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Article(id=1322917002452541489, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322916945741348911, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2025.12.441, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1765728000000, receivedDateStr=2025-12-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793337591994, onlineDateStr=2026-10-30, pubDate=1789401600000, pubDateStr=2026-09-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793337591994, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793337591994, creator=13701087609, updateTime=1793337591994, updator=13701087609, issue=Issue{id=1322916945741348911, tenantId=1146029695717560320, journalId=1281212831689347082, year='2026', volume='46', issue='9', pageStart='1039', pageEnd='1170', issueExtLink='null', onlineDate='null', pubDate='1789401600000', pubDateStr='2026-09-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793337578473, creator='13701087609', updateTime=1793337578473, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1107, endPage=1113, ext={EN=ArticleExt(id=1322917002653868082, articleId=1322917002452541489, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Surface Deformation Prediction in Xining City Based on Multi-Feature BiLSTM Model, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

SBAS-InSAR technology was employed to obtain the land surface deformation distribution in Xining city from October 2020 to November 2024. By integrating static features such as the spatial location of deformation points with dynamic climatic characteristics, a multi-feature-based BiLSTM prediction model was constructed to forecast deformation in areas with typical deformation characteristics. The main conclusions are as follows: 1) The average annual deformation rate in the study area ranges from -48 mm/a to 30.2 mm/a, with a maximum cumulative deformation value reaching -187.2 mm. 2) Compared with the LSTM model, the prediction accuracy of the multi-feature BiLSTM model in three typical deformation areas is improved by up to approximately 32.3%, and it can more accurately identify local details and abrupt change points. At last, the optimal model was used to predict the deformation trends in each typical deformation area for the next six months, providing a reasonable reference for surface deformation monitoring.

, authors=Longjiao WANG1, Shifang ZHANG2, Shangmin ZHAO1, *, authorsList=Longjiao WANG, Shifang ZHANG, Shangmin ZHAO, authorCompany=null, correspAuthors=Shangmin ZHAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322917005602463811, articleId=1322917002452541489, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=基于多特征BiLSTM模型的西宁市地表形变预测, columnId=1322566785874908034, journalTitle=大地测量与地球动力学, columnName=特色栏目: 地震大地测量学, runingTitle=null, highlight=null, articleAbstract=

利用SBAS-InSAR技术获取西宁市2020-10—2024-11的地表形变分布，并结合形变点空间位置等静态特征及动态气候特征构建基于多特征的BiLSTM预测模型, 对典型形变特征区域进行预测。主要结论为：1）研究区年均形变速率为-48~30.2 mm/a，最大累积形变值达-187.2 mm；2）在3个典型形变区, 基于多特征BiLSTM模型的预测精度较LSTM模型最高提升约32.3%，且能更精确地识别局部细节和突变点。最后，利用最优模型对各典型形变区未来6个月的形变趋势进行预测，为地表形变监测提供了合理参考。

, authors=王龙娇1, 章诗芳2, 赵尚民1, *, authorsList=王龙娇, 章诗芳, 赵尚民, authorCompany=null, correspAuthors=赵尚民, authorNote=

王龙娇，硕士生，主要研究方向为InSAR地表形变监测与预测，E-mail:

, correspAuthorsNote=
赵尚民，博士，教授，主要研究方向为GIS空间分析与生态环境遥感，E-mail:
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王龙娇，硕士生，主要研究方向为InSAR地表形变监测与预测，E-mail:

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王龙娇，硕士生，主要研究方向为InSAR地表形变监测与预测，E-mail:

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Sentinel-1A data information

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雷达影像参数 参量
轨道方向 升轨
波段 C
空间分辨率/m 5×20
影像数量/景 120
极化方式 VV+VH
波长/cm 5.6
重访周期/d 12
时间范围 2020-10—2024-11
), ArticleFig(id=1322917011956834420, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322917002452541489, language=CN, label=表1, caption=

Sentinel-1A数据信息

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雷达影像参数 参量
轨道方向 升轨
波段 C
空间分辨率/m 5×20
影像数量/景 120
极化方式 VV+VH
波长/cm 5.6
重访周期/d 12
时间范围 2020-10—2024-11
), ArticleFig(id=1322917012028137589, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322917002452541489, language=EN, label=Tab. 2, caption=

Results of prediction accuracy metrics

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区域 多特征BiLSTM/mm LSTM/mm RNN/mm
RMSE MAE RMSE MAE RMSE MAE
A 1.196 8 0.792 4 1.541 5 1.119 2 1.603 3 1.186 0
B 1.326 9 0.915 0 1.809 1 1.352 2 1.880 9 1.414 9
C 1.534 8 1.051 0 1.915 6 1.410 3 2.043 0 1.492 9
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预测精度指标结果

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区域 多特征BiLSTM/mm LSTM/mm RNN/mm
RMSE MAE RMSE MAE RMSE MAE
A 1.196 8 0.792 4 1.541 5 1.119 2 1.603 3 1.186 0
B 1.326 9 0.915 0 1.809 1 1.352 2 1.880 9 1.414 9
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基于多特征BiLSTM模型的西宁市地表形变预测
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王龙娇 1 , 章诗芳 2 , 赵尚民 1, *
大地测量与地球动力学 | 特色栏目: 地震大地测量学 2026,46(9): 1107-1113
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大地测量与地球动力学 |特色栏目: 地震大地测量学 2026 , 46 (9) : 1107 -1113
基于多特征BiLSTM模型的西宁市地表形变预测
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王龙娇1 , 章诗芳2, 赵尚民1, *
作者信息
  • 1 太原理工大学地球科学与测绘工程学院, 太原, 030024
  • 2 山西工程科技职业大学交通工程学院, 晋中, 030619
通讯作者:
赵尚民，博士，教授，主要研究方向为GIS空间分析与生态环境遥感，E-mail:
作者简介:

王龙娇，硕士生，主要研究方向为InSAR地表形变监测与预测，E-mail:

Surface Deformation Prediction in Xining City Based on Multi-Feature BiLSTM Model
Longjiao WANG1 , Shifang ZHANG2, Shangmin ZHAO1, *
Affiliations
  • 1 College of Geological and Surveying Engineering, Taiyuan University of Technology, Taiyuan 030024, China
  • 2 School of Traffic Engineering, Shanxi Vocational University of Engineering Science and Technology, Jinzhong 030619, China
出版时间: 2026-09-15 doi: 10.14075/j.jgg.2025.12.441
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摘要
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利用SBAS-InSAR技术获取西宁市2020-10—2024-11的地表形变分布，并结合形变点空间位置等静态特征及动态气候特征构建基于多特征的BiLSTM预测模型, 对典型形变特征区域进行预测。主要结论为：1）研究区年均形变速率为-48~30.2 mm/a，最大累积形变值达-187.2 mm；2）在3个典型形变区, 基于多特征BiLSTM模型的预测精度较LSTM模型最高提升约32.3%，且能更精确地识别局部细节和突变点。最后，利用最优模型对各典型形变区未来6个月的形变趋势进行预测，为地表形变监测提供了合理参考。

地表形变  /  SBAS-InSAR  /  BiLSTM  /  形变预测  /  西宁市

SBAS-InSAR technology was employed to obtain the land surface deformation distribution in Xining city from October 2020 to November 2024. By integrating static features such as the spatial location of deformation points with dynamic climatic characteristics, a multi-feature-based BiLSTM prediction model was constructed to forecast deformation in areas with typical deformation characteristics. The main conclusions are as follows: 1) The average annual deformation rate in the study area ranges from -48 mm/a to 30.2 mm/a, with a maximum cumulative deformation value reaching -187.2 mm. 2) Compared with the LSTM model, the prediction accuracy of the multi-feature BiLSTM model in three typical deformation areas is improved by up to approximately 32.3%, and it can more accurately identify local details and abrupt change points. At last, the optimal model was used to predict the deformation trends in each typical deformation area for the next six months, providing a reasonable reference for surface deformation monitoring.

surface deformation  /  SBAS-InSAR  /  BiLSTM  /  deformation prediction  /  Xining city
王龙娇, 章诗芳, 赵尚民. 基于多特征BiLSTM模型的西宁市地表形变预测. 大地测量与地球动力学, 2026 , 46 (9) : 1107 -1113 . DOI: 10.14075/j.jgg.2025.12.441
Longjiao WANG, Shifang ZHANG, Shangmin ZHAO. Surface Deformation Prediction in Xining City Based on Multi-Feature BiLSTM Model[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (9) : 1107 -1113 . DOI: 10.14075/j.jgg.2025.12.441
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地表形变主要由自然因素与人为因素共同驱动，对西宁市进行形变监测与预测分析不仅能揭示河谷盆地城市的地表运动规律，更可以为城市基础设施维护、地质灾害风险防控等提供关键的科学依据[1]。Berardino等[2]提出的短基线集合成孔径雷达干涉测量(small baseline subset InSAR, SABS-InSAR)技术能以毫米级精度[3]有效获取大范围区域的形变速率与累积位移，在城市形变监测领域应用广泛[4-5]。
目前，长短期记忆网络(long short-term memory, LSTM)作为一种改进型循环神经网络，在基于高精度InSAR技术获取的城市地表形变数据预测上展现出良好的预测性能[6-7]。在传统LSTM的基础上，双向长短期记忆网络(bidirectional LSTM, BiLSTM)引入了反向传播结构，能够同时建立时间序列的前向与后向依赖关系，尤其适用于结构复杂或受多因素驱动的时序建模任务[8-9]。同时，引入降水、地表温度、土壤湿度等因素能更精准地对地表形变情况进行分析与预测[10-11]。
本文选取西宁市为研究区，首先利用SBAS-InSAR技术获取研究区地表形变速率与累积形变量分布情况，并确定典型形变区；随后构建基于累积形变量数据的BiLSTM模型，结合研究区降水、地表温度、土壤湿度等时序数据对各典型形变区的累积形变量进行预测，对比其与循环神经网络(RNN)模型和LSTM模型的预测精度以及单点时序曲线预测效果；最后基于最优模型获取和分析研究区未来数月的形变趋势。
1 研究区概况与数据源
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1.1 研究区概况
西宁市位于青藏高原东北边缘、黄河上游湟水河谷处，是青海省省会。其地势西北高、东南低，东西呈条带状分布，平均海拔超2 000 m，境内主要为山地及高原河谷地貌，地层松散且植被稀少，多受地震、滑坡、塌陷等灾害影响；气候为高原半干旱气候，昼夜温差大，雨季集中。市内湟水河穿城而过，分出南川河、北川河等支流，研究区范围见图 1。
1.2 数据源
Sentinel-1卫星是欧洲航天局哥白尼计划(GMES)发射的地球观测卫星。本文采用时间范围为2020-10-11—2024-11-19的Sentinel-1A单视复数(SLC)影像，成像模式为干涉宽幅(IW)，数据来源于阿拉斯加卫星设备分布式活动档案中心(ASF DAAC), 具体数据见表 1。
辅助数据包括美国国家航空航天局提供的30 m分辨率SRTM DEM、欧空局提供的精密定轨数据(POD)、中国地震局第一监测中心提供的GNSS精密时间序列数据。气候数据包括CHIRPS的降水数据和ERA5-Land的地表温度数据以及土壤湿度数据(图 2)。
2 研究方法
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2.1 SBAS-InSAR
SBAS-InSAR技术是一种基于小基线集策略的时序InSAR分析方法，常用于获取大范围的缓慢地表形变，主要通过优化干涉组合的方式有效抑制大气相位延迟和时空失相干效应，其具体技术流程见图 3。
设置时间基线阈值为120 d、空间基线阈值为临界基线的35%，以避免完全的空间失相干问题。首先根据研究区范围对120景Sentinel-1A影像数据进行导入与裁剪，将处理后的SAR影像连接得到200对干涉像对，并选取2022-10-01的影像作为超级主影像，其余影像作为辅影像。在处理过程中利用30 m分辨率的SRTM DEM数据模拟地形相位、消除平地效应，采用Goldstein滤波[12]方法抑制时空失相干噪声，使用最小费用流法[13]完成相位解缠，并根据解缠结果将相干性阈值设为0.35；通过奇异值分解估算形变速率，并结合地形误差进行反演，再设置大气时间域高通滤波和空间域低通滤波阈值分离大气相位，最后经地理编码处理转换得到雷达视线(LOS)方向的地表形变速率场。
2.2 BiLSTM
LSTM网络[14]通过在网络单元中引入细胞状态和3类门控机制来解决传统RNN在训练长序列时梯度消失或爆炸的问题。在LSTM隐藏层里，单个神经元的运算过程涉及2个部分：细胞状态的更新和输出值的计算。其中神经元内包含3个门函数：遗忘门、输入门和输出门，计算公式分别为[15]：
$f_t =\sigma\left(\boldsymbol{W}_f \cdot\left[h_{t-1}, x_t\right]+\boldsymbol{b}_f\right)$
$u_t =\sigma\left(\boldsymbol{W}_u \cdot\left[h_{t-1}, x_t\right]+\boldsymbol{b}_u\right)$
$i_t =\sigma\left(\boldsymbol{W}_i \cdot\left[h_{t-1}, x_t\right]+\boldsymbol{b}_i\right)$
式中，ft、ut、it分别为遗忘门、输入门和输出门的结果；σ为激活函数；Wf、Wu、Wi为各门的权重矩阵；ht-1为上一时刻的LSTM输出信息；bf、bu、bi为各门的偏置项。
BiLSTM网络在LSTM网络的基础上增加了反向LSTM处理同一输入序列，其结构见图 4。一条正向链路沿序列开头至结尾捕捉历史信息，另一条反向链路从序列末尾到开头获取未来信息[16]。2条链路各自产生的隐藏状态在每个时间步通过拼接或加权方式融合，形成包含前后向综合特征的输出向量，这种双向信息交互机制能够更充分地挖掘序列中隐含的信息，具有更强的序列建模能力。
基于InSAR地表形变时序的特点，构建多源特征的BiLSTM模型进行预测，预测流程见图 4。首先对数据进行预处理，构建多尺度数据：一方面读取原始形变数据，通过插值和填充处理缺失值，按照长度为6的窗口切分训练样本，并提取各形变点的经纬度及高程信息作为静态特征数据；另一方面对降水量、地表温度、土壤湿度时间序列数据与形变时间序列进行时间对齐，然后按时间步进行拼接，获取动态特征数据；最后对所有动态、静态特征及预测目标值进行归一化处理并输入。为避免数据泄露，将数据集划分为训练集、验证集和测试集；其次，对形变数据进行预测，在模型结构上，采用双向LSTM网络结构，通过早停机制等正则化方法提升训练的稳定性和特征表达能力；同时，在模型训练过程中，使用贝叶斯优化方法自动搜索最优参数组合，并结合动态学习率调整策略，在保证模型稳定性的同时提升训练效率和收敛速度。
3 结果与分析
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3.1 研究区地表形变时空特征分析
图 5为研究区InSAR形变结果，其中正值表示向靠近卫星传感器的方向移动，负值表示向远离卫星传感器的方向移动。西宁市地表最大累积形变量为-187.2 mm，LOS向形变速率的范围为-48.0~30.2 mm/a，且主城区大部分地区形变速率小于10 mm/a，中心区域存在抬升。主要形变区域有3处，均位于西宁市主城区周边。其中A区域位于城北区海湖大道旁，覆盖区域内多为学校及企业建筑，此处最大形变速率达-47.8 mm/a，最大累积形变量为-187.2 mm；B区域位于城西区西、高速和沈家沟附近，最大形变速率达-31 mm/a，最大累积形变量为-132.5 mm；C区域位于城东区西、高速两旁及曹家沟附近，覆盖部分城区，此处最大形变速率达-44.4 mm/a，最大累积形变量为-184.9 mm。B、C两处均易发生滑坡。3个区域的形变整体呈现由形变中心向四周扩散的分布特点，研究区多为湿陷性黄土地层，因此周期性强降雨的特点可能使得地层承载力发生变化，导致不均匀形变发生[17]；此外，由于3个区域毗邻交通干线或工业园区，因此区域性工程也可能是引发地表形变的主要原因。
为验证InSAR结果的准确性，从中国地震局第一监测中心获取了XNIN测站的GNSS时间序列数据，然后将3个方向的形变量转换至LOS方向，再以该GNSS站点为中心提取SBAS-InSAR形变量，计算得出两者LOS方向形变量的RMSE为6.27 mm，且两者时间序列数据大部分相近(图 6)，证明了SBAS-InSAR结果具有较高的可靠性。
3.2 形变预测分析
3.2.1 模型参数设置
从上述3个典型形变区域中提取数据样本，分别对每个区域内的数据点进行划分，采用7∶2∶1的比例划分训练集、验证集与测试集，确保数据集之间无重叠。在模型训练前，首先设定超参数的初始搜索范围，并借助Optuna框架实施贝叶斯优化，从而高效确定最优超参数组合。最终确定模型结构为2层BiLSTM网络，激活函数为ReLU，训练过程中batch size设为8，最大训练轮次设为100次。同时为防止模型在训练过程中过拟合，设置丢弃层并采用早停机制，耐心值设为20个轮次。
3.2.2 基于多特征的BiLSTM模型的地表形变预测与分析
根据设置的最优超参数，使用LSTM模型和基于多特征的BiLSTM模型对典型形变区形变量进行预测，并基于SBAS-InSAR监测结果计算各区域的精度评价指标, 具体见表 2。
从表中各区域指标来看，3种模型在不同区域的形变预测中预测精度都较为稳定。相比RNN模型，LSTM模型在各区域的预测中表现出更好的性能，其RMSE与MAE的最大降幅分别达到约6.7%和6%；而基于多特征的BiLSTM模型在各评估指标上均显著优于LSTM。以区域B为例，多特征BiLSTM模型的RMSE较LSTM降低了约26.7%，MAE降低了约32.3%；在区域A与区域C，其精度提升幅度也均超过20%。值得注意的是，在不同样本规模的区域中，多特征BiLSTM模型与LSTM之间的精度差异未出现明显波动，且该模型在各区域上的RMSE均低于2 mm, MAE均低于1.1 mm。这表明，融合多特征输入的BiLSTM模型在西宁市地表形变时序预测任务中具备更强的泛化能力与稳定性。
预测精度提升的主要原因有2个方面：一方面基于多特征的BiLSTM模型的双向结构能同时利用序列的正反向信息，从而更充分地捕获形变过程中的滞后效应与突变特征，与只依赖单向传递信息的LSTM相比，该模型在捕捉长期依赖和周期性变化方面表现更为出色；另一方面，引入动态气候特征进一步为模型提供了形变过程中的关键驱动信息，通过将动态气候特征与时序信息结合，可以使模型进一步学习气候因素与形变值时间序列之间的动态关联和两者的时间滞后关系，提升预测精度。同时，相比于传统的网格搜索等方法，贝叶斯优化方法的应用使得模型能在数据多、参数复杂的情况下更有效地获取最优超参数，降低了时间成本。
为了更直观地分析各模型在形变预测中的差异，在3个区域中各随机选取形变点，并绘制各点SBAS-InSAR累积形变量结果随时间变化的曲线图，对应的形变点位置见图 5。由图 7可知，各区域中累积形变数据曲线变化趋势均呈现出波动性和不规律性的特点。随后分别使用3种模型对累积形变值进行预测，并与真实值进行比较。
从图 7可以看出，在对各区域进行单点预测时，3种模型对整体趋势都有较好的拟合效果，但在局部细节上存在差异。基于多特征的BiLSTM模型的预测曲线与SBAS-InSAR累积形变量时序曲线重合度更高，相对SBAS-InSAR真实值曲线波动最小、分布最为集中，能够在大部分突变节点和平缓过渡处保持较为一致的效果。LSTM模型对整体趋势的把握较为可靠，但在快速变化的部分出现滞后与平滑，在波动频繁或剧烈的阶段则出现偏离较大的情况。RNN模型可以把握整体趋势，但对于真实形变值时序曲线局部变化的拟合能力不如LSTM模型。由此可见，基于多特征的BiLSTM模型在兼顾长期依赖与短期突变方面具有明显优势，这一结果对于提升模型在单点形变时序预测中的稳定性与精度具有重要意义。
3.2.3 典型区域形变趋势预测
利用获取的最优模型参数，结合多源气候数据时间序列，对3个典型区域的形变数据进行预测，获取其未来6个月的形变情况。结果表明，研究区在2024-11—2025-04期间降水量较少，地表温度大多处于0 ℃以下，因此累积形变量变化小；2025-04之后降水量和地表气温显著升高，3个区域的形变趋势虽然在短周期内存在波动，但整体上开始呈现持续形变及影响范围逐步扩张的趋势。3个区域2024-11—2025-06的累积形变值分别预计增加至-213.4 mm、-164.4 mm和-193.0 mm(图 8)。其中, 区域A的形变趋势最明显，形变速率可达-49.8 mm/a，且该形变区位于企业建筑与学校设施旁，存在因降雨渗流或工程活动等因素诱发的沉降及滑坡等地质灾害隐患。
4 结语
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SBAS-InSAR所获取的西宁地区累积形变数据表现出波动性和不规律性的变化特点，直接采用基础的单向序列机器学习模型难以达到理想的预测效果。因此本文提出了基于多特征数据的BiLSTM模型，对西宁市典型形变区累积形变量时序数据进行预测，得到如下结论：
1) 研究区2020-10—2024-11整体形变速率较为平稳，主要形变区域有3个，其中最大年均形变速率达-48 mm/a，同时结合GNSS数据验证了结果的可靠性。
2) 在3个典型形变区域内构建基于多特征的BiLSTM模型，对SBAS-InSAR获取的地面形变数据进行预测，并与RNN模型和LSTM模型的预测效果进行对比。结果表明，LSTM模型在各区域的预测效果优于RNN模型；而引入动态特征数据的BiLSTM模型在预测精度上较LSTM模型最大可提升32.3%。在典型形变区域中, 随机选取形变点进行单点累积形变量的预测，本文模型能更好地拟合局部细节和突变点，证明了结合动态气候特征及双向LSTM结构的时序预测模型在复杂形变序列预测中的有效性。在后续研究中可进一步结合地下水位、岩层岩性、归一化植被指数(NDVI)等更多特征数据对模型进行优化。
3) 利用获得的最优模型对3个典型区域2024-11—2025-06的形变趋势进行预测，其未来6个月的累积形变值预计增加至-213.4 mm、-164.4 mm和-193.0 mm，需要注意持续形变可能带来的地质灾害影响。
基金
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  • 国家自然科学基金(42271432)
文献
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2026年第46卷第9期
26
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doi: 10.14075/j.jgg.2025.12.441
  • 接收时间：2025-12-15
  • 首发时间：2026-10-30
  • 出版时间：2026-09-15
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  • 收稿日期：2025-12-15
基金
国家自然科学基金(42271432)
作者信息
    1 太原理工大学地球科学与测绘工程学院, 太原, 030024
    2 山西工程科技职业大学交通工程学院, 晋中, 030619

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赵尚民，博士，教授，主要研究方向为GIS空间分析与生态环境遥感，E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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