Mapping forest canopy height across large areas by upscaling ALS estimates with freely available satellite data

Operational assessment of forest structure is an on-going challenge for land managers, particularly over large, remote or inaccessible areas. Here, we present an easily adopted method for generating a continuous map of canopy height at a 30 m resolution, demonstrated over 2.9 million hectares of highly heterogeneous forest (canopy height 0-70 m) in Victoria, Australia. A two-stage approach was utilized where Airborne Laser Scanning (ALS) derived canopy height, captured over ~18% of the study area, was used to train a regression tree ensemble method; random forest. Predictor variables, which have a global coverage and are freely available, included Landsat Thematic Mapper (Tasselled Cap transformed), Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index time series, Shuttle Radar Topography Mission elevation data and other ancillary datasets. Reflectance variables were further processed to extract additional spatial and temporal contextual and textural variables. Modeled canopy height was validated following two approaches; (i) random sample cross validation, and (ii) with 108 inventory plots from outside the ALS capture extent. Both the cross validation and comparison with inventory data indicate canopy height can be estimated with a Root Mean Square Error (RMSE) of ≤ 31% (~5.6 m) at the 95th percentile confidence interval. Subtraction of the systematic component of model error, estimated from training data error residuals, rescaled canopy height values to more accurately represent the response variable distribution tails e.g., tall and short forest. Two further experiments were carried out to test the applicability and scalability of the presented method. Results suggest that (a) no improvement in canopy height estimation is achieved when models were constructed and validated for smaller geographic areas, suggesting there is no upper limit to model scalability; and (b) training data can be captured over

LiDAR-Landsat Covariance for Predicting Canopy Fuels

Managing wildfires in the western United States is becoming increasingly complex. Visualizing and quantifying canopy structures allows fire managers to both plan for fire and track recovery. Light detecting and ranging, or LiDAR can measure forests in three dimensions, but has limited spatial and temporal coverage. LiDAR-Landsat covariance uses machine learning to fill in the spatial and temporal gaps of LiDAR coverage with supplemental Landsat imagery. However, in order to capture real forest dynamics, a model needs to be stable enough to detect long term trends, sensitive to episodic disturbance, and general enough to work on multiple landcovers. The purpose of this research is to refine the methodology behind LiDAR-Landsat covariance and assess if these predictions can yield sable and ecologically sensible time series to track forest fire recovery over time. Gradient boosted machine models (GBMs) were built to predict canopy cover, height, and base height. Then, they were tested on a series of validation sites in order to quantify the spatial and temporal sources of error associated with these models. Finally, the models were used to predict the trajectories of canopy cover, height, and base height on 164 fire scars in Montana, Idaho and Wyoming over the course of 36 years. The models were sensitive to moderate and high severity disturbance, both on an incident wide and pixel by pixel basis. Overall model R2 values were 0.89 for canopy cover, 0.84 for height, and 0.88 for base height. Year to year variability in canopy cover on validation sites was 2.3%. Height had more variability due to a sensor artifact from the transition from Landsat 5 to Landsat 8. On the Lost Fire the model found high severity fire corresponded with greater canopy fuel losses on a pixelwise basis. The models also detected canopy recovery, and found four distinct trajectories in which burned sites recover from disturbance. Seventy-seven percent of sites fully recovered canopy cover to pre-fire conditions within the 36-year time series. Further refinement of GBM based LiDAR-Landsat covariance can increase the sensitivity to smaller disturbances and reduce the impact of model error on performance

Quantifying urban forest structure in Greater Manchester with open-access remote sensing datasets

A growing body of evidence links the adverse impacts of expanding urbanism including increased air pollution, and exposure to heat stress with the removal of vegetation within cities. As the global population is estimated to reach 10 billion by 2050, urban trees and extended green infrastructure are advocated as a remedy to the effects of increasing urbanisation through delivering a multitude of ecosystem services including pollution abatement, reduction of urban heat islands and social benefits. To accurately quantify the services afforded by urban forests, it is vital to measure the extent and structure of urban forests, including through time, in addition for assessing the success of policy to maintain and promote green infrastructure assets. Current ground fieldwork methods rely on plot networks to measure a range of metrics across the tree population; these methods are locally comprehensive however do not fully describe the spatial heterogeneity of the urban fabric, given the limited sampling and often laborious data collection. The increasing availability and access to remote sensing/earth observation datasets provide an opportunity to collate synoptic measurements across large regions. Direct measurements though active sensors, particularly LiDAR, have seen wide adoption when measuring forest structure, however surveys can be expensive, and coverage limited. Fusing LiDAR with satellite imagery though machine learning methods such as Random Forests can drastically increase coverage through capturing complex non linear relationships. A framework is presented to estimate forest structure using open access data and software across Greater Manchester. This workflow estimates three forest structure metrics, canopy cover, canopy height and tree number/density. Random forest models were trained with airborne Environment Agency LiDAR, and predictor variables derived from Sentinel 2 and ancillary climatic and topographic datasets. Results indicate estimates in 2018, mean canopy cover of 14.9% (RMSE = 13.75), mean canopy height of 14.83m (RMSE = 6.14m) and home to ~2.6 million trees (RMSE = 0.95 per pixel). Results appear to illustrate higher canopy cover than i-Tree ground data but lower tree density and canopy heights. Altering input resolution was found to change structure estimations, attributed to methodological issues. Forest structure estimates were found to change from 2018 to 2021 indicating net decreases in canopy cover and number of trees, while average canopy height was found to increase, although change distribution of metrics across boroughs is not equal. Presented methods can augment traditional inventory methods and can assist urban forest/land managers to produce consistent monitoring information to support the sustainability of urban forests worldwide

Quantifying urban forest structure in Greater Manchester with open-access remote sensing datasets

A growing body of evidence links the adverse impacts of expanding urbanism including increased air pollution, and exposure to heat stress with the removal of vegetation within cities. As the global population is estimated to reach 10 billion by 2050, urban trees and extended green infrastructure are advocated as a remedy to the effects of increasing urbanisation through delivering a multitude of ecosystem services including pollution abatement, reduction of urban heat islands and social benefits. To accurately quantify the services afforded by urban forests, it is vital to measure the extent and structure of urban forests, including through time, in addition for assessing the success of policy to maintain and promote green infrastructure assets. Current ground fieldwork methods rely on plot networks to measure a range of metrics across the tree population; these methods are locally comprehensive however do not fully describe the spatial heterogeneity of the urban fabric, given the limited sampling and often laborious data collection. The increasing availability and access to remote sensing/earth observation datasets provide an opportunity to collate synoptic measurements across large regions. Direct measurements though active sensors, particularly LiDAR, have seen wide adoption when measuring forest structure, however surveys can be expensive, and coverage limited. Fusing LiDAR with satellite imagery though machine learning methods such as Random Forests can drastically increase coverage through capturing complex non linear relationships. A framework is presented to estimate forest structure using open access data and software across Greater Manchester. This workflow estimates three forest structure metrics, canopy cover, canopy height and tree number/density. Random forest models were trained with airborne Environment Agency LiDAR, and predictor variables derived from Sentinel 2 and ancillary climatic and topographic datasets. Results indicate estimates in 2018, mean canopy cover of 14.9% (RMSE = 13.75), mean canopy height of 14.83m (RMSE = 6.14m) and home to ~2.6 million trees (RMSE = 0.95 per pixel). Results appear to illustrate higher canopy cover than i-Tree ground data but lower tree density and canopy heights. Altering input resolution was found to change structure estimations, attributed to methodological issues. Forest structure estimates were found to change from 2018 to 2021 indicating net decreases in canopy cover and number of trees, while average canopy height was found to increase, although change distribution of metrics across boroughs is not equal. Presented methods can augment traditional inventory methods and can assist urban forest/land managers to produce consistent monitoring information to support the sustainability of urban forests worldwide

Comparaison des stratégies directe et indirecte pour la cartographie du volume forestier : cas de la forêt boréale à Terre-Neuve, Canada.

La gestion des ressources forestières sur de grands territoires requiert une cartographie précise des attributs de la forêt. Les programmes d’inventaire forestiers traditionnels s’appuient sur des photographies aériennes et des interprètes expérimentés pour cartographier les attributs forestiers. Cela nécessite une grande quantité de ressources. Une stratégie directe de cartographie alternative consiste à établir des relations statistiques entre (i) les attributs mesurés dans des placettes forestières et (ii) les données spectrales des images satellitaires combinées à des couches spatiales disponibles (par ex., relief, climat). Malheureusement, l’utilisation de l’imagerie multispectrale à elle seule n’atteint souvent pas un niveau de précision satisfaisant à cause de la saturation connue du signal optique à fort volume forestier. La donnée LiDAR aéroportée (ALS) sur une portion du territoire peut être utilisée pour améliorer la précision des cartes forestières par une stratégie de cartographie indirecte à deux phases. La phase 1 combine les mesures des placettes et la donnée ALS. La phase 2 combine la carte produite à la phase 1 avec des données satellitaires/spatiales pour une carte du territoire étendu. Ce projet vise à comparer la prédiction du volume total de bois de l’île de Terre-Neuve obtenue selon les stratégies directe et indirecte, ainsi qu’à comparer les approches de modélisation statistiques paramétriques et non paramétriques (regression Ordinary least squares (OLS) vs Random Forest (RF)) pour chacune des stratégies. Les modèles de la stratégie indirecte utilisés pour prédire le volume total sur les placettes de validation, basées sur les données ALS, expliquaient systématiquement une faible variance (16 % pour OLS et 11 % pour RF), avec des erreurs de prédiction relatives élevées pour les deux approches (47 % et 50 %). Les modèles de la stratégie directe, développés avec les placettes au sol, expliquaient une variance similaire de celle obtenue par la stratégie indirecte pour les deux approches (11 % et 14 %), avec des erreurs de prédiction tout aussi élevées (50 % et 56 %). Les modèles développés selon la stratégie indirecte n’ont pas entrainé une augmentation significative de la correspondance entre les valeurs observées et prédites, et ce, pour les deux approches. Ces résultats peuvent être expliqués par de nombreux facteurs, tels que le faible niveau de représentativité des placettes, la résolution différente des images satellitaires ou la densité de points des données ALS

Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area

Assessment of vegetation over large, remote and inaccessible areas is an ongoing challenge for land managers in Australia and around the world. This research aimed to develop metrics, techniques and acquisition specifications that are suitable for characterising vegetation across large forested areas. New methods were therefore required to be transferable between forest types as well as robust where forest structure is unknown a priori. Remote sensing techniques were utilised as they have been previously identified as key in forest assessment, owing to their synoptic capture as well as relative cost. Additionally, active remote sensing instruments, such as LiDAR, are capable of sensing 3-dimensional canopy structure. Canopy height and the canopy height profile are fundamental descriptors of forest structure and can be used for estimating biomass, habitat suitability and fire susceptibility. To investigate the ability of remote sensing to characterise vegetation structure across large areas, three key research questions were formulated: I. Which metrics of canopy height and vertical canopy structure are suitable for application across forested landscapes? II. What is the appropriate ALS sampling frequency for attribution of forest structure across different forest types? III. How can plot level estimates of canopy structure be scaled to generate continuous large area maps? A number of inventory measured canopy height metrics were compared with LiDAR analogues, these were shown to be accurate at estimating canopy height and transferable between forest types. Existing techniques for attributing the canopy height profile were found to be inadequate when applied across heterogeneous forests. Therefore a new technique was developed that utilised a nonparametric regression of LiDAR derived gap probability that identified major canopy features e.g. dominant canopy strata and shade tolerant layers beneath. The impact of sampling frequency was assessed using three key descriptors of canopy structure at six sites across Australia covering a range of forest types. The research concluded that forest structure can be adequately characterised with a pulse density of 0.5 pulses m-2 when compared to a higher density acquisition - 10 pulses m-2. At pulse density of <0.5 pulses m-2, the inability to generate an adequate ground surface model lead to poor results, particularly in high biomass forest. The outcomes of this research will allow land managers to specify lower pulse densities when commissioning LiDAR capture, which may result in significant cost savings. Finally, LiDAR derived plot estimates were scaled to an area of 2.9 million hectares of forest, where forest type ranged from short, open woodland to tall, closed canopy rainforest. Attribution was achieved using a two-stage sampling approach utilising the ensemble regression technique Random Forest. Predictor variables included freely available datasets such as Landsat TM and MODIS satellite imagery. Canopy height was estimated with a RMSE of 30% or ~5.5 m when validated with an independent forest inventory dataset. Attribution of the canopy height profile was less successful for a number of reasons, for example, the relatively high spatial variability of shade tolerant vegetation. Inclusion of additional synoptic datasets, such as radar, may improve this in the future

NEW, MULTI-SCALE APPROACHES TO CHARACTERIZE PATTERNS IN VEGETATION, FUELS, AND WILDFIRE

Pattern and scale are key to understanding ecological processes. My dissertation research aims for novel quantification of vegetation, fuel, and wildfire patterns at multiple scales and to leverage these data for insights into fire processes. Core to this motivation is the 3-dimensional (3-D) characterization of forest properties from light detection and ranging (LiDAR) and structure-from-motion (SfM) photogrammetry. Analytical methods for extracting useable information currently lag the ability to collect such 3-D data. The chapters that follow focus on this limitation blending interests in machine learning and data science, remote sensing, wildland fuels (vegetation), and wildfire. In Chapter 2, forest canopy structure is characterized from multiple landscapes using LiDAR data and a novel data-driven framework to identify and compare structural classes. Motivations for this chapter include the desire to systematically assess forest structure from landscape to global scales and increase the utility of data collected by government agencies for landscape restoration planning. Chapter 3 endeavors to link 3-D canopy fuels attributes to conventional optical remote sensing data with the goal of extending the reach of laser measurements to the entire western US while exploring geographic differences in LiDAR-Landsat relationships. Development of predictive models and resulting datasets increase accuracy and spatial variation over currently used canopy fuel datasets. Chapters 4 and 5 characterize fire and fuel variability using unmanned aerial systems (UAS) and quantify trends in the influence of fuel patterns on fire processes

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