Segmentation and classification of individual tree crowns

By segmentation and classification of individual tree crowns in high spatial resolution aerial images, information about the forest can be automatically extracted. Segmentation is about finding the individual tree crowns and giving each of them a unique label. Classification, on the other hand, is about recognising the species of the tree. The information of each individual tree in the forest increases the knowledge about the forest which can be useful for managements, biodiversity assessment, etc. Different algorithms for segmenting individual tree crowns are presented and also compared to each other in order to find their strengths and weaknesses. All segmentation algorithms developed in this thesis focus on preserving the shape of the tree crown. Regions, representing the segmented tree crowns, grow according to certain rules from seed points. One method starts from many regions for each tree crown and searches for the region that fits the tree crown best. The other methods start from a set of seed points, representing the locations of the tree crowns, to create the regions. The segmentation result varies from 73 to 95 % correctly segmented visual tree crowns depending on the type of forest and the method. The former value is for a naturally generated mixed forest and the latter for a non-mixed forest. The classification method presented uses shape information of the segments and colour information of the corresponding tree crown in order to decide the species. The classification method classifies 77 % of the visual trees correctly in a naturally generated mixed forest, but on a forest stand level the classification is over 90 %

Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

Censusing and modeling the dynamics of a population of eastern hemlock (Tsuga canadensis L.) using remote sensing

A population of eastern hemlock (Tsuga canadensis L.) was censused from the ground using traditional field methods and from the air using large scale, high-resolution, aerial imagery in the early spring of 1997, 1998 and 1999. A manual crown survey map of the population, prepared from aerial imagery, was compared to a traditional field census. Over 60% of the individuals measured on the ground were not detected in the aerial census. Tree size, crown density and crown position all played roles in determining a crown\u27s visibility from the air. Nearly all large, upper canopy hemlocks were visible in the aerial census. An important minority of small, lower canopy hemlocks were also visible in the aerial census. An automated spatial segmentation procedure was developed to identify and measure individual population units, or blobs, within the forest population. A blob was defined as a distinct portion of crown segmented from its neighbors on the basis of size, shape, and connectivity. To ensure the comparability of multi-year segmentation maps, an automated blob reconciliation procedure was also developed to make certain that no hemlock pixels were assigned to different blobs in different years. Following spatial segmentation and reconciliation, a large majority of hemlock blobs (∼64--72%) were found to be closely associated with ground referenced, manually delineated, individual hemlock crowns. The remaining blobs consisted of spatially distinct parts of a crown or closely clumped multiple crowns. Matrix population models were constructed from the ground-derived and aerial-derived population data. Matrix analysis produced a number of useful population characteristics including overall population growth rate (lambda), stable stage distributions, reproductive values, and sensitivity values. lambda\u27s calculated from the aerial and ground-derived matrices were compared using randomization tests. While providing a different perspective and description of a population than traditional ground studies, demographic studies using remote sensing provide some promising advantages. The spatially explicit nature of the data permits more biologically realistic modeling of the population and the investigation of potential environmental influences on population dynamics. Automated extraction of demographic or megademographic data from remotely sensed images represents an important first step toward scaling population analysis to the landscape and regional levels

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

Taxonomic identification of Amazonian tree crowns from aerial photography

Question: To what extent can aerial photography be used for taxonomic identification of Amazonian tree crowns? Objective: To investigate whether a combination of dichotomous keys and a web-based interface is a suitable approach to identify tree crowns. Location: The fieldwork was conducted at Tiputini Biodiversity Station located in the Amazon, eastern Ecuador. Methods: High-resolution imagery was taken from an airplane flying at a low altitude (600 m) above the ground. Imagery of the observable upper layer of the tree crowns was used for the analysis. Dichotomous identification keys for different types of crowns were produced and tested. The identification keys were designed to be web-based interactive, using Google Earth as the main online platform. The taxa analysed were Iriartea, Astrocaryum, Inga, Parkia, Cecropia, Pourouma, Guarea, Otoba, Lauraceae and Pouteria. Results: This paper demonstrates that a combination of photo-imagery, dichotomous keys and a web-based interface can be useful for the taxonomic identification of Amazonian trees based on their crown characteristics. The keys tested with an overall identification accuracy of over 50% for five of the ten taxa with three of them showing accuracy greater than 70% (Iriartea, Astrocaryum and Cecropia). Conclusions: The application of dichotomous keys and a web-based interface provides a new methodological approach for taxonomic identification of various Amazonian tree crowns. Overall, the study showed that crowns with a medium-rough texture are less reliably identified than crowns with smoother or well-defined surfaces

Forest Remote Sensing in Canada and the Individual Tree Crown (ITC) Approach to Forest Inventories

After a brief description of Canada’s forest situation and the role of the federal government in forestry, some Natural Resources Canada’country-wide project will be introduced. These include the National Forest Inventories (past and present), the National Forest Information System, the EOSD programs to map land cover, monitor change and evaluate biomass, mostly from Canada-wide coverages with Landsat images. The accounting of carbon and the monitoring of deforestation at a map scale level will also be introduced. The second and most significant part of this paper will describe our Individual Tree Crown (ITC) approach to forest inventories used with high spatial resolution images (better than 1m/pixel). Techniques for individual crown delineation, species classification and regrouping into forest stands that are leading to a semi-automatic production of forest inventories will be described.A locally adaptive technique for tree counts, mostly reserved for young regenerating areas, will also be presented. The synergy of multispectral and LIDAR data (atmany levels) will be examined and, the normalization of spectral values within and among aerial images will be considered.Article信州大学農学部紀要. 46(1-2): 85-92 (2010)departmental bulletin pape

A higher-order active contour model of a `gas of circles' and its application to tree crown extraction

Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when the topology of the region is unknown a priori, for example when the entity is composed of an unknown number of similar objects. Higher-order active contours (HOACs) represent one method for the modelling of non-trivial prior knowledge about shape without necessarily constraining region topology, via the inclusion of non-local interactions between region boundary points in the energy defining the model. The case of an unknown number of circular objects arises in a number of domains, e.g. medical, biological, nanotechnological, and remote sensing imagery. Regions composed of an a priori unknown number of circles may be referred to as a `gas of circles'. In this report, we present a HOAC model of a `gas of circles'. In order to guarantee stable circles, we conduct a stability analysis via a functional Taylor expansion of the HOAC energy around a circular shape. This analysis fixes one of the model parameters in terms of the others and constrains the rest. In conjunction with a suitable likelihood energy, we apply the model to the extraction of tree crowns from aerial imagery, and show that the new model outperforms other techniques

A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment.

Unmanned Aerial Systems (UAS) have emerged as a capable platform for measuring vegetation health, structure and productivity. Products derived from UAS imagery typically have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity of vegetation to be mapped and monitored with more detail. This study uses UAS-captured imagery from the Chobe Enclave of northern Botswana. Flights were conducted across a gradient of savanna sites classified as grass-, shrub-, or tree-dominated. We compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. Sensor types were also compared, to determine whether multispectral data improves estimates of vegetation structure at the expense of spatial resolution. We found that leveraging multispectral reflectance information aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. Comparisons are made between two crown delineation techniques, and the efficacy of each technique within savanna environments is discussed. The methods presented hold potential to inform field sampling protocols and UAS-based techniques for autonomous crown delineation in future dryland systems research. These findings advance research for field and remote sensing analyses assessing degradation in heterogeneous landscapes where varying vegetation structure has implications on land use and land functions