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学者姓名：杨凯杰
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Abstract ：
Fractional vegetation cover (FVC) is an essential metric for valuating ecosystem health and soil erosion. Traditional ground-measuring methods are inadequate for large-scale FVC monitoring, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, as well as limited sample representativeness. This study proposes a method for FVC estimation integrating uncrewed aerial vehicle (UAV) and satellite imagery using machine learning (ML) models. First, we assess the vegetation extraction performance of three classification methods (OBIA-RF, threshold, and K-means) under UAV imagery. The optimal method is then selected for binary classification and aggregated to generate high-accuracy FVC reference data matching the spatial resolutions of different satellite images. Subsequently, we construct FVC estimation models using four ML algorithms (KNN, MLP, RF, and XGBoost) and utilize the SHapley Additive exPlanation (SHAP) method to assess the impact of spectral features and vegetation indices (VIs) on model predictions. Finally, the best model is used to map FVC in the study region. Our results indicate that the OBIA-RF method effectively extract vegetation information from UAV images, achieving an average precision and recall of 0.906 and 0.929, respectively. This method effectively generates high-accuracy FVC reference data. With the improvement in the spatial resolution of satellite images, the variability of FVC data decreases and spatial continuity increases. The RF model outperforms others in FVC estimation at 10 m and 20 m resolutions, with R2 values of 0.827 and 0.929, respectively. Conversely, the XGBoost model achieves the highest accuracy at a 30 m resolution, with an R2 of 0.847. This study also found that FVC was significantly related to a number of satellite image VIs (including red edge and near-infrared bands), and this correlation was enhanced in coarser resolution images. The method proposed in this study effectively addresses the shortcomings of conventional FVC estimation methods, improves the accuracy of FVC monitoring in soil erosion areas, and serves as a reference for large-scale ecological environment monitoring using UAV technology.
Keyword ：
fractional vegetation cover (FVC) fractional vegetation cover (FVC) machine learning (ML) machine learning (ML) multi-scale satellite multi-scale satellite uncrewed aerial vehicle (UAV) uncrewed aerial vehicle (UAV)
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| GB/T 7714 | Chen, Xunlong , Sun, Yiming , Qin, Xinyue et al. Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning [J]. | REMOTE SENSING , 2024 , 16 (19) . |
| MLA | Chen, Xunlong et al. "Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning" . | REMOTE SENSING 16 . 19 (2024) . |
| APA | Chen, Xunlong , Sun, Yiming , Qin, Xinyue , Cai, Jianwei , Cai, Minghui , Hou, Xiaolong et al. Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning . | REMOTE SENSING , 2024 , 16 (19) . |
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本实用新型公开了可变坡的微型径流小区，包括填土土槽箱本体与稳定底板连接架，所述填土土槽箱本体一端的内侧固定连接有径流收集平台，所述填土土槽箱本体一端的内侧活动连接有翻盖，所述翻盖位于径流收集平台的上端，所述稳定底板连接架的上端均定位有千斤顶和固定支架，所述千斤顶位于固定支架的一端。本实用新型所述的可变坡的微型径流小区，具有可重现性好的效果，且可以满足人工降水的试验条件，并且生产和使用成本相对较低，可以完成限制因素研究，单独量化坡度，并且除了千斤顶外，其他的都是不锈钢材质，也加强了防腐效果，延长使用的寿命，通过调节千斤顶实现调节坡度的过程。
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| GB/T 7714 | 杨凯杰 , 张厚喜 , 刘海立 et al. 可变坡的微型径流小区 : CN202323113278.3[P]. | 2023-11-17 . |
| MLA | 杨凯杰 et al. "可变坡的微型径流小区" : CN202323113278.3. | 2023-11-17 . |
| APA | 杨凯杰 , 张厚喜 , 刘海立 , 何燕 , 严鸿林 . 可变坡的微型径流小区 : CN202323113278.3. | 2023-11-17 . |
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Future climate change is expected to impact the natural systems. This study used future climate data of general circulation models (GCMs) to investigate the impacts of climate change during the future period (2062-2095) relative to the historical period (1981-2014) on the hydrological system of the Minjiang river watershed, China. A previously calibrated soil and water assessment tool (SWAT) was employed to simulate the future hydrology under the impacts of changes in temperature, precipitation, and atmospheric CO2 concentration for four shared socioeconomic pathways (SSP 1, 2, 3, and 5) of the CMIP6. The study revealed that the impacts of increase in future temperature, i.e., increase in ET, and decrease in surface runoff, water, and sediment yield will be countered by increased atmospheric [CO2], and changes in the hydrological parameters in the future will be mostly associated to changes in precipitation. Data of the GCMs for all the SSPs predicts increase in precipitation of the watershed, which will cause increase in surface runoff, water yield, and sediment yield. Surface runoff will increase more in SSP 5 (47%), while sediment and water yield will increase more in SSP 1, by 33% and 23%, respectively. At the seasonal scale, water yield and surface runoff will increase more in autumn and winter in SSP 1, while in other scenarios, these parameters will increase more in the spring and summer seasons. Sediment yield will increase more in autumn in all scenarios. Similarly, the future climate change is predicted to impact the important parameters related to the flow regime of the Minjiang river, i.e., the frequency and peak of large floods (flows > 14,000 m(3)/s) will increase along the gradient of scenarios, i.e., more in SSP 5 followed by 3, 2, and 1, while duration will increase in SSP 5 and decrease in the other SSPs. The frequency and duration of extreme low flows will increase in SSP 5 while decrease in SSP 1. Moreover, peak of extreme low flows will decrease in all scenarios except SSP 1, in which it will increase. The study will improve the general understanding about the possible impacts of future climate change in the region and provide support for improving the management and protection of the watershed's water and soil resources.
Keyword ：
climate change climate change Minjiang river watershed Minjiang river watershed soil erosion soil erosion SWAT model SWAT model water balance water balance
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| GB/T 7714 | Rashid, Haroon , Yang, Kaijie , Zeng, Aicong et al. Predicting the Hydrological Impacts of Future Climate Change in a Humid-Subtropical Watershed [J]. | ATMOSPHERE , 2022 , 13 (1) . |
| MLA | Rashid, Haroon et al. "Predicting the Hydrological Impacts of Future Climate Change in a Humid-Subtropical Watershed" . | ATMOSPHERE 13 . 1 (2022) . |
| APA | Rashid, Haroon , Yang, Kaijie , Zeng, Aicong , Ju, Song , Rashid, Abdur , Guo, Futao et al. Predicting the Hydrological Impacts of Future Climate Change in a Humid-Subtropical Watershed . | ATMOSPHERE , 2022 , 13 (1) . |
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Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U-2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A-D) in the study area were selected to evaluate the performance of the U-2-Net model in olive crown extraction in different scenarios, and the U-2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U-2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U-2-Net model increased by 14.03-23.97 percentage points, 7.57-12.85 percentage points, and 8.15-14.78 percentage points, respectively. In addition, the U-2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R-2) higher than 0.93 in all four subareas, suggesting that the U-2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U-2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees.
Keyword ：
deep learning deep learning individual tree segmentation individual tree segmentation tree crown extraction tree crown extraction UAV-based remote sensing UAV-based remote sensing visible-light image visible-light image
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| GB/T 7714 | Ye, Zhangxi , Wei, Jiahao , Lin, Yuwei et al. Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model [J]. | REMOTE SENSING , 2022 , 14 (6) . |
| MLA | Ye, Zhangxi et al. "Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model" . | REMOTE SENSING 14 . 6 (2022) . |
| APA | Ye, Zhangxi , Wei, Jiahao , Lin, Yuwei , Guo, Qian , Zhang, Jian , Zhang, Houxi et al. Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model . | REMOTE SENSING , 2022 , 14 (6) . |
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Sustainable restoration of degraded ecosystems is a major environmental concern in several regions of China. Changting is one of the severely affected water- and soil-loss areas in southern China that have been under continuous management for the last 30 years. Taking the typical red soil erosion area in Changting, Fujian, as the research object, an evaluation index system with 30 m resolution was developed based on the Sensitivity-Resilience-Pressure (SRP) model. Spatial principal component analysis, Global Moran's I, the LISA cluster map, and the CA-Markov model were employed to dynamically evaluate and predict the ecological vulnerability of the red soil erosion area in Changting. The findings revealed that the ecological vulnerability of the red soil erosion area in Changting has obvious spatial differences and topography, meteorological, and economic and social variables are the primary driving factors of ecological vulnerability. The analysis of spatial distribution of ecological vulnerability showed significant sets of contiguous locations of severe and mild ecological vulnerability. The total index of ecological vulnerability in the study area reduced by 9.49% from 2000 to 2020, yet it was still just mildly vulnerable. The proportion of severe and extremely vulnerable areas declined by 4.87% and 5.61%, respectively. The prediction results for the coming ten years showed that the ecological vulnerability of red soil erosion in Changting will tend to improve. In summary, it is found that after years of continuous ecological management in the red soil erosion area of Changting, the ecological restoration effect of the soil erosion area is obvious.
Keyword ：
CA-Markov model CA-Markov model ecological vulnerability ecological vulnerability red soil area red soil area spatial autocorrelation spatial autocorrelation spatial principal component analysis spatial principal component analysis SRP model SRP model
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| GB/T 7714 | Wu, Xinyi , Zhu, Chenlu , Yu, Junbao et al. Ecological Vulnerability in the Red Soil Erosion Area of Changting under Continuous Ecological Restoration: Spatiotemporal Dynamic Evolution and Prediction [J]. | FORESTS , 2022 , 13 (12) . |
| MLA | Wu, Xinyi et al. "Ecological Vulnerability in the Red Soil Erosion Area of Changting under Continuous Ecological Restoration: Spatiotemporal Dynamic Evolution and Prediction" . | FORESTS 13 . 12 (2022) . |
| APA | Wu, Xinyi , Zhu, Chenlu , Yu, Junbao , Zhai, Lin , Zhang, Houxi , Yang, Kaijie et al. Ecological Vulnerability in the Red Soil Erosion Area of Changting under Continuous Ecological Restoration: Spatiotemporal Dynamic Evolution and Prediction . | FORESTS , 2022 , 13 (12) . |
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Changes in the climate and landcover are the two most important factors that influence terrestrial hydrological systems. Today, watershed-scale hydrological models are widely used to estimate the individual impacts of changes in the climate and landcover on watershed hydrology. The Minjiang river watershed is an ecologically and economically important, humid, subtropical watershed, located in south-eastern China. Several studies are available on the impacts of recent climate change on the watershed; however, no efforts have been made to separate the individual contributions of climate and landcover changes. This study is an attempt to separate the individual impacts of recent (1989-2018) climate and landcover changes on some of the important hydrological components of the watershed, and highlight the most influential changes in climate parameters and landcover classes. A calibrated soil and water assessment tool (SWAT) was employed for the study. The outcomes revealed that, during the study period, water yield decreased by 6.76%, while evapotranspiration, surface runoff and sediment yield increased by 1.08%, 24.11% and 33.85% respectively. The relative contribution of climate change to landcover change for the decrease in the water yield was 95%, while its contribution to the increases in evapotranspiration, surface runoff and sediment yield was 56%, 77% and 51%, respectively. The changes in climate parameters that were most likely responsible for changes in ET were increasing solar radiation and temperature and decreasing wind speed, those for changes in the water yield were decreasing autumn precipitation and increasing solar radiation and temperature, those for the increase in surface runoff were increasing summer and one-day maximum precipitation, while those for the increasing sediment yield were increasing winter and one-day maximum precipitation. Similarly, an increase in the croplands at the expense of needle-leaved forests was the landcover change that was most likely responsible for a decrease in the water yield and an increase in ET and sediment yield, while an increase in the amount of urban land at the expense of broadleaved forests and wetlands was the landcover change that was most likely responsible for increasing surface runoff. The findings of the study can provide support for improving management and protection of the watershed in the context of landcover and climate change.
Keyword ：
climate change climate change hydrology hydrology landcover change landcover change Minjiang river watershed Minjiang river watershed soil erosion soil erosion SWAT model SWAT model
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| GB/T 7714 | Rashid, Haroon , Yang, Kaijie , Zeng, Aicong et al. The Influence of Landcover and Climate Change on the Hydrology of the Minjiang River Watershed [J]. | WATER , 2021 , 13 (24) . |
| MLA | Rashid, Haroon et al. "The Influence of Landcover and Climate Change on the Hydrology of the Minjiang River Watershed" . | WATER 13 . 24 (2021) . |
| APA | Rashid, Haroon , Yang, Kaijie , Zeng, Aicong , Ju, Song , Rashid, Abdur , Guo, Futao et al. The Influence of Landcover and Climate Change on the Hydrology of the Minjiang River Watershed . | WATER , 2021 , 13 (24) . |
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近十年来,以"高技术、高投入、高产出"为主要特征的设施农业在青藏高原地区逐渐发展壮大,已成为高原农业发展的新亮点。识别青藏高原设施农业现状发展格局及时空变化特征,有利于掌握其发展态势,为其合理规划未来发展提供数据支撑。本研究首先以2018年Google Earth高分影像数据为数据源,通过目视解译获取青藏高原2018年设施农业用地,结合地统计学分析方法,揭示了其现状空间分布格局;其次,选择西宁市和拉萨市为典型区,在目视解译获取2008年和2018年两期设施农业用地的基础上,识别了两市设施农业的时空变化特征。结果表明:①2018年青藏高原地区设施农业总面积为9,426.95 hm~2,集中分布在西藏南部、东南部和青海省东部的主要城市及周边地区,其中青海省和西藏自治区设施农业面积占比分别为65.53%和29.96%;②十年间,西宁市和拉萨市设施农业发展迅速,分别由2008年的537.32 hm~2和616.12 hm~2增长为2018年的2,231.68 hm~2和1,448.30 hm~2,且均呈由市区向外围不断蔓延的空间变化态势,设施农业的区域分布格局发生了较大变化。基于该数据集的部分研究成果发表在《资源科学》2019年第41卷第6期。
Keyword ：
空间分布 空间分布 设施农业 设施农业 资源科学 资源科学 青藏高原 青藏高原 高分影像 高分影像
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| GB/T 7714 | 魏慧 , 吕昌河 , 杨凯杰 et al. 青藏高原及其典型地区设施农业空间分布数据集 [J]. | 全球变化数据学报(中英文) , 2019 , 3 (04) : 364-369,471-476 . |
| MLA | 魏慧 et al. "青藏高原及其典型地区设施农业空间分布数据集" . | 全球变化数据学报(中英文) 3 . 04 (2019) : 364-369,471-476 . |
| APA | 魏慧 , 吕昌河 , 杨凯杰 , 刘亚群 . 青藏高原及其典型地区设施农业空间分布数据集 . | 全球变化数据学报(中英文) , 2019 , 3 (04) , 364-369,471-476 . |
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设施农业的快速增长是近10年来青藏高原农业发展的一个突出亮点。揭示设施农业用地的空间分布和变化特征,有助于理解青藏高原设施农业的发展态势,为其规划布局提供决策支持。本文基于2018年Google Earth高分影像数据,采用目视解译和地统计学分析相结合的方法,获取了青藏高原设施农业用地的空间分布格局;并选择西宁和拉萨为典型区,对比2008年和2018年设施农业用地的时空变化特征。结果表明:①2018年青藏高原共有设施农业面积7821.74 hm~2,主要集中在河流两侧的城市周边,与河流走向大致吻合,其中青海和西藏设施农业面积分别占青藏高原设施农业总面积的58.10%和36.49%;②受海拔、地貌类型和城市分布的影响,设施农业分布海拔在1400～4600 m之间,但在2200～2600 m和3600～3900 m高程区间分布最为集中;③2008—2018年西宁和拉萨设施农业增长迅速,分别从293.73 hm~2和429.01 hm~2增至2111.45 hm~2和1422.30 hm~2。同时,因城市发展,两个城市超过60%的设施农业用地被建设占用,造成空间格局的显著变化;④设施农业在青藏高原发展前景良好,但也存在温室类型单一、变动频繁和"过程性浪费"等问题,应加强保护和规划管理,促进设施农业的良性发展。
Keyword ：
时空变化 时空变化 空间分异 空间分异 设施农业 设施农业 青藏高原 青藏高原 高分影像 高分影像
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| GB/T 7714 | 魏慧 , 吕昌河 , 刘亚群 et al. 青藏高原设施农业分布格局及变化 [J]. | 资源科学 , 2019 , 41 (06) : 1093-1101 . |
| MLA | 魏慧 et al. "青藏高原设施农业分布格局及变化" . | 资源科学 41 . 06 (2019) : 1093-1101 . |
| APA | 魏慧 , 吕昌河 , 刘亚群 , 杨凯杰 . 青藏高原设施农业分布格局及变化 . | 资源科学 , 2019 , 41 (06) , 1093-1101 . |
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