Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform

Chun Liu,Yuan Zhou,Zhuo Chen,Hangbin Wu,Mengchi Ai

doi:10.1155/2018/6869807

Abstract

The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km2) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km2 in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments.

Highlights

Forests play a vital role on global carbon cycle
The objective of this study is to verify the feasibility of mapping Firmiana danxiaensis (FD) canopies from remotely sensed images acquired by a customized unmanned aerial vehicle (UAV) imaging system and to test the effectiveness of a proposed image classifier, a hierarchical classification schema powered by support vector machine (SVM), to extract FD from the remotely sensed imagery
Customized imaging systems mounted on UAV platforms possess the capability and flexibility of collecting low-altitude remote sensing data for classifying land cover with plant species

Summary

Introduction

Forests play a vital role on global carbon cycle. Understanding constitution and spatial distribution patterns of plant species in forestry is a major concern of botanists and environmentalists [1]. Several studies have conducted mapping of forest distribution and extraction of the species composition by applying remote sensing mounted on satellites and unmanned aerial vehicles (UAVs) [2]. More coarse remote sensing imageries, such as the medium-resolution products from Landsat Thematic Mapper (TM) and high-resolution imagery Enhanced Thematic Mapper Plus (ETM+), were probably only suitable for mapping distribution of grouped plant species with an advantage of covering a larger area [6]. The application of UAV remote sensing included vegetation health monitoring, detailed composition analysis of plant species, and biomass estimation [8]. Remotely sensed images by UAVs provided important data resources for building high-resolution airborne orthophoto maps and point clouds which have been extensively applied in ecological studies [9]

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Methods

Conclusion