Mariela Soto-Berelov

Understanding the Effects of ALS Pulse Density for Metric Retrieval across Diverse Forest Types

Pulse density, the number of laser pulses that intercept a surface per unit area, is a key consideration when acquiring an Airborne Laser Scanning (ALS) dataset. This study compares area-based vegetation structure metrics derived from multireturn ALS simulated at six pulse densities (0.05 to 4 pl m-2) across a range of forest types: from savannah woodlands to dense rainforests. Results suggest that accurate measurement of structure metrics (canopy height, canopy cover, and vertical canopy structure) can be achieved with a pulse density of 0.5 pl m-2 across all forest types when compared to a dataset of 10 pl m-2. For pulse densities <0.5 pl m-2, two main sources of error lead to inaccuracies in estimation: the poor identification of the ground surface and sparse vegetation cover leading to under sampling of the canopy profile. This analysis provides useful information for land managers determining capture specifications for large-area ALS acquisitions.

Historical seagrass mapping in Port Phillip Bay, Australia

Seagrass beds are highly productive ecosystems and a decline in this habitat has become a global concern in recent decades. This study mapped seagrass at three sites in Port Phillip Bay between 1939 and 2011 and reviewed possible influences on seagrass cover changes. Historical aerial photographs from multiple sources were digitally scanned and orthorectified. Automated image processing techniques incorporating an unsupervised classification combined with minor editing in a GIS were applied to map seagrass cover and analyse variations in the size and distribution of seagrass beds. Large declines in seagrass cover were observed at all three sites after 1998. In contrast to other world-wide observations, these recent declines were preceded by a period of sustained seagrass expansion between the 1960s and 1990s and lower levels of seagrass cover were observed in the 1930s/40s. The recent and earlier low levels of seagrass cover coincided with extended droughts characterised by large reductions in nutrient inputs to the Bay. However, recent declines were not consistent across the Bay with three other sites remaining relatively stable during this period. The sites with large declines are all subject to longshore drift and changes in nearshore sediment transport driven by variations in weather patterns coinciding with extended periods of drought may be important influences on seagrass cover at these locations.

Using discrete-return airborne laser scanning to quantify number of canopy strata across diverse forest types

The vertical arrangement of forest canopies is a key descriptor of canopy structure, a driver of ecosystem function and indicative of forest successional stage. Yet techniques to attribute for canopy vertical structure across large and potentially heterogeneously forested areas remain elusive. This study introduces a new technique to estimate the Number of Strata (NoS) that comprise a canopy profile, using discrete-return Airborne Laser Scanning (ALS) data. Vertically resolved gap probability (P-gap) aggregated over a plot is generalized with a nonparametric cubic spline regression (P-s). Subsequently a count of the positive zero-crossings of second derivative of 1 - P-s is used to estimate NoS. Comparison with inventory derived estimates at 24 plots across three diverse study areas shows a good agreement between the two techniques (RMSE=041 strata). Furthermore, this is achieved without altering model parameters, indicating the transferability of the technique across diverse forest types. NoS values ranged from 0 to 4 at a further 239 plots, emphasizing the need for a method to quantify canopy vertical structure across forested landscapes. Comparison of NoS with other commonly derived ALS descriptors of canopy structure (canopy height, canopy cover and return height coefficient of determination) returned only a moderate correlation (r(2)<04). It is proposed the presented method provides a primary descriptor of canopy structure to complement canopy height and cover, as well as a candidate Ecological Biodiversity Variable for characterizing habitat structure.

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.

Mapping forest disturbance and recovery for forest dynamics over large areas using Landsat time-series remote sensing

Sustainable forest management requires consistent and simple approaches for characterizing forest changes through time and space at the landscape scale. Landsat satellite data, with its long archive and comprehensive spatial, temporal and spectral detail, could enable us to achieve this goal. This study develops a consistent approach for mapping both disturbance and recovery for forest dynamic estimation across large areas over a 30 year period (1988 to 2016) using Landsat time series data. We analyzed dynamic Eucalypt/ Sclerophyll public forests in south eastern Australia which have been impacted by a series of disturbances including fire and logging over the last 30 years. We first prepared annual satellite composites and fitted spectral time series trajectories on a per-pixel basis using the LandTrendr algorithm, from which we derived a range of spatial disturbance and recovery metrics. We then simultaneously modeled disturbance and consequent recovery levels using the Random Forest classifier. Using derived change information and a one-off forest cover dataset, we estimated change in forest extent throughout the time series. Disturbance and consequent recovery were simultaneously detected with an overall accuracy of 80.2%, while the model of change levels classification obtained an overall accuracy of 76.5%. Over the 30 year period, approximately 49.5% of the study area was disturbed, 92% of which has fully recovered. Forest extent was found to be quite dynamics throughout the time period and comprised between 80.2% to 88.3% of public forest estate.

A collaborative framework for vegetated systems research: A perspective from Victoria, Australia

Collaborative ventures in research infrastructure can allow multiple stakeholders to benefit from outcomes that may otherwise be cost prohibitive. In this study, we discuss how the investment in research infrastructure by various sectors of the academic, scientific, and land management community is promoting high end forest ecosystem research in Australia. Three 25km2 woodland and open canopy forests that are representative of Victorias 8 million hectares of public forests were incorporated into the Terrestrial Ecosystem Research Networks calibration/validation campaign. The sites are being used to develop algorithms that will assist land management agencies across various states to characterize fundamental forest attributes at a landscape level. Wireless technology (VegNet) is also being trialed at these sites to investigate forest condition over time. This study provides an example of how the establishment and co-investment in research infrastructure amongst different sectors of the scientific community promote data sharing and ultimately expand our understanding of forest ecosystems, which can in turn be used for monitoring and to inform policy and land management decision making.

Toward a Grand Narrative of Bronze Age Vegetation Change and Social Dynamics in the Southern Levant

We introduce a grand narrative on the development of Levantine Bronze Age civilization by applying digital technology to integrate long-term paleovegetation modeling with regional settlement patterns over the course of two millennia in the mid-Holocene. We consider the implications of shifts in potential vegetation linked with ancient climate change as a means of refreshing archaeological narratives on the causes and consequences of regional social dynamics, especially those related to the rise and collapse of agrarian town-based complex society. This study applies GIS, remote sensing, and statistical modeling tools (MAXENT) to nearly 1700 historical and modern plant species observation points across the southern Levant to create maps of modern and past potential vegetation based on annual precipitation and temperature values generated by a macrophysical climate model. We model the past potential vegetation of the Levant in 100-year intervals between ca. 5500 and 3000 years BP. Modeled ancient environmental dynamics are linked to settlement pattern shifts as they reflect the coalescence and dissolution of aggregated Bronze Age communities and town-based social systems. Junctures of environmental, archaeological, and historical change serve as interpretive touchstones around which grand narratives of the Levantine Bronze Age can be built.

Assessing two large area burnt area products across Australian Southern Forests

ABSTRACT Burnt area is a critical parameter for estimating emissions of greenhouse gases associated with biomass burning. Several burnt area products (BAPs) derived from Earth Observation satellites/sensors have been released; these are based on different spatial resolutions and derived using different methodologies so that accuracies can vary amongst them. This study validates a global (MODIS) and a national (AVHRR) BAP across Australian southern forests using two reference datasets: state fire histories (SFHs) from 2000 to 2013 and a forest cover map derived through high resolution air photo interpretation (API). The spatial and temporal agreement between fires in the BAPs and reference SFH were analysed based on 2610 sample points representative of Australian southern forest types (successful detection was evaluated according to fire type: planned burn vs. wildfire, size of fire, and land tenure). Results show that both BAPs were most successful when identifying large wildfires (>5000 ha). Overall accuracy for AVHRR and MODIS was 73.9% and 62.5%, respectively. When compared to the API derived forest cover map as reference dataset, both products achieved higher overall accuracies (94.1% for AVHRR and 87.1% for MODIS); an expected result given that the fires detected in this dataset are known to be observable using Earth observation data. But regardless of reference dataset, the AVHRR BAP which is tailored to Australian conditions achieved better results than the MODIS global BAP. Also, the AVHRR archive in Australia goes back to 1988, which is an important consideration for calculating wildfire history for greenhouse gas accounting.

Woody vegetation landscape feature generation from multispectral and LiDAR data (A CRCSI 2.07 woody attribution paper)

There is a need for accurate estimation of Australian woody vegetation parameters. State and Commonwealth land management agencies are mandated to report about forest condition every five years. The CRCSI 2.07 “Australian woody vegetation landscape feature generation from multi-source airborne and space-borne imaging and ranging data” aims at producing ready-to-use methods to report forest condition based on remote sensing data. The first efforts have focus on field data techniques and canopy structure characterization using LiDAR data. Results demonstrate canopy profile can be accurately estimated using Weibull probability density functions at 30×30m pixel size. Moreover different field techniques to measure vegetation fractional cover has been tested and compare finding differences up to 15%.

MAUP and LiDAR derived canopy structure (A CRCSI 2.07 woody attribution paper)

MAUP theory is applied to a LiDAR dataset acquired over a forested scene. The Weibull Probability Density Function (PDF) has been fit to LiDAR derived canopy height profiles for plots covering the complete 1 × 1 km scene. Ten plot sizes are tested from 10 - 300 m. Parameters describing the location and scale of the PDF are used as analogous of canopy height and canopy length respectively. Results suggest that, for a structurally homogenous forested scene, localised variance decreases for canopy height with increasing plot dimensions. The opposite is apparent for canopy length, it is suggested this is a result of a spatially heterogeneous understorey layer negatively skewing the distribution.