Detection of dryland forest through spectral signatures generalization
DOI:
https://doi.org/10.25260/EA.25.35.2.0.2518Keywords:
open woodland, satellite images, Landsat, Random Forest, Support Vector MachineAbstract
Dryland forests are among the most threatened and least studied forests in the world. Their detection and monitoring using medium-resolution satellite imagery present limitations derived from their naturally open structure. Therefore, methodologies incorporating information from very high-resolution images have been proposed, though these are compromised by the low availability of such imagery in some regions. This work aims to evaluate the feasibility of implementing spectral signature generalization to support multitemporal detection of dryland forests, taking a carob forest in Mendoza (Argentina) as a case study. This approach uses spectral signatures from images obtained on dates different from those classified. Signature generalization was compared with the traditional approach (i.e., using signatures only from the date under analysis). For each of the three dates studied, nine types of classifications were evaluated, varying 1) the addition or absence of a spectral index to the mosaic being classified; 2) the classification scheme; and 3) the classifier. Error matrices were constructed, and map accuracy metrics were calculated. The overall accuracy of classifications based on spectral signature generalization was equal to or greater than that of classifications performed using signatures from the analyzed date. Be�er results were obtained for classification types by implementing a simple classification scheme (two categories: forest and non-forest) and using Random Forest. The evaluated methodology constitutes a valuable contribution to designing strategies for efficient remote monitoring of these types of forests, particularly bec ause it can be implemented on dates and in regions with insufficient numbers of high-resolution samples for classification and map validation, or where these data are highly spatially concentrated.
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