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.
Highlights
The objectives of the present study were to (1) assess the suitability of SPOT7 imagery to delineate between canopy gaps and intact canopy in sub-tropical forests; (2) evaluate and compare the accuracy levels achieved with SPOT7 5 m versus 1.5 m resolution imagery in conjunction with the Normalised Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR), and to determine the significance of individual wavebands based on variable importance that are the most reliable in classifying intact canopy versus canopy gaps
The results from this study suggest that a lower resolution multispectral imagery in conjunction with SAR, vegetation indices and GLCM can be used to identify, characterise and represent canopy gaps in dynamic subtropical forests with acceptable levels of accuracy
The SPOT7 1.5 m pan-sharpened imagery is more accurate for delineating between intact canopy versus canopy gaps in sub-tropical forests
Summary
These disturbance events affect the overall forest system, i.e., increased light penetration, changes in soil nutrient availability, increased evapotranspiration from underlying vegetation on the forest floor, exposure of the seed bank to excessive levels of light and higher temperatures and increased opportunities for alien plant invasions [1,8] These abiotic and biotic changes to the status quo induced by gap formation will determine the future plant species composition within the gap and eventual recovery patterns of the canopy within it [9,10]. Indigenous forests are recognised as important biodiversity and ecosystem hubs but avrearuianbdleerimportance increased pressure, namely from climate change and anthropogenic impacts This has led to a significant decline in forest cover in recent years [1,2,3].
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