained by multi-station scanning is high, and the correlation coefficient with manually measured data is 0.9864. The shape of the upper tree trunk extracted by the point cloud is equivalent to that of the sample trees (height of 10 to 20 m) with points at H25 % and H50 % of the height. These two parameters also show a high correlation with volume. Results show that H25 % can better associated with tree volume, with R2 at 0.951, 0.957, and 0.901 at three stages, respectively. The volume dynamic change calculated by model 2 is linearly correlated with the rate in point cloud restoration, the intercept is −0.081, and the slope is 1.14. Compared with previous researches, the volume model established based on point cloud hierarchical parameters in this study could be used for monitoring the dynamic volume changes in Liriodendron forest. The Hz value extracted from multi-station scanning point cloud data could be used to represent the dynamic forest structure. The results of this study contribute to further development of terrestrial laser scanning-based modeling and estimation methods for individual tree and forest growth, thereby improving the accuracy of forest inventories estimation and providing better tools for decision-making processes.

This study aims at searching for characteristic parameters of tree trunks to establish a volume model and dynamic analysis of volume based on terrestrial laser scanning (TLS). We collected three phases of data over 5 years from an artificial Liriodendron chinense forest. The upper diameters of the tree stump and tree height data were obtained by using the multi-station scanning method. A novel hierarchical TLS point cloud feature named the height cumulative percentage (Hz%) was designed. The shape of the upper tree trunk extracted by the point cloud was equivalent to that of the analytical tree with inflection points at 25% and 50% of the height, and the dynamic volume change of the model, which was established by hierarchical features, was highly related to the volume change of the actual point cloud extraction. The obtained results reflected the fact that the Hz% value provided by multi-station scanning was closely related to the characteristic stumpage parameters and could be used to invert the dynamic forest structure. The volume model established based on point cloud hierarchical parameters in this study could be used to monitor the dynamic changes of forest volume and to provide a new reference for applying TLS point clouds for the dynamic monitoring of forest resources.

BackgroundThe rapid development of active remote sensing laser scanning technology has led to the accumulation of substantial data on long-term forest changes. Accurate selection of the key parameters from larger amounts of cloud data is a prerequisite for volume estimates of standing trees. This study collected three phases of data over 5 years from Liriodendron chinense plantation forest. A series of the height-related characteristic parameters was extracted from the scanned points of each tree stem, which includes a novel feature of the height cumulative percentage (Hz%) proposed by us. Meanwhile, taking the manually measurements directly from the terrestrial laser scanning (TLS) data as the ground truth, the performance of various models combined with the characteristic parameters on the prediction of the wood volume of each tree was evaluated for the purpose of determination of the optimal parameters and prediction models suitable for the tree species of Liriodendron chinense. ResultsThe shape of the upper tree trunk extracted by the point cloud is equivalent to that of the analytical tree with inflection points at 25% and 50% of the height. Among the correlations between the hierarchical features and volumes, the parameters with the highest correlations are H25 and H50. The hierarchical parameters were selected for volume modeling. H25 and diameter at breast height (DBH) were used for all three phases, for which the fitted R² reached 0.951, 0.957 and 0.901. The modeled dynamic volume change was highly correlated with the actual point cloud-extracted volume change. In the linear relation, the intercept is -0.081, and the slope is 1.14. ConclusionsThe H_z% value provided by multi-station scanning was closely related to the characteristic stumpage parameters and could be used to invert the dynamic forest structure. The volume model based on point cloud hierarchical parameters could be used to monitor the dynamic changes in forest volume and provide an updated reference for applying TLS point clouds for the dynamic monitoring of forest growth information.