Object-Based Image Analysis of Downed Logs in Disturbed Forested Landscapes Using Lidar

Abstract

Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In addition, optical remote sensing methods have not been able to map these ground targets due to the lack of optical sensor penetrability into the forest canopy and limited sensor spectral and spatial resolutions. Lidar (light detection and ranging) sensors have become a more viable and common data source in forest science for detailed mapping of forest structure. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms. Downed log objects were successfully delineated and classified from lidar derived metrics using an OBIA framework. 73% of digitized downed logs were completely or partially classified correctly. Over classification occurred in areas with large numbers of logs clustered in close proximity to one another and in areas with vegetation and tree canopy. The OBIA methods were found to be effective but inefficient in terms of automation and analyst's time in the delineation and classification of downed logs in the lidar data.

A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images

“…Blanchard and Muecke reported extraction completeness results of 73% and 75.6%, respectively. The main reason for the high completeness is that windthrown trees have larger diameters [14,15]. However, the reported completeness for trees with diameters smaller than 300 mm was only 43% in a study conducted by Mattias and Nyström.…”

“…As mentioned previously, over-classification is a common problem occurring in concentrated areas, creating difficulties for the extraction of individual windthrown trees [14]. Lindbergand and Nyström reduced interference from other objects by executing binary classification based on height and then extracted fallen trees using template matching.…”

“…The focus of current research is the identification and extraction of individual fallen trees using airborne laser scanning. Blanchard et al used the object-oriented image analysis method to extract fallen pines and cypresses in a forest ecosystem under small canopy coverage, achieving a completeness of 73% [14]. However, due to over-division, the outline of individual fallen trees was not complete.…”

Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

“…All LiDAR-derived grids were re-sampled to 5 m resolution to be consistent with RapidEye imagery. Since LiDAR-derived CHM, DSM, DEM, slope, as well as the standard deviation and skewness of these grids have proven to be useful in differentiating vegetation types [43]; they were also included as the spatial predictors for the NN and CART classification. Both NN and CART classification algorithms were built into eCognition.…”

Mapping forests with Lidar provides flexible, accurate data with many uses

“…Blanchard et al (2011) presented an objectbased image analysis (OBIA) approach to delineate and classify downed logs using Lidar-derived metrics. Despite its success in identifying downed logs, the OBIA method requires significant analyst interpretation for classification, and so it can be considered a complement to fieldbased methods but not a replacement for them.…”