Renxu Yang

Point cloud filtering plays a crucial role in ground point extraction in urban environments. It can effectively distinguish ground points from object points, reduce data redundancy, and improve processing efficiency, providing accurate foundational data for urban 3D modeling, environmental monitoring, and intelligent management. However, current point cloud filtering algorithms have significant limitations in multi-scale structural complexity and sparse-to-dense balancing, hindering accurate extraction in complex urban environments. To address those issues, this paper proposes a point cloud filtering algorithm based on cloth simulation and progressive TIN densification (CAP). The algorithm first applies the cloth simulation filtering (CSF) algorithm to perform an initial filtering of the point cloud data and extract the initial ground points. It then constructs a TIN model based on the initial ground points, incorporating the concept of the progressive TIN densification (PTD) algorithm. Through point-by-point thresholding, the ground and object points are further refined and optimized. In the urban public point cloud datasets provided by ISPRS, the average total error is 5.90% after CAP algorithm filtering. For 12 sets of point cloud data in the North Rhine–Westphalia experimental sample area, the results show that the CAP algorithm achieves an average total error of 2.86%, which is 2.01% lower than the PTD algorithm and 0.60% lower than the CSF algorithm. The average Kappa coefficient is 94.04%, which is an improvement of 4.17% and 1.22% over the PTD and CSF algorithms, respectively. This study demonstrates that the CAP algorithm exhibits superior accuracy and adaptability for point cloud filtering tasks in urban environments, with significant application potential.

Accurate mapping of rice planting areas is of great significance in terms of food security and market stability. However, the existing research into high-resolution rice mapping has relied heavily on fine-scale temporal remote sensing image data. Due to cloud occlusion and banding problems, data extraction from Landsat series remote sensing images with medium spatial resolution is not optimal. Therefore, this study proposes a rice mapping method (LR) using Google Earth Engine (GEE), which uses Landsat images and integrates automatic generation of training samples and a machine learning algorithm, with the assistance of phenological methods. The proposed LR method initially generated rice distribution maps based on phenology, and 300 sample points were selected for meta-identification of rice images via an enhanced pixel-based phenological feature composite method (Eppf-CM) utilizing high-resolution imagery. Subsequently, the inundation frequency (F) and an improved sample point statistical feature, i.e., the ratio of change amplitude of LSWI to NDVI (RCLN), were introduced to combine Eppf-CM with combined consideration of vegetation phenology and surface water variation (CCVS) methods, to automate the generation of training data with the aid of phenology. The sample data were optimized by an alternate iterative method involving extraction of neighborhood information. Finally, a random forest (RF) probabilistic model trained by integrating data from different phenological periods was used for rice mapping. To test its performance, we mapped rice distribution at 30 m resolution (“LR_Rice”) across Heilongjiang Province, China from 2010 to 2022, with annual overall accuracy (OA) and Kappa coefficients greater than 0.97 and 0.95, respectively, and compared them with four existing rice mapping products. The spatial distribution characteristics of rice cultivation extracted by the LR algorithm were accurate and the performance was optimal. In addition, the extracted area of LR_Rice was highly consistent with the agricultural statistical area; the coefficient of determination R² was 0.9915, and the RMSE was 22.5 kha. The results show that this method can accurately obtain large-scale rice planting information, which is of great significance for food security, water resource management, and environmentally sustainable development.

The detection and counting of Camellia oleifera trees are important parts of the yield estimation of Camellia oleifera. The ability to identify and count Camellia oleifera trees quickly has always been important in the context of research on the yield estimation of Camellia oleifera. Because of their specific growing environment, it is a difficult task to identify and count Camellia oleifera trees with high efficiency. In this paper, based on a UAV RGB image, three different types of datasets, i.e., a DOM dataset, an original image dataset, and a cropped original image dataset, were designed. Combined with the YOLOv8 model, the detection and counting of Camellia oleifera trees were carried out. By comparing YOLOv9 and YOLOv10 in four evaluation indexes, including precision, recall, mAP, and F1 score, Camellia oleifera trees in two areas were selected for prediction and compared with the real values. The experimental results show that the cropped original image dataset was better for the recognition and counting of Camellia oleifera, and the mAP values were 8% and 11% higher than those of the DOM dataset and the original image dataset, respectively. Compared to YOLOv5, YOLOv7, YOLOv9, and YOLOv10, YOLOv8 performed better in terms of the accuracy and recall rate, and the mAP improved by 3–8%, reaching 0.82. Regression analysis was performed on the predicted and measured values, and the average R² reached 0.94. This research shows that a UAV RGB image combined with YOLOv8 provides an effective solution for the detection and counting of Camellia oleifera trees, which is of great significance for Camellia oleifera yield estimation and orchard management.

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO₂ stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO₂ stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO₂ stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO₂ stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO₂ stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO₂ stress. (2) The 1.2-order derivative dual-band index (RVI (R₇₂₀, R₅₂₂)) constructed by combining the sensitive spectral bands of the CO₂ response of winter wheat leaves achieved a high-precision estimation of the LCC under CO₂ stress conditions (R² = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R² = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO₂ impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R² = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO₂ stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO₂ microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model’s accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO₂ microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO₂ microleakage stress showed a “red shift” of the green peak and a “blue shift” of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO₂ at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R² of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO₂ trace-leakage stress and to dynamically monitor CO₂ trace-leakage stress in crops.

Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km² and 2293 km², while the maximum nighttime differences were 484 km² and 265 km². Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands.