Ash Tree Identification Based on the Integration of Hyperspectral Imagery and High-density Lidar Data

Monitoring and management of ash trees has become particularly important in recent years due to the heightened risk of attack from the invasive pest, the emerald ash borer (EAB). However, distinguishing ash from other deciduous trees can be challenging. Both hyperspectral imagery and Light detection and ranging (LiDAR) data are two valuable data sources that are often used for tree species classification. Hyperspectral imagery measures detailed spectral reflectance related to the biochemical properties of vegetation, while LiDAR data measures the three-dimensional structure of tree crowns related to morphological characteristics. Thus, the accuracy of vegetation classification may be improved by combining both techniques. Therefore, the objective of this research is to integrate hyperspectral imagery and LiDAR data for improving ash tree identification. Specifically, the research aims include: 1) using LiDAR data for individual tree crowns segmentation; 2) using hyperspectral imagery for extraction of relative pure crown spectra; 3) fusing hyperspectral and LiDAR data for ash tree identification. It is expected that the classification accuracy of ash trees will be significantly improved with the integration of hyperspectral and LiDAR techniques. Analysis results suggest that, first, 3D crown structures of individual trees can be reconstructed using a set of generalized geometric models which optimally matched LiDAR-derived raster image, and crown widths can be further estimated using tree height and shape-related parameters as independent variables and ground measurement of crown widths as dependent variables. Second, with constrained linear spectral mixture analysis method, the fractions of all materials within a pixel can be extracted, and relative pure crown-scale spectra can be further calculated using illuminated-leaf fraction as weighting factors for tree species classification. Third, both crown shape index (SI) and coefficient of variation (CV) can be extracted from LiDAR data as invariant variables in tree’s life cycle, and improve ash tree identification by integrating with pixel-weighted crown spectra. Therefore, three major contributions of this research have been made in the field of tree species classification:1) the automatic estimation of individual tree crown width from LiDAR data by combining a generalized geometric model and a regression model, 2) the computation of relative pure crown-scale spectral reflectance using a pixel-weighting algorithm for tree species classification, 3) the fusion of shape-related structural features and pixel-weighted crown-scale spectral features for improving of ash tree identification

Moniajalliset aaltomuotolaserpiirteet metsäpuissa – fenologian, puulajien ja skannausgeometrian vaikutus

Ilmalaserkeilauksella ”airborne LiDAR” (Light Detection and Ranging) tuotetaan korkearesoluutioista 3D-tietoa erittäin kustannustehokkaasti. Tämänhetkiset metsien inventointimenetelmät yhdistävät sekä LiDARin että passiivisen ilmakuvauksen. Mahdollisuus pelkän LiDARin käyttöön on erittäin houkutteleva, koska se johtaisi ainakin osittain kustannusten alenemiseen. Tässä tutkimuksessa keskitytään ns. täyden aaltomuodon havaintoihin, mitkä sisältävät enemmän tietoa lähetetystä ja vastaanotetusta signaalista kuin ’tavanomaiset’ pistepilvet. Tässä tutkimuksessa tarkastellaan metsän latvuston rakenteellisten ominaisuuksien ja LiDAR-signaalien välisiä riippuvuuksia ja pyritään lisäämään ymmärrystämme LiDARin ja kasvillisuuden välisistä vuorovaikutuksista ja tekijöistä, jotka rajoittavat nykyistä kykyä käyttää LiDAR-dataa mm. puulajitulkintaan, ja sitä, kuinka erilaisin prosessointi ja laskentamenetelmin voimme parantaa LiDARin tulkintaa metsässä. Tämän tutkimuksen tarkoituksena on ymmärtää, kuinka erilaisia aaltomuotopiirteitä voidaan tulkita ja kuinka piirteet käyttäytyvät muuttuvan fenologian mukaan. Tutkimusaineisto koostuu kolmesta peräkkäisestä LiDAR- ja ilmakuva kampanjasta, jotka on tehty alueella 38 kuukauden aikana sekä tämän ajanjakson aikana mitatuista maastoreferenssipuista. Käytössä on monen ajankohdan dataa, mikä koostuu kolmesta toistetusta laserkeilauksesta, jotka kaikki käyttivät samaa sensoria, lentoratoja ja keilausasetuksia. Koska LiDAR-havainnot ovat vertailukelpoisia ja samoista puista, voidaan ns. "puutekijää" tutkia ja vaihtelua aaltomuodon ominaisuuksien välillä toistuvissa keilauksissa seurata. Fenologiset muutokset ovat havaittavissa, koska aineistot sisältävät talven (lehdetön aika), alkukesän (alhainen lehtialaindeksi (LAI) havupuilla) ja loppukesän (täyslehti, korkea LAI). Myös skannauszeniittikulman (SZA) vaikutus aaltomuodon ominaisuuksiin ja piirteisiin otettiin huomioon, koska sama puu voitiin nähdä usealta lentolinjalta. Tulokset osoittavat, että huolellisella koeasettelulla on mahdollista havaita lajien sisäisiä ja lajien välisiä fenologisia eroja ja muutoksia moniajallisista aaltomuotopiirteistä. SZA:lla ei ollut merkittävää vaikutusta tuloksiin. Puulajiluokitus onnistui hyvin vaihtelevissa fenologisissa olosuhteissa ja erirakenteellisissa metsiköissä. Fenologiset muutokset olivat hyvin ilmeisiä kausivihannoilla puilla, mutta melko pieniä ainavihannilla havupuilla. Kokonaistarkkuudet puulajiluokituksessa olivat talvella 92 %, alkukesällä 88 % ja loppukesällä 84 % kasvatusmetsässä ja talvella 84 %, alkukesällä 81 % ja loppukesällä 83 % vanhassa puustossa. "puutekijän" osoitettiin olevan merkittävä. Lajien sisäinen varianssi johtuu pääasiassa puutekijästä eli lajinsisäinen ominaisuusvarianssi edustaa luonnollista vaihtelua saman lajin puiden välillä.Airborne LiDAR (Light Detection And Ranging) produces high-resolution and cost-efficient 3D data. Currently, forest inventories combine the use of both LiDAR and passive imaging by cameras, and the possibility of using LiDAR only is very tempting as it would lead to cost reduction. Focus of this study is on the full-waveform observations that extent the information content compared to conventional point clouds and are somewhat rarer to have access to. This study explores basic dependencies between structural canopy features and LiDAR signals over time and aims at augmenting our understanding of LiDAR-vegetation interactions and factors limiting our current ability to use pulsed LiDAR data for species detection, and how possibilities to overcome those limitations. Motivation is to understand how different waveform features can be interpreted and how the features behave over time with changing vegetation phenology. The study material consists of three consecutive LiDAR campaigns and aerial imaging surveys done in the area during a 38-month period and field reference trees that have been measured during this period. I use multi-temporal data that comprise three repeated acquisitions, which all applied same sensor, trajectories, as well as sensor and acquisition settings. As I had repeated LiDAR observations of the same trees where the acquisition settings are comparable, I could study the so-called ‘tree effect’ and overall co-variation between waveform features in the repeated acquisitions. Phenological changes are available as the data comprises winter (leaf-off), early summer (low LAI in conifers) and late summer data (full leaf, high LAI). The influence of scan zenith angle (SZA) on waveform features and attributes is also considered, as the same tree can be seen from multiple strips. The results showed that by using careful experimentation it is possible to detect intra- and interspecies phenological changes from multitemporal full-waveform data, while SZA did not have markable effect on the WF features. I was also able to perform well with the tree species classification task in varying phenological conditions. The phenological changes were very apparent on deciduous trees, but rather small on evergreen conifers. In a 45-year-old stand, the overall accuracies in tree species classification were 92, 87 and 88 % for winter, early summer, and late summer, respectively. These figures were 84, 81, and 83 % for in an old growth forest. The ‘tree effect’ was shown to be significant, i.e., many of the WF features of trees were correlated over time. The intra-species feature variance that is due to the tree effect represents natural variation between trees of the same species

Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks

During the last two decades, forest monitoring and inventory systems have moved from field surveys to remote sensing-based methods. These methods tend to focus on economically significant components of forests, thus leaving out many factors vital for forest biodiversity, such as the occurrence of species with low economical but high ecological values. Airborne hyperspectral imagery has shown significant potential for tree species classification, but the most common analysis methods, such as random forest and support vector machines, require manual feature engineering in order to utilize both spatial and spectral features, whereas deep learning methods are able to extract these features from the raw data. Our research focused on the classification of the major tree species Scots pine, Norway spruce and birch, together with an ecologically valuable keystone species, European aspen, which has a sparse and scattered occurrence in boreal forests. We compared the performance of three-dimensional convolutional neural networks (3D-CNNs) with the support vector machine, random forest, gradient boosting machine and artificial neural network in individual tree species classification from hyperspectral data with high spatial and spectral resolution. We collected hyperspectral and LiDAR data along with extensive ground reference data measurements of tree species from the 83 km2 study area located in the southern boreal zone in Finland. A LiDAR-derived canopy height model was used to match ground reference data to aerial imagery. The best performing 3D-CNN, utilizing 4 m image patches, was able to achieve an F1-score of 0.91 for aspen, an overall F1-score of 0.86 and an overall accuracy of 87%, while the lowest performing 3D-CNN utilizing 10 m image patches achieved an F1-score of 0.83 and an accuracy of 85%. In comparison, the support-vector machine achieved an F1-score of 0.82 and an accuracy of 82.4% and the artificial neural network achieved an F1-score of 0.82 and an accuracy of 81.7%. Compared to the reference models, 3D-CNNs were more efficient in distinguishing coniferous species from each other, with a concurrent high accuracy for aspen classification. Deep neural networks, being black box models, hide the information about how they reach their decision. We used both occlusion and saliency maps to interpret our models. Finally, we used the best performing 3D-CNN to produce a wall-to-wall tree species map for the full study area that can later be used as a reference prediction in, for instance, tree species mapping from multispectral satellite images. The improved tree species classification demonstrated by our study can benefit both sustainable forestry and biodiversity conservation.peerReviewe

Is field-measured tree height as reliable as believed A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest

Quantitative comparisons of tree height observations from different sources are scarce due to the difficulties in effective sampling. In this study, the reliability and robustness of tree height observations obtained via a conventional field inventory, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) were investigated. A carefully designed non-destructive experiment was conducted that included 1174 individual trees in 18 sample plots (32 m x 32 m) in a Scandinavian boreal forest. The point density of the ALS data was approximately 450 points/m(2). The TLS data were acquired with multi-scans from the center and the four quadrant directions of the sample plots. Both the ALS and TLS data represented the cutting edge point cloud products. Tree heights were manually measured from the ALS and TLS point clouds with the aid of existing tree maps. Therefore, the evaluation results revealed the capacities of the applied laser scanning (LS) data while excluding the influence of data processing approach such as the individual tree detection. The reliability and robustness of different tree height sources were evaluated through a cross-comparison of the ALS-, TLS-, and field- based tree heights. Compared to ALS and TLS, field measurements were more sensitive to stand complexity, crown classes, and species. Overall, field measurements tend to overestimate height of tall trees, especially tall trees in codominant crown class. In dense stands, high uncertainties also exist in the field measured heights for small trees in intermediate and suppressed crown class. The ALS-based tree height estimates were robust across all stand conditions. The taller the tree, the more reliable was the ALS-based tree height. The highest uncertainty in ALS-based tree heights came from trees in intermediate crown class, due to the difficulty of identifying treetops. When using TLS, reliable tree heights can be expected for trees lower than 15-20 m in height, depending on the complexity of forest stands. The advantage of LS systems was the robustness of the geometric accuracy of the data. The greatest challenges of the LS techniques in measuring individual tree heights lie in the occlusion effects, which lead to omissions of trees in intermediate and suppressed crown classes in ALS data and incomplete crowns of tall trees in TLS data.Peer reviewe

Doctor of Philosophy

dissertationWith increasing wildfire activity throughout the western United States comes an increased need for wildland firefighters to protect civilians, structures, and public resources. In order to mitigate threats to their safety, firefighters employ the use of safety zones (SZ: areas where firefighters are free from harm) and escape routes (ER: pathways for accessing SZ). Currently, SZ and ER are designated by firefighters based on ground-level information, the interpretation of which can be error-prone. This research aims to provide robust methods to assist in the ER and SZ evaluation processes, using remote sensing and geospatial modeling. In particular, I investigate the degree to which lidar can be used to characterize the landscape conditions that directly affect SZ and ER quality. I present a new metric and lidar-based algorithm for evaluating SZ based on zone geometry, surrounding vegetation height, and number of firefighters present. The resulting map contains a depiction of potential SZ throughout Tahoe National Forest, each of which has a value that indicates its wind- and slope-dependent suitability. I then inquire into the effects of three landscape conditions on travel rates for the purpose of developing a geospatial ER optimization model. I compare experimentally-derived travel rates to lidar-derived estimates of slope, vegetation density, and ground surface roughness, finding that vegetation density had the strongest negative effect. Relative travel impedances are then mapped throughout Levan Wildlife Management Area and combined with a route-finding algorithm, enabling the identification of maximally-efficient escape routes between any two known locations. Lastly, I explore a number of variables that can affect the accurate characterization of understory vegetation density, finding lidar pulse density, overstory vegetation density, and canopy height all had significant effects. In addition, I compare two widely-used metrics for understory density estimation, overall relative point density and normalized relative point density, finding that the latter possessed far superior predictive power. This research provides novel insight into the potential use of lidar in wildland firefighter safety planning. There are a number of constraints to widespread implementation, some of which are temporary, such as the current lack of nationwide lidar data, and some of which require continued study, such as refining our ability to characterize understory vegetation conditions. However, this research is an important step forward in a direction that has potential to greatly improve the safety of those who put themselves at risk to ensure the safety of life and property

Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms

Remote sensing technology is an efficient tool for various practical applications of environmental resources management. Advances in this technology include the diverse range of high quality data sources and image analysis techniques. Object-based image analysis (OBIA) and machine learning algorithms are recent advances, which this thesis evaluates. OBIA and machine learning algorithms are first tested using a combination of multiple datasets for identifying individual tree species. These datasets include Quickbird, LiDAR, and GIS derived terrain data. Improvements in tree species classification were obtained and the best data combination was terrain context (based on slope, elevation, and wetness), tree height, canopy shape, and branch density (based on LiDAR return intensity). The availability of a range of classifiers and different data pre-processing techniques adds to the complexity of image analysis. The combinations of these techniques result in a large number of potential outcomes and these need to be evaluated. Therefore, the second part of this research investigated and compared tree species classification performance for different methods (Naïve Bayes - NB , Logistic Regression - LR, Random Forest - RF, and Support Vector Machine - SVM), combined with various dimensionality reduction (DR) methods (Correlation-based feature selection filter, Information Gain, Wrapper methods, and Principal Component Analysis). When DR was used prior to classification, only the NB classifier had a significant improvement in accuracy. SVM and RF had the best classification accuracy, and this was achieved without DR. The final part of this thesis demonstrates a new method using OBIA for mapping the biomass change of mangrove forests in Vietnam between 2000 and 2011 from SPOT images. First, three different mangrove associations were identified using two levels of image segmentation followed by a SVM classifier and a range of spectral, texture and GIS information for classification. The RF regression model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy

Integrating forest structural diversity measurement into ecological research

The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity—an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research

Assessing a Template Matching Approach for Tree Height and Position Extraction from Lidar-Derived Canopy Height Models of Pinus Pinaster Stands

In this paper, an assessment of a method using a correlation filter over a lidar-derived digital canopy height model (CHM) is presented. The objective of the procedure is to obtain stem density, position, and height values, on a stand with the following characteristics: ellipsoidal canopy shape (Pinus pinaster), even-aged and single-layer structure. The process consists of three steps: extracting a correlation map from CHM by applying a template whose size and shape resembles the canopy to be detected, applying a threshold mask to the correlation map to keep a subset of candidate-pixels, and then applying a local maximum filter to the remaining pixel groups. The method performs satisfactorily considering the experimental conditions. The mean tree extraction percentage is 65% with a coefficient of agreement of 0.4. The mean absolute error of height is ~0.5 m for all plots except one. It can be considered a valid approach for extracting tree density and height in regularly spaced stands (i.e., poplar plantations) which are fundamental for extracting related forest parameters such as volume and biomass

Ecological impacts of deforestation and forest degradation in the peat swamp forests of northwestern Borneo

Tropical peatlands have some of the highest carbon densities of any ecosystem and are under enormous development pressure. This dissertation aimed to provide better estimates of the scales and trends of ecological impacts from tropical peatland deforestation and degradation across more than 7,000 hectares of both intact and disturbed peatlands in northwestern Borneo. We combined direct field sampling and airborne Light Detection And Ranging (LiDAR) data to empirically quantify forest structures and aboveground live biomass across a largely intact tropical peat dome. The observed biomass density of 217.7 ± 28.3 Mg C hectare-1 was very high, exceeding many other tropical rainforests. The canopy trees were ~65m in height, comprising 81% of the aboveground biomass. Stem density was observed to increase across the 4m elevational gradient from the dome margin to interior with decreasing stem height, crown area and crown roughness. We also developed and implemented a multi-temporal, Landsat resolution change detection algorithm for identify disturbance events and assessing forest trends in aseasonal tropical peatlands. The final map product achieved more than 92% user’s and producer’s accuracy, revealing that after more than 25 years of management and disturbances, only 40% of the area was intact forest. Using a chronosequence approach, with a space for time substitution, we then examined the temporal dynamics of peatlands and their recovery from disturbance. We observed widespread arrested succession in previously logged peatlands consistent with hydrological limits on regeneration and degraded peat quality following canopy removal. We showed that clear-cutting, selective logging and drainage could lead to different modes of regeneration and found that statistics of the Enhanced Vegetation Index and LiDAR height metrics could serve as indicators of harvesting intensity, impacts, and regeneration stage. Long-term, continuous monitoring of the hydrology and ecology of peatland can provide key insights regarding best management practices, restoration, and conservation priorities for this unique and rapidly disappearing ecosystem