School of Ecosystem and Forest Sciences - Research Publications

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    A forest fuel dryness forecasting system that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models
    Lyell, CS ; Nattala, U ; Joshi, RC ; Joukhadar, Z ; Garber, J ; Mutch, S ; Inbar, A ; Brown, T ; Gazzard, T ; Gower, A ; Hillman, S ; Duff, T ; Sheridan, G (Imprensa da Universidade de Coimbra, 2022)
    Accurate and timely forecasting of forest fuel moisture is critical for decision making in the context of bushfire risk and prescribed burning. The moisture content in forest fuels is a driver of ignition probability and contributes to the success of fuel hazard reduction burns. Forecasting capacity is extremely limited because traditional modelling approaches have not kept pace with rapid technological developments of field sensors, weather forecasting and data-driven modelling approaches. This research aims to develop and test a 7-day-ahead forecasting system for forest fuel dryness that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models. The integrated system was established across a diverse range of 30 sites in south-eastern Australia. Fuel moisture was measured hourly using 10-hour automated fuel sticks. A subset of long-term sites (5 years of data) was used to evaluate the relative performance of a selection of machine learning (Light Gradient Boosting Machine (LightGBM) and Recurrent Neural Network (RNN) based Long-Short Term Memory (LSTM)), statistical (VARMAX) and process-based models. The best performing models were evaluated at all 30 sites where data availability was more limited, demonstrating the models' performance in a real-world scenario on operational sites prone to data limitations. The models were driven by daily 7-day continent-scale gridded weather forecasts, in-situ fuel moisture observation and site variables. The model performance was evaluated based on the capacity to successfully predict minimum daily fuel dryness within the burnable range for fuel reduction (11 – 16%) and bushfire risk (
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    Using Bayesian multitemporal classification to monitor tropical forest cover changes in Kalimantan, Indonesia
    Sari, IL ; Weston, CJ ; Newnham, GJ ; Volkova, L (TAYLOR & FRANCIS LTD, 2022-12-31)
    Significant areas of native forest in Kalimantan, on the island of Borneo, have been cleared for the expansion of plantations of oil palm and rubber. In this study multisource remote sensing was used to develop a time series of land cover maps that distinguish native forest from plantations. Using a study area in east Kalimantan, Landsat images were combined with either ALOS PALSAR or Sentinel-1 images to map four land cover classes (native forest, oil palm plantation, rubber plantation, non-forest). Bayesian multitemporal classification was applied to increase map accuracy and maps were validated using a confusion matrix; final map overall accuracy was >90%. Over 18 years from 2000 to 2018 nearly half the native forests in the study area were converted to either non-forest or plantations of either rubber or oil palm, with the highest losses between 2015 and 2016. Trending upwards from 2008 large areas of degraded or cleared forests, mapped as non-forest, were converted to oil palm plantation. Conversion of native forests to plantation mainly occurred in lowland and wetland forest, while significant forest regrowth was detected in degraded peatland. These maps will help Indonesia with strategies and policies for balancing economic growth and conservation.
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    Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia
    Sari, IL ; Weston, CJ ; Newnham, GJ ; Volkova, L (MDPI, 2022-01-01)
    Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference (>7.5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92%; producer’s accuracy for all classes was higher than 90%, except for rubber (65%), and user’s accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia’s national forest monitoring system and inform monitoring of plantation expansion.
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    Long-Term Response of Fuel to Mechanical Mastication in South-Eastern Australia
    Pickering, BJ ; Burton, JE ; Penman, TD ; Grant, MA ; Cawson, JG (MDPI, 2022-06-01)
    Mechanical mastication is a fuel management strategy that modifies vegetation structure to reduce the impact of wildfire. Although past research has quantified immediate changes to fuel post-mastication, few studies consider longer-term fuel trajectories and climatic drivers of this change. Our study sought to quantify changes to fuel loads and structure over time following mastication and as a function of landscape aridity. Measurements were made at 63 sites in Victoria, Australia. All sites had been masticated within the previous 9 years to remove over-abundant shrubs and small trees. We used generalised additive models to explore trends over time and along an aridity gradient. Surface fuel loads were highest immediately post-mastication and in the most arid sites. The surface fine fuel load declined over time, whereas the surface coarse fuel load remained high; these trends occurred irrespective of landscape aridity. Standing fuel (understorey and midstorey vegetation) regenerated consistently, but shrub cover was still substantially low at 9 years post-mastication. Fire managers need to consider the trade-off between a persistently higher surface coarse fuel load and reduced shrub cover to evaluate the efficacy of mastication for fuel management. Coarse fuel may increase soil heating and smoke emissions, but less shrub cover will likely moderate fire behaviour.
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    Optimum plant density and harvest age for maximizing productivity and minimizing competition in a Calliandra short-rotation-coppice plantation in West Java, Indonesia
    Widyati, E ; Sutiyono, ; Darwo, ; Mindawati, N ; Yulianti, M ; Prameswari, D ; Abdulah, L ; Yuniarti, K ; Baral, H (Informa UK Limited, 2022-01-01)
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    Multi-scale politics in climate change: the mismatch of authority and capability in federalizing Nepal
    Khatri, DB ; Nightingale, AJ ; Ojha, H ; Maskey, G ; 'Tsumpa', PNL (TAYLOR & FRANCIS LTD, 2022-06-30)
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    Performance of GEDI Space-Borne LiDAR for Quantifying Structural Variation in the Temperate Forests of South-Eastern Australia
    Dhargay, S ; Lyell, CS ; Brown, TP ; Inbar, A ; Sheridan, GJ ; Lane, PNJ (MDPI, 2022-08-01)
    Monitoring forest structural properties is critical for a range of applications because structure is key to understanding and quantifying forest biophysical functioning, including stand dynamics, evapotranspiration, habitat, and recovery from disturbances. Monitoring of forest structural properties at desirable frequencies and cost globally is enabled by space-borne LiDAR missions such as the global ecosystem dynamics investigation (GEDI) mission. This study assessed the accuracy of GEDI estimates for canopy height, total plant area index (PAI), and vertical profile of plant area volume density (PAVD) and elevation over a gradient of canopy height and terrain slope, compared to estimates derived from airborne laser scanning (ALS) across two forest age-classes in the Central Highlands region of south-eastern Australia. ALS was used as a reference dataset for validation of GEDI (Version 2) dataset. Canopy height and total PAI analyses were carried out at the landscape level to understand the influence of beam-type, height of the canopy, and terrain slope. An assessment of GEDI’s terrain elevation accuracy was also carried out at the landscape level. The PAVD profile evaluation was carried out using footprints grouped into two forest age-classes, based on the areas of mountain ash (Eucalyptus regnans) forest burnt in the Central Highlands during the 1939 and 2009 wildfires. The results indicate that although GEDI is found to significantly under-estimate the total PAI and slightly over-estimate the canopy height, the GEDI estimates of canopy height and the vertical PAVD profile (above 25 m) show a good level of accuracy. Both beam-types had comparable accuracies, with increasing slope having a slightly detrimental effect on accuracy. The elevation accuracy of GEDI found the RMSE to be 10.58 m and bias to be 1.28 m, with an R2 of 1.00. The results showed GEDI is suitable for canopy densities and height in complex forests of south-eastern Australia.
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    Using Remote Sensing to Estimate Understorey Biomass in Semi-Arid Woodlands of South-Eastern Australia
    Riquelme, L ; Duncan, DH ; Rumpff, L ; Vesk, PA (MDPI, 2022-05-01)
    Monitoring ground layer biomass, and therefore forage availability, is important for managing large, vertebrate herbivore populations for conservation. Remote sensing allows for frequent observations over broad spatial scales, capturing changes in biomass over the landscape and through time. In this study, we explored different satellite-derived vegetation indices (VIs) for their utility in estimating understorey biomass in semi-arid woodlands of south-eastern Australia. Relationships between VIs and understorey biomass data have not been established in these particular semi-arid communities. Managers want to use forage availability to inform cull targets for western grey kangaroos (Macropus fuliginosus), to minimise the risk that browsing poses to regeneration in threatened woodland communities when grass biomass is low. We attempted to develop relationships between VIs and understorey biomass data collected over seven seasons across open and wooded vegetation types. Generalised Linear Mixed Models (GLMMs) were used to describe relationships between understorey biomass and VIs. Total understorey biomass (live and dead, all growth forms) was best described using the Tasselled Cap (TC) greenness index. The combined TC brightness and Modified Soil Adjusted Vegetation Index (MSAVI) ranked best for live understorey biomass (all growth forms), and grass (live and dead) biomass was best described by a combination of TC brightness and greenness indices. Models performed best for grass biomass, explaining 70% of variation in external validation when predicting to the same sites in a new season. However, we found empirical relationships were not transferrable to data collected from new sites. Including other variables (soil moisture, tree cover, and dominant understorey growth form) improved model performance when predicting to new sites. Anticipating a drop in forage availability is critical for the management of grazing pressure for woodland regeneration, however, predicting understorey biomass through space and time is a challenge. Whilst remotely sensed VIs are promising as an easily-available source of vegetation information, additional landscape-scale data are required before they can be considered a cost-efficient method of understorey biomass estimation in this semi-arid landscape.
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    Mainstreaming Ecosystem Services from Indonesia's Remaining Forests
    Nugroho, HYSH ; Nurfatriani, F ; Indrajaya, Y ; Yuwati, TW ; Ekawati, S ; Salminah, M ; Gunawan, H ; Subarudi, S ; Sallata, MK ; Allo, MK ; Muin, N ; Isnan, W ; Putri, IASLP ; Prayudyaningsih, R ; Ansari, F ; Siarudin, M ; Setiawan, O ; Baral, H (MDPI, 2022-10-01)
    With 120 million hectares of forest area, Indonesia has the third largest area of biodiversity-rich tropical forests in the world, and it is well-known as a mega-biodiversity country. However, in 2020, only 70 percent of this area remained forested. The government has consistently undertaken corrective actions to achieve Sustainable Development Goal targets, with a special focus on Goals #1 (no poverty), #2 (zero hunger), #3 (good health and well-being), #7 (affordable and clean energy), #8 (decent work and economic growth), #13 (climate action), and #15 (life on land). Good environmental governance is a core concept in Indonesia’s forest management and includes mainstreaming ecosystem services as a framework for sustainable forest management. This paper analyzes efforts to mainstream Indonesia’s remaining forest ecosystem services. We review the state of Indonesia’s forests in relation to deforestation dynamics, climate change, and ecosystem service potential and options and provide recommendations for mainstreaming strategies regarding aspects of policy, planning, and implementation, as well as the process of the articulation of ecosystem services and their alternative funding.
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    Recovery of Carbon and Vegetation Diversity 23 Years after Fire in a Tropical Dryland Forest of Indonesia
    Adinugroho, WC ; Prasetyo, LB ; Kusmana, C ; Krisnawati, H ; Weston, CJ ; Volkova, L (MDPI, 2022-06-01)
    Understanding the recovery rate of forest carbon stocks and biodiversity after disturbance, including fire, is vital for developing effective climate-change-mitigation policies and actions. In this study, live and dead carbon stocks aboveground, belowground, and in the soil to a 30 cm depth, as well as tree and shrub species diversity, were measured in a tropical lowland dry forest, 23 years after a fire in 1998, for comparison with adjacent unburned reference forests. The results showed that 23 years since the fire was insufficient, in this case, to recover live forest carbon and plant species diversity, to the level of the reference forests. The total carbon stock, in the recovering 23-year-old forest, was 199 Mg C ha−1 or about 90% of the unburned forest (220 Mg C ha−1), mainly due to the contribution of coarse woody debris and an increase in the 5–10 cm soil horizon’s organic carbon, in the burned forest. The carbon held in the live biomass of the recovering forest (79 Mg C ha−1) was just over half the 146 Mg C ha−1 of the reference forest. Based on a biomass mean annual increment of 6.24 ± 1.59 Mg ha−1 yr−1, about 46 ± 17 years would be required for the aboveground live biomass to recover to equivalence with the reference forest. In total, 176 plant species were recorded in the 23-year post-fire forest, compared with 216 in the unburned reference forest. The pioneer species Macaranga gigantea dominated in the 23-year post-fire forest, which was yet to regain the similar stand structural and compositional elements as those found in the adjacent unburned reference forest.