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学者姓名：汪小钦
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Abstract ：
生态环境健康评价对于促进生态保护、引导区域经济发展战略、调整和衡量生态文明建设结果具有重要意义。综合指标体系模型是现今国内外主流的评价方法，然而，如何构建不同地区通用、普适性强的指标体系，如何从众多繁杂的指标通过客观、科学的方法自动筛选出能表征研究区特点的重要指标是目前所面临的难点。本文集成压力-状态-响应模型（PressureState-Response,PSR）和生态层次网络模型（Ecological Hierarchy Network,EHN），并考虑部分指标所存在的信息重叠，建立了目标层-准则层-要素层-指标层-同类指标层的5层网状指标体系，提出了基于优劣解距离法（Technique for Order Preference by Similarity to Ideal Solution,TOPSIS）的同类指标层约简和基于目标优化理论的指标层约简相结合的两段式自适应指标约简模式。结合两者完成面向生态环境健康评价的自适应指标约简模型的构建，并在地理大数据的支持下，应用于云南、福建、京津冀、陕西、湖北、新疆和吉林7个生态环境迥异区域的2001—2021年生态环境健康评价。研究结果表明：(1)利用两段式自适应指标约简模型所筛选出的中选指标可以较好地体现不同地区生态系统特点，中选指标中权重靠前的指标被较多文献应用于各地区指标体系构建，说明所构建的指标体系和两段式自适应指标约简模型具有较好的普适性和合理性，有效避免了人为指标体系构建的主观性；(2) 7个地区生态环境健康状况的空间分布和时间变化趋势符合实际情况，并且能与现有的文献、资料进行互相印证，从侧面证实了本文所提出模型的有效性。本文所提出的模型可为其他领域指标体系构建和筛选提供参考，也为大范围不同区域的生态环境健康评价提供方法支撑。
Keyword ：
CRITIC法 CRITIC法 优劣解距离法 优劣解距离法 压力-状态-响应模型 压力-状态-响应模型 指标约简 指标约简 最大化偏差模型 最大化偏差模型 熵权法 熵权法 生态层次网络模型 生态层次网络模型 目标优化模型 目标优化模型 网状指标体系 网状指标体系
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| GB/T 7714 | 陈建辉 , 汪小钦 , 孔令凤 . 面向生态环境健康评价的自适应指标约简模型构建 [J]. | 地球信息科学学报 , 2024 , 26 (05) : 1193-1211 . |
| MLA | 陈建辉 等. "面向生态环境健康评价的自适应指标约简模型构建" . | 地球信息科学学报 26 . 05 (2024) : 1193-1211 . |
| APA | 陈建辉 , 汪小钦 , 孔令凤 . 面向生态环境健康评价的自适应指标约简模型构建 . | 地球信息科学学报 , 2024 , 26 (05) , 1193-1211 . |
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面向美丽中国生态文明建设所需，亟待有效实现流域精细治理的科学决策，以根据流域综合治理愿景目标，优化流域管理措施（BMP）的空间布局方案（即BMP情景）、制定符合实际需求的实施路线图。对此，“流域系统模拟—情景优化”方法框架近年展现出广阔应用前景。本文介绍了该框架在应对实际应用需求中尚存的一系列问题，开展了体系性的方法研究：(1)提出新的流域过程建模框架，以兼顾建模灵活性和高性能计算、高效实现流域系统模拟；(2)提出以坡位单元作为BMP空间配置单元、并在情景优化过程中可进行单元边界动态调整的BMP情景优化方法，可有效考虑流域综合治理的经验知识，保障优化结果合理性；(3)提出考虑分阶段投资约束的BMP情景实施次序优化方法，可推荐出符合实际落地需求的实施路线图；(4)设计研发用户友好的参与式流域规划系统，供各方利益相关者协商决策。通过典型小流域应用案例验证了上述新方法、工具和原型系统的有效性和实用价值。
Keyword ：
决策支持 决策支持 情景分析 情景分析 智能优化 智能优化 流域管理措施 流域管理措施 流域系统模拟 流域系统模拟
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| GB/T 7714 | 秦承志 , 朱良君 , 申申 et al. 基于流域系统模拟一情景优化的精细治理决策支持方法 [J]. | 地理学报 , 2024 , 79 (01) : 58-75 . |
| MLA | 秦承志 et al. "基于流域系统模拟一情景优化的精细治理决策支持方法" . | 地理学报 79 . 01 (2024) : 58-75 . |
| APA | 秦承志 , 朱良君 , 申申 , 吴彤 , 肖桂荣 , 吴升 et al. 基于流域系统模拟一情景优化的精细治理决策支持方法 . | 地理学报 , 2024 , 79 (01) , 58-75 . |
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建筑物变化检测在城市环境监测、土地规划管理和违章违规建筑识别等应用中具有重要作用。针对传统孪生神经网络在影像变化检测中存在的检测边界与实际边界吻合度低的问题，本文结合面向对象图像分析技术，提出一种基于面向对象孪生神经网络（Obj-SiamNet）的高分辨率遥感影像变化检测方法，利用模糊集理论自动融合多尺度变化检测结果，并通过生成对抗网络实现训练样本迁移。该方法应用在高分二号和高分七号高分辨率卫星影像中，并与基于时空自注意力的变化检测模型（STANet）、视觉变化检测网络（ChangeNet）和孪生UNet神经网络模型（Siam-NestedUNet）进行比较。结果表明：（1）融合面向对象多尺度分割的检测结果较单一尺度分割的检测结果，召回率最高提升32%,F1指数最高提升25%，全局总体误差（GTC）最高降低7%;(2)在样本数量有限的情况下，通过生成对抗网络进行样本迁移，与未使用样本迁移前的检测结果相比，召回率最高提升16%,F1指数最高提升14%,GTC降低了9%;(3) Obj-SiamNet方法较其他变化检测方法，整体检测精度得到提升，F1指数最高提升23%,GTC最高降低9%。该方法有效提高了建筑物变化检测在几何和属性方面的精度，并能有效利用开放地理数据集，降低了模型训练样本制作成本，提升了检测效率和适用性。
Keyword ：
孪生神经网络 孪生神经网络 模糊集融合 模糊集融合 生成对抗网络 生成对抗网络 遥感变化检测 遥感变化检测 面向对象多尺度分析 面向对象多尺度分析 高分辨率遥感影像 高分辨率遥感影像
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| GB/T 7714 | 刘宣广 , 李蒙蒙 , 汪小钦 et al. 基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测 [J]. | 遥感学报 , 2024 , 28 (02) : 437-454 . |
| MLA | 刘宣广 et al. "基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测" . | 遥感学报 28 . 02 (2024) : 437-454 . |
| APA | 刘宣广 , 李蒙蒙 , 汪小钦 , 张振超 . 基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测 . | 遥感学报 , 2024 , 28 (02) , 437-454 . |
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遥感技术已成为快速有效获取农业大棚覆盖信息的重要途径，但遥感影像空间分辨率大小对提取精度的影响具有双重性，选择适宜分辨率影像具有重要意义。以南方农业塑料大棚为研究对象，利用GF-1、GF-2和Sentinel-2形成1～16 m间6个不同空间分辨率影像数据集，基于面向对象影像分析方法（Object-Based Image Analysis,OBIA），分别利用面向对象卷积神经网络（Convolutional Neural Network,CNN）方法和随机森林（Random forest,RF）方法开展大棚提取，分析提取精度和不同方法下的差异性。结果表明：(1)CNN和RF方法下，农业大棚的提取精度随着影像分辨率降低总体呈下降趋势，在1～16 m的影像上均能检测到农业大棚；(2)相对于RF方法，CNN方法对影像空间分辨率要求更高，在1～2 m分辨率下，CNN方法有更少的漏提和误提，但在4m及更低分辨率下，RF方法的适用性更高；(3)2 m分辨率影像是大棚信息提取的最佳空间分辨率，可经济有效地实现大棚监测。
Keyword ：
农业大棚提取 农业大棚提取 空间分辨率 空间分辨率 随机森林 随机森林 面向对象CNN方法 面向对象CNN方法 高分辨率遥感数据 高分辨率遥感数据
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| GB/T 7714 | 林欣怡 , 汪小钦 , 汤紫霞 et al. 基于面向对象CNN和RF的不同空间分辨率遥感影像农业大棚提取研究 [J]. | 遥感技术与应用 , 2024 , 39 (02) : 315-327 . |
| MLA | 林欣怡 et al. "基于面向对象CNN和RF的不同空间分辨率遥感影像农业大棚提取研究" . | 遥感技术与应用 39 . 02 (2024) : 315-327 . |
| APA | 林欣怡 , 汪小钦 , 汤紫霞 , 李蒙蒙 , 吴瑞姣 , 黄德华 . 基于面向对象CNN和RF的不同空间分辨率遥感影像农业大棚提取研究 . | 遥感技术与应用 , 2024 , 39 (02) , 315-327 . |
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[目的]模拟未来土地利用和气候影响下的流域水沙变化有利于制定适合的流域管理计划。[方法]基于土地利用和气象数据，结合CMIP6气候模式数据、PLUS模型和SWAT模型，定量模拟2030年土地利用及不同气候情景下径流和泥沙的时空变化。[结果](1)SWAT模型在闽江流域月尺度模拟精度较好，其中径流模拟的R~2范围为0.80～0.95,NSE范围为0.75～0.91;泥沙模拟的R~2范围为0.75～0.98,NSE范围为0.64～0.94。(2)利用2020年土地利用数据对PLUS模型进行精度评估的Kappa系数为0.77,模拟2030年闽江流域建设用地和耕地将分别增加325.64,1 157.51 km~2。(3)SSP2-4.5和SSP5-8.5情景下，2025—2035年平均降水量分别增加0.15%和2.18%,年平均气温分别增加0.23,0.62℃。(4)低碳情景和高碳情景下，仅土地利用变化导致年平均径流量相较于基准期分别增加0.08%和0.07%,年平均输沙量分别增加0.24%和减少0.05%;仅气候变化导致年平均径流量相较基准期分别减少4.76%和4.11%,年平均输沙量分别增加18.12%和0.13%;土地利用和气候综合影响导致年平均径流量相较于基准期分别减少4.57%和3.93%,年平均输沙量分别增加18.28%和0.33%。(5)未来气候和土地利用综合变化情景下，地表径流和产沙量较高且增幅较大的区域集中在以南平邵武市为中心的流域西北部和以三明将乐县为中心的流域西南部。[结论]研究结果为未来闽江流域的合理开发建设提供一定参考依据。
Keyword ：
土地利用变化 土地利用变化 径流 径流 模拟 模拟 气候情景 气候情景 输沙量 输沙量 闽江流域 闽江流域
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| GB/T 7714 | 余文广 , 陈芸芝 , 唐丽芳 et al. 气候和土地利用变化情景下闽江流域水沙变化模拟 [J]. | 水土保持学报 , 2024 , 38 (02) : 216-233,245 . |
| MLA | 余文广 et al. "气候和土地利用变化情景下闽江流域水沙变化模拟" . | 水土保持学报 38 . 02 (2024) : 216-233,245 . |
| APA | 余文广 , 陈芸芝 , 唐丽芳 , 汪小钦 . 气候和土地利用变化情景下闽江流域水沙变化模拟 . | 水土保持学报 , 2024 , 38 (02) , 216-233,245 . |
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The evaluation of regional ecological status has far-reaching significance for understanding regional ecological conditions and promoting sustainable development. Herein, a geospatial ecological index (GEI) was developed on the basis of Landsat data and the principles of soil lines and spatial geometry. Specifically, the GEI integrates four remote sensing indicators: Perpendicular Vegetation Index (PVI) representing greenness, Modified Perpendicular Drought Index (MPDI) representing drought, Normalized Difference Built-up and Soil Index (NDSI) representing the dryness of land surface, and Land Surface Temperature (LST) representing the hotness of land surface. Two typical regions, Fuzhou City and Zijin mining area, in Fujian Province, China, were selected to evaluate regional ecological quality via the proposed GEI. The results show an improvement in the overall ecological quality of Fuzhou City, with an increase in the average GEI value from 0.49 in 2001 to 0.53 in 2020. In the case of the Zijin mining area, regions with poor ecological status are concentrated in the main mining areas. However, the average GEI value rose from 0.51 in 1992 to 0.57 in 2020, illustrating an improvement in its ecological conditions. The study demonstrates the robustness and effectiveness of GEI, objectively revealing the spatial distribution and ecological status.
Keyword ：
ecological evaluation ecological evaluation fujian fujian GEI (Geospatial Ecological Index) GEI (Geospatial Ecological Index) geometric space geometric space soil line soil line
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| GB/T 7714 | Lin, Mengjing , Zhao, Yang , Shi, Longyu et al. A novel ecological evaluation index based on geospatial principles and remote sensing techniques [J]. | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY , 2024 . |
| MLA | Lin, Mengjing et al. "A novel ecological evaluation index based on geospatial principles and remote sensing techniques" . | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY (2024) . |
| APA | Lin, Mengjing , Zhao, Yang , Shi, Longyu , Wang, Xiaoqin . A novel ecological evaluation index based on geospatial principles and remote sensing techniques . | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY , 2024 . |
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Aerosol optical depth（AOD）is one of the most important parameters for aerosols. Existing AOD products derived from remote sensing data are seriously affected by clouds，snow，and many other factors. It is thus of great significance to generate AOD with large spatial coverage. This paper proposes a spatio-temporal continuous AOD reconstructed method（i.e.，IDW-CatBoost）based upon Inverse Distance Weight interpolation（IDW）and CatBoost models. It fuses the MAIAC AOD of MODIS，the AHI AOD of Himawari-8，and meteorological and elevation data. The IDW-CatBoost model was applied to the AOD reconstruction of Beijing-Tianjin-Hebei（BTH）and Taiwan Island，and compared with IDW and CatBoost methods. We validated reconstruction results using the ground-based monitoring AERONET AOD，where 352 samples were used for BTH and 641 for Taiwan Island. Results showed that the AOD obtained by IDW had star-dotted features in spatial distribution，while CatBoost and IDW-CatBoost AODs exhibited texture features of continuous spatial distribution. The AOD results of IDW were close to those of the IDW-CatBoost method in the BTH region when verifying by ground monitoring AERONET AOD data. Compared with IDW and CatBoost methods，the AOD results of the IDW-CatBoost in Taiwan Island were improved by 10% and 5%，respectively，for the R2 measure. Compared with single-sensor AHI L2，L3，and MAIAC AODs，the accuracy of the IDW-CatBoost AOD fused with multi-sensor data was significantly improved，leading to an R2 improvement of 15%，35% and 12%，and a RMSE decrease of 25%，38% and 22%，respectively，in the BTH region. Moreover，in Taiwan Island，the R2 was improved by 14%，76% and 76%，and the RMSE decreased by 6%，24% and 24%，respectively. We conclude that the IDW-CatBoost method fused with multi-sensor data is suitable to reconstruct accurate AOD products for large areas. © 2023 Science Press. All rights reserved.
Keyword ：
aerosol optical thickness（AOD） aerosol optical thickness（AOD） AOD reconstruction AOD reconstruction CatBoost CatBoost multi-sensor fusion multi-sensor fusion spatial and temporal continuous distribution spatial and temporal continuous distribution
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| GB/T 7714 | Zhang, C. , Wang, X. , Wu, Q. et al. Spatio-temporal continuous AOD reconstruction model based on multi-sensor fusion; [基于多传感器融合的时空连续 AOD 重构模型] [J]. | Acta Scientiae Circumstantiae , 2023 , 43 (5) : 353-365 . |
| MLA | Zhang, C. et al. "Spatio-temporal continuous AOD reconstruction model based on multi-sensor fusion; [基于多传感器融合的时空连续 AOD 重构模型]" . | Acta Scientiae Circumstantiae 43 . 5 (2023) : 353-365 . |
| APA | Zhang, C. , Wang, X. , Wu, Q. , Zheng, H. , Wang, H. , Yin, Y. . Spatio-temporal continuous AOD reconstruction model based on multi-sensor fusion; [基于多传感器融合的时空连续 AOD 重构模型] . | Acta Scientiae Circumstantiae , 2023 , 43 (5) , 353-365 . |
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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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Two main issues are faced when using very-high-spatial-resolution (VHR) satellite images for building change detection: 1) the boundaries of detected changes are hard to be consistent with the ground truth and 2) detected changes are easily affected by different viewing angles of bitemporal images, leading to noticeable false changes. To deal with these issues, this study develops a new Siamese change detection network [i.e., Siamese multitask change detection network (SMCD-Net)] based on a multitask learning framework to improve building change detection, particularly in the geometric aspect. Boundary information is formulated as an auxiliary task to constrain the learning of high-level semantic features. To enhance the identification of real changes from false changes, we model the directional relationships between buildings and their shadows by fuzzy sets, and incorporate the relationship information into SMCD-Net, leading to a network variant, labeled as SMCD-Net-m. Experiments were conducted on three datasets: a publicly available dataset, a Chinese GaoFen-2 dataset, and a French Pleiades dataset. We compared our methods with seven other methods, i.e., object-based Siamese network, ChangeStar, ChangeFormer, BIT, STANet, FC-Siam-diff, and Siam-NestedUNet. Results showed that the proposed SMCD-Net obtained the best detection results, achieving the lowest global total errors on all datasets. By incorporating directional information, SMCD-Net-m evidently improved detection accuracy, particularly when using bitemporal images with a large viewing angle difference. The improvement was positively correlated with the accuracy of building shadows extracted from VHR images.
Keyword ：
Building change detection Building change detection directional relationship modeling directional relationship modeling multitask learning multitask learning Siamese multitask change detection network (SMCD-Net) Siamese multitask change detection network (SMCD-Net) Siamese neural network (SNN) Siamese neural network (SNN) very-high-resolution satellite images very-high-resolution satellite images
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| GB/T 7714 | Li, Mengmeng , Liu, Xuanguang , Wang, Xiaoqin et al. Detecting Building Changes Using Multimodal Siamese Multitask Networks From Very-High-Resolution Satellite Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
| MLA | Li, Mengmeng et al. "Detecting Building Changes Using Multimodal Siamese Multitask Networks From Very-High-Resolution Satellite Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61 (2023) . |
| APA | Li, Mengmeng , Liu, Xuanguang , Wang, Xiaoqin , Xiao, Pengfeng . Detecting Building Changes Using Multimodal Siamese Multitask Networks From Very-High-Resolution Satellite Images . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
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Cotton is an economically important crop that plays a crucial role in improving human standards of living. Accurate spatial information about cotton is essential for efficient cotton production and management. In this paper, we employed a robust and lightweight model, namely SegFormer, to extract cotton planting areas from high-resolution remote sensing images. SegFormer combines the Transformer with a Multilayer Lightweight Perceptron (MLP), which not only exhibits stronger feature representation, but also has a smaller network size than the traditional convolutional neural networks (CNNs). We conducted experiments to extract cotton planting areas during the cotton maturity period in Shandong and Xinjiang using Google Earth images with a resolution of 1.19 meters. To demonstrate the effectiveness and accuracy of SegFormer, we compared it with U-Net and Swin-Unet networks. The results showed that SegFormer performs well in both areas, and the extracted cotton field boundaries are clear and smooth with complete shapes. Moreover, the overall extraction accuracy exceeds 95%, which is better than the performance of U-Net and Swin-Unet. We concluded that the semantic segmentation method based on SegFormer is effective and robust for extracting mature cotton planting areas from high-resolution remote sensing images. © 2023 IEEE.
Keyword ：
cotton planting areas cotton planting areas deep learning deep learning high-resolution remote sensing images high-resolution remote sensing images SegFormer SegFormer
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| GB/T 7714 | Zhang, H. , Lin, X. , Long, J. et al. SegFormer-Based Cotton Planting Areas Extraction from High-Resolution Remote Sensing Images [未知]. |
| MLA | Zhang, H. et al. "SegFormer-Based Cotton Planting Areas Extraction from High-Resolution Remote Sensing Images" [未知]. |
| APA | Zhang, H. , Lin, X. , Long, J. , Wang, X. , Dong, Y. , Guo, J. et al. SegFormer-Based Cotton Planting Areas Extraction from High-Resolution Remote Sensing Images [未知]. |
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