The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R² and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively; those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework.

Accurate, cost-efficient vegetation mapping is critical for managing afforestation projects, particularly in resource-limited areas. This study used a consumer-grade RGB unmanned aerial vehicle (UAV) to evaluate the optimal spatial and temporal resolutions (leaf-off and leaf-on) for precise, economically viable tree species mapping. This study conducted in 2024 in Kasho, Bannu district, Pakistan, using UAV missions at multiple altitudes captured high-resolution RGB imagery (2, 4, and 6 cm) across three sampling plots. A Support Vector Machine (SVM) classifier with 5-fold cross-validation was assessed using accuracy, Shannon entropy, and cost–benefit analyses. The results showed that the 6 cm resolution achieved a reliable accuracy (R² = 0.92–0.98) with broader coverage (12.3–22.2 hectares), while the 2 cm and 4 cm resolutions offered higher accuracy (R² = 0.96–0.99) but limited coverage (4.8–14.2 hectares). The 6 cm resolution also yielded the highest benefit–cost ratio (BCR: 0.011–0.015), balancing cost-efficiency and accuracy. This study demonstrates the potential of consumer-grade UAVs for affordable, high-precision tree species mapping, while also accounting for other land cover types such as bare earth and water, supporting budget-constrained afforestation efforts.

Levee-like features are critical for water regulation in arid regions, but their automated extraction from remote sensing imagery remains challenging due to the scarcity of high-resolution labeled datasets. This data descriptor introduces a high-resolution remote sensing image dataset for semantic segmentation of levee-like features. The dataset covers 11 regions across Xinjiang and Gansu Province in northwestern China. It includes 459 single-phase base images with a spatial resolution of 0.50 m, as well as multi-temporal images of the same regions captured at different times. All annotations were manually drawn in polygon mode using the LabelMe tool and converted into YOLO format label files. The dataset adopts a strict strategy to prevent data leakage: first, training, validation and test sets are divided based on single-phase images, and then multi-temporal images are allocated to the corresponding data subsets according to their spatial locations. The dataset has been publicly released on the ScienceDB platform under the CC BY 4.0 license. YOLO and U-Net segmentation experiments on the test set achieved promising results, demonstrating its usability for levee-like feature segmentation. This dataset can provide fundamental data support for research on levee-like feature extraction, remote sensing change detection, and cross-region model transfer learning.