Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costl...Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costly procedure of forest inventory,it is imperative to generate and use the resulting data optimally.Integrating field inventory information with remote sensing data increases the value of field approaches,such as national forest inventories.This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models(CHM)for forest growing stock estimation.For this purpose,fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis.Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots,the paired t-test results revealed no statistical difference between the two methods.The R2 increased by 0.08 points and the RMSE decreased by 7.7 percentage points(24.8m^(3)·ha^(−1))using fixed-area plots.Since tree height is the most critical variable essential for modeling forest growing stock using aerial images,we also compared the tree heights obtained from CHM to those from the typical field inventory approach.The result shows a high correlation(R^(2)=0.781)between the tree heights extracted from the CHM and those measured in the field.However,the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points(1.04m)when the allometrically derived tree heights were analyzed.Moreover,the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field,but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared.This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations.Overall,the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock.The produced maps reflect the variability of growth conditions and developmental stages of different forest stands.This information is required to characterize the status and changes,e.g.,in forest structure diversity,parameters for volume,and can be used for forest aboveground biomass estimation,which plays an important role in managing and controlling forest resources in mid-term forest management.This is of particular interest to forest managers and forest ecologists.展开更多
Bitterlich sampling is an extensively used technique in worldwide forest inventories. Although it has been proved that estimates of basal area from Bitterlich sampling are mathematically unbiased, its precision for in...Bitterlich sampling is an extensively used technique in worldwide forest inventories. Although it has been proved that estimates of basal area from Bitterlich sampling are mathematically unbiased, its precision for individual forest stands may be fairly poor. An extension of validation efforts to different forest biomes could therefore provide more comprehensive assessment and understanding of the Bitterlich sampling technique. In this study, this technique was quantitatively evaluated by using simulated sparse boreal forests and dense tropical forests from an empirical forest structure model (EFSM). Theoretical estimation of basal areas and practical estimation influenced by the hidden-tree effect were both compared with true basal areas of the simulated forests. The evaluation results indicated that: 1) Bitterlich sampling can yield acceptable accuracy and precision when the count number (CN) of trees was set to 10 for the studied boreal and tropical forests with distinct characteristics, 2) the theoretical estimation of basal area can be improved by increasing the CN values for both forests, and 3) when the hidden-tree effect is encountered, the accuracy for tropical forests will be decreased by increasing the CN values, whereas the accuracy for boreal forests can still be improved. Accordingly, a relatively high CN, at a reasonable cost, is recommended for sparse boreal forests to improve the accuracy of basal area estimation. In contrast, for dense tropical forests, a CN of ten is appropriate to mitigate the hidden-tree effect.展开更多
基金supported by grants provided within the research project»EO4Forest:Use of multi-temporal Sentinel-2 and VHR Pleiades stereo data for sustainable forest monitoring and management«funded by the Austrian Federal Ministry for Climate Action,Environ-ment,Energy,Mobility,Innovation and Technology(BMK)within the FFG Austrian Space Applications Program ASAP 12(grant agreement number 854027).
文摘Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costly procedure of forest inventory,it is imperative to generate and use the resulting data optimally.Integrating field inventory information with remote sensing data increases the value of field approaches,such as national forest inventories.This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models(CHM)for forest growing stock estimation.For this purpose,fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis.Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots,the paired t-test results revealed no statistical difference between the two methods.The R2 increased by 0.08 points and the RMSE decreased by 7.7 percentage points(24.8m^(3)·ha^(−1))using fixed-area plots.Since tree height is the most critical variable essential for modeling forest growing stock using aerial images,we also compared the tree heights obtained from CHM to those from the typical field inventory approach.The result shows a high correlation(R^(2)=0.781)between the tree heights extracted from the CHM and those measured in the field.However,the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points(1.04m)when the allometrically derived tree heights were analyzed.Moreover,the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field,but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared.This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations.Overall,the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock.The produced maps reflect the variability of growth conditions and developmental stages of different forest stands.This information is required to characterize the status and changes,e.g.,in forest structure diversity,parameters for volume,and can be used for forest aboveground biomass estimation,which plays an important role in managing and controlling forest resources in mid-term forest management.This is of particular interest to forest managers and forest ecologists.
文摘Bitterlich sampling is an extensively used technique in worldwide forest inventories. Although it has been proved that estimates of basal area from Bitterlich sampling are mathematically unbiased, its precision for individual forest stands may be fairly poor. An extension of validation efforts to different forest biomes could therefore provide more comprehensive assessment and understanding of the Bitterlich sampling technique. In this study, this technique was quantitatively evaluated by using simulated sparse boreal forests and dense tropical forests from an empirical forest structure model (EFSM). Theoretical estimation of basal areas and practical estimation influenced by the hidden-tree effect were both compared with true basal areas of the simulated forests. The evaluation results indicated that: 1) Bitterlich sampling can yield acceptable accuracy and precision when the count number (CN) of trees was set to 10 for the studied boreal and tropical forests with distinct characteristics, 2) the theoretical estimation of basal area can be improved by increasing the CN values for both forests, and 3) when the hidden-tree effect is encountered, the accuracy for tropical forests will be decreased by increasing the CN values, whereas the accuracy for boreal forests can still be improved. Accordingly, a relatively high CN, at a reasonable cost, is recommended for sparse boreal forests to improve the accuracy of basal area estimation. In contrast, for dense tropical forests, a CN of ten is appropriate to mitigate the hidden-tree effect.
