LiDAR patch metrics for object-based clustering of forest types in a tropical rainforest

Tropical rainforests support a large proportion of the Earth’s plant and animal species within a restricted global distribution, and play an important role in regulating the Earth’s climate. However, the existing knowledge of forest types or habitats is relatively poor and there are large uncertainties in the quantification of carbon stock in these forests. Airborne Laser Scanning, using LiDAR, has advantages over other remote sensing techniques for describing the three-dimensional structure of forests. With respect to the habitat requirements of different species, forest structure can be defined by canopy height, canopy cover and vertical arrangement of biomass. In this study, forest patches were identified based on classification and hierarchical merging of a LiDAR-derived Canopy Height Model in a tropical rainforest in Sumatra, Indonesia. Attributes of the identified patches were used as inputs for k-medoids clustering. The clusters were then analysed by comparing them with identified forest types in the field. There was a significant association between the clusters and the forest types identified in the field, to which arang forests and mixed agro-forests contributed the most. The topographic attributes of the clusters were analysed to determine whether the structural classes, and potentially forest types, were related to topography. The tallest clusters occurred at significantly higher elevations (> 850 m) and steeper slopes (> 26°) than the other clusters. These are likely to be remnants of undisturbed primary forests and are important for conservation and habitat studies and for carbon stock estimation. This study showed that LiDAR data can be used to map tropical forest types based on structure, but that structural similarities between patches of different floristic composition or human use histories can limit habitat separability as determined in the field

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

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

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

Evaluation of the impact of climate and human induced changes on the Nigerian forest using remote sensing

The majority of the impact of climate and human induced changes on forest are related to climate variability and deforestation. Similarly, changes in forest phenology due to climate variability and deforestation has been recognized as being among the most important early indicators of the impact of environmental change on forest ecosystem functioning. Comprehensive data on baseline forest cover changes including deforestation is required to provide background information needed for governments to make decision on Reducing Emissions from Deforestation and Forest Degradation (REED). Despite the fact that Nigeria ranks among the countries with highest deforestation rates based on Food and Agricultural Organization estimates, only a few studies have aimed at mapping forest cover changes at country scales. However, recent attempts to map baseline forest cover and deforestation in Nigeria has been based on global scale remote sensing techniques which do not confirm with ground based observations at country level. The aim of this study is two-fold: firstly, baseline forest cover was estimated using an ‘adaptive’ remote sensing model that classified forest cover with high accuracies at country level for the savanna and rainforest zones. The first part of this study also compared the potentials of different MODIS data in detecting forest cover changes at regional (cluster level) scale. The second part of this study explores the trends and response of forest phenology to rainfall across four forest clusters from 2002 to 2012 using vegetation index data from the MODIS and rainfall data obtained from the TRMM.Tertiary Education Trust Fund, Nigeri

CLUSTERING KABUPATEN BERDASARKAN LUAS HUTAN MENGGUNAKAN METODE K-MEANS DI PROVINSI JAWA TENGAH

Indonesia is one of the countries with the largest forest in the world. The tropical climate and high rainfall cause a lot of biodiversity in Indonesia’s forests. The existence of these forests can be utilized by many parties, both the government and the community in accordance with their functions to improve welfare. The government through the Central Statistic Agency has provided data information related to the forest area in various regions, one of which is Central Jawa Province but still requires development to obtain important information in the data. This study aims to divide the district based on forest area including protected forest, protected area, area for production, and area for other users in Central Java province using the K-Means Data Mining method. The data is obtained from Central Statistic Agency for the Central Java area, where four types of forest are to be grouped. The results of this study indicate that the grouping of districts based on the area of forest owned is based on the smallest Davies Bouldin (DB), which is 0.436 in the grouping with 2 clusters. The two clusters are distinguished based on the value of the proximity of the forest type attribute with the centroid point in each cluster. The clustering process grouped 26 districts in the province of Central Java into cluster 1, while cluster 2 consisted of 3 districts in Central Java, namely Grobogan, Blora, and Brebes districts. Keywords: clustering, forests, K-Mean

CLUSTERING KABUPATEN BERDASARKAN LUAS HUTAN MENGGUNAKAN METODE K-MEANS DI PROVINSI JAWA TENGAH

Indonesia is one of the countries with the largest forest in the world. The tropical climate and high rainfall cause a lot of biodiversity in Indonesia’s forests. The existence of these forests can be utilized by many parties, both the government and the community in accordance with their functions to improve welfare. The government through the Central Statistic Agency has provided data information related to the forest area in various regions, one of which is Central Jawa Province but still requires development to obtain important information in the data. This study aims to divide the district based on forest area including protected forest, protected area, area for production, and area for other users in Central Java province using the K-Means Data Mining method. The data is obtained from Central Statistic Agency for the Central Java area, where four types of forest are to be grouped. The results of this study indicate that the grouping of districts based on the area of forest owned is based on the smallest Davies Bouldin (DB), which is 0.436 in the grouping with 2 clusters. The two clusters are distinguished based on the value of the proximity of the forest type attribute with the centroid point in each cluster. The clustering process grouped 26 districts in the province of Central Java into cluster 1, while cluster 2 consisted of 3 districts in Central Java, namely Grobogan, Blora, and Brebes districts. Keywords: clustering, forests, K-Mean

INVESTIGATING THE SPATIAL BEHAVIOR AND HABITAT USE OF THE MATSCHIE’S TREE-KANGAROO (DENDROLAGUS MATSCHIEI) USING GPS COLLARS AND UNMANNED AIRCRAFT SYSTEMS (UAS)

Understanding the movement patterns and habitat needs of the endangered Matschie’s tree-kangaroo (Dendrolagus matschiei) is important for their conservation and management. Endemic to the montane cloud forests of the Huon Peninsula in northeastern Papua New Guinea, these elusive arboreal marsupials are tremendously challenging to study using traditional observational methods. This study is an assessment of novel techniques to overcome the significant challenges to in-situ data collection in remote and rugged tropical cloud forests. Animal locations are remotely tracked using purpose built altitude and motion logging GPS collars and habitat structure data is measured using photogrammetry from small Unmanned Aircraft Systems (UAS) aerial imagery. Leveraging the autocorrelation of regular GPS location sampling, this study applied a Time-Local Convex Hull (T-LoCoH) analysis to investigate particular locations that may be important to D. matschiei as well as potential barriers to movement that would be inside of the home range as identified in previous studies. A novel technique of ground surface interpolation from canopy gaps is presented to overcome the challenges of photogrammetric reconstruction of terrain surfaces under closed canopy forests. From this a variety of forest structure variables were calculated to understand the 3D complexity of these heterogeneous cloud forests. This investigation found that custom GPS collars can provide high fix success rates in dense multilayer forests found at the research site. The regular sampling intervals resulted in areas of utilization that were notably smaller than with traditional home range analyses, and provided insight into landscape features that the animals do not use. D. matschiei were found to preferentially use trees that were taller than average and were found in closer than average proximity to canopy emergent trees. The reconstruction of 3D habitat data from UAS aerial photogrammetry resulted in forest structure maps that have significant potential to overcome the necessity of manual habitat data collection that hinders large scale habitat research, for this and many other species

Characterizing Fire-Induced Forest Structure and Aboveground Biomass Changes in Boreal Forests Using Multi-Temporal Lidar and Landsat

Wildfire is the dominant stand-replacing disturbance regime in Canadian boreal forests. An accurate quantification of post-fire changes in forest structure and aboveground biomass density (AGBD) provides a means to understand the magnitudes of ecosystem changes through wildfires and related linkages with global climate. While multispectral remote sensing has been extensively utilized for burn severity assessment, its capacity for post-fire forest structure and AGBD change monitoring has been more limited to date. This study evaluates the interactions among burn severity, forest structure, and fire-return intervals for two representative sites in the western Canadian boreal forest. We adopted burn severity measurements from Landsat to characterize the heterogeneity of wildfire effects, while vertical forest structure information from lidar was utilized to inform on realized forest changes and carbon fluxes associated with fire. Dominant trees in biomass-rich stands showed higher tolerance to low- and moderate-severity wildfires, while understory vegetation in these same stands showed a severity-invariant response to wildfires indicated by high vegetation mortality regardless of burn severity levels. Compared to a site without previous burn, canopy height and AGBD experienced lower magnitudes of change after subsequent wildfires, explained by a negative feedback between high frequency wildfires and biomass loss ( ΔCanopyCanopy Height single wildfire = 3.03 m; ΔCanopyCanopy Height successive wildfire = 2.47 m; ΔAGBDAGBD single wildfire = 8.40 Mg/ha; ΔAGBDAGBD successive wildfire = 6.69 Mg/ha). This study provides new insights into forest recovery dynamics following fire disturbance, which is particularly relevant given increased fire frequency and intensity in boreal ecosystems resulting from climate change