Can trail spatial attributes predict trail use level in urban forest park? An examination integrating GPS data and space syntax theory

Understanding users’ spatial distribution in forest park is crucial for providing visitors with quality recreation experiences and for park planning and management. Utilizing users’ spatial distribution data, this study aims at investigating associations between trail use level and trail spatial attributes, through examining two large urban forest parks (Gongqing forest park and Paotaiwan forest park) in Shanghai, China. Users’ spatial distribution was measured utilizing GPS trackers with the interval of 10 seconds. This study conceptualizes trail spatial attributes as trail metric attributes and trail configurational attributes. Trail metric attributes include trail mean distance to gates, length, width and level of enclosure, which are calculated based on park map and on-site observation. We computed trail configurational attributes utilizing space syntax theory, which comprise measures of global integration, control, and connectivity. Trail connection with features/facilities, visual connection with water and shading are included as covariate variables. In total, we obtained 134 valid samples in Gongqing forest park and 210 valid samples in Paotaiwan forest park for analysis. Multivariate regression analyses indicate that when involving covariate variables, consistently in both parks, a greater trail usage is significantly related to trail shorter mean distance to park gate, trail width wider than 3 meters, higher global integration and higher control values. Collectively, these four trail spatial attributes explained 31.7 % (p < 0.001) and 27.3 % (p < 0.001) of the variances in trail use level in Gongqing forest park and Paotaiwan forest park. These findings provide direct implications to park designers and managers for providing visitors with different desirable social conditions, and ultimately improve users’ experiences and satisfactions.     

Exploring the characteristics of successful volunteer-led urban forest tree committees in Massachusetts

Citizen engagement through urban forest tree committee volunteer service may aid in providing essential experience, ideas, and skills that support municipal tree management. Using semi-structured, research interviews with tree committee (TC) representatives from across the Commonwealth of Massachusetts, this study addresses current knowledge gaps concerning the general composition, processes, and relationships of volunteer-led urban forest tree committees. Our findings indicate that TC representatives are typically motivated, passionate volunteers who generally desire to work cooperatively with the many associations, organizations, and agencies that comprise the local socio-political landscape. Our findings also indicate it is important that TC representatives make a sustained, concerted effort to work collaboratively with their local tree warden to advance the care of their community’s urban trees. Furthermore, it is also essential that municipal managers and decision-makers attempt to provide TC volunteers with appropriate training opportunities, resources, as well as demonstrate appreciation, to further encourage and solidify volunteer-engagement in urban forestry at the local level.     

Measuring trail users’ perception of crowding in a peri-urban nature park: A best-worst scaling experiment

This paper evaluates the perception of crowding, its determinants and relationship with trail users’ experience in a peri-urban nature park. Existing crowding surveys rely heavily on a single-item 9-point crowding scale which was originally developed for backcountry areas. Receiver operating characteristic (ROC) analysis was used to test the validity and reliability of this measure in detecting crowding issues and formulating visitor capacity limits. Low validity and reliability raised concerns about the applicability of this scale in similar frontcountry areas. Inability of this simple crowding measure to detect a visitor threshold brings to the fore its another important limitation – ignorance of the complexity of crowding phenomenon. The perception of crowding varies with various social, psychological and situational factors, and a best-worst scaling (BWS) experiment was designed to simultaneously evaluate different aspects of crowding in the Medvednica Nature Park, a peri-urban nature park near Zagreb, Croatia. The findings of BWS experiment revealed that occupancy of mountain huts, number of other hikers on the trail and amount of litter along the trail on average contributed most to the trail users’ sense of crowding. A latent class analysis identified a large heterogeneity in the perception of crowding; while one group of trail users (63%) was more disturbed by the direct social impacts, the others (37%) were more disturbed by the secondary impacts of other visitors, especially by the negative externalities from road traffic in the park. Older visitors had a greater tendency to primarily associate crowding with the environmental conditions. A higher impact of trail use level in the perception of crowding, younger age and more frequent use were associated with a more negative evaluation of crowding. The park management should closely monitor road traffic in the park and visitor activity on highly used trails, especially on those popular among younger trail users.     

Modeling height-diameter relationship for artificial monoculture Metasequoia glyptostroboides in sub-tropic coastal megacity Shanghai,China

Tree height is a key variable in forest monitoring studies and for forest management. However, tree height measurement is time consuming, and the recommended procedure is to use estimates from tree height (H) - diameter at breast height (DBH) models. Increasingly, H-DBH models are being developed for urban forests, providing tools to forest management and ecosystem services estimation. Here, we compared model forms and approaches for predicting H as a function of DBH and additional stand level covariates variables. Four model forms were evaluated: (i) basic models (which only used DBH as predictor variable); (ii) generalized models (which used DBH and other predictor variables based on the best basic model); (iii) a mixed-effects model based on the best basic model; and (iv) a mixed-effects model based on the generalized model. Several sampling designs aimed at minimizing height measurement were tested in terms of accuracy and applicability. Taking predicted accuracy and investigation cost into account, we recommend generalized non-linear mixed-effects model (NLME) when there were two or less tree height measurements taken in a given stand. The basic NLME model could be calibrated when there were 3 or more tree height measurements, depending on the required level of accuracy and investigation cost. Additionally, we first reported that soil pH as a covariate variable in H-DBH model and our generalized NLME model implied that the difference in the H-DBH relationship caused by pH varies among different stands. This finding may be attributable to differing biological properties of the similar alkaline tolerance species.     

运用机载LiDAR数据对橡胶林地上生物量估测的采样尺度效应分析

气候变化下,森林生物量遥感监测是当前研究的热点,机载LiDAR作为重要的遥感信息源,其采样大小对生物量估测精度有着一定的影响。以机载LiDAR数据为信息源,以44块30m×30m的方形橡胶林实测样地数据为基础,对机载激光雷达数据进行不同尺寸采样(共21个采样尺寸,边长从10m至30m,间隔为1m),提取不同采样尺寸下的激光雷达参数,并与橡胶林地上生物量建立PLSR模型,就机载激光雷达采样大小对橡胶林地上生物量估测精度的影响进行研究。研究表明:当采样尺寸小于18m时,估测精度随着采样尺寸的增大而增大;而当采样尺寸大于18m时,估测精度随着采样尺寸的增大而减小,进而趋于平缓。结果虽然呈现出一定的规律性,但是差异并不是很明显。当采样尺寸为18m时估测效果最佳,模型决定系数(R^2)为0.718,均方根误差(RMSE)为17.830 t/hm^2;交叉验证精度P和RMSEcv分别为82.741%和18.874t/hm^2。相较于实际样地(30m)尺寸下的估测结果,18m采样尺寸下的R^2提高了1.989%,RMSEcv降低了2.611%。因此,生物量的估测精度受机载激光雷达数据采样尺寸大小的影响,在生物量估测过程中需结合研究对象和研究区的实际情况对采样尺寸进行选择,从而提高生物量估测精度。     

王正非林火蔓延模型改进研究

王正非林火蔓延模型是目前国内比较先进的森林火灾蔓延模型,但是由于林火行为的复杂性,此模型在准确率和普适性方面仍存在许多不足。本文结合已有的成果进一步改进王正非模型,增加了可燃物湿度对林火蔓延速度的影响,改进了林火初始速度的计算方式。针对各种坡度对林火蔓延行为的影响给出了一个计算公式。与改进前相比,改进后的模型使其对林火蔓延情况的预估更为精准。     

基于无人机LiDAR的天然林与人工林林隙提取

为研究主动遥感进行森林地物分类和林隙提取的效果,分别在天然林和人工林中比较了无人机激光雷达(Li DAR)数据的阈值法、逐像元法和面向对象法3种方法的分类精度和适用性。选取天然林(黑龙江省哈尔滨市帽儿山林场)和人工林(内蒙古自治区赤峰市旺业甸林场)两处试验区,应用阈值法、逐像元法和面向对象法3种方法,对两个试验区采集的无人机Li DAR数据进行林隙、非林隙、其他类型划分。研究结果表明,面向对象法在天然林和人工林试验区中的分类精度和Kappa系数均最高,天然林为82. 43%、0. 73,人工林为91. 74%、0. 88;逐像元法次之,天然林为76. 62%、0. 64,人工林为78. 68%、0. 68;阈值法的分类精度和Kappa系数差异较大,在天然林中的精度极低,为50. 54%、0. 27,人工林的精度较高,为79. 12%、0. 69。面向对象法和逐像元法在天然林和人工林普遍适用,均可以达到理想的分类精度和Kappa系数。阈值法在天然林的精度较低,更适合于人工林的分类,即林分高度趋于一致,且建筑、道路等其他类型干扰较少的区域。天然林的最佳分类方法为面向对象法,人工林的最佳分类方法为阈值法。     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

Response of soil respiration to nitrogen addition in two subtropical forest types

Anthropogenic activities have increased nitrogen (N) deposition in terrestrial ecosystems, which directly and indirectly affects soil biogeochemical processes, including soil respiration. However, the effects of the increases in N availability on soil respiration are not fully understood. In this study, soil respiration was measured using an infrared gas analyzer system with soil chambers under four N treatments (0, 5, 15, and 30 g N m^-2 year^-1 as control, low N (LN), moderate N (MN), and high N (HN), respectively) in camphor tree and slash pine forests in subtropical China. Results showed that soil respiration rates decreased by 37% in the camphor tree forest and 27% in the slash pine forest on average on an annual base, respectively, in the N-fertilized treatments when compared with the control. No significant differences were found in the soil respiration rate among the LN, MN, and HN treatments in both forest types as these fertilized plots reached an adequate N content zone. In addition, soil microbial biomass carbon (C) content and fine root biomass declined in N-treated plots compared to the control. Our results indicated that elevated N deposition might alter the tree growth pattern, C partitioning, and microbial activity, which further affect soil C sequestration by reducing soil respiration in subtropical forests of China.     

喀斯特森林近地表层覆盖对碳酸盐岩红土蒸发过程的影响

蒸发是西南喀斯特地区薄层土壤水分损失的主要途径,浅层土壤水分的存蓄对喀斯特地区农业生产和生态恢复至关重要。以西南喀斯特森林碳酸盐岩红土为研究对象,基于室内蒸渗试验设置4个苔藓生物量(0,0.32,0.64,0.95 kg/m~2)和3个松针生物量(0,0.32,0.64 kg/m~2)共12种处理,分析森林近地表层覆盖对碳酸盐岩红土蒸发过程及表层温度时空分布的影响规律,并对比3种蒸发模型(Black、Rose、空气动力学蒸发模型)在喀斯特森林碳酸盐岩红土的适用性。结果表明:苔藓和松针覆盖显著降低累积蒸发量和蒸发速率(P0.05),接种苔藓0.95 kg/m~2和覆盖松针0.64 kg/m~2处理比裸土累积蒸发量小36.9%;苔藓和松针导致土壤含水量显著增加(P0.05);苔藓和松针增加了表层土壤的平均温度,松针对土壤温度的提升作用强于苔藓;Black、Rose和空气动力学模型均能较好地模拟碳酸盐岩红土蒸发过程,Black蒸发模型的拟合精度高于Rose和空气动力学蒸发模型。研究结果能为西南喀斯特地区的水量平衡分析提供理论支撑并加强对喀斯特森林地表水文过程的认知。