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学者姓名：周小成
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Abstract ：
福建省作为中国的产茶大省，快速准确地获取茶园的空间分布对于福建省的农业经济发展以及生态环境建设具有重大的决策意义。本研究在GEE云平台调用与处理Sentinel-1(S1)雷达数据和Sentinel-2(S2)多光谱数据，结合地形数据从中提取光谱特征、纹理特征、地形特征等98个特征，利用递归消除支持向量机算法（SVM＿RFE）对特征变量进行筛选，共设计4种特征组合方案，通过支持向量机分类器（SVM）进行茶园提取，并分别对4种分类方案进行精度评价，获得了福建省2020年10 m分辨率茶园空间分布数据。在此基础上，利用GEE云平台获取福建省2000–2020年植被干扰信息，以2020年茶园提取结果掩膜剔除2000–2015年影像中非茶园区域，得到2000–2020年每隔5年的福建省10 m分辨率茶园空间分布数据集。本数据集利用样本点对重点产茶县市进行人工验证，结果表明：2020年茶园提取精度在92%以上，利用干扰数据剔除法获得的2000年、2005年、2010年、2015年茶园提取精度均在80%以上。提取茶园精度较高，可为有关部门进行茶园管理提供支持。
Keyword ：
Google Earth Engine Google Earth Engine 支持向量机 支持向量机 植被干扰 植被干扰 福建省 福建省 茶园 茶园
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| GB/T 7714 | 王祎帆 , 周小成 , 熊皓丽 et al. 福建省2000–2020年10 m分辨率茶园空间分布数据集 [J]. | 中国科学数据(中英文网络版) , 2024 , 9 (02) : 354-365 . |
| MLA | 王祎帆 et al. "福建省2000–2020年10 m分辨率茶园空间分布数据集" . | 中国科学数据(中英文网络版) 9 . 02 (2024) : 354-365 . |
| APA | 王祎帆 , 周小成 , 熊皓丽 , 吴善群 , 谭芳林 , 郝优壮 et al. 福建省2000–2020年10 m分辨率茶园空间分布数据集 . | 中国科学数据(中英文网络版) , 2024 , 9 (02) , 354-365 . |
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Accurately delineating sediment export dynamics using high-quality vegetation factors remains challenging due to the spatio-temporal resolution imbalance of single remote sensing data and persistent cloud contamination. To address these challenges, this study proposed a new framework for estimating and analyzing monthly sediment inflow to rivers in the cloud-prone Minjiang River Basin. We leveraged multi-source remote sensing data and the Continuous Change Detection and Classification model to reconstruct monthly vegetation factors at 30 m resolution. Then, we integrated the Chinese Soil Loss Equation model and the Sediment Delivery Ratio module to estimate monthly sediment inflow to rivers. Lastly, the Optimal Parameters-based Geographical Detector model was harnessed to identify factors affecting sediment export. The results indicated that: (1) The simulated sediment transport modulus showed a strong Coefficient of Determination (R2 = 0.73) and a satisfactory Nash-Sutcliffe Efficiency coefficient (0.53) compared to observed values. (2) The annual sediment inflow to rivers exhibited a spatial distribution characterized by lower levels in the west and higher in the east. The monthly average sediment value from 2016 to 2021 was notably high from March to July, while relatively low from October to January. (3) Erosive rainfall was a decisive factor contributing to increased sediment entering the rivers. Vegetation factors, manifested via the quantity (Fractional Vegetation Cover) and quality (Leaf Area Index and Net Primary Productivity) of vegetation, exert a pivotal influence on diminishing sediment export.
Keyword ：
Chinese soil loss equation Chinese soil loss equation cloud-prone regions cloud-prone regions monthly remote sensing vegetation index monthly remote sensing vegetation index optimal parameters-based geographical detector optimal parameters-based geographical detector sediment delivery ratio sediment delivery ratio sediment inflow to rivers sediment inflow to rivers
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| GB/T 7714 | Wang, Xiaoqin , Yu, Zhichao , Li, Lin et al. Unveiling the Intra-Annual and Inter-Annual Spatio-Temporal Dynamics of Sediment Inflow to Rivers and Driving Factors in Cloud-Prone Regions: A Case Study in Minjiang River Basin, China [J]. | WATER , 2024 , 16 (22) . |
| MLA | Wang, Xiaoqin et al. "Unveiling the Intra-Annual and Inter-Annual Spatio-Temporal Dynamics of Sediment Inflow to Rivers and Driving Factors in Cloud-Prone Regions: A Case Study in Minjiang River Basin, China" . | WATER 16 . 22 (2024) . |
| APA | Wang, Xiaoqin , Yu, Zhichao , Li, Lin , Li, Mengmeng , Lin, Jinglan , Tang, Lifang et al. Unveiling the Intra-Annual and Inter-Annual Spatio-Temporal Dynamics of Sediment Inflow to Rivers and Driving Factors in Cloud-Prone Regions: A Case Study in Minjiang River Basin, China . | WATER , 2024 , 16 (22) . |
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森林地上生物量(Above Ground Biomass,AGB)是衡量森林生态系统碳存储、能量流动和生物多样性的关键指标,对于气候变化研究和森林资源管理至关重要.福建省地处多云多雨的亚热带,地形和森林类型复杂,森林地上生物量估算难度大.为提升森林地上生物量估算效果,将最新星载激光雷达数据全球生态系统动态调查(GEDI)、Landsat以及Sentinel系列卫星等多源遥感数据进行集成和综合利用,通过Landsat影像计算的林龄对GEDI_V27冠层高度产品进行优化,结合优化后的MGEDI_V27冠层高度产品,建立传统遥感特征结合冠层高度的极端梯度提升模型(XGBoost)生物量反演模型,实现了福建省森林地上生物量的有效估算与制图.研究结果表明:(1)通过林龄优化后的GEDI冠层高度精度评价结果为R2=0.67,RMSE=2.24m;(2)通过递归特征消除算法对三种森林类型进行特征优选,得到10个遥感特征,其中,三种森林类型最重要的遥感特征均为森林冠层高度,并且对比评价了在包含传统遥感特征因子的情况下有无冠层高度对于模型精度的影响,结果表明,在冠层高度因子参加特征构建时,森林AGB回归分析的精度明显提高,证实了冠层高度在生物量估算中具有显著的重要性;(3)研究得到的福建省森林AGB范围为0.001-363.331Mg/hm2,整体精度评价结果为R2=0.75,RMSE=17.34Mg/hm2,2020年全省AGB总量为8.22亿Mg,平均值为101.24Mg/hm2.通过优化GEDI中的森林冠层高度,并且结合传统遥感特征,可以实现对福建省森林地上生物量的精确估算和监测,研究成果有助于区域森林碳汇的评估.
Keyword ：
全球生态系统动态调查(GEDI) 全球生态系统动态调查(GEDI) 冠层高度 冠层高度 极端梯度提升模型(XGBoos t)回归 极端梯度提升模型(XGBoos t)回归 森林地上生物量 森林地上生物量 森林类型 森林类型 遥感 遥感
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| GB/T 7714 | 田国帅 , 周小成 , 郝优壮 et al. 结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化 [J]. | 生态学报 , 2024 , 44 (16) : 7264-7277 . |
| MLA | 田国帅 et al. "结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化" . | 生态学报 44 . 16 (2024) : 7264-7277 . |
| APA | 田国帅 , 周小成 , 郝优壮 , 谭芳林 , 王永荣 , 吴善群 et al. 结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化 . | 生态学报 , 2024 , 44 (16) , 7264-7277 . |
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无人机遥感在森林树种精细和高效分类制图中具有巨大的潜力。为了快速准确获取森林的优势树种分布信息，该研究探讨了半监督学习方法在树种分类方面的有效性。以福建省福州市、龙岩市和三明市的4个试验区为例，构建精简的ResNet18为主干的UNet树种分类模型（UNet-ResNet14~*），使用交叉熵和Dice系数的联合损失函数来优化模型参数，对比分析Self-training和Mean Teacher两种不同的半监督学习方法在无人机影像森林树种分类模型的泛化能力。结果表明，以ResNet14~*作为主干的分类模型与其他模型相比精度更高且预测速度更快，当联合损失函数权重值为0.5的情况下模型预测效果最好，总体精度达到了91.15%。经过Self-training的模型在木荷、马尾松、杉木3个样本充足的类别中精度均有所提升，总精度为91.08%，比原始模型略低，但在独立验证区的精度为88.50%，比原始模型高；Mean Teacher方法的总精度为88.56%，在独立验证区的精度为73.56%。因此，研究认为可以采用Self-trainin半监督方法结合UNet-ResNet14~*的方案快速得到试验区的树种组成信息。
Keyword ：
ResNet ResNet UNet UNet 半监督学习 半监督学习 可见光 可见光 无人机 无人机 树种分类 树种分类 森林 森林 遥感 遥感
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| GB/T 7714 | 陈龙伟 , 周小成 , 李传昕 et al. 基于UNet-ResNet14~*半监督学习的无人机影像森林树种分类 [J]. | 农业工程学报 , 2024 , 40 (01) : 217-226 . |
| MLA | 陈龙伟 et al. "基于UNet-ResNet14~*半监督学习的无人机影像森林树种分类" . | 农业工程学报 40 . 01 (2024) : 217-226 . |
| APA | 陈龙伟 , 周小成 , 李传昕 , 林华章 , 王永荣 , 崔永红 . 基于UNet-ResNet14~*半监督学习的无人机影像森林树种分类 . | 农业工程学报 , 2024 , 40 (01) , 217-226 . |
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Unmanned aerial vehicle (UAV) remote sensing has the promising potential for the precise and efficient classification and mapping of forest tree species. Deep learning also requires a large number of datasets for training, typically on manual annotation. In this study, the framework of forest tree species classification was proposed to fully utilize a large amount of unlabeled data and a small amount of annotated data using semi-supervised learning. A rapid and accurate classification was also achieved in the high-precision distribution of dominant tree species in forests. The experimental areas were taken as the complex mountainous forest environment in Fujian Province. The composition of tree species was then obtained in a rapid, effective, and cost-saving manner. Taking four experimental areas in Fuzhou, Longyan, and Sanming in Fujian Province as examples, the simplified classification was constructed in the UNet tree species (ResNet14*) model with ResNet18 as the backbone. ResNet14* was different from ResNet18: ResNet14* was used to remove the layer4 part of ResNet18, i.e., the last downsampled cascaded block, which retained slightly higher spatial information; At the end of the layer2 and layer3 sections of ResNet14*, a max pooling layer was added to reduce the training parameters of the neural network while retaining the original features. A joint loss function of cross entropy and Dice coefficient was used to optimize the model parameters. The generalization of Self-training and Mean teacher was evaluated on the classification models with semi-supervised learning using UVA images. The results show that the overall accuracy of the ResNet14* network reached 91.15%, with a Kappa coefficient of 0.827, which was within 1% of the accuracy of the rest ResNet models. At the same time, a smaller number of parameters and the shortest prediction time were achieved to balance the accuracy and efficiency of tree species classification. The best prediction performance of ResNet14* was achieved with the joint loss function weight of 0.5, indicating an overall accuracy of 91.15%. Therefore, the joint loss function weight of 0.5 was an optimal value for semi-supervised learning in this case. Self-training and Mean teacher semi-supervised learning were implemented with UNet (ResNet14*) as the main network. The experiment showed that the overall accuracy of the Self-training on the test set reached 91.08%, slightly lower than the original. The higher category accuracy was also achieved in the categories of Schima superba, Pinus massoniana, and Chinese fir with sufficient samples. Furthermore, the overall accuracy of the self-training with pseudo labels was improved among two semi-supervised models in experimental area D, reaching 88.50% compared with the original; There was a significant decrease in the overall accuracy of the Mean teacher model with consistency loss. The total accuracy of the Mean teacher model was 88.56%, where the accuracy was 73.56% in the independent validation area. Accuracy evaluation was also conducted on an independent validation area. The classification accuracy of above 80% was found in the three types of tree species, namely Schima superba, Pinus Massoniana, and Chinese fir. A relatively large area was accounted for to meet the accuracy requirements of tree species mapping in the experimental area. Therefore, the semi-supervised learning of the Self-training model can be expected to rapidly obtain the composition of tree species in the experimental area. © 2024 Chinese Society of Agricultural Engineering. All rights reserved.
Keyword ：
Antennas Antennas Classification (of information) Classification (of information) Deep learning Deep learning Forestry Forestry Large datasets Large datasets Multilayer neural networks Multilayer neural networks Personnel training Personnel training Remote sensing Remote sensing Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Chen, Longwei , Zhou, Xiaocheng , Li, Chuanxin et al. Classification of tree species based on UNet-ResNet14* semi-supervised learning using UAV images [J]. | Transactions of the Chinese Society of Agricultural Engineering , 2024 , 40 (1) : 217-226 . |
| MLA | Chen, Longwei et al. "Classification of tree species based on UNet-ResNet14* semi-supervised learning using UAV images" . | Transactions of the Chinese Society of Agricultural Engineering 40 . 1 (2024) : 217-226 . |
| APA | Chen, Longwei , Zhou, Xiaocheng , Li, Chuanxin , Lin, Huazhang , Wang, Yongrong , Cui, Yonghong . Classification of tree species based on UNet-ResNet14* semi-supervised learning using UAV images . | Transactions of the Chinese Society of Agricultural Engineering , 2024 , 40 (1) , 217-226 . |
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森林地上生物量（Above Ground Biomass,AGB）是衡量森林生态系统碳存储、能量流动和生物多样性的关键指标，对于气候变化研究和森林资源管理至关重要。福建省地处多云多雨的亚热带，地形和森林类型复杂，森林地上生物量估算难度大。为提升森林地上生物量估算效果，本文将最新星载激光雷达数据GEDI、Landsat以及Sentinel系列卫星等多源遥感数据进行集成和综合利用，首先通过Landsat影像计算的林龄对GEDI＿V27冠层高度产品进行优化，之后结合优化后的MGEDI＿V27冠层高度产品，建立传统遥感特征结合冠层高度的XGBoost生物量反演模型，实现了福建省森林地上生物量的有效估算与制图。研究结果表明：（1）通过林龄优化后的GEDI冠层高度精度评价结果为R2=0.67,RMSE=2.24m;(2)通过递归特征消除算法对三种森林类型进行特征优选，得到10个遥感特征，其中，三种森林类型最重要的遥感特征均为森林冠层高度，并且对比评价了在包含传统遥感特征因子的情况下有无冠层高度对于模型精度的影响，结果表明，在冠层高度因子参加特征构建时，森林AGB回归分析的精度明显提高，证实了冠层高度在生物量估算中具有显著的重要性；（3）研究得到的福建省森林AGB范围为0.001—363.331Mg/hm~2，整体精度评价结果为R~2=0.75,RMSE=17.34 Mg/hm~2,2020年全省AGB总量为8.22亿Mg，平均值为101.24Mg/hm~2。通过优化GEDI中的森林冠层高度，并且结合传统遥感特征，可以实现对福建省森林地上生物量的精确估算和监测，研究成果有助于区域森林碳汇的评估。
Keyword ：
GEDI GEDI XGBoost回归 XGBoost回归 冠层高度 冠层高度 森林地上生物量 森林地上生物量 森林类型 森林类型 遥感 遥感
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| GB/T 7714 | 田国帅 , 周小成 , 郝优壮 et al. 结合MGEDI冠层高度的森林地上生物量模型优化——以福建省为例 [J]. | 生态学报 , 2024 , (16) . |
| MLA | 田国帅 et al. "结合MGEDI冠层高度的森林地上生物量模型优化——以福建省为例" . | 生态学报 16 (2024) . |
| APA | 田国帅 , 周小成 , 郝优壮 , 谭芳林 , 王永荣 , 吴善群 et al. 结合MGEDI冠层高度的森林地上生物量模型优化——以福建省为例 . | 生态学报 , 2024 , (16) . |
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【目的】森林生态质量是从生态角度反映森林质量的内涵，对森林的生态功能和生态服务、生长状况以及自我调节功能进行综合测度，以期提高森林改善生态环境、维护生态平衡的能力。【方法】利用中、高分辨率多源遥感数据，获取大范围尺度下能表征森林生态质量的关键指标信息，在此基础上，分析福建省海岸带40个县域的森林生态质量状况。首先，基于2016年2 m分辨率多源遥感数据为主要数据源，利用双层尺度集模型选定最佳分割尺度，多分类器集成算法集，自动选择最优分类算法进行森林类型提取，并结合2020年Sentinel遥感数据及森林分类产品，更新2020年福建省海岸带森林类型精细分布图；其次，利用LandTrendr算法衍生的干扰开始时间特征推算现存森林年龄，通过GEDI冠层高度产品获取海岸带森林冠层高度分布图；在以上关键森林质量指标提取基础上，对遥感手段获取的8项森林生态质量评价指标进行主成分分析，获得福建省海岸带县域森林生态质量综合评价结果。【结果】2020年福建海岸带40个县域约50%的县域森林生态质量处于优良水平，其中仙游县、闽侯县、南安市、霞浦县、柘荣县及厦门海沧区、思明区、集美区、同安区等森林生态质量为优；森林生态质量较差的县域有惠安县、秀屿区、石狮市、福安市、平潭实验区、诏安县。【结论】结合中、高分辨率多源遥感数据，能够发挥遥感大范围监测优点，客观评价福建省海岸带40个县域的森林生态质量；研究结果表明2020年福建沿海县域森林生态质量还存在较大的提升空间，需要针对存在的问题采取相应森林管理措施提升森林生态质量。
Keyword ：
冠层高度 冠层高度 林龄 林龄 森林生态质量 森林生态质量 森林类型 森林类型 福建海岸带 福建海岸带 遥感 遥感
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| GB/T 7714 | 闫谨 , 周小成 , 黄婷婷 et al. 基于多源遥感数据的福建省海岸带县域森林生态质量评价 [J]. | 北京林业大学学报 , 2024 , 46 (05) : 12-25 . |
| MLA | 闫谨 et al. "基于多源遥感数据的福建省海岸带县域森林生态质量评价" . | 北京林业大学学报 46 . 05 (2024) : 12-25 . |
| APA | 闫谨 , 周小成 , 黄婷婷 , 乐通潮 , 王永荣 , 吴善群 . 基于多源遥感数据的福建省海岸带县域森林生态质量评价 . | 北京林业大学学报 , 2024 , 46 (05) , 12-25 . |
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Abstract ：
森林地上生物量(Above Ground Biomass, AGB)是衡量森林生态系统碳存储、能量流动和生物多样性的关键指标，对于气候变化研究和森林资源管理至关重要。福建省地处多云多雨的亚热带，地形和森林类型复杂，森林地上生物量估算难度大。为提升森林地上生物量估算效果，将最新星载激光雷达数据全球生态系统动态调查(GEDI)、Landsat以及Sentinel系列卫星等多源遥感数据进行集成和综合利用，通过Landsat影像计算的林龄对GEDI＿V27冠层高度产品进行优化，结合优化后的MGEDI＿V27冠层高度产品，建立传统遥感特征结合冠层高度的极端梯度提升模型(XGBoost)生物量反演模型，实现了福建省森林地上生物量的有效估算与制图。研究结果表明：(1)通过林龄优化后的GEDI冠层高度精度评价结果为R~2=0.67,RMSE=2.24m;(2)通过递归特征消除算法对三种森林类型进行特征优选，得到10个遥感特征，其中，三种森林类型最重要的遥感特征均为森林冠层高度，并且对比评价了在包含传统遥感特征因子的情况下有无冠层高度对于模型精度的影响，结果表明，在冠层高度因子参加特征构建时，森林AGB回归分析的精度明显提高，证实了冠层高度在生物量估算中具有显著的重要性；(3)研究得到的福建省森林AGB范围为0.001—363.331Mg/hm~2,整体精度评价结果为R~2=0.75,RMSE=17.34Mg/hm~2,2020年全省AGB总量为8.22亿Mg,平均值为101.24Mg/hm~2。通过优化GEDI中的森林冠层高度，并且结合传统遥感特征，可以实现对福建省森林地上生物量的精确估算和监测，研究成果有助于区域森林碳汇的评估。
Keyword ：
全球生态系统动态调查(GEDI) 全球生态系统动态调查(GEDI) 冠层高度 冠层高度 极端梯度提升模型(XGBoost)回归 极端梯度提升模型(XGBoost)回归 森林地上生物量 森林地上生物量 森林类型 森林类型 遥感 遥感
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| GB/T 7714 | 田国帅 , 周小成 , 郝优壮 et al. 结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化——以福建省为例 [J]. | 生态学报 , 2024 , 44 (16) : 7264-7277 . |
| MLA | 田国帅 et al. "结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化——以福建省为例" . | 生态学报 44 . 16 (2024) : 7264-7277 . |
| APA | 田国帅 , 周小成 , 郝优壮 , 谭芳林 , 王永荣 , 吴善群 et al. 结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化——以福建省为例 . | 生态学报 , 2024 , 44 (16) , 7264-7277 . |
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Forest canopy height plays an important role in forest resource management and conservation. The accurate estimation of forest canopy height on a large scale is important for forest carbon stock, biodiversity, and the carbon cycle. With the technological development of satellite-based LiDAR, it is possible to determine forest canopy height over a large area. However, the forest canopy height that is acquired by this technology is influenced by topography and climate, and the canopy height that is acquired in complex subtropical mountainous regions has large errors. In this paper, we propose a method for estimating forest canopy height by combining long-time series Landsat images with GEDI satellite-based LiDAR data, with Fujian, China, as the study area. This approach optimizes the quality of GEDI canopy height data in topographically complex areas by combining stand age and tree height, while retaining the advantage of fast and effective forest canopy height measurements with satellite-based LiDAR. In this study, the growth curves of the main forest types in Fujian were first obtained by using a large amount of forest survey data, and the LandTrendr algorithm was used to obtain the forest age distribution in 2020. The obtained forest age was then combined with the growth curves of each forest type in order to determine the tree height distribution. Finally, the obtained average tree heights were merged with the GEDI_V27 canopy height product in order to create a modified forest canopy height model (MGEDI_V27) with a 30 m spatial resolution. The results showed that the estimated forest canopy height had a mean of 15.04 m, with a standard deviation of 4.98 m. In addition, we evaluated the accuracy of the GEDI_V27 and the MGEDI_V27 using the sample dataset. The MGEDI_V27 had a higher accuracy (R-2 = 0.67, RMSE = 2.24 m, MAE = 1.85 m) than the GEDI_V27 (R-2 = 0.39, RMSE = 3.35 m, MAE = 2.41 m). R-2, RMSE, and MAE were improved by 71.79%, 33.13%, and 22.53%, respectively. We also produced a forest age distribution map of Fujian for the year 2020 and a forest disturbance map of Fujian for the past 32 years. The research results can provide decision support for forest ecological protection and management and for carbon sink analysis in Fujian.
Keyword ：
canopy height canopy height forest age forest age Fujian Fujian GEDI GEDI LiDAR LiDAR time-series remote sensing time-series remote sensing
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| GB/T 7714 | Zhou, Xiaocheng , Hao, Youzhuang , Di, Liping et al. Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China [J]. | REMOTE SENSING , 2023 , 15 (2) . |
| MLA | Zhou, Xiaocheng et al. "Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China" . | REMOTE SENSING 15 . 2 (2023) . |
| APA | Zhou, Xiaocheng , Hao, Youzhuang , Di, Liping , Wang, Xiaoqin , Chen, Chongcheng , Chen, Yunzhi et al. Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China . | REMOTE SENSING , 2023 , 15 (2) . |
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With the rapid development of Unmanned Aerial Vehicle (UAV) technology, more and more UAVs have been used in forest survey. UAV (RGB) images are the most widely used UAV data source in forest resource management. However, there is some uncertainty as to the reliability of these data when monitoring height and growth changes of low-growing saplings in an afforestation plot via UAV RGB images. This study focuses on an artificial Chinese fir (Cunninghamia lancelota, named as Chinese Fir) young forest plot in Fujian, China. Divide-and-conquer (DAC) and the local maximum (LM) method for extracting seedling height are described in the paper, and the possibility of monitoring young forest growth based on low-cost UAV remote sensing images was explored. Two key algorithms were adopted and compared to extract the tree height and how it affects the young forest at single-tree level from multi-temporal UAV RGB images from 2019 to 2021. Compared to field survey data, the R-2 of single saplings' height extracted from digital orthophoto map (DOM) images of tree pits and original DSM information using a divide-and-conquer method reached 0.8577 in 2020 and 0.9968 in 2021, respectively. The RMSE reached 0.2141 in 2020 and 0.1609 in 2021. The R-2 of tree height extracted from the canopy height model (CHM) via the LM method was 0.9462. The RMSE was 0.3354 in 2021. The results demonstrated that the survival rates of the young forest in the second year and the third year were 99.9% and 85.6%, respectively. This study shows that UAV RGB images can obtain the height of low sapling trees through a computer algorithm based on using 3D point cloud data derived from high-precision UAV images and can monitor the growth of individual trees combined with multi-stage UAV RGB images after afforestation. This research provides a fully automated method for evaluating the afforestation results provided by UAV RGB images. In the future, the universality of the method should be evaluated in more afforestation plots featuring different tree species and terrain.
Keyword ：
forest survey forest survey height change height change RGB images RGB images saplings saplings tree height tree height unmanned aerial vehicle unmanned aerial vehicle
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| GB/T 7714 | Zhou, Xiaocheng , Wang, Hongyu , Chen, Chongcheng et al. Detection of Growth Change of Young Forest Based on UAV RGB Images at Single-Tree Level [J]. | FORESTS , 2023 , 14 (1) . |
| MLA | Zhou, Xiaocheng et al. "Detection of Growth Change of Young Forest Based on UAV RGB Images at Single-Tree Level" . | FORESTS 14 . 1 (2023) . |
| APA | Zhou, Xiaocheng , Wang, Hongyu , Chen, Chongcheng , Nagy, Gabor , Jancso, Tamas , Huang, Hongyu . Detection of Growth Change of Young Forest Based on UAV RGB Images at Single-Tree Level . | FORESTS , 2023 , 14 (1) . |
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