Improving satellite images classification using remote and ground data integration by means of stochastic simulation

A methodology is proposed, to assess land surface cover classification using a geostatistical methodology of stochastic simulation, direct sequential cosimulation, to combine field observations with remotely sensed data classified with the classical algorithm of maximum likelihood classification. This procedure has two main advantages: (1) incorporation of a spatial continuity statistics; and (2) integration of different scales of information, contained in polygons (training areas) and point information (field observations), which also involves different qualities of information that is less reliable and more reliable, respectively. Moreover, this methodology allows production not only of a classified map, but also of maps of occupation proportions and of uncertainty for each thematic class. Local co-regionalization models are applied to account for local differences in both field data availability and distribution, and the correlation between these hard data and the classified satellite images as soft data. The methodology is based on two criteria: the influence of the hard data dependent on their availability and proportional to their proximity; and the influence of the soft data dependent on their local correlation to the hard data. The method is applied to a study of four economically important forest tree species on the Setu´bal Peninsula (south of Lisbon, Portugal). The results show more contiguous forest covers, i.e. more spatial contiguity, than the classical classification. In comparison to a contemporary field inventory, the proposed method improved forest cover estimations, showing a difference of only 3%.