dc.creatorAlmeida J.
dc.creatordos Santos J.A.
dc.creatorMiranda W.O.
dc.creatorAlberton B.
dc.creatorMorellato L.P.C.
dc.creatorda S. Torres R.
dc.date2015
dc.date2015-06-25T12:53:32Z
dc.date2015-11-26T15:10:55Z
dc.date2015-06-25T12:53:32Z
dc.date2015-11-26T15:10:55Z
dc.date.accessioned2018-03-28T22:21:00Z
dc.date.available2018-03-28T22:21:00Z
dc.identifier
dc.identifierEcological Informatics. Elsevier, v. 26, n. P3, p. 61 - 69, 2015.
dc.identifier15749541
dc.identifier10.1016/j.ecoinf.2015.01.003
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84922719400&partnerID=40&md5=68059736e1c28e8c439876e1e3197073
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/85484
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/85484
dc.identifier2-s2.0-84922719400
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1258031
dc.descriptionPlant phenology studies recurrent plant life cycle events and is a key component for understanding the impact of climate change. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful strategies relies on the use of digital cameras, which are used as multi-channel imaging sensors to estimate color changes that are related to phenological events. We monitor leaf-changing patterns of a cerrado-savanna vegetation by taking daily digital images. We extract individual plant color information and correlate with leaf phenological changes. For that, several vegetation indices associated with plant species are exploited for both pattern analysis and knowledge extraction. In this paper, we present a novel approach for deriving appropriate vegetation indices from vegetation digital images. The proposed method is based on learning phenological patterns from plant species through a genetic programming framework. A comparative analysis of different vegetation indices is conducted and discussed. Experimental results show that our approach presents higher accuracy on characterizing plant species phenology.
dc.description26
dc.descriptionP3
dc.description61
dc.description69
dc.descriptionAhrends, H., Etzold, S., Kutsch, W., Stoeckli, R., Bruegger, R., Jeanneret, F., Wanner, H., Eugster, W., Tree phenology and carbon dioxide fluxes: use of digital photography for process-based interpretation at the ecosystem scale (2009) Clim. Res., 39, pp. 261-274
dc.descriptionAlberton, B., Almeida, J., Henneken, R., da S.Torres, R., Menzel, A., Morellato, L.P.C., Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation (2014) Ecol. Inform., 19, pp. 62-70
dc.descriptionAlmeida, J., dos Santos, J.A., Alberton, B., da S.Torres, R., Morellato, L.P.C., Remote phenology: applying machine learning to detect phenological patterns in a cerrado savanna (2012) IEEE International Conference on eScience (eScience'12), pp. 1-8
dc.descriptionAlmeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., da S.Torres, R., Plant species identification with phenological visual rhythms (2013) IEEE International Conference on eScience (eScience'13), pp. 148-154
dc.descriptionAlmeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., da S.Torres, R., Visual rhythm-based time series analysis for phenology studies (2013) IEEE International Conference on Image Processing (ICIP'13), pp. 4412-4416
dc.descriptionAlmeida, J., dos Santos, J.A., Alberton, B., da S.Torres, R., Morellato, L.P.C., Applying machine learning based on multiscale classifiers to detect remote phenology patterns in cerrado savanna trees (2014) Ecol. Inform., 23, pp. 49-61
dc.descriptionAndrade, F.S.P., Almeida, J., Pedrini, H., da S.Torres, R., Fusion of local and global descriptors for content-based image and video retrieval (2012) Iberoamerican Congress on, Pattern Recognition (CIARP'12), pp. 845-853
dc.descriptionBaeza-Yates, R.A., Ribeiro-Neto, B.A., (1999) Modern Information Retrieval, , Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA
dc.descriptionBimbo, A., (1999) Visual Information Retrieval, , Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
dc.descriptionCalumby, R.T., da S.Torres, R., Gonçalves, M.A., Multimodal retrieval with relevance feedback based on genetic programming (2014) Multimed. Tools Appl., 69 (3), pp. 991-1019
dc.descriptionConti, J.C., Faria, F.A., Almeida, J., Alberton, B., Morellato, L.P.C., Camolesi, L., da S.Torres, R., Evaluation of time series distance functions in the task of detecting remote phenology patterns (2014) IEEE International Conference on Pattern Recognition (ICPR'14), pp. 1-6
dc.descriptionda S.Torres, R., Falcão, A.X., Gonçalves, M.A., Papa, J.P., Zhang, B., Fan, W., Fox, E.A., A genetic programming framework for content-based image retrieval (2009) Pattern Recogn., 42 (2), pp. 283-292
dc.descriptionDavis, J., Goadrich, M., The relationship between precision-recall and roc curves (2006) ACM International Conference on, Machine Learning (ICML'06), pp. 233-240
dc.descriptionFan, W., Fox, E.A., Pathak, P., Wu, H., The effects of fitness functions on genetic programming-based ranking discovery forweb search (2004) J. Am. Soc. Inf. Sci. Technol., 55 (7), pp. 628-636
dc.descriptionFaria, F.A., Veloso, A., Almeida, H.M., Valle, E., da S.Torres, R., Gonçalves, M.A., Meira, W., Learning to rank for content-based image retrieval (2010) ACM International Conference on Multimedia Information Retrieval (MIR'10), pp. 285-294
dc.descriptionFerreira, C.D., dos Santos, J.A., da S.Torres, R., Gonçalves, M.A., Rezende, R.C., Fan, W., Relevance feedback based on genetic programming for image retrieval (2011) Pattern Recogn. Lett., 32 (1), pp. 27-37
dc.descriptionFishburn, P.C., (1988) Nonlinear Preference and Utility Theory, , Johns Hopkins University Press
dc.descriptionGillespie, A.R., Kahle, A.B., Walker, R.E., Color enhancement of highly correlated images. II. Channel ratio and "chromaticity" transformation techniques (1987) Remote Sens. Environ., 22 (3), pp. 343-365
dc.descriptionGonzalez, R.C., Woods, R.E., (2007) Digital Image Processing, , Prentice-Hall Inc., Upper Saddle River, NJ, USA
dc.descriptionGuigues, L., Cocquerez, J., Le Men, H., Scale-sets image analysis (2006) Int. J. Comput. Vis., 68, pp. 289-317
dc.descriptionHenneken, R., Dose, V., Schleip, C., Menzel, A., Detecting plant seasonality from webcams using bayesian multiple change point analysis (2013) Agric. For. Meteorol., 168, pp. 177-185
dc.descriptionIde, R., Oguma, H., Use of digital cameras for phenological observations (2010) Ecol. Inform., 5 (5), pp. 339-347
dc.descriptionIde, R., Oguma, H., A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems (2013) Ecol. Inform., 16, pp. 25-34
dc.descriptionKoza, J.R., (1992) Genetic Programming: on the Programming of Computers by Means of Natural Selection, , MIT Press
dc.descriptionKurc, S., Benton, L., Digital image-derived greenness links deep soil moisture to carbon uptake in a creosotebush-dominated shrubland (2010) J. Arid Environ., 74, pp. 585-594
dc.descriptionMigliavacca, M., Galvagno, M., Cremonese, E., Rossini, M., Meroni, M., Sonnentag, O., Cogliati, S., Richardson, A.D., Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic co2 uptake (2011) Agric. For. Meteorol., 151 (10), pp. 1325-1337
dc.descriptionMorellato, L.P.C., Rodrigues, R.R., Leitão Filho, H.F., Joly, C.A., Estudo comparativo da fenologia de espécies arbóreas de floresta de altitude e floresta mesófila semidecídua na Serra do Iapí, Jundiaí, São Paulo (1989) Braz. J. Bot., 12, pp. 85-98
dc.descriptionMorellato, L.P.C., Camargo, M.G.G., Gressler, E., A review of plant phenology in south and central america (2013) Phenology: An Integrative Environmental Science, pp. 91-113. , Springer, (chapter 6), M.D. Schwartz (Ed.)
dc.descriptionMorisette, J.T., Richardson, A.D., Knapp, A.K., Fisher, J.I., Graham, E.A., Abatzoglou, J., Wilson, B.E., Liang, L., Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century (2009) Front. Ecol. Environ., 7 (5), pp. 253-260
dc.descriptionMuttil, N., Lee, J.H.W., Genetic programming for analysis and real-time prediction of coastal algal blooms (2005) Ecol. Model., 189 (3-4), pp. 363-376
dc.descriptionNagai, S., Maeda, T., Gamo, M., Muraoka, H., Suzuki, R., Nasahara, K.N., Using digital camera images to detect canopy condition of deciduous broad-leaved trees (2011) Plant Ecol. Divers., 4, pp. 79-89
dc.descriptionRichardson, A.D., Jenkins, J.P., Braswell, B.H., Hollinger, D.Y., Ollinger, S.V., Smith, M.L., Use of digital webcam images to track spring greep-up in a deciduous broadleaf forest (2007) Oecologia, 152, pp. 323-334
dc.descriptionRichardson, A.D., Braswell, B.H., Hollinger, D.Y., Jenkins, J.P., Ollinger, S.V., Near-surface remote sensing of spatial and temporal variation in canopy phenology (2009) Ecol. Appl., 19, pp. 1417-1428
dc.descriptionRocha, A., Almeida, J., Nascimento, M.A., da, S., Torres, R., Goldenstein, S., Efficient and flexible cluster-and-search approach for CBIR (2008) Advanced Concepts for Intelligent Vision Systems (ACIVS'08), pp. 77-88
dc.descriptionSchwartz, M.D., (2013) Phenology: An Integrative Environmental Science, , Springer
dc.descriptionSonnentag, O., Hufkens, K., Teshera-Sterne, C., Young, A.M., Friedl, M., Braswell, B.H., Milliman, T.O., Richardson, A.D., Digital repeat photography for phenological research in forest ecosystems (2012) Agric. For. Meteorol., 152, pp. 159-177
dc.descriptionWoebbecke, D.M., Meyer, G.E., Von-Bargen, K., Mortensen, A.D., Color indices for weed identification under various soil, residue, and lighting conditions (1995) Trans. ASAE, 38 (1), pp. 259-269
dc.descriptionZhao, J., Zhang, Y., Tan, Z., Song, Q., Liang, N., Yu, L., Zhao, J., Using digital cameras for comparative phenological monitoring in an evergreen broad-leaved forest and a seasonal rain forest (2012) Ecol. Inform., 10, pp. 65-72
dc.descriptionZhou, L., He, H., Sun, X., Zhang, L., Yu, G., Ren, X.-L., Wang, J.-Y., Zhao, F.-H., Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data (2013) Ecol. Inform., 18, pp. 69-78
dc.languageen
dc.publisherElsevier
dc.relationEcological Informatics
dc.rightsfechado
dc.sourceScopus
dc.titleDeriving Vegetation Indices For Phenology Analysis Using Genetic Programming
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución