Mapping eucalypts trees using high resolution multispectral images: A study comparing WorldView 2 vs. SPOT 7

Abstract

Invasive alien plants are considered as a major threat to many ecological and socio-economic systems. Nevertheless, the management of some of these plants is often controversial due to the positive socio-economic and ecosystem roles they play. This necessitates proper mapping and monitoring of the extent and spatial distribution of such plants to prioritize resource allocation and management. However, mapping plant species using remote sensing in a heterogeneous environment such as an urban area is often challenged by high levels of spectral muddle. This study investigated the utility of the high and medium spectral and spatial resolution imageries from the WorldView-2 (WV-2) and SPOT-7 satellites, respectively, to map eucalypts trees in urban areas. Furthermore, the classification performances of Random Forest (RF) and Support Vector Machines (SVM) were compared. Both WV-2 and SPOT-7 imageries attained overall accuracies of 81.67% (0.78 kappa) and 72.78% (0.67 kappa), respectively, when the RF algorithm was used and 80% (0.76 Kappa) and 71.11% (0.65 Kappa), respectively when SVM algorithm was used. The user’s accuracies for the eucalypts class in both WV-2 and SPOT 7 imageries were 73.33% and 60%, respectively, for the RF and 70% and 56.67% for the SVM algorithm, respectively. Thus, WV-2 imagery is more suitable for mapping eucalypts trees in a heterogeneous urban environment. Therefore, the classification of WV-2 imageries using RF produced a relatively more accurate map of the eucalypts trees for the study areas, the southern part of Johannesburg city.

Keywords

• Eucalyptus
• Random Forest
• Support Vector Machine
• Urban forest
• Ecosystem service
• Street trees