Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques.

Mozhgan Abbasi,Hamid Reza Riyahi Bakhtiari,Jochem Verrelst,Mohsen Mirzaei,Safar Marofi

doi:10.3390/rs12010063

Abstract

Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm.

Highlights

In Iran, cultivations and orchards cover about 12% of the total land area
Many researchers indicate that multispectral data such as Landsat and SPOT images produce general land cover classifications that are too broad to be utilized for identification of orchards species
analysis of variance (ANOVA) was applied to identify the optimum wavelengths with distinct spectral behavior in the studied species. 2151 wavelengths were analyzed and the result was reported at confidence level of 99%

Summary

Introduction

In Iran, cultivations and orchards cover about 12% of the total land area. According to the Agriculture Jihad Ministry, Iran ranks first in the Middle East and 9th in the world in fruit production.In Chaharmahal va Bakhtiyari province there are 40,890 hectares of orchards, whereby almonds, walnuts, and grapes cover most of the area with 37, 20, and 12 percent respectively. Optical remote sensing devices reached maturity, with a diversity of sensors covering a wide range of spatial and spectral resolutions. Both types of multispectral and hyperspectral remote sensing data have been used for discrimination and classification of vegetation [5,6,7]. A field spectroradiometer is able to record a unique spectral curve (spectral fingerprint) for any object [18] These spectral signatures can be brought together into a spectral library, and so contribute to the remote sensing community with web-based platforms and enhanced data browsing/search capabilities [18,30,31,32]. By using field spectroscopy a large amount of data is obtained in the form of spectral curves, that in turn can be analyzed to identify the desirable objectives [16,33,34]

Objectives

Results

Discussion

Conclusion