Accurate characterization of individual tree attributes and spatial distributions is pivotal for scientifically guided forest thinning decisions, yet traditional methods for identifying target trees are subjective and labor-intensive. To address these challenges within the context of typical Chinese fir (Cunninghamia lanceolata) plantations in southern China, we propose a Geographic Object-based Integrated Multi-module Image Analysis (GEOIMIA) framework, incorporating three interconnected modules. First, an object-based classification module applies heuristic sequential optimization to achieve high object-level accuracy (97% on the validation set) for four categories-Chinese fir, broadleaf trees, other tree species, and others-across entire images, thereby enabling precise global semantic masks and robust species delineation. Second, we developed a dedicated detection network, AMC-Net, tailored to high-resolution UAV remote sensing data under low illumination conditions, enabling precise crown detection for Chinese fir with AP50 = 96.3% and AP50-95 = 58.3%. Model compression experiments further reduced AMC-Net's parameters to 1.1 million (only 45.8% of the original) while maintaining comparable detection performance (APweighted = 62.1%). Third, a decision module supported by Maskable Proximal Policy Optimization (Maskable PPO) dynamically optimizes thinning priorities, effectively balancing canopy closure (CC), stem density (SD), and stand stability (SS) in real-time. Adhering to the Regulations for forest tending (GB/T15781-2015), this module implements selective thinning strategies that remove denser, smaller, or weaker trees and retain those with higher ecological and silvicultural value. Consequently, GEOIMIA offers an economical, efficient, and scalable solution for automated thinning operations in mixed-species forests.

Thinning is a cornerstone of sustainable forest management (SFM), yet decisions are often constrained by subjective experience and a lack of quantitative spatial data. To address this, we developed a precision thinning framework for Cunninghamia lanceolata plantations by fusing terrestrial and aerial LiDAR data. Instead of relying on manual selection, this study established a quantitative mechanism for identifying harvesting targets. We employed a layer-stacking seed-point algorithm to accurately segment individual trees and extracted key parameters (tree height, DBH, and crown width) with high precision (Overall R-2 >= 0.85). Furthermore, an objective multi-criteria weighting method (AHP - CRITIC) was constructed to prioritize thinning targets based on stand stability and growth status. Results indicated that the proposed framework achieved an individual tree segmentation F1-score of 89.24%. Simulated thinning based on the calculated weights significantly optimized the stand spatial structure: canopy openness increased by 11.8%, and spatial indices converged toward optimal ranges. Compared with conventional practices, the proposed approach effectively reduced growth dispersion and enhanced population coordination. These findings demonstrate that integrating multi-platform LiDAR with objective weighting algorithms offers a scientifically rigorous pathway for precision forestry, promoting both productivity and the long-term sustainability of plantation ecosystems.

Mangroves are important coastal blue-carbon ecosystems, and accurate biomass estimation is essential for carbon stock assessment and ecological monitoring. To address the limitation of regional-scale biomass estimation caused by the scale mismatch between field plots and satellite pixels, this study selected the Zhangjiangkou National Mangrove Nature Reserve in Fujian Province as the study area and the mangrove distribution region of Fujian Province as the extrapolation area. A multiscale biomass estimation framework integrating field plots, unmanned aerial vehicles (UAVs), and satellite remote sensing was established. The results showed that (1) the optimal UAV-scale models achieved R2 values of 0.69 and 0.78 for aboveground biomass (AGB) and belowground biomass (BGB), respectively, with corresponding root mean square error (RMSE) values of 18.55 and 9.52 t·ha−1 and normalized root mean square error (nRMSE) values of 0.14 and 0.17 demonstrating reliable predictive performance; (2) after introducing UAV-derived bridging labels, the R2 of the AGB model increased from 0.24 to 0.64, while the RMSE decreased from 29.02 to 10.86 t·ha−1. Similarly, the R2 of the BGB model increased from 0.43 to 0.63, accompanied by a reduction in RMSE from 14.89 to 6.35 t·ha−1, demonstrating a substantial improvement in satellite-scale biomass estimation accuracy; (3) the total AGB and BGB of mangroves in Fujian Province were estimated at 58,768.60 t and 24,575.14 t, respectively, with high-biomass areas mainly distributed along the coastal regions of Zhangzhou and Quanzhou. Unlike conventional field-to-satellite extrapolation approaches, the proposed framework introduces UAV-derived biomass maps as intermediate bridging labels for pixel-level supervised learning, thereby establishing an effective link between field measurements and satellite observations. This strategy effectively reduces the scale mismatch between field and satellite data, significantly improves satellite-scale biomass estimation accuracy, and provides a transferable and scalable framework for regional mangrove biomass mapping and blue-carbon assessment.

Accurate identification of moso bamboo (Phyllostachys edulis) age classes is essential for effective forestry resource management, yet existing methods often struggle to achieve a satisfactory balance between accuracy and computational efficiency under complex field conditions. To address this challenge, this study proposes a lightweight object detection model, termed YOLO11-GCR, for fine-grained moso bamboo age-class classification based on close-range imagery. The proposed approach builds upon the YOLO11 framework and incorporates Ghost convolution, the Convolutional Block Attention Module (CBAM), and a Receptive Field Block (RFB) to reduce model complexity, enhance discriminative feature representation, and improve sensitivity to subtle texture variations among age classes. A dataset consisting of 9538 annotated bamboo culm images covering four age classes (I-du to IV-du) was constructed and divided into training, validation, and independent test sets with strict spatiotemporal separation. Experimental results indicate that YOLO11-GCR achieves robust detection performance with a lightweight architecture of 2.62 × 106 parameters and 6.2 GFLOPs, yielding an mAP@0.5 of 0.913 and an mAP@0.5–0.95 of 0.895 on the independent test set. Notably, the model demonstrates improved classification stability for visually similar age classes, such as II-du and III-du. Overall, this study presents an efficient and practical imaging-based solution for automated moso bamboo age-class recognition in complex natural environments.

This study aimed to streamline the determination of chlorophyll content in Cunninghamia lanceolate while achieving precise measurements of canopy chlorophyll content. Relative chlorophyll content (SPAD) in the Cunninghamia lanceolate canopy were assessed in the study area using the SPAD-502 portable chlorophyll meter, alongside spectral data collected via onboard multispectral imaging. And based on the unmanned aerial vehicle (UAV) multispectral collection of spectral values in the study area, 21 vegetation indices with significant correlation with Cunninghamia lanceolata canopy SPAD (CCS) were constructed as independent variables of the model’s various regression techniques, including partial least squares regression (PLSR), random forests (RF), and backpropagation neural networks (BPNN), which were employed to develop a SPAD inversion model. The BPNN-based model emerged as the best choice, exhibiting test dataset coefficients of determination (R2) at 0.812, root mean square error (RSME) at 2.607, and relative percent difference (RPD) at 1.942. While the model demonstrated consistent accuracy across different slope locations, generalization was lower for varying slope directions. By creating separate models for different slope directions, R2 went up to about 0.8, showcasing favorable terrain applicability. Therefore, constructing inverse models with different slope directions samples separately can estimate CCS more accurately.

The sustainable development of scenic spots have attracted much attention in academic circles, and the influence mechanism of anthropomorphic information framework to guide tourists to enhance environmental responsibility behavior is not clear. Based on the framework theory and prospect theory, the study constructed a theoretical model of anthropomorphic information framework acting on environmental responsibility behavior. The results of three experimental studies showed that: (1) The anthropomorphic information framework could significantly improve tourists’ environmental responsibility behavior, and compared with the positive anthropomorphic information framework, the negative anthropomorphic information framework can significantly improve tourists’ environmental responsibility behavior. (2) Natural empathy played an intermediary role between anthropomorphic information framework and tourists’ environmental responsibility behavior. (3) Self-construal moderated the relationship between anthropomorphic information framework and tourists’ environmental responsibility behavior. Among the interdependent tourists, compared with the positive anthropomorphic information framework, the negative anthropomorphic information framework was more likely to produce stronger environmental responsibility behavior and natural empathy. Among independent tourists, tourists’ environmental responsibility behavior and natural empathy generated by negative anthropomorphic information and positive anthropomorphic information were not significant. Through the above findings, we hope to improve the management level of tourist attractions.

[Objective] The driving factors and interaction of dynamic changes ecological environmental quality in the Minjiang River basin were studied in order to provide theoretical support for improving the quality of the ecological environment and for realizing human and natural development. [Methods] MODIS remote sensing data were obtained from 2001 to 2020 to calculate the remote sensing ecological index (RSEI) of the Minjiang River basin based on the Google Earth Engine (GEE) platform. The spatio-temporal changes in ecological environmental quality for the Minjiang River basin were studied in combination with trend analysis, and the driving factors of RSEI were identified by geographical detectors. [Results] ① The average contribution rate of the first principal component of the four ecological indicators reached 69.01% in the RSEI calculation results, indicating that RSEI could fully reflect the ecological environmental quality of the Minjiang River basin. ② The ecological environment of the Minjiang River basin was in the good quality category, and exhibited improvement over time. From 2001 to 2020, the average RSEI of the Minjiang River basin increased from 0.618 to 0.701. The proportion of area with excellent and good ecological environmental quality increased by 19.16%, and the regional ecological environmental quality improved for 90.28% of the area of the Minjiang River basin. [Conclusion] The ecological environmental quality of the Minjiang River basin was jointly affected by multiple factors, among which altitude and nighttime light were the main influencing factors and driving forces with a high degree of explanation. The interaction between factors will increase the impact on ecological environmental quality.

Citrus (Citrus reticulata), which is an important economic crop worldwide, is often managed in a labor-intensive and inefficient manner in developing countries, thereby necessitating more rapid and accurate alternatives to field surveys for improved crop management. In this study, we propose a novel method for individual tree segmentation from unmanned aerial vehicle remote sensing (RS) using a combination of geographic object-based image analysis (GEOBIA) and layer-adaptive Euclidean distance transformation-based watershed segmentation (LAEDT-WS). First, we use a GEOBIA support vector machine classifier that is optimized for features and parameters to identify the boundaries of citrus tree canopies accurately by generating mask images. Thereafter, our LAEDT workflow separates connected canopies and facilitates the accurate segmentation of individual canopies using WS. Our method exhibited an F1-score improvement of 10.75% compared to the traditional WS method based on the canopy height model. Furthermore, it achieved 0.01% and 1.38% higher F1-scores than the state-of-the-art deep learning detection networks YOLOX and YOLACT, respectively, on the test plot. Our method can be extended to detect larger-scale or more complex structured crops or economic plants by introducing more finely detailed and transferable RS images, such as high-resolution or LiDAR-derived images, to improve the mask base map.

It is challenging to accurately and rapidly extract crops based on the ultra-high spatial resolution images of uncrewed aerial vehicle (UAV). Object-based image analysis (OBIA) was regarded as an effective technique for high-spatial-resolution image classification because of its ability to achieve high accuracy by integrating multidimensional features. In recent years, deep learning (DL) techniques, with their ability to automatically learn image features from a large number of images, have shown great potential for crop monitoring. However, a systematic comparison of these two mainstream methods for monitoring the crop phenotype has not been conducted. Therefore, this study compares the performance of two advanced methods, DL and OBIA, in individual cabbage plant detection tasks. The results show that the Mask R-CNN deep learning model outperforms the object-based image analysis-multilevel distance transform watershed segmentation (OBIA-MDTWS) method in crop extraction and counting, with an overall mean F1-Score, accuracy of 2.70, 4.15 percentage points higher, respectively. Moreover, the Mask R-CNN deep learning model has higher computing efficiency, which is 3.74 times higher than the OBIA-MDTWS model. In summary, this study shows that the Mask R-CNN deep learning model performs better in vegetable extraction and quantity estimation, providing technical support for subsequent field nursery management and fine planting.

为探究杉木高生产力的核心区域及其影响因素,采用改进的CASA模型基于遥感数据、森林资源调查数据和气象台站数据估算2006年、2011年和2016年福建省杉木林植被净初级生产力(Net Primary Productivity,NPP),分析其时空分异特征,探究其与各影响因子的关系.研究结果显示,福建省杉木林在2006年、2011年和2016年NPP年均值分别为804.03、854.76、884.46 g/(m2·a),呈现出夏季高冬季低的季相特征;杉木林的分级统计以中、高生产力占优势,其中南平市和三明市是福建省杉木林的核心产区,各期的中、高生产力杉木林分单元分别占全省杉木林分总面积的36.04％、41.98％和45.60％;杉木林NPP与年降水量、年太阳总辐射、海拔、坡度、土壤有机质、全氮和全钾呈显著正相关,与年平均气温、坡向、全磷呈显著负相关;杉木林NPP总体上随海拔、坡度、太阳总辐射、年降水量和土壤有机质的升高而递增,随年平均气温的升高而递减.研究表明海拔、坡度、年太阳总辐射、年降水量、年平均气温和土壤有机质是影响福建省杉木林NPP的重要因子.

The rationality and efficiency of the spatial structure of an urban park system are critical in building a livable urban environment. Fractal theory is currently treated as the frontier theory for exploring the law of complex systems; however, it has rarely been applied to urban park systems. This study applied the aggregation, grid and correlation dimension models of fractal theory in Fuzhou, China. The spatial structure and driving factors of the urban park system were analyzed and an innovative model was proposed. The evidence shows that the spatial structure of the park system has fractal characteristics, although self-organization and optimization have not yet been fully formed, revealing a multi-core nesting pattern. Moreover, the core is cluster of four popular parks with weakening adsorption, and the emerging Baima River Park is located at the geometric center, which is likely to be further developed. The system structure is primarily driven by geographical conditions, planning policies, and transportation networks. Against this backdrop, an innovative model for the park system was proposed. The central park has heterogeneity and synergistic development, relying on the kinds of flow which can lead to the formation of a park city, a variation of a garden city. At the regional scale, relying on the geographical lines, the formation of a regional park zone could be realized. These findings provide new perspectives to reveal the spatial structure of urban park systems. The information derived can assist policy makers and planners in formulating more scientific plans, and may contribute to building a balanced and efficient urban park system.

In the context of global warming, although the coordinated development of tourism has led to regional economic growth, the high energy consumption-driven effects of such development have also led to environmental degradation. This research combines the undesired output of the Super-SBM model and social network analysis methods to determine the eco-efficiency of provincial tourism in China from 2010-2019 and analyzes its spatial correlation characteristics as well as its influencing factors. The aim of the project is to improve China's regional tourism eco-efficiency and promote cross-regional tourism correlation. The results show that (1) the mean value of provincial tourism eco-efficiency in China is maintained at 0.405~0.612, with an overall fluctuating upward trend. The tourism eco-efficiency of eastern China is higher than that of central, western and northeastern China, but the latter three regions have not formed a stable spatial distribution pattern. (2) The spatial network of provincial tourism eco-efficiency in China is multithreaded, dense and diversified. Throughout the network, affiliations are becoming closer, and network structure robustness is gradually improving, although the "hierarchical" spatial network structure remains. In individual networks, Jiangsu, Guangdong and Shandong provinces in eastern China have higher centrality degrees, closeness centrality and betweenness centrality than other provinces, which means they are dominant in the network. Hainan Province, also located in eastern China, has not yet built a "bridge" for tourism factor circulation. In the core-periphery model, the core-periphery areas of China's provincial tourism eco-efficiency are distributed in clusters, and the number of "core members" has increased. (3) The economic development level, information technology development level, and tourism technology level collectively drive the development and evolution of China's provincial tourism eco-efficiency spatial network.

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images.

为探究目标树经营对杉木人工林林分空间结构的改善效果,在3个林龄(8、12、21 a)的杉木人工林中分别设置3块标准地,选取4个空间结构参数(角尺度、大小比数、开敞度和竞争指数)与胸径因子构建单木综合评价指标模型来确定目标树、干扰木和一般木,通过模拟采伐干扰木,计算并分析间伐前后林分空间结构参数的变化.结果表明,林分空间结构单元多以1株中心木和5～6株近邻木构成;林龄为8、12和21 a的杉木人工林样地的干扰木数量分别占林分的33.93％、31.80％、11.20％;3个林龄各样地林木均为随机分布,林木分化明显,生长空间不足,竞争压力较大.经目标树抚育模拟间伐干扰木后,林龄为8、12和21 a的杉木人工林样地大小比数和竞争指数的平均值均降低,林木间竞争压力减小,竞争优势地位提升;林龄为8、12 a的杉木人工林样地角尺度和开敞度的平均值均增大,林木空间分布格局越来越优,生长空间大幅增加,而林龄为21 a的杉木人工林样地的改善程度不明显.

基于南平松溪林场九个人工杉木林标准地的实测数据,对人工杉木林林分空间结构参数进行了评价和相关性分析,为制定杉木林和其他纯林林分空间结构优化和调整措施奠定基础.采用n=4确定林木的空间结构单元,用大小比数、角尺度和竞争指数等3个林分空间结构参数分析杉木林的林分空间结构特征.结果表明,9个标准地的杉木林大小比数都接近中庸状态,林木个体差异不大,林木分化不严重;角尺度处于均匀和随机分布状态之间,与水平分布理想模式存在一定差距;竞争指数较为稳定,没有因为年龄的不同而出现大幅度的变动.大小比数U和竞争指数H间为中度相关,大小比数U和角尺度W,以及角尺度W和竞争指数H间几乎不相关.研究结果有助于杉木人工林的可持续经营.

长汀县是我国水土流失极为严重的地区,及时快速地监测区域内水土流失敏感性并开展相关治理显得尤为重要.以长汀县为研究区,选择1994、2006和2016年三期Landsat遥感影像为主要数据源,采用熵权法及多因子加权求和模型,以降雨、地形因子、土壤类型、植被覆盖度与土地利用类型5个指标作为水土流失敏感性评价指标,构建水土流失敏感性综合评价指标体系及各年份评价模型,对研究区水土流失敏感性分布情况进行综合评价.再采用自然分界法将其水土流失敏感性划分为不敏感、轻度敏感、中度敏感、高度敏感、极敏感5个等级,结合海拔、坡度分析其空间分异情况.结果表明:研究区水土流失敏感性等级以轻度敏感和中度敏感为主,空间格局表现为内高外低的分布特征.在水土流失敏感性等级变化中,1994—2016年,不同敏感性等级均有不同程度的转化,仅有局部地区敏感性等级有所上升,但总体呈高等级敏感区向低等级敏感区转移的趋势,该现象与长汀县政府对水土流失治理的重视密切相关.各年份敏感性等级随着海拔、坡度的上升均表现为增加后减少的趋势,与人类活动的频繁程度密切相关.研究结果能为研究区生态环境管控措施的制定提供一定的参考与指导.

Land surface temperature (LST) is a joint product of physical geography and socio-economics. It is important to clarify the spatial heterogeneity and binding factors of the LST for mitigating the surface heat island effect (SUHI). In this study, the spatial pattern of UHI in Fuzhou central area, China, was elucidated by Moran’s I and hot-spot analysis. In addition, the study divided the drivers into two categories, including physical geographic factors (soil wetness, soil brightness, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), water density, and vegetation density) and socio-economic factors (normalized difference built-up index (NDBI), population density, road density, nighttime light, park density). The influence analysis of single factor on LST and the factor interaction analysis were conducted via Geodetector software. The results indicated that the LST presented a gradient layer structure with high temperature in the southeast and low temperature in the northwest, which had a significant spatial association with industry zones. Especially, LST was spatially repulsive to urban green space and water body. Furthermore, the four factors with the greatest influence (q-Value) on LST were soil moisture (influence = 0.792) > NDBI (influence = 0.732) > MNDWI (influence = 0.618) > NDVI (influence = 0.604). The superposition explanation degree (influence (Xi ∩ Xj)) is stronger than the independent explanation degree (influence (Xi)). The highest and the lowest interaction existed in ”soil wetness ∩ MNDWI” (influence = 0.864) and “nighttime light ∩ population density” (influence = 0.273), respectively. The spatial distribution of SUHI and its driving mechanism were also demonstrated, providing theoretical guidance for urban planners to build thermal environment friendly cities.

当前农林类特别是林科类学生对专业学习存在一定的偏见和畏难情绪,投入精力不足,对专业发展方向和学习目标不清晰,学习态度不够积极,学习效果不佳.文章基于《3S技术》课程20年的教学实践,利用自建"金课"和自编实践教材,采用线上线下混合教学模式,将学生自主学习和教师引导相结合,以全过程评价对学生进行考...>>详细当前农林类特别是林科类学生对专业学习存在一定的偏见和畏难情绪,投入精力不足,对专业发展方向和学习目标不清晰,学习态度不够积极,学习效果不佳.文章基于《3S技术》课程20年的教学实践,利用自建"金课"和自编实践教材,采用线上线下混合教学模式,将学生自主学习和教师引导相结合,以全过程评价对学生进行考核,充分利用校企平台、以赛促学等方式进行教学创新改革,实现理论与实践无缝衔接.实践效果表明,学生专业素养得到提高,自主学习能力得到加强,潜力得到激发,团队协作意识得到增强,创新成果得到多方好评,并以在线课程形式得到推广应用.

在实地调查的基础上,对分布于福建天台山半枫荷(Semiliquidambar cathayensis Chang)天然群落的物种组成、生态位特征及种间关系进行分析.结果表明:半枫荷群落乔木层和灌木层植物共有27科39属70种645株,其中,乔木层有23科36属47种424株,灌木层有26科38属52种221株.乔木层中半枫荷的重要值为7.19％,位居第2,为乔木层的优势种,Levins生态位宽度和Shannon-Weaver生态位宽度位居第4,而灌木层中半枫荷的重要值仅为0.94％,生态位宽度排名也靠后.乔木层和灌木层中主要种类(重要值排名前31位)生态位重叠值的平均值分别为0.571和0.500,并且,乔木层中半枫荷与赤杨叶〔Alniphyllum fortunei(Hemsl.)Makino〕、米槠〔Castanopsis carlesii(Hemsl.)Hayata.〕和红楠(Machilus thunbergii Sieb.et Zucc.)等7个种类的生态位重叠值较大.χ2检验结果显示:半枫荷群落乔木层和灌木层中多数种对呈不显著正关联,种间独立性相对较强.从共同出现百分率看,半枫荷仅与栲(Castanopsis fargesii Franch.)紧密关联;从联结系数看,半枫荷与木油桐(Vernicia montana Lour.)、毛锥(Castanopsis fordii Hance)、木荷(Schima superba Gardn.et Champ.)、山胡椒〔Lindera glauca(Sieb.et Zucc.)Bl.〕及甜槠〔Castanopsis eyrei(Champ.ex Benth.)Tutch.〕紧密关联.半枫荷群落稳定性交点坐标为(35.45,65.19),群落稳定性较差.综上所述,福建天台山半枫荷天然群落中,半枫荷与多数种类种间联结性不显著,其分布具有一定的独立性和随机性.由于群落中缺乏半枫荷幼苗,群落稳定性较差,种间竞争较激烈,在未来群落演替过程中,半枫荷可能会被偏阴性种类取代,建议对其进行就地保护.

城市道路绿带是城市绿地系统的重要组成部分,绿地绿量对热岛效应的调控功能受到广泛关注.以福州市仓山区道路绿带为研究对象,基于2019年9月的Landsat-8 OLI/TIRS和12月的GF-1影像数据,利用辐射传输方程法进行地表温度反演,并采用面向对象分类法和分层分类法提取典型树种,结合实地测定叶面积指数完成对道路绿带绿量的计算,进而分析城市道路绿带绿量与地温的关系.结果表明:1)道路绿带对周边环境具有一定的降温作用;2)道路绿带的位置与面积差异会对其发挥降温效应产生影响;3)在不同绿带面积范围内,绿量与地温的关系不同.当绿带面积<1 hm2或>10 hm2时,地温分布几乎与绿量无关,在1～10 hm2面积范围内,二者呈负相关;4)绿量在绿带面积为1～4 hm2的降温效率优于在4～10 hm2,单位绿量可分别平均降温0.343℃ ～0.373℃、0.0878℃ ～0.1572℃.通过分析不同面积范围内城市道路绿带绿量与地温的关系,以期为缓解城市热岛效应,科学规划城市道路绿地提供新思路与理论指导.