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学者姓名：许章华
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Abstract ：
针对当前难以在多种因素干扰下准确测出不同状态植物叶片面积的问题,研制一种可测算植物健康与病态叶片面积的装置.该装置由叶片面积信息采集系统和叶片面积计算终端系统组成.通过获取健康与病态植物叶照片,利用叠加分析、亮度检验等技术实时测算叶片面积.以毛竹叶片为测算对象,并设计对照实验.结果表明,该装置适用于室内外多种复杂光照拍摄环境下植物健康与病态叶片面积的测算,可在植物叶生理参数的相关教学实验中推广应用.
Keyword ：
健康与病态叶 健康与病态叶 叶片面积测算 叶片面积测算 图像采集与处理 图像采集与处理 教学实验设计 教学实验设计 装置研制 装置研制
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| GB/T 7714 | 孙磊 , 蓝文静 , 许章华 et al. 健康与病态叶片面积测算装置研制及教学实验设计 [J]. | 实验室研究与探索 , 2025 , 44 (2) : 15-18,41 . |
| MLA | 孙磊 et al. "健康与病态叶片面积测算装置研制及教学实验设计" . | 实验室研究与探索 44 . 2 (2025) : 15-18,41 . |
| APA | 孙磊 , 蓝文静 , 许章华 , 袁现茂 , 詹俊杰 , 朱苡萱 . 健康与病态叶片面积测算装置研制及教学实验设计 . | 实验室研究与探索 , 2025 , 44 (2) , 15-18,41 . |
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Rapid urbanization has increased carbon dioxide (CO2) emissions, exacerbating ecological issues and prompting global shift towards low-carbon development. However, current studies at the county-level face challenges such as incomplete monitoring systems and insufficient statistical granularity, which restrict the detailed analysis of carbon emission spatial distribution and driving mechanisms. To address this, the study utilized high-resolution Luojia1-01 nighttime light (NTL) data combined with the optimal parameters-based geographical detector (OPGD) model, taking Fuzhou, a typical "furnace city" as a case study to reveal the spatial differentiation characteristics and driving mechanisms of carbon emissions at the county-level. The results indicate that carbon emissions in Fuzhou exhibit a "core-edge" spatial differentiation pattern, with the central urban areas having higher emissions than the surrounding counties, and a positive spatial correlation was observed; the proportion of the tertiary production (PTP), the proportion of the primary production (PTP), the urbanization rate (UR), and the level of social capital (SC) are core driving factors of carbon emissions, with dual-factor interactions exhibiting significant bilinear enhancement effects. Based on the carbon emission differentiation characteristics, the study proposes a "five-zone differentiated" governance strategy, which includes low-carbon transformation of the service industry in the core urban areas, green industrial upgrading in high-emission zones, and strengthening the carbon sink function in ecological protection areas. This study provides methodological support and decision-making guidance for refined carbon emission management and low-carbon development planning at the county-level.
Keyword ：
Carbon emission Carbon emission Energy consumption Energy consumption Geographical detector Geographical detector Luojial-01 Luojial-01 Nighttime light Nighttime light
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| GB/T 7714 | Liu, Wang , Yue, Xupan , Wang, Xiaowen et al. Spatial distribution and driving factors of carbon emission in a furnace city using Luojia1-01 nighttime data and optimal parameters-based geodetector [J]. | URBAN CLIMATE , 2025 , 61 . |
| MLA | Liu, Wang et al. "Spatial distribution and driving factors of carbon emission in a furnace city using Luojia1-01 nighttime data and optimal parameters-based geodetector" . | URBAN CLIMATE 61 (2025) . |
| APA | Liu, Wang , Yue, Xupan , Wang, Xiaowen , Lin, Zhongli , Yao, Xiong , Xu, Zhanghua . Spatial distribution and driving factors of carbon emission in a furnace city using Luojia1-01 nighttime data and optimal parameters-based geodetector . | URBAN CLIMATE , 2025 , 61 . |
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Leaf area index (LAI) and chlorophyll content are crucial variables in photosynthesis, respiration, and transpiration, playing a vital role in monitoring vegetation stress, estimating productivity, and evaluating carbon cycling processes. Currently, physical models are widely adopted for estimating LAI and canopy chlorophyll content (CCC). However, the main challenges of physical model-based methods for estimating LAI and CCC are the high computational cost and the fact that different combinations of canopy variables result in similar spectral reflectance for local minima. To address this limitation, a hybrid model was proposed to invert the LAI and CCC in Moso bamboo (Phyllostachys pubescens) forests. This approach utilized the PROSAIL canopy radiation transfer model, established look-up table (LUT) for LAI and CCC, and employed the Stacking ensemble learning framework. Compared with the PROSAIL LUT method, the hybrid model demonstrated higher performance in predicting LAI and CCC by incorporating the strengths of different models within the hybrid framework. The R-2 values between predicted and measured values were improved by 3.28% and 7.15%, while the RMSE values were reduced by 19.71% and 16.14%, respectively. Moreover, the hybrid model based on Stacking ensemble learning achieved an 86% reduction in running time. Therefore, the hybrid model, which integrates the PROSAIL model with the Stacking ensemble learning framework, offers a more efficient and accurate approach for remotely estimating the LAI and CCC in Moso bamboo forests. The high efficiency of this method makes it promising and suitable for application to other types of vegetation.
Keyword ：
Canopy chlorophyll content (CCC) Canopy chlorophyll content (CCC) hybrid method hybrid method leaf area index (LAI) leaf area index (LAI) Moso bamboo forests Moso bamboo forests PROSAIL RTM PROSAIL RTM
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| GB/T 7714 | Xu, Zhanghua , Zhang, Chaofei , Xiang, Songyang et al. A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (Phyllostachys pubescens) Forests From Sentinel-2 MSI Data [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 : 3125-3143 . |
| MLA | Xu, Zhanghua et al. "A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (Phyllostachys pubescens) Forests From Sentinel-2 MSI Data" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18 (2025) : 3125-3143 . |
| APA | Xu, Zhanghua , Zhang, Chaofei , Xiang, Songyang , Chen, Lingyan , Yu, Xier , Li, Haitao et al. A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (Phyllostachys pubescens) Forests From Sentinel-2 MSI Data . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 , 3125-3143 . |
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The on-year and off-year phenomenon is a distinctive phenological characteristic of Moso bamboo, reflecting variations in nutrient dynamics and endogenous hormonal rhythms during the transition from bamboo shoot to the culm. This phenomenon also influences pest resistance between the on-year and off-year cycles of Moso bamboo. Pantana phyllostachysae Chao is a leaf-feeding pest that affects Moso bamboo. However, monitoring P. phyllostachysae damage using remote sensing data is challenging because the off-year Moso bamboo has physiological characteristics similar to on-year Moso bamboo infested with P. phyllostachysae. This study utilizes the Recursive Feature Elimination (RFE) algorithm to investigate hyperspectral remote sensing characteristics of P. phyllostachysae in Moso bamboo forests. We analyzed the impact of on-year and off-year phenological characteristics on the accuracy of hazard extraction and developed detection models for P. phyllostachysae hazard levels in on-year and off-year Moso bamboo using Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and one-dimensional Convolutional Neural Network (1D-CNN). The results demonstrate that classical machine learning and deep learning models can effectively detect P. phyllostachysae damage, with the 1D-CNN algorithm achieving the best performance. Analyzing the impact of the phenological differences between on-year and off-year Moso bamboo on pest identification accuracy revealed that when four machine learning models accounted for these phenological characteristics, their accuracy in identifying pests was significantly higher than that of a model which did not take into account the bamboo phenology. This finding highlights that considering the phenological characteristics of on-year and off-year Moso bamboo can substantially improve the detection accuracy of UAV hyperspectral remote sensing in monitoring P. phyllostachysae damage. This provides more accurate technical support for the health management and resource protection of bamboo forests and offers a scientific basis for maximizing the ecological and economic benefits of bamboo forests.
Keyword ：
machine learning machine learning Moso bamboo forests Moso bamboo forests on-year and off-year phenological characteristics on-year and off-year phenological characteristics Pantana phyllostachysae Chao Pantana phyllostachysae Chao unmanned aerial vehicle (UAV) hyperspectral images unmanned aerial vehicle (UAV) hyperspectral images
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| GB/T 7714 | He, Anqi , Xu, Zhanghua , Li, Yifan et al. Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV hyperspectral images [J]. | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
| MLA | He, Anqi et al. "Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV hyperspectral images" . | GEO-SPATIAL INFORMATION SCIENCE (2025) . |
| APA | He, Anqi , Xu, Zhanghua , Li, Yifan , Li, Bin , Huang, Xuying , Zhang, Huafeng et al. Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV hyperspectral images . | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
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BACKGROUNDMoso bamboo (Phyllostachys edulis) plays a pivotal role in the global carbon cycle because of its rapid growth and significant ecological benefits. Accurate estimation of its aboveground biomass (AGB) is therefore essential for effective carbon management. However, the influence of its primary leaf-feeding pest, Pantana phyllostachysae Chao (P. phyllostachysae), on AGB remains poorly understood, potentially compromising estimation accuracy. This study aims to develop allometric equations and integrate them with machine learning algorithms to accurately estimate the AGB of Moso bamboo forests under varying levels of pest stress.RESULTSAllometric equations exhibited strong estimation performance across all pest infestation levels, with R2 values exceeding 0.93, root mean square error (RMSE) values below 0.66 kg, and mean absolute error (MAE) values under 0.51 kg. Among the machine learning approaches evaluated, the Extreme Gradient Boosting (XGBoost) algorithm demonstrated superior performance, yielding an R2 of 0.8593, RMSE of 0.5176 kg, and MAE of 0.4313 kg. A clear negative correlation was identified between the severity of P. phyllostachysae infestation and AGB, with biomass values decreasing progressively from healthy to severely infested stands.CONCLUSIONIncorporating pest factors into AGB estimation models significantly enhances model accuracy and captures the nuanced effects of pest stress on biomass accumulation. This integration improves model generalizability and ecological relevance, offering valuable insights for sustainable forest management and carbon accounting. The findings highlight the importance of explicitly considering pest dynamics in biomass modeling and carbon management strategies, laying a robust foundation for future research on pest-biomass interactions in forest ecosystems. (c) 2025 Society of Chemical Industry.
Keyword ：
aboveground biomass aboveground biomass allometric equations allometric equations machine learning machine learning Moso bamboo forests Moso bamboo forests Pantana phyllostachysae Chao Pantana phyllostachysae Chao UAV multispectral images UAV multispectral images
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| GB/T 7714 | He, Anqi , Xu, Zhanghua , Li, Guantong et al. Accurate estimation of aboveground biomass in Moso bamboo (Phyllostachys edulis) forests under Pantana phyllostachysae Chao stress using UAV multispectral remote sensing and self-establish allometric equations [J]. | PEST MANAGEMENT SCIENCE , 2025 , 81 (10) : 6650-6666 . |
| MLA | He, Anqi et al. "Accurate estimation of aboveground biomass in Moso bamboo (Phyllostachys edulis) forests under Pantana phyllostachysae Chao stress using UAV multispectral remote sensing and self-establish allometric equations" . | PEST MANAGEMENT SCIENCE 81 . 10 (2025) : 6650-6666 . |
| APA | He, Anqi , Xu, Zhanghua , Li, Guantong , Chen, Lingyan , Zhang, Huafeng , Li, Bin et al. Accurate estimation of aboveground biomass in Moso bamboo (Phyllostachys edulis) forests under Pantana phyllostachysae Chao stress using UAV multispectral remote sensing and self-establish allometric equations . | PEST MANAGEMENT SCIENCE , 2025 , 81 (10) , 6650-6666 . |
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Pantana phyllostachysae Chao is a destructive leaf-eating pest that poses a significant threat to the health of bamboo forests and the bamboo industry. However, the spatial and temporal spread mechanisms of this pest are still unclear. To better understand and predict the spread of this pest, we used Sentinel-2 A/B images from the pest detection period of 2018-2021, to identify association factors from five dimensions, including forest stand, meteorology, topography, pest sources, and human environment factors. The association factor sets for the spread of P. phyllostachysae were established under both existence and nonexistence pest control scenarios. The extreme gradient boosting (XGBoost) model was employed to derive conversion rules for the respective spread models, enabling the determination of suitability probabilities for both healthy and damaged bamboo forests. These probabilities were then utilized in conjunction with cellular automata (CA) to simulate the spread of P. phyllostachysae under two scenarios. The results showed that the OA and Kappa reached more than 85% and 0.7% in both scenarios, respectively. Meanwhile, the division of pest control scenarios and the selection of XGBoost both help to improve the spreading simulation accuracy. Our models effectively coupled the research results of leaf hosts of different damage levels, simulated the spread of P. phyllostachysae, and identified the dynamic mechanisms of the pest's spread. These findings provide decision support for interrupting the spread path of the pest and achieving precise control, thus safeguarding forest ecological security.
Keyword ：
Accuracy Accuracy Adaptation models Adaptation models Bamboo Bamboo Biological system modeling Biological system modeling Forests Forests Hazards Hazards Hazard spread simulation Hazard spread simulation Meteorology Meteorology Moso bamboo Moso bamboo Pest control Pest control P. phyllostachysae Chao P. phyllostachysae Chao Predictive models Predictive models Sentinel-2 A/B images Sentinel-2 A/B images Surfaces Surfaces XGBoost-CA XGBoost-CA
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| GB/T 7714 | He, Anqi , Xu, Zhanghua , Zhang, Hongbin et al. Simulation of Pantana phyllostachysae Chao Hazard Spread in Moso Bamboo (Phyllostachys pubescens) Forests Based on XGBoost-CA Model [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | He, Anqi et al. "Simulation of Pantana phyllostachysae Chao Hazard Spread in Moso Bamboo (Phyllostachys pubescens) Forests Based on XGBoost-CA Model" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | He, Anqi , Xu, Zhanghua , Zhang, Hongbin , Zhou, Xin , Li, Guantong , Zhang, Huafeng et al. Simulation of Pantana phyllostachysae Chao Hazard Spread in Moso Bamboo (Phyllostachys pubescens) Forests Based on XGBoost-CA Model . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
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Moso bamboo forests (MBFs) are unique subtropical ecosystems characterized by distinct leaf phenology, bamboo shoots, rapid growth, and carbon sequestration capability. Leaf area index (LAI) is an essential metric for evaluating the productivity and ecological quality of MBFs. However, accurate and large-scale methods for remote-sensing-based LAI monitoring during the winter growth stage remain underdeveloped. This study introduces a novel method integrating hyperspectral indices from Zhuhai-1 Orbit Hyperspectral Satellites (OHS) imagery with the particle swarm optimization-support vector machine (PSO-SVM) coupling model to estimate LAI in winter MBFs. Five traditional vegetation indices (VIRs) and their red-edge variants (VIREs) were optimized to build empirical models. Machine learning algorithms, including SVM, Random Forest, extreme gradient boosting, and partial linear regression, were also applied. The PSO-SVM model, integrating three VIRs and three VIREs, achieved the highest accuracy (R-2 = 0.721, RMSE = 0.490), outperforming traditional approaches. LAI was strongly correlated with indices, such as NDVIR, RVIR, EVIRE, and SAVI(R) (R > 0.77). LAI values of MBFs primarily ranged from 2.1 to 5.5 during winter, with values exceeding 4.5 indicating high winter bamboo shoot harvesting. These findings demonstrate the potential of OHS data to improve LAI retrieval models for large-scale LAI mapping, offering new insights into MBFs monitoring and contributing to sustainable forest management practices.
Keyword ：
hyperspectral remote sensing hyperspectral remote sensing leaf area index leaf area index machine learning machine learning multi-purpose bamboo plant multi-purpose bamboo plant particle swarm algorithm particle swarm algorithm red edge vegetation index red edge vegetation index
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| GB/T 7714 | Guo, Xiaoyu , Wang, Weisen , Meng, Fangyu et al. LAI Mapping of Winter Moso Bamboo Forests Using Zhuhai-1 Hyperspectral Images and a PSO-SVM Model [J]. | FORESTS , 2025 , 16 (3) . |
| MLA | Guo, Xiaoyu et al. "LAI Mapping of Winter Moso Bamboo Forests Using Zhuhai-1 Hyperspectral Images and a PSO-SVM Model" . | FORESTS 16 . 3 (2025) . |
| APA | Guo, Xiaoyu , Wang, Weisen , Meng, Fangyu , Li, Mingjing , Xu, Zhanghua , Zheng, Xiaoman . LAI Mapping of Winter Moso Bamboo Forests Using Zhuhai-1 Hyperspectral Images and a PSO-SVM Model . | FORESTS , 2025 , 16 (3) . |
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This study estimated aboveground carbon stock (AGC) using field data and integrated multi-source remote sensing imagery to understand the effects of Pantana phyllostachysae Chao (P. phyllostachysae) stress. AGC remote sensing inversion was performed while accounting for P. phyllostachysae stress, and changes were analyzed. Results indicate: (1) Carbon content coefficients of Moso bamboo leaves, branches, and culms under pest stress ranged from 0.422 to 0.543 g/g, decreasing with increased stress. (2) A random forest model using multi-source data demonstrated the best performance (R2 = 0.688), estimating average AGC at 28.427 t/ha and total carbon sequestration at 913.902 MtC (Million tons of Carbon). (3) Increased pest stress resulted in gradual reductions in AGC. (4) Pest stress is estimated to result in a carbon sequestration loss of 77.443 MtC. The AGC estimation model indicates that P. phyllostachysae significantly reduces AGC, providing crucial data for understanding carbon cycling and enhancing carbon sink management in Moso bamboo forests.
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| GB/T 7714 | Yang, Yuanyao , Xu, Zhanghua , Chen, Lingyan et al. The Aboveground Carbon Stock of Moso Bamboo Forests Is Significantly Reduced by Pantana phyllostachysae Chao Stress: Evidence from Multi-source Remote Sensing Imagery [J]. | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING , 2025 , 91 (4) . |
| MLA | Yang, Yuanyao et al. "The Aboveground Carbon Stock of Moso Bamboo Forests Is Significantly Reduced by Pantana phyllostachysae Chao Stress: Evidence from Multi-source Remote Sensing Imagery" . | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 91 . 4 (2025) . |
| APA | Yang, Yuanyao , Xu, Zhanghua , Chen, Lingyan , Shen, Wanling , Li, Haitao , Zhang, Chaofei et al. The Aboveground Carbon Stock of Moso Bamboo Forests Is Significantly Reduced by Pantana phyllostachysae Chao Stress: Evidence from Multi-source Remote Sensing Imagery . | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING , 2025 , 91 (4) . |
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To address gaps in understanding how external stresses influence remote-sensing inversion of vegetation biochemical components, a P-PROSAIL model incorporating stress factors was developed, with Shunchang County and Yanping District in Fujian Province as the study areas. The models' effectiveness was assessed, yielding R2 values of 0.7133, 0.7066, 0.6441, 0.6392, 0.6057, 0.7038, 0.5323, and 0.5149 for leaf area index (LAI), canopy dry matter content (CDMC), canopy cellulose content (CCC), canopy lignin content (CLC), canopy protein content (CPC), canopy nitrogen content (CNC), canopy tannin content (CTC), and canopy flavonoid content (CFC), respectively. While CDMC and most other components showed stable inversions, CTC and CFC exhibited uncertainties due to pest stress. This study clarified the internal and external change characteristics and mechanisms of Moso bamboo forests under Pantana phyllostachysae stress, providing empirical support for the ecological health of bamboo forests.
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| GB/T 7714 | Xu, Zhanghua , Sun, Lei , Zhang, Yiwei et al. PROSAIL Modeling Coupled with Environmental Stress: Remote Sensing Retrieval of Multiple Dry Matters in the Canopy of Moso Bamboo Forests under the Stress of Pantana phyllostachysae Chao [J]. | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING , 2025 , 91 (5) . |
| MLA | Xu, Zhanghua et al. "PROSAIL Modeling Coupled with Environmental Stress: Remote Sensing Retrieval of Multiple Dry Matters in the Canopy of Moso Bamboo Forests under the Stress of Pantana phyllostachysae Chao" . | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 91 . 5 (2025) . |
| APA | Xu, Zhanghua , Sun, Lei , Zhang, Yiwei , Zhang, Huafeng , Zhang, Hongbin , Guan, Fengying et al. PROSAIL Modeling Coupled with Environmental Stress: Remote Sensing Retrieval of Multiple Dry Matters in the Canopy of Moso Bamboo Forests under the Stress of Pantana phyllostachysae Chao . | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING , 2025 , 91 (5) . |
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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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