Keywords
phenology
biophysical variables
cover map
global data
How to Cite
Abstract
The capacity of the phenological data of the CYCLOPES project LAI series for the year 2000 to distinguish general vegetation types (evergreen forest, deciduous forest, crops and pastureshrubland) in Michoacán, Mexico, was explored. Using the TIMESAT program, 11 phenological variables of each 1-km pixel of that series were extracted. The behavior of each variable was analyzed using the INF2000 map as a reference. The main differences relate to the deciduous or evergreen character of the vegetation. The 11 variables were reduced to five principal components (98% of the variance) to make an unsupervised classification of 250 phenological classes or groups. Each class was associated with one of the cover types, with a criterion of maximum area matched with the INF2000 reference map, in order to construct the CYCL2000 cover map. Comparing the two maps yielded modest results, with 63 % total accuracy. Deciduous forests were better identified (80.1 % of pixels correctly identified and 62.1 % correctly classified), followed by evergreen forests (74.1 %, 69.9 %), crops (62.9 %, 61.1 %) and pasture shrubland (16.9 %, 52.3 %). The relatively good identification of forests shows that this approach could be used to estimate deforestation.
References
Achard, F., & Hansen, M. C. (2013). Use of Earth observation technology to monitor forest across the Globe. In F. Achard, & M. C. Hansen (Eds.), Global forest monitoring from Earth observation (pp. 39–54). New York, USA: CRC Press, Taylor & Francis Group
Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M…, Leroy, M. (2007). LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm. Remote Sensing of Environment, 110, 275–286. doi: https://doi.org/10.1016/j.rse.2007.02.018
Campbell, J. B. (2002). Introduction to remote sensing (3rd. ed.). New York, USA: The Guilford Press.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of Environment, 37(1), 35–46. doi: https://doi.org/10.1016/0034-4257(91)90048-B
Couturier, S., Mas, J. F., López-Granados, E., Benítez, J., Tapia, V., & Vega, A. (2010). Accuracy assessment of the Mexican National Forest Inventory map: a study in four ecogeographical areas. Singapore Journal of Tropical Geography, 31(2), 163–79. doi: https://doi.org/10.1111/j.1467-9493.2010.00399.x
DeFries, R. (2013). Why forest monitoring matters for people and the planet. In F. Achard, & M. C. Hansen (Eds.), Global forest monitoring from Earth observation (pp. 1–14). New York, USA: CRC Press, Taylor & Francis Group.
Fisher, J. A. D., Frank, K. T., & Leggett, W. C. (2010). Dynamic macroecology on ecological time-scales. Global Ecology and Bio-geography, 19, 1–15. doi: https://doi.org/10.1111/j.1466- 8238.2009.00482.x
García, M. E. (1989). Apuntes de climatología (6a ed.). México, D.F.: UNAM.
Geoland2. (2014). Dataset series catalogue. http://www.geoland2.eu
Gillespie, T. W., Foody, G. M., Rocchini, D., Giorgi, A. P., & Saatchi, S. (2008). Measuring and modelling biodiversity from space. Progress in Physical Geography, 32, 203–221. doi: https://doi.org/10.1177/0309133308093606
Houghton, R. A. (2013). Role of forests and impact of deforestation in the global carbon cycle. In F. Achard, & M. C. Hansen (Eds.), Global forest monitoring from Earth observation (pp. 15–38). New York, USA: CRC Press, Taylor & Francis Group.
Instituto Nacional de Estadística y Geografía (INEGI). (1985). Síntesis geográfica del estado de Michoacán. México: Autor.
Instituto Nacional de Estadística y Geografía (INEGI) (2012). Marco geoestadístico municipal 2000, áreas geoestadísticas estatales. Consultado 12-06-2012 http://www.inegi.org.mx/geo/contenidos/geoestadistica/m_geoestadistico.aspx
Jones, H. G., & Vaughan, R. A. (2010). Remote sensing of vegetation. Principles, techniques and applications. New York, USA: Oxford University Press.
Jönsson, P., & Eklundh, L. (2004). TIMESAT: A program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30, 833–845. doi: https://doi.org/10.1016/j.cageo.2004.05.006
Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2012). A comparison of methods for smoothing and gap filling time series of remote sensing observations: Application to MODIS LAI products. Biogeosciences Discussions, 9, 17053–17097. doi: https://doi.org/10.5194/bgd-9-17053-2012
Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 881–892. doi: https://doi.org/10.1109/TPAMI.2002.1017616
Mas, J. F., Velázquez, A., Palacio-Prieto, J. L., Bocco, G., Peralta, A., & Prado, J. (2002). Assessing forest resources in Mexico: Wall to wall land use/cover mapping. Photogrammetric Engineering & Remote Sensing, 68(10), 966–968. http://dialnet.unirioja.es/servlet/ejemplar?codigo=76311
Matlab, Mathematics Laboratory (2012). Matlab: The language of technical computing. Massachusetts, USA: The MathWorks, Inc.
Miranda, F., & Hernández, X. (1963). Los tipos de vegetación de México y su clasificación. Boletin de la Sociedad Botánica Mexicana, 28, 29–179. http://www.cvirtual1.uaem.mx/observatorio/cen_documen/articulos/art_ eco_1963.pdf
Palacio-Prieto, J. L., Bocco, G., Velázquez, A., Mas, J. F., Takaki- Takaki, F., Victoria, A…, González, M. F. (2000). La condición actual de los recursos forestales en México: Resultados del Inventario Forestal Nacional 2000. Investigaciones Geográficas, Boletín del Instituto de Geografía, UNAM, 43, 183–203.
Petitjean, F., Inglada, J., & Gancarski, P. (2012). Satellite image time series analysis under time warping. IEEE Transactions on Geoscience and Remote Sensing, 50(8), 3081–3095. doi: https://doi.org/10.1109/TGRS.2011.2179050
Pfeifer, M., Disney, M., Quaife, T., & Marchant, R. (2011). Terrestrial ecosystems from space: A review of earth observation products for macroecology applications. Global Ecology and Biogeography, 21(6), 603–624. doi: https://doi.org/10.1111/j.1466-8238.2011.00712.x
Rzendowsky, J. (1978). Vegetación de México. México, D.F: Editorial Limusa
Secretaría de Medio Ambiente, Recursos Naturales y Pesca (SEMARNAP). (1997). Estadísticas del Medio Ambiente. México, D.F.: INEGI. http://www.semarnat.gob.mx/archivosanteriores/informacionambiental/Documents/01_informes/informe_1997.pdf
Valderrama-Landeros, L., Baret, F., & España-Boquera, M. L. (2011). Consistency between global and regional land cover maps over Michoacán state, Mexico. Revista Chapingo, Serie Ciencias Forestales y del Ambiente, 17, 343–360. doi: https://doi.org/105154./r.rchscfa2010.09.075
Velázquez, A., Mas, J. F., Bocco, G., & Palacio‐Prieto, J. L. (2010). Mapping land cover changes in Mexico, 1976–2000 and applications for guiding environmental management policy.Singapore Journal of Tropical Geography, 31(2), 152–162. doi: https://doi.org/10.1111/j.1467-9493.2010.00398
Weiss, M., Baret, F., Garrigues, S., Lacaze, R., & Bicheron, P. (2007). LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 2: Validation and comparison with MODIS Collection 4 products. Remote Sensing of the Environment, 110, 317–331. doi: https://doi.org/10.1016/j.rse.2007.02.018
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2014 Revista Chapingo Serie Ciencias Forestales y del Ambiente