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

Quantifying the urban forest environment using dense discrete return LiDAR and aerial color imagery for segmentation and object-level biomass assessment

The urban forest is becoming increasingly important in the contexts of urban green space and recreation, carbon sequestration and emission offsets, and socio-economic impacts. In addition to aesthetic value, these green spaces remove airborne pollutants, preserve natural resources, and mitigate adverse climate changes, among other benefits. A great deal of attention recently has been paid to urban forest management. However, the comprehensive monitoring of urban vegetation for carbon sequestration and storage is an under-explored research area. Such an assessment of carbon stores often requires information at the individual tree level, necessitating the proper masking of vegetation from the built environment, as well as delineation of individual tree crowns. As an alternative to expensive and time-consuming manual surveys, remote sensing can be used effectively in characterizing the urban vegetation and man-made objects. Many studies in this field have made use of aerial and multispectral/hyperspectral imagery over cities. The emergence of light detection and ranging (LiDAR) technology, however, has provided new impetus to the effort of extracting objects and characterizing their 3D attributes - LiDAR has been used successfully to model buildings and urban trees. However, challenges remain when using such structural information only, and researchers have investigated the use of fusion-based approaches that combine LiDAR and aerial imagery to extract objects, thereby allowing the complementary characteristics of the two modalities to be utilized. In this study, a fusion-based classification method was implemented between high spatial resolution aerial color (RGB) imagery and co-registered LiDAR point clouds to classify urban vegetation and buildings from other urban classes/cover types. Structural, as well as spectral features, were used in the classification method. These features included height, flatness, and the distribution of normal surface vectors from LiDAR data, along with a non-calibrated LiDAR-based vegetation index, derived from combining LiDAR intensity at 1064 nm with the red channel of the RGB imagery. This novel index was dubbed the LiDAR-infused difference vegetation index (LDVI). Classification results indicated good separation between buildings and vegetation, with an overall accuracy of 92% and a kappa statistic of 0.85. A multi-tiered delineation algorithm subsequently was developed to extract individual tree crowns from the identified tree clusters, followed by the application of species-independent biomass models based on LiDAR-derived tree attributes in regression analysis. These LiDAR-based biomass assessments were conducted for individual trees, as well as for clusters of trees, in cases where proper delineation of individual trees was impossible. The detection accuracy of the tree delineation algorithm was 70%. The LiDAR-derived biomass estimates were validated against allometry-based biomass estimates that were computed from field-measured tree data. It was found out that LiDAR-derived tree volume, area, and different distribution parameters of height (e.g., maximum height, mean of height) are important to model biomass. The best biomass model for the tree clusters and the individual trees showed an adjusted R-Squared value of 0.93 and 0.58, respectively. The results of this study showed that the developed fusion-based classification approach using LiDAR and aerial color (RGB) imagery is capable of producing good object detection accuracy. It was concluded that the LDVI can be used in vegetation detection and can act as a substitute for the normalized difference vegetation index (NDVI), when near-infrared multiband imagery is not available. Furthermore, the utility of LiDAR for characterizing the urban forest and associated biomass was proven. This work could have significant impact on the rapid and accurate assessment of urban green spaces and associated carbon monitoring and management

Spatio-temporal dynamics of woody vegetation structure in a human-modified South African savanna

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. 25 October 2016 in Johannesburg, South Africa.Ecosystem services, nature’s benefit to people, contribute to human well-being. Extensive reliance on, and unsustainable use of, natural resources is typical of the rural poor in developing countries and can lead to ecosystem degradation, decreased ecosystem service provision, and increased vulnerabilities of rural populations. Most ecosystem services are intangible or difficult to quantify, but fuelwood provisioning can be measured directly and can serve as a proxy for the status of other ecosystem services (e.g. aesthetic and spiritual services, nutrient cycling, carbon sequestration). South African rural communities have a high reliance on fuelwood despite extensive access to electricity. Within this context, live wood harvesting occurring around rural settlements in increasing amounts has been deemed unsustainable. However, the ‘fuelwood crisis’ of the 1970s, and subsequent predictions of woodland collapse through fuelwood supply-demand models, has still not occurred despite substantial population growth in developing countries. Hypothesised reasons for modelled supply-demand mismatches are based on underestimation of fuelwood supply and woodland regeneration, as well as overestimation of fuelwood demand by discounting behavioural adaptability of users. It is likely that the spatial configuration of fuelwood use allows for the co-adaptability of both humans and ecosystems. A lack of understanding of the spatial configuration of these social-ecological dynamics limits our insights into current and future adaptive responses and thus, the degree of sustainability. This thesis aimed to assess woody biomass stocks and vertical structure changes, as a proxy for provisioning ecosystem services, in a spatially and temporally explicit manner, to describe the status and impact of wood extraction in semi-arid, savanna communal lands. Using repeat, airborne light detection and ranging (LiDAR) data from 2008 and 2012, we surveyed three-dimensional woodland structure in Bushbuckridge Municipality communal lands – the grazing and harvesting areas for densely populated rural settlements in former Apartheid ‘homelands’ in South Africa. Woody biomass in 2008 ranged from 9 Mg ha-1 on gabbro geology to 27 Mg ha-1 on granitic geology. Land-use pressure was associated with compensatory regrowth of savanna tree species through post-harvest coppice in the 1-3m height class. Woody biomass increased at all sites, contrary to previous fuelwood models of the area. Change detection in the vertical canopy structure revealed that biomass increases were almost solely attributable to the 1-3m and 3-5m height classes. These changes were exacerbated by wood extraction intensity in the communal lands – the communal land with the highest wood extraction pressure experienced the greatest biomass increases, likely a strong regrowth response to high harvesting levels. Within communal lands, areas closest to roads and settlements experienced substantial biomass increases as a result of shrub level gains. This relationship was mediated by the usage gradient – the greater the wood extraction pressure on the communal land, the larger and more spatially coalesced the ‘hotspots’ of shrub-level increases were in relation to ease of access to the communal land. However, biomass increases are not necessarily indicative of woodland recovery, as shrub-level increases were coupled with losses of trees >3m in height. To explore these tall tree dynamics further, we tracked >450 000 individual tree canopies over two years over contrasting landscapes – a private reserve containing elephants, two communal lands under different wood extraction pressures, and a nature reserve fenced off from both elephants and humans. Humans are considerable drivers of treefall (defined here as a ≥75% reduction in the maximum height of each tree canopy) in communal lands. Humanmediated biennial treefall rates were 2-3.5 fold higher than the background treefall rate of 1.5% treefall ha-1 (in the control site – the reserve containing neither elephants nor humans). Elephant-mediated treefall was five fold higher than the background rate. Rate and spatial patterns of treefall were mediated by geology and surface water provision in the elephantutilised site where relative treefall was higher on nutrient-rich geology, and intense treefall hotspots occurred around permanent water points. Human-mediated rates and spatial patterns of treefall were influenced by settlement and crop-land expansion, as well as ease of access to communal lands. Frequent fires facilitated the persistence of trees >3m in height, but was associated with height loss in trees <3m. The combined loss of large trees and gain in shrubs could result in a structurally simple landscape with reduced functional capacity. Shrub-level increases in the communal lands are likely an interactive combination of newly established woody encroachers and strong coppice regrowth in harvested species. The more intensely used the communal land, the greater the bush thickening and the stronger the relationship between biomass gains and structural changes in the lowest height classes. The exacerbation of bush thickening in natural resource-dependent communities has critical implications for ecosystem service provision. There is potential for coppice regrowth to provide fuelwood to communities using ‘tree thinning’ programmes, but there is a lack of data on the quantity and quality of the regrowth, as well as the sustainability of coppice, the impacts of different harvesting methods, and the potential feedbacks with changing climate and CO2 fertilisation. Woody resource spatial distribution in communal lands is centred around settlement-level wood extraction pressure, as well as natural resource accessibility in the woodlands. In highly utilised areas, woodland regenerative capacity has been underestimated. Additionally, natural resource extraction is still highly localised, even at the communal land scale, with major structural changes occurring around the periphery or close to existing infrastructure. However, it is these underrated coupled adaptive responses in social-ecological systems that explain the failure of fuelwood supply-demand models’ predictive abilities. Nevertheless, loss of large trees in the landscape and the persistence of ‘functionally juvenile’ coppice stands will have implications for seedling production and establishment in the landscape with repercussions for the future population structure and ecosystem service provision. I discuss the implications of increased natural resource reliance in an African development context and the positive feedback between rural poverty and environmental impoverishment. Potential constraints to the data are unpacked, together with opportunities for further research in this area.LG201

Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning

"© 2022 The Author(s). Published by Elsevier B.V. on behalf of International Society of Photogrammetry and Remote Sensing (isprs). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)"Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation.publishedVersio

Developing laser scanning applications for mapping and monitoring single tree characteristics for the needs of urban forestry

Urban forests provide various ecosystem services. However, they also require fairly intensive management, which can be supported with up-to-date tree-level data. Until recently, the data have been collected using traditional field measurements. Laser scanning (LS) techniques provide efficient means for acquiring detailed three-dimensional (3D) data from the vegetation. The objective of this dissertation was to develop methods for mapping and monitoring urban forests at tree level. In substudy I, a method (MS-STI) utilizing multiple data sources was developed for extracting tree-level attributes. The method combined airborne laser scanning (ALS), field measurements, and tree locations. The field sample was generalized using the non-parametric nearest neighbor (NN) approach. The relative root mean square error (RMSE) of diameter at breast height (DBH) varied between 18.8–33.8%. The performance of MS-STI was assessed in substudy II by applying it to an existing tree register. 88.8% of the trees were successfully detected, and the relative RMSE of DBH for the most common diameter classes varied between 21.7–24.3%. In substudy III, downed trees were mapped from a recreational forest area by detecting changes in the canopy. 97.7% of the downed trees were detected and the commission error was 10%. Species group, DBH, and volume were estimated for all downed trees using ALS metrics and existing allometric models. For the DBH, the relative RMSE was 20.8% and 34.1% for conifers and deciduous trees respectively. Finally, in substudy IV, a method utilizing terrestrial laser scanning (TLS) and tree basic density was developed for estimating tree-level stem biomass for urban trees. The relative RMSE of the stem biomass estimates varied between 8.4–10.5%. The dissertation demonstrates the applicability of LS data in assessing tree-level attributes for urban forests. The methods developed show potential in providing the planning and management of urban forests with cost-efficient and up-to-date tree-level data

Tree Species Classification : Analyzing Multitemporal Satellite Imagery and Multispectral Airborne Laser Scanning Data

Tree species composition of forests affects the whole ecosystem and is part of the information needed for an efficient planning of forest management. This thesis explores how recent developments in remote sensing can provide more accurate tree species mapping. I try to answer the question of how the properties of these data can be used to derive more information on tree species. Out of the four papers in this thesis, two papers examine how multitemporal satellite imagery from the Sentinel-2 mission can be of use, and the other two papers investigate what properties of multispectral airborne laser scanning (MSALS) data that contain the most information on tree species. We applied a Bayesian method to multitemporal satellite imagery for tree species classification of pixels in the hemiboreal forest of Remningstorp in southwestern Sweden. The Bayesian method was applied to 142 Sentinel-2 images, and to a subset of images ranked and selected by the separability of tree species classes. The method was also compared to a Random Forest classifier for 45 Sentinel-2 images of boreal forest in mid-Sweden. The Bayesian method performed better for homogeneous tree species classes, while Random Forest performed better for heterogeneous classes. Data from two MSALS systems were used for classifying the tree species of individual trees. Optech Titan-X data were used to classify free-standing trees of nine species in Remningstorp. By using Riegl VQ-1560i-DW data, we performed a tree species classification in a more operational setting for three tree species in closed-canopy hemiboreal forest in Asa in southern Sweden. Multispectral intensity features provided a great improvement in classification accuracy in both cases, compared to using only structural features or combining them with monospectral intensity features. For Optech Titan-X, the green wavelength performed poorly, but for Riegl VQ-1560i-DW, the green wavelength provided the most information for separability, especially for birch (Betula spp.). There are two main conclusions in this thesis. The first is that Bayesian methods that updates probabilities as new observations are made provides an opportunity to automate the addition of satellite images for an updated classification. The second is that MSALS data provides more information on tree species than monospectral data and tree crown structure do, with the most information coming from the upper parts of the canopy. Nonetheless, what wavelengths of light that contribute most to tree species classification accuracy is highly dependent on what MSALS system that is used

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

New Metrics for Spatial and Temporal 3D Urban Form Sustainability Assessment Using Time Series Lidar Point Clouds and Advanced GIS Techniques

Monitoring sustainability of urban form as a 3D phenomenon over time is crucial in the era of smart cities for better planning of the future, and for such a monitoring system, appropriate tools, metrics, methodologies and time series 3D data are required. While accurate time series 3D data are becoming available, a lack of 3D sustainable urban form (3D SUF) metrics, appropriate methodologies and technical problems of processing time series 3D data has resulted in few studies on the assessment of 3D SUF over time. In this chapter, we review volumetric building metrics currently under development and demonstrate the technical problems associated with their validation based on time series airborne lidar data. We propose new metrics for application in spatial and temporal 3D SUF assessment. We also suggest a new approach in processing time series airborne lidar to detect three-dimensional changes of urban form. Using this approach and the developed metrics, we detected a decreased volume of vegetation and new areas prepared for the construction of taller buildings. These 3D changes and the proposed metrics can be used to numerically measure and compare urban areas in terms of trends against or in favor of sustainability goals for caring for the environment

Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data

Machine learning techniques have gained a distinguished position in the automatic processing of Light Detection and Ranging (LiDAR) data area. They represent the actual research topic in the remote sensing domain. Indeed, this paper presents one method of supervised machine learning, which is called Random Forest. This algorithm is discussed, and their primary applications in automatic vegetation extraction and modelling in the LiDAR data area are presented here

3D Forest structure analysis from optical and LIDAR dataAnálise 3D da estrutura da floresta com dados ópticos e da LIDAR

In Austria about half of the entire area (46 %) is covered by forests. The majority of these forests are highly managed and controlled in growth. Besides timber production, forest ecosystems play a multifunctional role including climate control, habitat provision and, especially in Austria, protection of settlements. The interrelationships among climatic, ecological, social and economic dimensions of forests require technologies for monitoring both the state and the development of forests. This comprises forest structure, species and age composition and, forest integrity in general. Assessing forest structure for example enables forest managers and natural risk engineers to evaluate whether a forest can fulfil its protective function or not. Traditional methods for assessing forest structure like field inventories and aerial photo interpretation are intrinsically limited in providing spatially continuous information over a large area. The Centre for Geoinformatics (Z_GIS) in collaboration with the National Park Bayerischer Wald, Germany and the Stand Montafon, Austria, has tested and applied advanced approaches of integrating multispectral optical data and airborne laser scanning (ALS) data for (1) forest stand delineation, (2) single tree detection and (3) forest structure analysis. As optical data we used RGBI line scanner data and CIR air-photos. ALS data were raw point data (10 pulses per sqm) and normalised crown models (nCM) at 0.5 m and 1 m resolution. (1) Automated stand delineation was done by (a) translating a key for manual mapping of forest development phases into a rule-based system via object-relationship modelling (ORM); and (b) by performing multi-resolution segmentation and GIS analysis. (2) Strategies for single tree detection using raw ALS data included (a) GIS modelling based on a region-growth local maxima algorithm and (b) object-based image analysis using super class information class-specific rule sets. (3) Vertical forest structure has been assessed statistically by (a) applying basic statistics (like mean, standard deviation, and variation coefficient) on the raw data using a moving window approach; and (b) by applying landscape metrics (Shannon Evenness Index, SHEI, and division index, DIVI) for different strata extracted from the nCM.Resumo Aproximadamente 46% da superfície da Áustria é coberta por florestas. A sua maior parte é manejada tendo o seu crescimento controlado. Além da produção de madeira, os ecossistemas florestais desempenham um papel multi-funcional, incluindo o controle climático e particularmente, proteção aos assentamentos humanos. As inter-relações entre as dimensões climáticas, ecológicas, sociais e econômicas de florestas requerem tecnologias para o monitoramento, tanto do estado como do desenvolvimento das florestas. Isto inclui a estrutura das florestas, espécies e composição etária bem como integridade da floresta em geral. A avaliação da estrutura florestal permite, por exemplo, aos gerentes de floresta e engenheiros prognosticar os riscos naturais e avaliar se uma área florestal pode satisfazer a sua função protetora ou não. Métodos tradicionais de avaliação da estrutura florestal, tal como inventários de campo e interpretação de fotografias aéreas são intrinsecamente limitados para fornecer informação espacialmente contínua numa grande área. O Centro de Geo-Informática (Z_GIS) em colaboração com o Parque Nacional Bayerischer Wald, (Alemanha) e o Stand Montafon (Áustria), testaram e aplicaram aproximações de pesquisas avançadas, integrando dados ópticos multi-espectrais e varredura a laser aerotransportados (ALS – airborne laser scanning) para (1) delineamento dos talhões florestais, (2) detecção de árvores individuais, (3) análise da estrutura de florestas. Os dados ópticos usados foram obtidos por scanner em linha RGBI e fotografias aéreas infra-vermelho coloridas (CIR). Dados ALS tiveram como característica uma resolução de 10 pontos por m2 e de modelos normalizado de copa (nCM) com uma resolução de 0,5 e 1,0 m. (1) A delineação automática dos talhões foi feita através de (a) migração de uma chave elaborada para mapeamento manual de fases de desenvolvimento de florestas para um sistema baseado em regras via modelagem do relacionamento dos objetos (ORM); e (b) da segmentação multi-resolução e análise de SIG. (2) Estratégias para a detecção de árvores individuais usando dados ALS brutos incluem (a) modelagem GIS baseado em algoritmos de máximas locais por crescimento de regiões, e (b) análise orientada a objetos usando a informação de super-classes de conjuntos e séries de regras de classes específicas. (3) a estrutura vertical de florestas foi avaliada estatisticamente através de (a) aplicação de estatística básica (como média, desvio-padrão e coeficiente de variação) nos dados brutos usando uma abordagem de janela móvel; e (b) aplicando métricas de paisagem (Shannon Evenness Index, SHEI e Índice de Divisão, DIVI) para diferentes estratos obtidos do nCM