Mapping of crop calendar events by object-based analysis of MODIS and ASTER images

Revista oficial de la Asociación Española de Teledetección[EN] A method to generate crop calendar and phenology-related maps at a parcel level of four major irrigated crops (rice, maize, sunflower and tomato) is shown. The method combines images from the ASTER and MODIS sensors in an object-based image analysis framework, as well as testing of three different fitting curves by using the TIMESAT software. Averaged estimation of calendar dates were 85%, from 92% in the estimation of emergence and harvest dates in rice to 69% in the case of harvest date in tomato.[ES] Se presenta un procedimiento para generar mapas de calendario de cultivo y otras variables fenológicas a nivel de parcela de cuatro tipos de cultivo de regadío (arroz, maíz, girasol y tomate). El método combina imágenes de los sensores ASTER y MODIS en un entorno de análisis de imágenes basado en objetos, y la aplicación de tres curvas de ajuste diferentes analizadas con el programa TIMESAT. Los resultados obtenidos tuvieron una exactitud media del 85%, con valores entre el 92% en las fechas de emergencia y cosecha del arroz y el 69% en la estimación de la fecha de cosecha del tomate.Este trabajo fue financiado por un proyecto de la Fundación Kearny de Ciencias del Suelo de la Universidad de California - Davis. El trabajo del Dr. José M. Peña fue financiado por un contrato postdoctoral del programa MEC-Fulbright, financiado por la Secretaría de Estado e Investigación del Ministerio Español de Ciencia e Innovación. Las imágenes ASTER y MODIS se obtuvieron de la plataforma NASA-EOS a través de una afiliación de investigación.De Castro, A.; Plant, R.; Six, J.; Peña, J. (2014). Mapas de calendario de cultivo y variables fenológicas mediante el análisis de imágenes MODIS y ASTER basado en objetos. Revista de Teledetección. (41):29-36. doi:10.4995/raet.2014.2307.SWORD293641Beck, P. S. A., Atzberger, C., Høgda, K. A., Johansen, B., & Skidmore, A. K. (2006). Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 100(3), 321-334. doi:10.1016/j.rse.2005.10.021Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16. doi:10.1016/j.isprsjprs.2009.06.004Gao, F., Morisette, J. T., Wolfe, R. E., Ederer, G., Pedelty, J., Masuoka, E., … Nightingale, J. (2008). An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series. IEEE Geoscience and Remote Sensing Letters, 5(1), 60-64. doi:10.1109/lgrs.2007.907971Jönsson, P., & Eklundh, L. (2004). TIMESAT—a program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30(8), 833-845. doi:10.1016/j.cageo.2004.05.006Peña-Barragán, J. M., López-Granados, F., García-Torres, L., Jurado-Expósito, M., Sánchez de la Orden, M., & García-Ferrer, A. (2008). Discriminating cropping systems and agro-environmental measures by remote sensing. Agronomy for Sustainable Development, 28(2), 355-362. doi:10.1051/agro:2007049Peña-Barragán, J. M., Ngugi, M. K., Plant, R. E., & Six, J. (2011). Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115(6), 1301-1316. doi:10.1016/j.rse.2011.01.009Rojas, O., Vrieling, A., & Rembold, F. (2011). Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment, 115(2), 343-352. doi:10.1016/j.rse.2010.09.006Tan, B., Morisette, J. T., Wolfe, R. E., Gao, F., Ederer, G. A., Nightingale, J., & Pedelty, J. A. (2011). An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2), 361-371. doi:10.1109/jstars.2010.2075916Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F., Gao, F., … Huete, A. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3), 471-475. doi:10.1016/s0034-4257(02)00135-9Zhong, L., Gong, P., & Biging, G. S. (2014). Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery. Remote Sensing of Environment, 140, 1-13. doi:10.1016/j.rse.2013.08.023Wang, J., Rich, P. M., Price, K. P., & Kettle, W. D. (2004). Relations between NDVI and tree productivity in the central Great Plains. International Journal of Remote Sensing, 25(16), 3127-3138. doi:10.1080/0143116032000160499Thenkabail, P. S., Knox, J. W., Ozdogan, M., Gumma, M. K., Congalton, R. G., Wu, Z., Miseli, C., Finkral, A., Marshall, M., Mariotto, I., You, S., Giri, C.P., Nagler, P. L., 2012. Assessing future risks to agricultural productivity, water resources and food security-how can remote sensing help? Photogrammetric Engineering and Remote Sensing, 78(8), 773-782

Lombardi Drawings of Graphs

We introduce the notion of Lombardi graph drawings, named after the American abstract artist Mark Lombardi. In these drawings, edges are represented as circular arcs rather than as line segments or polylines, and the vertices have perfect angular resolution: the edges are equally spaced around each vertex. We describe algorithms for finding Lombardi drawings of regular graphs, graphs of bounded degeneracy, and certain families of planar graphs.Comment: Expanded version of paper appearing in the 18th International Symposium on Graph Drawing (GD 2010). 13 pages, 7 figure