Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

In this study, regression-based prediction of volume and aboveground biomass (AGB) of coniferous forests in a mountain test site was conducted. Two datasets — one with applied topographic correction and one without applied topographic correction — consisting of four spectral bands and six vegetation indices were generated from SPOT 5 multispectral image. The relationships between these data and ground data from field plots and national forest inventory polygons were examined. Strongest correlations of volume and AGB were observed with the near infrared band, regardless of the topographic correction. The maximal correlation coefficients when using plotwise data were ?0.83 and ?0.84 for the volume and AGB, respectively. The maximal correlation with standwise data was ?0.63 for both parameters. The SCS+C topographic correction did not significantly affect the correlations between spectral data and forest parameters, but visually removed much of the topographically induced shading. Simple linear regression models resulted in relative RMSE of 32–33% using the plotwise data, and 43–45% using the standwise data. The importance of the source and the methodology used to obtain ground data for the successful modelling was pointed out.     

遥感与地理信息系统中的数学模型及其应用

本文探讨了遥感与地理信息系统中应用的数学模型的特点;并综述了中国研究人员在遥感图象识别、农林牧估产、水文过程模拟、沙漠化和水土流失动态估计以及土地资源分析评价中的应用实例;最后,根据遥感与地理信息系统的发展趋势,展望了其中应用的数学模型的发展趋势。     

基于遗传神经网络的克钦湖叶绿素反演研究

叶绿素a浓度能够在一定程度上反映内陆湖泊水质情况。为实现对克钦湖水体叶绿素a浓度的监测,于2010年8月15日对克钦湖进行了现场光谱测量和同步采样。通过分析叶绿素a浓度和光谱数据之间的关系,建立基于反射比、人工神经网络和遗传神经网络的叶绿素a浓度估测模型。结果表明:利用R700nm/R670nm反射比建立的模型估测精度为R2=0.67;人工神经网络模型的估测精度较高,R2=0.882;将遗传算法引入神经网络之后,模型的估测精度进一步提高,R2达到0.956,将模型预测的结果与克里格内插法相结合对研究区的叶绿素a空间分布情况进行定量估测,发现北湖的叶绿素a浓度明显高于南湖,有由北向南逐渐递减的趋势,这为今后利用高光谱数据对克钦湖叶绿素a浓度大面积遥感反演提供了研究基础。     

Regional mineral resources assessment based on rasterized geochemical data: A case study of porphyry copper deposits in Manzhouli,China

This study used regional geochemical survey data (1:200,000 scale) from the Manzouli area of China to assess mineral resources. Geochemical survey data was rasterized and a geochemical atlas was generated, with the image pixel size determined according to geochemical exploration sample point spacing. The Wunugetushan, Babayi, and Badaguan porphyry copper deposits were selected as model areas for the assessment of copper mineral resources. Three parameters were considered for the calculation of the mineral resources. An ore-bearing hydrothermal alteration coefficient was determined based on geological characteristics and geochemical characteristics of the model area, in order to determine alteration intensity; a denudation coefficient was calculated to determine denudation extent; and a mineralization intensity coefficient was calculated to determine the intensity of mineralization within each pixel. Resource estimation was conducted through regression analysis of model deposit resources and coefficients. The results can be used to determine prospecting target areas based on frequency classification and can be used to estimate the number of ore deposits. Results show that resource estimation using rasterized geochemical data provides high prediction precision and accurate positioning.     

半干旱区内陆湖泊透明度高光谱估测模型研究——以松嫩平原查干湖为例

通过实测查干湖高光谱数据,建立透明度(Secchi Disk Depth,SDD)单波段估测模型、比值估测模型以及神经网络高光谱估测模型,并以确定性系数R2以及剩余残差RMSE为指标进行了验证.通过对单波段估测模型和比值估测模型进行比较发现,单波段模型估测结果与比值模型相差无几,而水体透明度经对数处理有利于模型精度提高,但是神经网络模型是三者中最优的.查干湖透明度高光谱定量估测模型的建立,有利于今后利用遥感影像,对查干湖水体透明度进行全面估测,对于研究和监测查干湖水体水质状况有重要意义.     

Deadwood assessment in different developmental stages of beech (Fagus orientalis Lipsky) stands in Caspian forest ecosystems

Deadwood is an indicator that addresses many parameters of naturalness and is becoming a general reference for natural forests. If there are enough of the different kinds of deadwood in a forest, then it is likely to be properly natural. Also, it is a practical indicator, representing the health and biodiversity of forests. The aim of this research was to find out how much deadwood should be present in different developmental stages in a natural forest as a reference. For this purpose, a natural forest ecosystem in Mazandaran province, north of Iran, which is located in Noshahr, was selected. Species, diameter and height of all (living and dead) trees in each area were assessed. Then, developmental stages were determined, and their map was produced. The amount of deadwood was determined in different stages. Results showed that all three stages (initial, optimal and decay stage) could be recognized in the studied beech stands. Deadwood rate varied and greatest volume and number of it occurred in decay and initial stages, respectively. The frequency and volume of deadwood depend on the pattern of natural disturbance, developmental stages and stand structure. The amount of deadwood within managed forests is open to debate and requires detailed knowledge about beech stands in local conditions. So, based on these results in natural forests of beech in north of Iran, deadwood volume between 4.9 and 54.3 m³ ha^?1 or 1.1–9.6 % of total volume of wood could be considered as a reasonable amount and each developmental stage must be different.     

Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (K_s) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict K_s, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict K_s using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points?×?7 factors?=?791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R²) in comparison with the initial FIS and the GA-FIS model. EF and R² values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME?=?10.4 versus 11.5 and RMSE?=?5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.     

基于MODIS数据的东北地区积雪覆盖率估算

东北地区是我国三大积雪区之一,森林覆盖面积占总面积的40%左右。受森林冠层的影响,当前的MODIS雪盖产品（V6）提供的积雪覆盖率标准模型对东北地区积雪覆盖率估算结果存在明显的低估现象。基于此,采用分区建模的方式：在森林地区,计算归一化差值林地积雪指数（NDFSI）,建立像元积雪覆盖率（FSC）与NDFSI及NDVI之间的线性关系;在非森林地区,采用MOD10A1 V6提供的归一化差值积雪指数（NDSI）,建立像元积雪覆盖率（FSC）与NDSI及NDVI之间的线性关系。采用Landsat 8 OLI数据提取的积雪覆盖率（FSC）对分区建模的估算结果与标准模型的估算结果进行对比,发现进行估算的过程中均方根误差和平均绝对误差这两项指标的数值相对于标准模型有了大幅下降,这一结果在林区有更显著的表现。计算得到的决定系数R²,在本文模型也有提高。以T1林区影像为例,本文模型的均方根误差和平均绝对误差分别为0.246、0.055,而标准模型的两项指标则分别为0.420、0.348。本文模型和标准模型的决定系数分别为0.675、0.641。     

Nitric oxide emissions from soils: a case study with temperate soils from Saxony, Germany

Nitrogen oxides (NO _x ) are involved in acid rain and ozone formation, as well as destruction. NO _x are climate-relevant trace gases in the atmosphere. Atmospheric NO _x originate from anthropogenic emissions (mainly combustion processes). NO from natural processes derives from thunderstorms and soil microbial processes. They may play a crucial role in soil?Catmosphere feedback processes. This study aims to investigate NO _x -emissions from soils under different land use, geographical and meteorological conditions. NO _x -emissions were quantified in both field and laboratory experiments with a closed static chamber. Disturbed soil samples have been used for laboratory experiments. A climate chamber was used to regulate soil temperature of the samples. Field experiments showed that NO-soil emissions strongly depend on soil temperature. NO-emissions from a soil under meadow showed significant daily variations, unlike soil below spruce forest. Peak emission values were 18???g NO?CN?m^?2?h^?1 above meadow and 1.3???g NO?CN?m^?2?h^?1 under forest canopy. In addition, NO-emissions of meadow and forest soil were studied in a climate chamber, enhanced by an additional experiment with agricultural soil. These experiments revealed strong exponential correlations of NO-emissions and soil temperature. Maximum values reached above 400???g NO?CN?m^?2?h^?1 from agricultural soils at soil temperatures above 50°C. This study shows that soil NO-emissions strongly depend on temperature, vegetation type and geographical position. Consequently, NO-emissions may have a positive feedback effect on climate change.     

A New Analytic Integration of the Rate Equation for Batch Dissolution of Salts in the Presence of Common Ion

Following a recent suggestion of a new rate equation specifically for the batch dissolution of salts in solutions containing a common ion, this paper describes an analytic solution to its integration. The equation has been tested by dissolving 250???m gypsum- rock particles in water (26.7?g?l^?1) containing various mixtures of sodium and calcium chlorides, all at an ionic strength of 0.060?M. The model fitted the experimental curves very well and showed that the dissolution slowed slightly overall when the initial calcium concentration was increased from 0 to 0.020?M. The dissolution curves were also modelled as a simple exponential, whence the fit was comparable to that with the new equation, with the exponential rate constant varying between 0.025 and 0.019 (±0.0004) for 0 and 0.020?M initial calcium concentration, respectively. Conventional Electrolyte theory from thermodynamics is used to show that the new equation is an inevitable consequence of modelling the net rate of dissolution in terms of a back reaction that is first order with respect to the dissolved substance, as per the recently described Shrinking Object model. Moreover, it is shown how the simple exponential model (which is a well-used plot in dissolution kinetics) provides the linear end-member to an infinite number of curvilinear plots of rate of dissolution versus reaction progress developed by the new model??it is the special case where common ion is absent. The results are now judged good enough to identify a generic batch dissolution rate equation for all salts dissolving without significant complication from either contaminants or their own gaseous species, as in calcium carbonate dissolution.     

An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran

Vegetation indices have been introduced for analyzing and assessing the status of quantitative and qualitative characteristics of vegetation using satellite images. However, choosing the best indices to be used in forest biodiversity and vegetation is one of the important problems faced by the users. The purpose of this research is to evaluate six vegetation indices in the analysis of tree species diversity in the northern forests of Iran. The present research uses LISS III sensor data from IRS-P6 satellite. Geometric rectification of images was performed using ground control points, and Chavez model was used for atmospheric correction of the data. The six spectral vegetation indices included NDVI, IPVI, Ashburn Vegetation Index (AVI), TVI, TTVI, and RVI. Shannon–Wiener species diversity index was used to analyze diversity, and the value of the index was calculated in each sample plot. Then, the spectral values of each sample plot were extracted from different bands. The best subset regression was used to analyze the relationship between species diversity and the related bands. The results obtained from the regression showed that polynomial equations under scrutiny as independent variables can assess tree and shrub species diversity better than other bands and compounds used (R ²?=?0.47). The obtained results also indicated a higher capacity in the case of the AVI index for estimating tree species diversity in the under study area.     

基于LIDAR数据的森林参数反演方法研究

森林结构参数诸如林分平均高、平均冠幅、平均胸径、林分密度、地上生物量等的空间分布对于森林可持续经营管理具有重要意义。以黑河流域祁连山大野口典型森林区为研究区,采用高密度LIDAR小脚印点云数据,在进行单木结构参数提取的基础上,按20 m×20 m大小的网格进行了小区域森林参数反演研究。首先由LIDAR点云数据生成冠层高度模型(Canopy Height Model,CHM),从CHM中估测单株木结构参数树的位置、树高、冠幅。然后采用多元逐步回归分析法建立样地(20 m×20 m)尺度上LIDAR估测的平均树高、冠幅等与实测森林参数(林分平均高、林分算数平均高、平均冠幅、平均胸径、林分密度、地上生物量)之间的关系。结果表明,林分平均高、林分算术平均高、地上生物量的估测方程精度较高,R2均大于0.7,平均冠幅、平均胸径、林分密度的估测方程R2均大于0.5,根据建立的方程得到了森林参数的空间分布图。高密度LIDAR数据可以得到较高精度的森林参数空间分布图,对于森林可持续经营管理以及林相图的更新等具有重要意义,同时对小流域森林水文科学的研究具有重要的应用价值。     

Runout and entrainment analysis of an extremely large rock avalanche—a case study of Yigong,Tibet, China

An extremely large rock avalanche occurred on April 9, 2000 at Yigong, Tibet, China. It started with an initial volume of material of 90?×?10⁶ m³ comprising mainly of loose material lying on the channel bed. The rock avalanche travelled around 10 km in horizontal distance and formed a 2.5-km-long by 2.5-km-wide depositional fan with a final volume of approximately 300?×?10⁶ m³. An energy-based debris flow runout model is used to simulate the movement process with a new entrainment model. The entrainment model considers both rolling and sliding motions in calculating the volume of eroded material. Entrainment calculation is governed by a second order partial differential equation which is solved using the finite difference method. During entrainment, it is considered that the total mass is changed due to basal erosion. Also the profile of the channel bed is adjusted accordingly due to erosion at the end of each calculation time step. For Yigong, the profile used in the simulation was extracted from a digital elevation model (DEM) with a resolution of 30 m?×?30 m. Measurements obtained from site investigation, including deposition depth and flow height at specific location, are used to verify the model. Ground elevation-based DEM before and after the event is also used to verify the simulation results where access was difficult. It is found that the calculated runout distance and the modified deposition height agree with the field observations. Moreover, the back-calculated flow characteristics based on field observations, such as flow velocity, are also used for model verifications. The results indicate that the new entrainment model is able to capture the entrainment volume and depth, runout distance, and deposition height for this case.     

Species composition and forest structure in tropical moist deciduous forest of Bangladesh—a case study in Thakurgaon

This study was conducted in the tropical moist deciduous forest in Bangladesh to describe the species composition, diversity, and the forest structure. There were three plots established in Ranishonkoil, Ruhia, and Baliadangi forest beat in Thakurgaon. A total of 126 tree species, 1,991 stems (663 ha^?1) of ≥10-cm girth were listed. Tree communities in these forest region differed in dominance, composition, diversity, and structure; and tree stand density varied from 651 to 685 ha^?1. Species diversity (H¹) ranges from 3.11 to 3.48. Meliaceae, Myrtaceae, and Rubiaceae were the most abundant families within the three plot area. Study site 2 is more diverse at spatial scale and taxonomic levels due to high rainfall and favorable edaphic condition. This study will help the foresters as baseline information for monitoring and sustaining diversity of tropical moist deciduous forests in Bangladesh.     

Biomonitoring of trace element accumulation in plants growing at Murat Mountain

The purpose of this study is to determine the present levels of atmospheric trace element pollution in the Murat Mountain of Aegean Region in the western part of Turkey. Therefore, 29 different plants were selected as potential biomonitors of trace elements including nickel, zinc, iron, lead, cadmium and manganese (??g/g, dry weight). The samples were collected from two different heights of Murat Mountain. Atomic absorption spectrometry was used to determine the concentrations of trace elements. The mean concentrations determined at 1,000?m altitude ranged from 0.139 to 4.518, 0.223 to 0.986, 0.359 to 6.930, 0.443 to 0.727 and 0.077 to 3.222???g/g, dry weight, for nickel, zinc, iron, lead and manganese, respectively. At 1,600?m altitude, the values ranged from 0.191 to 6.248, 0.302 to 1.008, 2.387 to 8.896, 0.345 to 0.570 and 0.195 to 3.502???g/g, dry weight, for nickel, zinc, iron, lead and manganese, respectively. No cadmium was found at both altitudes. For determination of existence of any differences between the averages of the herbaceous and woody plants, comparisons were made by the independent sample t test. In the statistical analysis, comparison of trace element pollution values of herbaceous and woody plants for nickel, lead and manganese was significant (P?<?0.05), while it was not for iron and zinc.     

云南石林景区恢复植被的土壤水文结构功能研究

基于喀斯特恢复植被生态系统功能评价的要求,采用土壤水文结构功能模型评价云南石林景区恢复植被的土壤水文结构功能。选取原生滇青冈林、恢复26a的次生林B、恢复21a次生林A、恢复8a的灌丛、石漠化灌草丛和恢复30a云南松林6个样地,分别调查它们的土壤水文结构特征。相对于原生林,石漠化样地经过8a的封禁,其土壤水文结构功能可恢复31%,21a可恢复45%,人工飞播的云南松林经过30a恢复的只有38%,而砍伐林地封禁26a可恢复71%。土壤水文结构功能评价模型基本适用于岩溶区不同恢复林地的土壤水文结构功能的恢复评价。      

天山冰川储量变化和面积变化关系分析研究

冰川储量的变化对海平面和区域水资源、生态环境以及社会经济等有重要影响,但是对大量冰川的储量变化很难通过实地观测获得。基于天山9条监测冰川储量变化的估算结果,通过统计分析,探讨冰川储量变化和面积变化的相互关系,以期通过测定冰川面积变化来估算冰川储量变化。结果表明：冰川储量变化与面积变化具有线性关系,其决定系数为0.67~0.70。加入其他山区冰川样本以后,决定系数并没有提高。其原因在于,一方面对冰川变化监测和冰川储量估算存在误差;另一方面,不同的冰川区由于冰川的类型、规模及区域气候特征的差异,冰川变化对气候变化响应的敏感程度不同。因此,未来不仅需要更多更准确的冰川储量估算,还需要按冰川形态类型和区域分别研究。     

基于样本数据的红土型铝土矿定量遥感建模与反演研究

定量遥感找矿是遥感大数据在找矿应用中的一项前沿技术。本文采用多元回归分析方法,通过对多组不同数量地表铝土矿样本进行遥感建模与反演实验,探索样本量对定量遥感建模的影响。实验中,首先以地表采样获得的Al2O3、SiO2含量分析结果作为样本数据,再根据样本位置从Landsat 8遥感数据中分别读取1~7个波段反射率数据,然后利用SPSS软件,将Al2O3、SiO2含量与相应的1~7波段反射率进行多元回归分析,并分别建立Al2O3、SiO2含量遥感定量反演模型。为了得到最佳反演模型,随机选出6批不同数量的样本进行建模实验,每批样本大约2/3用于建模,其余1/3用于模型检验。实验结果表明:随着样本量增加,模型的判定系数(R2)均呈先快速升高后缓慢下降的趋势,反演结果的均方根误差则与之相反。当样本量为50左右时,判定系数取得极大值,均方根误差取得极小值,总体具有偏态分布特征。最后,利用遥感影像对样本量为50时建立的模型进行反演验证,研究区地表Al2O3、SiO2含量的反演结果与实际情况吻合得很好,从而证实了该建模方法具有可靠性,可以进一步推广使用。     

Use of DEM data to monitor height changes due to deforestation

Deforestation has been a major cause of climate change and other environmental problems. An accurate estimation of the volume of deforested area is needed for United Nations Reducing Emissions from Deforestation and Forest Degradation (UN-REDD+) policies implementation and global carbon accounting. Accurate information about three-dimensional (3-D) structure of forests is required to quantify forest carbon stock. This study demonstrates the use of different digital elevation models (DEMs) to monitor changes in height due to deforestation in Cambodia to support climate change mitigation policies of UN-REDD+. The Shuttle Radar Topographic Mission-DEM (SRTM-DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER-GDEM) and Panchromatic Remote sensing Instrument for Stereo Mapping-Digital Surface Model (PRISM-DSM) data were calibrated using Ice Cloud and land Elevation Satellite Geoscience Laser Altimeter System (ICESat–GLAS) data. The results obtained from this study clearly indicate the changes in the height of forests due to deforestation activity. The height of cutover forest generated from the PRISM-DSM and SRTM-DEM is more reliable than that from the PRISM-DSM and ASTER-GDEM data. Field data has also been used to validate the height of the cutover forests, which shows ±5 m uncertainties in the estimation.     

Optimized height of noise barrier for non-urban highway using artificial neural network

This study applies artificial neural network (ANN) for the determination of optimized height of a highway noise barrier. Field measurements were carried out to collect traffic volume, vehicle speed, noise level, and site geometry data. Barrier height was varied from 2 to 5 m in increments of 0.1 m for each measured data set to generate theoretical data for network design. Barrier attenuation was calculated for each height increment using Federal Highway Administration model. For neural network design purpose, classified traffic volume, corresponding traffic speed, and barrier attenuation data have been taken as input parameters, while barrier height was considered as output. ANNs with different architectures were trained, cross validated, and tested using this theoretical data. Results indicate that ANN can be useful to determine the height of noise barrier accurately, which can effectively achieve the desired noise level reduction, for a given set of traffic volume, vehicular speed, highway geometry, and site conditions.