Ecological systems are dynamic and disturbance is an important factor for change. Fire is an agent of environmental change globally at various spatial and temporal scales determining land use, productivity, biodiversity and has impacts on hydrologic, biogeochemical and atmospheric processes [1
]. Fires occur over the majority of the Australian landscape and in most vegetation types [2
], and Australian dry sclerophyll forests are amongst the more fire-prone forest communities in the world [3
]. Land managers in such fire-prone countries mitigate the threat posed by catastrophic wildfires using prescribed burning (fuel reduction). These low intensity burns involve the deliberate application of fire to forest fuels under specified conditions in order to achieve well-defined management goals [4
]. These goals include reduction of wildfire hazard, protecting biodiversity and protecting infrastructures at the urban interface [5
Measuring the effects of a prescribed burn on the landscape (i.e.
, burn severity) is important for burn efficacy reporting [6
] and allowing land managers to manage post-fire rehabilitation and remediation (e.g., to prevent runoff and erosion) [7
]. Reporting following a prescribed burn can also help monitor ecosystem recovery [9
] and quantify carbon emissions [10
]. As such, land managers require an effective and meaningful way of quantifying burn severity.
Established techniques for describing or quantifying the effects of a fire include destructive sampling of the remaining fuel, in-situ
visual estimates or measurement of post-burn variables [4
]. Post-burn variables include percentage surface burnt, percentage understorey cover burnt (grass and litter), percentage canopy scorch and burnt and litter depth post-burn amongst others. Routinely used field measures of burn severity such as Composite Burn Index (CBI) are based on ocular estimation and judgement [11
]. In Australia too, the techniques aimed at reporting the nature of the burn event using a number of variables to ascertain severity are also based on visual field assessments which are both subjective and qualitative [12
]. The ability to draw accurate links between fire effects and operational fire models whilst overcoming cost, time and technical challenges posed to forest managers involved in collecting field data has also been acknowledged by several researchers [13
]. In order to improve the reporting procedures around prescribed burns and quantify fire effects, a need for repeatable and quantified description has been identified in this paper.
Knowledge of the understorey environment is essential for fire behaviour modelling [15
], wildlife habitat assessment and modeling [18
] and carbon stocks and sequestration [19
]. Hence, estimates of change in the understorey vegetation become important in burnt landscapes, however this has utility beyond fire severity mapping.
Airborne LiDAR technology has found wide utility in forest attribution [20
]. Terrestrial Laser Scanning (TLS) is also increasingly being used to quantify forest properties such as canopy height [27
], tree diameter [28
], LAI [31
] and canopy gap fraction [33
]. These studies have focused on deriving a biophysical description of forests at a single point in time. Change detection in forested landscapes using LiDAR (both terrestrial and airborne) has been reported by many researchers [34
]. However, this research has focused primarily on biomass accumulation or growth and dynamics of the canopy and emergent layers.
Detecting and quantifying the properties of the understorey using LiDAR technology has been less widely studied. Reported attempts [15
] quantifying various properties of the understorey have utilised a variety of metrics derived from both airborne and terrestrial LiDAR data for fire-behaviour monitoring purposes as outlined in Table 1
]). These metrics, which are primarily point density or height-based, have been used successfully to predict and estimate various understorey vegetation properties including volume, density, cover, height and biomass to varying degrees [17
LiDAR derived understorey metrics used by researchers to map understorey vegetation.
LiDAR derived understorey metrics used by researchers to map understorey vegetation.
|Metric||Property||Metric Type||Scale||LiDAR Platform||Application||Study|
|Proportion of corrected number of understorey laser hits||Cover||Point Density||Landscape||Airborne||Fire behaviour modelling||Riano
et al. |
|Proportion of corrected number of understorey laser hits||Cover||Point Density||Plot||Airborne||Ecological and forestry||Goodwin |
|Presence or absence of laser points within each cm3 space||Volume||Point density||Plot||Terrestrial||Fire behaviour modelling||Loudermilk
et al. |
|% of ground returns % of returns between 1 and 2.5 m||Cover Distribution||Point Density||Plot||Airborne||Ecological management||Martinuzzi
et al. |
|Difference between pre- and post-fire LiDAR elevation||Cover Biomass||Height||Landscape||Airborne||Fire severity||Wang and Glen |
|Variety of height metrics Ratio of Points Above and Below the Inflection Point||Cover||Both Point Density and Height||Plot||Terrestrial||Fire behaviour modelling||Rowel and Seielstad |
|Proportion of number of understorey laser hits after applying intensity filter Number of LiDAR points per square metre under 1.5 m||Cover||Point Density||Plot||Airborne||Ecological management||Wing
et al. |
|Variety of Height-based Metrics||Cover Height||Height||Plot||Airborne||Wildfire behaviour modelling||Jakubowski
et al. |
This paper is motivated by the successful use of point cloud based metrics to produce estimates of change in understorey properties. Given this success, LiDAR technology may provide an avenue to producing quantifiable and repeatable measurements of the effects of low intensity prescribed burns. This study explores the use of terrestrial LiDAR technology to produce estimates of understorey forest change which is accurate, repeatable, robust and sensitive to the low intensity nature of a prescribed burn and can be used by the land management agencies to supplement qualitative assessments of change in response to prescribed burns.
TLS technology is increasingly being used to produce accurate measurements of forest understorey conditions [15
]. However, the ability to monitor understorey forest dynamics (biomass loss or growth) using TLS has not been widely reported. Results obtained in this study indicate that fire-induced change, as an example of a disturbance in a forest understorey, is clearly discernible between multi-temporal TLS scans. The spatial distribution of change detected by most of the metrics in the fire-altered plot was found to be in agreement with visual field assessments demonstrating the concept of similarity
. TLS derived metrics were assessed for correctly reporting minimal or no change in unaltered natural landscapes. In this study, this concept of stability
was assessed using the control plot. All metrics showed only small changes between 1% and 5% in the control plot. The concept of sensitivity
was explored by the ability of the metrics to detect fire-induced change in the forest understorey. In the fire-altered plot, all metrics (except AGH skewnesschange
, AGH kurtosischange
and mean intensitychange
) showed a change between 30% and 52% whilst 10 of these exhibited bimodal distribution highlighting the subplot sensitivity of TLS metrics to detecting fire-induced change.
The methodology employed in this research is unique in that it applies bi-temporal TLS scans captured in single-scan mode to detect and quantify change in forest understorey. Scans were captured in single-scan mode and with a minimal fixed reference system which allowed for faster data acquisition and processing whilst also avoiding the need for co-registration [46
]. It has been demonstrated that whilst TLS data acquired in single-scan mode suffers from some limitations such as occlusion [43
], such datasets still have utility in change detection studies as has been demonstrated in this paper. However, in change detection studies in a forested environment, occlusion due to high tree densities needs to be carefully considered. The results of modelling the effects of occlusion in this paper show that high levels of occlusion are likely to bias the results towards changes occurring closer to the scanner’s location (i.e.
, the plot centre). Hence, for the change detection methods described in this paper using TLS to be successful, it is recommended that at least 50% plot visibility needs to be achieved.
Given that the control plot received no burn it was reasonable to expect that there would be little or no change detected between the two TLS data capture. TLS-derived metrics recorded a large change in metric values <10% voxels across the plot. The upper AGHchange percentiles (AGH90change, AGH95change and AGH99change) were relatively less stable as compared to some of the lower AGH percentiles (AGH10change to AGH50change) (for example AGH95change.σ = 0.21 and AGH50change σ = 0.14) in the control plot. It could be that in the event of a low intensity change event such as prescribed burns, environmental factors such as wind can potentially affect the stability of these metrics because of movement of features in the landscape. It must be noted that during the second set of TLS data capture the conditions in the study area were extremely windy with faint drizzle which may have also contributed to noise in the point clouds. Another reason is that these upper AGHchange percentiles are also most likely to contain change in response to phenological growth and senescence in the control plot. However, the histogram distribution for some lower AGHchange percentiles (AGH10change to AGH40change) was multimodal while for the other metrics it was normal. These lower AGHchange percentile metrics are likely to be affected by interaction with ground elements and thus may not be appropriate for describing unaltered understorey landscapes.
In the fire-altered plot, 16 TLS-derived metrics were reported as being sensitive
at detecting fire-induced change in the forest understorey. This is supported by the σ values being much larger in the fire-altered plot (0.32–1.67) in comparison to the control plot (0.14–0.45) as listed in Table 3
. A larger σ value is representative of unburnt patches interspersed with burnt areas in the fire-altered plot when examining the histograms. Histograms of AGHchange
percentiles such as AGH30change
exhibit bimodal distributions with peaks in the range of 0.3–0.5 and 0.8–1.1 in the fire-altered plot. The local maximum peak between 0.3 and 0.5 corresponds to voxels that have undergone fire-induced change. These voxels are also found to lie outside the dashed red line in Figure 5
which is indicative of a fire-induced change. Similarly, voxels around the local maximum peak centred at 0.8–1.1 are those belonging to unburnt patches in the forest understorey in the fire-altered plot. As stated earlier, values closer to 1 in ratio-based metrics is indicative of little or no change. Thus, the bimodal distribution exhibited by some TLS-derived metrics is able to account for populations belonging to two disparate groups. In this research these two groups would be burnt and unburnt forest understorey. The upper AGHchange
) and maximum AGHchange
are shown to record a lower fire-induced change (37%–53% voxels) as compared to lower AGHchange
percentiles (62%–67% voxels). The field based assessments recorded burn in 60%–70% of the plot area. This could be attributed to the environmental conditions (e.g. wind) and patchy nature of prescribed burns. If a voxel with dimensions 0.5 × 0.5 ×1.0 m was affected by fire, a few remaining stalks of grass may classify this voxel as being unburnt. These findings suggest that the upper AGHchange
percentiles may not actually be appropriate for reporting fire-induced change following low intensity prescribed burns. The mean intensitychange
metric which was stable in the control plot showed a change in the fire-altered plot. The change recorded was only 19% which suggests that the change detected by mean intensitychange
in the fire-altered plot could be due to a number of factors other than fire.
Although it has been established in this paper that TLS technology and its derived metrics are sensitive
at detecting fire-induced change in forested understorey, it is equally important to attempt to map where these changes have occurred on the ground. The patchy nature of prescribed burns is a well acknowledged fact [3
]. As shown in Figure 6
, vast areas of the control plot recorded no change which was to be expected. However, the majority of the change detected in the control plot for most metrics was found to occur in a small localised area of the plot. This corresponded and could be explained due to the defoliation of a fallen tree. However, this change was not detected by AGH99change
, mode AGHchange
and maximum AGHchange
metrics as the large woody component of the tree was still present. This defoliation appears to present a similar pattern to fire induced change within the fire-altered plot where large woody debris was still present following prescribed burns. Whilst it was ascertained that the ideal metric should remain stable and detect little or no-change in the control plot, it would be inaccurate if the metric did not detect a real and a substantial non fire-induced change in the forest understorey even in the undisturbed plot.
In the fire-altered plot, unburnt patches interspersed with burnt patches are reported by the majority of the metrics (except AGH skewnesschange
and AGH kurtosischange
). Figure 4
A shows an image from the fire-altered plot highlighting the mosaic landscape as a result of the prescribed burn. Although the degree of patchiness and plot area burnt is extremely variable amongst the metrics (22%–71%), the ability of TLS technology to map this “patchiness” is an extremely promising finding. The level of patchiness within burnt areas can determine the proportion of vegetation population exposed to heat. This can inform vegetation mortality rates and seed germination [48
]. Patchiness can also help predict fire intensity. Low intensity fires are shown to be significantly patchier than higher intensity fires [50
]. The pattern of the burn represented by the binary maps (Figure 6
) for many metrics (AGH10change
, mean AGHchange
and point countchange
) is similar
and closer to the field assessments of burnt areas. This includes both the percentage area burnt and spatial distribution of burnt areas in the fire-altered plot. These metrics consistently detected a much larger burnt area on the eastern side of the plot (Figure 4
B) in comparison to the west with a large unburnt patch in the southwest region of the plot.
From the above analysis AGH50change, mean AGHchange and point countchange seem to be the most suitable individual metrics for attributing change in an altered understorey forest whilst remaining stable in an undisturbed one. It is important to ensure that the metrics being used in change detection studies remain stable in an undisturbed landscape whilst remaining sensitive at attributing change in an altered landscape. It is also important to consider that the metrics being used report spatial distribution of change similar to change occurring on the ground. These findings suggest that TLS technology and TLS-derived metrics can be used to supplement the routine qualitative field assessments of change which are often based on visual estimates thereby providing a method to allow for a more quantified and accurate reporting approach. The burn maps showing the spatial distribution of change can be used by land managers to identify areas in need of urgent rehabilitation.
Future work could involve exploring the utility of the method presented in this research to quantify biomass change. This research could be further developed by exploring the binary change detection maps for mapping different burn severity levels. This could also help identify unburnt patches which can help in understanding ecological impacts on fire-sensitive plants, watershed hydrology and soil stability amongst others. This may involve using a combination of the metrics used in this paper given their demonstrated differences in each metric shown here. Given that post-burn TLS scans were carried out within two weeks of the burn event, they helped ascertain change in the landscape in response to the burn. A longitudinal study involving multi-temporal scans over longer time scales can help monitor fuel accumulation, post-fire regeneration dynamics and vegetation senescence.