Assessing the Trade-Offs of SPOT7 Imagery for Monitoring Natural Forest Canopy Intactness

Abstract

Natural and human-induced disturbances influence the biodiversity and functionality of forest ecosystems. Regular, repeated assessments of canopy intactness are essential to map site-specific forest disturbance and recovery patterns, an essential requirement for forest monitoring and management. However, accessibility to images required for this practice, uncertainty around the levels of accuracy achieved with images of different resolution, and the affordability of the practice challenges its application in many developing regions. This study aimed to compare the accuracy of forest gap detection (in subtropical forests) achieved with lower-resolution (SPOT7 5 m) and higher-resolution (SPOT7 1.5 m) pan-sharpened imagery. Additionally, the Normalised Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) were compared in terms of their ability to increase the accuracy of this detection when used in conjunction with both high and low resolution imagery. Results indicate that the SPOT7 1.5 m imagery produced an overall accuracy of 77.78% and a ϰ coefficient of 0.66 compared with the 69.44% accuracy and the 0.59 ϰ coefficient achieved with the SPOT7 5 m imagery. Computing image texture analysis within the Random Forest classifier (RF) framework increased classification accuracies to 75.00% for the SPOT 5 m and 86.11% for the SPOT7 1.5 m imagery, validating the usefulness of texture analysis. Variable importance was used to identify wavebands and texture-derived variables that were the most effective in discriminating canopy gaps from intact canopy. In this regard, near infrared, NDVI, SAR, contrast, mean, entropy and second moment were the most important. Collectively the results indicate that the approach adopted in this study, i.e., the use of SPOT7 1.5 m imagery in conjunction with image texture analysis and variable importance, can be used to accurately discriminate between canopy gaps and intact canopy, making it a cost-effective spatial approach for monitoring and managing natural forests.