Lidar In Coastal Storm Surge Modeling: Modeling Linear Raised Features

A method for extracting linear raised features from laser scanned altimetry (LiDAR) datasets is presented. The objective is to automate the method so that elements in a coastal storm surge simulation finite element mesh might have their edges aligned along vertical terrain features. Terrain features of interest are those that are high and long enough to form a hydrodynamic impediment while being narrow enough that the features might be straddled and not modeled if element edges are not purposely aligned. These features are commonly raised roadbeds but may occur due to other manmade alterations to the terrain or natural terrain. The implementation uses the TauDEM watershed delineation software included in the MapWindow open source Geographic Information System to initially extract watershed boundaries. The watershed boundaries are then examined computationally to determine which sections warrant inclusion in the storm surge mesh. Introductory work towards applying image analysis techniques as an alternate means of vertical feature extraction is presented as well. Vertical feature lines extracted from a LiDAR dataset for Manatee County, Florida are included in a limited storm surge finite element mesh for the county and Tampa Bay. Storm surge simulations using the ADCIRC-2DDI model with two meshes, one which includes linear raised features as element edges and one which does not, verify the usefulness of the method

Vegetation Detection and Classification for Power Line Monitoring

Electrical network maintenance inspections must be regularly executed, to provide a continuous distribution of electricity. In forested countries, the electrical network is mostly located within the forest. For this reason, during these inspections, it is also necessary to assure that vegetation growing close to the power line does not potentially endanger it, provoking forest fires or power outages. Several remote sensing techniques have been studied in the last years to replace the labor-intensive and costly traditional approaches, be it field based or airborne surveillance. Besides the previously mentioned disadvantages, these approaches are also prone to error, since they are dependent of a human operator’s interpretation. In recent years, Unmanned Aerial Vehicle (UAV) platform applicability for this purpose has been under debate, due to its flexibility and potential for customisation, as well as the fact it can fly close to the power lines. The present study proposes a vegetation management and power line monitoring method, using a UAV platform. This method starts with the collection of point cloud data in a forest environment composed of power line structures and vegetation growing close to it. Following this process, multiple steps are taken, including: detection of objects in the working environment; classification of said objects into their respective class labels using a feature-based classifier, either vegetation or power line structures; optimisation of the classification results using point cloud filtering or segmentation algorithms. The method is tested using both synthetic and real data of forested areas containing power line structures. The Overall Accuracy of the classification process is about 87% and 97-99% for synthetic and real data, respectively. After the optimisation process, these values were refined to 92% for synthetic data and nearly 100% for real data. A detailed comparison and discussion of results is presented, providing the most important evaluation metrics and a visual representations of the attained results.Manutenções regulares da rede elétrica devem ser realizadas de forma a assegurar uma distribuição contínua de eletricidade. Em países com elevada densidade florestal, a rede elétrica encontra-se localizada maioritariamente no interior das florestas. Por isso, durante estas inspeções, é necessário assegurar também que a vegetação próxima da rede elétrica não a coloca em risco, provocando incêndios ou falhas elétricas. Diversas técnicas de deteção remota foram estudadas nos últimos anos para substituir as tradicionais abordagens dispendiosas com mão-de-obra intensiva, sejam elas através de vigilância terrestre ou aérea. Além das desvantagens mencionadas anteriormente, estas abordagens estão também sujeitas a erros, pois estão dependentes da interpretação de um operador humano. Recentemente, a aplicabilidade de plataformas com Unmanned Aerial Vehicles (UAV) tem sido debatida, devido à sua flexibilidade e potencial personalização, assim como o facto de conseguirem voar mais próximas das linhas elétricas. O presente estudo propõe um método para a gestão da vegetação e monitorização da rede elétrica, utilizando uma plataforma UAV. Este método começa pela recolha de dados point cloud num ambiente florestal composto por estruturas da rede elétrica e vegetação em crescimento próximo da mesma. Em seguida,múltiplos passos são seguidos, incluindo: deteção de objetos no ambiente; classificação destes objetos com as respetivas etiquetas de classe através de um classificador baseado em features, vegetação ou estruturas da rede elétrica; otimização dos resultados da classificação utilizando algoritmos de filtragem ou segmentação de point cloud. Este método é testado usando dados sintéticos e reais de áreas florestais com estruturas elétricas. A exatidão do processo de classificação é cerca de 87% e 97-99% para os dados sintéticos e reais, respetivamente. Após o processo de otimização, estes valores aumentam para 92% para os dados sintéticos e cerca de 100% para os dados reais. Uma comparação e discussão de resultados é apresentada, fornecendo as métricas de avaliação mais importantes e uma representação visual dos resultados obtidos

Application of high-resolution airborne data using individual tree crowns in Japanese conifer plantations

The original publication is available at www.springerlink.comArticleJOURNAL OF FOREST RESEARCH. 14(1):10-19 (2009)journal articl

Remote sensing tools for the objective quantification of tree structural condition from individual trees to landscape scale assessment

Tree management is the practice of protecting and caring for trees for sustainable, defined objectives. However, there are often conflicts between maintaining trees and the obligation to protect targets, such as people or infrastructure, from the risks associated with the failure of trees and major limbs. Where there are targets worthy of protection, tree structural condition is typically monitored relative to the prescribed management objectives. Traditionally, field methods for capturing data on tree structural condition are manual with a tree surveyor taking very limited direct measurements, and only from parts of the tree that are within reach from the ground. Consequently, large sections of the tree remain unmeasured due to the logistical complications of accessing the aerial structure. Therefore, the surveyor estimates tree part sizes, approximates counts of relevant tree features and uses personal interpretation to infer the significance of the observations. These techniques are temporally and logistically demanding, and largely subjective. This thesis develops solutions to the limitations of traditional methods through the development of remote sensing (RS) tools for assessing tree structural condition, in order to inform tree management interventions. For individual trees, a proximal photogrammetry technique is developed for objectively quantifying tree structural condition by measuring the self-affinity of tree crowns in fractal dimensions. This can identify the individual tree crown complexity along a structural condition continuum, which is more effective than the traditional categorical approach for monitoring tree condition. Moving out in scale, a framework is developed which optimises the matchpairing agreement between ground reference tree data and RS-derived individual tree crown (ITC) delineations in order to quantify the accuracy of different ITC delineation algorithms. The framework is then used to identify an optimal ITC delineation algorithm which is applied to aerial laser scanning data to map individual trees and extract a point cloud for each tree. Metrics are then derived from the point cloud to classify a tree according to its structural condition, a process which is then applied to the tree population across an entire landscape. This provides information with which to spatially optimise tree survey and management resources, improve the decision making process and move towards proactive tree management. The research presented in this thesis develops RS tools for assessing tree structural condition, at a range of investigative scales. These objective, data-rich tools will enable resource-limited tree managers to direct remedial interventions in an optimised and precise way

Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

Abstract Background Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind. </jats:sec

Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain

We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwise selection and genetic algorithm selection) in combination with multiple linear regression models to estimate biomass. 43 predictor variables derived from discrete-return LiDAR data (4 pulses per m2 ) were used for estimating the forest biomass of Pinus sylvestris Linnaeus and Pinus nigra Arnold forests. Twelve circular plots – six for each species – and three different fixed-radius designs (i.e. 7, 15, and 30 m) were estab lished within the range of the airborne LiDAR. The Bayesian information criterion and R2 were used to select the best models. As expected, the models that included the largest plots (30 m) yielded the highest R2 value (0.91) for Pinus sp. using genetic algorithm models. Considering P. sylvestris and P. nigra models separately, the genetic algorithm approach also yielded the highest R2 values for the 30-m plots (P. nigra: R2 = 0.99, P. sylvestris: R2 = 0.97). The results we obtained with two species and different plot sizes revealed that increasing the size of plots from 15 to 30 m had a low effect on modelling attempts.European Commission (EC) FP7-315165Ministerio de Economía, Industria y Competitividad QUERCUSAT (CLG2013-40790-R

Multiscale forest health mapping: the potential of air- and space-borne remote sensing sensors

Forest health decline triggered by extensive periods of drought and high temperatures is increasingly common across Australia. In this respect remote sensing technology may help with understanding and managing forest health decline by providing information on a scale that field-based studies cannot match. In this thesis I explore the potential of air- and space-borne remote sensing in characterizing and monitoring forest health expressed in terms of tree dieback at multiple scales. I conducted my experiments in the largest river red gum forest in the world, located in the south-east of Australia that has experienced episodes of severe dieback over the past six decades. First, I propose a new algorithm that utilizes high point density airborne laser scans (ALS) for delineating individual trees with complex shapes, such as eucalypts, in Chapter 2. My algorithm was able to accurately delineate up to 68% of trees depending on forest and ALS point density. Second, I investigate the utility of ALS and imaging spectroscopy in classifying forest health at the individual tree level and diagnosing potential causes of forest health decline, in Chapter 3. According to my results the health of individual trees can be classified with an overall accuracy of 81% and a kappa score of 0.66, while infrequently flooded areas were most susceptible to tree health decline. Finally, I assess how low point density ALS, Synthetic Aperture Radar and multispectral satellite imagery can estimate forest health at the plot level, in Chapter 4. My findings demonstrate that individual tree health could be scaled up to the plot level with substantial level of accuracy (R2 of up to 0.64). Overall, my results provide a robust and peer-reviewed methodology that utilizes air- and space-borne remote sensing to accurately classify forest health at multiple scales. Moreover, the forest health map produced as a result of my research will potentially enable forest managers to perform demographic reporting on forest dynamics, diagnose ecological processes linked to forest health, and prioritize areas for forest health promotion and conservation of biodiversity

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

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources