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学者姓名：周小成
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Abstract ：
Normalization of satellite images collected under various atmospheric conditions is critical for the comprehensive, long-term global surveillance of terrestrial surface alterations. This study utilized remote sensing data from the Sentinel-2A Multispectral Instrument (MSI) in polar orbit and the Landsat-8 Operational Land Imager (OLI) sensors, with multispectral global coverage of 10-30 m, to derive reflectance products using inversion algorithms. Validation and assessment were conducted using synchronous surface measurement spectra collected from four sites across three Chinese provinces in 2019. We corrected surface reflectance and derived vegetation indices across blue, green, red, near-infrared (NIR), and two short-wave infrared (SWIR) bands and normalized discrepancies. The phenological spatial distribution map for late rice in Jiangxi Province was constructed using normalized data outcomes. A robust linear correlation in reflectance across corresponding bands of the two satellite sensors was observed. The NIR and SWIR bands showed the most significant difference because of differences in their spectral response functions. A high degree of congruence was observed between Landsat-8 OLI and Sentinel-2 MSI sensor reflectance products, with root mean square error values consistently below 0.05. The derived conversion equations were highly accurate for harmonizing data from both sensor systems.
Keyword ：
harmonization harmonization Landsat-8 OLI Landsat-8 OLI Sentinel-2 MSI Sentinel-2 MSI Surface reflectance (SR) Surface reflectance (SR) vegetation index vegetation index
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| GB/T 7714 | Zhang, Jiaqi , Zhou, Xiaocheng , Liu, Xueping et al. Harmonizing Landsat-8 OLI and Sentinel-2 MSI: an assessment of surface reflectance and vegetation index consistency [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Zhang, Jiaqi et al. "Harmonizing Landsat-8 OLI and Sentinel-2 MSI: an assessment of surface reflectance and vegetation index consistency" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Zhang, Jiaqi , Zhou, Xiaocheng , Liu, Xueping , Wang, Xiaoqin , He, Guojin , Zhang, Youshui . Harmonizing Landsat-8 OLI and Sentinel-2 MSI: an assessment of surface reflectance and vegetation index consistency . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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针对三维绿量计算过程中树木参数获取成本较高的问题,提出一种融合高分辨率遥感数据和街景数据的城市行道树三维绿量估算方法.以福州主要辖区为例,首先基于高分二号(GF-2)遥感影像,获取城市行道树的二维分布;然后结合街景地图实现对行道树的树木参数量测;最后基于行道树的水平分布和垂直特征完成三维绿量的估算.结果表明,研究区内行道树整体分布不均衡.基于街景测量获取的树木参数精度较高,与实测数据相比,R2 大于 0.9.单位面积上的三维绿量在白马路路段较高,在福马路等路段较低,榕树对该研究区的三维绿量贡献最大,占研究区总绿量的 80%.与二维指标相比,城市行道树的三维绿量值更能体现城市行道树的三维立体差异,反映绿地实际生态效益.
Keyword ：
三维绿量 三维绿量 百度街景 百度街景 立体景观 立体景观 虚拟测量 虚拟测量 行道树 行道树
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| GB/T 7714 | 孔令凤 , 汪小钦 , 周小成 . 融合遥感影像与街景的城市行道树三维绿量估算 [J]. | 福州大学学报（自然科学版） , 2025 , 53 (2) : 151-158 . |
| MLA | 孔令凤 et al. "融合遥感影像与街景的城市行道树三维绿量估算" . | 福州大学学报（自然科学版） 53 . 2 (2025) : 151-158 . |
| APA | 孔令凤 , 汪小钦 , 周小成 . 融合遥感影像与街景的城市行道树三维绿量估算 . | 福州大学学报（自然科学版） , 2025 , 53 (2) , 151-158 . |
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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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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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Above Ground Biomass (AGB) is a key indicator of forest ecosystem carbon storage, energy flow changes and biodiversity, and is crucial for climate change research and forest resource management. Fujian Province, as the largest collective forest area in southern China, has abundant forest resources, accurately estimating forest aboveground biomass can lay the foundation for estimating carbon storage and provide decision-making support for achieving the dual carbon goals. Fujian Province is located in a cloudy and rainy subtropical zone with complex terrain and forest types, making it difficult to estimate forest aboveground biomass, estimating forest aboveground biomass using traditional methods is difficult to meet accuracy requirements. In order to improve the accuracy of aboveground forest biomass, this study integrated and comprehensively utilized multi-source remote sensing data such as the latest spaceborne lidar data GEDI, Landsat and Sentinel series satellites. Above all, the GEDI_V27 canopy height product was optimized based on the forest age calculated from Landsat. Then combined with the optimized MGEDI_V27 canopy height product, by establishing an XGBoost biomass inversion model that combined traditional remote sensing features with canopy height, we effectively improved model accuracy,estimated and mapped the aboveground biomass of forests in Fujian Province. The research results showed that: (1) The GEDI canopy height accuracy evaluation result optimized by forest age was R2 = 0.67, RMSE = 2.24m; (2) The recursive feature elimination algorithm was used to optimize the features of the three forest types, and 10 remote sensing features were obtained. Among them, the most important remote sensing features of the three forest types were forest canopy height, and a comparative evaluation was performed on the features including traditional remote sensing features. The results showed that when the canopy height factor was included in the feature construction, the accuracy of the forest AGB regression analysis was significantly improved, confirming that canopy height played a significant role in biomass estimation; (3) The studied forest AGB range in Fujian Province was 0. 001———363. 331Mg / hm2, the overall accuracy evaluation result was R2 = 0.75, RMSE = 17.34 Mg / hm2, and the total AGB amount in the province in 2020 was 822 million Mg. The average value was 101.24Mg / hm2, reflecting the good ecological quality of Fujian Province. By optimizing the forest canopy height in GEDI and combining it with traditional remote sensing features, the accuracy of forest aboveground biomass modeling can be significantly improved, and it is possible to accurately estimate and monitor forest biomass in Fujian Province. The research results are helpful for the high-precision estimation of aboveground biomass in regional forests, and have certain guiding significance for the assessment of carbon sinks. © 2024 Science Press. All rights reserved.
Keyword ：
canopy height canopy height extreme gradient boosting(XGBoost) regression extreme gradient boosting(XGBoost) regression forest above ground biomass forest above ground biomass forest type forest type global ecosystem dynamics investigation (GEDI) global ecosystem dynamics investigation (GEDI) remote sensing remote sensing
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| GB/T 7714 | Tian, G. , Zhou, X. , Hao, Y. et al. Optimization model of forest aboveground biomass based on MGEDI canopy height: a case study in Fujian, China; [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]. | Shengtai Xuebao , 2024 , 44 (16) : 7264-7277 . |
| MLA | Tian, G. et al. "Optimization model of forest aboveground biomass based on MGEDI canopy height: a case study in Fujian, China; [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]" . | Shengtai Xuebao 44 . 16 (2024) : 7264-7277 . |
| APA | Tian, G. , Zhou, X. , Hao, Y. , Tan, F. , Wang, Y. , Wu, S. et al. Optimization model of forest aboveground biomass based on MGEDI canopy height: a case study in Fujian, China; [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] . | Shengtai Xuebao , 2024 , 44 (16) , 7264-7277 . |
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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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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
Cite：
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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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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，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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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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