ORCID Profile
0000-0002-9871-361X
Current Organisation
Victoria University
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Publisher: MDPI AG
Date: 07-04-2021
DOI: 10.3390/RS13081413
Abstract: Forest inventories play an important role in enabling informed decisions to be made for the management and conservation of forest resources however, the process of collecting inventory information is laborious. Despite advancements in mapping technologies allowing forests to be digitized in finer granularity than ever before, it is still common for forest measurements to be collected using simple tools such as calipers, measuring tapes, and hypsometers. Dense understory vegetation and complex forest structures can present substantial challenges to point cloud processing tools, often leading to erroneous measurements, and making them of less utility in complex forests. To address this challenge, this research demonstrates an effective deep learning approach for semantically segmenting high-resolution forest point clouds from multiple different sensing systems in erse forest conditions. Seven erse point cloud datasets were manually segmented to train and evaluate this model, resulting in per-class segmentation accuracies of Terrain: 95.92%, Vegetation: 96.02%, Coarse Woody Debris: 54.98%, and Stem: 96.09%. By exploiting the segmented point cloud, we also present a method of extracting a Digital Terrain Model (DTM) from such segmented point clouds. This approach was applied to a set of six point clouds that were made publicly available as part of a benchmarking study to evaluate the DTM performance. The mean DTM error was 0.04 m relative to the reference with 99.9% completeness. These approaches serve as useful steps toward a fully automated and reliable measurement extraction tool, agnostic to the sensing technology used or the complexity of the forest, provided that the point cloud has sufficient coverage and accuracy. Ongoing work will see these models incorporated into a fully automated forest measurement tool for the extraction of structural metrics for applications in forestry, conservation, and research.
Publisher: Springer Science and Business Media LLC
Date: 08-05-2014
Publisher: Elsevier BV
Date: 06-2020
Publisher: MDPI AG
Date: 09-04-2021
DOI: 10.3390/F12040455
Abstract: Australia’s large potential forest bioenergy resource is considerably underutilised, due largely to its high delivered costs. Drying forest biomass at the roadside can potentially reduce its delivered cost through weight reduction and increased net calorific value. There has been little research on the impact of roadside drying for Australian conditions and plantation species. This study compared delivered costs for three forest biomass types—Eucalyptus globulus plantation whole trees and logging residue (LR)-disaggregated (LR conventional) or aggregated (LR fuel-adapted)—and three roadside storage scenarios—no storage, ≤two-month storage and optimal storage—to supply a hypothetical thermal power plant in south-west Western Australia. The study was performed using a tactical linear programming tool (MCPlan). Roadside storage reduced delivered costs, with optimal storage (storage for up to 14 months) producing the lowest costs. Delivered costs were inversely related to forest biomass spatial density due to transport cost reductions. Whole trees, which had the highest spatial density, stored under the optimal storage scenario had the lowest delivered costs (AUD 7.89/MWh) while LR conventional, with the lowest spatial density, had the highest delivered costs when delivered without storage (AUD 15.51/MWh). For both LR types, two-month storage achieved ~60% of the savings from the optimal storage scenario but only 23% of the savings for whole trees. The findings suggested that roadside drying and high forest biomass spatial density are critical to reducing forest biomass delivered costs.
Publisher: Elsevier BV
Date: 05-2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 11-2021
Publisher: MDPI AG
Date: 20-01-2022
DOI: 10.3390/F13020153
Abstract: Unoccupied Aircraft Systems (UAS) are beginning to replace conventional forest plot mensuration through their use as low-cost and powerful remote sensing tools for monitoring growth, estimating biomass, evaluating carbon stocks and detecting weeds however, physical s les remain mostly collected through time-consuming, expensive and potentially dangerous conventional techniques. Such conventional techniques include the use of arborists to climb the trees to retrieve s les, shooting branches with firearms from the ground, canopy cranes or the use of pole-mounted saws to access lower branches. UAS hold much potential to improve the safety, efficiency, and reduce the cost of acquiring canopy s les. In this work, we describe and demonstrate four iterations of 3D printed canopy s ling UAS. This work includes detailed explanations of designs and how each iteration informed the design decisions in the subsequent iteration. The fourth iteration of the aircraft was tested for the collection of 30 canopy s les from three tree species: eucalyptus pulchella, eucalyptus globulus and acacia dealbata trees. The collection times ranged from 1 min and 23 s, up to 3 min and 41 s for more distant and challenging to capture s les. A vision for the next iteration of this design is also provided. Future work may explore the integration of advanced remote sensing techniques with UAS-based canopy s ling to progress towards a fully-automated and holistic forest information capture system.
Publisher: Springer Science and Business Media LLC
Date: 24-12-2010
Publisher: MDPI AG
Date: 29-10-2021
DOI: 10.3390/F12111495
Abstract: The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 2016
Publisher: MDPI AG
Date: 21-05-2020
DOI: 10.3390/RS12101652
Abstract: The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest canopy in challenging forest and terrain conditions. This UAS was deployed to assess under-canopy UAS photogrammetry as an alternative to field measurements for obtaining stem diameters as well as ultra-high-resolution (~400,000 points/m2) 3D models of forest study sites. There were 378 tape-based diameter measurements collected from 99 stems in a native, unmanaged eucalyptus pulchella forest with mixed understory conditions and steep terrain. These measurements were used as a baseline to evaluate the accuracy of diameter measurements from under-canopy UAS-based photogrammetric point clouds. The diameter measurement accuracy was evaluated without the influence of a digital terrain model using an innovative tape-based method. A practical and detailed methodology is presented for the creation of these point clouds. Lastly, a metric called the Circumferential Completeness Index (CCI) was defined to address the absence of a clearly defined measure of point coverage when measuring stem diameters from forest point clouds. The measurement of the mean CCI is suggested for use in future studies to enable a consistent comparison of the coverage of forest point clouds using different sensors, point densities, trajectories, and methodologies. It was found that root-mean-squared-errors of diameter measurements were 0.011 m in Site 1 and 0.021 m in the more challenging Site 2. The point clouds in this study had a mean validated CCI of 0.78 for Site 1 and 0.7 for Site 2, with a mean unvalidated CCI of 0.86 for Site 1 and 0.89 for Site 2. The results in this study demonstrate that under-canopy UAS photogrammetry shows promise in becoming a practical alternative to traditional field measurements, however, these results are currently reliant upon the operator’s knowledge of photogrammetry and his/her ability to fly manually in object-rich environments. Future work should pursue solutions to autonomous operation, more complete point clouds, and a method for providing scale to point clouds when global navigation satellite systems are unavailable.
Publisher: Elsevier BV
Date: 2016
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 05-2011
Publisher: MDPI AG
Date: 19-11-2021
DOI: 10.3390/RS13224677
Abstract: Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively s led manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the ersity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.
Publisher: Wiley
Date: 12-2021
DOI: 10.1111/EMR.12505
Abstract: The benefits of using remote sensing technologies for informing and monitoring ecological restoration of forests from the community to the in idual are presented. At the community level, we link remotely sensed measures of structural complexity with animal behaviour. At the plot level, we monitor the return of vegetation structure and ecosystem services (e.g. carbon sequestration) using data‐rich three‐dimensional point clouds. At the in idual‐level, we use high‐resolution images to accurately classify plants to species and provenance and show genetic‐based variation in canopy structural traits. To facilitate the wider use of remote sensing in restoration, we discuss the challenges that remain to be resolved.
Publisher: Elsevier BV
Date: 04-2020
No related grants have been discovered for Mohammad Sadegh Taskhiri.