[目的]通过对长白落叶松年轮特征进行分析,以探究径向生长的周向异质性及其影响因素,以及不同高度年轮宽度各向变异特征的差异性,为长白落叶松的经营管理提供理论依据.[方法]采用Wilcoxon符号秩检验方法对黑龙江省朗乡东折棱河林场的18株长白落叶松相对年轮宽度随树龄的变化进行检验,通过比较不同高度的相对年轮宽度变异系数,分析不同高度的年轮宽度变异特征的差异.借助加权Voronoi图计算树木各向生存半径,分析其与树木年轮宽度变异特征的关系.利用单因素方差分析和描述性统计方法,分析光照和树龄与径向生长的周向异质性的关系.[结果](1)人工长白落叶松5年定期平均年轮宽度和累积平均年轮宽度随树龄的增长在各方向上的差异特征没有显著变化.(2)7株样木的累积年轮宽度变异系数与横断面相对高度呈极显著或显著负相关,12株样木的近5年平均生长量在各方向上的变异系数与断面相对高度呈极显著或显著负相关.(3)胸高断面径向生长周向变异特征与光照强弱方位特征之间没有显著关系;成熟样木的年轮宽度各向变异系数的平均值最高;大部分样木胸高断面各向相对年轮宽度(近5年平均生长量)与各向生存半径之间呈显著相关性.[结论](1)同一高度不同树龄的年轮宽度在各个方向上不存在显著差异,径向生长周向差异程度随横断面高度的增加而减弱.(2)竞争对年轮宽度变异特征有显著影响.树木生存半径越大,竞争能力越强,此方向的年轮宽度越大;不同树龄中,成熟林的径向生长周向差异最大.

[目的]研究林木树冠形态对外界环境适应的可塑性机理.[方法]以小兴安岭地区的长白落叶松为研究对象,利用详细的树冠半径调查数据,研究长白落叶松的树冠偏冠现象,分析树冠偏冠与林木大小和竞争之间的关系.[结果]在单木水平上,不同林龄样地中林木的树冠变异系数的平均值均大于20％,大部分林木的树冠半径变异系数较大,只有不到1/5的林木的树冠半径变异系数小于15％.不同林龄样地中长白落叶松偏冠距离的平均值在0.35～0.51 m之间,各样地中林木偏冠距离的最大值均在1.2 m以上,其中成熟林中林木偏冠距离的最大值达到1.73 m.树冠的偏冠方向没有特定的方向偏向,是均匀分布的.在幼龄林中,林木的偏冠距离与胸径、冠长、平均树冠半径呈显著正相关,树冠偏冠距离随树木增大而增大.在中龄林和近熟林中,偏冠指数与胸径、树高和平均树冠半径呈显著负相关,与高径比呈显著正相关,树冠偏冠程度随树木增大而减小.在成熟林中,偏冠程度与林木大小的相关性不显著.在幼龄林、中龄林、近熟林中,树冠偏冠与竞争压力呈显著正相关,在成熟林中树冠偏冠与竞争压力的相关性不显著.基于树冠中心坐标计算的林分平均竞争指数均小于基于树干坐标计算的林分平均竞争指数.[结论]长白落叶松树冠偏冠距离一般随着林龄的增大而变大,在不同发育阶段受到不同程度竞争的影响,树冠偏冠在一定程度上降低了林分平均竞争指数,减少了林分竞争压力.

Forest biomass is an important index in forest development planning and forest resource monitoring. In order to provide a more efficient and low-biased method for estimating individual tree biomass, we introduced artificial neural network here. We used the data of aboveground biomass of 101 Larix olgensis trees harvested from the Dongzhelenghe Forest Farm in Heilongjiang Province to develop four aggregation model systems (AMS), based on different combination of the variables (diameter at breast height, tree height, crown width). The weighted functions were used to eliminate heteroscedasticity. Then, we trained artificial neural network (ANN) biomass model based on the optimal combination. The models were tested by the leave-one-out cross-validation method to compare the accuracy of the two biomass estimation methods. The results showed that biomass model based on only one variable, diameter at breast height, could accurately estimate the biomass of L. olgensis. Adding two indices, tree height and crown width, could improve the fitting performance of models, with AMS4 performing the best among the four addictive model systems. The biomass models developed by the two methods both could estimate biomass at tree level accurately, with the coefficient of determination (R2) of each component was higher than 0.87. Compared with the AMS4, R2 of leaf biomass model was about 0.05 higher, and that of other organs were also about 0.01 higher in artificial neural network model system. In addition, the root mean square error (RMSE) and other indicators were also significantly smaller. For example, the RMSE of tree stem and aboveground biomass were smaller by 2.135 kg and 3.908 kg, respectively. The model's validation statistics mean relative error (MRE) performed better. In general, ANN was a flexible and reliable biomass estimation method, which was worthy consideration when predicting tree component biomass or aboveground biomass.

[目的]针对树冠结构复杂、形状不规则等问题,实现树冠垂直投影面积自动计算并提高树冠投影区域面积计算精度.[方法]以研究区453棵杉木为研究对象,精确测量每木8向冠幅.以Visualstudio和ArcGIS混合编程,进行树冠测量半径的坐标转换,采用Bezier曲线进行边界平滑,实现树冠垂直投影面积自动提取,并与传统计算方法进行对比分析.利用"圆度"来衡量树冠偏冠,并计算树冠偏冠指数与树冠垂直投影面积计算的相关性.[结果]本研究提出的树冠垂直投影面积计算方法能够计算出树冠真实面积,计算过程中测量冠幅半径数量越多,计算结果越接近真实冠幅面积,相比于椭圆的计算方法,把树冠视为正圆所得面积更接近真实面积.不同树冠垂直投影面积计算方法的结果和树冠垂直投影面积差值与树冠偏冠指数呈显著正相关.[结论]基于树冠投影区域形状的面积计算相较于传统的面积计算方法更加准确,为林业遥感冠幅提取精度验证提供了一种新思路.

[目的]为了解长白落叶松单木叶生物量与径向生长之间的关系.[方法]以小兴安岭地区长白落叶松为研究对象,结合管道模型理论,研究单木个体水平和单木内不同方位区间水平上的叶生物量与径向生长的关系,分析树冠分布与径向生长之间的相关性.[结果]叶生物量与胸高处和枝下高处树干直径、断面积、边材面积,近1、2、3、5年断面积生长量均呈显著正相关,使用胸高断面积作为预测因子的叶生物量模型拟合效果最好.单木株内不同方位区域的叶生物量与对应的胸径处和枝下高处的树干半径、断面积,近1、2、3、5年断面积生长量均呈显著正相关.[结论]估测单株叶生物量时,使用胸高断面积作为预测因子的预测精度最高.株内不同方位叶生物量与对应方位的树干半径、断面积及断面积生长量均呈显著正相关,树冠偏冠与髓心偏心具有一定的相关性.

Pine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

[目的]雷达和光学遥感数据可以提供不同方面的信息,利用Sentinel?1与Sentinel?2联合估算亚热带地区森林地上生物量,探索光学数据与合成孔径雷达(SAR)数据结合对于提高森林地上生物量估测的优势.[方法]以福建省将乐国有林场杉木林为研究对象,以Sentinel?1 SAR数据和Sentinel?2光学数据为数据源,采用多元线性逐步回归方法进行建模,以决定系数(R2)、调整决定系数(R2adj)、均方根误差(RMSE)、方差膨胀因子(VIF)为模型评价指标,对比分析Sentinel?2光学数据与Sentinel?2结合Sentinel?1 SAR数据估算森林地上生物量的能力.[结果]基于Sentinel?2光学数据的森林地上生物量估算模型,其调整决定系数(R2adj)达到0.501、均方根误差(RMSE)为64.04 Mg/hm2;Sentinel?2光学数据结合Sentinel?1 SAR数据的森林地上生物量估算模型,其调整决定系数(R2adj)达到0.575、均方根误差(RMSE)为59.13 Mg/hm2,对比Sentinel?2估算模型,该模型精度有明显提高.[结论]Sentinel?2卫星的多光谱数据能够作为估算亚热带地区森林地上生物量的有效数据,加入Sentinel?1 SAR影像的极化纹理信息后,利用Sentinel?1雷达传感器的全天候获取数据能力与Sentinel?2多光谱传感器丰富的光谱波段信息特点,以及两者短重访周期的能力,能够有效提高估算森林地上生物量模型的精度.

在高精度曲面建模方法和地球表层系统建模基本定理研究结果基础上,演绎提出了生态环境曲面建模基本定理.以京津冀地区为案例,对基于生态环境曲面建模基本定理的空间升尺度、空间降尺度、空间插值、数据融合和模型-数据同化等算法进行了实证研究,与传统算法精度进行了比较分析.结果表明,由于基于生态环境曲面建模基本定理的各种算法综合了外蕴量信息和内蕴量信息,同时运用了理论上完善的信息综合方法,使海拔高度曲面的升尺度均方根误差至少降低了9m,年平均气温未来情景的降尺度精度至少提高16％,年平均气温过去变化趋势的数据融合精度至少提高70％,年平均降雨量过去变化趋势的空间插值精度至少提高0.2％,碳储量的模型-数据同化精度提高了40％.文章最后讨论了生态曲面建模基本定理亟待解决的五大理论问题和四大应用基础问题.

The height to crown base (HCB) of a tree is a necessary variable that includes many growth and yield models as a predictor, and it is important to develop an HCB model due to its contribution to forest management. In this study, we developed an individual tree HCB model for Larix olgensis using a generalized nonlinear mixed-effects model with 2510 Larix olgensis trees on 40 sample plots located on the Dongzhelenghe Forest Farm in northeastern China. According to the evaluated base model, a logistic model that most suited our data was selected as the base model. In addition to height and diameter at breast height, tree and stand level variables that represent tree size, site quality and competition, such as dominant height, basal area of trees larger than subject tree and relative spacing index, were added to the HCB model. The sample plot was set as the random effect, and the parameters describing sample plot-level random effects were included in the HCB model through the mixed-effects model. The variance heteroscedasticity in the residuals was reduced by including a constant plus power variance function in the HCB model. The results showed that the mixed-effects model described a larger part of the HCB variations (R-adj(2) = 0.7642, RMSE = 1.7225) than did the ordinary least square model (R-adj(2) = 0.7063, RMSE = 1.9224).

We used the data of 29 plots of Chinese fir located in national forest farm of Jiangle in Fujian Province to build height prediction model by BP neural network .First, the input variable and the hidden nodes were determined , then, by training and optimization, an optimum model was developed, with a model structure of 2∶5∶1, a determinate coefficient of 0.902 3 and error of mean square of 1.784 2.And then, it was compared with two traditional generalized height-diameter equations, the validation datasets were used to test the models , respectively .The fitting effect and prediction effect of BP neural network model are better than those of traditional equations , and BP neural network model can be used as effective tree height pre-diction technology .

Leaf area is an essential indicator of photosynthesis for the study of crop and forest productivity. The Levenberg-Marquardt back-propagation optimization algorithm was coupled with Bayesian regulation to train the artificial neural network (ANN), and the predictive model was developed to determinate rapidly and accurately Moso bamboo leaf area. The results showed that the best input variables were the combination of leaf width and leaf length for ANN model, whereas the leaf shape index did not significantly affect the variability of leaf area. The optimization ANN model possessed with excellent performance and predictable accuracy, with the high determination coefficient of 0.992 and mean relative prediction error of 4.28%. The ANN model would be allowed for estimating accuracy the leaf area of Moso bamboo.

So far,there is no evaluation index system of Cunninghamia lanceolata scenic and recreational forests by analytic network process.Based on the data of scenic and recreational forests at home and abroad,by taking C.lanceolata forests as studying objects,an evaluation index system of Cunninghamia lanceolata scenic and recreational forests was set up by analytic network process.The results show that in the first grade indexes,the weighting of forest aesthetics index was the biggest,that of other indexes followed by: forest measurement,stand spatial structure,Characteristics of surface cover,forest’s health.In the second grade indexes,the weightings ordered by magnitude: slope,tree height,transparency distance,DBH,life form,canopy density,crown width,density of trees number,show degree of trunk,height of shrub,green quantity,proportion of coniferous trees and broadleaved trees,fallen dead wood,height of grass,coverage,distribution of grass and shrub,stem form,color diversity,forest tree distribution,litter distribution,mean height under first alive branch.This study provides basis data for city forest planning,management and further research.