Characterizing Tree Spatial Distribution Patterns Using Discrete Aerial Lidar Data
نویسندگان
چکیده
منابع مشابه
Combining High Spatial Resolution Lidar Data with Aerial Photography to Automate Individual Tree Measurements
Detailed forest inventory information is critically required for many ecological applications as well as for forest management. However, traditional field measurements, which are labor intensive and time consuming, can provide only a very limited amount of information for large forest areas so that extrapolated estimation of forest characteristics tends to have large errors. With the help of ne...
متن کاملAerial Lidar Data Classification using Expectation-Maximization
We use the Expectation-Maximization (EM) algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged)...
متن کاملCharacterizing Urban Volumetry Using Lidar Data
Urban indicators are efficient tools designed to simplify, quantify and communicate relevant information for land planners. Since urban data has a strong spatial representation, one can use geographical data as the basis for constructing information regarding urban environments. One important source of information about the land status is imagery collected through remote sensing. Afterwards, us...
متن کاملDetection of Tree Crowns Based on Reclassification Using Aerial Images and Lidar Data
Tree detection using aerial sensors in early decades was focused by many researchers in different fields including Remote Sensing and Photogrammetry. This paper is intended to detect trees in complex city areas using aerial imagery and laser scanning data. Our methodology is a hierarchal unsupervised method consists of some primitive operations. This method could be divided into three sections,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2020
ISSN: 2072-4292
DOI: 10.3390/rs12040712