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1.
森林土壤呼吸在陆地生态系统的碳平衡中发挥了重要作用,准确估算森林土壤呼吸量对于了
解陆地碳平衡的变化至关重要。这项研究以全球气候数据、全球森林土壤呼吸数据库为基础数据,通过
开发人工神经网络（ANN）模型建立由年平均气温（MAT）、年平均降水（MAP）、森林类型驱动的土壤
呼吸模型,预测全球森林土壤呼吸变化。模型估算的结果表明,从 1960 年到 2017 年,全球森林平均年
土壤呼吸量为 40.10±0.48 Pg C yr-1,全球森林土壤对全球土壤呼吸的贡献在 40.9% - 49.8% 之间。人工神
经网络模型预测的准确度达到 0.63,进一步改善了全球森林土壤呼吸模型预测的精度。 相似文献
2.
基于BP神经网络的针阔混交林TM遥感图像自动分类技术研究 总被引：3，自引：0，他引：3
在对标准BP神经网络试验分析的基础上，通过输入矢量归一化处理、主成分分析、增加验证集、改进训练学习算法、扩大隐层和输出层规模等措施，对BP神经网络自动分类系统进行改进；利用改进后的BP系统对吉林省汪清林业局的典型针阔混交林TM遥感图像进行辩识、分类试验研究。结果表明：改进后的BP网络分类系统自动分类精度提高了19．14％，比传统无监督自动分类精度提高8．55％，达到了区分森林类型的分类要求。研究还显示了该改进系统应用于针阔混交林TM遥感图像自动分类识别的精度随网络规模增大而提高。 相似文献
3.
针对现有小尺度林火预测模型预测结果有效性、可扩展性等方面的不足,通过考虑多种火险因素,构建BP神经网络预测模型以提高预测精度,在此基础上借助粒子群算法加快BP神经网络收敛速度,进而提出一种混成的多因素森林火险等级预测模型particle swarm optimization based back-propagation neural network (PSO-BP)。所构建的预测模型,能够同时考虑气候因素(日最高气温、日平均气温、24 h降水量、连旱天数、日照时数、日平均相对湿度、日平均风速)、地形地貌因素(海拔、坡度、坡向、土壤含水量)、可燃物因素(植被类型、可燃物含水率、地被物载量)、人为因素(人口密度、距人类活动区域的距离) 16个变量。基于南京林业大学下蜀林场森林防火实验站传感器网络所采集的实际数据及现场测量数据,通过一组试验验证提出模型的有效性。结果表明:基于训练数据集及检验样本所构建的模型能够开展有效的火险等级预测;模型的计算复杂度较单独使用BP神经网络模型明显下降。 相似文献
4.
Axel Albrecht Marc Hanewinkel Jürgen Bauhus Ulrich Kohnle 《European Journal of Forest Research》2012,131(1):229-247
Storms represent the most important disturbance factor in forests of Central Europe. Using data from long-term growth and
yield experiments in Baden-Wuerttemberg (south-western Germany), which permit separation of storm damage from other causes
of mortality for individual trees, we investigated the influence of soil, site, forest stand, and tree parameters on storm
damage, especially focusing on the influence of silvicultural interventions. For this purpose, a four-step modeling approach
was applied in order to extract the main risk factors for (1) the general stand-level occurrence of storm damage, (2) the
occurrence of total stand damage, and (3) partial storm damage within stands. The estimated stand-level probability of storm
damage obtained in step 3 was then offset in order to describe the damage potential for the individual trees within each partially
damaged stand (4). Generalized linear mixed models were applied. Our results indicate that tree species and stand height are
the most important storm risk factors, also for characterizing the long-term storm risk. Additionally, data on past timber
removals and selective thinnings appear more important for explaining storm damage predisposition than for example stand density,
soil and site conditions or topographic variables. When quantified with a weighting method (summarizing the relative weight
of single predictors or groups of predictors), removals could explain up to 20% of storm risk. The stepwise modeling approach
proved an important methodological feature of the analysis, since it enabled consideration of the large number of observations
without damage (“zero inflation”) in a statistically correct way. These results form a reliable basis for quantifying forest
management’s direct impact on the risk of storm damage. 相似文献
5.
动态数据驱动的林火蔓延模型适宜性选择 总被引：1，自引：0，他引：1
基于BP人工神经网络方法设计林火模型适宜性选择技术框架结构,通过神经网络形成林火模型选择知识,实现林火模型的自动化和智能化选择;以火场环境因子为输入变量,以适宜火场环境模拟的林火蔓延模型作为输出变量,构建林火模型选择神经网络模型;研究输入、输出因子数据的获取与计算方式,实现动态数据驱动的林火模型自动选择机制.以北京市为例,选择有详细火场情况记录的72场林火作为试验样本,其中60条记录作为学习样本集,12条记录作为验证样本,对神经网络进行学习和验证,结果表明:模型选择精度可达到80%以上. 相似文献
6.
Christoph Hartebrodt 《Small-Scale Forestry》2004,3(2):203-222
Storm damage is considered to be one of the most important risk factors for forestry in Central Europe. At the end of 1999
a centennial storm event hit the south-east of Germany and Switzerland, as well as central and western parts of France, and
caused great damage. Forests in Baden-Württemberg were severely affected, with 30 M m3 of timber felled due to storm damage, three times the amount of the normal annual cut. Approximately 5.2 M m3 of the wind-thrown timber was in private forests, of which most were located in the central Black Forest. Smaller shares
came from other regions of Baden-Württemberg. The economic damages and strategies of the forest owners were analysed in a
multi-dimensional approach, using economic data from long-term accountancy networks, in combination with the results of a
qualitative opinion poll amongst private forest owners. A storm coefficient was devised as a suitable indicator for the concerns
of owners or ownership classes. The predicted operating income of the private forest owners is related to this coefficient.
Cash flow simulations suggest that enterprises with a coefficient of more than 100% suffer from a reduction of their economic
base. By combining the results derived from the accountancy networks with findings from the opinion poll it was found that
the owners took an active decision towards self-processing and were able to save more than 30 M ￡ by choosing this strategy.
State support which was provided in a variety of ways is also identified. A range of programs and institutional support measures
mitigated the impact of the storm disaster. The effectiveness and acceptance of these measures by forest owners was confirmed
by the results of the opinion poll. 相似文献
7.
人工神经网络在森林资源管理中的应用 总被引：6，自引：0，他引：6
传统的数量方法难以解决森林资源管理中的更多的非结构性问题.人工智能新技术能将人类积累的知识传输到决策支持系统中,作为人工智能的一个分支,人工神经网络已经成为一个在传统统计方法之外,十分引人注目的新方法,并开始用于预测生物系统中的非线性行为.文中从人工神经网络在森林立地分类和制图,森林生长和动态模拟、空间数据的分析和拟合、植物病虫害动态的预测及气候变化研究等方面的应用进行综合评述.并对人工神经网络在应用中的优缺点,存在的问题和局限性,以及对未来的前景等提出了自已的初步看法. 相似文献
8.
9.
基于第九次全国森林资源清查的中国竹资源分析 总被引：24，自引：18，他引：6
中国第九次全国森林资源清查结果显示,中国竹林面积为641.16万hm2,占森林面积的2.94%,其中毛竹林467.78万hm2、占竹林面积的72.96%。文章依据第九次全国森林资源清查结果,对其中竹资源的数量、质量及其分布特点进行了分析,并结合中国历次森林资源清查结果分析了竹资源的变化。 相似文献
10.
基于人工神经网络预测广东省森林火灾的发生 总被引：7，自引：0，他引：7
应用人工神经网络建立热带森林火灾发生情况预测的多层神经网络模型,并将林火发生影响因子的历史数据作为样本值,输入模型进行训练。结果表明:利用所选取的输入因子作为样本的人工神经网络,可以对林火的发生发展作出准确有效的预测。文中还对模型的准确性和训练精度进行讨论,进而分析人工神经网络在林火预测中的可行性,证明人工神经网络在林火预测中的应用价值。 相似文献
11.
12.
基于DRNN和ARIMA模型的森林火灾面积时空综合预测方法 总被引：1，自引：0，他引：1
森林火灾是一个跨空间发展的动态过程,不易被传统的分析方法和静态神经网络有效处理.提出一种基于动态回归神经网络(DRNN)和自回归集成移动平均(ARIMA)组合模型的森林火灾时空综合预测方法.该方法先用ARIMA对时空数据的时序进行预测,再用DRNN捕获时空数据间隐藏的空间相关,最后用统计回归将时间和空间预测结果组合起来,得到时空综合预测结果.以广东省森林火灾面积预测为例,说明其原理和建模过程,并对预测结果的精度进行验证.结果表明:由于考虑了数据间的空间关系,该时空综合预测模型可以对森林火灾面积进行较准确有效的预测,比单纯应用ARIMA模型预测精度高,是预测森林火灾等跨空间动态变化问题的有效工具. 相似文献
13.
《林业研究》2019,(6)
Forest fires are influenced by several factors,including forest location, species type, age and density,date of fire occurrence, temperatures, and wind speeds,among others. This study investigates the quantitative effects of these factors on the degree of forest fire disaster using nonparametric statistical methods to provide a theoretical basis and data support for forest fire management.Data on forest fire damage from 1969 to 2013 was analyzed. The results indicate that different forest locations and types, fire occurrence dates, temperatures, and wind speeds were statistically significant. The eastern regions of the study area experienced the highest fire occurrence,accounting for 85.0% of the total number of fires as well as the largest average forested area burned. April, May, and October had more frequent fires than other months,accounting for 78.9%, while September had the most extensive forested area burned(63.08 ha) and burnt area(106.34 ha). Hardwood mixed forest and oak forest had more frequent fires, accounting for 31.9% and 26.0%,respectively. Hardwood-conifer mixed forest had the most forested area burned(50.18 ha) and burnt area(65.09 ha).Temperatures, wind speeds, and their interaction had significant impacts on forested area burned and area burnt. 相似文献
14.
de Oliveira Neto Ricardo Rodrigues Leite Helio Garcia Gleriani José Marinaldo Strimbu Bogdan M. 《European Journal of Forest Research》2022,141(1):129-151
Prediction of plantation productivity from environmental data is challenged by the complex relationships defining the growth and yield processes. Artificial neural network (ANN) can be used to model complex nonlinear interrelations but are challenged by the choice of the training algorithm and the structure and size of the network. The objective of the present study is to find an efficient ANN to estimate the eucalyptus productivity from biotic, abiotic and silvicultural data. We defined the efficiency of an ANN as the processing time to supply an accurate solution. We increase the efficiency of the network in two steps: one outside the ANN, through principal component analysis (PCA), and one inside the ANN, through dedicated pruning methods. To test different network configurations, we used data from 507 Eucalyptus plantations, 3 to 7 years old, located in Minas Gerais, Brazil. An ANN configuration is a combination of the number of neurons in the hidden layers, training algorithm and pruning method. Each ANN was trained five times to assess the impact of the initial weights on the results, which led to a factorial experiment with 9000 combinations. The most accurate result was supplied in 38.81 s by an ANN using the data trimmed with PCA, trained with the Scaled Conjugate Gradient algorithm with four neurons and Magnitude-Based Pruning method. However, an accuracy loss of less than 1% (i.e., the second most accurate result) was obtained in 1.7 s using the same ANN configuration, but no pruning. Our results indicate that an efficient prediction of Eucalyptus productivity does not use all the data or the most complex training algorithms. The proposed efficient ANN supplies the most precise results compared with the existing models. Furthermore, the efficient ANN produces more accurate results from environmental variables than some models that contain dimensional variables. Our study suggests that modeling the yield of Eucalyptus plantations should be executed using a large set of environmental variables, as no clear edaphic or climatic variables supplied accurate results.
相似文献15.
16.
In Sweden, as in other countries with a growing and increasingly diverse population of forest owners, there is an apparent
need for more detailed quantitative data of high quality in order to describe and understand present forest conditions and
predict and explain future trends. Therefore, the Swedish University of Agricultural Sciences has developed a Data Base for
Forest Owner Analysis (DBFOA) by combining existing forest measurement statistics, gathered on a regular basis by the Swedish
Forest Agency since 1992, with records of the individual forest owners. The database consists of self-reported measurement
statistics in terms of cuttings, cleaning, scarification and planting from about 30,000 forest management units. It includes
information on the owner age, gender, residential proximity to the management unit and the extent of work undertaken by the
owner. From 1999 it also indicates whether the forest is certified. This paper demonstrates the use of the database by presenting
results from (1) a comparison of management practices on properties that are certified with those that are not, and (2) an
examination of how the area of planting and final felling have changed from 1999 to 2006 in total and between male and female
forest owners. Results from the first analysis show that the willingness to certify increases with the size of the forest
property and also that harvesting activities are more frequent on certified than non-certified properties. The second analysis,
show a higher ratio of final felling during 2003–2006 on properties owned by women than properties owned by men. 相似文献
17.
18.
Zhanghua Xu Xuying Huang Lu Lin Qianfeng Wang Jian Liu Kunyong Yu Chongcheng Chen 《林业研究》2020,(1):107-121
The construction of a pest detection algorithm is an important step to couple"ground-space"characteristics,which is also the basis for rapid and accurate monitoring and detection of pest damage.In four experimental areas in Sanming City,Jiangle County,Sha County and Yanping District in Fujian Province,sample data on pest damage in 182 sets of Dendrolimus punctatus were collected.The data were randomly divided into a training set and testing set,and five duplicate tests and one eliminating-indicator test were done.Based on the characterization analysis of the host for D.punctatus damage,seven characteristic indicators of ground and remote sensing including leaf area index,standard error of leaf area index(SEL)of pine forest,normalized difference vegetation index(NDVI),wetness from tasseled cap transformation(WET),green band(B2),red band(B3),near-infrared band(B4)of remote sensing image are obtained to construct BP neural networks and random forest models of pest levels.The detection results of these two algorithms were comprehensively compared from the aspects of detection precision,kappa coefficient,receiver operating characteristic curve,and a paired t test.The results showed that the seven indicators all were responsive to pest damage,and NDVI was relatively weak;the average pest damage detection precision of six tests by BP neural networks was 77.29%,the kappa coefficient was 0.6869 and after the RF algorithm,the respective values were 79.30%and 0.7151,showing that the latter is more optimized,but there was no significant difference(p>0.05);the detection precision,kappa coefficient and AUC of the RF algorithm was higher than the BP neural networks for three pest levels(no damage,moderate damage and severe damage).The detection precision and AUC of BP neural networks were a little higher for mild damage,but the difference was not significant(p>0.05)except for the kappa coefficient for the no damage level(p<0.05).An"over-fitting"phenomenon tends to occur in BP neural networks,while RF method is more robust,providing a detection effect that is better than the BP neural networks.Thus,the application of the random forest algorithm for pest damage and multilevel dispersed variables is thus feasible and suggests that attention to the proportionality of sample data from various categories is needed when collecting data. 相似文献
19.
We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP. 相似文献
20.
研究以小班为基本研究单元,按起源分林龄对广东省肇庆市国有北岭山林场生态公益林碳储
量进行了研究。国有北岭山林场乔木林总碳储量为 278.0×106kg,其中生态公益林碳储量为 244.9×106kg。生态公益林中天然林在不同林龄间的林分间平均碳密度不存在显著差异,范围（25.24±1.02）~ （28.01±1.69）×103 kg/hm2;而人工林则存在显著的差异,以 20~40 a 林龄的林分最大,为（34.22±2.77） ×103 kg/hm2,其次为大于 40 a 林龄的林分,为（23.34±0.72×103kg/hm2。在 20~40 a 林龄的生态公益林中,人工林平均碳密度显著大于天然林,但在大于 40 a 林龄的林分中,则显著小于天然林。 相似文献