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1.
以云浮市云城区和云安区森林土壤为研究对象,应用传统统计学和地统计学方法结合 GIS 技术分析其土壤养分的空间变异性,预测土壤养分空间分布。地统计学主要从空间插值模型和人工神经网络模型的角度对土壤养分的空间变异性进行诠释,并利用平均绝对误差(MAE)和均方根误差(RMSE)两个指标以及模型预测点与实测点间的相关系数作为判断模型好坏的标准。研究结果认为插值模型中泛克里格插值法在样点密度较小时显示出明显优势,而 BP-ANN 在模型的稳定性和推广性表现尤为突出,最后对泛克里格插值模型和 BP-ANN 下的有机碳、全氮、全磷、全钾4种养分空间预测分布特征进行描述。 相似文献
2.
生态系统服务功能社会价值评估是优化区域资源管理、提高社会治理水平的重要依据，但因其自身的无形性、主观性、不确定性常在研究中被忽视且难以有效量化。传统的生态系统服务价值评估主要以自然要素测度为主，既忽略了社会属性及价值，又导致评估结果缺乏完整性、客观性。SolVES模型以非货币化价值指数表示、定量化评估精度高、适用性广泛、空间可视化程度高等特点，广泛应用于不同生态系统及空间尺度的评估。文中概述SolVES模型的框架及原理，结合相关应用案例总结该模型当前的研究成果、研究热点及优缺点，对其未来发展趋势进行归纳和展望，可为SolVES模型更好地应用于生态系统服务功能社会价值评估提供参考。 相似文献
3.
《林业科学》2021,57(3)
【目的】比较多源数据对林分动态预测的影响,分析模型参数与预测不确定性的变化规律,从准确性和可靠性角度对模型进行评估,获取改进模型的数据需求,为森林调查中的数据收集策略提供建议。【方法】收集秦岭油松林3期调查(1990、2005和2012年)和4种信息类型(临时样地、固定样地、解析木和多源数据)建模数据,设计一组数据信息要求较低的可变密度全林模型,基于贝叶斯信息动态融合框架,分析传统森林调查数据与生长收获模型的关系。利用MCMC抽样技术获得的参数联合后验分布对森林动态模拟的不确定性进行量化:一方面比较相同类型的多期森林调查数据不断对模型进行训练后,模型在参数与预测中的概率分布变化过程;另一方面比较分别采用4种数据类型对模型预测产生的影响。数据与模型更新循环过程以先验信息和后验信息不断相互转化的方法实现,即前一次拟合得到的参数联合后验分布作为下一期数据加入时的先验。不同数据类型整合根据数据自身抽样和观测误差所设计的独立似然结构实现。为避免粗糙数据或异常值对模型产生的影响,描述误差分布的似然函数采用重尾正态分布。观测误差的异方差特性通过迭代中自动调整似然函数的方差控制。【结果】随着新一期调查数据加入,模型参数的边际或联合分布不断发生变化,但概率分布峰度总是逐渐升高,即参数不确定性逐步下降,从而降低林分预测的不确定性。与基于1990年调查数据的模型相比,经过2005和2012年数据校正后模型在成过熟林阶段的不确定性下降最为明显,同时树高生长极大值的参数也更高。不同数据类型在模型预测中的差异反映出不同调查方法本身的缺陷和优势,解析木数据倾向于在成过熟林阶段预测出更高的树高生长;固定样地和临时样地数据在林分平均高和平均胸径模拟中表现相似,但由于抽样方法和数据量等因素区别,导致其在林分断面积模拟中呈现明显差异。基于循环更新或多源数据的模型呈现出最稳定的预测结果。【结论】在生长收获模型构建中,不同类型森林调查数据会产生不同预测结果,不同数据信息特性也会对预测的不确定性产生规律性影响。以概率分布呈现信息的贝叶斯方法,既可反映模型的精准程度,又能解释数据信息中存在的缺陷。本研究以全林模型更新为例,展示了该方法不断循环、更新、融合的数据-模型逻辑框架,是架构生长收获模型与数据桥梁的有力工具。 相似文献
4.
《林业科学》2019,(11)
【目的】基于空间聚类对杉木分布区进行分组,在不同组内分别建立杉木生长模型,为适用于全国范围的杉木生长高精度预测提供方法。【方法】以杉木分布的16个省区为研究区,基于第七次、第八次全国森林资源连续清查杉木固定样地复测数据和研究区的地形、土壤、气象等环境数据,采用随机森林-递归特征消除(RF-RFE)算法对影响杉木生长的环境因子进行分析。选择对杉木生长影响较大的前8个环境因子,利用ArcGIS 102的分组分析功能,根据环境相似性对杉木分布区进行分组,分别建立林分分组和未分组蓄积生长率模型。以全国未分组模型作为参考,采用决定系数(R~2)、均方根误差(RMSE)、平均相对误差(MRE)、系统误差(SE)和剩余标准差(S)对建模结果进行分析。【结果】对杉木生长影响较大的前8个环境因子分别为bio4(温度季节变化标准差)、elevation(海拔)、bio3(等温性)、bio8(最湿季度平均温)、bio1(年均温)、bio14(最干月降水量)、bio12(年均降水量)和bio2(昼夜温差月均值)。将研究区划分为7组时可使组内环境相似性最大、组间环境相似性最小。7组中只有4组有杉木样地数据,在4组中采用2种模型分别建立杉木林分生长率模型,与全国未分组模型相比,分组模型的决定系数提高01左右,拟合度更好;分组建模精度也明显提高,RMSE降低05左右,MRE降低6%左右,SE降低3%左右,S降低1左右。【结论】根据环境相似性对研究区分组并在此基础上建立生长模型是一种适用于全国范围杉木生长的高精度预测方法,可用于全国区域范围杉木林分生长量的整体预估和森林资源小班数据的模型更新,为实现主要人工林树种的大区域生长预测提供新的方法。 相似文献
5.
[目的]随着全球气候变化,极端天气频繁出现,发生森林火灾的可能性也显著提高.在过去的森林火灾发生预测研究中并没有考虑林火发生与气象因子关系模型的不确定性.然而,在全球气候变化背景下,要充分考虑模型的不确定性,科学分析气象因子与森林火灾发生次数的关系,以更好地预测森林火灾的发生,为管理部门的预防工作提供参考依据.[方法]... 相似文献
6.
为探讨不同植被类型对喀斯特地区土壤微生物的影响,进一步研究植被恢复对喀斯特地区土壤生态系统功能的影响。以粤北地区典型植被类型(针叶林、阔叶林和天然草地)土壤为研究对象,对不同植被类型土壤的理化性质和细菌群落结构与功能进行研究,分析粤北喀斯特山区中不同植被类型土壤的理化性质、细菌群落多样性和功能组成的差异。结果显示：在细菌群落多样性方面,除了针叶林的均匀度和系统发育多样性水平较低,其他无显著差异。土壤细菌的群落组成在不同植被类型中具有显著差异,土壤理化参数如pH值、含水率、铵态氮、硝态氮、总氮、总磷和有机质对土壤细菌群落组成有显著影响。草地土壤的pH值接近中性,且显著高于针叶林和阔叶林土壤,草地土壤的氮、磷、有机质含量也更高。根据原核生物分类单元功能注释功能预测结果,不同植被类型土壤中,化能异养表达量最高,氮循环相关的功能中固氮功能表达量较高,且在不同植被类型土壤中产生了显著差异,总氮、总磷、p H值和土壤含水率是导致这一差异的主要环境因子。综上,喀斯特地区的植被恢复以及林-草之间的生态演替可能会影响土壤细菌群落结构与潜在功能,进而影响土壤生态系统功能。 相似文献
7.
《林业科学》2021,57(3)
【目的】探究河北省北部华北落叶松人工林立地生产力空间分布格局及其与环境因子的关系,为高效森林经营提供依据。【方法】基于研究区1 179块森林资源二类调查小班数据,建立以地形、气候和土壤因子为辅助变量的多元线性回归(MLR)、随机森林(RF)、回归克里格(RK)和地理加权回归克里格(GWRK)模型。通过模型评价,选择最优模型预测研究区华北落叶松人工林立地指数(SI)空间分布,采用相关和偏相关分析方法分析SI分布格局与主要环境因子的关系。【结果】海拔、最干月降水、土壤全氮和全磷是影响研究区华北落叶松人工林立地生产力的主要因子;地统计学的2种模型(RK和GWRK)拟合优度相近且均显著高于MLR和RF模型,与全局回归的RK模型相比,局部回归的GWRK模型具有较高的R~2(0.780)及较低的AIC (160.533)、RMSE(1.593 m)和MAE(1.113 m),为最优预测模型;不同立地生产力等级地区内SI对地形和土壤因子的变化表现更为敏感,对气候因子的变化反应较弱。【结论】海拔、最干月降水较高以及土壤氮磷含量适中的研究区西北部,是华北落叶松人工林适生区,而海拔、最干月降水较低以及土壤氮磷含量不平衡的东、南部,华北落叶松人工林的适应性较差,可通过造林活动或适当添加氮磷肥削弱环境因子对树木的不利影响。 相似文献
8.
物种分布模型(Species Distribution Models,SDMs)常被用于评价物种在不同地域的适生概率,但能否用于指导远距离引种栽培仍需试验证明。该研究以黄花风铃木和红花风铃木这两种外来树种为研究对象,对比理论预测结果与实际栽培分布的差异。结果显示：黄花风铃木和红花风铃木的理论适生阈值分别为0.433和0.469,均明显高于栽培样点的最低适生概率。其中,黄花风铃木的预测适生区面积约为7.96×104km2,仅包含4.19%的实际栽培样点,是实际可栽培面积的6.48%。红花风铃木的预测适生区面积约为28.91×104km2,包含71.79%的实际栽培样点,是实际可栽培面积的62.62%。回归分析结果显示土壤pH值、最冷月均温、土壤有机物碳含量对黄花风铃木在中国的适生概率有明显的正影响,而最冷月均温对红花风铃木的适生概率有明显的负影响。该研究证明了物种分布模型在指导远距离引种栽培时可能存在较大误差,并揭示了影响风铃木预测准确度和适应性分布的可能原因。该研究将为物种分布模型的应用优化以及风铃... 相似文献
9.
森林资源动态分析一般具有少数据和贫信息带来的灰色不确定性,灰色系统理论是解决这一问题重要工具。以伊春林区2001~2012年森林资源的统计数据为基础,采用灰色理论系统建立动态预测模型,对其森林蓄积量、有林地面积、森林覆盖率3个资源指标进行预测分析。结果表明,运用灰色预测建立的模型的预测结果经检验,精度较高;伊春林区森林资源在未来3 a内将会持续增加。 相似文献
10.
《绿色科技》2017,(20)
以宁夏平罗县龟裂碱土为研究对象,以实测植被光谱和土壤pH值为基础数据源,通过对原始光谱数据进行小波阈值去噪,和对数、一阶微分、多元散射校正、归一化等8种变换,筛选土壤碱化程度最佳光谱变换方式和敏感波段,用一阶傅里叶和三次多项式进行回归分析、比较,来构建更加精确的龟裂碱土信息预测模型。研究表明:植被光谱反射率一阶微分变换在波段861nm处为最佳敏感波段,相关系数为0.86;多项式拟合比傅里叶拟合效果好;以最佳光谱指标和土壤pH值为变量,构建的pH含量三次多项式预测模型精度最高,在0.01显著性水平上通过检验,该模型可为干旱区半干旱地区土壤碱化程度遥感定量反演提供依据。 相似文献
11.
《Forest Ecology and Management》1997,96(3):207-216
Forest growth is often projected for several subsequent periods. This being the case, the independent variables of a growth model will contain error due to prediction errors in the preceding periods, which cumulate through the simulation process. Thus, the accuracy of growth predictions depends on the number of growth periods. In this paper, the effect of cumulating errors is considered by conducting a simulation study using models estimated from thinning experiments carried out in Finland. The aim of the study is to estimate the prediction bias and the precision of long-term growth projections due to sequential use of growth models. The study demonstrates that it is important to take the residual variation of the models into account in long-term projections in order to reduce the prediction bias. 相似文献
12.
Nonparametric modelling has been popular in recent forestry applications. However, nonparametric modelling methods usually assume independent observations, that is, do not acknowledge the spatial relationships of most forest data sets. For these situations, mixed model and kriging approaches have been used. The aim of this paper was to compare accuracy of spatial parametric and nonparametric approaches, namely mixed models and a combination of k-nn method and mixed models, in prediction of tree height. The spatial approaches were compared to a nonspatial parametric model and k-nn method. Tree height was first modelled using either mixed model or k-nn. The residual error was divided into plot and tree effects. A nonspatial prediction was obtained using the fixed part of the models. The spatial prediction was obtained when this prediction was further adjusted using the estimates of within-plot correlation of errors and best linear predictor. The influence of the quality of modelling data was also considered. The adjustment of nonspatial estimates of both parametric and nonparametric approaches markedly improved the predictions in all study cases. For many applications, the combination of the nonparametric k-nn method for the fixed component of the model, along with random effects for spatial correlations to create a mixed model, could be used. This would allow for spatial prediction, which would likely provide improved predictions, as shown for predicting height in this paper. Also, there is the added benefit that the nonparametric k-nn does not require a particular model form. 相似文献
13.
Spatial prediction of forest stand variables 总被引：1，自引：1，他引：0
Arne Nothdurft Joachim Saborowski Johannes Breidenbach 《European Journal of Forest Research》2009,128(3):241-251
This study aims at the development of a model to predict forest stand variables in management units (stands) from sample plot
inventory data. For this purpose we apply a non-parametric most similar neighbour (MSN) approach. The study area is the municipal
forest of Waldkirch, 13 km north-east of Freiburg, Germany, which comprises 328 forest stands and 834 sample plots. Low-resolution
laser scanning data, classification variables as well rough estimations from the forest management planning serve as auxiliary
variables. In order to avoid common problems of k-NN-approaches caused by asymmetry at the boundaries of the regression spaces and distorted distributions, forest stands are
tessellated into subunits with an area approximately equivalent to an inventory sample plot. For each subunit only the one
nearest neighbour is consulted. Predictions for target variables in stands are obtained by averaging the predictions for all
subunits. After formulating a random parameter model with variance components, we calibrate the prior predictions by means
of sample plot data within the forest stands via BLUPs (best linear unbiased predictors). Based on bootstrap simulations,
prediction errors for most management units finally prove to be smaller than the design-based sampling error of the mean.
The calibration approach shows superiority compared with pure non-parametric MSN predictions. 相似文献
14.
Key message
When predicting forest growth at a regional or national level, uncertainty arises from the sampling and the prediction model. Using a transition-matrix model, we made predictions for the whole Catalonian forest over an 11-year interval. It turned out that the sampling was the major source of uncertainty and accounted for at least 60 % of the total uncertainty.Context
With the development of new policies to mitigate global warming and to protect biodiversity, there is a growing interest in large-scale forest growth models. Their predictions are affected by many sources of uncertainty such as the sampling error, errors in the estimates of the model parameters, and residual errors. Quantifying the total uncertainty of those predictions helps to evaluate the risk of making a wrong decision.Aims
In this paper, we quantified the contribution of the sampling error and the model-related errors to the total uncertainty of predictions from a large-scale growth model in Catalonia.Methods
The model was based on a transition-matrix approach and predicted tree frequencies by species group and 5-cm diameter class over an 11-year time step. Using Monte Carlo techniques, we propagated the sampling error and the model-related errors to quantify their contribution to the total uncertainty.Results
The sampling variance accounted for at least 60 % of the total variance in smaller diameter classes, with this percentage increasing up to 90 % in larger diameter classes.Conclusion
Among the few possible options to reduce sampling uncertainty, we suggest improving the variance–covariance estimator of the predictions in order to better account for the multivariate framework and the changing plot size.15.
Key message
We examine how the configurations in nearest neighbor imputation affect the performance of predicted species-specific diameter distributions. The simultaneous nearest neighbor imputation for all tree species and separate imputation by tree species are evaluated with total volume calibration as a prediction method for diameter distributions.Context
This study considers the predictions of species-specific diameter distributions in Finnish boreal forests by means of airborne laser scanning (ALS) data and aerial images.Aims
The aim was to investigate different configurations in non-parametric nearest neighbor (NN) imputation and to determine how changes in configurations affect prediction error rates for timber assortment volumes and the error indices of the diameter distributions.Methods
Non-parametric NN imputation was used as a modeling method and was applied in two different ways: (1) diameter distributions were predicted at the same time for all tree species by simultaneous NN imputation, and (2) diameter distributions were predicted for one tree species at a time by separate NN imputation. Calibration to a regression-based total volume prediction was applied in both cases.Results
The results indicated that significant changes in the volume prediction error rates for timber assortment and for error indices can be achieved by the selection of responses, calibration to total volume, and separate NN imputation by tree species.Conclusion
Overall, the selection of response variables in NN imputation and calibration to total volume improved the predicted diameter distribution error rates. The most successful prediction performance of diameter distribution was achieved by separate NN imputation by tree species.16.
戎桂凤 《内蒙古林业调查设计》2012,(1):95-97
ArcGlobe是基于全球视野下的三维可视化场景展示平台,广泛应用于涉及地形、地貌等自然地理要素的各种工程规划和设计中,应用卫星遥感数据、数字高程模型(DEM)叠加形成的三维场景,可逼真地显示出地形地貌景观。文章主要介绍了基于ArcGlobe的三维地形可视化在森林公园设计中的应用,并以五台山森林公园为例,论述了应用数据的选择、可视化场景的制作以及森林公园三维景观效果图的特点和制作过程。 相似文献
17.
【目的】确保立木材积和树皮材积预测的一致性并提高预测精度。【方法】以大兴安岭兴安落叶松为研究对象,分别采用控制法和分解法研建了可加性模型系统。利用SAS统计软件模型模块proc model中的NSUR法进行拟合及参数估计。拟合结果采用确定系数(R2)和均方根误差(RMSE)进行评价;检验结果则通过确定系数(R2)、均方根误差(RMSE)、平均相对误差(MRE)、平均误差绝对值(MAB)和相对误差绝对值(MPB)进行评价。【结果】从模型的整体评价结果来看,两种方法的拟合和检验效果均很好,基于分解法构建的模型略优于基于控制法构建的模型;不同径阶的检验表明,对于中等径阶的树木(20≤D<36 cm),基于控制法的模型相对较好,而对于小径阶(5≤D<20 cm)和大径阶的树木(D≤36 cm),基于分解法的带皮、去皮、树皮材积模型的预测精度要比基于控制法的各立木材积模型要稍好。【结论】总的来说,两种可加性模型系统均能很好地预测单木带皮材积、去皮材积和树皮材积,并确保得到满足一致性的预测结果,在具体应用时可根据实际情况选择适合的可加性材积模型系统。 相似文献
18.
A flexible stem taper and volume prediction method based on mixed-effects B-spline regression 总被引：1，自引：0，他引：1
Edgar Kublin Johannes Breidenbach Gerald Kändler 《European Journal of Forest Research》2013,132(5-6):983-997
Modelling stem taper and volume is crucial in many forest management and planning systems. Taper models are used for diameter prediction at any location along the stem of a sample tree. Furthermore, taper models are flexible means to provide information on the stem volume and assortment structure of a forest stand or other management units. Usually, taper functions are mean functions of multiple linear or nonlinear regression models with diameter at breast height and tree height as predictor variables. In large-scale inventories, an upper diameter is often considered as an additional predictor variable to improve the reliability of taper and volume predictions. Most studies on stem taper focus on accurately modelling the mean function; the error structure of the regression model is neglected or treated as secondary. We present a semi-parametric linear mixed model where the population mean diameter at an arbitrary stem location is a smooth function of relative height. Observed tree-individual diameter deviations from the population mean are assumed to be realizations of a smooth Gaussian process with the covariance depending on the sampled diameter locations. In addition to the smooth random deviation from the population average, we consider independent zero mean residual errors in order to describe the deviations of the observed diameter measurements from the tree-individual smooth stem taper. The smooth model components are approximated by cubic spline functions with a B-spline basis and a small number of knots. The B-spline coefficients of the population mean function are treated as fixed effects, whereas coefficients of the smooth tree-individual deviation are modelled as random effects with zero mean and a symmetric positive definite covariance matrix. The taper of a tree is predicted using an arbitrary number of diameter and corresponding height measurements at arbitrary positions along the stem to calibrate the tree-individual random deviation from the population mean estimated by the fixed effects. This allows a flexible application of the method in practice. Volume predictions are calculated as the integral over cross-sectional areas estimated from the calibrated taper curve. Approximate estimators for the mean squared errors of volume estimates are provided. If the tree height is estimated or measured with error, we use the “law of total expectation and variance” to derive approximate diameter and volume predictions with associated confidence and prediction intervals. All methods presented in this study are implemented in the R-package TapeR. 相似文献
19.
This study examined both statistical and biological behaviors of two nonlinear mixed models fitted by the first-order (FO) and first-order conditional expectation (FOCE) methods. The simpler FO method was found to alter the mathematical forms of the base models and produce biologically unreasonable predictions for some subjects. This was not the case for the more complex FOCE method. Since the computations for predicting random parameters and for making subject-specific (SS) predictions were different for the two methods, mixing them would potentially produce large prediction biases and unexpected biological behaviors. This was demonstrated on two data sets (basal area–age and height–diameter data sets). For basal area–age data, accurate and precise basal area predictions were produced as long as a consistent method was used for predicting random parameters and for making SS predictions, regardless of the method used for model fitting. Otherwise, less accurate and precise predictions were produced, with some predictions totally against biological expectations. For height–diameter data, both consistent and inconsistent methods produced similar prediction statistics, but this does not mean that inconsistent methods should be adopted. Overall the FOCE method was found to be superior to the FO method in terms of producing biologically more reasonable predictions. 相似文献
20.
《Forest Ecology and Management》1999,115(1):85-92
A stochastic optimization model is developed to select between planting and seed-tree regeneration methods. The model considers the uncertainty of, and the legal requirement on, the stocking level of the established seedlings in a given year after regeneration. Uncertainty is quantified as a variation of the mortality rate of the planted seedlings for the planting method and as the prediction error for the seed-tree method. The objective of the forest landowner is assumed to maximize the expected net present value (NPV). Numerical simulations show that the landowner should select the seed-tree method rather than planting for a sample site with a Scots pine stand. In addition, if a risk-free selection model is used, it over-estimates the financial return by about +1.8%. Sensitivity analysis shows that a less restrictive forest act may improve the expected NPV for both planting and the seed-tree method. Sensitivity analysis also shows that a decrease in the variation of the mortality rate (or prediction error) increases the expected NPV. Since these results are obtained only for the sample site, more work is needed to be done until a general conclusion has been drawn. 相似文献