Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping

Abstract

This paper assessed the performance of object-based supervised support vector machine (SVM) and rule-based techniques in classifying tropical vegetated floodplain using 0.6m QuickBird image and LIDAR dataset of 1.4 points per square meter (PPSM). This is particularly significant in hydraulic modelling in which vegetation roughness is a big uncertainty and largely relies on land cover classification. The supervised classification resulted in 79.40% overall accuracy whilst the results improved by 8% with rule-based classification. 40 sample plots of trees and shrubs were measured to be compared to obtain the best classification results. The results showed a linear relationship between tree diameters and NDVI with a high Pearson correlation of 0.76 and coefficient of determination (r 2 ) of 0.58. The canopy areas of shrubs were found to be representative spatially with an even higher Pearson correlation of 0.98 and r 2 of 0.95. The study concluded that the fusion of QuickBird image and low point density LIDAR in rule-based classification together with field data were useful in quantifying tropical trees and shrubs.

Identifying Conifer Tree vs. Deciduous Shrub and Tree Regeneration Trajectories in a Space-for-Time Boreal Peatland Fire Chronosequence Using Multispectral Lidar

“…For example, it was more difficult to identify deciduous shrubs due to the potential impact of shadows, which occlude shrubs, such as overstory canopies. One way to improve this study would be to use an objectoriented classification, which could include structural attributes such as tree crown shape and crown area to height ratio to better distinguish between conifer and deciduous shrubs and trees [29,66]. Though this could enhance uncertainty when applied to early postfire vegetation regeneration as small stature individuals may be missed within objects and require division of structures within pixels similar to spectral unmixing.…”

Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective

“…For example, canopy cover calculated from lidar data with large scan angles (>20°) tends to be overestimated [141]. By combining the structure information provided by lidar with optical imagery acquired from satellite or aircraft, vegetation communities in homogeneous forests can be identified more accurately [142], and shrubs can be distinguished from trees more easily than when using optical imagery alone [143].…”

Forest Road Detection Using LiDAR Data and Hybrid Classification

“…Conversely, identification of forest roads was difficult in non-vegetated areas and DR, because both types of cover are characterized by high density of ground points and similar intensity values. One possible solution for improving these results in non-vegetated areas wound be to add auxiliary information such as satellite or aerial images [63,96]. In the case of the presence of forest vegetation, this would not be a valid solution, as improving the identification of this type of cover in these areas require increasing the level of detail of the terrain, which images generally do not allow.…”