Karin Reinke

Development of a Multi-Spatial Resolution Approach to the Surveillance of Active Fire Lines Using Himawari-8

Satellite remote sensing is regularly used for wildfire detection, fire severity mapping and burnt area mapping. Applications in the surveillance of wildfire using geostationary-based sensors have been limited by low spatial resolutions. With the launch in 2015 of the AHI (Advanced Himawari Imaginer) sensor on board Himawari-8, ten-minute interval imagery is available covering an entire earth hemisphere across East Asia and Australasia. Existing active fire detection algorithms depend on middle infrared (MIR) and thermal infrared (TIR) channels to detect fire. Even though sub-pixel fire detection algorithms can detect much smaller fires, the location of the fire within the AHI 2 × 2 km (400 ha) MIR/TIR pixel is unknown. This limits the application of AHI as a wildfire surveillance and tracking sensor. A new multi-spatial resolution approach is presented in this paper that utilizes the available medium resolution channels in AHI. The proposed algorithm is able to map firelines at a 500 m resolution. This is achieved using near infrared (NIR) (1 km) and RED (500 m) data to detect burnt area and smoke within the flagged MIR (2 km) pixel. Initial results based on three case studies carried out in Western Australia shows that the algorithm was able to continuously track fires during the day at 500 m resolution. The results also demonstrate the utility for wildfire management activities.

Assessing metrics for estimating fire induced change in the forest understorey structure using terrestrial laser scanning

Quantifying post-fire effects in a forested landscape is important to ascertain burn severity, ecosystem recovery and post-fire hazard assessments and mitigation planning. Reporting of such post-fire effects assumes significance in fire-prone countries such as USA, Australia, Spain, Greece and Portugal where prescribed burns are routinely carried out. This paper describes the use of Terrestrial Laser Scanning (TLS) to estimate and map change in the forest understorey following a prescribed burn. Eighteen descriptive metrics are derived from bi-temporal TLS which are used to analyse and visualise change in a control and fire-altered plot. Metrics derived are Above Ground Height-based (AGH) percentiles and heights, point count and mean intensity. Metrics such as AGH50change, mean AGHchange and point countchange are sensitive enough to detect subtle fire-induced change (28%–52%) whilst observing little or no change in the control plot (0–4%). A qualitative examination with field measurements of the spatial distribution of burnt areas and percentage area burnt also show similar patterns. This study is novel in that it examines the behaviour of TLS metrics for estimating and mapping fire induced change in understorey structure in a single-scan mode with a minimal fixed reference system. Further, the TLS-derived metrics can be used to produce high resolution maps of change in the understorey landscape.

A Comparison of High and Low Gain DMSP/OLS Satellite Images for the Study of Socio-Economic Metrics

The Operational Linescan System (OLS) onboard the Defense Meteorological Satellite Program (DMSP) group of satellites, unlike other passive remote sensing sensors, is capable of recording the emissions from artificial lights on the earth surface. Along with detecting light from forest fires, shipping fleets and gas flares, the OLS sensor also records the light emitted from cities at night. This paper reports on a study that uses the DMSP Operational Linescan (DMSP-OLS) images with fixed gain settings of 20 dB and 50 dB to model selected metrics used in the Indian census for the state of Maharashtra. The study firstly looks into the utility of non-composited single fixed gain radiance calibrated DMSP-OLS products for proposing a method which might help to build a surrogate method for Indian census. Several parameters are considered in this analysis, with detailed focus on population density, total population and proportion of households with electricity access for 35 districts within the state of Maharashtra. Results show that spatial scale plays an important role in selection of the images and gains. Secondly, this study provides a relative assessment of gain setting for the DMSP-OLS images in an urban Indian context. Images with a gain of 50 dB prove suitable for larger areas while those with a gain of 20 dB give better results at a smaller spatial scale. Statistical analysis and residual maps of spatial distribution of total population and population density validate the result.

An assessment of pre- and post fire near surface fuel hazard in an Australian dry sclerophyll forest using point cloud data captured using a terrestrial laser scanner

Assessment of ecological and structrual changes induced by fire events is important for understanding the effects of fire, and planning future ecological and risk mitigation strategies. This study employs Terrestrial Laser Scanning (TLS) data captured at multiple points in time to monitor the changes in a dry sclerophyll forest induced by a prescribed burn. Point cloud data was collected for two plots; one plot undergoing a fire treatment, and the second plot remaining untreated, thereby acting as the control. Data was collected at three epochs (pre-fire, two weeks post fire and two years post fire). Coregistration of these multitemporal point clouds to within an acceptable tolerance was achieved through a two step process utilising permanent infield markers and manually extracted stem objects as reference targets. Metrics describing fuel height and fuel fragmentation were extracted from the point clouds for direct comparison with industry standard visual assessments. Measurements describing the change (or lack thereof) in the control plot indicate that the method of data capture and coregistration were achieved with the required accuracy to monitor fire induced change. Results from the fire affected plot show that immediately post fire 67% of area had been burnt with the average fuel height decreasing from 0.33 to 0.13 m. At two years post-fire the fuel remained signicantly lower (0.11 m) and more fragmented in comparison to pre-fire levels. Results in both the control and fire altered plot were comparable to synchronus onground visual assessment. The advantage of TLS over the visual assessment method is, however, demonstrated through the use of two physical and spatially quantifiable metrics to describe fuel change. These results highlight the capabilities of multitemporal TLS data for measuring and mapping changes in the three dimensional structure of vegetation. Metrics from point clouds can be derived to provide quantified estimates of surface and near-surface fuel loss and accumulation, and inform prescribed burn efficacy and burn severity reporting.

A model for solving the prescribed burn planning problem

The increasing frequency of destructive wildfires, with a consequent loss of life and property, has led to fire and land management agencies initiating extensive fuel management programs. This involves long-term planning of fuel reduction activities such as prescribed burning or mechanical clearing. In this paper, we propose a mixed integer programming (MIP) model that determines when and where fuel reduction activities should take place. The model takes into account multiple vegetation types in the landscape, their tolerance to frequency of fire events, and keeps track of the age of each vegetation class in each treatment unit. The objective is to minimise fuel load over the planning horizon. The complexity of scheduling fuel reduction activities has led to the introduction of sophisticated mathematical optimisation methods. While these approaches can provide optimum solutions, they can be computationally expensive, particularly for fuel management planning which extends across the landscape and spans long term planning horizons. This raises the question of how much better do exact modelling approaches compare to simpler heuristic approaches in their solutions. To answer this question, the proposed model is run using an exact MIP (using commercial MIP solver) and two heuristic approaches that decompose the problem into multiple single-period sub problems. The Knapsack Problem (KP), which is the first heuristic approach, solves the single period problems, using an exact MIP approach. The second heuristic approach solves the single period sub problem using a greedy heuristic approach. The three methods are compared in term of model tractability, computational time and the objective values. The model was tested using randomised data from 711 treatment units in the Barwon-Otway district of Victoria, Australia. Solutions for the exact MIP could be obtained for up to a 15-year planning only using a standard implementation of CPLEX. Both heuristic approaches can solve significantly larger problems, involving 100-year or even longer planning horizons. Furthermore there are no substantial differences in the solutions produced by the three approaches. It is concluded that for practical purposes a heuristic method is to be preferred to the exact MIP approach.

A current perspective on Australian woody vegetation maps and implications for small remnant patches

Digital map products are routinely used by land managers and policy makers for environmental decision-making. This paper assesses the ability of such products to detect woody vegetation, particularly remnant patches which serve as critical landscape structures. Comparisons are made between two map products (NCAS and a SPOT-based classification) and a high spatial resolution reference dataset, across contrasting landscapes. Spatial analysis and statistical association tests are used to determine the ability of these map products to produce accurate measurements of woody vegetation. It is demonstrated that landscape structure is fundamental in determining the fitness-for-use and function of the digital map products.

Non‐destructive estimation of above‐ground surface and near‐surface biomass using 3D terrestrial remote sensing techniques

Quantitative measurements of above-ground vegetation biomass are vital to a range of ecological and natural resource management applications. Remote-sensing techniques, such as terrestrial laser scanning (TLS) and image-based point clouds, are potentially revolutionary techniques for measuring vegetation biomass and deriving other related, structural metrics for these purposes. Surface vegetation biomass (up to 25 cm) in pasture, forest, and woodland environments is estimated from a 3D point cloud derived from a small number of digital images. Volume is calculated, using the 3D cloud and regressed against dry weight to provide an estimate of biomass. Assessment of the method is made through comparison to 3D point clouds collected through TLS surveys. High correlation between destructively sampled biomass and vegetation volume derived from TLS and image-based point clouds in the pasture (TLS r2=0·75, image based r2=0·78), dry grassy forest (TLS r2=0·73, image based r2=0·87) and lowland forest (TLS r2=0·74, image based r2=0·63) environments was found. Occlusion caused by standing vegetation in the woodland environment resulted in moderate correlation between TLS derived volume and biomass (r2=0·50). The effects of surrounding vegetation on the image-based technique resulted in 3D point clouds being resolved for only 40% of the samples in this environment. The results of this study demonstrate that image-based point cloud techniques are highly viable for the measurement of surface biomass. In contrast to TLS, volume and biomass data can be captured using low-cost equipment and relatively little expertise.

A Broad-Area Method for the Diurnal Characterisation of Upwelling Medium Wave Infrared Radiation

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location’s recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area’s local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

Changes in the spectral features of fuel layers of an Australian dry sclerophyll forest in response to prescribed burning

Prescribed burning is a landscape management tool often used for asset protection and ecological maintenance. Accordingly, there is a need to understand the effects fire has on the landscape and how these changes might be measured. Remote sensing pre- and post-burn has the potential to inform decisions about burn severity and ecosystem sensitivity to fire. The aim of this research was to identify changes in the electromagnetic radiation (EMR) following a prescribed burn in the fuel layers of an Australian dry sclerophyll forest using a hyperspectral radiometer (HSR). Results indicated three major changes in spectral features (1) absence of the green reflectance peak (550nm), (2) flattening or absence of red edge (680–750nm) and (3) disappearance of water absorption feature (970nm). The greatest difference in the intensity and shape of spectral signatures from pre-burn levels for all the targets occurred within the first 2 weeks post-burn. The trend of a return to the pre-burn spectral signature was seen to occur from week 5 onwards for most targets. These findings have important implications for identifying suitable remote sensing parameters for monitoring the effects of fire on vegetation.

The role of satellite data in census: Case study of an Indian State

Countries, such as India, conduct a census collection every ten years. Currently census in India is carried out manually, therefore suffering from a number of shortcomings including inconsistency issues, the Modifiable Areal Unit Problem (MAUP) and large temporal acquisition timeframes. This paper proposes a surrogate census method using satellite images captured at night by DMSP-OLS satellites to overcome some of these drawbacks. The lights on the earth surface captured by this satellite represent areas of human habitation. Correlations between stable lights and brightness information with available census metrics from the last Indian census (2001) were calculated using bootstrapping techniques. Linear regression and multivariate analyses were subsequently performed and models proposed for each of the selected census metrics (e.g population density, number of households per square Kilometre, percentage of households with cars, jeeps and vans, Per Capita District Domestic Product (PCDDP) and urban population density) with results ranging from r 2 of 0.8 to 0.9 at the 95% confidence interval. Census metrics unavailable at spatial scales lower than districts were also predicted using the proposed models and maps were derived showing the predicted measures. The results demonstrate that DMSP-OLS night-time images may be successfully used to estimate census variables in real time.