基于SPOT5数据的内蒙古辉腾锡勒草原生物量估算最佳植被指数选取

随着植被指数在草原估产中的广泛应用,不同研究区内最佳植被指数的选取为更多学者所探讨。基于Spot5遥感数据计算NDVI等6种植被指数,结合野外调查方法,对精准定位的样点进行不同植被指数与生物量进行相关性分析,确定研究区最佳植被指数。结果表明:1)Spot5数据计算6种植被指数与样点生物量之间其存在着明显的相关性。2)样点生物量与NDVI,SAVI相关系数R分别为0.823,0.802,相关系数R0.8,高度相关;PVI,DVI,RVI,MASVI相关系数R分别为0.738,0.735,0.672,0.671,相关系数R介于0.3~0.8之间,中度相关。3)试验性的应用相关性分析方法确定研究区最佳植被指数,为研究区确定其他植被特征指标如植被盖度,以及条件相似的研究区提供快速、有效的植被指数选取方法。     

基于RS的林分蓄积量估测模型研究——以内蒙古大青山人工油松林为例

以TM遥感影像和二类调查小班数据为研究资料,以内蒙古大青山人工油松林为研究对象,采用逐步回归的方法构建内蒙古大青山人工油松林蓄积量估测模型,其中遥感因子选取与植物生长相关的植被指数及比值波段,林分立地条件因子选取郁闭度、坡向、龄级。研究结果表明:NDVI、RVI、DVI、郁闭度、TM4*TM3/TM7通过了T检验,作为入选变量参与模型构建。所建立的模型线性关系显著,估测精度86.21%,达到了森林资源二类调查要求,有一定的应用价值。     

基于TM数据估测鞍山市不同林分蓄积量

文章以鞍山市2006年Landsat TM数据和ASTER DEM高程数据为数据源,在提取出波段1~5、7这6个波段后,通过波段运算、缨帽变换、主成分分析等方法获取差值植被指数、土壤调节植被指数、大气阻抗植被指数、垂直植被指数、归一化差值植被指数、比值植被指数等,以及亮度、绿度,前3个主成分等24个变量因子。基于这些变量因子和采样点数据,通过逐步回归模型选择最优模型并估测鞍山市不同林分蓄积量。鞍山市总蓄积量估测模型的相关系数R2是0.73,通过进行交叉验证,发现实测蓄积量和交叉估测值之间的相关系数R2是0.58。基于2007年的森林资源规划设计调查,从森林中提取出针叶林和阔叶林,分不同林分建立蓄积量估测模型;针叶林蓄积量、阔叶林蓄积量估测模型的相关系数R2分别是0.73和0.75。     

崂山林场森林冠层叶面积指数反演研究

本文以地处崂山林场东部林区作为研究区,运用逐步分析方法对6种植被指数和海拔、坡向、坡度等立地信息进行比较分析,筛选出3种植被指数NDVI、RVI、SAVI可敏感反映森林冠层LAI,建立分别以NDVI、DVI、SAVI为自变量的二次曲线模型、幂函数曲线模型、指数曲线模型以及包含这3种植被指数的多元线性模型,从决定系数(R2)和标准误差两个方面对基于不同植被指数LAI反演模型进行定量分析。结果表明,崂山林场LAI最佳的统计模型是多元线性模型,模型的R2是0.812,具有较好的估测效果。利用该模型反演了研究区的林分冠层LAI,并把崂山林场的林分冠层LAI分为5个等级,研究区的森林冠层LAI分布呈现西北部和东南部较低,而东北部和南部相对较高的特点。     

广西岩溶地区植被覆盖度与植被指数耦合模型研究

选择广西典型岩溶山区灌草植被类型设置91块样方,运用Green Seeker手持光谱仪实测不同覆盖度植被的光谱响应并构建归一化植被指数(NDVI)、差值植被指数(DVI)、比值植被指数(RVI)、修正植被指数(MVI)和修改型土壤调节植被指数(MSA VI)等5种植被指数,同时运用数码相机垂直拍摄样方照片并进行监督分类,提取植被覆盖度,最后对样方的植被覆盖度和植被指数进行相关分析、建立回归模型和精度验证。结果表明:NDVI、RVI和MVI与植被覆盖度高度相关,相关系数超过0.9;而且用NDVI反演的植被覆盖度与实测值在α=0.05显著水平下进行t检验,结果无显著差异,平均精度达到95%以上,比其他植被指数模型更优秀。研究结论对广西岩溶石漠化地区植被覆盖度的快速、连续监测与评估具有重要参考价值。     

马尾松人工幼林年生长规律研究

为了探明马尾松人工林的生长规律,在桂西北乐里林场设置了3个固定样地,对马尾松人工幼林(4~11a生)的胸径、树高等进行了连续8年的监测研究,得出如下结果:1)胸径逐年总生长为5.50~12.27cm,与林龄的关系式为YDBH/cm=3.9383Ln(X林龄)+3.7132,连年生长和年平均生长分别为0.77~2.23cm和1.10~1.31cm。2)树高逐年总生长为3.53~11.73m,与林龄的关系式为YH/m=1.2186 X林龄-1.5443,连年生长和年平均生长分别为0.73~1.47m和0.88~1.08m。3)林分逐年总蓄积量为6.5~137.8m3/hm2,与林龄的关系式为YV/m3/hm2=0.0932X3.1119林龄,连年生长和年平均生长分别为5.0~25.0m3/hm2和1.6~12.5m3/hm2。研究充分说明,该区马尾松人工幼林生长较为缓慢,林分蓄积量较低。     

基于3S技术和SPSS软件的香格里拉市森林蓄积量估测探析

以香格里拉市的森林作为研究对象,森林资源规划设计调查角规控制样地蓄积量为实测数据参照,美国Landsat8卫星遥感图像为矢量数据来源,通过对遥感图像进行处理,获取了多光谱影像的波段光谱值,运用SPSS软件以组合植被指数(DVI、RVI、ARVI)、归一化植被指数(NDVI)、多波段线性组合指数(VIS123)为自变量,建立了蓄积量估测模型,探索了将纹理特征应用到森林蓄积量估测提供新的途径和方法。从结果与验证可以看出:利用Landsat8全色波段的纹理特征建模进行蓄积量估测是可行的,能够达到对蓄积量估测的精度要求,具有良好的应用前景。     

鄂尔多斯市森林资源动态分析

文章以鄂尔多斯市2001～2009年森林资源调查数据为材料,对森林总面积、森林覆盖率、森林蓄积量、组成结构等进行了动态分析,并做出了各项森林指标间的线性回归方程。结果表明,2001年以来,森林总面积、森林覆盖率和森林蓄积量均稳步提升,年均增长速度分别为8.28万hm2、0.95%和33.7万m3。森林覆盖率与灌木林地面积、森林蓄积量与有林地面积有显著相关性,回归方程分别为y=0.1199x+2.4778(R2=0.9999)和y=10.003x2-439.31x+5471.7(R2=0.9071)。     

基于 SVM 方法的高山松林蓄积量遥感估测研究

以香格里拉县高山松为研究对象,利用2006年香格里拉县TM遥感影像、2006年森林资源二类调查小班数据、2009年精度为30 m 的DEM数据以及2013年香格里拉县高山松实测样地数据,提取研究区内高山松林影像分布图及筛选出17个因子（13个遥感因子、3个地形因子、1个地面调查因子）作为备选自变量,在MAT-LAB下利用LIBSVM模块建立研究区高山松林蓄积量单位面积（30 m ×30 m）估测模型。结果表明,选用RBF核函数在参数范围内寻找出SVM模型的最佳参数C＝3.5809, g＝0.1、 p＝0.01,利用最佳寻优参数建立SVM非参数模型,对SVM模型进行测试得到,均方根误差MSE＝0.0087,复相关系数R＝0.51,相对误差RE＝23.4％,估测精度为76.6％。以像元为单位,分块提取高山松林对应的各像元自变量因子,利用估测模型预测得到香格里拉县高山松林总蓄积量为13318476.5 m3。     

抚育间伐对人工诱导的阔叶红松林林分蓄积量的影响

对1973年设置的不同类型人工诱导的阔叶红松林试验地进行了抚育间伐促进林分总蓄积量增长的试验研究,结果表明:B试验区林分总蓄积量最高,为242.097 3 m3.hm-2,其次是D试验区,为212.109 3 m3.hm-2,分别较A试验区提高18.5%和3.8%;B试验区较C试验区林分总蓄积量提高22.4%。适时、合理地抚育间伐可有效促进人工诱导的阔叶红松林林分总蓄积量的增长。     

基于ALOS PALSAR数据的森林蓄积量估测技术研究

以吉林省汪清林业局为研究区,基于ALOS PALSAR和森林资源二类清查固定样地数据,利用非线性回归方法建立了固定样地蓄积量与所对应的PALSAR像元后向散射系数之间的关系,结果表明,除杨树(Populus us-suriensis)等树种组外,PALSAR的HV后向散射系数与蓄积量呈良好的正相关关系,对多数树种而言,交叉极化方式(HV)后向散射系数与蓄积量的决定系数比同极化方式(HH)的略高。若以林场为单位统计,采用回归方法得到的估测结果与直接利用固定样地估测的结果相差很小。     

Practical large-scale forest stand inventory using a small-footprint airborne scanning laser

Mean tree height, dominant height, mean diameter, stem number, basal area and timber volume of 116 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 6500 ha study area, and the size of each plot was 232.9 m². Regressions for coniferous forest explained 60–97% of the variability in ground reference values of the six studied characteristics. A proposed practical two-phase procedure for prediction of corresponding characteristics of entire forest stands was tested. Fifty-seven test plots within the study area with a size of approximately 3740 m² each were divided into 232.9 m² regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data using the estimated regression equations. Average values for each test plot were computed and compared with ground-based estimates measured over the entire plot. The bias and standard deviations of the differences between predicted and ground reference values (in parentheses) of mean height, dominant height, mean diameter, stem number, basal area and volume were ?0.58 to ?0.85 m (0.64–1.01 m), ?0.60 to ?0.99 m (0.67–0.84 m), 0.15–0.74 cm (1.33–2.42 cm), 34–108 ha^?1 (97–466 ha^?1), 0.43–2.51 m² ha^?1 (1.83–3.94 m² ha^?1) and 5.9–16.1 m³ ha^?1 (15.1–35.1 m³ ha^?1), respectively.     

Prediction of tree height,basal area and stem volume in forest stands using airborne laser scanning

The aim of this study was to develop prediction models using laser scanning for estimation of forest variables at plot level, validate the estimations at stand level (area 0.64 ha) and test the effect of different laser measurement densities on the estimation errors. The predictions were validated using 29 forest stands (80×80 m²), each containing 16 field plots with a 10 m radius. For the best tested case, mean tree height, basal area and stem volume were predicted with a root mean square error of 0.59 m (3% of average value), 2.7 m² ha^?1 (10% of average value) and 31 m³ ha^?1 (11% of average value), respectively, at stand level. There were small differences in terms of prediction errors for different measuring densities. The results indicate that mean tree height, basal area and stem volume can be estimated in small stands with low laser measurement densities producing accuracies similar to traditional field inventories.     

Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory(FRI) data, and SPOT Vegetation(SPOT-VGT) normalized difference vegetation index(NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China(with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg(1 Pg = 10~(15) g), with an average of 77.2 Mg ha~(-1), during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fineresolution biomass map.     

公益林林木材积生长率的调查

在公益林区布设固定样地,定期、定点对公益林林木材积生长率进行调查,结果表明,2010年与2007年相比较,样地1、样地2、样地3、样地4、样地5的活立木材积净生长量分别为0.642 8 m3、0.641 9 m3、0.952 6 m3、1.179 4 m3、0.237 4 m3、3.627 1 m3,生长率分别为12.80%、4.73%、8.12%、3.87%、1.52%。对公益林进行有效管护,可以使林分蓄积稳步增长,森林生态功能和经济效益逐年增强。     

A comparison of different methods for forest resource estimation using information from airborne laser scanning and CIR orthophotos

This article compares three methods for forest resource estimation based on remote sensing features extracted from Airborne laser scanning and CIR orthophotos. The estimation was made exemplarily for the total stem volume of trees for a given area, measured in cubic metres per hectare [m3 ha⁻¹] (as one of the most important quantitative parameters to characterise a forest stand). The following methods were compared: Regression Analysis (RA), k-NN (nearest neighbour) method and a method that utilises regional yield tables, referred to as the yield table method (YT-method). The estimation of stem volume was examined in a mixed forest in Southern Germany using 300 circular inventory plots, each with a size of 452 m2. Remote sensing features relating to vegetation height and structures were extracted and used as input variables in the different approaches. The accuracy of the estimation was analysed using scatter plots and quantified using absolute and relative root mean square errors (RMSE). The comparison was made for all plots, as well as for averaged plot values located within forest stands that have the same age class. On “plot level” the RMSE yielded 79.79 m3 ha⁻¹ (RA), 81.93 m3 ha⁻¹ (k-NN) and 81.78 m3 ha⁻¹ (YT-method) and for the averaged values 35.75 m3 ha⁻¹ (RA), 35.06 m3 ha⁻¹ (k-NN) and 42.98 m3 ha⁻¹ (YT-method). Advantages and disadvantages, as well as requirements, of the methods are discussed.     

基于遥感分层技术的平南县森林蓄积抽样调查方法研究

采用遥感数据辅助分层可解决分层抽样在大范围森林资源调查中分层面积不准确的缺点.以ALOS数据为基础,将平南县的森林资源分为A层（有林地、疏林地层）和B层（其它地类层）.在各层内机械预布设样地,比较预布样地缓冲区（角规控制检尺所能绕测到的最大范围）的SAVI值、DNN IR值及对明显地物的目视解译,确定各样地缓冲区的地类,A层样地数有578个,B层有978个.根据分层抽样各层所需样本数,在确定好地类的样地中,随机抽取各层所需样本数并调查其蓄积量.结果表明,抽样的估计精度为91.5%,全县森林蓄积量为5 900 186.7 m3.     

Characteristics of runoff and sediment generation of forest vegetation on a hill slope by use of artificial rainfall apparatus

We studied the impact of forest vegetation on soil erosion,surface runoff, and sediment generation by using field simulated rainfall apparatus. We measured runoff and sediment generation of five 4.5 × 2.1m runoff plots (a bare soil as a control; two Pinus tabulaeformis forestplots and two Platycladus orientalis forest with row spacing of 1 m × 1m and 1.5 m × 1.5 m, respectively) in Beijing Jiu Feng National ForestPark under three rainfall intensities (0.42, 0.83, 1.26 mm per minute).Forest vegetation significantly reduced soil erosion and sediment yield.Mean total runoff volume in the four tree stand plots was 93% of that inthe control plot, demonstrating the limited effectiveness of forest vegetation in runoff control. With increasing rainfall intensity, runoff reductionin forest plots declined from 28.32% to 2.1%. Similar trends in runoff coefficient and the relationship between runoff volume and rainfall duration was observed. Mean total sediment yield and mean sediment yield reduction rate under different treatments was 55.05% and 43.17% of those in the bare soil control plot, respectively. Rainfall intensity playedan important role in runoff and sediment generation processes, and had agreater impact on runoff than on soil erosion and sediment generation.When considering several factors in runoff and sediment transport processes, the P. tabulae form plot with row spacing at 1 × 1 m had a greater effect on soil and water conservation than did other forested plots.     

森林面积方法研究

本文通过对遥感图像的处理和光谱特征信息的分析,应用ETM 影像数据和地面调查数据,研究了遥感数据处理技术在植被信息提取中的应用,尝试对高山峡谷区的森林资源调查提出较为完善的计算机图像处理技术和自动分类方法。对道孚县台站林场和麻孜林场地类的分类结果表明:运用TM453波段融合能够达到较好的图像增强效果;运用无监督分类方法提取森林面积能达到较高的分类精度;NDVI比RVI更能突出植被信息和消除山体阴影的影响。