Does environmental data increase the accuracy of land use and land cover classification?
International Journal of Applied Earth Observation and Geoinformation - Tập 91 - Trang 102128 - 2020
Tài liệu tham khảo
Anjos, 2011, 2286
Anjos, 2017, Análise do nível de legenda de classificação de áreas urbanas empregando imagens multiespectrais e hiperespectrais com os métodos árvore de decisão C4.5 e floresta randômica, Bol. Ciênc. Geod., Curitiba, 23, 371, 10.1590/s1982-21702017000200024
Arruda, 2017, Combining climatic and soil properties better covers of Brazilian biomes, Sci Nat., 104, 32, 10.1007/s00114-017-1456-6
Bakula, 2016, Testing of land cover classification from multispectral airborne laser scanning data, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, Volume XLI-B7, XXIII ISPRS Congress
Bauer, 1975, The role of remote sensing in determining the distribution and yield of crops, Advances in Agronomy, 27, 271, 10.1016/S0065-2113(08)70012-9
Breiman, 2001, Random forest, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Bruzzone, 1997, Multisource classification of complex rural areas by statistical and neural-network approaches, Photogramm. Eng. Remote Sensing, 63, 523
Calvão, 2015, Remote sensing in food production – a review, Emir. J. Food Agric., 27, 138, 10.9755/ejfa.v27i2.19272
Cardoso-Leite, 2005, Hamburguer, D.S. Ecologia da paisagem, Acta Botanica Brasílica, 19, 233, 10.1590/S0102-33062005000200005
Comber, 2012, Spatial analysis of remote sensing image classification accuracy, Remote Sens. Environ., 127, 237, 10.1016/j.rse.2012.09.005
Congalton, 1991, A review of assessing the accuracy of classification of remotely sensed data, Remote Sens. Environ., 37, 35, 10.1016/0034-4257(91)90048-B
CONGALTON, 2009, 183
Costa, 2007, Organização comunitária de um cerrado sensu strictu no Bioma Caatinga, chapada do Araripe, Barbalha, Ceará, Acta Botanica Brasílica, 21, 281, 10.1590/S0102-33062007000200004
CURRAN, 1983, Problems in the remote sensing of vegetation canopies for biomass estimation, 84
Daughtry, 1998, Spectral discrimination of Cannabis sativa L. Leaves and canopies, Remote Sens. Env., 64, 192, 10.1016/S0034-4257(98)00002-9
Demarchi, 2018, Floristic composition, structure and soil-vegetation relations in three white-sand soil patches in central Amazonia, Acta Amazon., 48, 46, 10.1590/1809-4392201603523
Dias-Filho, 2004, Competição e sucessão vegetal em pastagens, 252
Diniz, 2013, RedFace: um sistema de reconhecimento facial baseado em técnicas de análise de componentes principais e autofaces: comparação com diferentes classificadores. Revista Brasileira de Computação Aplicada (ISSN 2176-6649), Passo Fundo, 5, 42
Domaç, 2006, Integration of environmental variables with satellite images in regional scale vegetation classification, Int. J. Remote Sens., 27, 1329, 10.1080/01431160500444806
Dorren, 2003, Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification, For. Ecol. Manage., 183, 31, 10.1016/S0378-1127(03)00113-0
Ducart, 2016, Mapping iron oxides with Landsat-8/OLI and EO-1/Hyperion imagery from the Serra Norte iron deposits in the Carajás Mineral Province, Brazil, Braz. J. Geol., 46, 331, 10.1590/2317-4889201620160023
Duguay, 1989, A software package for integrating digital elevation models into the digital analysis of remote-sensing data, Comput. Geosci., 15, 669, 10.1016/0098-3004(89)90075-7
Fahsi, 2000, Incorporation of digital elevation models with Landsat-TM data to improve land cover classification accuracy, For. Ecol. Manage., 128, 57, 10.1016/S0378-1127(99)00272-8
Fahsi, 2000, Incorporation of digital elevation models with Landsat-TM data to improve land cover classification accuracy, For. Ecol. Manage., 128, 57, 10.1016/S0378-1127(99)00272-8
Fang, 2017, Forest-type shift and subsequent intensive management affected soil organic carbon and microbial community in southeastern China, Eur. J. For. Res., 136, 689, 10.1007/s10342-017-1065-0
Foley, 1998, Ecological applications of near infrared reflectance spectroscopy - A tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance, Oecologia, 116, 293, 10.1007/s004420050591
Foody, 2002, Status of land cover classification accuracy assessment, Remote Sens. Environ., 80, 185, 10.1016/S0034-4257(01)00295-4
Framil, 2013, 58
Franklin, 1989, Ancillary data input to satellite remote sensing of complex terrain phenomena, Comput. Geosci., 15, 799, 10.1016/0098-3004(89)90082-4
Franklin, 1995, Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients, Prog. Phys. Geogr., 19, 494, 10.1177/030913339501900403
Franklin, 1991, Spatial and spectral classification of remote-sensing imagery, Comput. Geosci., 17, 1151, 10.1016/0098-3004(91)90075-O
Gerçek, 2004, Improvement of image classifcation with the integration of topographical data
Guedes, 2018, Sensoriamento remoto no estudo da vegetação: princípios físicos, sensores e métodos, ACTA Geográfica, 12, 127, 10.18227/2177-4307.acta.v12i29.4001
Gutman, 2002, Nasa Land Cover/Land use change program, 11
Harris, 1995, The integration of geographic data with remotely-sensed imagery to improve classification in an urban area, Photogramm. Eng. Remote Sensing, 61, 993
Hijmans, 2005, Very resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 65, 10.1002/joc.1276
HUETE, 1988, A Soil-adjusted vegetation index (SAVI), Remote Sens. Environ., 25, 295, 10.1016/0034-4257(88)90106-X
IBGE - Instituto Brasileiro de Geografia e Estatística, 2015
IBGE - Instituto Brasileiro de Geografia e Estatística, 2017
Janssen, 1990, Integrating topographic data with remote sensing for land cover classification, Photogramm. Eng. Remote Sensing, 56, 1503
Jiang, 2012, Effects of single and mixed species forest ecosystems on diversity and function of soil microbial community in subtropical China, J. Soils Sediments, 12, 228, 10.1007/s11368-011-0442-4
Kaya, 2014, Recursive feature selection based on non-parallel SVMs and its application to hyperspectral image classification
Khalid, 2014, A survey of feature selection and feature extraction techniques in machine learning, 372
Knipling, 1970, Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation, Remote Sens. Environ., 1, 155, 10.1016/S0034-4257(70)80021-9
Landis, 1977, The measurements of agreement for categorical data Biometrics, Washington, 33, 159
Li, 2010, Remotely sensed images and GIS data fusion for automatic change detection, Int. J. Image Data Fusion, 1, 99, 10.1080/19479830903562074
Li, 2014, Soil bacterial communities of different natural forest types in Northeast China, Plant Soil, 383, 203, 10.1007/s11104-014-2165-y
Lillesand, 2004, 763
Lopez-Paz, 2013, The randomized dependence coefficient, 1
Lu, 2007, A Survey of image classification methods and techniques for improving classification performance, Int. J. Remote Sens., 28, 823, 10.1080/01431160600746456
Lu, 2012, Land use/cover classification in the Brazilian Amazon using satellite images, Pesq. agropec. bras., 47, 1185, 10.1590/S0100-204X2012000900004
Lu, 2012, Application of time series landsat images to examining land-use/land-cover dynamic change, Photogramm. Eng. Remote Sensing, 78, 747, 10.14358/PERS.78.7.747
Lu, 2013, Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon, Int. J. Remote Sens., 34, 5953, 10.1080/01431161.2013.802825
Moges, 2005, Evaluation of green, red, and near infrared bands for predicting winter wheat biomass, nitrogen uptake, and final grain yield, J. Plant Nutr., 27, 1431, 10.1081/PLN-200025858
Mulder, 2011, The use of remote sensing in soil and terrain mapping: a review, Geoderma, 162, 1, 10.1016/j.geoderma.2010.12.018
Novo, 2010, 388
Perez Filho, 2007, Sistemas naturais e geografia, 01, 333
Pignatti, 2009, Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy, Remote Sens. Environ., 3, 622, 10.1016/j.rse.2008.11.006
Pinder, 1997, The relationship between vegetation type and topography in Lassen Volcanic National Park, Plant Ecol., 131, 17, 10.1023/A:1009792123571
Pinter Junior, 2003, Remote sensing for crop management, Photogramm. Eng. Remote Sensing, 647, 10.14358/PERS.69.6.647
Ponzoni, 2010, 127
Potter, 2003, Continental-scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982-1998, Glob. Planet. Chang., 39, 201, 10.1016/j.gloplacha.2003.07.001
R Core Team, 2016
Rajendran, 2016, Vegetation analysis study in and around Sultan Qaboos University, Oman, using Geoeye-1 satellite data, Egypt. J. Remote. Sens. Space Sci., 19, 297
Ricchetti, 2000, Multispectral satellite image and ancillary data integration for geological classification, Phtogramm. Eng. Remote Sens., 66, 429
Rogan, 2002, Operational detection of changes in forest and shrub cover in California using multitemporal landsat data, Proceedings of RS2002, 08–12 April, San Diego, California, CDROM
Rosa, 2009, 264
Rouse, 1973, Monitoring vegetation systems in the great plains with ERTS, Earth Resources Technology Satellite - 1 Symposium, 3, Washington, 1973. Proceedings… Whashington: NASA, 1974, 1, 309
Schmidt, 2001, Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: the Makhtesh Ramon Crater (Israel) case study, Int. J. Remote Sens., 22, 3503, 10.1080/01431160110063779
Scotford, 2005, Applications of spectral reflectance techniques in northern european cereal production: a review, Biosys. Eng., 90, 235, 10.1016/j.biosystemseng.2004.11.010
SEPLAN - Secretaria de Planejamento e Orçamento, 2016
Simonetti, 2014, 60
Smits, 1999, Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach, Int. J. Remote Sens., 20, 1461, 10.1080/014311699212560
Souza, 2009, 112
Souza, 2017, 144
Stathakis, 2014, Monitoring urban sprawl simulated PROBA-V data, Int. J. Remote Sens., 35, 2731, 10.1080/01431161.2014.883089
Stathakis, 2012, Efficient segmentation of urban areas by the VIBI, Int. J. Remote Sens., 33, 6361, 10.1080/01431161.2012.687842
Takyu, 2002, Effects of topography on tropical lower montane forest under different geological conditions on Mount Kinabalu, Borneo. Plant Ecol., 159, 35, 10.1023/A:1015512400074
Ter Steege, 2006, Continental-scale patterns of canopy tree composition and function across Amazonia, Nature, 443, 444, 10.1038/nature05134
Trietz, 2000, Integrating spectral, spatial and terrain variables for forest ecosystem classification, Photogramm. Eng. Remote Sensing, 66, 305
USGS - United States Geological Survey, 2012, 81
USGS - United States Geological Survey, 2015
USGS - United States Geological Survey, 2017, 36
Wan, 2018, Soil indicators of plant diversity for global ecoregions: implications for management practices, Glob. Ecol. Conserv., 14
Wan, 2019, Effects of soil properties on the spatial distribution of forest vegetation across China, Glob. Ecol. Conserv., 18
WORLDCLIM - Global Climate Data, 2016
Xue, 2017, Significant remote sensing vegetation indices: a review of developments and applications, J. Sens, 17
Zha, 2003, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, Int. J. Remote Sensing, 24, 583, 10.1080/01431160304987
Zukowskyj, 2003, Validation of a novel classification system: the integrated digital elevation model image classification system