Monitoring natural ecosystem and ecological gradients: Perspectives with EnMAP

Remote Sensing

Volumn 7, Issue 10, 2015, Pages 13098-13119

  Leitão, Pedro J ^a   Schwieder, Marcel ^a   Suess, Stefan ^a   Okujeni, Akpona ^a   Galvão, Lênio Soares ^b   van der Linden, Sebastian ^a   Hostert, Patrick ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

^b INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

Author keywords

Complex landscapes; Earth observation; Ecosystem monitoring; Ecosystem transitions; EnMAP; Gradients; Hyperspectral; Imaging spectroscopy; Savannah; Shrub encroachment

Indexed keywords

ENVIRONMENTAL IMPACT; FORESTRY; HYPERSPECTRAL IMAGING; PHOTOMAPPING; REMOTE SENSING; SUSTAINABLE DEVELOPMENT; VEGETATION;

COMPLEX LANDSCAPES; EARTH OBSERVATIONS; ECOSYSTEM MONITORING; ENMAP; GRADIENTS; HYPERSPECTRAL; IMAGING SPECTROSCOPY; SAVANNAH; SHRUB ENCROACHMENTS;

ECOSYSTEMS;

EID: 84937910505     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs71013098     Document Type: Article

References (91)

1. Walther, G.R.; Post, E.; Convey, P.; Menzel, A.; Parmesan, C.; Beebee, T.J.C.; Fromentin, J.M.; Hoegh-Guldberg, O.; Bairlein, F. Ecological responses to recent climate change. Nature 2002, 416, 389-395.
2. Kareiva, P.; Watts, S.; McDonald, R.; Boucher, T. Domesticated nature: Shaping landscapes and ecosystems for human welfare. Science 2007, 316, 1866-1869.
3. Sitch, S.; Smith, B.; Prentice, I.C.; Arneth, A.; Bondeau, A.; Cramer, W.; Kaplan, J.O.; Levis, S.; Lucht, W.; Sykes, M.T.; et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 2003, 9, 161-185.
4. McIntosh, R.P. Continuum concept of vegetation. Bot. Rev. 1967, 33, 130-187.
5. Muller, F. Gradients in ecological systems. Ecol. Model. 1998, 108, 3-21.
6. Gosz, J.R. Gradient analysis of ecological change in time and space: Implications for forest management. Ecol. Appl. 1992, 2, 248-261.
7. Gaston, K.J. Global patterns in biodiversity. Nature 2000, 405, 220-227.
8. Schimel, D.S. Terrestrial ecosystems and the carbon-cycle. Glob. Change Biol. 1995, 1, 77-91.
9. Ardö, J.; Mölder, M.; El-Tahir, B.A.; Abdalla, H.; Elkhidir, M. Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan. Carbon Balance Manag. 2008, 3, 7.
10. Serreze, M.C.; Walsh, J.E.; Chapin, F.S.; Osterkamp, T.; Dyurgerov, M.; Romanovsky, V.; Oechel, W.C.; Morison, J.; Zhang, T.; Barry, R.G. Observational evidence of recent change in the northern high-latitude environment. Clim. Change 2000, 46, 159-207.
11. McDonnell, M.J.; Pickett, S.T.A. Ecosystem structure and function along urban rural gradients: An unexploited opportunity for ecology. Ecology 1990, 71, 1232-1237.
12. Eldridge, D.J.; Bowker, M.A.; Maestre, F.T.; Roger, E.; Reynolds, J.F.; Whitford, W.G. Impacts of shrub encroachment on ecosystem structure and functioning: towards a global synthesis. Ecol. Lett. 2011, 14, 709-722.
13. Attiwill, P.M. The disturbance of forest ecosystems-The ecological basis for conservative management. For. Ecol. Manag. 1994, 63, 247-300.
14. Adler, P.B.; Raff, D.A.; Lauenroth, W.K. The effect of grazing on the spatial heterogeneity of vegetation. Oecologia 2001, 128, 465-479.
15. Mack, R.N.; Simberloff, D.; Lonsdale, W.M.; Evans, H.; Clout, M.; Bazzaz, F.A. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 2000, 10, 689-710.
16. Estel, S.; Kuemmerle, T.; Alcántara, C.; Levers, C.; Prishchepov, A.; Hostert, P. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sens. Environ. 2015, 163, 312-325.
17. Müller, H.; Leitão, P.J.; Hostert, P. Vegetation dynamics, carbon stocks and turnover rates in the Amazon-Upscaling local processes with remote sensing time series. In 43rd Annual Meeting of the Ecological Society of Germany, Austria and Switzerland, Potsdam, Germany, 9-13 September 2013.
18. Hooper, D.U.; Chapin, F.S.; Ewel, J.J.; Hector, A.; Inchausti, P.; Lavorel, S.; Lawton, J.H.; Lodge, D.M.; Loreau, M.; Naeem, S.; et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 2005, 75, 3-35.
19. Lavorel, S.; Grigulis, K.; Lamarque, P.; Colace, M.-P.; Garden, D.; Girel, J.; Pellet, G.; Douzet, R. Using plant functional traits to understand the landscape distribution of multiple ecosystem services. J. Ecol. 2011, 99, 135-147.
20. Sala, O.E.; Maestre, F.T. Grass-woodland transitions: determinants and consequences for ecosystem functioning and provisioning of services. J. Ecol. 2014, 102, 1357-1362.
21. Field, C.B.; Randerson, J.T.; Malmstrom, C.M. Global net primary production: Combining ecology and remote-sensing. Remote Sens. Environ. 1995, 51, 74-88.
22. De Fries, R.; Pagiola, S.; Adamowicz, W.L.; Akçakaya, H.R.; Arcenas, A.; Babu, S.; Balk, D.; Confalonieri, U.; Cramer, W.; Falconí, F.; et al. Analytical approaches for assessing ecosystem condition and human well-being. In Ecosystems and Human Well-Being: Current State and Trends; Hassan, R., Scholes, R., Ash, N., Eds.; Island Press: Washington, DC, USA, 2005; Chapter 2, pp. 37-71.
23. Hoare, D.; Frost, P. Phenological description of natural vegetation in southern Africa using remotely-sensed vegetation data. Appl. Veg. Sci. 2004, 7, 19-28.
24. Fisher, J.I.; Mustard, J.F.; Vadeboncoeur, M.A. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sens. Environ. 2006, 100, 265-279.
25. Kennedy, R.E.; Andrefouet, S.; Cohen, W.B.; Gomez, C.; Griffiths, P.; Hais, M.; Healey, S.P.; Helmer, E.H.; Hostert, P.; Lyons, M.B.; et al. Bringing an ecological view of change to landsat-based remote sensing. Front. Ecol. Environ. 2014, 12, 339-346.
26. Vogelmann, J.E.; Xian, G.; Homer, C.; Tolk, B. Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems. Remote Sens. Environ. 2012, 122, 92-105.
27. Homer, C.G.; Meyer, D.K.; Aldridge, C.L.; Schell, S.J. Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields. J. Appl. Remote Sens. 2013, 7 (1), 073508.
28. Okujeni, A.; van der Linden, S.; Hostert, P. Extending the vegetation-impervious-soil model using simulated EnMAP data and machine learning. Remote Sens. Environ. 2015, 158, 69-80.
29. Shoshany, M. Satellite remote sensing of natural Mediterranean vegetation: A review within an ecological context. Progr. Phys. Geogr. 2000, 24, 153-178.
30. Ustin, S.L.; Gamon, J.A. Remote sensing of plant functional types. New Phytol. 2010, 186, 795-816.
31. Lee, K.S.; Cohen, W.B.; Kennedy, R.E.; Maiersperger, T.K.; Gower, S.T. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sens. Environ. 2004, 91, 508-520.
32. Smith, M.L.; Ollinger, S.V.; Martin, M.E.; Aber, J.D.; Hallett, R.A.; Goodale, C.L. Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecol. Appl. 2002, 12, 1286-1302.
33. Mutanga, O.; Skidmore, A.K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 2004, 25, 3999-4014.
34. Fuentes, D.A.; Gamon, J.A.; Cheng, Y.; Claudio, H.C.; Qiu, H.-L.; Mao, Z.; Sims, D.A.; Rahman, A.F.; Oechel, W.; Luo, H. Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS. Remote Sens. Environ. 2006, 103, 312-323.
35. Asner, G.P.; Elmore, A.J.; Hughes, R.F.; Warner, A.S.; Vitousek, P.M. Ecosystem structure along bioclimatic gradients in Hawaii from imaging spectroscopy. Remote Sens. Environ. 2005, 96, 497-508.
36. Schmidtlein, S.; Zimmermann, P.; Schüpferling, R.; Weiß, C. Mapping the floristic continuum: Ordination space position estimated from imaging spectroscopy. J. Veg. Sci. 2007, 18, 131-140.
37. Leitão, P.J.; Schwieder, M.; Suess, S.; Catry, I.; Milton, E.J.; Moreira, F.; Osborne, P.E.; Pinto, M.J.; van der Linden, S.; Hostert, P. Mapping beta diversity from space: Sparse generalised dissimilarity modelling (SGDM) for analysing high-dimensional data. Methods Ecol. Evol. 2015, doi:10.1111/2041-1210X.12378.
38. Oldeland, J.; Dorigo, W.; Wesuls, D.; Jürgens, N. Mapping bush encroaching species by seasonal differences in hyperspectral imagery. Remote Sens. 2010, 2, 1416-1438.
39. Harris, A.T.; Asner, G.P. Grazing gradient detection with airborne imaging spectroscopy on a semi-arid rangeland. J. Arid Environ. 2003, 55, 391-404.
40. Underwood, E.; Ustin, S.; DiPietro, D. Mapping nonnative plants using hyperspectral imagery. Remote Sens. Environ. 2003, 86, 150-161.
41. Guanter, L.; Kaufmann, H.; Segl, K.; Chabrillat, S.; Förster, S.; Rogass, C.; Kuester, T.; Hollstein, A.; Rossner, G.; Chlebek, C.; et al. The EnMAP spaceborne imaging spectroscopy mission for Earth Observation. Remote Sens. 2015, 7, 8830-8857.
42. Segl, K.; Guanter, L.; Rogass, C.; Kuester, T.; Roessner, S.; Kaufmann, H.; Sang, B.; Mogulsky, V.; Hofer, S. EeteS-The EnMAP End-to-End Simulation Tool. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2012, 5, 522-530.
43. Schwieder, M.; Leitão, P.J.; Suess, S.; Senf, C.; Hostert, P. Estimating fractional shrub cover using simulated EnMAP data: A comparison of three machine learning regression techniques. Remote Sens. 2014, 6, 3427-3445.
44. Ustin, S.L.; Roberts, D.A.; Gamon, J.A.; Asner, G.P.; Green, R.O. Using imaging spectroscopy to study ecosystem processes and properties. BioScience 2004, 54, 523-534.
45. Asner, G.P.; Nepstad, D.; Cardinot, G.; Ray, D. Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. Proc. Natl. Acad. Sci. USA 2004, 101, 6039-6044.
46. Stagakis, S.; Markos, N.; Sykioti, O.; Kyparissis, A. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sens. Environ. 2010, 114, 977-994.
47. Torres-Madronero, M.C.; Velez-Reyes, M.; Van Bloem, S.J.; Chinea, J.D. Multi-temporal unmixing analysis of Hyperion images over the Guanica Dry Forest. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2012, 6, 329-338.
48. Campbell, P.K.E.; Middleton, E.M.; Thome, K.J.; Kokaly, R.F.; Huemmrich, K.F.; Lagomasino, D.; Novick, K.A.; Brunsell, N.A. EO-1 Hyperion reflectance time series at calibration and validation sites: Stability and sensitivity to seasonal dynamics. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2013, 6, 276-290.
49. Somers, B.; Asner, G.P. Hyperspectral time series analysis of native and invasive species in Hawaiian rainforests. Remote Sens. 2012, 4, 2510-2529.
50. Somers, B.; Asner, G.P. Invasive species mapping in Hawaiian rainforests using multi-temporal Hyperion spaceborne imaging spectroscopy. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2013, 6, 351-359.
51. Numata, I.; Cochrane, M.A.; Souza, C.M., Jr.; Sales, M.H. Carbon emissions from deforestation and forest fragmentation in the Brazilian Amazon. Environ. Res. Lett. 2011, 6,.044003
52. Kaufmann, H.; Förster, S.; Wulf, H.; Segl, K.; Guanter, L.; Bochow, M.; Heiden, U.; Müller, A.; Heldens, W.; Schneiderhan, T.; et al. Science Plan of the Environmental Mapping and Analysis Program (EnMAP); Deutsches Geo Forschungs Zentrum GFZ: Potsdam, Gemany, 2012.
53. Suess, S.; van der Linden, S.; Okujeni, A.; Schwieder, M.; Leitão, P.J.; Hostert, P. Using class-probabilities to map gradual transitions in shrub vegetation maps from simulated EnMAP data. Remote Sens. 2015, 7, 10668-10688.
54. Vapnik, V.N. An overview of statistical learning theory. IEEE Trans. Neural Netw. 1999, 10, 988-999.
55. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5-32.
56. Dormann, C.F.; Elith, J.; Bacher, S.; Buchmann, C.; Carl, G.; Carré, G.; Marquéz, J.R.G.; Gruber, B.; Lafourcade, B.; Leitão, P.J.; et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013, 36, 27-46.
57. Verrelst, J.; Munoz, J.; Alonso, L.; Delegido, J.; Rivera, J.P.; Camps-Valls, G.; Moreno, J. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and_3. Remote Sens. Environ. 2012, 118, 127-139.
58. Pal, M.; Foody, G.M. Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 2010, 48, 2297-2307.
59. Held, M.; Rabe, A.; Senf, C.; van der Linden, S.; Hostert, P. Analyzing hyperspectral and hypertemporal data by decoupling feature redundancy and feature relevance. IEEE Geosci. Remote Sens. Lett. 2015, 12, 983-987.
60. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352.
61. Serrano, L.; Peñuelas, J.; Ustin, S.L. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sens. Environ. 2002, 81, 355-364.
62. Ferwerda, J.G.; Skidmore, A.K.; Mutanga, O. Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species. Int. J. Remote Sens. 2005, 26, 4083-4095.
63. Austin, M.P. Spatial prediction of species distribution: An interface between ecological theory and statistical modelling. Ecol. Model. 2002, 157, 101-118.
64. van der Linden, S.; Rabe, A.; Held, M.; Jakimow, B.; Leitão, P.J.; Okujeni, A.; Suess, S.; Hostert, P. The EnMAP-Box-A toolbox and application programming interface for EnMAP data processing. Remote Sens. 2015, 7, 11249-11266.
65. Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S.; de Colstoun, E.B.; McMurtrey, J.E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 2000, 74, 229-239.
66. Galvão, L.S.; Formaggio, A.R.; Tisot, D.A. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sens. Environ. 2005, 94, 523-534.
67. Nagler, P.L.; Inoue, Y.; Glenn, E.P.; Russ, A.L.; Daughtry, C.S.T. Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sens. Environ. 2003, 87, 310-325.
68. Elith, J.; Leathwick, J.R.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77, 802-813.
69. Shirley, S.M.; Yang, Z.; Hutchinson, R.A.; Alexander, J.D.; McGarigal, K.; Betts, M.G. Species distribution modelling for the people: Unclassified landsat TM imagery predicts bird occurrence at fine resolutions. Divers. Distrib. 2013, 19, 855-866.
70. Cheong, Y.L.; Leitão, P.J.; Lakes, T. Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees. Spat. Spatio-Temporal Epidemiol. 2014, 10, 75-84.
71. Müller, D.; Leitão, P.J.; Sikor, T. Comparing the determinants of cropland abandonment in Albania and Romania using Boosted Regression Trees. Agric. Syst. 2013, 117, 66-77.
72. R Development Core Team. R: A Language and Environment for Statistical Computing, Version 2.6.0; R Foundation for Statistical Computing: Vienna, Austria, 2015.
73. Ridgeway, G. Generalized Boosted Models: A Guide to the GBM Package, Version 2.1.1; 2007.
74. Leitão, P.J.; Moreira, F.; Osborne, P.E. Breeding habitat selection by steppe birds in Castro Verde: A remote sensing and advanced statistics approach. Ardeola 2010, 57, 93-116.
75. Moreira, F.; Leitão, P.J.; Morgado, R.; Alcazar, R.; Cardoso, A.; Carrapato, C.; Delgado, A.; Geraldes, P.; Gordinho, L.; Henriques, I.; et al. Spatial distribution patterns, habitat correlates and population estimates of steppe birds in Castro Verde. Airo 2007, 17, 5-30.
76. Kuemmerle, T.; Hostert, P.; Radeloff, V.C.; Perzanowski, K.; Kruhlov, I. Post-socialist farmland abandonment in the Carpathians. Ecosystems 2008, 11, 614-628.
77. Marta-Pedroso, C.; Domingos, T.; Freitas, H.; de Groot, R.S. Cost-benefit analysis of the Zonal Program of Castro Verde (Portugal): Highlighting the trade-off between biodiversity and soil conservation. Soil Tillage Res. 2007, 97, 79-90.
78. Calvão, T.; Palmeirim, J.M. Mapping Mediterranean scrub with satellite imagery: Biomass estimation and spectral behaviour. Int. J. Remote Sens. 2004, 25, 3113-3126.
79. Klink, C.A.; Machado, R.B. Conservation of the Brazilian Cerrado. Conserv. Biol. 2005, 19, 707-713.
80. Myers, N.; Mittermeier, R.A.; Mittermeier, C.G.; Fonseca, G.A.B.; Kent, J. Biodiversity hotspots for conservation priorities. Nature 2000, 403, 853-858.
81. Ratter, J.A.; Ribeiro, J.F.; Bridgewater, S. The Brazilian cerrado vegetation and threats to its biodiversity. Ann. Bot. 1997, 80, 223-230.
82. Ferreira, M.E.; Ferreira, L.G.; Miziara, F.; Soares-Filho, B.S. Modeling landscape dynamics in the central Brazilian savanna biome: future scenarios and perspectives for conservation. J. Land Use Sci. 2012, 8, 403-421.
83. Batlle-Bayer, L.; Batjes, N.H.; Bindraban, P.S. Changes in organic carbon stocks upon land use conversion in the Brazilian Cerrado: A review. Agric. Ecosyst. Environ. 2010, 137, 47-58.
84. Sano, E.E.; Rosa, R.; Brito, J.L.S.; Ferreira, L.G. Land cover mapping of the tropical savanna region in Brazil. Environ. Monit. Assess. 2010, 166, 113-124.
85. Lopes, A.S.; Cox, F.R. Cerrado vegetation in Brazil: An edaphic gradient. Agron. J. 1977, 69, 828-831.
86. De Castro, E.A.; Kauffman, J.B. Ecosystem structure in the Brazilian Cerrado: A vegetation gradient of aboveground biomass, root mass and consumption by fire. J. Trop. Ecol. 1998, 14, 263-283.
87. Ribeiro, L.F.; Tabarelli, M. A structural gradient in cerrado vegetation of Brazil: Changes in woody plant density, species richness, life history and plant composition. J. Trop. Ecol. 2002, 18, 775-794.
88. Schwieder, M.; Leitão, P.J.P.; Rabe, A.; Bustamante, M.M.C.; Ferreira, L.G.; Hostert, P. Mapping Cerrado physiognomies using Landsat time series based phenological profiles. In proceedings of the XVII Simpósio Brasileiro de Sensoriamento Remoto, INPE, João Pessoa, Brazil, 27 April 2015; pp. 3656-3663.
89. Rogass, C.; Guanter, L.; Mielke, C.; Scheffler, D.; Boesche, N.K.; Lubitz, C.; Brell, M.; Spengler, D.; Segl, K. An automated processing chain for the retrieval of georeferenced reflectance data from hyperspectral EO-1 Hyperion acquisitions. In Proceedings of the 34th EARSeL Symposium, Warsaw, Poland, 16-20 June 2014; doi:10.12760/03-2014-06.
90. Ferreira, L.G.; Yoshioka, H.; Huete, A.; Sano, E.E. Seasonal landscape and spectral vegetation index dynamics in the Brazilian Cerrado: An analysis within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). Remote Sens. Environ. 2003, 87, 534-550.
91. Ratana, P.; Huete, A.R.; Ferreira, L. Analysis of cerrado physiognomies and conversion in the MODIS seasonal-temporal domain. Earth Interact. 2005, 9, 1-22.