Quantifying the progress and interactions of the 11 indicators of Sustainable Development Goal 11 plays a crucial role in improving urban living and promoting urban prosperity. SDG 11, focused on sustainable cities and communities, employs forward-thinking strategies to address challenges arising from urban prosperity and development, such as land scarcity and resource shortages. This paper positions the indicators of SDG 11, analyzing the patterns, trends, dynamics, and issues of urbanization development in Guilin using a combination of geospatial satellite resource data and categorical statistical data. The study introduces a framework and positioning method for assessing sustainable development at the city–county scale, exploring the current state, spatial aggregation, synergies, and trade-offs in the development of Guilin City. The study introduces a framework and positioning method for assessing sustainable development at the city–county scale. Utilizing a localized evaluation system, it explores the developmental status of Guilin City. The application of Moran’s Index observes spatial aggregation among entities. By investigating Spearman’s rank correlation coefficient, it delves into the interplay of synergies and trade-offs within the studied region. Ultimately, it reveals significant disparities in the developmental landscape of the evaluated area, with a comprehensive spatial distribution indicating higher levels of development in the central and western regions and lower levels in the southeastern part. Strengthened cross-leverage and coordination are imperative to address the interconnections and harmonization of the developmental trends of the six synergistic indicators and nine trade-off indicators during the developmental process. The sustainable development of Guilin lays the groundwork for urban planning, construction, conservation, and management, positioning it as a potential model for successful sustainable development practices.

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture.

This study tested image fusion quality aiming at vegetation classification in the Kongquegou scenic location on the southern slope of Huaguo Mountain in Lianyungang, Jiangsu Province, China. Four fusion algorithms were used to fuse WorldView-2 multispectral and panchromatic images: GS (Gram-Schmidt) transform, Ehlers, Wavelet transform, and Modified IHS. The fusion effect was evaluated through visual comparison, quantitative index analysis, and vegetation classification accuracy. The study result revealed that GS and Wavelet transformation produced higher spectral fidelity and better-quality fusion images, followed by Modified IHS and Ehlers. In terms of vegetation classification, for the Wavelet transform, both spectral information and adding spatial structure provided higher accuracy and displayed suitability for vegetation classification in the selected area. Meanwhile, although the spectral features obtained better classification accuracy using the Modified IHS, adding spatial structure to the classification process produced less improvement and a lower robustness effect. The GS transform yielded better spectral fidelity but relatively low vegetation classification accuracy using spectral features only and combined spectral features and spatial structure. Lastly, the Ehlers method’s vegetation classification results were similar to those of the GS transform image fusion method. Additionally, the accuracy was significantly improved in the fused images compared to the multispectral image. Overall, Wavelet transforms showed the best vegetation classification results in the study area among the four fusion algorithms.

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination of multi-source and multi-temporal remote sensing images for vegetation classification is currently a hot topic in the field. In species-rich mountainous environments, this study selected four remote sensing images from different seasons (two aerial images, one WorldView-2 image, and one UAV image) and proposed a vegetation classification method integrating hierarchical extraction and object-oriented approaches for 11 vegetation types. This method innovatively combines the Random Forest algorithm with a decision tree model, constructing a hierarchical strategy based on multi-temporal feature combinations to progressively address the challenge of distinguishing vegetation types with similar spectral characteristics. Compared to traditional single-temporal classification methods, our approach significantly enhances classification accuracy through multi-temporal feature fusion and comparative experimental validation, offering a novel technical framework for fine-grained vegetation classification under complex land cover conditions. To validate the effectiveness of multi-temporal features, we additionally performed Random Forest classifications on the four individual remote sensing images. The results indicate that (1) for single-temporal images classification, the best classification performance was achieved with autumn images, reaching an overall classification accuracy of 72.36%, while spring images had the worst performance, with an accuracy of only 58.79%; (2) the overall classification accuracy based on multi-temporal features reached 89.10%, which is an improvement of 16.74% compared to the best single-temporal classification (autumn). Notably, the producer accuracy for species such as Quercus acutissima Carr., Tea plantations, Camellia sinensis (L.) Kuntze, Pinus taeda L., Phyllostachys spectabilis C.D.Chu et C.S.Chao, Pinus thunbergii Parl., and Castanea mollissima Blume all exceeded 90%, indicating a relatively ideal classification outcome.

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

In complex agroforestry mosaics, similarities in canopy structure and spectral response among tree species constrain medium-resolution peach orchard identification. Using 2023 multi-temporal Sentinel-2 imagery from Ganyu District, Lianyungang, Jiangsu, we assessed NIR–SWIR features, vegetation indices, and phenological parameters for classifying peach orchards, poplar, pine, and willow. First, spectral curves, Jeffries–Matusita (JM) distances, and bootstrap confidence intervals compared separability across leaf-off, early-spring, and leaf-on periods. Subsequently, HANTS reconstructed the time series from which SOS and EOS were extracted; multi-temporal bands, MSI, NDVI, EVI, and NDRE were used in Random Forest schemes. Furthermore, McNemar tests, repeated random splits, and permutation feature importance assessed differences, stability, and feature contributions; the 2023 model was transferred to 2025 imagery. Results showed phenological dependence, with leaf-off and early-spring performance exceeding leaf-on. MSI and NDII showed similar separability and performance, indicating effects from shared B8 and B11 information rather than index formulation. The optimal scheme yielded OA = 94.545%, Kappa = 0.924, peach orchard PA = 97.619%, and UA = 91.111%. Ten splits gave OA = 92.83% ± 2.08% and peach orchard F1-score = 94.34% ± 2.27%. At fixed validation locations, cross-year transfer achieved OA = 90.184% and Kappa = 0.863, indicating temporal transfer potential within the forestland and orchard mask.