Dehesa environment mapping with transference of a Random Forest classifier to neighboring ultra-high spatial resolution
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ORIGINAL PAPER
Dehesa environment mapping with transference of a Random Forest classifier to neighboring ultra-high spatial resolution imagery at class and macro-class land cover levels Laura Fragoso-Campo´n1
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Elia Quiro´s1
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Jose´ Antonio Gutie´rrez Gallego1
Accepted: 19 September 2020 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Accurate vegetation cover maps of forested areas are crucial for ecosystems monitoring, as well as for management of water balance, flood and fire risk, and other forest-associated resources. With this regard, remote sensing techniques have been used for land cover mapping worldwide. Here, we propose a vegetation-mapping methodology in a dehesa environment using ultra-high spatial resolution imagery (UHSR) with a spatial resolution of 0.25 m and four bands in the visible and near-infrared spectrum. Land cover categories were defined by their runoff generation capability and considered two levels of disaggregation: among species (macro-class level) and within species (class level). Additionally, we developed a method to reduce field campaigns and manual work by transferring random forest classifiers trained with a group of images (training group) to neighboring images (validation group). The training group was remarkably accurate, achieving an overall accuracy of 91.6% (k = 0.89) at the class level and 95.8% (k = 0.94) at the macro-class level. The results for the validation group were also very high, with an overall accuracy of 78.3% (k = 0.74) at the class level and 86.3% (k = 0.82) at the macro-class level. Moreover, we found that the blue band, soil color index, and texture features have a great influence on species discrimination, especially within shrub species in dehesa environments. Notably, having accurate land cover maps is crucial, given that the use of a global database led to underestimating the potential runoff in the most representative land cover in the dehesa environment. Future research will focus on the automatic generation of new samples extracted from the classified UHSR images, which could be used as training datasets for the supervised classification of other high spatial resolution images (e.g., Sentinel imagery) for regional-scale hydrological models. Keywords Jeffries–Matusita distance National Plan of Aerial Orthophotography (PNOA) Run off Shrub Training-data Tree grass ecosystem
1 Introduction
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00477-020-01880-3) contains supplementary material, which is available to authorized users. & Laura Fragoso-Campo´n [email protected] Elia Quiro´s [email protected] Jose´ Antonio Gutie´rrez Gallego [email protected] 1
Department of Graphic Expression, Universidad de Extremadura, Escuela Polite´cnica, Avda. Universidad s/n, 10003 Ca´ceres, Spain
Accurate vegetation maps of forested areas are widely known to be crucial for forest management. Notably, vegetation cover has a great influence on biodiversity and ecosystem monitoring (Fang
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