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学者姓名：阙翔
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Abstract ：
Drawing upon an empirical case study of China's Quanzhou Bay Estuary Wetland (QBEW) area, this study underscores spatial and temporal variations of underlying forces that shape changes in the habitat quality (HQ) of wetlands. Specifically, by integrating the InVEST model-based ecosystem services or HQ assessment and the STWR-based statistical modeling, we have found evident spatial and temporal variations among the effects of different environmental conditions and socioeconomic factors on HQ. HQ in areas above 200 m is significantly better than that of areas below an altitude of 50 m in our study region when holding other factors constant. Additionally, locations with higher values of the Normalized Difference Vegetation Index (NDVI) are associated with better HQ, especially in areas near extensive mangrove forests. In contrast, night light (NL) reflectance, a proxy of socioeconomic activities, negatively impacts HQ, but its influence has been weakened throughout the study period. Another indicator of urbanization, i.e., population density (PD), however, has had a weak and unstable effect on HQ. Our findings suggest that wetland conservation would be more effective if it could be more closely tied to place- and time-specific socioeconomic and environmental contexts, and the integrated framework developed in this research would assist in effective ecological improvement efforts and resource allocation for sustaining wetland HQ.
Keyword ：
Habitat quality Habitat quality Improvement strategies Improvement strategies InVEST InVEST Spatiotemporal weighted regression (STWR) Spatiotemporal weighted regression (STWR) Wetlands Wetlands
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| GB/T 7714 | Wang, Chen , Ding, Xiaoting , Lian, Haifeng et al. Wetland Habitat Quality Assessment and Improvement From A Perspective of Spatiotemporal Heterogeneity [J]. | WETLANDS , 2025 , 45 (7) . |
| MLA | Wang, Chen et al. "Wetland Habitat Quality Assessment and Improvement From A Perspective of Spatiotemporal Heterogeneity" . | WETLANDS 45 . 7 (2025) . |
| APA | Wang, Chen , Ding, Xiaoting , Lian, Haifeng , Ma, Xiaogang , Li, Chenhao , Li, Zhizhen et al. Wetland Habitat Quality Assessment and Improvement From A Perspective of Spatiotemporal Heterogeneity . | WETLANDS , 2025 , 45 (7) . |
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地质领域研究正经历以构建新知识体系为核心、大数据为驱动的深刻变革。地质知识图谱的构建能够有效地解决在数据分散状态下的知识发现与推理受限等问题。关系抽取技术作为知识图谱构建的关键技术之一，在地质实体关系识别中发挥关键作用。传统关系抽取技术高度依赖大规模标注数据。然而地质领域中实体关系复杂且专业性强，人工标注数据耗时费力，致使大规模标注数据短缺。因此，传统关系抽取技术在地质领域的有效应用受限。针对上述困境，本研究提出基于原型网络的地质关系抽取小样本学习方法，创新性地引入增强提示学习机制，并通过对比学习优化实例表示和关系描述表示，显著地提升了原型代表性。同时，采用加权损失函数和困难任务辅助训练策略，增强模型对困难任务的关注度，有效地提高了整体准确率。实验结果表明，本文提出的模型在地质小样本关系抽取数据集的5way 1-shot场景下准确率达到82.16%,相比通用领域先进模型SimpleFSRE提升1.94%,相比原型网络Proto-BERT方法提升9.01%,验证了所提方法的有效性。
Keyword ：
关系抽取 关系抽取 原型网络 原型网络 地质知识图谱 地质知识图谱 小样本学习 小样本学习 提示学习 提示学习
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| GB/T 7714 | 张志庭 , 彭帅 , 阙翔 et al. 基于提示和度量学习的小样本地质关系抽取 [J]. | 地学前缘 , 2025 , 32 (04) : 250-261 . |
| MLA | 张志庭 et al. "基于提示和度量学习的小样本地质关系抽取" . | 地学前缘 32 . 04 (2025) : 250-261 . |
| APA | 张志庭 , 彭帅 , 阙翔 , 陈麒玉 . 基于提示和度量学习的小样本地质关系抽取 . | 地学前缘 , 2025 , 32 (04) , 250-261 . |
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本发明涉及一种基于时空异质性的能源碳减排路径提取方法，属于能源、环境科学与资源利用的交叉应用领域。所述方法，包括：基于遥感影像反演估算县级能源碳排放ECE；利用时空加权回归STWR生成的显著系数，检测县级主导因素的转变；基于县级主导因素的转变和县级ECE动态变化之间的关联，进行县级路径分析。本发明可探测能源碳排放与不同驱动因素之间的时空异质性关系，通过匹配局部碳排放的变化量与其主导因素之间的转移（路径）关系，学习和提取出不同时间和不同区域的最优的转移路径，为决策者制定差异化碳减排策略提供依据。
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| GB/T 7714 | 阙翔 , 赖雨婷 , 费婷婷 et al. 一种基于时空异质性的能源碳减排路径提取方法 : CN202510415606.X[P]. | 2025-04-03 . |
| MLA | 阙翔 et al. "一种基于时空异质性的能源碳减排路径提取方法" : CN202510415606.X. | 2025-04-03 . |
| APA | 阙翔 , 赖雨婷 , 费婷婷 , 庄馨涵 , 徐小莹 , 张燕娇 et al. 一种基于时空异质性的能源碳减排路径提取方法 : CN202510415606.X. | 2025-04-03 . |
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Riparian ecosystems are ecologically sensitive landscapes that are highly vulnerable to drought, yet the spatial and temporal dynamics of riparian vegetation response to drought severity remain poorly understood, especially in large, human-influenced river basins. This study examines how riparian vegetation in the Lower Mississippi River Basin (LMRB) responds to varying drought severity levels using a multi-method approach that integrates U.S. Drought Monitor classifications (D0-D4) with MODIS-derived NDVI indices for vegetation resistance and recovery. We combined regression models, spatial autocorrelation (Moran's I), hot spot analysis, and ANOVA to assess vegetation health across 2010-2014. Results reveal counterintuitive patterns: high-resistance areas corresponded to more weeks of moderate drought (D1), suggesting the influence of land management practices, while extreme drought (D4) was linked to low resistance. Recovery patterns varied by drought exposure, with paradoxical associations between higher recovery and moderate drought (D1-D2). Spatial clustering of vegetation responses diminished over time, highlighting potential legacy effects and homogenization. These findings underscore the need to consider spatial variability, drought history, and land use in managing drought impacts on riparian ecosystems, offering practical insights for resilience strategies in similar dynamic regions.
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| GB/T 7714 | Wang, Hui , Wang, Zhe , Qu, Shijin et al. Unraveling the link between riparian vegetation health and drought severity [J]. | ECOLOGICAL INDICATORS , 2025 , 178 . |
| MLA | Wang, Hui et al. "Unraveling the link between riparian vegetation health and drought severity" . | ECOLOGICAL INDICATORS 178 (2025) . |
| APA | Wang, Hui , Wang, Zhe , Qu, Shijin , Que, Xiang , Yao, Zhiyuan . Unraveling the link between riparian vegetation health and drought severity . | ECOLOGICAL INDICATORS , 2025 , 178 . |
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Monitoring mangrove phenology requires frequent, high-resolution remote sensing data, yet satellite imagery often suffers from coarse resolution and cloud interference. Traditional methods, such as denoising and spatiotemporal fusion, faced limitations: denoising algorithms usually enhance temporal resolution without improving spatial quality, while spatiotemporal fusion models struggle with prolonged data gaps and heavy noise. This study proposes an optimized mangrove phenology extraction approach (OMPEA), which integrates Landsat and MODIS data with a denoising algorithm (e.g., Gap Filling and Savitzky-Golay filtering, GF-SG) and a spatiotemporal fusion model (e.g., Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model, ESTARFM). The key of OMPEA is that GF-SG algorithm filled data gaps from cloud cover and satellite transit gaps, providing high-quality input to ESTARFM and improving its accuracy of NDVI imagery reconstruction in mangrove phenology extraction. By conducting experiments on the GEE platform, OMPEA generates 1-day, 30 m NDVI imagery, from which phenological parameters (i.e., the start (SoS), end (EoS), length (LoS), and peak (PoS) of the growing season) are derived using the maximum separation (MS) method. Validation in four mangrove areas along the coastal China shows that OMPEA significantly improves the potential to capture mangrove phenology in the presence of incomplete data. The OMPEA significantly increased usable data, adding 7-33 Landsat images and 318-415 MODIS images per region. The generated NDVI series exhibits strong spatiotemporal consistency with original data (R2: 0.788-0.998, RMSE: 0.007-0.253) and revealed earlier SoS and longer LoS at lower latitudes. Cross-correlation analysis showed a 2-3 month lagged effects of temperature on mangroves' growth, with precipitation having minimal impact. The proposed OMPEA improves the possibility of capturing mangrove phenology under non-continuous and low-resolution data, providing valuable insights for large-scale and long-term mangrove conservation and management.
Keyword ：
denoising algorithm denoising algorithm mangrove forests mangrove forests OMPEA OMPEA phenology monitoring phenology monitoring spatiotemporal interpolation spatiotemporal interpolation
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| GB/T 7714 | Hong, Yu , Zhou, Runfa , Liu, Jinfu et al. Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA) [J]. | REMOTE SENSING , 2025 , 17 (3) . |
| MLA | Hong, Yu et al. "Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA)" . | REMOTE SENSING 17 . 3 (2025) . |
| APA | Hong, Yu , Zhou, Runfa , Liu, Jinfu , Que, Xiang , Chen, Bo , Chen, Ke et al. Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA) . | REMOTE SENSING , 2025 , 17 (3) . |
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统计学科与自然资源学科的交叉融合有助于实现“双碳”目标的人才培养需求。福建农林大学统计学硕士点采取统计学与林学、生态学、计算机科学与技术等学科交叉融合创新，形成统计学专业（生态与资源统计方向）的创新人才培养体系。培养途径包括：树立多学科交叉融合人才培养理念，明确培养“双碳”人才的科学定位；构建多学科交叉的“双碳”课程体系，把握重点学科，建设优势专业；加强慕课平台建设，提倡互动式教学；鼓励研究生积极参与多平台学术讲座，促进学术交流，及时掌握“双碳”研究领域动向；深化产教融合，量身定制差异化的科研实训方案；构建实践创新教学平台，积极鼓励研究生参加各类学科竞赛、项目研究等活动，教师团队提供必要的指导。多学科交叉融合人才培养体系通过整合福建农林大学生态与资源统计研究方向的人才和科技创新力量，取得较好的科研教学发展和“双碳”人才培养成果，为各高校开展相关教学改革工作提供参考。
Keyword ：
人才培养体系 人才培养体系 多学科交叉融合 多学科交叉融合 生态与资源 生态与资源 碳达峰碳中和 碳达峰碳中和 统计学 统计学
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| GB/T 7714 | 刘金福 , 徐道炜 , 潘燕萍 et al. 构建服务“双碳”目标的统计学专业研究生人才培养体系——以福建农林大学为例 [J]. | 中国林业教育 , 2025 , 43 (01) : 46-50 . |
| MLA | 刘金福 et al. "构建服务“双碳”目标的统计学专业研究生人才培养体系——以福建农林大学为例" . | 中国林业教育 43 . 01 (2025) : 46-50 . |
| APA | 刘金福 , 徐道炜 , 潘燕萍 , 阙翔 . 构建服务“双碳”目标的统计学专业研究生人才培养体系——以福建农林大学为例 . | 中国林业教育 , 2025 , 43 (01) , 46-50 . |
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本发明提供一种基于可达时间约束的城市资源享有不平等性动态评估方法，包括以下步骤：独立构建供给端模型、需求端模型与连接端模型，当供给端、需求端或连接端任一参数变化时，仅更新受影响模型的数据，并基于动态可达性重新计算不平等性评估结果；根据供给端能力、需求端权重与连接端可达性的动态交互关系，生成空间资源享有不平等性量化指标；结合所述不平等性量化指标，通过粒子群算法生成新增资源选址方案，所述方案满足最小间距约束、服务覆盖率约束及功能多样性约束。
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| GB/T 7714 | 阙翔 , 庄馨涵 , 杨好好 et al. 一种基于可达时间约束的城市资源享有不平等性动态评估方法 : CN202510593710.8[P]. | 2025-05-09 . |
| MLA | 阙翔 et al. "一种基于可达时间约束的城市资源享有不平等性动态评估方法" : CN202510593710.8. | 2025-05-09 . |
| APA | 阙翔 , 庄馨涵 , 杨好好 , 付权利 , 郑煜滨 , 元文丽 et al. 一种基于可达时间约束的城市资源享有不平等性动态评估方法 : CN202510593710.8. | 2025-05-09 . |
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Guangdong, Fujian, and Zhejiang (GFZ), located on China's southeast coast, have long been economically active and rapidly growing provinces in China. However, the rising energy consumption in these provinces poses a major challenge to their carbon emissions reduction. Due to the spatial variation in the natural environment and socio-economic activities, energy carbon emissions (ECEs) and their reduction may vary among counties. The matter of scientifically formulating localized carbon reduction paths has therefore become a critical issue. This study proposed a novel path analysis framework based on exploring spatiotemporal heterogeneity using a spatiotemporal statistic model (i.e., spatiotemporal weighted regression). The path's learning procedure was based on linking the changes in the amount of ECEs to the shifts in dominant factors, which were detected through local significance tests on the coefficients of STWR. To verify its effectiveness, we conducted a county-level empirical study considering four drivers (i.e., population (P), impervious surfaces (I), the proportion of secondary industry (manufacturing, M), and the proportion of tertiary industry (services, S)) in GFZ from 2014 to 2021. The ECEs show two different trends that may be affected by the COVID-19 pandemic and economic recession; hence, we divided them into two periods: an active period (2014-2018) and a stable period (2018-2021). Many interpretable paths and their occurrences were derived from our results, including the following: (1) P and S showed higher sensitivity to the changes in ECEs compared with I and M. Most counties (more than 50%) were dominated by P, but the dominator P may shift to I, M, and S during the active period. Many S-dominated counties reverted to being P-dominated ones during the stable period. (2) For the active period, the two most significant paths, M+ -> S- and M+ -> P+ (+/- denotes positive or negative impacts of dominated driver), reduced ECEs by about 7.747 x 105 tons and 3.145 x 105 tons, respectively. Meanwhile, the worst path, S+ -> P+, increased ECEs by nearly 1.186 x 106 tons. (3) For the stable period, the best path (S+ -> I+) significantly reduced ECEs by 1.122 x 106 tons, while the worst two paths, M- -> P+ and I+ -> P+, increased ECEs by 1.978 x 106 tons and 4.107 x105 tons, respectively. These findings verify the effectiveness of our framework and further highlight the need for tailored, region-specific policies to achieve carbon reduction goals.
Keyword ：
energy carbon emissions energy carbon emissions path analysis path analysis remote sensing remote sensing spatiotemporal heterogeneity spatiotemporal heterogeneity spatiotemporal weighted regression spatiotemporal weighted regression
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| GB/T 7714 | Lai, Yuting , Fei, Tingting , Wang, Chen et al. Energy Carbon Emission Reduction Based on Spatiotemporal Heterogeneity: A County-Level Empirical Analysis in Guangdong, Fujian, and Zhejiang [J]. | SUSTAINABILITY , 2025 , 17 (7) . |
| MLA | Lai, Yuting et al. "Energy Carbon Emission Reduction Based on Spatiotemporal Heterogeneity: A County-Level Empirical Analysis in Guangdong, Fujian, and Zhejiang" . | SUSTAINABILITY 17 . 7 (2025) . |
| APA | Lai, Yuting , Fei, Tingting , Wang, Chen , Xu, Xiaoying , Zhuang, Xinhan , Que, Xiang et al. Energy Carbon Emission Reduction Based on Spatiotemporal Heterogeneity: A County-Level Empirical Analysis in Guangdong, Fujian, and Zhejiang . | SUSTAINABILITY , 2025 , 17 (7) . |
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统计学科与自然资源学科的交叉融合有助于实现"双碳"目标的人才培养需求.福建农林大学统计学硕士点采取统计学与林学、生态学、计算机科学与技术等学科交叉融合创新,形成统计学专业(生态与资源统计方向)的创新人才培养体系.培养途径包括:树立多学科交叉融合人才培养理念,明确培养"双碳"人才的科学定位;构建多学科交叉的"双碳"课程体系,把握重点学科,建设优势专业;加强慕课平台建设,提倡互动式教学;鼓励研究生积极参与多平台学术讲座,促进学术交流,及时掌握"双碳"研究领域动向;深化产教融合,量身定制差异化的科研实训方案;构建实践创新教学平台,积极鼓励研究生参加各类学科竞赛、项目研究等活动,教师团队提供必要的指导.多学科交叉融合人才培养体系通过整合福建农林大学生态与资源统计研究方向的人才和科技创新力量,取得较好的科研教学发展和"双碳"人才培养成果,为各高校开展相关教学改革工作提供参考.
Keyword ：
人才培养体系 人才培养体系 多学科交叉融合 多学科交叉融合 生态与资源 生态与资源 碳达峰碳中和 碳达峰碳中和 统计学 统计学
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| GB/T 7714 | 刘金福 , 徐道炜 , 潘燕萍 et al. 构建服务"双碳"目标的统计学专业研究生人才培养体系 [J]. | 中国林业教育 , 2025 , 43 (1) : 46-50 . |
| MLA | 刘金福 et al. "构建服务"双碳"目标的统计学专业研究生人才培养体系" . | 中国林业教育 43 . 1 (2025) : 46-50 . |
| APA | 刘金福 , 徐道炜 , 潘燕萍 , 阙翔 . 构建服务"双碳"目标的统计学专业研究生人才培养体系 . | 中国林业教育 , 2025 , 43 (1) , 46-50 . |
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Mangroves are an essential component of coastal ecosystems. Accurate and effective identification and extraction of mangrove areas from remote sensing imagery is crucial for monitoring changes in the nearshore ecological environment. High -resolution remote sensing imagery is often difficult or expensive to obtain and usually lacks sufficient temporal coverage, so most monitoring of mangrove forests still relies on medium- or low -resolution imagery, resulting in inaccurate distribution of extracted mangrove areas. The super -resolution (SR) images generated by increasingly widely used deep learning (DL) models may be an alternative. To validate this, we evaluated the extraction of mangroves from SR images generated by four DL models (i.e., enhanced superresolution generative adversarial networks (ESRGAN), Real-ESRGAN, vast -receptive -field pixel attention network (VapSR), and image restoration methods using swin transformer (SwinIR)). The models were trained on paired (high -resolution) Chinese GF-1 satellite images and (low -resolution) Landsat 8 datasets. The performance of model fitting, vegetation indices, and extracted mangrove areas was evaluated using three commonly used classifiers (i.e., support vector machines (SVM), random forest (RF), and gradient -boosted decision tree (GBDT)) in combination with ground -truth sampling points and public mangrove datasets. Results showed that: (1) the SR images generated by DL -based models can facilitate the extraction of mangrove features. (2) The quality evaluation metrics peak signal-to-noise ratio (PSNR) and structural similarity index measurements (SSIM) cannot be regarded as absolute criteria for SR images, especially when the SR images were used for mangrove feature extractions. (3) The SR image generated by the VapSR model was best suited for extracting mangrove forests and performed even better than the original high -resolution images. (4) Taking a public dataset as a benchmark, the mangrove areas extracted from the SR image generated by the VapSR model were closer to the benchmark than the original Landsat-8 and GF-1. Overall, the DL -based SR models can enhance mangrove extractions and potentially have widespread applications.
Keyword ：
Deep learning Deep learning Mangrove extraction Mangrove extraction Remote sensing Remote sensing Super-resolution models Super-resolution models
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| GB/T 7714 | Hong, Yu , Que, Xiang , Wang, Zhe et al. Mangrove extraction from super-resolution images generated by deep learning models [J]. | ECOLOGICAL INDICATORS , 2024 , 159 . |
| MLA | Hong, Yu et al. "Mangrove extraction from super-resolution images generated by deep learning models" . | ECOLOGICAL INDICATORS 159 (2024) . |
| APA | Hong, Yu , Que, Xiang , Wang, Zhe , Ma, Xiaogang , Wang, Hui , Salati, Sanaz et al. Mangrove extraction from super-resolution images generated by deep learning models . | ECOLOGICAL INDICATORS , 2024 , 159 . |
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