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A methodology to highlight changes in the landscape based on satellite image classification has been developed
involving unsupervised and supervised approaches.
With past acquisitions, ground truth data are in general not known, therefore the classification can only be unsupervised.
These classifications provide labels but not surface types. The main difficulty lies in the interpretation of these classes.
An automatic interpretation method has been developed to allocate semantics to classes thanks to a radiometric value
catalogue. However, it requires radiometrically comparable images. After radiometric correction, the images are not free
from defects; this is why a normalization method has been developed.
We propose a specific methodology to evaluate changes consisting in regrouping classes of the same theme, smoothing
and eroding contours without taking "mixels" into account and comparing the classified images to provide statistics and
image changes. The different steps of the process are essential to avoid false changes and to quantify land cover change
with a high degree of accuracy. Various statistical results are given: changes or no changes, types of changes, and crop
rotations over several years.
Land use /cover change (LUCC) can provide an estimate of carbon capture and storage. Reforestation, changing land use
and best practices can increase carbon sequestration in biomass and soils for a period of several decades, which may
constitute a significant contribution to the fight against the greenhouse effect. Deforestation, conversely, can lead to
significant levels of CO2 emission.
By application to the South-West region of Toulouse, we observe significant land cover changes over 11 years (1991-
2002). The crop rotations are given for 4 years (year per year 2002-2005).
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