Fig. 1
Fig. 2
Fig. 3
The area of crops and the results of accuracy assessment_
| Crop type | Area] | Number of reference polygons | Omission error | Commission error |
|---|---|---|---|---|
| [ha] | [−] | [%] | ||
| Wheat | 21,803 | 214 | 6.59 | 14.01 |
| Alfalfa | 4892 | 137 | 17.02 | 15.59 |
| Fruit tree | 5561 | 162 | 8.43 | 9.87 |
| Vegetable | 2816 | 99 | 7.36 | 11.11 |
| Kappa Coefficient = 82.84% | Overall Accuracy = 87.41% | |||
Description of landscape metrics used in this research, adapted from Leitão et al_ (2012)_
| Landscape metric | Acronym | Aspect of pattern | Range [unit] |
|---|---|---|---|
| Number of patches | NP | Composition | NP > 0, without limit [−] |
| Mean patch size | MPS | Composition | MPS > 0, without limit [ha] |
| Mean shape index | MSI | Structure | MSI > 1, without limit [−] |
| Perimeter-to-area ratio | PARA | Structure | PARA > 1, without limit [−] |
| Euclidian nearest-neighbor distance | ENN | Configuration | ENN > 0, without limit [m] |
The classification scheme is used to recognise crop types_
| Crop type | Object code | Final code | ||
|---|---|---|---|---|
| L1 | L2 | L3 | ||
| Wheat | 1 | 1 | 0 | 110 |
| Alfalfa | 1 | 1 | 1 | 111 |
| Fruit tree | 0 | 1 | 1 | 011 |
| Vegetable | 0 | 0 | 1 | 001 |