*5.1. Applicability Assessment of BRDF Characteristic Types*

For promoting the realization of the relationship between the BRDF and plant leaves and vegetation canopy structure parameters, the following subsections are given to discuss the role of the ground object BRDF characteristic parameters in the fine classification of vegetation.

(1) Performance of BRDF\_0◦:

Table 4 shows that compared with the classification results based on the DOM, the classification accuracy based on BRDF\_0◦ was greatly improved. Figure 7 shows the producer's accuracy and user's accuracy of each type of land feature using two classification features.

**Figure 7.** Vegetation classification accuracy based on DOM and BRDF\_0◦.

Figure 7 indicates that BRDF\_0◦ was instrumental in the distinction among different objects. The vertical reflectance data of DOM were obtained using statistical methods. However, the method of using a semi-empirical model to construct the BRDF and invert the vertically observed reflectance combines the advantages of an empirical model and a physical model. Although the model parameters are empirical parameters, they have certain physical significance. Consequently, the observation angle of the ground objects is unified with the vertical observations through the BRDF model, which weakens the reflection characteristics of the same type of vegetation affected by the observation angle difference. Compared with the classification results obtained using DOM data, the classification accuracy obtained using BRDF\_0◦ was greatly improved, but the recognition accuracy of dirt roads, peach trees, and ash trees was still very low. The producer's accuracy of weeds, soybeans, and maize improved to greater than 90%, but the user's accuracy improved only slightly, which indicates that the results for these three types of land features were overclassified.

(2) Hot and dark spot reflectance signatures:

Six feature sets were used to classify vegetation, namely, the vertical observation direction (BRDF\_0◦); hot spot observation direction (BRDF\_HS); dark spot observation direction (BRDF\_DS); vertical observation direction and hot spot direction (BRDF\_0◦+HS); vertical observation direction and

dark spot direction (BRDF\_0◦+DS); and vertical observation direction, hot spot direction, and dark spot direction (BRDF\_0◦+HS+DS). The overall classification accuracy and kappa coefficients of the six feature sets are shown in Figure 8. The classification accuracy of BRDF\_0◦+HS+DS was the highest at about 77%. The classification effect of vegetation types using BRDF\_DS was slightly worse than that using BRDF\_0◦, while the classification effect of vegetation types using BRDF\_HS was better than that using BRDF\_0◦. The results show that the hot spot reflectance signature had an excellent effect in the recognition of complex vegetation types. This was because the reflection characteristics of different objects in the direction of dark spots were lower than those in the direction of hot spots, and the hot spot effects between crops and tree species were quite different. The producer and user accuracies of each type of land feature are shown in Figure 9.

**Figure 8.** Overall accuracy and kappa coefficient of vegetation classification based on hot and dark spot characteristics.

**Figure 9.** *Cont.*

**Figure 9.** Producer's accuracy and user's accuracy for each vegetation type based on hot and dark spot characteristics.

From Figure 9, the combined application of dark spot and hot spot directional reflectance features improved the classification accuracy. The classification results for soybean, peach trees, mulberry trees, and ash trees using BRDF\_HS were more accurate than those using BRDF\_DS. In contrast, the ground objects with a high accuracy included dirt roads and shadows based on the reflection features in the dark spot direction. The research shows that the tree structure features had a high sensitivity in the hot spot direction.

(3) Multi-angle reflectance characteristics of the observed principal plane and cross-principal plane: Four feature sets were used to classify the vegetation, namely the reflectance values from the vertical observation direction (BRDF\_0◦), principal plane (BRDF\_PP), cross plane (BRDF\_CP), principal and cross planes (BRDF\_PP+CP). The corresponding classification results are shown in Figure 10. The classification accuracy using BRDF\_PP+CP was the highest (OA = 88%). The reflectance characteristics from the principal plane were more conducive to the classification of complex vegetation species than those in the vertical main plane. The producer's accuracy and user's accuracy of each type of land feature are shown in Figure 11.

**Figure 10.** Overall accuracy and kappa coefficient of the vegetation classification based on the multi-angle reflectance characteristics for the observed principal plane and cross-principal plane.

**Figure 11.** Producer's accuracy and user's accuracy of each vegetation type based on the multi-angle reflectance characteristics for the observed principal plane and cross-principal plane.

Figure 11 shows that the combined application of reflectance characteristics from the principal and cross planes could improve the classification accuracy. The joint classification results for the reflectance characteristics in the two main planes show that the producer's accuracy of other land features was greater than 90%, the producer's accuracy of peach seedlings was approximately 52%, and the peach seedlings were misclassified as soybean and ash trees.

(4) BRDF model parameters:

Three feature sets were used to classify vegetation, namely the reflectance from the vertical observation direction (BRDF\_0◦), BRDF model parameters (BRDF\_3f), and reflectance from the vertical observation direction and BRDF model parameters (BRDF\_0◦+3f). The corresponding classification results are shown in Figure 12. The classification accuracy of BRDF\_0◦+3f was the highest (OA = 78%). The proportions of uniform reflection, bulk reflection, and geometric optical reflection were expressed as parameters. The addition of model parameters increased the descriptive information for the physical structure of vegetation, which contributed to the classification. The producer's and user's accuracies of each type of land feature are shown in Figure 13.

**Figure 12.** Overall accuracy and kappa coefficient of vegetation classification based on the BRDF model parameters.

**Figure 13.** Producer's accuracy and user's accuracy of each vegetation type based on the BRDF model parameters.
