The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Airborne Light Detection and Ranging (LIDAR) remote sensing based forest inventory at the individual tree level is a valuable and effective alternative to manual inventory, due to factors such as higher accuracy, easy repeatability of sampling, and economic benefits. However, individual tree detection in multi-storied forests is challenging due to high tree proximity and forest structure complexity...
The knowledge about the species of trees is essential for precision forest management practices. Modern high density airborne Light Detection and Ranging (LiDAR) systems have the ability to acquire large number of LiDAR points, allowing a very detailed characterization of the forest at the individual tree level. In this context, it is possible to use LiDAR data for accurate classification of the tree...
Light Detection And Ranging (LiDAR) data have proven to be very effective in the estimation of parameters for forestry applications. However, little research has been done regarding the multitemporal analysis of these data. In this paper we propose a novel hierarchical change detection approach that first performs the detection of major changes (e.g., harvested trees) and then focuses on the detection...
When dealing with optical images, the most common approach to unsupervised change detection is Change Vector Analysis (CVA) which computes the multispectral difference image and exploits its statistical distribution in (hyper-)spherical coordinates. The latter step usually requires assumptions on both the model of class distributions and the number of changes. However, both assumptions are seldom...
In this paper we present a hierarchical approach to the segmentation of high-density LiDAR data which aims to automatically detect and delineate the single tree crowns of both the dominant and the dominated layers of the forest. First, we detect the dominant tree crowns by using both the image derived from the LiDAR data and the LiDAR point cloud. Hence, the detected crowns are delineated directly...
Modern forest inventory is based on the accurate and precise characterization of the 3D structure of the forest. Although LiDAR (Light Detection and Ranging) is an effective tool to estimate forest parameters, when acquired from single view point it is not able to represent accurately the entire scene. To solve this problem, in this paper we present a method that integrates the terrestrial and airborne...
Hyperspectral data classification problems have been extensively studied in the past decade. However, well designed features and a robust classifier are still open issues that impact on the performance of an automatic land-cover classification system. In this paper, we propose a deep feature represenation method that generates very good features and a classifier for pixel-wise hyperspectral data classification...
The rise in global temperature accelerates the melting of the ice sheets. This highlights the importance of understanding the ice sheet structure and dynamics. Although fundamental for a reliable 3D modeling of the ice subsurface, the development of automatic techniques for the integration of multisensor data acquired over the ice sheets is still very limited. To address this issue, in this paper...
This paper presents an adaptive framework for detection of changes of relevance occurring in image time series in a recursive way. With the availability of reference data for only one image pair from the time series (source domain), the proposed methodology employs change vector analysis in the 3-dimensional spherical domain to determine a decision region R associated with the change of relevance...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.