The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 383–390, 2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 383–390, 2015

  29 Apr 2015

29 Apr 2015

A proper Land Cover and Forest Type Classification Scheme for Mexico

S. Gebhardt1, P. Maeda1, T. Wehrmann1, J. Argumedo Espinoza2, and M. Schmidt1 S. Gebhardt et al.
  • 1National Commission for the Knowledge and Use of Biodiversity (CONABIO), Liga Periférico-Insurgentes Sur 4903, Parques del Pedregal, 14010 Tlalpan, Mexico City, Mexico
  • 2National Institute for Statistics and Geography (INEGI), Héroe de Nacozari 2301 Sur, Jardines del Parque, 20270 Aguascalientes, Aguascalientes, Mexico

Keywords: Land cover, Forest type, Landsat time series, Automatic classification, Wall-2-wall, REDD+

Abstract. The imminent implementation of a REDD+ MRV system in Mexico in 2015, demanding operational annual land cover change reporting, requires highly accurate, annual and high resolution forest type maps; not only for monitoring but also to establish the historical baseline from the 1990s onwards. The employment of any supervised classifier demands exhaustive definition of land cover classes and the representation of all classes in the training stage. This paper reports the process of a data driven class separability analysis and the definition and application of a national land cover classification scheme. All Landsat data recorded over Mexico in the year 2000 with cloud coverage below 10 percent and a national digital elevation model have been used. Automatic wall-2-wall image classification has been performed trained by national reference data on land use and vegetation types with 66 classes. Validation has been performed against field plots of the national forest inventory. Groups of non-separable classes have subsequently been discerned by automatic iterative class aggregation. Class aggregations have finally been manually revised and modified towards a proposed national land cover classification scheme at 4 levels with 35 classes at the highest level including 13 classes for primary temperate and tropical forests, 2 classes for secondary temperate and tropical forest, 1 for induced or cultivated forest, as also 8 different scrubland classes. The remaining 11 classes cover agriculture, grassland, wetland, water bodies, urban and other vegetation land cover classes. The remaining 3 levels provide further hierarchic aggregations with 14, 10, and 8 classes, respectively. Trained by the relabeled training dataset wall-2-wall classification towards the 35 classes has been performed. The final national land cover dataset has been validated against more than 200,000 polygons randomly distributed all over the country with class labels derived by manual interpretation. The agreement for all 35 classes at level 4 was 71%. Primary forest classes have been identified with accuracies between 60% and 83%. Secondary forest classes rated only 50% finding major confusion with the primary forest classes. Accuracies over the scrubland classes have been calculated between 60% and 90%. Agreements for aggregated temperate and tropical forest classes was 85% and 80%, respectively. Separation of forest and non-forest has been achieved with an agreement of 87%.