Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light...Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.展开更多
Updating eucalyptus carbon stock data in a timely manner is essential for better understanding and quantifying its effects on ecological and hydrological processes.At present,there are no suitable methods to accuratel...Updating eucalyptus carbon stock data in a timely manner is essential for better understanding and quantifying its effects on ecological and hydrological processes.At present,there are no suitable methods to accurately estimate the eucalyptus carbon stock in a large area.This research aimed to explore the transferability of the eucalyptus carbon stock estimation model at temporal and spatial scales and assess modeling performance through the strategy of combining sample plots,airborne LiDAR and Landsat time series data in subtropical regions of China.Specifically,eucalyptus carbon stock estimates in typical sites were obtained by applying the developed models with the combination of airborne LiDAR and field measurement data;the eucalyptus plantation ages were estimated using the random localization segmentation approach from Landsat time series data;and regional models were developed by linking LiDAR-derived eucalyptus carbon stock and vegetation age(e.g.,months or years).To examine the models’robustness,the developed models at the regional scale were transferred to estimate carbon stocks at the spatial and temporal scales,and the modeling results were evaluated using validation samples accordingly.The results showed that carbon stock can be successfully estimated using the age-based models(both age variables in months and years as predictor variables),but the month-based models produced better estimates with a root mean square error(RMSE)of 6.51 t⋅ha1 for Yunxiao County,Fujian Province,and 6.33 t⋅ha1 for Gaofeng Forest Farm,Guangxi Zhuang Autonomous Region.Particularly,the month-based models were superior for estimating the carbon stocks of young eucalyptus plantations of less than two years.The model transferability analyses showed that the month-based models had higher transferability than the year-based models at the temporal scale,indicating their possibility for analysis of carbon stock change.However,both the month-based and year-based models expressed relatively poor transferability at a spatial scale.This study provides new insights for cost-effective monitoring of carbon stock change in intensively managed plantation forests.展开更多
Forest canopy height is one of the important forest parameters for accurately assessing forest biomass or carbon sequestration. ICESat-2 ATLAS provides the potential for retrieval of forest canopy height at global or ...Forest canopy height is one of the important forest parameters for accurately assessing forest biomass or carbon sequestration. ICESat-2 ATLAS provides the potential for retrieval of forest canopy height at global or regional scale, but the current canopy height product (ATL08) has coarse resolution and high uncertainty compared to airborne LiDAR-derived canopy height (hereafter ALCH) in mountainous regions, and is not ready for such applications as biomass modeling at finer scale. The objective of this research was to explore the approach to accurately retrieve canopy height from ATLAS data by incorporating an airborne-derived digital terrain model(DTM) and a data-filtering strategy. By linking ATLAS ATL03 with ATL08 products, the geospatial locations,types, and (absolute) heights of photons were obtained, and canopy heights at different lengths (from 20 to 200 m at 20-m intervals) of segments along a track were computed with the aid of airborne LiDAR DTM. Based on the relationship between the numbers of canopy photons within the segments and accuracy of ATLAS mean canopy height compared to ALCH, a filtering method for excluding a certain portion of unreliable segments was proposed.This method was further applied to different ATLAS ground tracks for retrieval of canopy heights and the results were evaluated using corresponding ALCH. The results show that the incorporation of high-precision DTM and ATLAS products can considerably improve the retrieval accuracy of forest canopy height in mountainous regions.Using the proposed filtering approach, the correlation coefficients (r) between ATLAS canopy height and corresponding ALCH were 0.61–0.91, 0.65–0.92, 0.68–0.94 for segment lengths of 20, 60, and 100 m, respectively;RMSE were 1.90–4.35, 1.55–3.63, and 1.34–3.23 m for the same segment lengths. The results indicate the necessity of using high-precision DTM and using the proposed filtering method to retrieve accurate canopy height from ICESat-2 ATLAS in mountainous regions with dense forest cover and complex terrain conditions.展开更多
基金funded by the Guangxi Natural Science Fund for Innovation Research Team (No. 2019JJF50001)the Natural Science Foundation of Fujian Province,China (No. 2019 J01396)+1 种基金the Special Fund for Guangxi Innovation and Driving Development (Major science and technology projects)(No. 2018AA13005)the Youth Innovation Promotion Association CAS (2019130)。
文摘Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.
基金supported by the National Key R&D Program of China(Grant No.2021YFD2200400102)Fujian Provincial Science and Technology Department(Grant No.2021R1002008).
文摘Updating eucalyptus carbon stock data in a timely manner is essential for better understanding and quantifying its effects on ecological and hydrological processes.At present,there are no suitable methods to accurately estimate the eucalyptus carbon stock in a large area.This research aimed to explore the transferability of the eucalyptus carbon stock estimation model at temporal and spatial scales and assess modeling performance through the strategy of combining sample plots,airborne LiDAR and Landsat time series data in subtropical regions of China.Specifically,eucalyptus carbon stock estimates in typical sites were obtained by applying the developed models with the combination of airborne LiDAR and field measurement data;the eucalyptus plantation ages were estimated using the random localization segmentation approach from Landsat time series data;and regional models were developed by linking LiDAR-derived eucalyptus carbon stock and vegetation age(e.g.,months or years).To examine the models’robustness,the developed models at the regional scale were transferred to estimate carbon stocks at the spatial and temporal scales,and the modeling results were evaluated using validation samples accordingly.The results showed that carbon stock can be successfully estimated using the age-based models(both age variables in months and years as predictor variables),but the month-based models produced better estimates with a root mean square error(RMSE)of 6.51 t⋅ha1 for Yunxiao County,Fujian Province,and 6.33 t⋅ha1 for Gaofeng Forest Farm,Guangxi Zhuang Autonomous Region.Particularly,the month-based models were superior for estimating the carbon stocks of young eucalyptus plantations of less than two years.The model transferability analyses showed that the month-based models had higher transferability than the year-based models at the temporal scale,indicating their possibility for analysis of carbon stock change.However,both the month-based and year-based models expressed relatively poor transferability at a spatial scale.This study provides new insights for cost-effective monitoring of carbon stock change in intensively managed plantation forests.
基金financially supported by the National Natural Science Foundation of China (No. 32171787)
文摘Forest canopy height is one of the important forest parameters for accurately assessing forest biomass or carbon sequestration. ICESat-2 ATLAS provides the potential for retrieval of forest canopy height at global or regional scale, but the current canopy height product (ATL08) has coarse resolution and high uncertainty compared to airborne LiDAR-derived canopy height (hereafter ALCH) in mountainous regions, and is not ready for such applications as biomass modeling at finer scale. The objective of this research was to explore the approach to accurately retrieve canopy height from ATLAS data by incorporating an airborne-derived digital terrain model(DTM) and a data-filtering strategy. By linking ATLAS ATL03 with ATL08 products, the geospatial locations,types, and (absolute) heights of photons were obtained, and canopy heights at different lengths (from 20 to 200 m at 20-m intervals) of segments along a track were computed with the aid of airborne LiDAR DTM. Based on the relationship between the numbers of canopy photons within the segments and accuracy of ATLAS mean canopy height compared to ALCH, a filtering method for excluding a certain portion of unreliable segments was proposed.This method was further applied to different ATLAS ground tracks for retrieval of canopy heights and the results were evaluated using corresponding ALCH. The results show that the incorporation of high-precision DTM and ATLAS products can considerably improve the retrieval accuracy of forest canopy height in mountainous regions.Using the proposed filtering approach, the correlation coefficients (r) between ATLAS canopy height and corresponding ALCH were 0.61–0.91, 0.65–0.92, 0.68–0.94 for segment lengths of 20, 60, and 100 m, respectively;RMSE were 1.90–4.35, 1.55–3.63, and 1.34–3.23 m for the same segment lengths. The results indicate the necessity of using high-precision DTM and using the proposed filtering method to retrieve accurate canopy height from ICESat-2 ATLAS in mountainous regions with dense forest cover and complex terrain conditions.
