Discriminating pure Tamarix species and their putative hybrids using field spectrometer. Issue 25 (13th December 2022)

• Record Type: Journal Article
• Authors: Tesfamichael, Solomon G.; Newete, Solomon W.; Adam, Elhadi; Byrne, Marcus J.
• Is Part Of: Geocarto international. Volume 37:Issue 25(2023)
• Page Start: 7733
• Page End: 7752
• Publication Date: 2022-12-13
• Subjects: Tamarix spp -- machine learning classification -- field spectrometer -- leaf-level classification -- Canopy-level classification -- multispectral simulation; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.1983033
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: South Africa is home to a native Tamarix species, while two were introduced in the early 1900s to mitigate the effects of mining on soil. The introduced species have spread to other ecosystems resulting in ecological deteriorations. The problem is compounded by hybridization of the species making identification between the native and exotic species difficult. This study investigated the potential of remote sensing in identifying native, non-native and hybrid Tamarix species recorded in South Africa. Leaf- and canopy-level classifications of the species were conducted using field spectroradiometer data that provided two inputs: original hyperspectral data and bands simulated according to Landsat-8, Sentinel-2, SPOT-6 and WorldView-3. The original hyperspectral data yielded high accuracies for leaf- and plot-level discriminations (>90%), while promising accuracies were also obtained using Landsat-8, Sentinel-2 and Worldview-3 simulations (>75%). These findings encourage for investigating the performance of actual space-borne multispectral data in classifying the species.