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

Authors

DOI:

https://doi.org/10.4995/raet.2022.16698

Keywords:

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

Abstract

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

References

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. https://doi.org/10.1016/j.rse.2012.05.019

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. https://doi.org/10.1016/j.isprsjprs.2010.09.008

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. https://doi.org/10.1016/j.isprsjprs.2016.01.011

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

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. http://doi.org/10.1016/j.rse.2014.10.004

Breiman, L. 2001. Random Forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324

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. https://doi.org/10.3390/rs13061166

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. https://doi.org/10.1016/j.rse.2018.05.005

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. https://doi.org/10.1016/j.rse.2015.03.028

Generalitat Valenciana. 2020. Estadísticas agrícolas. Superficies y producción de la Comunitat Valenciana (Principales cultivos). https://agroambient.gva.es/es/ 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. https://doi.org/10.1016/j.isprsjprs.2016.08.010

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. https://doi.org/10.1016/j.isprsjprs.2020.07.013

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. https://doi.org/10.1016/j.ecolind.2017.06.022

Hijmans, R. H. 2021. raster: Geographic Data Analysis and Modeling. R package version 3.4-10. https://CRAN.R-project.org/package=raster

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. https://doi.org/10.14358/PERS.80.9.863

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. https://doi.org/10.3390/rs70708300

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. https://doi.org/10.3390/rs10020159

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. gob.es/es/estadistica/temas/estadisticas-agrarias/ 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. https://doi.org/10.3390/rs12122062

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. https://doi.org/10.3390/rs13040681

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. https://doi.org/10.1016/j.rse.2018.04.025

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. https://doi.org/10.3390/rs8070582

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. https://doi.org/10.1016/j.foreco.2012.12.044

Pal, M. 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222. https://doi.org/10.1080/01431160412331269698

Prishchepov, A.V. 2020. Agricultural Land Abandonment. Oxford Bibliographies Environmental Science. Oxford University Press., https://doi.org/10.1093/obo/9780199363445-0129

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. https://doi.org/10.1016/j.rse.2012.08.017

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

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. https://doi.org/10.1016/j.compag.2012.10.005

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. https://doi.org/10.1016/j.agee.2005.11.027

Roussel, J. R., Auty D. 2021. Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R package version 3.1.2. https://cran.rproject.org/package=lidR

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. https://doi.org/10.1109/JSTARS.2020.3026724

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). https://doi.org/10.1016/j.envdev.2021.100681

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. https://doi.org/10.1080/22797 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, https://doi.org/10.2760/26277, 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. https://doi.org/10.3390/rs12142195

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. https://doi.org/10.1080/01431161.2018.1452075

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. https://doi.org/10.1016/j.rse.2018.02.050

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. http://doi.org/10.1109/TGRS.2003.810682

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. https://doi.org/10.1016/j.compag.2020.105581

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. https://doi.org/10.3390/rs8060501

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). https://doi.org/10.1109/Multi-Temp.2017.8035246

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

Published

2022-01-31

Issue

Section

Research articles