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学者姓名：樊仲谋
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Abstract ：
Remote sensing-based forest tree species classification plays a crucial role in forest resource management and ecological monitoring. In recent years, hyperspectral reconstruction technology has emerged as an effective approach to enhance the spectral resolution of multispectral imagery. By leveraging the advantages of both multispectral and hyperspectral data, this technique offers the potential to improve tree species classification accuracy. However, the application of hyperspectral reconstruction methods in forestry remains limited, with existing approaches often suffering from insufficient spectral information utilization and a strong reliance on large-scale datasets. To address the specific demands of hyperspectral reconstruction in forestry, this study proposes HSCNN-Unet, a model that employs U-Net as its overall architecture and integrates HSCNN-D for spectral reconstruction. Additionally, a Spectral Attention mechanism and a feature fusion module are incorporated to enhance spectral feature extraction and improve data reconstruction capability. We evaluated the proposed model on multiple sample sets and in comparison, with various baseline models. Experimental results show that HSCNN-Unet demonstrates superior spectral reconstruction performance on smaller-sized samples compared to all baseline models (MPSNR values of 51.9587 and 49.1999, MSSIM of 0.9954, and SAM values of 0.0141 and 0.0213), while still maintaining strong reconstruction performance on larger-sized samples. In addition, the effectiveness of the reconstructed hyperspectral data in classification tasks was also verified. Furthermore, the model was applied to the spectral reconstruction of Sentinel-2 imagery and tested in combination with dominant tree species classification in the study area. The experimental results show that compared to the classification performance of the original multispectral data, the reconstructed hyperspectral data achieved improvements of 6.72%, 6.71%, and 8.88% in Weighted F1, Overall Accuracy, and Kappa, respectively. Other metrics also improved by 8.95%-9.20%. These results demonstrate the excellent reconstruction performance and practical classification utility of HSCNN-Unet, confirming its real-world application value in forest resource monitoring.
Keyword ：
HSCNN-D HSCNN-D Hyperspectral reconstruction Hyperspectral reconstruction Remote sensing Remote sensing Tree identification Tree identification
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| GB/T 7714 | Lin, Zhiping , Yuan, Yuhao , Zang, Qun et al. Tree species classification via a two-stage deep learning framework: integration of enhanced spectral reconstruction with classifiers [J]. | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2025 , 46 (17) : 6350-6376 . |
| MLA | Lin, Zhiping et al. "Tree species classification via a two-stage deep learning framework: integration of enhanced spectral reconstruction with classifiers" . | INTERNATIONAL JOURNAL OF REMOTE SENSING 46 . 17 (2025) : 6350-6376 . |
| APA | Lin, Zhiping , Yuan, Yuhao , Zang, Qun , Fan, Zhongmou . Tree species classification via a two-stage deep learning framework: integration of enhanced spectral reconstruction with classifiers . | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2025 , 46 (17) , 6350-6376 . |
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This study introduces the hierarchical pixel-wavelength fusion network (HPWF-Net), a novel hyperspectral reconstruction framework designed to address the challenges of tree species classification and spectral reconstruction in complex forest ecosystems. The proposed method employs a hierarchical pixel-wise and target-wavelength-wise reconstruction strategy, progressively recovering hyperspectral images by dynamically selecting and refining target wavelengths at each layer. HPWF-Net utilizes an enhanced U-Net architecture with multilevel loss supervision and convolutional consistency constraints, enabling accurate reconstruction by supervising intermediate encoder outputs, convolutional regressions, and final decoder predictions. This design preserves consistency with the original multispectral (MS) input while enhancing spectral fidelity and robustness. The model is trained on hyperspectral data from the NEON dataset and applied to Sentinel-2 MS imagery of the Daiyun Mountain National Nature Reserve. Experimental results demonstrate that the reconstructed hyperspectral data improve tree species classification accuracy by 5.9% compared to the original Sentinel-2 MS data. This work contributes to hyperspectral reconstruction and fine-grained ecological monitoring, offering a practical solution for remote sensing applications in forest ecosystems. Furthermore, we offer a Python-based package that enables to replicate our results at https://github.com/DarkFlameMaster02/Hierarchical-Pixel-Wavelength-Fusion-Network-for-Progressive-Hyperspectral-Image-Reconstruction
Keyword ：
Accuracy Accuracy Biological system modeling Biological system modeling Data models Data models Ecosystems Ecosystems Forestry Forestry Hierarchical reconstruction Hierarchical reconstruction hyperspectral image reconstruction hyperspectral image reconstruction Hyperspectral imaging Hyperspectral imaging Image reconstruction Image reconstruction neural network for remote sensing neural network for remote sensing Spatial resolution Spatial resolution Training Training Vegetation Vegetation
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| GB/T 7714 | Yuan, Yuhao , Zang, Qun , Lin, Zhiping et al. Hierarchical Pixel-Wavelength Fusion Network for Progressive Hyperspectral Image Reconstruction [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | Yuan, Yuhao et al. "Hierarchical Pixel-Wavelength Fusion Network for Progressive Hyperspectral Image Reconstruction" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | Yuan, Yuhao , Zang, Qun , Lin, Zhiping , Liu, Jincheng , Fan, Zhongmou . Hierarchical Pixel-Wavelength Fusion Network for Progressive Hyperspectral Image Reconstruction . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
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Airborne Laser Scanner (ALS), Terrestrial Laser Scanning (TLS), and Backpack Laser Scanning (BLS) exhibit distinct data acquisition perspectives and spatial distribution characteristics, leading to non-uniform point cloud distribution and localized data gaps. These limitations may compromise the accuracy of tree species classification. Previous classification studies focused on the quality of data itself, without investigating the impact of data absence on classification outcomes. Therefore, to verify the effect of partial point cloud absence on model classification results, this research selected 12 tree species (low density data) from the Minjiang River Estuary Wetland Park and Sanjiangkou Ecological Park in Fuzhou City, and 8 tree species (high density data) from Fuzhou University City as research subjects. The study employed a random mask method to generate datasets with varying scales of under-canopy point cloud absence corresponding to ALS and lateral point cloud absence corresponding to BLS and TLS, using PointNet+ + as the classification model for experiments. The research found that high-density point cloud data demonstrated significant robustness, with the impact of various local absences merely fluctuating around the baseline. For low-density data, the absence of lower point cloud led to a decrease in average accuracy ranging from 1.89 % to 7.70 %, while partial absence of lateral point cloud in some cases actually improved classification performance, with a maximum increase of 6.08 %. In random combination tests simulating real-world scenarios, the classification accuracy only decreased by approximately 5 %, demonstrating the model's good generalization ability for mixed missing data. The results indicate that data absence caused by different collection equipment has a relatively limited impact on classification effectiveness, and incomplete point cloud data can still be utilized for tree species classification tasks.
Keyword ：
Airborne lidar Airborne lidar Backpack lidar Backpack lidar Deep learning Deep learning Uneven distribution Uneven distribution
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| GB/T 7714 | Jiang, Xinhao , Zhang, Wenxuan , Wei, Jinhuang et al. Impact of partial point cloud deficiency on tree species classification in Fuzhou, China [J]. | URBAN FORESTRY & URBAN GREENING , 2025 , 112 . |
| MLA | Jiang, Xinhao et al. "Impact of partial point cloud deficiency on tree species classification in Fuzhou, China" . | URBAN FORESTRY & URBAN GREENING 112 (2025) . |
| APA | Jiang, Xinhao , Zhang, Wenxuan , Wei, Jinhuang , Fan, Zhongmou . Impact of partial point cloud deficiency on tree species classification in Fuzhou, China . | URBAN FORESTRY & URBAN GREENING , 2025 , 112 . |
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为了解决高校课程思政建设过程中出现的与思想政治理论课同向同行的各类课程的思政教育元素同质化内容所占比例过高导致的“超限效应”问题，通过问卷调查，对高校思想政治教育教学中“超限效应”的影响进行分析并提出应对策略，以期提高大学生的学习效率和高校的思政教育效果。问卷调查主要围绕“对思政教学方式的接受程度”“课堂上是否有思政教学案例重复使用现象”“是否需要避免思政教学案例的重复使用”“能够容忍的思政教学案例重复使用限度”等4个主题进行，调查对象为2020年9月—2022年6月期间福建农林大学交通与土木工程学院的在校研究生和4个本科教学班的学生。问卷调查结论为：案例教学方式得到学生的普遍认可，高校应明确继续采用并优化案例教学方式开展思想政治教育的方向；思政教学案例重复使用普遍存在，但可通过优化内容和创新讲授角度等增加新的学习点；思政教学案例除了考虑质量和适用性外，提高新鲜度仍是不可回避的必须解决的问题；经典型思政教学案例内容的新鲜度和有趣性有更强烈的需求，时事热点型思政教学案例短期内少量重复使用更容易被接受。为了应对高校思想政治教育教学中“超限效应”的影响，教师需要在思政教学案例的选择、教学角度和案例更新等方面下更多工夫。为此，提出建设智慧课程案例管理系统的应对策略。该系统主要包括用户管理、课表信息管理、案例信息管理、超限管理（提醒）、学生评价等功能模块，旨在帮助各类课程的任课教师更加高效地选择和管理思政教学案例。其中，案例信息管理功能可为教师提供案例的使用频率和受欢迎程度以及学生对案例的评价等方面的统计数据和报告，超限管理（提醒）功能可对案例的重复使用情况进行检测以及将上传到系统的新案例与现有的案例库进行比对，学生评价功能可为学生提供反馈案例使用情况和改进建议的专门路径。实践证明，该系统在减少“超限效应”和增强思政教育教学效果方面具有实际价值，但仍需完善，如增设监控功能、预警系统、案例新颖度的评价机制、案例推荐系统、案例共享平台等；同时，关注研究生思政教育教学与本科生的差异性以及两者的衔接与整合。为此，本研究认为除了建设智慧课程案例管理系统之外，还可以在以下方面进行实践探索。一是按专业统筹规划思政教育元素和思政教学案例，二是建设一个能够实时更新思政教育教学资源的系统，三是构建支持教师间协作和交流的机制，从而增强高校思政教育的连贯性、深度和广度。
Keyword ：
思想政治教育 思想政治教育 思政教学案例 思政教学案例 智慧课程案例管理系统 智慧课程案例管理系统 案例教学 案例教学 超限效应 超限效应 问卷调查 问卷调查 高校 高校
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| GB/T 7714 | 樊仲谋 , 危金煌 , 胡喜生 et al. 高校思想政治教育教学中“超限效应”的影响及对策研究 [J]. | 中国林业教育 , 2025 , 43 (01) : 12-18 . |
| MLA | 樊仲谋 et al. "高校思想政治教育教学中“超限效应”的影响及对策研究" . | 中国林业教育 43 . 01 (2025) : 12-18 . |
| APA | 樊仲谋 , 危金煌 , 胡喜生 , 周成军 , 阮云凯 . 高校思想政治教育教学中“超限效应”的影响及对策研究 . | 中国林业教育 , 2025 , 43 (01) , 12-18 . |
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The constraints of spatiotemporal heterogeneity and spatial resolution constitute two crucial challenges in the establishment of remote sensing inversion models. Spatiotemporal heterogeneity gives rise to an inadequate generalization capacity of remote sensing models, demanding extensive manual parameter adjustment for each model construction. This not only escalates the task's work intensity but also leads to unstable performance. The limited spatial resolution of remote sensing images leads to suboptimal inversion accuracy for sampling points influenced by mixed pixel effects. To tackle these problems, we take the case of non-photosensitive water quality parameter inversion in the narrow rivers of Longnan area. By integrating advanced Hyper-Parameter Optimization (HPO) techniques, such as Optuna from machine learning, an inversion model was developed, incorporating the bands of Sentinel-2 and Sentinel-3 as model features. Among these features, bands with lower spatial resolution are employed to furnish surrounding information, thereby enhancing the inversion accuracy. The research outcomes demonstrate that: 1) The model constructed based on the HPO method, Optuna, attained favourable inversion results, with R2 values of 0.68, 0.77, 0.35, and 0.60 for Total Nitrogen (TN), Total Phosphorus (TP), Ammonia Nitrogen (NH3-N), and Chemical Oxygen Demand (COD), respectively. 2) The fusion of Sentinel-2 and Sentinel-3 data enhanced the inversion accuracy compared to using them separately, highlighting the considerable significance of multi-source data fusion methods in improving inversion accuracy. This research fills a void in the remote sensing inversion domain and lays the groundwork for future endeavours.
Keyword ：
Hyper-Parameter Optimization Hyper-Parameter Optimization inverse problems inverse problems Remote sensing Remote sensing
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| GB/T 7714 | Yuan, Yuhao , Lin, Zhiping , Jiang, Xinhao et al. Hyper-Parameter Optimization-based multi-source fusion for remote sensing inversion of non-photosensitive water quality parameters [J]. | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2024 , 45 (18) : 6838-6859 . |
| MLA | Yuan, Yuhao et al. "Hyper-Parameter Optimization-based multi-source fusion for remote sensing inversion of non-photosensitive water quality parameters" . | INTERNATIONAL JOURNAL OF REMOTE SENSING 45 . 18 (2024) : 6838-6859 . |
| APA | Yuan, Yuhao , Lin, Zhiping , Jiang, Xinhao , Fan, Zhongmou . Hyper-Parameter Optimization-based multi-source fusion for remote sensing inversion of non-photosensitive water quality parameters . | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2024 , 45 (18) , 6838-6859 . |
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To address the disorderliness issue of point cloud data when directly used for tree species classification, this study transformed point cloud data into projected images for classification. Building upon this foundation, the influence of incorporating multiple distinct projection perspectives, integrating depth information, and utilising various classification models on the classification of tree point cloud projected images was investigated. Nine tree species in Sanjiangkou Ecological Park, Fuzhou City, were selected as samples. In the single-direction projection classification, the X-direction projection exhibited the highest average accuracy of 80.56%. In the dual-direction projection classification, the XY-direction projection exhibited the highest accuracy of 84.76%, which increased to 87.14% after adding depth information. Four classification models (convolutional neural network, CNN; visual geometry group, VGG; ResNet; and densely connected convolutional networks, DenseNet) were used to classify the datasets, with average accuracies of 73.53%, 85.83%, 87%, and 86.79%, respectively. Utilising datasets with depth and multidirectional information can enhance the accuracy and robustness of image classification. Among the models, the CNN served as a baseline model, VGG accuracy was 12.3% higher than that of CNN, DenseNet had a smaller gap between the average accuracy and the optimal result, and ResNet performed the best in classification tasks.
Keyword ：
convolutional neural network convolutional neural network deep learning deep learning image recognition image recognition projection image projection image residual neural network residual neural network three-dimensional point cloud three-dimensional point cloud tree species classification tree species classification
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| GB/T 7714 | Fan, Zhongmou , Zhang, Wenxuan , Zhang, Ruiyang et al. Classification of Tree Species Based on Point Cloud Projection Images with Depth Information [J]. | FORESTS , 2023 , 14 (10) . |
| MLA | Fan, Zhongmou et al. "Classification of Tree Species Based on Point Cloud Projection Images with Depth Information" . | FORESTS 14 . 10 (2023) . |
| APA | Fan, Zhongmou , Zhang, Wenxuan , Zhang, Ruiyang , Wei, Jinhuang , Wang, Zhanyong , Ruan, Yunkai . Classification of Tree Species Based on Point Cloud Projection Images with Depth Information . | FORESTS , 2023 , 14 (10) . |
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Green barriers are regarded as a viable strategy for improving road air quality, and yet there lacks consensus regarding how they can actively regulate air pollution, particularly under dynamic traffic, meteorology and built environments. To address this, field measurements were conducted to assess the levels of fine particulate matter (PM2.5) before and after green barriers along an urban expressway. The measurements revealed distinct spatial attenuation patterns in vegetationrich and vegetation-sparse areas. Specifically, the first-layer green barriers were found to significantly reduce PM2.5 levels by 8.4-9.3% in the vegetation-rich area. The magnitude of PM2.5 reduction at all locations distant from the motorway varied depending on the wind direction. On average, there are reductions of 7.8% (winter) and 13.0% (summer) in PM2.5 levels under crossroad winds. A validated hybrid model further demonstrated the significant impact of green barriers on pollutant diffusion, showing an unstable spatial variation trend under changing wind conditions. A combination of trees and hedgerows with a tree spacing of 3 m and obovate or spherical tree crowns proved effective in reducing roadside pollution. Simulations also suggested significant pollution reduction when considering appropriate spatial distribution of traffic flow and solid barrier alongside green barriers. These findings underscore the potential of combining green barriers with diverse measures to maximize the reduction of traffic-related pollution at roadside.
Keyword ：
Air pollution Air pollution Field measurement Field measurement Numerical simulation Numerical simulation Road greenbelt Road greenbelt Spatial variation Spatial variation
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| GB/T 7714 | Chen, Xin , Wu, Jie , Yang, Wenbin et al. Measuring and modeling the effects of green barriers on the spatial distribution of fine particulate matter at roadside [J]. | URBAN CLIMATE , 2023 , 52 . |
| MLA | Chen, Xin et al. "Measuring and modeling the effects of green barriers on the spatial distribution of fine particulate matter at roadside" . | URBAN CLIMATE 52 (2023) . |
| APA | Chen, Xin , Wu, Jie , Yang, Wenbin , Wang, Zhanyong , Chen, Shuting , Hu, Xisheng et al. Measuring and modeling the effects of green barriers on the spatial distribution of fine particulate matter at roadside . | URBAN CLIMATE , 2023 , 52 . |
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Compared with ground-based light detection and ranging (LiDAR) data, the differential distribution of the quantity and quality of point cloud data from airborne LiDAR poses difficulties for tree species classification. To verify the feasibility of using the PointNet++ algorithm for point cloud tree species classification with airborne LiDAR data, we selected 11 tree species from the Minjiang River Estuary Wetland Park in Fuzhou City and Sanjiangkou Ecological Park. Training and testing sets were constructed through pre-processing and segmentation, and direct and enhanced down-sampling methods were used for tree species classification. Experiments were conducted to adjust the hyperparameters of the proposed algorithm. The optimal hyperparameter settings used the multi-scale sampling and grouping (MSG) method, down-sampling of the point cloud to 2048 points after enhancement, and a batch size of 16, which resulted in 91.82% classification accuracy. PointNet++ could be used for tree species classification using airborne LiDAR data with an insignificant impact on point cloud quality. Considering the differential distribution of the point cloud quantity, enhanced down-sampling yields improved the classification results compared to direct down-sampling. The MSG classification method outperformed the simplified sampling and grouping classification method, and the number of epochs and batch size did not impact the results.
Keyword ：
airborne lidar airborne lidar hyperparameters hyperparameters Pointnet plus plus Pointnet plus plus tree species classification tree species classification
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| GB/T 7714 | Fan, Zhongmou , Wei, Jinhuang , Zhang, Ruiyang et al. Tree Species Classification Based on PointNet plus plus and Airborne Laser Survey Point Cloud Data Enhancement [J]. | FORESTS , 2023 , 14 (6) . |
| MLA | Fan, Zhongmou et al. "Tree Species Classification Based on PointNet plus plus and Airborne Laser Survey Point Cloud Data Enhancement" . | FORESTS 14 . 6 (2023) . |
| APA | Fan, Zhongmou , Wei, Jinhuang , Zhang, Ruiyang , Zhang, Wenxuan . Tree Species Classification Based on PointNet plus plus and Airborne Laser Survey Point Cloud Data Enhancement . | FORESTS , 2023 , 14 (6) . |
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树种分类在城市景观提升、生态结构调整、林业资源管理等方面具有重要作用，但如何科学、快速、简便地进行树种分类一直是实际工作的难点。本研究选取福州三江口生态公园8种优势树种为研究对象，采用单木摄影代替传统近景摄影测量中的立体像对，提取单木地径、树高、枝下高、冠高、冠幅等特征参数的像素值，考虑到像素值不能直接反映单木因子的真值，尝试采用比值组合参数代替特征真值作为分类特征因子。将比值组合参数通过重要性排序选出其中5个重要的特征因子，利用模型融合方法和3种传统机器学习方法进行分类。结果表明：所提出比值组合参数可以用于树种分类，并且在选用的分类模型中，Stacking融合模型对树种分类效果最好，分类准确率可达85.12%,相较于传统机器学习K最近邻、支持向量机、随机森林分类准确度分别提高18.38%,14.40%,10.93%。研究结果可以为树种识别提供新的思路和方法。
Keyword ：
Stacking Stacking 单目视觉 单目视觉 树种识别 树种识别 组合参数因子 组合参数因子
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| GB/T 7714 | 杨森 , 樊仲谋 , 张文宣 . 基于Stacking单目视觉组合参数的树种分类研究 [J]. | 林业工程学报 , 2023 , 8 (03) : 173-181 . |
| MLA | 杨森 et al. "基于Stacking单目视觉组合参数的树种分类研究" . | 林业工程学报 8 . 03 (2023) : 173-181 . |
| APA | 杨森 , 樊仲谋 , 张文宣 . 基于Stacking单目视觉组合参数的树种分类研究 . | 林业工程学报 , 2023 , 8 (03) , 173-181 . |
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Dynamic traffic and complex roadside environments always cause fine variations in traffic pollutants with many uncertainties in nonmotorized lanes located close to motorways; thus, reliable methods for identifying pollution risks are urgently needed so that measures can be taken to reduce these slow-moving risks. Focusing on the nonmotorized lanes along an expressway in Fuzhou, China, in this study, we established a cycling platform instrumented by portable detectors to collect fine particle (PM2.5), coarse particle (PM10), and black carbon (BC) concentrations at a high spatiotemporal resolution; then, wavelet transform (WT) and random forest (RF) methods were combined to reveal the fine-scale distribution of different particulate matter types. The results indicated that WT was able to accurately decompose the total measurement value (C-t) of each particulate matter into immediate vehicle-emitted (C-v) and background-contributed (C-b) values, thereby successfully identifying the spatiotemporal variations in traffic-induced pollution hotspots rather than background-disguised hotspots. Furthermore, the RF results were substantially better than the land-use regression results with regards to the fine-scale prediction of each particle in nonmotorized lanes. Although the RF predictions of C(t )and C-v particles differed, traffic pollution hotspots could still be captured by the results. Compared to the measurements, the spatial distributions of the PM2.5 and PM10 predictions presented R-2 values larger than 0.96, higher than those of BC (R-2 = 0.77); this was the result of the different impacts of the same predictors, especially their differentiated determinants such as barometric pressure, relative humidity and air temperature. This study highlights the potential of using WT and RF methods to reveal fine-scale variations in roadside traffic pollution, which is beneficial for preventing and controlling air pollution in road microenvironments.
Keyword ：
Atmospheric particulate matter Atmospheric particulate matter Machine learning Machine learning Slow-moving traffic Slow-moving traffic Spatiotemporal variation Spatiotemporal variation Wavelet transform Wavelet transform
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| GB/T 7714 | Luo, Binru , Cao, Ruhui , Yang, Wenbin et al. Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods [J]. | STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT , 2023 , 37 (7) : 2657-2676 . |
| MLA | Luo, Binru et al. "Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods" . | STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 37 . 7 (2023) : 2657-2676 . |
| APA | Luo, Binru , Cao, Ruhui , Yang, Wenbin , Wang, Zhanyong , Hu, Xisheng , Xu, Jinqiang et al. Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods . | STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT , 2023 , 37 (7) , 2657-2676 . |
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