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学者姓名：王琳
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Abstract ：
The nighttime economy is instrumental in driving economic growth, particularly in the post-pandemic era. Nighttime Light (NTL) data is a key source in nighttime economy remote sensing study, with its angular effect directly affecting result accuracy. This study compares the accuracy of identifying nighttime economic agglomerations (NEAs) in Shanghai using Black Marble NTL and POI data at three observation angles: near-nadir, off-nadir, and all-angle. The results indicate that under all three angles, landmark NEAs can be identified fairly well. However, near-nadir demonstrates superior sample library identification accuracy and Theil index performance compared to all-angle and off-nadir. The study reveals that near-nadir observations offer higher accuracy and better suppression of "pseudo-accuracy units", making them more suitable for studying the nighttime economy. Furthermore, the study analyzes the spatial distribution characteristics of NEAs in Shanghai and finds a distinct "center-periphery" development pattern, suggesting imbalances in overall development. The presence of buildings with scattered high-low distribution and complex urban structures contributes to the variations in NEA identification under different satellite-observed angles. This study provides valuable insights into selecting the appropriate satellite-observed angle for studying NEAs using NTL data. It also explores the potential application of Black Marble NTL data products in socioeconomic remote sensing.
Keyword ：
Angular effects Angular effects Black marble nighttime lights product suite Black marble nighttime lights product suite Nighttime economy agglomeration (NEA) Nighttime economy agglomeration (NEA) Shanghai city Shanghai city Socioeconomic remote sensing Socioeconomic remote sensing
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| GB/T 7714 | Ou, Caihong , Tang, Fei , Deng, Xiaohui et al. The Impact of Angular Effects on Nighttime Economy Observations: Determining the Optimal Observation Angle of Nighttime Light Remote Sensing [J]. | APPLIED SPATIAL ANALYSIS AND POLICY , 2025 , 18 (1) . |
| MLA | Ou, Caihong et al. "The Impact of Angular Effects on Nighttime Economy Observations: Determining the Optimal Observation Angle of Nighttime Light Remote Sensing" . | APPLIED SPATIAL ANALYSIS AND POLICY 18 . 1 (2025) . |
| APA | Ou, Caihong , Tang, Fei , Deng, Xiaohui , Wang, Lin . The Impact of Angular Effects on Nighttime Economy Observations: Determining the Optimal Observation Angle of Nighttime Light Remote Sensing . | APPLIED SPATIAL ANALYSIS AND POLICY , 2025 , 18 (1) . |
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As a geological disaster widely distributed in the southern regions of China, rainfall-induced shallow landslides pose a significant threat to affected areas. Timely detection of landslides is crucial in the effective response to such disasters. However, landslide detection faces adverse impacts from various factors, such as insufficient sample data, complex model structures, and limitations in detection accuracy during the actual detection process. In this study, high-quality image samples were collected from multiple landslide disaster areas in southern China, and a rainfall-induced shallow landslide sample database was constructed in the region. Based on this, a lightweight attention-guided YOLO model (LA-YOLO) was proposed to improve the detection performance of YOLO model for rainfall-induced shallow landslides. First, CG block is introduced to enhance the C2f module, enriching the feature representation capability through multiscale feature fusion and reducing the model's parameters and computational complexity. Second, the SimAM attention module is used to focus on the target regions, improving feature extraction effectiveness. Experimental results show that the model parameters of LA-YOLO were reduced by approximately 30%, with precision, recall, and mean average precision (mAP) on the landslide sample dataset increasing by 2.6%, 0.7%, and 2.2%, respectively. While ensuring model detection performance, the model structure was significantly optimized, achieving both lightweight and accuracy goals, confirming the model's superiority in monitoring rainfall-induced shallow landslide disasters.
Keyword ：
Accuracy Accuracy Attention mechanism Attention mechanism Biological system modeling Biological system modeling context guidance module context guidance module Data models Data models Disasters Disasters Feature extraction Feature extraction landslide detection landslide detection Neurons Neurons Rain Rain rainfall-induced shallow landslide rainfall-induced shallow landslide Terrain factors Terrain factors Training Training YOLO YOLO YOLO v8 YOLO v8
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| GB/T 7714 | Wang, Lin , Lei, Henggang , Jian, Wenbin et al. Enhancing Landslide Detection: A Novel LA-YOLO Model for Rainfall-Induced Shallow Landslides [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2025 , 22 . |
| MLA | Wang, Lin et al. "Enhancing Landslide Detection: A Novel LA-YOLO Model for Rainfall-Induced Shallow Landslides" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 22 (2025) . |
| APA | Wang, Lin , Lei, Henggang , Jian, Wenbin , Wang, Wenjia , Wang, Hao , Wei, Nan . Enhancing Landslide Detection: A Novel LA-YOLO Model for Rainfall-Induced Shallow Landslides . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2025 , 22 . |
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Human activity is an important factor affecting regional thermal environment change, and it is of great significance to explore the response relationship between human activity intensity (HAI) and land surface temperature (LST) for regional sustainable development. Based on MOD11A1 ground temperature data and combined with multi-source data such as land use (LU),population density (PD),night light (NTL),grassland use intensity (GUI) and existing biomass (EB),this paper proposed a method suitable for measuring the HAI in Three-River Source Region,and analyzed the spatio-temporal variation characteristics of HAI and LST in Three-River Source Region from 2000 to 2020. Comprehensive use of spatial autocorrelation and spatial autoregressive model to explore the spatial relationship between them. The results show that: (1) According to the regional ecological characteristics of Three-River Source Region, the HAI index model constructed in this paper can effectively identify the spatial distribution of HAI in Three-River Source Region,and can better distinguish the spatial differences of HAI in Three-River Source Region;(2) The average value of HAI in Three-River Source Region in the past 20 years was 0.285,and the overall intensity was low.The spatial distribution characteristics of HAI and LST were both high in the east and low in the west,and the two were significantly positively correlated.(3) LU,PD,GUI and EB in HAI index significantly affect the change of LST in Three-River Source Region,while there is a phenomenon of ' warming lag' between NTL and LST,and its response to LST is not obvious.In general,in order to slow down the further rise of LST in this region and alleviate the challenges faced by the regional ecological security system,it is necessary to limit the spread of high human activity areas. © 2025 Editorial Board of Journal of Basic Science and Engineering. All rights reserved.
Keyword ：
Population statistics Population statistics
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| GB/T 7714 | Liu, Zhicai , Zheng, Weiwen , Long, Zihan et al. Modeling of Human Activity Intensity Index and Its Spatial Relationship with Land Surface Temperature in Three-River Source Region [J]. | Journal of Basic Science and Engineering , 2025 , 33 (2) : 349-361 . |
| MLA | Liu, Zhicai et al. "Modeling of Human Activity Intensity Index and Its Spatial Relationship with Land Surface Temperature in Three-River Source Region" . | Journal of Basic Science and Engineering 33 . 2 (2025) : 349-361 . |
| APA | Liu, Zhicai , Zheng, Weiwen , Long, Zihan , Wang, Lin , Xu, Zhanghua . Modeling of Human Activity Intensity Index and Its Spatial Relationship with Land Surface Temperature in Three-River Source Region . | Journal of Basic Science and Engineering , 2025 , 33 (2) , 349-361 . |
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The Russia-Ukraine conflict has persisted for over a year, posing challenges in assessing and verifying the extent of damage through on-site investigations. Nighttime light (NTL) remote sensing, an emerging approach for studying regional conflicts, can complement traditional methods. This article employs National Aeronautics and Space Administration's Black Marble products to reveal the response characteristics of NTL intensity at national and state scales during the first anniversary of the conflict (January 2022 to February 2023) in Ukraine. The article used the NTL ratio index to assess the relative intensity of NTL and month-on-month change rate, nighttime light change rate index (NLCRI), and the rate (R value) of linear regression analysis to depict spatiotemporal dynamics. In addition, Theil-Sen median trend analysis and Mann-Kendall tests were employed to analyze intensity trends, with a "dual-threshold method" to reduce extensive noise interference. The results showed: At the national scale, the conflict resulted in an 84.0% decrease in NTL across Ukraine. At the state scale, the most severe NTL decline occurred near the southwestern border and eastern conflict zone under Ukrainian government control, witnessing over 80% decline rates. The correlation of decreases in NLCRI and R values with population displacement, infrastructure damage, or curfew measures demonstrated that the concentration of refugees and electricity facility restoration led to increased NLCRI and R values. Overall, NTL reflects critical moments at the national scale and provides insights into military intentions and humanitarian measures at the state scale. Therefore, NTL can effectively serve as a tool for observation and assessment in military conflicts.
Keyword ：
Black Marble nighttime lights (NTLs) product suite Black Marble nighttime lights (NTLs) product suite multiscale analysis multiscale analysis Russia-Ukraine conflict Russia-Ukraine conflict spatiotemporal dynamics spatiotemporal dynamics VNP46A3 product VNP46A3 product
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| GB/T 7714 | Wang, Lin , Lei, Henggang , Xu, Hanqiu . Analysis of Nighttime Light Changes and Trends in the 1-Year Anniversary of the Russia-Ukraine Conflict [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 : 4084-4099 . |
| MLA | Wang, Lin et al. "Analysis of Nighttime Light Changes and Trends in the 1-Year Anniversary of the Russia-Ukraine Conflict" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17 (2024) : 4084-4099 . |
| APA | Wang, Lin , Lei, Henggang , Xu, Hanqiu . Analysis of Nighttime Light Changes and Trends in the 1-Year Anniversary of the Russia-Ukraine Conflict . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 , 4084-4099 . |
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Bamboo groves predominantly thrive in tropical or subtropical regions. Assessing the efficacy of remote sensing data of various types in extracting bamboo forest information from bright and shadow areas is a critical issue for achieving precise identification of bamboo forests in complex terrain. In this study, 34 features were obtained from Sentinel-1 SAR and Sentinel-2 optical images using the Google Earth Engine platform. The normalized shaded vegetation index (NSVI) was then employed to segment the bright and shadow woodlands. Different features from diverse data sources were evaluated to extract bamboo forest information in the bright and shadow areas, then use the random forest (RF) classification algorithm to extract bamboo forest. The results showed that (1) the red-edge and short-wave infrared bands of Sentinel-2 optical images and their corresponding vegetation indices are significant in bamboo forest information extraction. (2) The dissimilarity and homogeneity of Sentinel-2 texture features in the bright area and dissimilarity in the shadow area, the Sentinel-1 backscatter features in the bright area and the VV and VH in the bright area and VV-VH in the shadow area have some variability between bamboo and nonbamboo forests, which can be used as effective features for bamboo forest extraction. (3) The combination of spectral, texture and backscatter features yields the highest overall classification accuracy and Kappa coefficient, at 87.96% and 0.7435, respectively. This study has the potential for remote sensing refinement of bamboo forest identification in complex terrain areas by utilizing subregion classification methods combined with optical and radar image features.
Keyword ：
Bamboo forest Bamboo forest Google Earth Engine Google Earth Engine Sentinel-1 Sentinel-1 Sentinel-2 Sentinel-2 subregion classification subregion classification
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| GB/T 7714 | Xiang, Songyang , Xu, Zhanghua , Shen, Wanling et al. Mapping of bamboo forest bright and shadow areas using optical and SAR satellite data in Google Earth Engine [J]. | GEOCARTO INTERNATIONAL , 2023 , 38 (1) . |
| MLA | Xiang, Songyang et al. "Mapping of bamboo forest bright and shadow areas using optical and SAR satellite data in Google Earth Engine" . | GEOCARTO INTERNATIONAL 38 . 1 (2023) . |
| APA | Xiang, Songyang , Xu, Zhanghua , Shen, Wanling , Chen, Lingyan , Hao, Zhenbang , Wang, Lin et al. Mapping of bamboo forest bright and shadow areas using optical and SAR satellite data in Google Earth Engine . | GEOCARTO INTERNATIONAL , 2023 , 38 (1) . |
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Leaf water content (LWC) is very important in the growth of vegetation. LWC and leaf spectra change when the leaves are under pest stress; exploring the change mechanism between LWC, leaf spectra, and pest stress can lay the foundation for pest detection. In this study, we measured the LWC and leaf spectra of moso bamboo leaves under different damage levels, used the Pearson-Lasso method to screen the features, and established a multiple linear regression (MLR) and random forest regression (RFR) model to estimate the LWC. We analyzed the relationship between LWC and spectral features of moso bamboo leaves under Pantana phyllostachysae Chao (PPC) stress and their changes. The results showed that: (1) the LWC showed a decreasing trend as the pest level increased. (2) The spectra changed substantially when the leaves were under pest stress. (3) The number and significance of response features associated with the LWC were diverse under different damage levels. (4) The estimation of LWC under different damage levels differed significantly. LWC, leaf spectra, response features, and the model estimation effect were diverse under different damage levels. The correlation between LWC and features was higher for healthy leaves than for damaged and off-year leaves. The two models were more effective in estimating the LWC of healthy leaves but less effective for damaged and off-year leaves. This study provides theoretical support for the prediction of PPC stress and lays the foundation for remote sensing monitoring.
Keyword ：
changing relationships changing relationships moso bamboo moso bamboo Pantana phyllostachysae Chao Pantana phyllostachysae Chao Pearson-Lasso Pearson-Lasso spectral features spectral features water content water content
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| GB/T 7714 | Xu, Zhanghua , Li, Bin , Yu, Hui et al. Changing Relationships between Water Content and Spectral Features in Moso Bamboo Leaves under Pantana phyllostachysae Chao Stress [J]. | FORESTS , 2023 , 14 (4) . |
| MLA | Xu, Zhanghua et al. "Changing Relationships between Water Content and Spectral Features in Moso Bamboo Leaves under Pantana phyllostachysae Chao Stress" . | FORESTS 14 . 4 (2023) . |
| APA | Xu, Zhanghua , Li, Bin , Yu, Hui , Zhang, Huafeng , Guo, Xiaoyu , Li, Zenglu et al. Changing Relationships between Water Content and Spectral Features in Moso Bamboo Leaves under Pantana phyllostachysae Chao Stress . | FORESTS , 2023 , 14 (4) . |
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Chlorophyll is an important physiological parameter reflecting the health status of green vegetation. The change mechanism of chlorophyll and leaf spectrum under pest stress is complex. It is of great significance to analyze the relationship between chlorophyll and leaf spectrum in depth for pest detection. Taking Shunchang County, Nanping City, Fujian Province as the experimental area, the leaf SPAD and leaf spectrum of Phyllostachys pubescens under different damage scenarios were measured. Pearson correlation method was used to screen the leaf spectrum characteristic indexes, and multiple linear regression, ridge regression, random forest and XGBoost estimation models of leaf SPAD were established. By comparing the screening results of spectral characteristics and the estimation effect of the model, the relationship between chlorophyll and leaf spectral characteristics of Phyllostachys pubescens under the stress of Pantana phyllostachysae was analyzed. The results showed that: (1) SPAD of Phyllostachys pubescens leaves showed a downward trend with the increase of insect pests; (2) Compared with the undamaged state, the spectral characteristics of Phyllostachys pubescens leaves changed obviously under the stress of Pantana phyllostachysae, and the "green peak" and "red valley" tended to disappear, the slope of "red edge" decreased, and the reflectance of near infrared wavelength decreased. (3) The best spectral characteristics of leaf SPAD based on full sample fitting are VOG(2), R-515/R-570, CIred, PRI and NDVI705, and the best estimation model is multiple linear regression model (R-2=0.7537, RMSE=3.0150). (4) SPAD of Phyllostachys pubescens leaves was fitted based on samples with different damage degrees. The optimal spectral characteristic indexes were health: CIred, VOG(2), ARVI, R-515/R-570, DVI; mild hazard: RENDVI, RERVI and REDVI; moderate hazard: RENDVI, RERVI and REDVI; severe hazard: VOG(2), CIred, NDVI705; off year: PRI, NDVI705, VOG(1), CIred. The best estimation model is the multiple linear regression model, and the model accuracy is healthy (R-2 = 0.8823; RMSE=1.6388); mild hazard(R-2=0.1802; RMSE=3.3354); moderate hazard(R-2 = 0.3604; RMSE=3.8867); severe hazard (R-2=0.4677; RMSE=2.6018); off year (R-2=0.7324; RMSE=2.3754). It was found that with the increase of the damage grade, the spectral characteristic index of Phyllostachys pubescens leaves changed, and the estimation accuracy of the relational model showed a trend of sharp decline at first and then slowly rising. The model had better estimation effect on SPAD of healthy and young leaves, but poor estimation effect on SPAD of light-medium-severe damaged leaves. When the relationship between SPAD and spectral characteristics of Phyllostachys pubescens leaves tends to be disordered, it indicates that the harm of Pantana phyllostachysae may occur.
Keyword ：
Correlation analysis Correlation analysis Machine learning Machine learning Pest stress Pest stress SPAD SPAD Spectral characteristics of leaves Spectral characteristics of leaves
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| GB/T 7714 | Hu Xin-yu , Xu Zhang-hua , Huang Xu-ying et al. Relationship Between Chlorophyll and Leaf Spectral Characteristics and Their Changes Under the Stress of Phyllostachys Praecox [J]. | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2022 , 42 (9) : 2726-2739 . |
| MLA | Hu Xin-yu et al. "Relationship Between Chlorophyll and Leaf Spectral Characteristics and Their Changes Under the Stress of Phyllostachys Praecox" . | SPECTROSCOPY AND SPECTRAL ANALYSIS 42 . 9 (2022) : 2726-2739 . |
| APA | Hu Xin-yu , Xu Zhang-hua , Huang Xu-ying , Zhang Yi-wei , Chen Qiu-xia , Wang Lin et al. Relationship Between Chlorophyll and Leaf Spectral Characteristics and Their Changes Under the Stress of Phyllostachys Praecox . | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2022 , 42 (9) , 2726-2739 . |
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To control the negative effects resulting from the disorderly development of aquaculture ponds and promote the development of the aquaculture industry, rapid and accurate identification and extraction techniques are essential. An aquaculture pond is a special net-like water body divided by complex roads and dikes. Simple spectral features or spatial texture features are not sufficient to accurately extract it, and the mixed feature rule set is more demanding on computer performance. Supported by the GEE platform, and using the Landsat satellite data set and corresponding DEM combined with field survey data, we constructed a decision-making model for the extraction of aquaculture ponds in the coastal waters, and applied this method to the coastal waters of Southeast China. This method combined the image spectral information, spatial features, and morphological operations. The results showed that the total accuracy of this method was 93%, and the Kappa coefficient was 0.86. The overlapping proportions of results between the automated extraction and visual interpretation for test areas were all more than 90%, and the average was 92.5%, which reflected the high precision and reliability of this extraction method. Furthermore, in 2020, the total area of coastal aquaculture ponds in the study area was 6348.51 km(2), which was distributed primarily in the cities of Guangdong and Jiangsu. Kernel density analysis suggested that aquaculture ponds in Guangdong and Jiangsu had the highest degree of concentration, which means that they face higher regulatory pressure in the management of aquaculture ponds than other provinces. Therefore, this method can be used to extract aquaculture ponds in coastal waters of the world, and holds great significance to promote the orderly management and scientific development of fishery aquaculture.
Keyword ：
aquaculture pond area aquaculture pond area GEE platform GEE platform spatial convolution spatial convolution threshold segmentation threshold segmentation
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| GB/T 7714 | Wang, Lin , Li, Yefan , Zhang, Dongzhu et al. Extraction of Aquaculture Pond Region in Coastal Waters of Southeast China Based on Spectral Features and Spatial Convolution [J]. | WATER , 2022 , 14 (13) . |
| MLA | Wang, Lin et al. "Extraction of Aquaculture Pond Region in Coastal Waters of Southeast China Based on Spectral Features and Spatial Convolution" . | WATER 14 . 13 (2022) . |
| APA | Wang, Lin , Li, Yefan , Zhang, Dongzhu , Liu, Zhicai . Extraction of Aquaculture Pond Region in Coastal Waters of Southeast China Based on Spectral Features and Spatial Convolution . | WATER , 2022 , 14 (13) . |
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为控制水产养殖塘无序发展带来的负面效应,促进水产养殖业进一步发展,首要解决的就是对其快速、准确识别和提取的问题.水产养殖塘是被复杂道路和堤坝分割的特殊网状水体,单纯的光谱特征或空间纹理特征都不足以对其准确提取,且混合特征规则集对计算机性能要求越发苛刻.鉴于此,以Landsat影像序列为数据源,基于谷歌地球引擎(Google Earth Engine,GEE)平台,提出了一种结合影像光谱信息、空间特征和形态学操作的沿海水产养殖塘自动提取方法.该方法联用了双特征水体光谱指数(改进型组合水体指数(modified combined index for water identification,MCIWI)与改进的归一化差异水体指数(modified normalized difference water index,MNDWI))以突出大面积水体与养殖塘的网格特征,再利用低频滤波空间卷积运算拉伸养殖与非养殖水体之间的差异特征,将水产养殖塘区作为一个整体准确识别和快速提取.研究结果表明:①该方法总精度达到93％,Kappa系数为0.86,典型区域叠加比对检验流程验证,提取结果和实际结果重叠比例均在90％以上,平均重叠比例达92.5％,反映了提取方法的高精度和可靠性;②2020年福建省近岸海域水产养殖塘区总面积为511.73 km2,主要分布在漳州市、福州市和宁德市;③核密度分析结果表明漳州市的水产养殖塘集聚度高,相应其养殖塘管理压力也较大.该方法可以实现近岸海域水产养殖塘的自动化提取,对促进渔业养殖的有序管理和科学发展具有重要的意义.
Keyword ：
GEE平台 GEE平台 水产养殖塘区 水产养殖塘区 空间卷积 空间卷积 阈值分割 阈值分割
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| GB/T 7714 | 李叶繁 , 王琳 , 张冬珠 . 光谱特征和空间卷积相协同的近岸海域养殖塘遥感信息提取 [J]. | 自然资源遥感 , 2022 , 34 (4) : 42-52 . |
| MLA | 李叶繁 et al. "光谱特征和空间卷积相协同的近岸海域养殖塘遥感信息提取" . | 自然资源遥感 34 . 4 (2022) : 42-52 . |
| APA | 李叶繁 , 王琳 , 张冬珠 . 光谱特征和空间卷积相协同的近岸海域养殖塘遥感信息提取 . | 自然资源遥感 , 2022 , 34 (4) , 42-52 . |
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以往研究生态脆弱性多集中于宏观区域角度,而针对工程建设项目等微观尺度的生态脆弱性影响研究则较为少见.为了探究道路建设对路域生态环境的影响,以Landsat 8 OLI/TIRS遥感影像和DEM为数据源,以莆炎高速YA12段公路两侧1000 m路域为研究区,将定量化和空间化的理念引入南方红壤丘陵区公路建设项目尺度下的生态脆弱性研究,利用"缓冲区阶梯"探寻了公路建设活动对路域生态脆弱性影响的空间分异规律.结果表明:路域生态脆弱性程度与缓冲距离成反比,随着与公路距离的接近而逐渐增大,路域30 m缓冲范围内是受公路建设影响最强的区域,根据该缓冲范围内平均生态脆弱性指数相较于背景值的上升程度(仅升高0.293),可推断该标段的公路建设行为所引起的地表扰动对路域生态脆弱性的影响较为有限;公路两侧300 m缓冲区范围认定为路域生态环境重点保护区域,而针对路基、桥梁为主的建设形式则需要外延至360 m缓冲范围;隧道出入口建设对生态脆弱性的影响远大于隧道内部,因此隧道出入口也是公路建设期间及竣工之后的生态监控、生态重建的重点区域.研究可为高速公路等各类公路工程建设对生态环境影响的监测与评价提供帮助和参考.
Keyword ：
环境工程 环境工程 生态脆弱性指数 生态脆弱性指数 空间分异 空间分异 缓冲区阶梯 缓冲区阶梯 遥感 遥感
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| GB/T 7714 | 李春强 , 王琳 , 刘智才 et al. 南方红壤丘陵区公路路域生态脆弱性空间分异规律 [J]. | 公路交通科技 , 2022 , 39 (12) : 224-230 . |
| MLA | 李春强 et al. "南方红壤丘陵区公路路域生态脆弱性空间分异规律" . | 公路交通科技 39 . 12 (2022) : 224-230 . |
| APA | 李春强 , 王琳 , 刘智才 , 刘辉 , 张冬珠 . 南方红壤丘陵区公路路域生态脆弱性空间分异规律 . | 公路交通科技 , 2022 , 39 (12) , 224-230 . |
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