Comparing the suitability of classified land cover data and remote sensing variables for modeling distribution patterns of plants

Ecological Modelling

Volumn 272, Issue , 2014, Pages 129-140

  Cord, Anna F ^a   Klein, Doris ^b   Mora, Franz ^c   Dech, Stefan ^b,d  

^a HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

^c National Commission for the Knowledge and Use of Biodiversity (CONABIO)   (Mexico)

^d UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

Land cover classification; Mexico; MODIS; Species distribution model; Time series; Vegetation phenology

Indexed keywords

LAND COVER CLASSIFICATION; ME-XICO; MODIS; SPECIES DISTRIBUTION MODELING; VEGETATION PHENOLOGY;

BIODIVERSITY; CONSERVATION; ECOSYSTEMS; IMAGE RESOLUTION; METADATA; REMOTE SENSING; TIME SERIES;

POPULATION DISTRIBUTION;

ALGORITHM; BIODIVERSITY; ECOSYSTEM FUNCTION; ECOSYSTEM SERVICE; GEOGRAPHICAL DISTRIBUTION; LAND CLASSIFICATION; LAND COVER; MAXIMUM ENTROPY ANALYSIS; MODIS; NATURE CONSERVATION; PHENOLOGY; REMOTE SENSING; SPATIAL RESOLUTION; TIME SERIES; VEGETATION STRUCTURE;

MEXICO [NORTH AMERICA];

EID: 84886780998     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2013.09.011     Document Type: Article

References (84)

1. Araújo M.B., Alagador D., Cabeza M., Nogués-Bravo D., Thuiller W. Climate change threatens European conservation areas. Ecology Letters 2011, 14:484-492.
2. Baldwin R.A. Use of maximum entropy modeling in wildlife research. Entropy 2009, 11:854-866.
3. Bradley B.A., Fleishman E. Can remote sensing of land cover improve species distribution modelling?. Journal of Biogeography 2008, 35:1158-1159.
4. Brando P.M., Goetz S.J., Baccini A., Nepstad D.C., Beck P.S.A., Christman M.C. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proceedings of the National Academy of Sciences of the United States of America 2010, 107(33):14685-14690.
5. Buermann W., Saatchi S., Smith T.B., Zutta B.R., Chaves J.A., Mila B., Graham C.H. Predicting species distributions across the Amazonian and Andean regions using remote sensing data. Journal of Biogeography 2008, 300:1-17.
6. Butchart S.H.M., Walpole M., Collen B., van Strien A., Scharlemann J.P.W., Almond R.E.A., Baillie J.E.M., Bomhard B., Brown C., Bruno J., Carpenter K.E., Carr G.M., Chanson J., Chenery A.M., Csirke J., Davidson N.C., Dentener F., Foster M., Galli A., Galloway J.N., Genovesi P., Gregory R.D., Hockings M., Kapos V., Lamarque J.-F., Leverington F., Loh J., McGeoch M.A., McRae L., Minasyan A., Morcillo M.H., Oldfield T.E.E., Pauly D., Quader S., Revenga C., Sauer J.R., Skolnik B., Spear D., Stanwell-Smith D., Stuart S.N., Symes A., Tierney M., Tyrrell T.D., Vié J.-C., Watson R. Global biodiversity: indicators of recent declines. Science 2010, 328(5982):1164-1168.
7. Cavazos T., Hastenrath S. Convection and rainfall over Mexico and their modulation by the Southern Oscillation. International Journal of Climatology 1990, 10:377-386.
8. Cevallos-Ferriz S.R.S., González-Torres E.A. Geological setting and phytodiversity in Mexico. Studies on Mexican Paleontology 2005, 1-18. Springer, Dortrecht. F.J. Vega, T.G. Nyborg, M. del Carmen Perrilliat, M. Montellano-Ballesteros, S.R.S. Cevallos-Ferriz, S.A. Quiroz-Barroso (Eds.).
9. Colditz R.R., Conrad C., Wehrmann T., Schmidt M., Dech S. TiSeG: a flexible software tool for time-series generation of MODIS data utilizing the Quality Assessment Science Data Set. IEEE Transactions on Geoscience and Remote Sensing 2008, 46(10):20-30.
10. Colditz R.R., Cord A., Conrad C., Mora F., Maeda P., Ressl R. Analyzing phenological characteristics of Mexico with MODIS time series products. Proceedings of the MultiTemp2009, 5th International Workshop on the Analysis of Multitemporal Remote Sensing Images 2009.
11. Colditz R.R., Maeda P., López G., Cruz I., Ressl R. Land cover classification of Mexico in the framework of the North American Land Change Monitoring System. Proceedings of 2010 ASPRS Annual Conference 2010.
12. CONAFOR Inventario Nacional Forestal y de Suelos, Manual y procedimientos para elmuestreo de campo (RE-MUESTREO 2009). Technical Report 2009, Comision Nacional Forestal, Mexico-City, Mexico, (in Spanish).
13. Cord A., Rödder D. Inclusion of habitat availability in species distribution models through multi-temporal remote sensing data?. Ecological Applications 2011, 21(8):3285-3298.
14. Cord A.F., Klein D., Gernandt D., Pérez de la Rosa J.A., Dech S. Remote sensing data can reduce the overestimation of species richness by stacked species distribution models: a case study for Mexican pines. Journal of Biogeography 2013, (in press). 10.1111/jbi.12225.
15. DiGregorio A., Jansen L.J.M. Land Cover Classification System. Classification Concepts and User Manual, Software Version 2 2005, Food and Agriculture Organization of the United Nations, Rome.
16. Dormann C.F., Elith J., Bacher S., Buchmann C., Carl G., Carré G., García Marquéz J.R., Gruber B., Lafourcade B., Leitão P.J., Münkemüller T., McClean C., Osborne P.E., Reineking B., Schröder B., Skidmore A.K., Zurell D., Lautenbach S. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013, 36:27-46.
17. Elith J., Graham C.H., Anderson R.P., Dudík M., Ferrier S., Guisan A., Hijmans R.J., Huettmann F., Leathwick J.R., Lehmann A., Li J., Lohmann L.G., Loiselle B.A., Manion G., Moritz C., Nakamura M., Nakazawa Y., Overton J.M., Peterson A.T., Phillips S.J., Richardson K., Scachetti-Pereira R., Schapire R.E., Soberón J., Williams S., Wisz M.S., Zimmermann N.E. Novel methods improve prediction of species' distributions from occurrence data. Ecography 2006, 29:129-151.
18. Elith J., Phillips S.J., Hastie T., Dudík M., Chee Y.E., Yates C.J. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 2011, 17:43-57.
19. Eyre M.D., Rushton S.P., Luff M.L., Telfer M.G. Predicting the distribution of ground beetle species (Coleoptera, Carabidae) in Britain using land cover variables. Journal of Environmental Management 2004, 72:163-174.
20. Feilhauer H., Faude U., Schmidtlein S. Combining Isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape. Remote Sensing of Environment 2011, 115:2513-2524.
21. Fielding A.H., Bell J.F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 1997, 24(1):38-49.
22. Franklin J. Mapping Species Distributions: Spatial Inference and Prediction. Ecology, Biodiversity and Conservation 2009, Cambridge University Press, Cambridge.
23. Gong P., Wang J., Yu L., Zhao Y., Zhao Y., Liang L., Niu Z., Huang X., Fu H., Liu S., Li C., Li X., Fu W., Liu C., Xu Y., Wang X., Cheng Q., Hu L., Yao W., Zhang H., Zhu P., Zhao Z., Zhang H., Zheng Y., Ji L., Zhang Y., Chen H., Yan A., Guo J., Yu L., Wang L., Liu X., Shi T., Zhu M., Chen Y., Yang G., Tang P., Xu B., Giri C., Clinton N., Zhu Z., Chen J., Chen J. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing 2013, 34(7):2607-2654.
24. Goodchild M.F., Guoqing S., Shiren Y. Development and test of an error model for categorical data. International Journal of Geographical Information Systems 1992, 6(2):87-104.
25. Graham C.H., Hijmans R.J. A comparison of methods for mapping species ranges and species richness. Global Ecology and Biogeography 2006, 15:578-587.
26. Guisan A., Theurillat J.-P. Equilibrium modeling of alpine plant distribution: how far can we go?. Phytocoenologia 2000, 30:353-384.
27. Guisan A., Zimmermann N.E. Predictive habitat distribution models in ecology. Ecological Modelling 2000, 135:147-186.
28. Guisan A., Zimmermann N.E., Elith J., Graham C.H., Phillips S., Peterson A.T. What matters for predicting the occurrences of trees: techniques, data, or species' characteristics?. Ecological Monographs 2007, 77(4):615-630.
29. Hall F.G., Huemmrich K.F., Strebel D.E., Goetz S.J., Nickeson J.E., Woods K.D. Biophysical, Morphological, Canopy Optical Property, and Productivity Data from the Superior National Forest 1992, National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD, USA, NASA Technical Memorandum 104568.
30. Heikkinen R.K., Luoto M., Virkkala R., Rainio K. Effects of habitat cover, landscape structure and spatial variables on the abundance of birds in an agricultural-forest mosaic. Journal of Applied Ecology 2004, 41:824-835.
31. Heikkinen R.K., Luoto M., Araújo M.B., Virkkala R., Thuiller W., Sykes M.T. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography 2006, 30(6):751-777.
32. Hernandez P.A., Graham C.H., Master L.L., Albert D.L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 2006, 29:773-785.
33. Herold M., Mayaux P., Woodcock C.E., Baccini A., Schmullius C. Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1km datasets. Remote Sensing of Environment 2008, 112:2538-2556.
34. Herold M. Assessment of the Status of the Development of the Standards for the Terrestrial Climate Variables, Land Cover, T9 Report 2009, GTOS Secretariat, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy.
35. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002, 83:195-213.
36. Jaynes E. Information theory and statistical mechanics. Physical Reviews 1957, 106:620-630.
37. Jeschke J.M., Strayer D.L. Usefulness of bioclimatic models for studying climate change and invasive species. Annals of the New York Academy of Sciences 2008, 1134:1-24.
38. Jönsson P., Eklundh L. TIMESAT-a program for analyzing time-series of satellite sensor data. Computers and Geosciences 2004, 30:833-845.
39. Kerr J., Ostrovsky M. From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution 2003, 18(6):299-305.
40. Krishnaswamy J., Bawa K.S., Ganeshaiah K.N., Kiran M.C. Quantifying and mapping biodiversity and ecosystem services: utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment 2009, 113:857-867.
41. Kuemmerle T., Perzanowski K., Chaskovskyy O., Ostapowicz K., Halada L., Bashta A.-T., Kruhlov I., Hostert P., Waller D.M., Radeloff C. European Bison habitat in the Carpathian Mountains. Biological Conservation 2010, 143:908-916.
42. Lambin E.F. Monitoring forest degradation in tropical regions by remote sensing: some methodological issues. Global Ecology and Biogeography 1999, 8:191-198.
43. Luoto M., Heikkinen R.K., Pöyry J., Saarinen K. Determinants of the biogeographical distribution of butterflies in boreal regions. Journal of Biogeography 2006, 33:1764-1778.
44. Luoto M., Virkkala R., Heikkinen R.K. The role of land cover in bioclimatic models depends on spatial resolution. Global Ecology and Biogeography 2007, 16:34-42.
45. Maes D., Gilbert M., Titeux N., Goffart P., Dennis R.L.H. Prediction of butterfly diversity hotspots in Belgium: a comparison of statistically focused and land use-focused models. Journal of Biogeography 2003, 30:1907-1920.
46. McPherson J.M., Jetz W. Effects of species' ecology on the accuracy of distribution models. Ecography 2007, 30:135-151.
47. Millennium Ecosystem Assessment Ecosystems and Human Well-being: Biodiversity Synthesis 2005, World Resources Institute, Washington.
48. Miranda F., Hernández X. Los tipos de vegetación de México y su clasificación. Boletín de la Sociedad Botánica de México 1963, 28:29-179. (in Spanish).
49. Nagendra H. Using remote sensing to assess biodiversity. International Journal of Remote Sensing 2001, 22(12):2377-2400.
50. NALCMS North American Land Cover at 250m Spatial Resolution 2005, Produced by: Natural Resources Canada/Canadian Centre for Remote Sensing (NRCan/CCRS), United States Geological Survey (USGS), Insituto Nacional de Estadística y Geografía (INEGI), Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), and Comisión Nacional Forestal (CONAFOR). (accessed 11.04.13). http://www.cec.org/Page.asp%3FPageID=122%26ContentID=25501.
51. Nemani R., Pierce L., Running S., Goward S. Developing satellite-derived estimates of surface moisture status. Journal of Applied Meteorology 1993, 32:548-557.
52. Pearson R.G., Dawson T.P., Berry P.M., Harrison P.A. SPECIES: a spatial evaluation of climate impact on the envelope of species. Ecological Modelling 2002, 154:289-300.
53. Pearson R.G., Dawson T.P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?. Global Ecology and Biogeography 2003, 12:361-371.
54. Pearson R.G., Dawson T.P., Liu C. Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography 2004, 27:285-298.
55. Pearson R.G., Raxworthy C.J., Nakamura M., Peterson A.T. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography 2007, 34:102-117.
56. Pettorelli N., Vik J.O., Mysterud A., Gaillard J.-M., Tucker C.J., Stenseth N.C. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution 2005, 20:503-510.
57. Phillips S.J., Anderson R.P., Schapire R.E. Maximum entropy modeling of species geographic distributions. Ecological Modelling 2006, 190:231-259.
58. Phillips S.J., Dudík M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 2008, 31:161-175.
59. Prates-Clark C.D., Saatchi S.S., Agosti D. Predicting geographical distribution models of high-value timber trees in the Amazon Basin using remotely sensed data. Ecological Modelling 2008, 211:309-323.
60. Quattrochi D.A., Luvall J.C. Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications. Landscape Ecology 1999, 14:577-598.
61. Ramamoorthy J.P., Bye R., Lot A., Fa J. Introduction. Biological Diversity of Mexico: Origins and Distributions 1993, 1-2. Oxford University Press, Oxford. J.P. Ramamoorthy, R. Bye, A. Lot, J. Fa (Eds.).
62. R Development Core Team R: A Language and Environment for Statistical Computing 2011, R Foundation for Statistical Computing, Vienna.
63. Reed B.C., Brown J.F., VanderZee D., Loveland T.R., Merchant J.W., Ohlen D.O. Measuring phenological variability from satellite imagery. Journal of Vegetation Science 1994, 5:703-714.
64. Ricker M., Ramírez-Krauss I., Ibarra-Manríquez G., Martínez E., Ramos C.H., González-Medellín G., Gómez-Rodríguez G., Palacio-Prieto J.L., Hernández H.M. Optimizing conservation of forest diversity: a country-wide approach in Mexico. Biodiversity and Conservation 2007, 16:1927-1957.
65. Rogers D.J., Randolph S.E., Snow R.W., Hay S.I. Satellite imagery in the study and forecast of malaria. Nature 2002, 415:710-715.
66. Rzedowski J. Vegetación de México. 1ra. Edición Digital 2006, Comision Nacional para el Conocimiento y Uso de la Biodiversidad, Mexico, 504 p (in Spanish).
67. Saatchi S., Buermann W., ter Steege H., Mori S., Smith T.B. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sensing of Environment 2008, 112:2000-2017.
68. Sánchez-Cordero V., Illoldi-Rangel P., Linaje M., Sarkar S., Townsend Peterson A. Deforestation and extant distributions of Mexican endemic mammals. Biological Conservation 2005, 126:465-473.
69. Sarukhán J., Koleff P., Carabias J., Soberón J., Dirzo R., Llorente-Bousquets J., Halffter G., González R., March I., Mohar A., Anta S., de la Maza J. Natural Capital of Mexico. Synopsis: Current Knowledge, Evaluation, and Prospects for Sustainability 2010, Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Mexico-City, Mexico.
70. SEMARNAT Norma Oficial Mexicana NOM-059-SEMARNAT-2001, Lista de especies en riesgo 2010, Secretaría de medio ambiente y recursos naturales (SEMARNAT), Mexico-City, (in Spanish).
71. Sinclair S.J., White M.D., Newell G.R. How useful are species distribution models for managing biodiversity under future climates?. Ecology and Society 2010, 15(1):8.
72. Siriwardena G.M., Crick H.Q.P., Baillie S.R., Wilson J.D. Agricultural land-use and the spatial distribution of granivorous lowland farmland birds. Ecography 2000, 23:702-719.
73. Swets J. Measuring the accuracy of diagnostic systems. Science 1988, 240:1285-1293.
74. Thuiller W., Araújo M.B., Lavorel S. Do we need land-cover data to model species distributions in Europe?. Journal of Biogeography 2004, 31:353-361.
75. Toledo V.M., Ordóñez M.J. The biodiversity scenario of Mexico: a review of terrestrial habitats. Biological Diversity of Mexico: Origins and Distributions 1993, 757-777. Oxford University Press, Oxford. J.P. Ramamoorthy, R. Bye, A. Lot, J. Fa (Eds.).
76. Tuanmu M.-N., Viña A., Bearer S., Xu W., Ouyang Z., Zhang H., Liu J. Mapping understory vegetation using phenological characteristics derived from remotely sensed data. Remote Sensing of Environment 2010, 114:1833-1844.
77. Tuanmu M.-N., Viña A., Roloff G.J., Liu W., Ouyang Z., Zhang H., Liu J. Temporal transferability of wildlife habitat models: implications for habitat monitoring. Journal of Biogeography 2011, 38:1510-1523.
78. USGS MODIS Reprojection Tool (MRT), Version 4.0. Land Processes DAAC 2008, USGS Center for Earth Resources Observation and Science (EROS) in collaboration with the South Dakota School of Mines and Technology (SDSM&T).
79. White M.A., de Beurs K., Didan K., Inouye D., Richardson A.D., Jensen O., O'Keefe J., Zhang G., Nemani R.R., van Leeuwen W., Brown J., de Wit A., Schaepman M., Lin X., Dettinger M., Bailey A., Kimball J.S., Schwartz M.D., Baldocchi D.D., Lee J., Lauenroth W. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology 2009, 15:2335-2359.
80. Wilting A., Cord A., Hearn A.J., Hesse D., Mohamed A., Traeholdt C., Cheyne S.M., Sunarto S., Jayasilan M.-A., Ross J., Shapiro A.C., Sebastian A., Dech S., Breitenmoser C., Sanderson J., Duckworth J.W., Hofer H. Modelling the species distribution of flat-headed cats (Prionailurus planiceps), an endangered South-East Asian small felid. PLoS ONE 2010, 5(3):e9612.
81. Wisz M.S., Hijmans R.J., Li J., Peterson A.T., Graham C.H., Guisan A., NCEAS Predicting Species Distributions Working Group Effects of sample size on the performance of species distribution models. Diversity and Distributions 2008, 14:763-773.
82. Xie Y., Sha Z., Yu M. Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology 2008, 1(1):9-23.
83. Yates C.J., McNeill A., Elith J., Midgley G.F. Assessing the impacts of climate change and land transformation on Banksia in the South West Australian Floristic region. Diversity and Distributions 2010, 16:187-201.
84. Zhang X., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C.F., Gao F., Reed B.C., Huete A. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 2003, 84:471-475.