Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph

Digital aerial photograph(DAP)data is processed based on Structure from Motion(Sf M)algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging(LiDAR)and digital orthophoto mosaic(DOM)similar to optical remote sensing image.In this study,we obtained highresolution images of mature forests of Chinese fir by unmanned aerial vehicle(UAV)flying through crossroute flight,and then reconstructed the threedimensional point clouds in the UAV aerial area by SfM technique.The point cloud segmentation(PCS)algorithm was used for the individual tree segmentation,and the F-score of the three sample plots were 0.91,0.94,and 0.94,respectively.Individual tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly segmented.The relative root mean squared error(rRMSE)values of random forest(RF),bagged tree(BT)and support vector regression(SVR)were 34.48%,35.74%and 40.93%,respectively.Our study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

Effects of site productivity on individual tree maximum basal area growth rates of Eucalyptus pilularis in subtropical Australia

Inventory data were available from 96 plots of even-aged,monoculture,tall-open forests of Eucalyptus pilularis Smith,aged 2-63 years,growing in sub-tropical regions along the east coast of Australia.A model was developed relating the maximum possible stem basal area growth rate of individual trees to their stem basal area.For any tree size,this maximum increased as site productivity increased.However,the size at which this maximum occurred decreased as productivity increased.Much research has shown that,at any stand age,trees of a particular stem basal area are taller on more productive sites than on less productive ones.Taller trees incur greater respiratory costs to ensure maintenance of the photo synthetic capacity of their canopies;this reduces their growth rates.It was concluded that trees with larger basal areas will have the maximum possible growth rate on a less productive site,whilst trees with smaller basal areas will have the maximum possible on a more productive site.The model developed may constitute the first stage of a complete individual tree growth model system to predict wood yields from these forests.

An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests： a case study using the Nova Scotia permanent sample plot network

Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.

Development and evaluation of an individual tree growth and yield model for the mixed species forest of the Adirondacks Region of New York, USA

Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.

Automated tree detection and crown delineation using airborne laser scanner data in heterogeneous East-Central Europe forest with different species mix

Identifying tree locations is a basic step in the derivation of other tree parameters using remote sensing techniques, particularly when using airborne laser scanning. There are several techniques for identifying tree positions. In this paper, we present a raster-based method for determining tree position and delineating crown coverage. We collected data from nine research plots that supported different mixes of species. We applied a raster-based method to raster layers with six different spatial resolutions and used terrestrial measurement data as reference data. Tree identification at a spatial resolution of 1.5 m was demonstrated to be the most accurate, with an average identification ratio (IR) of 95% and average detection ratio of 68% being observed. At a higher spatial resolution of 0.5 m, IR was overestimated by more than 600%. At a lower spatial resolution of 3 m, IR was underestimated at less than 44% of terrestrial measurements. The inventory process was timed to enable evaluation of the time efficiency of automatic methods.

An automatic extraction method for individual tree crowns based on self-adaptive mutual information and tile computing

Forest data acquisition,which is of crucial importance for modeling global biogeochemical cycles and climate,makes a contribution to building the ecological Digital Earth(DE).Due to the complex calculations and large volumes of data associated with high-resolution images of large areas,accurate and effective extraction of individual tree crowns remains challenging.In this study,two GeoEye-1 panchromatic images of Beihai and Ningbo in China with areas of 5 and 25 km2,respectively,were used as experimental data to establish a novel method for the automatic extraction of individual tree crowns based on a self-adaptive mutual information(SMI)algorithm and tile computing technology(SMI-TCT).To evaluate the performance of the algorithm,four commonly used algorithms were also applied to extract the individual tree crowns.The overall accuracy of the proposed method for the two experimental areas was superior to that of the four other algorithms,with maximum extraction accuracies of 85.7%and 63.8%.Moreover,the results also indicated that the novel method was suitable for individual tree crowns extraction in sizeable areas because of the multithread parallel computing technology.

Net Primary Productivity and Management Potential of Artificial Pinus tabulaeformis Forest in Shanxi Province

The dynamic variation of net primary productivity of artificial Pinus tabulaeformis forest was studied in Shanxi Province,and potential productivity of artificial forest was predicted to provide reference for improving quality of regional forest stand. The regression equation was established by using the stratification and harvesting method with the relative growth model. Cumulative method and Thornthwaite Memorial model was used to estimate the actual and potential productivity of the forest. The productivity of P. tabulaeformis forest increased with the increase of age and started decrease with the mature period. The actual productivity of P. tabulaeformis forest was 4. 462 t/( ha·year); the contribution rate of trees was 72. 17% of the total productivity,and with the increase of age,the total biomass increased but productivity decreased at late near-mature forest; the contribution rate of herb layer was 21. 16% in the young forest stage,and then decreased gradually. On the contrary,the contribution rate of shrub layer increased gradually,and the contribution rate of the grassland was more than that of the herb layer,so as the key period of structural management; the average potential productivity of forest was 8. 422 t/( ha·year),and the potential space of P. tabulaeformis was at least 32% in Shanxi Province. In conclusion,the potential space of productivity of P. tabulaeformis was at least 32%,and the primary limiting factor of P. tabulaeformis forest productivity in Shanxi Province was rainfall.

A tree counting algorithm for precision agriculture tasks

This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data.Depending on site-specific pruning practices,the morphologic characteristics of tree crowns may generate one or more brightness peaks(tree top)on the imagery.To optimize tree counting and to minimize typical background noises from orchards(i.e.bare soil,weeds,and man-made objects),a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands(asymmetrical smoothing filter,local minimum filter,mask layer,and spatial aggregation operator).System perfor-mance was evaluated through objective criteria,showing consistent results in fast capturing tree position for precision agriculture tasks.