Updating land cover classification using a rule‐based decision system

This paper proposes a land cover classification methodology in agricultural contexts that provides satisfactory results with a single satellite image per year acquired during the growing period. Our approach incorporates ancillary data such as cropping history (inter‐annual crop rotations), context (altitude, soil type) and structure (parcels size and shape) to compensate for the lack of radiometric data resulting from the use of a single image. The originality of the proposed method resides in the three successive steps used: S1: per‐pixel classification of a single SPOT XS image with a restricted number of land cover classes (RL) chosen to ensure good accuracy; S2: conversion of RLs into a per‐parcel classification system using ancillary parcel boundaries; and S3: parcel allocation using exhaustive land cover classes (EL) and its refinement through the application of decision rules. The method was tested on a 120 km2 area (Sousson river basin, Gers, France) where exhaustive knowledge of land cover for two successive years allowed complete validation of our method. It allocated 87% of the parcels with a 75% accuracy rate according to the exhaustive list (EL). This is a satisfactory result obtained with one SPOT XS image in a small agricultural parcel context.