The impact of training data characteristics on ensemble classification of land cover

Supervised classification of remote sensing imagery has long been recognised as an essential technology for large area land cover mapping. Remote sensing derived land cover and forest classification maps are important sources of information for understanding environmental processes and informing natural resource management decision making. In recent years, the supervised transformation of remote sensing data into thematic products has been advanced through the introduction and development of machine learning classification techniques. Applied to a variety of science and engineering problems over the past twenty years (Lary et al., 2016), machine learning provides greater accuracy and efficiency than traditional parametric classifiers, capable of dealing with large data volumes across complex measurement spaces. The Random forest (RF) classifier in particular, has become popular in the remote sensing community, with a range of commonly cited advantages, including its low parameterisation requirements, excellent classification results and ability to handle noisy observation data and outliers, in a complex measurement space and small training data relative to the study area size. In the context of large area land cover classification for forest cover, using multisource remote sensing and geospatial data, this research sets out to examine proposed advantages of the RF classifier - insensitivity to training data noise (mislabelling) and handling training data class imbalance. Through margin theory, the research also investigates the utility of ensemble learning – in which multiple base classifiers are combined to reduce generalisation error in classification – as a means of designing more efficient classifiers, improving classification performance, and reducing reference (training and test) data redundancy. The first part of the thesis (chapters 2 and 3) introduces the experimental setting and data used in the research, including a description (in chapter 2) of the sampling framework for the reference data used in classification experiments that follow. Chapter 3 evaluates the performance of the RF classifier applied across 7.2 million hectares of public land study area in Victoria, Australia. This chapter describes an open-source framework for deploying the RF classifier over large areas and processing significant volumes of multi-source remote sensing and ancillary spatial data. The second part of this thesis (research chapters 4 through 6) examines the effect of training data characteristics (class imbalance and mislabelling) on the performance of RF, and explores the application of the ensemble margin, as a means of both examining RF classification performance, and informing training data sampling to improve classification accuracy. Results of binary and multiclass experiments described in chapter 4, provide insights into the behaviour of RF, in which training data are not evenly distributed among classes and contain systematically mislabelled instances. Results show that while the error rate of the RF classifier is relatively insensitive to mislabelled training data (in the multiclass experiment, overall 78.3% Kappa with no mislabelled instances to 70.1% with 25% mislabelling in each class), the level of associated confidence falls at a faster rate than overall accuracy with increasing rates of mislabelled training data. This study section also demonstrates that imbalanced training data can be introduced to reduce error in classes that are most difficult to classify. The relationship between per-class and overall classification performance and the diversity of members in a RF ensemble classifier, is explored through experiments presented in chapter 5. This research examines ways of targeting particular training data samples to induce RF ensemble diversity and improve per-class and overall classification performance and efficiency. Through use of the ensemble margin, this study offers insights into the trade-off between ensemble classification accuracy and diversity. The research shows that boosting diversity among RF ensemble members, by emphasising the contribution of lower margin training instances used in the learning process, is an effective means of improving classification performance, particularly for more difficult or rarer classes, and is a way of reducing information redundancy and improving the efficiency of classification problems. Research chapter 6 looks at the application of the RF classifier for calculating Landscape Pattern Indices (LPIs) from classification prediction maps, and examines the sensitivity of these indices to training data characteristics and sampling based on the ensemble margin. This research reveals a range of commonly used LPIs to have significant sensitivity to training data mislabelling in RF classification, as well as margin-based training data sampling. In conclusion, this thesis examines proposed advantages of the popular machine learning classifier, Random forests - the relative insensitivity to training data noise (mislabelling) and its ability to handle class imbalance. This research also explores the utility of the ensemble margin for designing more efficient classifiers, measuring and improving classification performance, and designing ensemble classification systems which use reference data more efficiently and effectively, with less data redundancy. These findings have practical applications and implications for large area land cover classification, for which the generation of high quality reference data is often a time consuming, subjective and expensive exercise

Property protection from extreme bushfire events under the influence of climate change

Natural disasters give rise to loss of life, property (including homes, industry and livelihood) and environmental values and may be increasing with the impacts of climate change. Bushfires are a natural part of the Australian landscape and the ecology of the range of biota found within the various landscapes. They pose significant risks to people and property and require increasing demands for management in the face of these risks. Bushfires (also known as wildland fires) can be highly complex both spatially and temporally within the landscape. Attempts to better explain such events has given rise to a range of fire behaviour models to quantify fire characteristics such as rate of spread, fire line intensity, flame heights and spotting distances. However, there is a need to develop clear criteria when applying these models in land use planning and construction practice for bushfire protection. In Australia, a number of empirical models have been developed to quantify bushfire behaviour. These models have limitations, both in their application and in their capacity to draw upon data with which to utilise them. Two such models are used in the current study, being the McArthur Forest Fire Danger Meter (Mark 5) and the more recent Dry Eucalypt Forest Fire Model, and both have been used to develop design bushfire(dimensions and characteristics of a bushfire in a regional setting) conditions for the state of New South Wales (NSW). These models use different input parameters, as well as different intermediate parameters to describe fire behaviour. In addition, the study utilises and extends the forest fire danger index (FFDI) andKeetch-Byram Drought Index (KBDI) data to all 21 NSW fire weather districts. It also provides a new database for daily fuel moisture content (FMC). By using case studies that show 'validation' of methodological approaches, it can be confirmed that suitable extreme value assessment statistical techniques can be applied to the outputs of the identified models for the purposes of determining design bushfires. The study also seeks to give greater understanding of the frequency and shifts in the seasonality of fire weather, and changes in bushfire severity as consequences of climate change. A technique of generalised extreme value analysis based on moving data window to detect the impact of climate change on recurrence values of various indices has been developed. The evaluation of trends in fire weather through various metrics for FFDI, FMC and KBDI have revealed that a number of districts in NSW exhibit pronounced shifts at the extreme arising from climate change. However, the role of the El Nino Southern Oscillation does not appear to play a major role in these shifts over the long term. The current investigations have provided significant improvements on previous investigations such as improved datasets providing wider representation of all the NSW fire weather districts and covering a longer period of time; the use of new metrics, including the use of the GEV assessment through a moving period approach; the metrics being applied to fire weather parameters other than FFDI; and, trends in fire weather parameters being considered in conjunction with other global factors. The methodology and the technique developed in the current study have the potential to be utilised in many parts of the world for the development of design conditions and to study the impact of the climate change on the local fire weather conditions

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Using earth observation satellites to explore forest dynamics across large areas

A third of the land on earth is covered by forests. Forests provide valuable resources and essential ecosystem services, including filtering air and water, harbouring biodiversity and managing the carbon cycle. Regular monitoring and reporting across various indicators is necessary to manage forests sustainably. Due to the vastness of forests, satellite Earth observation is one of the most practical and cost-effective ways to monitor forests. The regular and consistent measurements provided from space enable time series analysis, which can reveal trends over time. The temporal, spatial and radiometric depth of the Landsat archive, which extends back to 1972 in some cases, is one of the most useful resources for monitoring forest dynamics across large areas. Analysing forest disturbance and recovery trends using Landsat has recently become widespread, particularly since the opening of the image archive in 2008. However, deriving useful information from the data is challenging on many fronts, including overcoming cloud-cover, differentiating true changes from noise and relating spectral measurements to meaningful outputs. In addition, large data volumes create hurdles for processing and storage. This study presents new techniques for exploiting the Landsat archive in relation to monitoring and measuring forest disturbance and recovery across large areas. Landsat data were processed through a series of steps, analysed in time series, and combined with other data sources to produce mapped outputs and statistical summaries, which can be interpreted by non-experts. The spatial extent of the analysis expands across multiple scales - from local and regional to global (temperate and boreal forests). Firstly, eight Landsat spectral indices were assessed to determine their sensitivity to forest disturbance (caused by wildfire) and recovery in southeast Australian forests. Results indicated that indices making use of the shortwave infrared wavelengths were more reliable indicators of forest disturbance and recovery than indices using only the red and near-infrared wavelengths. Following this exploratory analysis, three indices and two change detection algorithms were evaluated in terms of their ability to detect forest disturbance. Results showed that the LandTrendr algorithm with the Normalised Burn Ratio (NBR) was the most accurate single algorithm/index combination (overall error 21%). However, results were greatly improved by using an ensemble approach. A Random Forests model combining several Landsat-derived metrics with multiple indices, trained with human interpreted reference data, had an overall error of 7%. A notable finding was that priming the training data with confusing cases (commission errors from the change detection algorithms) led to increased accuracy. One Random Forests model was used to create annual forest disturbance maps (1989-2017) across the state of Victoria, Australia. These maps, in conjunction with each pixel's temporal trajectory, were used to extract metrics for spectral disturbance magnitude and recovery length across 2 million ha of burned forest in southeast Australia. The association between disturbance magnitude and forest recovery length, as measured spectrally, was then explored. A novel patch-based technique was used to isolate the disturbance-recovery relationship from confounding factors such as climate, elevation and soil type. The results showed statistically significant differences across bioregions and forest types. The patch-based method demonstrated how Landsat time series can be harnessed to explore ecological changes. The methods developed above were then employed over a much larger area, to investigate trends in fire disturbance and forest recovery in temperate and boreal forests worldwide. This work used both MODIS and Landsat data, through the Google Earth Engine platform, to look at trends in burned area, fire severity and forest recovery across almost 2 billion ha of forests, over the last 18 years. Burned area results showed significant increasing trends in two cases: coniferous forests in Canada and Mediterranean forests in Chile. A significant decreasing trend was found in temperate mixed forests in China. An assessment of fire severity, as measured by Landsat spectral change, highlighted possible trends in a few cases; most notably, the Russian taiga, where increasing severity was observed. An analysis of forest recovery, based on Landsat time series, indicated recovery times were accelerating in many regions. However, given the relatively short time-period analysed, these results should be interpreted with caution. The results presented in this thesis demonstrate the power of Earth observation satellites in monitoring forests at the landscape scale. Although forests are complex systems that are influenced by a myriad of factors, the regular and consistent measurements provided by satellites can be analysed in time series to provide inter-comparable results across large areas. This can broaden our understanding of the dynamic nature of forests, and in doing so, help progress towards their sustainable management

Classification and mapping of the woody vegetation of Gonarezhou National Park, Zimbabwe

Within the framework of the Great Limpopo Transfrontier Conservation Area (GLTFCA), the purpose of this study was to produce a classification of the woody vegetation of the Gonarezhou National Park, Zimbabwe, and a map of its potential distribution. Cover-abundance data of woody species were collected in 330 georeferenced relevés across the Park. These data were used to produce two matrices: the first one using the cover-abundance values as collected in five height layers and the second one based on merging the layers into a single cover value for each species. Automatic classifications were produced for both matrices to determine the optimal number of vegetation types. The two classification approaches both produced 14 types belonging to three macro-groups: mopane, miombo and alluvial woodlands. The results of the two classifications were compared looking at the constant, dominant and diagnostic species of each type. The classification based on separate layers was considered more effective and retained. A high-resolution map of the potential distribution of vegetation types for the whole study area was produced using Random Forest. In the model, the relationship between bioclimatic and topographic variables, known to be correlated to vegetation types, and the classified relevés was used. Identified vegetation types were compared with those of other national parks within the GLTFCA, and an evaluation of the main threats and pressures was conducted. Conservation implications: Vegetation classification and mapping are useful tools for multiple purposes including: surveying and monitoring plant and animal populations, communities and their habitats, and development of management and conservation strategies. Filling the knowledge gap for the Gonarezhou National Park provides a basis for standardised and homogeneous vegetation classification and mapping for the entire Great Limpopo Transfrontier Conservation Area

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models