Spectral Discrimination of Common Karoo Shrub and Grass Species Using Spectroscopic Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 20; p. 3869
• Main Authors: Harmse, Christiaan Johannes, van Niekerk, Adriaan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: Accuracy; Aerial surveys; Assessments; Cameras; Carbon dioxide; Classification; Degradation; Drone aircraft; Ecological function; Ecologists; Ecosystems; Environmental degradation; Feature selection; Flowers & plants; Grasses; hyperspectral remote sensing; Imagery; Machine learning; Plant species; plant species discrimination; Productivity; Range ecology; rangeland management; Rangelands; Remote sensing; Satellites; Scientific equipment and supplies industry; Sensors; Species; Species classification; UAV-based remote sensing; Unmanned aerial vehicles; Vegetation; Visual discrimination; Wavelengths; United States; South Africa; Karoo
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16203869

Rangelands represent about 25% of the Earth’s land surface but are under severe pressure. Rangeland degradation is a gradually increasing global environmental problem, resulting in temporary or permanent loss of ecosystem functions. Ecological rangeland studies aim to determine the productivity of rangelands as well as the severity of their degradation. Rigorous in situ assessments comprising visual identification of plant species are required as such assessments are perceived to be the most accurate way of monitoring rangeland degradation. However, in situ assessments are expensive and time-consuming exercises, especially when carried out over large areas. In situ assessments are also limited to areas that are accessible. This study aimed to evaluate the effectiveness of multispectral (MS) and hyperspectral (HS) remotely sensed, unmanned aerial vehicle (UAV)-based data and machine learning (random forest) methods to differentiate between 15 dominant Nama Karoo plant species to aid ecological impact surveys. The results showed that MS imagery is unsuitable, as classification accuracies were generally low (37.5%). In contrast, much higher classification accuracies (>70%) were achieved when the HS imagery was used. The narrow bands between 398 and 430 nanometres (nm) were found to be vital for discriminating between shrub and grass species. Using in situ Analytical Spectral Device (ASD) spectroscopic data, additional important wavebands between 350 and 400 nm were identified, which are not covered by either the MS or HS remotely sensed data. Using feature selection methods, 12 key wavelengths were identified for discriminating among the plant species with accuracies exceeding 90%. Reducing the dimensionality of the ASD data set to the 12 key bands increased classification accuracies from 84.8% (all bands) to 91.7% (12 bands). The methodology developed in this study can potentially be used to carry out UAV-based ecological assessments over large and inaccessible areas typical of Karoo rangelands.