Mapping and monitoring forest extent is a common requirement of regional forest inventories and public land natural resource management, including in Australia. The state of Victoria, Australia, has approximately 7.2 million hectares of mostly forested public land, comprising ecosystems that present a diverse range of forest structures, composition and condition. In this paper, we evaluate the performance of the Random Forest (RF) classifier, an ensemble learning algorithm that has recently shown promise using multi-spectral satellite sensor imagery for large area feature classification. The RF algorithm was applied using selected Landsat Thematic Mapper (TM) imagery metrics and auxiliary terrain and climatic variables, while the reference data was manually extracted from systematically distributed plots of sample aerial photography and used for training (75%) and accuracy (25%) assessment. The RF algorithm yielded an overall accuracy of 96% and a Kappa statistic of 0.91 (confidence interval (CI) 0.909–0.919) for the forest/non-forest classification model, given a Kappa maximised binary threshold value of 0.5. The area under the receiver operating characteristic plot produced a score of 0.91, also indicating high model performance. The framework described in this study contributes to the operational deployment of a robust, but affordable, program, able to collate and process large volumes of multi-sourced data using open-source software for the production of consistent and accurate forest cover maps across the full spectrum of Victorian sclerophyll forest types.

Surveying of woody debris left over from harvesting operations on managed forests is an important step in monitoring site quality, managing the extraction of residues and reconciling differences in pre-harvest inventories and actual timber yields. Traditional methods for post-harvest survey involving manual assessment of debris on the ground over small sample plots are labor-intensive, time-consuming, and do not scale well to heterogeneous landscapes. In this paper, we propose and evaluate new automated methods for the collection and interpretation of high-resolution, Unmanned Aerial Vehicle (UAV)-borne imagery over post-harvested forests for estimating quantities of fine and coarse woody debris. Using high-resolution, geo-registered color mosaics generated from UAV-borne images, we develop manual and automated processing methods for detecting, segmenting and counting both fine and coarse woody debris, including tree stumps, exploiting state-of-the-art machine learning and image processing techniques. Results are presented using imagery over a post-harvested compartment in a Pinus radiata plantation and demonstrate the capacity for both manual image annotations and automated image processing to accurately detect and quantify coarse woody debris and stumps left over after harvest, providing a cost-effective and scalable survey method for forest managers.

Recent advances and commercialisation of unmanned aerial vehicle/red blue green (RGB) camera systems and digital photogrammetric techniques now provide a cheap and flexible alternative to higher-cost airborne platforms for routine monitoring of canopy health in timber plantations. Structure-from-Motion photogrammetry produces very dense three-dimensional (3D) point clouds which can be used to derive metrics for inventory estimation. Unmanned aerial vehicle RGB photography also captures data that can relate to tree health. In contrast to the more common use of orthorectified RGB photography to extract this spectral information, we used the software package Agisoft Photoscan to assign a simple Vegetation Index value directly to each point in the 3D point cloud. Using data acquired by a DJI Phantom 4 Pro, we present a simple processing and photogrammetric workflow solution for detecting dead and dying trees in a young Eucalyptus pellita plantation located in the provenance of Riau, Sumatra. Trees affected by the bacterial wilt Ralstonia sp. present symptoms of necrotic foliage on individual branches or the whole crown. Assigning the Visible Atmospheric Resistant Index Vegetation Index colour-coded values to individual points in the 3D point cloud significantly enhanced visualisation of necrotic foliage on individual trees in both the point cloud and the associated orthophoto compared to the RGB equivalent images. This approach could easily be operationally deployed for the rapid detection and mapping of unhealthy trees with symptoms of necrotic foliage.

Better information regarding the spatial variability of height, Diameter at Breast Height (DBH) and stocking could improve inventory estimates at the operational Planning Unit since these parameters are used extensively in allometric equations, including stem volume, biomass and carbon calculations. In this study, the influence of stand stocking on height and DBH of two even aged radiata pine (Pinus radiata D. Don) stands were investigated using airborne Light Detection and Ranging (LiDAR) data at a study site in New South Wales, Australia. Both stands were characterized by irregular stocking due to patchy establishment and self-thinning in the absence of any silvicultural thinning events. For the purpose of this study, a total of 34 plots from a 34 year old site and 43 plots from a nine year old site were established, from which a total of 447 trees were sampled. Within these plots, DBH and height measurements were measured and their relationships with stocking were evaluated. LiDAR was used for height estimation as well as stem counts in fixed plots (stocking). The results showed a significant relationship between stem DBH and stocking. At both locations, trees with larger diameters were found on lower stocking sites. Height values were also significantly correlated with stocking, with taller trees associated with high stocking. These results were further verified of additional tree samples, with independent field surveys for DBH and LiDAR-derived metrics for height analysis. This study confirmed the relationship between P. radiata tree heights and stem diameter with stocking and demonstrated the capacity of LiDAR to capture sub-compartment variation in these tree-level attributes.

Reducing uncertainty in forest carbon estimates at local and regional scales has become increasingly important due to the centrality of the terrestrial carbon cycle in issues of climate change. In Victoria, Australia, public natural forests extend over 7.2 M ha and constitute a significant and important carbon stock. Recently, a wide range of approaches to estimate carbon stocks within these forests have been developed and applied. However, there are a number of data and estimation limitations associated with these studies. In response, over the last five years, the State of Victoria has implemented a pragmatic plot-based design consisting of pre-stratified permanent observational units located on a state-wide grid. Using the ground sampling grid, we estimated aboveground and belowground carbon stocks (including soil to 0.3 m depth) in both National Parks and State Forests, across a wide range of bioregions. Estimates of carbon stocks and associated uncertainty were conducted using simple design based estimators. We detected significantly more carbon in total aboveground and belowground components in State Forests (408.9 t ha⁻¹, 95% confidence interval 388.8–428.9 t ha⁻¹) than National Parks (267.6 t ha⁻¹, 251.9–283.3 t ha⁻¹). We were also able to estimate forest carbon stocks (and associated uncertainty) for 21 strata that represent all of Victoria’s bioregions and public tenures. It is anticipated that the lessons learnt from this study may support the discussion on planning and implementing low cost large area forest carbon stock sampling in other jurisdictions.

Accurate mapping of timber resources in commercial forestry is essential to support planning and management operations of forest growers. Over the last two decades, Light Detection and Ranging (LiDAR) systems have been successfully deployed for the collection of point-cloud data for accurate modelling of forest attributes that are traditionally obtained from plot-based inventory. In recent years, studies conducted in North America and Scandinavia have shown that three-dimensional point clouds derived from digital aerial photogrammetric (AP) data can be used to model forest attributes with a level of accuracy similar to traditional LiDAR-based approaches. A comparative analysis of the performance of the two point-cloud technologies has never been attempted in Australian plantations. In this study, we compared the performance of LiDAR-based and AP-based point clouds for estimating total recoverable volume in a Pinus radiata plantation at Springfield in north-eastern Tasmania, using volume data collected by harvesting machines as a reference. Our results showed that AP point clouds can be used for mapping total recoverable volume in P. radiata plantations with levels of accuracy that are comparable to LiDAR-based estimates. Plot-level relative root mean squared error (RMSE%) values were 23.85% for LiDAR and ranged from 22.07% to 27.10% for the three AP dense point-cloud settings evaluated. At the stand level, RMSE% decreased to 9.86% and 8.91% for LiDAR and AP, respectively. Both LiDAR-based and AP-based modelled volumes showed a close agreement with volumes measured using harvester head data, demonstrating the potential of AP technology for the management and planning of forestry operations in softwood plantations.