Classificação do uso do solo de uma bacia hidrográfica por meio de algoritmos de machine learning
Palavras-chave:
Aprendizado de máquinas, Uso do solo, Inteligência artificialResumo
O monitoramento do uso de solo em uma bacia hidrográfica é fundamental para gestão dos recursos hídricos, sendo essa importância ampliada em bacias com altas taxas de urbanização, em que se eleva o grau do efeito antrópico no ambiente. O uso de algoritmos de aprendizado de máquinas aplicado a imagens de satélite tem se apresentado nos últimos anos como uma alternativa promissora para convecção de mapas de cobertura do solo. Este trabalho analisa o desempenho dos algoritmos árvore de decisão (DT), floresta aleatória (RF), máquina de vetor suporte (SVM) e k vizinhos mais próximos (K-NN) para classificar o uso do solo na bacia do rio Iguaçu – Sarapuí – RJ por meio de uma imagem Landsat 8 OLI/TIRS. Esta bacia é fortemente degradada, possuindo grandes áreas urbanas e industriais. Foram utilizados 5 mil pontos de amostragem, divididos em um conjunto de treinamento (77%) e teste (33%). As sete primeiras bandas espectrais mais o Índice de Vegetação Normalizado (NDVI) foram combinadas em treze ensaios diferentes. O algoritmo árvore de decisão e K-NN foram que obtiveram melhores desempenhos, alcançando 75% de acerto. Entre os ensaios, os melhores ensaios consideraram as bandas 1, 2, 3, 4, 5 e 6. As maiores taxas de acerto em relação as classes foram em áreas urbanas (83%) e floresta (88%).
Referências
Aha DW (1990) A study of instance-based algorithms for supervised learning tasks: mathematical, empirical, and psychological evaluations. PhD dissertation, University of California, Irvine, CA, USA.
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Belmont, CA, USA.
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Carneiro PRF, Cardoso AL, Azevedo JPS (2008) O planejamento do uso do solo urbano e a gestão de bacias hidrográficas: o caso da bacia dos rios Iguaçu/Sarapuí na Baixada Fluminense. Cadernos Metrópole 19:165–190.
Chan JCW, Chan KP, Yeh AGO (2001) Detecting the nature of change in an urban environment: a comparison of machine learning algorithms. Photogramm Eng Remote Sens 67(2):213–225.
Feng Y, Cui N, Gong D, Zhang Q, Zhao L (2017) Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agric Water Manag 193:163–173. https://doi.org/10.1016/j.agwat.2017.08.003
Galiano VFR, Chica-Rivas M (2014) Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and digital terrain models. Int J Digit Earth 7:492–509. https://doi.org/10.1080/17538947.2012.748848
Gong P, Howarth PJ (1990) An assessment of some factors influencing multispectral land-cover classification. Photogramm Eng Remote Sens 56(5):597–603.
Hepner GF, Logan T, Ritter N, Bryant N (1990) Artificial neural network classification using a minimal training set: comparison to conventional supervised classification. Photogramm Eng Remote Sens 56(4):469–473.
Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174. https://doi.org/10.2307/2529310
Lawrence R, Bunn A, Powell S, Zambon M (2004) Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sens Environ 90(3):331–336. https://doi.org/10.1016/j.rse.2004.01.007
Li M, Im J, Beier C (2013) Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest. GISci Remote Sens 50(4):361–384. https://doi.org/10.1080/15481603.2013.819161
Li C, Wang J, Wang L, Hu L, Gong P (2014) Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery. Remote Sens 6(2):964–983. https://doi.org/10.3390/rs6020964
Lyu H, Lu H, Mou L, Li W, Wright JS, Li X, Li X, Zhu XX, Wang J, Yu L, Gong P (2018) Long-term annual mapping of four cities on different continents by applying a deep information learning method to Landsat data. Remote Sens 10(3):471. https://doi.org/10.3390/rs10030471
Long H, Liu Y, Wu X, Dong G (2009) Spatio-temporal dynamic patterns of farmland and rural settlements in Su-Xi-Chang region: implications for building a new countryside in coastal China. Land Use Policy 26(2):322–333. https://doi.org/10.1016/j.landusepol.2008.04.001
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66(3):247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Paiva ACE (2014) Previsibilidade de cheias na bacia do rio Iguaçu/Sarapuí - RJ, usando modelagem hidrológica distribuída. MSc dissertation, Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP, Brazil.
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217–222. https://doi.org/10.1080/01431160412331269698
QGIS Development Team (2019) QGIS geographic information system. Open Source Geospatial Foundation Project. http://qgis.osgeo.org. Accessed 20 Apr 2019.
Rogan J, Franklin J, Stow DA, Miller J, Woodcock C, Roberts D (2008) Mapping land-cover modifications over large areas: a comparison of machine learning algorithms. Remote Sens Environ 112(5):2272–2283. https://doi.org/10.1016/j.rse.2007.10.004
Rogan J, Chen DM (2004) Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog Plan 61:301–325.
Schneider A (2012) Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sens Environ 124:689–704. https://doi.org/10.1016/j.rse.2012.06.006
Schlerf M, Atzberger C, Hill J (2005) Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens Environ 95(2):177–194. https://doi.org/10.1016/j.rse.2004.12.016
Shao Y, Lunetta RS (2012) Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS J Photogramm Remote Sens 70:78–87. https://doi.org/10.1016/j.isprsjprs.2012.04.001
Small C, Lu J (2006) Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis. Remote Sens Environ 100(4):441–456. https://doi.org/10.1016/j.rse.2005.10.023
Vapnik VN (1979) Estimation of dependences based on empirical data. Nauka, Moscow, USSR. (English translation: Springer-Verlag, New York, 1982.)
Zhao H, Ma L, Wang L, Li J (2016) Examining urban expansion using multi-temporal Landsat imagery: a case study of Montreal census metropolitan area. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 2923–2926. IEEE. https://doi.org/10.1109/IGARSS.2016.7729741
Zhen Z, Quackenbush LJ, Stehman SV, Zhang L (2013) Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification. Int J Remote Sens 34(19):6914–6930. https://doi.org/10.1080/01431161.2013.810822
Zhu G, Blumberg DG (2002) Classification using ASTER data and SVM algorithms: the case study of Beer Sheva, Israel. Remote Sens Environ 80(2):233–240. https://doi.org/10.1016/S0034-4257(01)00305-4
