Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods
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Using satellite data to extract forest structure mapping parameters assists forest management. In this research, structural parameters including species, density, canopy, and gaps were extracted from SPOT-7 satellite data over Hyrcanian forests (Iran). A detailed ground inventory was initially conducted, over 12 × 1 ha (100 m × 100 m) plots, in which tree coordinates were plotted, using a differential global positioning system (DGPS), along with data on tree species, diameter-at-breast-height and height, as well as canopy dimensions, and canopy gap shapes, sizes, and positions, for each plot. Then, spectral transformations, vegetation indices, and simple spectral ratios were extracted from SPOT-7 data, and a supervised, pixel-based classification method and a support-vector machine algorithm were used to classify and determine tree species types. In addition, canopy tree borders and gaps were classified, using an object-based method, and tree densities per unit area were determined, using the canopy gravity center. Finally, the original ground data was used to perform an accuracy assessment on the extracted information, with the results showing that forest type could be determined with 95% accuracy and a Kappa coefficient of 0.8. Canopy and gap coverage achieved an overall accuracy of 91% (Kappa coefficient: 0.7), and tree densities per hectare were determined, on average, to be 47 trees fewer than reality. In conclusion, we have shown that forest structural parameters could be extracted, with good accuracy, using a combination of pixel- and object-based methods applied to SPOT-7 imaging.
KeywordsFagus orientalis Hyrcanian forest Object-based classification SPOT-7 Support-vector machine algorithm
The authors would like to appreciate those who assisted in conducting this work. Also thanks to Remote sensing Institute of K.N. Toosi University of Tech. for providing the opportunity to use the remote sensing lab for this research. We thank anonymous reviewers who provided many helpful comments and suggestions for improving this manuscript.
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