Marco A. Contreras

Applying ant colony optimization metaheuristic to solve forest transportation planning problems with side constraints

Forest transportation planning problems (FTPP) have evolved from considering only the financial aspects of timber management to more holistic problems that also consider the environmental impacts of roads. These additional re- quirements have introduced side constraints, making FTPP larger and more complex. Mixed-integer programming (MIP) has been used to solve FTPP, but its application has been limited by the difficulty of solving large, real-world problems within a reasonable time. To overcome this limitation of MIP, we applied the ant colony optimization (ACO) metaheuristic to develop an ACO-based heuristic algorithm that efficiently solves large and complex forest transportation problems with side constraints. Three hypothetical FTPP were created to test the performance of the ACO algorithm. The environmental impact of forest roads represented by sediment yields was incorporated into the economic analysis of roads as a side con- straint. Four different levels of sediment constraints were analyzed for each problem. The solutions from the ACO algo- rithm were compared with those obtained from a commercially available MIP solver. The ACO solutions were equal to or slightly worse than the MIP solution, but the ACO algorithm took only a fraction of the computation time that was re- quired by the MIP solver.

A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data

Abstract This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown information such as steepness and height on-the-fly to delineate crown boundaries, and most importantly, does not require a priori assumptions of crown shape and size. The approach segments trees iteratively starting from the tallest within a given area to the smallest until all trees have been segmented. To evaluate its performance, the approach was applied to the University of Kentucky Robinson Forest, a deciduous closed-canopy forest with complex terrain and vegetation conditions. The approach identified 94% of dominant and co-dominant trees with a false detection rate of 13%. About 62% of intermediate, overtopped, and dead trees were also detected with a false detection rate of 15%. The overall segmentation accuracy was 77%. Correlations of the segmentation scores of the proposed approach with local terrain and stand metrics was not significant, which is likely an indication of the robustness of the approach as results are not sensitive to the differences in terrain and stand structures.

Forest Understory Trees Can Be Segmented Accurately Within Sufficiently Dense Airborne Laser Scanning Point Clouds

Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis.

Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds

Abstract Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies. We applied the proposed approach to the University of Kentucky Robinson Forest – a natural deciduous forest with complex and highly variable terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting understory trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented understory trees (increased from 1% to 16%), while barely affecting the overall segmentation quality of overstory trees. Results of vertical stratification of the canopy showed that the point density of understory canopy layers were suboptimal for performing a reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds would allow more improvements in segmenting understory trees. As shown by inspecting correlations of the results with forest structure, the segmentation approach is applicable to a variety of forest types.

A scalable approach for tree segmentation within small-footprint airborne LiDAR data

This paper presents a distributed approach that scales up to segment tree crowns within a LiDAR point cloud representing an arbitrarily large forested area. The approach uses a single-processor tree segmentation algorithm as a building block in order to process the data delivered in the shape of tiles in parallel. The distributed processing is performed in a master-slave manner, in which the master maintains the global map of the tiles and coordinates the slaves that segment tree crowns within and across the boundaries of the tiles. A minimal bias was introduced to the number of detected trees because of trees lying across the tile boundaries, which was quantified and adjusted for. Theoretical and experimental analyses of the runtime of the approach revealed a near linear speedup. The estimated number of trees categorized by crown class and the associated error margins as well as the height distribution of the detected trees aligned well with field estimations, verifying that the distributed approach works correctly. The approach enables providing information of individual tree locations and point cloud segments for a forest-level area in a timely manner, which can be used to create detailed remotely sensed forest inventories. Although the approach was presented for tree segmentation within LiDAR point clouds, the idea can also be generalized to scale up processing other big spatial datasets. A scalable distributed approach for tree segmentation was developed and analyzed.~2 million trees in a 7440 ha forest was segmented in 2.5h using 192 cores.2% false positive trees were identified as a result of the distributed run.Estimated number of trees and the tree height distribution aligned with field data.The approach can be used to scale up processing other big spatial data.

Automatically configuring ACO using multilevel ParamILS to solve transportation planning problems with underlying weighted networks

Abstract Configuring parameter settings for ant colony optimisation (ACO) based algorithms is a challenging and time consuming task, because it usually requires evaluating a large number of parameter combinations to find the most appropriate setting. In this study, a multilevel ParamILS (MParamILS) technique, that combines a graph coarsening method and the ParamILS framework, has been developed for configuring ACO algorithms to solve transportation planning problems with underlying weighted networks. The essential idea is to first use the graph coarsening method to recursively produce a set of increasingly coarser level problems from the original problem, and then apply ParamILS sequentially to the coarser level problems to select high-quality settings from a parameter combination domain. From the coarsest level to the finest (original) level problem, the parameter domain is refined by removing the low-quality settings identified by ParamILS. The size of the combination domain continues to decrease, resulting in fewer number of parameter combinations evaluated at finer level problems, hence the computing time is reduced. The performance of MParamILS was compared with ParamILS. Experimental results showed that MParamILS matches ParamILS in solution quality with significant reduction in computing time for all test cases.

A multilevel ACO approach for solving forest transportation planning problems with environmental constraints

Abstract This paper presents a multilevel ant colony optimization (MLACO) approach to solve constrained forest transportation planning problems (CFTPPs). A graph coarsening technique is used to coarsen a network representing the problem into a set of increasingly coarser level problems. Then, a customized ant colony optimization (ACO) algorithm is designed to solve the CFTPP from coarser to finer level problems. The parameters of the ACO algorithm are automatically configured by evaluating a parameter combination domain through each level of the problem. The solution obtained by the ACO for the coarser level problems is projected into finer level problem components, which are used to help the ACO search for finer level solutions. The MLACO was tested on 20 CFTPPs and solutions were compared to those obtained from other approaches including a mixed integer programming (MIP) solver, a parameter iterative local search (ParamILS) method, and an exhaustive ACO parameter search method. Experimental results showed that the MLACO approach was able to match solution qualities and reduce computing time significantly compared to the tested approaches.

Multi-camera surveillance systems for time and motion studies of timber harvesting equipment

A multi-camera security system was tested to conduct a time and motion study.The system was installed on a John Deere 540G cable skidder.Time stamped video footage was inspected to obtain time consumption of work tasks.Accurate calculation of total cycle times and delays as well as productivity metrics.The system can be adopted as a reliable approach to conduct time and motion studies. We evaluated the feasibility of using a multi-camera security system to conduct time and motion studies. It was installed on a John Deere 540G cable skidder and connected to the skidders battery for continuous recording with minimal effort and intervention. After recording the skidders work for eleven experimental skidding cycles, time stamped video footage was visually inspected to obtain time consumption of work tasks, which provided for accurate calculation of total cycle times and delays. Several advantages of the security camera system including quick and non-invasive installation, large memory storage, transferability, resistance to weather elements, and the capacity to capture different views, offer a great potential for this method to be adopted as a reliable approach to accurately conduct time and motion studies. Along with distance and gradient information for skid-trail segments, we also explored the influence of gradient on travel time for loaded and unloaded skidding. There is a need for future studies to formally explore this relationship and develop more detailed cycle time equations that explicitly take into account skid-trail gradient for individual segments.

Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems

Problems related to the transportation of timber products have traditionally involved finding routes that minimize timber hauling and road construction costs. However, increasing environmental concerns have introduced negative impacts (i.e., soil erosion and water quality) into forest transportation planning problems (FTPPs). In this paper, we designed and implemented a multi-objective ant colony optimization algorithm (MOACO) to solve a bi-objective FTPP that considers both transportation cost and environmental impacts. The goal is to provide decision makers with different timber transportation planning alternatives to help them make informed decisions. The MOACO incorporates various design choices that have been identified to have better performances in recent research literature. To test for performance, we applied the algorithm to ten FTPPs. Experimental results demonstrate the MOACO was able to solve all test problems under different stop conditions.