Abstract
This study assessed the accuracy of land cover change (2000–2018) maps compiled from Landsat images with either automated digital processing or with visual interpretation for a tropical forest area in Indonesia. The accuracy assessment used a two-stage stratified random sampling involving a confusion matrix for assessing map accuracy and by estimating areas of land cover change classes and associated uncertainty. The reference data were high-resolution images from SPOT 6/7 and high-resolution images finer than 5 m obtained from Open Foris Collect Earth. Results showed that the map derived from automated digital processing had lower accuracy (overall accuracy 73–77%) compared to the map based on visual interpretation (overall accuracy 80–84%). The automated digital processing map error was in differentiating between native forest and plantation areas. While the visual interpretation map had a higher accuracy, it did not consistently differentiate between native forest and shrub areas. Future improvement of the digital map requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.
Highlights
This study addresses the development of land cover change maps for Indonesia, which has lost more than 68 million hectares to deforestation over recent decades (1950–2015) [4]
This study presents a comprehensive assessment of the accuracy of forest cover maps derived from automated digital processing and visual interpretation from 2000–2018
The Indonesian Government uses the visual interpretation (VI) maps supported by the automated digital processing (ADP) maps for national forest monitoring using Landsat images as the main input data
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The fastest rate of forest degradation is occurring in the tropics, where more than 7 million hectares per year are deforested annually [1]. Addressing the drivers of forest loss requires accurate maps to enable a time-series analysis of land-use change to support the development of effective mitigation actions. While remotely sensed images can be automatically processed to predict global forest cover [2], the value of the maps for conservation management at local and regional scales relies on the accurate identification of land cover classes [3]. In many tropical areas, the quality of remotely sensed information is degraded by cloud cover or impacted by the nature of the terrain, posing technical challenges for accurate digital mapping
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