UAV based Multi Seasonal Deciduous Tree Species Analysis in the Hainich National Park using Multi Temporal and Point Cloud Curvature Features

Low cost UAV systems are a flexible and mobile platform for very detailed spatial high-resolution point cloud and surface height mapping projects. This study investigates the potential of the DJI Phantom 4 Pro 3D point clouds and derived crown surface height information in combination with RGB spectral information for mapping of deciduous tree species in the Hainich national park area. RGB image data was captured in August, early October and November 2018 to create a multi seasonal spectral dataset for a 100 ha test area. The flight campaigns were controlled from the Hainich flux tower platform in 40 m height owned and operated by University of Göttingen in the central part of the park area. Absolut georeferencing accuracy of the datasets was improved using 7 DGPS measured control points within the stand structure on small forest clearings. Image files and ground control points were processed to a dense point cloud model with 2.6 billion points (approximately 200k points per tree crown object) using the Agisoft Metashape cluster processing environment. Additionally, a digital surface model and a true ortho image mosaic with 3 cm spatial resolution was generated. For the differentiation of deciduous tree species, a reference data set with coordinates for the tree species Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), Carpinus betulus (hornbeam) and dead trees and early defoliated trees was defined. The study site is however dominated by Fagus sylvatica and Fraxinus excelsior. We studied two different groups of features: tree crown surface height variability parameters using point cloud densities, point cloud height variance, local standard deviation of gaussian curvature, standard deviation of local point cloud roughness and multi temporal normalised spectral features using multi seasonal uncalibrated UAV RGB data. Analysis of feature separability showed that very high-resolution point cloud surface curvature properties with small neighbourhood radii can differentiate some tree species types but we also found multitemporal spectral ratios based on RGB data to be very successful in differentiating the main tree species. Results of this work show that super fine very dense point cloud models and derived roughness measures of mixed forest stand surfaces hold valuable information for deciduous species discrimination and will likely also be very useful for morphological analysis of tree crown types

Novel UAV Flight Designs for Accuracy Optimization of Structure from Motion Data Products

Leveraging low-cost drone technology, specifically the DJI Mini 2, this study presents an innovative method for creating accurate, high-resolution digital surface models (DSMs) to enhance topographic mapping with off-the-shelf components. Our research, conducted near Jena, Germany, introduces two novel flight designs, the “spiral” and “loop” flight designs, devised to mitigate common challenges in structure from motion workflows, such as systematic doming and bowling effects. The analysis, based on height difference products with a lidar-based reference, and curvature estimates, revealed that “loop” and “spiral” flight patterns were successful in substantially reducing these systematic errors. It was observed that the novel flight designs resulted in DSMs with lower curvature values compared to the simple nadir or oblique flight patterns, indicating a significant reduction in distortions. The results imply that the adoption of novel flight designs can lead to substantial improvements in DSM quality, while facilitating shorter flight times and lower computational needs. This work underscores the potential of consumer-grade unoccupied aerial vehicle hardware for scientific applications, especially in remote sensing tasks

Tree Stem Detection and Crown Delineation in a Structurally Diverse Deciduous Forest Combining Leaf-On and Leaf-Off UAV-SfM Data

Accurate detection and delineation of individual trees and their crowns in dense forest environments are essential for forest management and ecological applications. This study explores the potential of combining leaf-off and leaf-on structure from motion (SfM) data products from unoccupied aerial vehicles (UAVs) equipped with RGB cameras. The main objective was to develop a reliable method for precise tree stem detection and crown delineation in dense deciduous forests, demonstrated at a structurally diverse old-growth forest in the Hainich National Park, Germany. Stem positions were extracted from the leaf-off point cloud by a clustering algorithm. The accuracy of the derived stem co-ordinates and the overall UAV-SfM point cloud were assessed separately, considering different tree types. Extracted tree stems were used as markers for individual tree crown delineation (ITCD) through a region growing algorithm on the leaf-on data. Stem positioning showed high precision values (0.867). Including leaf-off stem positions enhanced the crown delineation, but crown delineations in dense forest canopies remain challenging. Both the number of stems and crowns were underestimated, suggesting that the number of overstory trees in dense forests tends to be higher than commonly estimated in remote sensing approaches. In general, UAV-SfM point clouds prove to be a cost-effective and accurate alternative to LiDAR data for tree stem detection. The combined datasets provide valuable insights into forest structure, enabling a more comprehensive understanding of the canopy, stems, and forest floor, thus facilitating more reliable forest parameter extraction

Development and application of geostatistical methods for forest structure classification in high resolution data of the HRSC-A

Auf dem Gebiet der flugzeuggestützten Fernerkundungssensoren sehr hoher geometrischer Auflösung vollzieht sich eine Umorientierung von der Luftbildkamera hin zu digitalen Stereokamerasystemen mit multispektralen Kanälen. Mit der vollautomatischen Ableitung orthoprojizierter Multispektraldaten und digitaler Oberflächenmodelle stehen in Zukunft Datensätze zur Verfügung, die in den Bereichen Fernerkundung, Geographische Informationssysteme, Photogrammetrie und Kartographie zu erheblichen Veränderungen führen werden. Automatische Klassifikationsmethoden oder Objekterkennungsverfahren für geometrisch sehr hochauflösende Fernerkundungsdaten im Bereich von 15 bis 40 cm sind jedoch bisher für viele Anwendungen, so auch für die Forstfernerkundung, nur ansatzweise verfügbar. Bekannte pixelorientierte Klassifizierungsverfahren, die bisher in der Forstfernerkundung genutzt wurden und aus Entwicklungen für geringer auflösende Satellitenbilddaten hervorgegangen sind, können nur bedingt erfolgreich für die Auswertung sehr hochauflösender Daten eingesetzt werden. Einzelne Pixel stellen nicht mehr eine Mischung unterschiedlicher Objekte dar, sondern bilden vielmehr Teile eines einzigen Objektes. Diese Komponenten weisen oft keine objektspezifischen spektralen Charakteristika auf. Dagegen hat die räumliche Anordnung der Bildelemente einen sehr hohen Beschreibungswert für die Ableitung der im Forstanwendungsbereich relevanten Objekteigenschaften wie z.B. Bestandsdichte, Kronendurchmesser, Bestandsstruktur oder Bestandsart. Es besteht daher für viele Anwendungsbereiche ein hoher Entwicklungsbedarf für Verfahren, die Nachbarschaften von Pixelwerten in hohem Maße berücksichtigen, um Objekte zu erfassen, die außerdem objektorientiert vorgehen und die hohe spektrale Variabilität von Objekten und Klassen in geometrisch sehr hochauflösenden Bilddatensätzen bei einer Auswertung untersuchen und nutzen. In dieser Arbeit werden die multispektralen Daten, die Stereodaten und die digitalen Oberflächenmodelle des digitalen Stereozeilenscanners HRSC-A (High Resolution Stereo Camera-Airborne) hinsichtlich der Eignung für eine Beschreibung der strukturellen Eigenschaften von Forstbeständen untersucht. Digitale Oberflächenmodelle, die mit stereoabbildenden Sensoren ableitbar sind, stellen für die Forstwirtschaft potentiell eine sehr hochwertige Informationsquelle dar, da die vertikalen Bestandsstrukturen wirtschaftlich wichtige Parameter (z.B. Bestandshöhe und Holzvolumen) ableitbar machen können. In dieser Arbeit wird das Potential digitaler Bestandsoberflächenmodelle daher hinsichtlich einer derartigen Anwendung im direkten Vergleich mit Laserscannerdatensätzen untersucht. Ergebnisse zeigen, daß Oberflächenmodelle von Nadelbeständen nicht mit ausreichend hoher Genauigkeit aus HRSC-A Daten ableitbar sind, um die Wuchshöhe von einzelnen Bäumen und Beständen automatisch festzustellen. Die Geländehöhe in älteren Nadelbeständen ist nur im Bereich größerer Bestandslücken erkennbar, was die Berechnung eines Bestandshöhenmodells mit nur wenigen, inhomogen verteilten Stützpunkten bedingt. Auch durch das indirekte photogrammetrische Verfahren der Bestimmung von Objekthöhen treten bei Forstoberflächen Ungenauigkeiten bei der Wiedergabe von Kronenhöhen auf. Eine Ableitung bestandsspezifischer Eigenschaften der vertikalen Strukturen eines Forstgebietes aus HRSC-A Bestandsoberflächenmodellen ist daher nur sehr eingeschränkt möglich. Aufgrund dieser Einschränkungen wird ein erweitertes Verfahren entwickelt, das die hochauflösenden Multiblickwinkeldaten und Multispektraldaten des Kamerasystems HRSC-A nutzt, um bestandsstrukturspezifische Texturparameter abzuleiten. Die Beleuchtungsbedingungen und die Blickwinkel der Stereo- und Multispektralkanäle bestimmen die Textureigenschaften und die Geometrieveränderungen in Bilddatensätzen von Forstbeständen in erheblichem Maße. Die so verursachten richtungsabhängigen Eigenschaften sind bei der Ableitung von Texturparametern zu berücksichtigen. Zur Untersuchung der Pixelnachbarschaften wird die geostatistische Variogrammfunktion genutzt. Eine Miteinbeziehung von Beleuchtungsrichtung und blickwinkelbedingten Objektgeometrieveränderungen bei der Berücksichtigung richtungsabhängiger Eigenschaften für eine Bestandsstrukturklassifikation wird über die gesonderte Untersuchung der entsprechenden Winkel für Beleuchtungsrichtung (Azimut) und Flugrichtung bzw. den Objektversatz in allen Stereokanälen ermöglicht. Trennbare und hochdimensionale Merkmalssignaturen, bestehend aus modellierten Variogramm-rangewerten, können so für einzelne Bestandsstrukturen abgeleitet werden. Dies führt zu hohen Klassifikationsgenauigkeiten für Bestandsstrukturen von bis zu 93%. Durch die Reduzierung auf geometrische Merkmale ist auch eine Ableitung von Bestandsobjekten durch eine Segmentierung der Texturmerkmale zu Bestandsregionen möglich. Das so entwickelte Verfahren zeigt ein hohes Anwendungspotential in der Beschreibung der strukturellen Diversität von Beständen, in der Ableitung von Bestandesgrenzen und der Differenzierung der Bestandsdichte. So ist der Einsatz in den Anwendungsbereichen Forstinventur und Biodiversitätserfassung denkbar. Durch die Integration des Verfahrens in das Geoinformationssystem GRASS (Geographic Resources Analysis Support System) können externe Informationen (z.B. Bestandesgrenzen aus der Forstinventur) genutzt werden. So kann das entwickelte Verfahren der Variogramm Textur Klassifikation für die Erfassung von Wachstumsunterschieden oder Strukturdiversitätsveränderungen über bereits bestehende Informationen der Bestandesgrenzen genutzt werden. Dies zeigt auch Anwendungsmöglichkeiten im Bereich der Landschaftsstrukturerfassung. Geometrisch hochauflösende Zeilenscannerdaten zeigen somit durch die konstante Multiblickwinkel-geometrie und auch durch den geringen öffnungswinkel des optischen Systems der HRSC-A ein hohes Potential bei der Ableitung von Forstbestandseigenschaften über Textur erfassende Methoden. Mit der Operationalisierung derartiger anwendungsspezifischer Methoden können aus hochauflösenden Fernerkundungsdaten in Zukunft Informationen bereitgestellt werden, die bisher terrestrisch erstellt oder visuell aus Luftbilddaten generiert wurden. Ein entscheidender Schritt in diese Richtung wird die Integration von Methoden der Geoinformationssysteme und anwendungsspezifischen Verfahren der Fernerkundung in Form von hybriden und objektorientierten Ansätzen darstellen.In the field of airborne high-resolution remote sensing a paradigm shift from analogue aerial photographic systems towards digital sensor systems with stereo and multispectral imaging capabilities is recognisable. In addition there is a trend towards the use of high-resolution data of orbital remote-sensing systems. New strategies and adapted tools for the analyses of high-resolution remote sensing data for different applications and in the field of forest remote sensing are up to now not available. There is however an increased interest of the scientific community in relating textural information to biophysical properties ever since the appearance of the first airborne stereo line scanners in the 80's. The problem which dominates the analysis of high-resolution data and which confuses most traditional methodologies is the very much increased interclass spectral variability compared with low-resolution satellite scanner data. Well known pixel-based supervised and unsupervised classification methods used for the analysis of low-resolution satellite data cannot be applied with much success. Pixels in very high resolution data do not represent a mixture of objects anymore. In fact pixels now represent a small portion of one object which is not a representative measure of reflectance for the object or a forest stand attribute anymore as reflectance depends also on illumination direction, sensor viewing geometry, object shape, object structure and surface. In contrast the spatial distribution and neighbourhood of pixels is now of increased interest. The spatial information can be used to measure important forest stand characteristics (for example stand density, crown diameter and structural information of forest stands). This leads to image processing methods which focus on the investigation of neighbourhood relationships of pixel values by analysing and classifying texture or which define object geometry and location at first (segmentation) and analyse their spectral properties thereafter (classification). In this work a new methodology is developed in order to use high-resolution stereo scanner data in the applicational field of forestry. Main purpose is the classification and differentiation of forest stands. High-resolution data shows a potential for the differentiation of stand properties, which is of interest for forest inventory, monitoring and applications of landscape structure analysis. Digital stereo line scanner cameras also provide panchromatic stereo information which is used to derive digital surface models which could be valuable information source for stand height and for the prediction of timber volume. In this work the potential of HRSC-A digital stand surface models is analysed. Tree heights and stand surface information of laser scanner surface models and HRSC-A surface models are compared with terrestrial measurements. One finding is that indirect measurements of object heights in photogrammetric generated HRSC-A digital surface models do not reproduce tree height of spruce stands with adequate accuracy. Besides terrain height in medium dense stands is not observable. This reduces the applicational potential of digital surface models in forestry as absolute stand height models cannot be derived with sufficient accuracy. Analysis of vertical properties of stand surfaces and attribution to specific stand structures and species is therefore complicated. Due to this restriction a new enhanced method of texture classification is developed that uses the multiviewangle data characteristics of the HRSC-A stereo and multispectral data. The basis of the textural analysis of the multiviewangle data is the variogram and its characteristic parameters: "range" and "sill". The range parameter of the variogram as a measure of auto correlation and textural coarseness is calculated for variograms in illumination direction, in flight direction and perpendicular to these directions. This captures the viewangle and sun azimuth dependent texture anisotropy in high resolution HRSC-A stereo data. Stand structure can be differentiated and classified using these modelled multidirectional variogram parameters with success. This is performed using range values as n-dimensional geometrical signatures generated from a multi-layer range raster data set (in a similar way as hyperspectral data spectra are used). This method shows potential for a supervised stand structure classification using range signatures (with up to 93% accuracy) (variogram texture classification -VTC). Variogram texture measures can also be used with success for a stand segmentation when range values are used for a region growing and region merging strategy. Possible applications are forest inventory, estimation of biodiversity and as a measure for structural stand diversity in the context of landscape metrics methodologies. By integrating this method into the geographic information system GRASS (Geographic Resources Analysis Support System) enhanced capabilities of map operations following the concept of Tomlin (1990) and zonal functionality can be applied (area thresholding). This increases the attribution accuracy furthermore and allows the integration of vector data formats into application scenarios of the proposed method. This opens the perspective for applications of variogram texture classification for monitoring purposes or change detection of structural diversity of forest stands. If forest stands can be investigated as objects using external information about stand borders than structural homogeneity can be analysed with success. Another advantage of the integration of the presented method for forest stand structure mapping into GRASS is the combined use with landscape metrics tools. High-resolution multispectral and stereo scanner data of the HRSC-A show a high potential for textural analysis of forest stands. This is explained with high geometric accuracy of the data correction, low across-track viewing angles, stereo viewing-angles with a defined geometry and multispectral image information. The application-specific analysis of high-resolution remote sensing data (including the data from high-resolution orbital sensor systems) will benefit from the development of integrated RS/GIS systems that use GIS functionality together with specialised image processing tools (e.g. the VTC) in a hybrid and object oriented style

Forest damage assessment in the Black Triangle area using landsat TM, MSS and forest inventory data

The main objective of this work was the evaluation of TM (Thematic Mapper) and MSS (Multi Spectral Scanner) data of Landsat 1 and 5 for a large scale forest damage assessment in the black triangle region. The area covers the Krusne Hory and neighboring mountain areas (border area of Germany, Poland and the Czech Republic. A temporal comparison of land use of 1975 and 1989 of a sub area and a classification of three forest damage categories of coniferous forest was accomplished. A geometrical corrected database was produced and TM data was radiometric corrected to at satellite spectral reflectance. To achieve more comparable TM data sets(3), a relative atmospheric correction of one TM data set was performed using forest and lake test sites in overlapping areas of the data and a linear regression algorithm. A land use classification of TM and MSS data was performed using a supervised maximum likelihood classifier. Logit regressions for an estimation of the degree of needle loss were used to discriminate among 3 damage categories. The results indicated that a relationship between spectral properties and forest damage exists. The land use classification showed an increase of clear cuts from 1975 to 1989 and a decrease of coniferous forest. Classification results verified that atmospheric conditions in the study area had an effect on the accuracy that was achieved. It was concluded that large areas (more than one TM data set)(4) are not accurate to classify without additional ground truth data for every data set. (3) TM data sets used for this work contained a sub area of channel 1 to 5 and channel 7. (4) For this work subareas of 3 TM data sets were used

Erhöhte Buchenmortalität im Nationalpark Hainich?

Hitze und Trockenheit der letzten drei Jahre haben auch im Buchen-Nationalpark Hainich zu Veränderungen im Wald geführt. Diese Veränderungen werden seit 2019 mit Methoden der Fernerkundung erfasst, analysiert und ausgewertet. Erste Ergebnisse werden vorgestellt und diskutiert. Der Prozess ist im vollen Gange, ein Ende nicht absehbar, umfassende Auswertungen, Analysen und Bilanzen sind noch nicht möglich. Es wird die Frage erörtert, was diese Veränderungen für den Nationalpark bedeuten

UAV-based dead wood mapping in a natural deciduous forest in mid-Germany

The utilization of UAVs for the acquisition of ultra-high resolution imagery has heavily increased during the past decade. Once the hardware is purchased, images can be recorded almost at any time and at low cost. The image parameters can be determined in terms of spectral channels, image overlap, and geometric resolution. The overlap between the images enables stereoscopic image processing, the delineation of point clouds, and the generation of seamless image mosaics. UAV image data products have gathered high interest in the forestry community, as structural and spectral features can be delineated. Accordingly, regular forest monitoring and inventory can be supported using UAV data. In this study, the potential of DJI Phantom 4 Pro RTK imagery based orthomosaics and point clouds to map dead wood on the forest floor is investigated. The test site is located in the center of the Hainich national park. The Hainich national park is an unmanaged forest comprising deciduous tree species such as Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), and Carpinus betulus (hornbeam). The flight campaigns were controlled from the Hainich flux tower in the central part of the park area. RGB image data was captured in March 2019 during leaf-off conditions. Agisoft Metashape was used for processing the imagery to orthomosaics and point clouds. The living/standing trees were virtually removed from the point clouds as follows: 1.) normalizing the point cloud for topography, 2.) dropping all points above 5 m height. The remaining points were converted to an orthorectified RGB raster file, which solely contains the forest floor including the deadwood (lying stems) and tree stumps of the virtually cut trees. This raster was eventually used for dead wood mapping. The mapping task was accomplished using the OBIA software eCognition using the line extraction function as major method. The detection rate of the automatic mapping was approximately 70%. The dead wood mapping was complicated dead wood of several years of age featuring almost the same color and elevation level as the surrounding forest floor. Due to the latter, no elevation information was used. For regular monitoring considering recent dead wood only elevation information can be implemented and higher detection rates are feasible

A fractional vegetation cover remote sensing product on pan-arctic scale with link to GeoTIFF image

The paper presents first results of a pan-boreal scale land cover harmonization and classification. A methodology is presented that combines global and regional vegetation datasets to extract percentage cover information for different vegetation physiognomy and barren for the pan-arctic region within the ESA Data User Element Permafrost. Based on the legend description of each land cover product the datasets are harmonized into four LCCS (Land Cover Classification System) classifiers which are linked to the MODIS Vegetation Continuous Field (VCF) product. Harmonized land cover and Vegetation Continuous Fields products are combined to derive a best estimate of percentage cover information for trees, shrubs, herbaceous and barren areas for Russia. Future work will concentrate on the expansion of the developed methodology to the pan-arctic scale. Since the vegetation builds an isolation layer, which protects the permafrost from heat and cold temperatures, a degradation of this layer due to fire strongly influences the frozen conditions in the soil. Fire is an important disturbance factor which affects vast processes and dynamics in ecosystems (e.g. biomass, biodiversity, hydrology, etc.). Especially in North Eurasia the fire occupancy has dramatically increased in the last 50 years and has doubled in the 1990s with respect to the last five decades. A comparison of global and regional fire products has shown discrepancies between the amounts of burn scars detected by different algorithms and satellite data