1 Introduction
2 Proposed approach
2.1 Step 1: identifying parameters and structure of LCC prediction model
2.1.1 Choice of parameters
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Uncertainty sources of spectral parameters Several studies investigated effects of spectral parameters [28]. Among these effects, we list: spectral reflectance of the surface (S1), sensor calibration (S2), effect of mixed pixels (S3), effect of a shift in the channel location (S4), pixel registration between several spectral channels (S5), atmospheric temperature and moisture profile (S6), effect of haze particles (S7), instrument’s operation conditions (S8), atmospheric conditions (S9), as well as by the stability of the instrument itself characteristics (S10).
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Uncertainty sources of texture parameters Among these sources, we list: the spatial interaction between the size of the object in the scene and the spatial resolution of the sensor (S11), a border effect (S12), and ambiguity in the object/background distinction (S13).
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Uncertainty sources of shape parameters Uncertainty related to shape parameters can rely to the following factors [28]: accounting for the seasonal position of the sun with respect to the Earth (S14), conditions in which the image was acquired changes in the scene’s illumination (S15), atmospheric conditions (S16), and observation geometry (S17).
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Uncertainty sources of NDVI Among factors that affect NDVI, we can list: variation in the brightness of soil background (S18), red and NIR bands (S19), atmospheric perturbations (S20), and variability in the sub-pixel structure (S21).
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Uncertainty sources of climate parameters According to [29], uncertainty sources related to climate parameters can be: atmospheric correction (S22), noise of the sensor (S23), land surface emissivity (S24), aerosols and other gaseous absorbers (S25), angular effects (S26), wavelength uncertainty (S27), full-width half-maximum of the sensor (S28), and bandpass effects (S29).
2.1.2 Description of model structure
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Similarity measure step:
Distance between states (\(d(S_{t},S_{t_1})\ge 0.9\) indicates a higher similarity between the query and the retrieved states). In addition, similarity measure between states is based on time assumption. -
Spatiotemporal change tree building
step:
The aim of this step is to determine the confidence degrees and the percentage of changes of the model between two dates and for different land-cover types. The confidence degree of changes is achieved by a fuzzy decision tree (fuzzy ID3). This method is based on a number of assumptions such as: the proportion of a data set of land-cover type, the size of a data set, etc. The percentage of changes is achieved by computing the distances between two states and the centroid of the classes.
2.2 Step 2: propagating the uncertainty
2.2.1 Basics of possibility theory
2.2.2 Propagation of parameter uncertainty
2.2.3 Propagation of structural uncertainty
2.3 Step 3: performing the sensitivity analysis
2.4 Step 4: constructing the knowledge base
3 Experimental results
3.1 Case study 1
3.1.1 Description of the study area and data
3.1.2 Results of uncertainty propagation
Water (%) | Urban (%) | Forest (%) | Bare soil (%) | Vegetation (%) | |
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Predicted changes in 2025 | 1.9 | 37.4 | 39.31 | 26.95 | 26.7 |
Output of proposed model | 1.5 | 23.18 | 35.97 | 22.87 | 20.08 |
Real changes in 2011 | 1.7 | 21.4 | 36.1 | 24.1 | 16.7 |
3.1.3 Results of sensitivity analysis
3.1.4 Results of LCC prediction maps
3.1.5 Evaluation of the proposed approach
Water (%) | Urban (%) | Forest (%) | Bare soil (%) | Vegetation (%) | |
---|---|---|---|---|---|
Proposed approach | 1.5 | 23.18 | 35.97 | 22.87 | 20.08 |
Approach applied to model in [40] | 1.5 | 25.32 | 34.98 | 20.03 | 16.24 |
Real changes in 2011 | 1.7 | 21.4 | 36.1 | 24.1 | 16.7 |
3.2 Case study 2
3.2.1 Description of the study area and data
3.2.2 Results of uncertainty propagation
Urban (%) | Agriculture (%) | Water (%) | Desert (%) | |
---|---|---|---|---|
Output of proposed model | 15.63 | 13.80 | 0.01 | 4.03 |
Real changes in 2014 | 17.32 | 13.00 | 0.02 | 5.00 |
Urban (%) | Agriculture (%) | Water (%) | Desert (%) | |
---|---|---|---|---|
Predicted changes in 2025 | 20.16 | 14.72 | 0.03 | 6.11 |
Real changes in 2014 | 17.32 | 13.00 | 0.02 | 5.00 |
3.2.3 Results of sensitivity analysis
3.2.4 Results of LCC prediction maps
Urban (%) | Agriculture (%) | Water (%) | Desert (%) | |
---|---|---|---|---|
Proposed approach | 15.63 | 13.80 | 0.01 | 4.03 |
Approach applied to model in [40] | 14.93 | 13.61 | 0.01 | 5.92 |
Real changes in 2014 | 17.32 | 13.00 | 0.02 | 5.00 |