Mapping dominant tree species of miombo woodlands in Western Tanzania using PlanetScope imagery

Abstract

Abstract Mapping dominant tree species in miombo woodlands is essential for enhancing their monitoring and management. We evaluated PlanetScope imagery to map Julbernardia globiflora, Brachystegia spiciformis, and Pterocarpus tinctorius in Tongwe Forest Reserve, Tanzania. The study assessed the effectiveness of PlanetScope bands in discriminating tree species and investigated how different months/seasons influenced tree species classification. Optimal months (seasons) and spectral bands were selected using Principal Component loading, temporal pattern analysis, mean decrease in accuracy, and mean decrease Gini techniques. Random forest classification was employed for tree species classification, and accuracy was assessed using an error matrix. The study identified March, July, and September as optimal months for acquiring imagery, with effective bands including blue, green-1, green, yellow, red, and red-edge. July and September imagery in the dry season achieved higher overall accuracies of 65% and 69%, respectively, while March imagery in the wet season reached 55%. The highest overall accuracy of 72% was achieved using images from different seasons. Producer’s accuracy was highest for Brachystegia spiciformis (79%) and Julbernardia globiflora (95%), whereas Pterocarpus tinctorius had lower accuracy (25%). User’s accuracy varied with 74% for Brachystegia spiciformis, 70% for Julbernardia globiflora, and 67% for Pterocarpus tinctorius. Mapping accuracy was notably high for Brachystegia spiciformis and Julbernardia globiflora, reflecting their high sample size (dominance) and distinct phenology. The yellow and red bands were particularly effective in distinguishing miombo tree species demonstrating PlanetScope’s capability. Future research should focus on scaling up PlanetScope’s application for broad miombo tree species mapping.

Keywords

• Unmanned aerial vehicle (UAV)
• Temporal pattern analysis
• Principal component analysis (PCA)
• Mixed forests
• Variable importance
• Random forest