a b s t r a c tThis work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures.The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m 2 and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m 2 .More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent.
Post-stratified model-assisted (MA) and hybrid (HY) estimators are used with repeated airborne laser scanning (ALS) strip sampling and national forest inventory field data for stratum-wise and overall estimation of aboveground biomass (AGB) stock and change. The study area covered the southern portion of the Hedmark County in Norway. Both MA and HY estimation substantially reduced the uncertainty in AGB change when compared with estimation using the field survey only. Relative efficiencies (relative variance) of 4.15 (MA) and 3.36 (HY) for overall estimates were found. The results suggest the MA estimator for single-time estimation and the HY as more appropriate for change estimation by cover class. With the HY estimator, a nested post-stratification scheme is demonstrated, combining cover classes with change classes, which enables detailed reporting for change according to cause within each cover class, and has the potential to improve the estimation precision. Finally, parametric bootstrapping is demonstrated as an empirical alternative to estimate the model-error component in the HY estimator. The model error estimated with parametric bootstrapping converged to the analytically determined value of the HY estimator within 1000 bootstrap samples.
The use of heuristic techniques in forest planning has been promoted by the need to solve complex problems that cannot be solved using mixed integer programming. We proved that for merchantability standards ensuring the perfect bin-packing theorem (PBPT), the maximum volume that can be harvested annually equals the sum of the maximum MAI of the stands. The method accommodates optimality criteria at the stand level, regarded as maximum MAI, and at the forest level, regarded as maximum annual allowable cut. We scheduled the harvesting by adjusting the first fit decreasing algorithm (FFD) to the PBPT conditions. When PBPT conditions were not met, we developed a mixed integer programming solution to adjust the merchantability standards of the stands to the distributional requirements of the PBPT, an adjustment that ensured the optimal performance of the FFD. The adjusted FFD was compared with linear programming (LP) and simulated annealing (SA) using two harvesting ages (i.e., one based on MAI maximization and one determined as the minimal age) and the same set of spatial temporal constraints for three areas in north-eastern British Columbia, Canada. We found that the adjusted FFD performed 100 times faster than SA and for annual allowable cut (AAC) supplied results that were more homogenous and at least 10% greater than the AAC supplied by SA. Furthermore, the adjusted FFD seemed to be relatively insensitive to spatial constraints (i.e., adjacency), while SA displayed a 70% reduction in AAC in response to an increase in adjacency delay from 1 year to 20 years. The results suggest that both adjusted FFD and SA are impacted by the selection of the harvesting age, but the adjusted FFD could still outperform SA.
Stimulating climate change mitigation actions in the forest sector requires methods to quantify the biomass stocks and changes at different geographical levels. Often, differences in data and estimation methods that are available at each level cause inconsistencies in forest parameters estimated at different levels. We propose a method to align model-based and model-assisted estimators to ensure cross-sectional and time series consistency of stock and change estimates of above-ground biomass (AGB). The method adjusts estimates within their confidence intervals using heuristic optimization to minimize the estimation errors. The method is evaluated under simulated sampling in a case study representing a forested area of approximately 50 km2 in southeastern Norway. The area is divided into 93 forest properties encompassing 3324 forest stands. The artificial forest population is generated for two time points using wall-to-wall airborne laser scanning (ALS) data acquired in 2001 and 2016, as well as field surveys conducted within a similar timeframe. The adjusted AGB stock and change estimators at different levels of aggregation are compared to the original unadjusted estimators in terms of bias and RMSE. The results show that the adjusted estimators do not introduce bias, and the increase in RMSE is small for the forest stand-level estimators, and even decreasing for the forest property-level estimators. The method can easily be adapted to complex systems of estimators that need to be consistent.
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