Mapping urban tree species in a tropical environment using airborne multispectral and LiDAR data

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAccurate and up-to-date urban tree inventory is an essential resource for the development of strategies towards sustainable urban planning, as well as for effective management and preservation of biodiversity. Trees contribute to thermal comfort within urban centers by lessening heat island effect and have a direct impact in the reduction of air pollution. However, mapping individual trees species normally involves time-consuming field work over large areas or image interpretation performed by specialists. The integration of airborne LiDAR data with high-spatial resolution and multispectral aerial image is an alternative and effective approach to differentiate tree species at the individual crown level. This thesis aims to investigate the potential of such remotely sensed data to discriminate 5 common urban tree species using traditional Machine Learning classifiers (Random Forest, Support Vector Machine, and k-Nearest Neighbors) in the tropical environment of Salvador, Brazil. Vegetation indices and texture information were extracted from multispectral imagery, and LiDAR-derived variables for tree crowns, were tested separately and combined to perform tree species classification applying three different classifiers. Random Forest outperformed the other two classifiers, reaching overall accuracy of 82.5% when using combined multispectral and LiDAR data. The results indicate that (1) given the similarity in spectral signature, multispectral data alone is not sufficient to distinguish tropical tree species (only k-NN classifier could detect all species); (2) height values and intensity of crown returns points were the most relevant LiDAR features, combination of both datasets improved accuracy up to 20%; (3) generation of canopy height model derived from LiDAR point cloud is an effective method to delineate individual tree crowns in a semi-automatic approach

An object-based classification approach for mapping "migrant housing" in the mega-urban area of the Pearl River Delta (China)

Urban areas develop on formal and informal levels. Informal development is often highly dynamic, leading to a lag of spatial information about urban structure types. In this work, an object-based remote sensing approach will be presented to map the migrant housing urban structure type in the Pearl River Delta, China. SPOT5 data were utilized for the classification (auxiliary data, particularly up-to-date cadastral data, were not available). A hierarchically structured classification process was used to create (spectral) independence from single satellite scenes and to arrive at a transferrable classification process. Using the presented classification approach, an overall classification accuracy of migrant housing of 68.0% is attained

CHARACTERIZING FOREST STANDS USING UNMANNED AERIAL SYSTEMS (UAS) DIGITAL PHOTOGRAMMETRY: ADVANCEMENTS AND CHALLENGES IN MONITORING LOCAL SCALE FOREST COMPOSITION, STRUCTURE, AND HEALTH

Present-day forests provide a wide variety of ecosystem services to the communities that rely on them. At the same time, these environments face routine and substantial disturbances that direct the need for site-specific, timely, and accurate monitoring/management (i.e., precision forestry). Unmanned Aerial Systems (UAS or UAV) and their associated technologies offer a promising tool for conducting such precision forestry. Now, even with only natural color, uncalibrated, UAS imagery, software workflows involving Structure from Motion (SfM) (i.e., digital photogrammetry) modelling and segmentation can be used to characterize the features of individual trees or forest communities. In this research, we tested the effectiveness of UAS-SfM for mapping local scale forest composition, structure, and health. Our first study showed that digital (automated) methods for classifying forest composition that utilized UAS imagery produced a higher overall accuracy than those involving other high-spatial-resolution imagery (7.44% - 16.04%). The second study demonstrated that natural color sensors could provide a highly efficient estimate of individual tree diameter at breast height (dbh) (± 13.15 cm) as well as forest stand basal area, tree density, and stand density. In the final study, we join a growing number of researchers examining precision applications in forest health monitoring. Here, we demonstrate that UAS, equipped with both natural color and multispectral sensors, are more capable of distinguishing forest health classes than freely available high-resolution airborne imagery. For five health classes, these UAS data produced a 14.93% higher overall accuracy in comparison to the airborne imagery. Together, these three chapters present a wholistic approach to enhancing and enriching precision forest management, which remains a critical requirement for effectively managing diverse forested landscapes

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Methodical basis for landscape structure analysis and monitoring: inclusion of ecotones and small landscape elements

Habitat variation is considered as an expression of biodiversity at landscape level in addition to genetic variation and species variation. Thus, effective methods for measuring habitat pattern at landscape level can be used to evaluate the status of biological conservation. However, the commonly used model (i.e. patch-corridor-matrix) for spatial pattern analysis has deficiencies. This model assumes discrete structures within the landscape without explicit consideration of “transitional zones” or “gradients” between patches. The transitional zones, often called “ecotones”, are dynamic and have a profound influence on adjacent ecosystems. Besides, this model takes landscape as a flat surface without consideration of the third spatial dimension (elevation). This will underestimate the patches’ size and perimeter as well as distances between patches especially in mountainous regions. Thus, the mosaic model needs to be adapted for more realistic and more precise representation of habitat pattern regarding to biodiversity assessment. Another part of information that has often been ignored is “small biotopes” inside patches (e.g. hedgerows, tree rows, copse, and scattered trees), which leads to within-patch heterogeneity being underestimated. The present work originates from the integration of the third spatial dimension in land-cover classification and landscape structure analysis. From the aspect of data processing, an integrated approach of Object-Based Image Analysis (OBIA) and Pixel-Based Image Analysis (PBIA) is developed and applied on multi-source data set (RapidEye images and Lidar data). At first, a general OBIA procedure is developed according to spectral object features based on RapidEye images for producing land-cover maps. Then, based on the classified maps, pixel-based algorithms are designed for detection of the small biotopes and ecotones using a Normalized Digital Surface Model (NDSM) which is derived from Lidar data. For describing habitat pattern under three-dimensional condition, several 3D-metrics (measuring e.g. landscape diversity, fragmentation/connectivity, and contrast) are proposed with spatial consideration of the ecological functions of small biotopes and ecotones. The proposed methodology is applied in two real-world examples in Germany and China. The results are twofold. First, it shows that the integrated approach of object-based and pixel-based image processing is effective for land-cover classification on different spatial scales. The overall classification accuracies of the main land-cover maps are 92 % in the German test site and 87 % in the Chinese test site. The developed Red Edge Vegetation Index (REVI) which is calculated from RapidEye images has been proved more efficient than the traditionally used Normalized Differenced Vegetation Index (NDVI) for vegetation classification, especially for the extraction of the forest mask. Using NDSM data, the third dimension is helpful for the identification of small biotopes and height gradient on forest boundary. The pixel-based algorithm so-called “buffering and shrinking” is developed for the detection of tree rows and ecotones on forest/field boundary. As a result the accuracy of detecting small biotopes is 80 % and four different types of ecotones are detected in the test site. Second, applications of 3D-metrics in two varied test sites show the frequently-used landscape diversity indices (i.e. Shannon’s diversity (SHDI) and Simpson’s diversity (SIDI)) are not sufficient for describing the habitats diversity, as they quantify only the habitats composition without consideration on habitats spatial distribution. The modified 3D-version of Effective Mesh Size (MESH) that takes ecotones into account leads to a realistic quantification of habitat fragmentation. In addition, two elevation-based contrast indices (i.e. Area-Weighted Edge Contrast (AWEC) and Total Edge Contrast Index (TECI)) are used as supplement to fragmentation metrics. Both ecotones and small biotopes are incorporated into the contrast metrics to take into account their edge effect in habitat pattern. This can be considered as a further step after fragmentation analysis with additional consideration of the edge permeability in the landscape structure analysis. Furthermore, a vector-based algorithm called “multi-buffer” approach is suggested for analyzing ecological networks based on land-cover maps. It considers small biotopes as stepping stones to establish connections between patches. Then, corresponding metrics (e.g. Effective Connected Mesh Size (ECMS)) are proposed based on the ecological networks. The network analysis shows the response of habitat connectivity to different dispersal distances in a simple way. Those connections through stepping stones act as ecological indicators of the “health” of the system, indicating the interpatch communications among habitats. In summary, it can be stated that habitat diversity is an essential level of biodiversity and methods for quantifying habitat pattern need to be improved and adapted to meet the demands for landscape monitoring and biodiversity conservation. The approaches presented in this work serve as possible methodical solution for fine-scale landscape structure analysis and function as “stepping stones” for further methodical developments to gain more insights into the habitat pattern.Die Lebensraumvielfalt ist neben der genetischen Vielfalt und der Artenvielfalt eine wesentliche Ebene der Biodiversität. Da diese Ebenen miteinander verknüpft sind, können Methoden zur Messung der Muster von Lebensräumen auf Landschaftsebene erfolgreich angewandt werden, um den Zustand der Biodiversität zu bewerten. Das zur räumlichen Musteranalyse auf Landschaftsebene häufig verwendete Patch-Korridor-Matrix-Modell weist allerdings einige Defizite auf. Dieses Modell geht von diskreten Strukturen in der Landschaft aus, ohne explizite Berücksichtigung von „Übergangszonen“ oder „Gradienten“ zwischen den einzelnen Landschaftselementen („Patches“). Diese Übergangszonen, welche auch als „Ökotone“ bezeichnet werden, sind dynamisch und haben einen starken Einfluss auf benachbarte Ökosysteme. Außerdem wird die Landschaft in diesem Modell als ebene Fläche ohne Berücksichtigung der dritten räumlichen Dimension (Höhe) betrachtet. Das führt dazu, dass die Flächengrößen und Umfänge der Patches sowie Distanzen zwischen den Patches besonders in reliefreichen Regionen unterschätzt werden. Daher muss das Patch-Korridor-Matrix-Modell für eine realistische und präzise Darstellung der Lebensraummuster für die Bewertung der biologischen Vielfalt angepasst werden. Ein weiterer Teil der Informationen, die häufig in Untersuchungen ignoriert werden, sind „Kleinbiotope“ innerhalb größerer Patches (z. B. Feldhecken, Baumreihen, Feldgehölze oder Einzelbäume). Dadurch wird die Heterogenität innerhalb von Patches unterschätzt. Die vorliegende Arbeit basiert auf der Integration der dritten räumlichen Dimension in die Landbedeckungsklassifikation und die Landschaftsstrukturanalyse. Mit Methoden der räumlichen Datenverarbeitung wurde ein integrierter Ansatz von objektbasierter Bildanalyse (OBIA) und pixelbasierter Bildanalyse (PBIA) entwickelt und auf einen Datensatz aus verschiedenen Quellen (RapidEye-Satellitenbilder und Lidar-Daten) angewendet. Dazu wird zunächst ein OBIA-Verfahren für die Ableitung von Hauptlandbedeckungsklassen entsprechend spektraler Objekteigenschaften basierend auf RapidEye-Bilddaten angewandt. Anschließend wurde basierend auf den klassifizierten Karten, ein pixelbasierter Algorithmus für die Erkennung von kleinen Biotopen und Ökotonen mit Hilfe eines normalisierten digitalen Oberflächenmodells (NDSM), welches das aus LIDAR-Daten abgeleitet wurde, entwickelt. Zur Beschreibung der dreidimensionalen Charakteristika der Lebensraummuster unter der räumlichen Betrachtung der ökologischen Funktionen von kleinen Biotopen und Ökotonen, werden mehrere 3D-Maße (z. B. Maße zur landschaftlichen Vielfalt, zur Fragmentierung bzw. Konnektivität und zum Kontrast) vorgeschlagen. Die vorgeschlagene Methodik wird an zwei realen Beispielen in Deutschland und China angewandt. Die Ergebnisse zeigen zweierlei. Erstens zeigt es sich, dass der integrierte Ansatz der objektbasierten und pixelbasierten Bildverarbeitung effektiv für die Landbedeckungsklassifikation auf unterschiedlichen räumlichen Skalen ist. Die Klassifikationsgüte insgesamt für die Hauptlandbedeckungstypen beträgt 92 % im deutschen und 87 % im chinesischen Testgebiet. Der eigens entwickelte Red Edge-Vegetationsindex (REVI), der sich aus RapidEye-Bilddaten berechnen lässt, erwies sich für die Vegetationsklassifizierung als effizienter verglichen mit dem traditionell verwendeten Normalized Differenced Vegetation Index (NDVI), insbesondere für die Gewinnung der Waldmaske. Im Rahmen der Verwendung von NDSM-Daten erwies sich die dritte Dimension als hilfreich für die Identifizierung von kleinen Biotopen und dem Höhengradienten, beispielsweise an der Wald/Feld-Grenze. Für den Nachweis von Baumreihen und Ökotonen an der Wald/Feld-Grenze wurde der sogenannte pixelbasierte Algorithmus „Pufferung und Schrumpfung“ entwickelt. Im Ergebnis konnten kleine Biotope mit einer Genauigkeit von 80 % und vier verschiedene Ökotontypen im Testgebiet detektiert werden. Zweitens zeigen die Ergebnisse der Anwendung der 3D-Maße in den zwei unterschiedlichen Testgebieten, dass die häufig genutzten Landschaftsstrukturmaße Shannon-Diversität (SHDI) und Simpson-Diversität (SIDI) nicht ausreichend für die Beschreibung der Lebensraumvielfalt sind. Sie quantifizieren lediglich die Zusammensetzung der Lebensräume, ohne Berücksichtigung der räumlichen Verteilung und Anordnung. Eine modifizierte 3D-Version der Effektiven Maschenweite (MESH), welche die Ökotone integriert, führt zu einer realistischen Quantifizierung der Fragmentierung von Lebensräumen. Darüber hinaus wurden zwei höhenbasierte Kontrastindizes, der flächengewichtete Kantenkontrast (AWEC) und der Gesamt-Kantenkontrast Index (TECI), als Ergänzung der Fragmentierungsmaße entwickelt. Sowohl Ökotone als auch Kleinbiotope wurden in den Berechnungen der Kontrastmaße integriert, um deren Randeffekte im Lebensraummuster zu berücksichtigen. Damit kann als ein weiterer Schritt nach der Fragmentierungsanalyse die Randdurchlässigkeit zusätzlich in die Landschaftsstrukturanalyse einbezogen werden. Außerdem wird ein vektorbasierter Algorithmus namens „Multi-Puffer“-Ansatz für die Analyse von ökologischen Netzwerken auf Basis von Landbedeckungskarten vorgeschlagen. Er berücksichtigt Kleinbiotope als Trittsteine, um Verbindungen zwischen Patches herzustellen. Weiterhin werden entsprechende Maße, z. B. die Effective Connected Mesh Size (ECMS), für die Analyse der ökologischen Netzwerke vorgeschlagen. Diese zeigen die Auswirkungen unterschiedlicher angenommener Ausbreitungsdistanzen von Organismen bei der Ableitung von Biotopverbundnetzen in einfacher Weise. Diese Verbindungen zwischen Lebensräumen über Trittsteine hinweg dienen als ökologische Indikatoren für den „gesunden Zustand“ des Systems und zeigen die gegenseitigen Verbindungen zwischen den Lebensräumen. Zusammenfassend kann gesagt werden, dass die Vielfalt der Lebensräume eine wesentliche Ebene der Biodiversität ist. Die Methoden zur Quantifizierung der Lebensraummuster müssen verbessert und angepasst werden, um den Anforderungen an ein Landschaftsmonitoring und die Erhaltung der biologischen Vielfalt gerecht zu werden. Die in dieser Arbeit vorgestellten Ansätze dienen als mögliche methodische Lösung für eine feinteilige Landschaftsstrukturanalyse und fungieren als ein „Trittsteine” auf dem Weg zu weiteren methodischen Entwicklungen für einen tieferen Einblick in die Muster von Lebensräumen

Earth Resources Laboratory research and technology

The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed