Detection of dead standing Eucalyptus camaldulensis without tree delineation for managing biodiversity in native Australian forest

In Australia, many birds and arboreal animals use hollows for shelters, but studies predict shortage of hollows in near future. Aged dead trees are more likely to contain hollows and therefore automated detection of them plays a substantial role in preserving biodiversity and consequently maintaining a resilient ecosystem. For this purpose full-waveform LiDAR data were acquired from a native Eucalypt forest in Southern Australia. The structure of the forest significantly varies in terms of tree density, age and height. Additionally, Eucalyptus camaldulensis have multiple trunk splits making tree delineation very challenging. For that reason, this paper investigates automated detection of dead standing Eucalyptus camaldulensis without tree delineation. It also presents the new feature of the open source software DASOS, which extracts features for 3D object detection in voxelised FW LiDAR. A random forest classifier, a weighted-distance KNN algorithm and a seed growth algorithm are used to create a 2D probabilistic field and to then predict potential positions of dead trees. It is shown that tree health assessment is possible without tree delineation but since it is a new research directions there are many improvements to be made

Spatio-temporal analysis of coastal sediment erosion in Cape Town through remote sensing and geoinformation science

Coastal erosion can be described as the landward or seaward propagation of coastlines. Coastal processes occur over various space and time scales, limiting in-situ approaches of monitoring change. As such it is imperative to take advantage of multisensory, multi-scale and multi-temporal modern spatial technologies for multi-dimensional coastline change monitoring. The research presented here intends to showcase the synergy amongst remote sensing techniques by showcasing the use of coastal indicators towards shoreline assessment over the Kommetjie and Milnerton areas along the Cape Town coastline. There has been little progress in coastal studies in the Western Cape that encompass the diverse and dynamic aspects of coastal environments and in particular, sediment movement. Cape Town, in particular; is socioeconomically diverse and spatially segregated, with heavy dependence on its 240km of coastline. It faces sea level rise intensified by real-estate development close to the high-water mark and on reclaimed land. Spectral indices and classification techniques are explored to accommodate the complex bio-optical properties of coastal zones. This allows for the segmentation of land and ocean components to extract shorelines from multispectral Landsat imagery for a long term (1991-2021) shoreline assessment. The DSAS tool used these extracted shorelines to quantify shoreline change and was able to determine an overall averaged erosional rate of 2.56m/yr. for Kommetjie and 2.35m/yr. for Milnerton. Beach elevation modelling was also included to evaluate short term (2016-2021) sediment volumetric changes by applying Differential Interferometry to Sentinel-1 SLC data and the Waterline method through a combination of Sentinel -1 GRD and tide gauge data. The accuracy, validation and correction of these elevation models was conducted at the pixel level by comparison to an in-field RTK GPS survey used to capture the current state of the beaches. The results depict a sediment deficit in Kommetjie whilst accretion is prevalent along the Milnerton coastline. Shoreline propagation and coastal erosion quantification leads to a better understanding of geomorphology, hydrodynamic and land use influences on coastlines. This further informs climate adaptation strategies, urban planning and can support further development of interactive coastal information systems

Regional climate, federal land management, and the social-ecological resilience of southeastern Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2007Complex systems of humans and nature often experience rapid and unpredictable change that results in undesirable outcomes for both ecosystems and society. In circumpolar regions, where multiple converging drivers of change are reshaping both human and natural communities, there is uncertainty about future dynamics and the capacity to sustain the important interactions of social-ecological systems in the face of rapid change. This research addresses this uncertainty in the region of Southeast Alaska, where lessons learned from other circumpolar regions may not be applicable because of unique social and ecological conditions. Southeast Alaska contains the most productive and diverse ecosystems at high latitudes and a human population almost entirely isolated and embedded in National Forest lands; these qualities underscore the importance of the region's climate and federal management systems, respectively. This research presents a series of case studies of the drivers, dynamics, and outcomes of change in regional climate and federal management, and theoretically grounds these studies to understand the regional resilience to change. Climate change in Southeast Alaska is investigated with respect to impacts on temperate rainforest ecosystems. Findings suggest that warming is linked to emergence of declining cedar forests in the last century. Dynamics of federal management are investigated in several studies, concerning the origins and outcomes of national conservation policy, the boom-bust history of the regional timber economy, and the factors contributing to the current 'deadlock' in Tongass National Forest management. Synthesis of case study findings suggests both emergent phenomena (yellow-cedar decline) and cyclic dynamics (timber boom-bust) resulting from the convergence of ecological and social drivers of change. Adaptive responses to emergent opportunities appear constrained by inertia in management philosophies. Resilience to timber industry collapse has been variable at local scales, but overall the regional economy has experienced transition while retaining many of its key social-ecological interactions (e.g., subsistence and commercial uses of fish and wildlife). An integrated assessment of regional datasets suggests a high integrity of these interactions, but also identifies critical areas of emergent vulnerability. Overall findings are synthesized to provide policy and management recommendations for supporting regional resilience to future change.Introduction : Southeast Alaska as a social-ecological system -- Climate change and forest decline in Southeast Alaska -- Significance of wilderness conservation in Southeast Alaska : outcomes of the Alaska lands debate over the Tongass National Forest -- Dynamics of federal land management during the 20th century -- Factors influencing the reorganization of federal land management -- Conclusions : regional dynamics and social-ecological resilience of Southeast Alaska

Forest Fire Effects on Snow Storage and Melt Across Scales of Forest Recovery in the Western Oregon Cascades

Snow is the largest component of water storage in the western United States, it serves as a key moisture source for forested ecosystems and is fundamentally linked to streamflow and nutrient cycling. Snow is vulnerable to climatic warming, and a key consequence of declining mountain snowpack is the escalation in wildfire frequency, extent, intensity, and duration across the seasonal snow zone. Fire modifies the spatial extent of snow in watersheds, reducing snow water storage and timing of melt across burned forests. Forested mountain ecosystems and water supplies are facing shifts in their structure, function, and succession. Previous research has focused on short-term forest fire effects on snow hydrology. However, no previous study has empirically investigated the recovery of forest fire effects on snow-storage and melt over decades following fire. With the intensity and frequency of forest fires increasing and snowpack declining in the western United States, a common question is how to reduce forest fire risk while increasing watersheds efficiency at generating water supplies? Here we present a potential answer to such a question, where snowpack observations taken from the western Oregon Cascades illustrate that over decades following fire, snow in burned forests store more snow volume and delay melt timing for similar to an open area. We evaluate the long-term recovery of forest fire effects on snow accumulation and melt. We combined in-situ point based measurements, continuous time-lapse photography within three burned forests, and a remote sensing and multivariate analysis of basin scale forest fire effects on snow cover in the western Oregon Cascades. We found that forest fires increase snow accumulation and eventually delay snowmelt around 10 days later 10 years following fire compared to immediately following fire Decades following forest fire, burned forests may retain more snow longer in spring and result in long term benefits for water resources. Allowing forest fire to burn in snow dominated headwaters may increase snow storage for water resources management

Tree Structure Retrieval for Apple Trees from 3D Pointcloud

3D reconstruction is a challenging problem and has been an important research topic in the areas of remote sensing and computer vision for many years. Existing 3D reconstruction approaches are not suitable for orchard applications due to complicated tree structures. Current tree reconstruction has included models specific to trees of a certain density, but the impact of varying Leaf Area Index(LAI) on model performance has not been studied. To better manage an apple orchard, this thesis proposes methods for evaluating an apple canopy density mapping system as an input for a variable-rate sprayer for both trellis-structured (2D) and standalone (3D) apple orchards using a 2D LiDAR (Light Detection and Ranging). The canopy density mapping system has been validated for robustness and repeatability with multiple scans. The consistency of the whole row during multiple passes has a correlation R^2 = 0.97. The proposed system will help the decision-making in a variable-rate sprayer. To further study the individual tree structure, this thesis proposes a novel and fast approach to reconstruct and analyse 3D trees over a range of Leaf Area Index (LAI) values from LiDAR for morphology analysis for height, branch length and angles of real and simulated apple trees. After using Principal Component Analysis (PCA) to extract the trunk points, an improved Mean Shift algorithm is introduced as Adapted Mean Shift (AMS) to classify different branch clusters and extract the branch nodes. A full evaluation workflow of tree parameters including trunk and branches is introduced for morphology analysis to investigate the accuracy of the approach over different LAI values. Tree height, branch length, and branch angles were analysed and compared to the ground truth for trees with a range of LAI values. When the LAI is smaller than 0.1, the accuracy for height and length is greater than 90\% and the accuracy for the angles is around 80\%. When the LAI is greater than 0.1, the branch accuracy reduces to 40\%. This analysis of tree reconstruction performance concerning LAI values, as well as the combination of efficient and accurate structure reconstruction, opens the possibility of improving orchard management and botanical studies on a large scale. To improve the accuracy of traditional tree structure analysis, a deep learning approach is introduced to pre-process and classify unbalanced, in-homogeneous, and noisy point cloud data. TreeNet is inspired by 3D U-Net, adding classes and median filters to segment trunk, branch, and leave parts. TreeNet outperformed 3D U-Net and SVM in the case of Kappa, Matthews Correlation Coefficient(MCC), and F1-score value in segmentation. The TreeNet-AMS combined method also showed improvement in tree structure analysis than the traditional AMS method mentioned above. Following on from this research, efficient tree structure analysis on tree height, trunk length, branch position, and branch length could be conducted. Knowing the tree morphology is proved to be closely relevant to thinning, spraying and yield, the proposed work will then largely benefit the relevant studies in agriculture and forestry

Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms

Forests are one of the major carbon sinks that significantly contribute towards achieving targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG) emissions. In order to contribute to regular National Inventory Reporting, and as part of the on-going development of the Irish national GHG reporting system (CARBWARE), improvements in characterisation of changes in forest carbon stocks have been recommended to provide a comprehensive information flow into CARBWARE. The Irish National Forest Inventory (NFI) is updated once every six years, thus there is a need for an enhanced forest monitoring system to obtain annual forest updates to support government agencies and forest management companies in their strategic decision making and to comply with international GHG reporting standards. Sustainable forest management is imperative to promote net carbon absorption from forests. Based on the NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become a net emitter of carbon. Disturbances from human induced activities such as clear felling, thinning and deforestation results in carbon emissions back into the atmosphere. Funded by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR) satellite based sensors for monitoring changes in the small stand forests of Ireland. Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2 PALSAR-2 sensors have been used to map forest areas and characterise the different disturbances observed within three different regions of Ireland. Forest mapping and disturbance characterisation was achieved by combining the machine learning supervised Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis (ISODATA) classification techniques. The lack of availability of ground truth data supported use of this unsupervised approach which forms natural clusters based on their multi-temporal signatures, with divergence statistics used to select the optimal number of clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial where there is a dearth of ground-based information. When applied to the forests, mapped with an accuracy of up to 97% by RF, the ISODATA technique successfully identified the unique multi-temporal pattern associated with clear-fells which exhibited a decrease of 4 to 5 decibels (dB) between the images acquired before and after the event. The clustering algorithm effectively highlighted the occurrence of other disturbance events within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas of tree growth and afforestation. A highlight of the work is the successful transferability of the algorithm, developed using ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential continuity of annual forest monitoring. The higher spatial and radiometric resolutions of ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images. Moreover, even with some different backscatter characteristics of images acquired in different seasons, similar signature patterns between the sensors were retrieved that helped to define the cluster groups, thus demonstrating the robustness of the algorithm and its successful transferability. Having proven the potential to monitor forest disturbances, the results from both the sensors were used to detect deforestation over the time period 2007-2016. Permanent land-use changes pertaining to conversion of forests to agricultural lands and windfarms were identified which are important with respect to forest monitoring and carbon reporting in Ireland. Overall, this work has presented a viable approach to support forest monitoring operations in Ireland. By providing disturbance information from SAR, it can supplement projects working with optical images which are generally limited by cloud cover, particularly in parts of northern, western and upland Ireland. This approach adds value to ground based forest monitoring by mapping distinct forests over large areas on an annual basis. This study has demonstrated the ability to apply the algorithm to three different study areas, with a vision to operationalise the algorithm on a national scale. The main limitations experienced in this study were the lack of L-band SAR data availability and reference datasets. With typically only one image acquired per year, and discrepancies and omissions existing within reference datasets, understanding the behaviour of certain cluster groups representing disturbances was challenging. However, this approach has addressed some issues within the reference datasets, for example locating areas for which a felling licence was granted but where trees were never cut, by providing detailed systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B, P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited SAR image acquisitions provided more images per year are available, especially during the summer months

A GIS approach to sustainable livestock planning from carbon dynamics analysis: case study of a cattle ranch in Serra da Mantiqueira (Brazil)

Dissertation presented as partial requirement for obtaining the Master’s degree in Geographical Information Systems and ScienceA avaliação da dinâmica do carbono como indicador de serviços ecossistêmicos de regulação climática, através da modelagem de diferentes cenários sobre mudanças de uso e cobertura do solo (LULC), é amplamente utilizada em estudos de conservação ambiental para apoiar processos decisórios atrelados a políticas públicas. Todavia, são raros os estudos em escala local que analisam a relação de impacto e custo-benefício da simulação de cenários agrícolas sustentáveis na prestação de serviços ecossistêmicos. Neste trabalho, realizamos a quantificação, a avaliação econômica e o mapeamento da captura e do estoque de carbono de cenários LULC passados (2007-2017) e futuros (2027), em uma fazenda pecuarista da Serra da Mantiqueira, para entender como diferentes mudanças de paisagens podem impactar o serviço de regulação climática e contribuir economicamente com o setor agrícola. Sob uma abordagem SIG, empregamos técnicas de detecção remota, para elaborar os mapas LULC, ferramentas de modelagem Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), para a construção dos cenários futuros e para avaliação das dinâmicas de carbono, e ferramentas de modelagem da família Sis para simular a produção resultante do manejo florestal. Todos os cenários avaliados promoveram o aumento da captura e do estoque de carbono na área de estudo, assim como revelaram oportunidades econômicas rentáveis associadas à sua implementação. A introdução de árvores de eucalipto no sistema de produção agropecuário é uma alternativa interessante para a diversificação e aumento de renda, contribuindo para o equilíbrio dos gases de efeito estufa (GEE) da atividade pecuária e agregando valor à produção. Esses resultados são úteis para apoiar o planejamento e o desenvolvimento de políticas de conservação ambiental e de produção agrícola sustentável.The assessment of carbon dynamics as indicator of climate-regulation ecosystem services (ES) through the modeling of different scenarios on land use and land cover (LULC) changes is widely used in environmental conservation studies to support the decision-making process regarding public policies. However, studies at local scales that address the subject under the farm property perspective, through impact and cost-benefit analyses of simulated sustainable farming scenarios on the provision of ecosystem services, are rare or nonexistent. In this paper, we performed the quantification, valuation and mapping of carbon capture and storage of past (2007-2017) and future LULC (2027) sustainable scenarios in a cattle ranch of Serra da Mantiqueira to understand how different LULC change scenarios may affect the provision of ES and contribute to economic opportunities to the farming sector. Under a GIS-approach, we used remote sensing techniques to LULC mapping, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for scenario building, carbon assessment and valuation, as well as Sis family software modeling for forest management production. All the sustainable scenarios contributed to the increase of carbon capture and storage in the study area, in addition to showing profitable economic opportunities arising from their implementation. The introduction of eucalyptus trees in livestock and agricultural production systems is an interesting alternative for diversification and income increase, contributing to the balance of greenhouse gases (GHG) from livestock activity and adding value to production. These results are useful to support the development and planning for both environmental conservation policies and sustainable farming production

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems