Abstract
The application of remote sensing techniques in threatened ecosystems such as the Galapagos Islands has shown to be a powerful tool for decision-making. Specifically in the case of San Cristobal Island, it will allow accurate mapping and modeling techniques at relatively low costs for battling invasive species such as guava and wax apple. This research evaluates the performance of three classification techniques for land cover mapping in the agricultural area of San Cristobal in the Galapagos: (a) pixel-based hybrid (supervised/unsupervised classification), (b) principal component pixel-based hybrid, and (c) object-oriented image hybrid classifications. An evaluation of three parametric classification algorithms (maximum likelihood, Mahalanobis distance, and minimum distances) for classification technique was also performed. The goal was to compare and identify the best approach for determining LULC with a focus on invasive species such as guava in the highland territory. The results for both pixel-based approaches are superior than the object-based approach. Nevertheless, it was evident that the principal component classifications tend to mix signature responses and did not show the same discrimination ability. Per-pixel/hybrid classification with maximum likelihood and Mahalanobis distance performs a superior kappa index of 0.8640 and 0.8610, respectively, proving to be more sensitive toward identifying invasive species such as guava and wax apple fields.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aguirre-Gutiérrez J, Seijmonsbergen AC, Duivenvoorden JF (2012) Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico. Appl Geogr 34:29–37. https://doi.org/10.1016/j.apgeog.2011.10.010
Aplin P, Atkinson PM (2001) Sub-pixel cover mapping for per-field classification. Int J Remote Sens 22(14):2853–2858
Ceballos JC, Bottino MJ (1997) The discrimination of scenes by principal components analysis of multi-spectral imagery. Int J Remote Sens 18(11):2437–2449. https://doi.org/10.1080/014311697217701
Coello S, Saunders A (2011). Final project evaluation: control of invasive species in the Galapagos Archipelago, ECU/00/G31
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46
Cremers L (2002) Irrigated agriculture on the Galapagos Islands: fit for survival. Wageningen University, Wageningen
Desclée B, Bogaert P, Defourny P (2006) Forest change detection by statistical object-based method. Remote Sens Environ 102:1–11
Duro DC, Franklin SE, Dubé MG (2012) A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens Environ 118:259–272. https://doi.org/10.1016/j.rse.2011.11.020
ENVI EX (2009) ENVI EX user’s guide. ENVI EX tutorial
Erdas Inc (1999) ERDAS field guide. Imagine, 5th edn. ERDAS, Atlanta
Fleiss J (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76:378–382
Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185–201. https://doi.org/10.1016/S0034-4257(01)00295-4
González JA, Montes C, Rodríguez J, Tapia W (2008) Rethinking the Galapagos Islands as a complex social-ecological system: implications for conservation and management. Ecol Soc 13(2):13
Grenier C (2000) Conservation contre nature: Les Iles Galápagos (IRD). Collection Latitudes 23, Paris
Huang C-Y, Asner GP (2009) Applications of remote sensing to alien invasive plant studies. Sensors (Basel, Switzerland) 9(6):4869–4889. https://doi.org/10.3390/s90604869
Im J, Jensen J, Tullis J (2008) Object-based change detection using correlation image analysis and image segmentation techniques. Int J Remote Sens 29:399–423
INEC (2010) Censo de Población y Vivienda. Ecuador.
Joshi C (2006) Mapping cryptic invaders and invasibility of tropical forest ecosystems: Chromolaena odorata in Nepal. International Institute for Geo-information Science & Earth Observation (ITC), Enschede
Li X, Yeh AGO (1998) Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta. Int J Remote Sens 19(8):1501–1518. https://doi.org/10.1080/014311698215315
Lloyd CD, Berberoglu S, Atkinson PM, Curran PJ (2004) A comparison of texture measures for the per-field classification of Mediterranean land cover. Int J Remote Sens 25:3943–3965
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870. https://doi.org/10.1080/01431160600746456
Magnani R (1999) Sampling guide: food security and nutrition monitoring. USAID, Washington, DC
Messina J, Walsh S (2001) 2.5D morphogenesis: modeling landuse and landcover dynamics in the Ecuadorian Amazon. Plant Ecol 156(1):75–88
Messina JP, Crews-Meyer KA, Walsh SJ (2000) Scale dependent pattern metrics and panel data analysis as applied in a multiphase hybrid landcover classification scheme. In: Proc. 2000 ASPRS conf
Ministerio de Medio Ambiente, S.B (2010) Protocolo Metodológico para la generación del Mapa de Deforestación Histórica en el Ecuador continental. MAE, Quito
Ministerio del Ambiente (2013) Programa de control y erradicación de especies invasoras prioritarias para la reducción de la vulnerabilidad de especies endémicas y nativas de las islas Galápagos. San Cristobal, Parque Nacional Galápagos
Myint SW, Giri CP, Wang L, Zhu Z, Gillette S (2008) Identifying mangrove species and their surrounding land use and land cover classes using an object oriented approach with a lacunarity spatial measure. GISci Remote Sens 45:188–208
Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ 115(5):1145–1161. https://doi.org/10.1016/j.rse.2010.12.017
Pal M, Mather P (2003) An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 86:554–565
Pal M, Mather P (2004) Assessment of the effectiveness of support vector machines for hyperspectral data. Futur Gener Comput Syst 20:1215–1225
Registro Oficial No. 520 (2015) Ley Orgánica de Régimen Especial de la Provincia de Galápagos. Asamblea Nacional del Ecuador, Quito
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67:93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002
Rozenstein O, Karnieli A (2011) Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Appl Geogr 31(2):533–544. https://doi.org/10.1016/j.apgeog.2010.11.006
Schott JR (1997) Remote sensing: the image chain approach. Oxford University Press, New York
Smits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and proposal for a cost-based approach. Int J Remote Sens 20:1461–1486
South S, Qi J, Lusch D (2004) Optimal classification methods for mapping agricultural tillage practices. Remote Sens Environ 91:90–97
Tayor PJ (1977) Quantitative methods in geography: an introduction to spatial analysis. Houghton Mifflin Company, Boston
Taylor JE, Hardner J, Stewart M (2006) Ecotourism and economic growth in the Galapagos: an island economy-wide analysis. Environ Dev Econ 14:139–162
UICN (2007) Convention concerning the protection of the world cultural and natural heritage: thirty-fourth session (31 COM). Paris, France
UICN (2010a) Convention concerning the protection of the world cultural and natural heritage: thirty-fourth session (34 COM). Understanding the politics of heritage. Paris, France. https://doi.org/10.1016/j.sbspro.2010.05.048
UICN (2010b) Decision: 34 COM 8C.3. http://whc.unesco.org/en/decisions/4242. Accessed 13 Sept 2016
UICN (2016) Convention concerning the protection of the world cultural and natural heritage: thirty-fourth session (40 COM). Paris, France
Van der Meer F, Scmidt KS, Bakker A, Bokler W (2002) Environmental modelling with GIS and RS. In: New environmental RS systems. Taylor and Francis, London, p 26–51
Whiteside TG, Boggs GS, Maier SW (2011) Comparing object-based and pixel-based classifications for mapping savannas. Int J Appl Earth Observ Geoinform 13(6):884–893. https://doi.org/10.1016/j.jag.2011.06.008
Wilkinson GG (2005) Results and implications of a study of fifteen years of satellite image classification experiments. Computer 43(3):433–440
Xie Z, Roberts C, Johnson B (2008) Object-based target search using remotely sensed data: a case study in detecting invasive exotic Australian Pine in south Florida. J Photogramm Remote Sens 63(6):647–660
Yan G, Maathuis BHP, Xiangmin Z, Van Dijk PM (2006) Comparison of pixel based and object oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China. Int J Remote Sens 27(18):4039–4055
Messina, J. P., Crews-Meyer, K. A. & Walsh, S. J (2000) Scale dependent pattern metrics and panel data analysis as applied in a multiphase hybrid landcover classification scheme. Proc. 2000 ASPRS Conf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Sampedro, C., Mena, C.F. (2018). Remote Sensing of Invasive Species in the Galapagos Islands: Comparison of Pixel-Based, Principal Component, and Object-Oriented Image Classification Approaches. In: Torres, M., Mena, C. (eds) Understanding Invasive Species in the Galapagos Islands. Social and Ecological Interactions in the Galapagos Islands. Springer, Cham. https://doi.org/10.1007/978-3-319-67177-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-67177-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67176-5
Online ISBN: 978-3-319-67177-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)