Aravind Harikumar

Aravind Harikumar
University of Toronto | U of T · Department of Biology at Mississauga

Doctor of Philosophy

About

17
Publications
3,386
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82
Citations
Introduction
My current reseach focus is on individual tree genotype classification in UAV based photogrammetric optical time series data.
Additional affiliations
October 2019 - December 2021
University of Toronto
Position
  • PostDoc Position
October 2012 - March 2014
University of Twente
Position
  • Master's Student
February 2011 - March 2012
Università degli Studi di Trento
Position
  • PhD Student
Education
March 2012 - March 2014
University of Twente
Field of study
  • Geoinformatics

Publications

Publications (17)
Article
Full-text available
Precise delineation of individual tree crowns is critical for accurate forest biophysical parameter estimation, species classification, and ecosystem modelling. Multispectral optical remote sensors mounted on low-flying unmanned aerial vehicles (UAVs) can rapidly collect very-high-resolution (VHR) photogrammetric optical data that contain the spect...
Article
Full-text available
A comprehensive 3-D structural mapping of stem is essential for an accurate 3-D crown modeling and tree parameter estimation. Terrestrial laser scanning (TLS) is an effective technology for a comprehensive collection of individual tree level data, compared to destructive and costly field measurements. The performance of 3-D stem modeling techniques...
Article
Crown features derived from high-density airborne laser scanning (ALS) data have proven to be effective for forest species classification at the individual tree level. Most of the general state-of-the-art (SoA) techniques rely on coarse-level crown features extracted from ALS data and under-utilize both the spatial and the spectral information avai...
Thesis
Full-text available
The ecological, climatic, and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high-density multi-return small footprint Light Detection and Ranging (LiDAR) d...
Article
Full-text available
Accurate crown detection and delineation of dominant and subdominant trees are crucial for accurate inventorying of forests at the individual tree level. The state-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exhibits a reduced accuracy when dealing with subdominant trees. In this...
Conference Paper
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...
Article
Full-text available
The knowledge of the tree species is a crucial information that governs the success of precision forest management practice. High-density small footprint multireturn airborne light detection and ranging (LiDAR) scanning can collect a huge amount of point samples containing structural details of the forest vertical profile, which can reveal importan...
Conference Paper
The knowledge about individual trees in forest is highly beneficial in forest management. High density small foot-print multi-return airborne Light Detection and Ranging (LiDAR) data can provide a very accurate information about the structural properties of individual trees in forests. Every tree species has a unique set of crown structural charact...
Conference Paper
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 characterisation of the forest at the individual tree level. In this context, it is possible to u...
Article
Full-text available
This article presents a hybrid fuzzy classifier for effective land-use/land-cover (LULC) mapping. It discusses a Bayesian method of incorporating spatial contextual information into the fuzzy noise classifier (FNC). The FNC was chosen as it detects noise using spectral information more efficiently than its fuzzy counterparts. The spatial informatio...

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