## 4 Methodology
### Decision Tree Modeling and Evaluation  
A decision tree (DT) classifier based on the algorithm proposed by [PERSON] _et al._[47] has been used in this work. The major reason for its choice lies in that the results from this classifier are simple to understand. In fact, in [PERSON] _et al._[24], the DT classifier is described as a \"white box\", because both its structure (formed by several splits) and its final results (terminal leaves) are easy to interpret. In fact, the DT classifier could be implemented directly in eCognition by means of rule sets. DT is a non-parametric rule-based method in which each node makes a binary decision that separates either one class or some of the classes from the remaining classes. Classification through the DT algorithm is increasingly applied in remote sensing data. In fact, it has been the selected classifier in recent OBIA investigations focused on outdoor crop identification [24, 25, 48]. Other advantages of DT classifiers are: (1) they fit well in the OBIA procedure; (2) DTs are computationally fast and make no assumptions regarding the distribution of the data; (3) they are able to take numerous input variables and perform rapid classification without being severely affected by the \"curse of dimensionality\"; and (4) the DT methods provide quantitative measurements of each variable's relative contribution to the classification results, so allowing users to rank the importance of input variables. Once the object-based features corresponding to the 694 reference greenhouses were computed from Landsat 8 time series and WV2 imagery, they were exported by means of eCognition software in order to generate the following three different datasets: (1) 192 feature dataset (56 mean values; 56 standard deviation (SD), 56 VIs and 24 GLCMs) from Landsat 8 time series; (2) 46 features dataset (8  
Figure 3: Segmentations for a detailed area of \(225\) m \(\times\) 240 m showing orthoimages as the background: (**a**) manual digitizing on WV2 pan-sharpened orthoimage MS RGB; (**b**) WV2 MS segmentation using an enhanced resolution of 0.8 m per pixel; (**c**) Landsat 8 pan-sharpened orthoimage with the original GSD (15 m); (**d**) Landsat 8 pan-sharpened orthoimage using 0.9375 m GSD. means, 8 SD, 24 VIs and 6 GLCMs) from the WV2 MS orthoimage; and (3) the combination of all 238 features from both satellites. The StatSoft STATISTICA 10 software was used for the classification and regression decision tree analysis (StatSoft Inc., Tulsa, OK, United States). Briefly, the DT algorithm seeks to split the data into segments as homogeneous as possible with respect to the dependent or response variable. In this case, the categorical dependent variable was the kind of crop that had been grown under plastic coverings in the reference greenhouses during autumn 2013 (Table 1). The DT node-splitting rule was the Gini index, which is a measure of impurity for a given node [49]. Its application attempts to maximize the homogeneity of the child nodes with respect to the values of the dependent variable. Cross-validation provided an objective measure of quality for the computed DT model [25, 50, 51]. The experimental design was implemented within the STATISTICA environment through a stratified 10-fold cross-validation procedure, leading to one confusion matrix for each fold. In 10-fold cross-validation, the data are randomly partitioned into 10 equally-sized samples. Of the 10 samples, a single sample is used to validate the model, and the remaining 9 samples are used to train the model. The cross-validation process is then repeated 10 times, with each of the 10 samples used exactly once as the validation data. A final confusion matrix was obtained by summing all of them. The classification accuracy assessment in this work was finally based on this final error matrix provided by cross-validation [52]. Hence, the accuracy measures computed were user's accuracy (UA), producer's accuracy (PA) and overall accuracy (OA).