Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

Courage Kamusoko,Hitomi Murakami,Jonah Gamba

doi:10.3390/land3020524

Abstract

Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT), random forests (RF), and support vector machines (SVM) in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran’s I) variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem.

Highlights

Miombo woodlands are extensive in the Democratic Republic of Congo (DRC), Angola, Tanzania, Mozambique, Malawi, Zambia and Zimbabwe [1,2]
The objective of this study is to evaluate the performance of decision trees (DT), support vector machines (SVM), and random forests (RF) classifiers in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Southern Africa
The objective of this study was to evaluate the performance of RF, SVM and DT classifiers for the classification of woodland and non-woodland cover in the Miombo ecosystem in Zimbabwe

Summary

Introduction

Miombo woodlands are extensive in the Democratic Republic of Congo (DRC), Angola, Tanzania, Mozambique, Malawi, Zambia and Zimbabwe [1,2]. These broad-leaved deciduous woodlands—dominated by tree species, such as Brachystegia, Julbernardia, and Isoberlinia—provide important ecosystem, socioeconomic and cultural services in Central and Southern Africa [3]. Rapid population growth and tobacco farming by newly resettled farmers have increased deforestation and woodland degradation in the Miombo ecosystems [4]. The livelihoods of two-thirds of the rural population dependent on the Miombo ecosystem is under threat unless sustainable agro-forestry development policies are implemented [5]

Objectives

Methods

Results

Conclusion