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.
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Notes
Please note that temporal resolution was not analyzed in this work.
It was not until 1994 that the Soil Conservation Service (SCS) changed its name to the National Resources Conservation Service (NRCS), so the methodology is also known as the SCS-CN method.
Online Resource 2: see Tables S1 and S2 for more details on the training group at the class level. For the macro-class-level analysis, see Tables S3 and S4. For the validation group at class level, see Tables S5 and S6. For the analysis at the macro-class level, see Tables S7 and S8.
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Funding
This research was funded by the Junta de Extremadura and the European Social Fund: A way of doing Europe, through the “Financing of Predoctoral Contracts for the Training of Doctors in Public Research and Development Centers belonging to the Extremadura System of Science, Technology, and Innovation [file PD16018].” This work was also supported by the Government of Extremadura (Spain) and co-funded by the European Regional Development Foundation under Grants GR18052 (DESOSTE) and GR18028 (KRAKEN). We thank the Junta de Extremadura (CICTEX) for providing the necessary high-resolution PNOA ortophotographs (PNOA 2007-CC-BY 4.0 scne.es).
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Laura Fragoso-Campón: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing—Original Draft, Writing—Review & Editing. Elia Quirós: Conceptualization, Methodology, Resources, Writing—Original Draft, Writing—Review & Editing, Supervision. José Antonio Gutiérrez Gallego: Resources, Writing—Review & Editing.
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Fragoso-Campón, L., Quirós, E. & Gutiérrez Gallego, J.A. 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. Stoch Environ Res Risk Assess 34, 2179–2210 (2020). https://doi.org/10.1007/s00477-020-01880-3
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DOI: https://doi.org/10.1007/s00477-020-01880-3