Cartography of citrus crops abandonment using altimetric data: LiDAR and SfM photogrammetry




LiDAR, 3D photogrammetry, crop status, land abandonment, citrus


The Comunitat Valenciana region (Spain) is the largest citrus producer in Europe. However, it has suffered an accelerated land abandonment in recent decades. Agricultural land abandonment is a global phenomenon with environmental and socio-economic implications. The small size of the agricultural parcels, the highly fragmented landscape and the low spectral separability between productive and abandoned parcels make it difficult to detect abandoned crops using moderate resolution images. In this work, an approach is applied to monitor citrus crops using altimetric data. The study uses two sources of altimetry data: LiDAR from the National Plan for Aerial Orthophotography (PNOA) and altimetric data obtained through an unmanned aerial system applying photogrammetric processes (Structure from Motion). The results showed an overall accuracy of 67,9% for the LiDAR data and 83,6% for the photogrammetric data. The high density of points in the photogrammetric data allowed to extract texture features from the Gray Level Co-Occurrence Matrix derived from the Canopy Height Model. The results indicate the potential of altimetry information for monitoring abandoned citrus fields, especially high-density point clouds. Future research should explore the fusion of spectral, textural and altimetric data for the study of abandoned citrus crops.


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Author Biographies

María-Teresa Sebastiá-Frasquet, Universitat Politècnica de València

Instituto de Investigación para la Gestión Integrada de Zonas Costeras

Javier Estornell, Universitat Politècnica de València

Geo-Environmental Cartography and Remote Sensing Group


Alcantara, C., Kuemmerle, T, Prishchepov, A. V., Radeloff, V. C. 2012. Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sensing of Environment, 124, 334-347.

Amorós Lopez, J., Izquierdo Verdiguier, E., Gómez Chova, L., Muñoz Marí, J., Rodríguez Barreiro J. Z., Camps Valls, G., Calpe Maravilla, J. 2011. Land cover classification of VHR airborne images for citrus grove identification. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 115-123.

Belgiu, M., Drăguţ L. 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.

Bivand, R., Keitt, T., Rowlingson, B. 2021. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.5-23.

Bouvier, M., Durrieu, S., Fournier, R. A., Renaud, J. 2015. Generalizing predictive models of forest inventory attributes using an area-based approach with airborne las data. Remote Sensing of Environment, 156, 322-334.

Breiman, L. 2001. Random Forests. Machine Learning, 45, 5-32.

Compés, R., García, J. M., Martínez, V. 2019. La crisis citrícola en la Comunidad Valenciana y el Acuerdo de Asociación Económica con el sur de África. Comunicación. Universitat Politècnica de València.

Czesak, B., Różycka-Czas, R., Salata, T., DixonGough, R., Hernik, J. 2021. Determining the Intangible: Detecting Land Abandonment at Local Scale. Remote Sensing, 13, 1166.

Dara, A., Baumann, M., Kuemmerle, T., Pflugmacher, D., Rabe, A., Griffiths, P., Hölzel, N., Kamp, J., Freitag, M., Hostert, P. 2018. Mapping the timing of cropland abandonment and recultivation in northern Kazakhstan using annual Landsat time series. Remote Sensing of Environment, 213, 49-60.

Estel, S., Kuemmerle, T., Alcántara, C., Levers, C., Prishchepov, A. V., Hostert, P. 2015. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sensing of Environment, 163, 312-325.

Generalitat Valenciana. 2020. Estadísticas agrícolas. Superficies y producción de la Comunitat Valenciana (Principales cultivos). estadistiques-agricoles

Gil-Yepes, J. L., Ruiz, L. A., Recio, J. A., BalaguerBeser, A., Hermosilla, T. 2016. Description and validation of a new set of object-based temporal geostatistical features for land-use/ land-cover change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 77-91.

Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., Hasanlou, M. 2020. Improved land cover map of Iran using Sentinel imagery withing Google Earth Engine and novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 276-288.

Grădinaru, S. R., Kienast, F., Psomas, A. 2019. Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl. Ecological Indicators, 96(2), 79-86.

Hijmans, R. H. 2021. raster: Geographic Data Analysis and Modeling. R package version 3.4-10.

Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., Hussin, Y. A. 2014. Generating pitfree canopy height models from airborne Lidar. Photogrammetric Engineering & Remote Sensing, 80(9), 863-872.

Kolecka, N., Kozak, J., Kaim, D., Dobosz, M., Ginzler, C., Psomas, A. 2015. Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography. Remote Sensing, 7(7), 8300-8322.

Löw, F., Prishchepov, F., Waldner, F., Dubovyk, O., Akramkhanov, A., Biradar, C., Lamers, J. 2018. Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series. Remote Sensing, 10(2), 159.

Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente. 2020. ESYRCE: Encuesta Sobre Superficies y Rendimientos del año 2019; Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente: Madrid, Spain, 2020. https://www.mapa. agricultura/esyrce/

Morell-Monzó, S., Estornell, J., Sebastiá-Frasquet, M.-T. 2020. Comparison of Sentinel-2 and HighResolution Imagery for Mapping Land Abandonment in Fragmented Areas. Remote Sensing, 12(12), 2062.

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

Neigh, C. S. R., Carroll, M. L., Wooten, M. R., McCarty, J. L., Powell, B. F., Husak, G. J., Enenkel, M., Hain C. R. 2018. Smallholder crop area mapped with wall-to-wall WorldView sub-meter panchromatic image texture: A test case for Tigray, Ethiopia. Remote Sensing of Environment, 212, 8-20.

Niemi, M. T., Vauhkonen, J. 2016. Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics. Remote Sensing, 8(7), 582.

Ozdemir, I., Donoghue D. N. M. 2013. Modelling tree size diversity from airborne laser scanning using canopy height models with image texture measures. Forest, Ecology and Management, 295, 28-37.

Pal, M. 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222.

Prishchepov, A.V. 2020. Agricultural Land Abandonment. Oxford Bibliographies Environmental Science. Oxford University Press.,

Prishchepov, A.V., Radeloff, V.C., Dubinin, M., Alcantara, C. 2012. The effect of Landsat ETM/ ETM image acquisition dates on the detection of agricultural land abandonment in Eastern Europe. Remote Sensing of Environment, 126, 195-209.

R Core Team. 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL

Recio, J. A., Hermosilla, T., Ruiz, L. A., Palomar, J. 2013. Automated extraction of tree and plot-based parameters in citrus orchards from aerial images. Computer and Electronics in Agriculture, 90, 24-34.

Rounsevell, M. D. A., Reginster, I., Araújo, M. B., Carter, T. R., Dendoncker, N., Ewert, F., House, J. I., Kankaanpää, S., Leemans, R., Metzger, M. J. 2006. A coherent set of future land use change scenarios for Europe. Agriculture Ecosystems and Environment, 114, 57-68.

Roussel, J. R., Auty D. 2021. Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R package version 3.1.2.

Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., Homayouni, S. 2020. Support Vector Machine versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308-6325.

Subedi, Y. R., Kristiansen, P., Cacho, O. 2021. Drivers and consecuences of agricultural land abandonment and its reutilisation pathways: A systematic review. Environmental Development, (in press).

Szostak, M., Hawryło, P., Piela, D. 2017. Using of Sentinel-2 images for automation of the forest succession detection. European Journal of Remote Sensing, 51, 142-149. 254.2017.1412272

Vajsová, B., Fasbender, D., Wirnhardt, C., Lemajic, S. 2019. Applicability limits of Sentinel-2 data compared to higher resolution imagery for CAP checks by monitoring, Sima, A. and Aastrand, P. editor(s), EUR 29721 EN, Publications Office of the European Union, Ispra, 2019, ISBN 978- 92-76-01935-0,, JRC115564.

Vajsová, B., Fasbender, D., Wirnhardt, C., Lemajic, S., Devos, W., 2020. Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring. Remote Sensing, 12(14), 2195.

Wulder, M. A., Coops, N.C., Roy, D.P., White, J.C., Hermosilla, T. 2018. Land cover 2.0. International Journal of Remote Sensing, 39, 4254-4284.

Yin, H., Prishchepov, A. V., Kuemmerle, T., Bleyhl, B., Buchner, J., Radeloff, V. C. 2018. Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sensing of Environment, 210, 12-24.

Zhang, K., Chen, S. C., Whitman, D., Shyu, M. L., Yan, J., Zhang, C. 2003. A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(4 PART I), 872-882.

Zhang, P., Hu, S., Li, W., Zhang, C. 2020. Parcellevel mapping of crops in smallholder agricultural area: A case study of central China using single-temporal VHSR imagery. Computer and Electronics in Agriculture, 175, 105581.

Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., Yan, G. 2016. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sensing, 8(6), 501.

Zurita-Milla, R., Izquierdo.Verdiguier, E., de By, R.A. 2017. Identifying crops in smallholder farms using time series of WorldView-2 images. 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp).

Zvoleff, A. 2020. glcm: Calculate Textures from Grey-Level Co-Occurrence Matrices (GLCMs). R package version 1.6.5. package=glcm





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