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Article

Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information

1
Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paraninfo, 1, 46730 Grau de Gandia, Spain
2
Geo-Environmental Cartography and Remote Sensing Group, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Salman Qureshi
Remote Sens. 2021, 13(4), 681; https://doi.org/10.3390/rs13040681
Received: 25 January 2021 / Revised: 5 February 2021 / Accepted: 10 February 2021 / Published: 13 February 2021
Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 × 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now. View Full-Text
Keywords: land abandonment; land use; agriculture; citrus; gray level co-occurrence matrix; random forests; VHR; image classification; semantic segmentation; remote sensing land abandonment; land use; agriculture; citrus; gray level co-occurrence matrix; random forests; VHR; image classification; semantic segmentation; remote sensing
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MDPI and ACS Style

Morell-Monzó, S.; Sebastiá-Frasquet, M.-T.; Estornell, J. Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information. Remote Sens. 2021, 13, 681. https://doi.org/10.3390/rs13040681

AMA Style

Morell-Monzó S, Sebastiá-Frasquet M-T, Estornell J. Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information. Remote Sensing. 2021; 13(4):681. https://doi.org/10.3390/rs13040681

Chicago/Turabian Style

Morell-Monzó, Sergio, María-Teresa Sebastiá-Frasquet, and Javier Estornell. 2021. "Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information" Remote Sensing 13, no. 4: 681. https://doi.org/10.3390/rs13040681

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