Quantifying marine plastic debris in a beach environment using spectral analysis

Marine plastic debris (MPD) is a globally relevant environmental challenge, with an estimated 8 million tons of synthetic debris entering the marine environment each year. Plastic has been found in all parts of the marine environment, including the surface layers of the ocean, within the water column, in coastal waters, on the benthic layer and on beaches. While research on detecting MPD using remote sensing is increasing, most of it focuses on detecting floating debris in open waters, rather than detecting MPD on beaches. However, beaches present challenges that are unique from other parts of the marine environment. In order to better understand the spectral properties of beached MPD, we present the SWIR reflectance of weathered MPD and virgin plastics over a sandy substrate. We conducted spectral feature analysis on the different plastic groups to better understand the impact that polymers have on our ability to detect synthetic debris at sub-pixel surface covers that occur on beaches. Our results show that the minimum surface cover required to detect MPD on a sandy surface varies between 2–8% for different polymer types. Furthermore, plastic composition affects the magnitude of spectral absorption. This suggests that variation in both surface cover and polymer type will inform the efficacy of beach litter detection methods

Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible

Intercomparison of Real and Simulated GEDI Observations across Sclerophyll Forests

Forest structure is an important variable in ecology, fire behaviour, and carbon management. New spaceborne lidar sensors, such as the Global Ecosystem Dynamics Investigation (GEDI), enable forest structure to be mapped at a global scale. Virtual GEDI-like observations can be derived from airborne laser scanning (ALS) data for given locations using the GEDI simulator, which was a tool initially developed for GEDI’s pre-launch calibration. This study compares the relative height (RH) and ground elevation metrics of real and simulated GEDI observations against ALS-derived benchmarks in southeast Australia. A total of 15,616 footprint locations were examined, covering a large range of forest types and topographic conditions. The impacts of canopy cover and height, terrain slope, and ALS point cloud density were assessed. The results indicate that the simulator produces more accurate canopy height (RH95) metrics (RMSE: 4.2 m, Bias: −1.3 m) than the actual GEDI sensor (RMSE: 9.6 m, Bias: −1.6 m). Similarly, the simulator outperforms GEDI in ground detection accuracy. In contrast to other studies, which favour the Gaussian algorithm for ground detection, we found that the Maximum algorithm performed better in most settings. Despite the determined differences between real and simulated GEDI observations, this study indicates the compatibility of both data sources, which may enable their combined use in multitemporal forest structure monitoring

Regional Variation in Forest Canopy Height and Implications for Koala (Phascolarctos cinereus) Habitat Mapping and Forest Management

Previous research has shown that the Koala (Phascolarctos cinereus) prefers larger trees, potentially making this a key factor influencing koala habitat quality. Generally, tree height is considered at regional scales which may overlook variation at patch or local scales. In this study, we aimed to derive a set of parameters to assist in classifying koala habitat in terms of tree height, which can then be used as an overlay for existing habitat maps. To determine canopy height variation within a specific forest community across a broad area in eastern Australia, we used freely available Airborne Laser Scanning (ALS) data and adopted a straightforward approach by extracting maximum-height ALS returns within a total of 288 30 m × 30 m “virtual” ALS plots. Our findings show that while maximum tree heights generally fall within published regional-scale parameters (mean height 33.2 m), they vary significantly between subregions (mean height 28.8–39.0 m), within subregions (e.g., mean height 21.3–29.4 m), and at local scales, the tree heights vary in response to previous land-use (mean height 28.0–34.2 m). A canopy height dataset useful for habitat management needs to recognise and incorporate these variations. To examine how this information might be synthesised into a usable map, we used a wall-to-wall canopy height map derived from ALS to investigate spatial and nonspatial clustering techniques that capture canopy height variability at both intra-subregional (100s of hectares) and local (60 hectare) scales. We found that nonspatial K-medians clustering with three or four height classes is suited to intra-subregional extents because it allows for simultaneous assessment and comparison of multiple forest community polygons. Spatially constrained clustering algorithms are suited to individual polygons, and we recommend the use of the Redcap algorithm because it delineates contiguous height classes recognisable on a map. For habitat management, an overlay combining these height classification approaches as separate attributes would provide the greatest utility at a range of scales. In addition to koala habitat management, canopy height maps could also assist in managing other fauna; identifying forest disturbance, regenerating forest, and old-growth forest; and identifying errors in existing forest maps

Exploring the Influence of Forest Tenure and Protection Status on Post-Fire Recovery in Southeast Australia

Research Highlights: We used Landsat time series data to investigate the role forest tenure and protection status play in the recovery of a forest after a fire. Background and Objectives: Changing fire regimes put forests in southeast Australia under increasing pressure. Our investigation aimed to explore the impact of different forest management structures on a forest’s resilience to fire by looking at the post-fire recovery duration. Materials and Methods: The analysis included a total of 60.6 Mha of land containing 25.4 Mha of forest in southeast Australia. Multispectral time series data from Landsat satellites and a local reference dataset were used to model attributes of disturbance and recovery over a period of 33 years. Results: Protected public forest spectrally recovered 0.4 years faster than protected private forest. No other significant effects in relation to different tenure and protection status were found. Climatic and topographic variables were found to have much greater influence on post-fire spectral recovery. Conclusions: Protected area status in public forests resulted in slightly faster recovery, compared with the private protected forest estate. However, factors outside the control of land managers and policy makers, i.e., climatic and topographic variables, appear to have a much greater impact on post-fire recovery

Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2

Burn severity has been widely studied. Typical approaches use spectral differencing indices from remotely sensed data to extrapolate in-situ severity assessments. Next generation geostationary data offer near-continuous fire behaviour information, which has been used for fire detection and monitoring but remains underutilized for fire impact estimation. Here, we explore the association between remotely sensed fire intensity metrics and spectral differencing severity indices to understand whether and where they describe similar wildfire effects. The commonly used Differenced Normalised Burn Ratio (dNBR) severity index was calculated for Advanced Himawari Imager (AHI − 2 km) and Sentinel-2 (20 m) data and compared to different Fire Radiative Power (FRP) metrics derived from fire hotspot detections from AHI data across Australia. The comparison was implemented through different stratifications based on biogeographical region, land cover, fire type, and percentage of AHI pixel burned (fire fractional cover). The results indicate that FRP and dNBR metrics do not correlate in most scenarios, noting correlations being marginally stronger for hotter fires. However, correlations become significantly stronger when data are grouped using fire type information and fire fractional cover, with correlations peaking (R = 0.75) for large fires that burned 41–60 % of an AHI pixel. In conclusion, remotely sensed fire intensity and severity proxies capture different aspects of wildfire impact, that only correlate with each other after using auxiliary data. Spectral differencing severity metrics have been used extensively during the past decades, however high-frequency fire intensity estimations have the potential to augment the existing information and reveal new ways of characterizing wildfire impact over large areas

A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data

The prediction of forest biomass at the landscape scale can be achieved by integrating data from field plots with satellite imagery, in particular data from the Landsat archive, using k-nearest neighbour (kNN) imputation models. While studies have demonstrated different kNN imputation approaches for estimating forest biomass from remote sensing data and forest inventory plots, there is no general agreement on which approach is most appropriate for biomass estimation across large areas. In this study, we compared several imputation approaches for estimating forest biomass using Landsat time-series and inventory plot data. We evaluated 18 kNN models to impute three aboveground biomass (AGB) variables (total AGB, AGB of live trees and AGB of dead trees). These models were developed using different distance techniques (Random Forest or RF, Gradient Nearest Neighbour or GNN, and Most Similar Neighbour or MSN) and different combinations of response variables (model scenarios). Direct biomass imputation models were trained according to the biomass variables while indirect biomass imputation models were trained according to combinations of forest structure variables (e.g., basal area, stem density and stem volume of live and dead-standing trees). We also assessed the ability of our imputation method to spatially predict biomass variables across large areas in relation to a forest disturbance history over a 30-year period (1987⁻2016). Our results show that RF consistently outperformed MSN and GNN distance techniques across different model scenarios and biomass variables. The lowest error rates were achieved by RF-based models with generalized root mean squared difference (gRMSD, RMSE divided by the standard deviation of the observed values) ranging from 0.74 to 1.24. Whereas gRMSD associated with MSN-based and GNN-based models ranged from 0.92 to 1.36 and from 1.04 to 1.42, respectively. The indirect imputation method generally achieved better biomass predictions than the direct imputation method. In particular, the kNN model trained with the combination of basal area and stem density variables was the most robust for estimating forest biomass. This model reported a gRMSD of 0.89, 0.95 and 1.08 for total AGB, AGB of live trees and AGB of dead trees, respectively. In addition, spatial predictions of biomass showed relatively consistent trends with disturbance severity and time since disturbance across the time-series. As the kNN imputation method is increasingly being used by land managers and researchers to map forest biomass, this work helps those using these methods ensure their modelling and mapping practices are optimized

Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery

Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel values over time, through the use of various spectral indices. This study examines the utility of eight spectral indices for characterizing fire disturbance and recovery in sclerophyll forests, in order to determine their relative merits in the context of Landsat time-series. Although existing research into Landsat indices is comprehensive, this study presents a new approach, by comparing the distributions of pre and post-fire pixels using Glass’s delta, for evaluating indices without the need of detailed field information. Our results show that in the sclerophyll forests of southeast Australia, common indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), both accurately capture wildfire disturbance in a pixel-based time-series approach, especially if images from soon after the disturbance are available. However, for tracking forest regrowth and recovery, indices, such as NDVI, which typically capture chlorophyll concentration or canopy ‘greenness’, are not as reliable, with values returning to pre-fire levels in 3–5 years. In comparison, indices that are more sensitive to forest moisture and structure, such as NBR, indicate much longer (8–10 years) recovery timeframes. This finding is consistent with studies that were conducted in other forest types. We also demonstrate that additional information regarding forest condition, particularly in relation to recovery, can be extracted from less well known indices, such as NBR2, as well as textural indices incorporating spatial variance. With Landsat time-series gaining in popularity in recent years, it is critical to understand the advantages and limitations of the various indices that these methods rely on