Classifying Mountain Vegetation Types Using Object-Oriented Machine Learning Methods Based on Different Feature Combinations

Abstract

Mountainous vegetation type classification plays a fundamental role in resource investigation in forested areas, making it necessary to accurately identify mountain vegetation types. However, Mountainous vegetation growth is readily affected by terrain and climate, which often makes interpretation difficult. This study utilizes Sentinel-2A images and object-oriented machine learning methods to map vegetation types in the complex mountainous region of Jiuzhaigou County, China, incorporating multiple auxiliary features. The results showed that the inclusion of different features improved the accuracy of mountain vegetation type classification, with terrain features, vegetation indices, and spectral features providing significant benefits. After feature selection, the accuracy of mountain vegetation type classification was further improved. The random forest recursive feature elimination (RF_RFE) algorithm outperformed the RliefF algorithm in recognizing mountain vegetation types. Extreme learning machine (ELM), random forest (RF), rotation forest (ROF), and ROF_ELM algorithms all achieved good classification performance, with an overall accuracy greater than 84.62%. Comparing the mountain vegetation type distribution maps obtained using different classifiers, we found that classification algorithms with the same base classifier ensemble exhibited similar performance. Overall, the ROF algorithm performed the best, achieving an overall accuracy of 89.68%, an average accuracy of 88.48%, and a Kappa coefficient of 0.8789.

Identification of Dominant Species and Their Distributions on an Uninhabited Island Based on Unmanned Aerial Vehicles (UAVs) and Machine Learning Models

“…2024, 16, x FOR PEER REVIEW 16 of 22 Topography can differentiate environmental gradients, and results in significant zonal distributions of species at different altitudes, aspects, or slopes [3,30], ultimately improving the OA of the dominant species in the SVM model. We find that with the addition of topographic factors (G3), the OA of dominant species in the SVM model increased by 2.43%, which is relatively similar to that reported by Zhang et al [35] (4.21%), and Fu et al [28] (5.40%). Our results show that the OA of dominant species on the wind- Topography can differentiate environmental gradients, and results in significant zonal distributions of species at different altitudes, aspects, or slopes [3,30], ultimately improving the OA of the dominant species in the SVM model.…”

“…Our results show that the OA of dominant species on the wind- Topography can differentiate environmental gradients, and results in significant zonal distributions of species at different altitudes, aspects, or slopes [3,30], ultimately improving the OA of the dominant species in the SVM model. We find that with the addition of topographic factors (G3), the OA of dominant species in the SVM model increased by 2.43%, which is relatively similar to that reported by Zhang et al [35] (4.21%), and Fu et al [28] (5.40%). Our results show that the OA of dominant species on the windward slope (Figure 9b) decreases by 14.61% compared with that of the whole island (Figure 9a), while an increase by 1.52% is observed on the leeward slope (Figure 9c), using the SVM model with G4 feature combinations to identify the dominant species on two different slopes.…”

“…of four bands in UAVs images are selected, which are commonly used for species classification in machine learning models (Table 2). Vegetation indices (VIs) are mathematical transformations of multi-band reflectance based on comprehensive consideration of relevant spectral signals [28]. We conduct two field surveys before the collection of UAVs images and find that bare land and rock are widely exposed on Anyu Island, particularly on the windward slope due to vegetation degradation (Figure 1).…”

Crop Mapping and Spatio–Temporal Analysis in Valley Areas Using Object-Oriented Machine Learning Methods Combined with Feature Optimization

“…Object-based classification is developed to fully exploit the information contained in high-resolution imagery. This method can utilize spatial, textural, and contextual features of image-objects [16]. Some researchers have demonstrated the advantages and potential of object-based classification even in medium-resolution remote sensing imagery [17].…”