Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series

Oleg Antropov,Frank Martin Seifert,Yrjö Rauste,Tuomas Häme,Jaan Praks

doi:10.3390/rs13040740

Abstract

Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user’s accuracy for disturbed forest varied from 76.7% to 94.9% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests.

Highlights

Assessment and monitoring of the state of the forests forms basis for definition and implementation of preventive and corrective measures for sustainable forest management, preservation of forests and their restoration after disturbances
Time series of C-band synthetic aperture radar (SAR) data are to our understanding feasible in detecting the areas that are affected by selective logging
The change detection approach using C-band satellite SAR data can be used to define a stratified sample for the accurate evaluation of disturbed area and removed forest biomass

Summary

Introduction

Assessment and monitoring of the state of the forests forms basis for definition and implementation of preventive and corrective measures for sustainable forest management, preservation of forests and their restoration after disturbances. While more than fifty definitions of forest degradation have been formulated [1], the majority of them seem to be either too broadly defined for practical use or concentrate on specific aspects of reduction in productivity, biomass or biodiversity [2]. In the framework of this study, the selective logging was done in concession areas where sustainable forestry is practiced. Areas of ground damage typically exhibit gaps, logging trails and roads. Selective logging does not necessarily cause forest degradation but it can strongly affect the forest ecosystem [3,4,5]. Monitoring of logging activities improves potential for measuring forest degradation and contributes to sustainable forest management practices

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Conclusion