Soil Organic Matter Fractions under Different Vegetation Types in Permafrost Regions along the Qinghai-Tibet Highway, North of Kunlun Mountains, China

As a key attribute of soil quality, soil organic matter(SOM) and its different fractions play an important role in regulating soil nutrient cycling and soil properties.This study evaluated the soil carbon(C) and nitrogen(N) concentrations in different SOM fractions(light– and heavy fractions,microbial biomass) under different vegetation types and analyzed their influencing factors in continuous permafrost regions along the Qinghai-Tibet Highway in the North of Kunlun Mountains, China.Soil samples were collected in pits under four vegetation types — Alpine swamp meadow(ASM), Alpine meadow(AM), Alpine steppe(AS) and Alpine desert(AD) — at the depth of 0-50 cm.The vegetation coverage was the highest at ASM and AM, followed byAS and AD.The results indicated that the concentrations of light fraction carbon(LFC) and nitrogen(LFN), and microbial biomass carbon(MBC)and nitrogen(MBN) decreased as follows: ASM > AM >AS > AD, with the relatively stronger decrease of LFC,whereas the heavy fraction carbon(HFC) and nitrogen(HFN) concentrations were lower in AS soils than in the AD soils.The relatively higher proportions of LFC/SOC and MBC/SOC in the 0-10 cm depth under the ASM soils are mainly resulted from its higher substrate input and soil moisture content.Correlation analysis demonstrated that aboveground biomass, soil moisture content, soil organic carbon(SOC) and total nitrogen(TN) positively correlated to LFC, LFN, HFC, HFN, MBC and MBN, while p H negatively correlated to LFC, LFN, HFC, HFN, MBC and MBN.There was no relationship between active layer thickness and SOM fractions, except for the LFC.Results suggested that vegetation cover, soil moisture content, and SOC and TN concentrations were significantly correlated with the amount and availability of SOM fractions, while permafrost had less impact on SOM fractions in permafrost regions of the central Qinghai–Tibet Plateau.

Soil Organic Carbon and Its Fractions Across Vegetation Types:Effects of Soil Mineral Surface Area and Microaggregates

Soil organic carbon(SOC)can act as a sink or source of atmospheric carbon dioxide;therefore,it is important to understand the amount and composition of SOC in terrestrial ecosystems,the spatial variation in SOC,and the underlying mechanisms that stabilize SOC.In this study,density fractionation and acid hydrolysis were used to assess the spatial variation in SOC,the heavy fraction of organic carbon(HFOC),and the resistant organic carbon(ROC)in soils of the southern Hulun Buir region,northeastern China,and to identify the major factors that contribute to this variation.The results showed that as the contents of clay and silt particles(0–50μm)increased,both methylene blue(MB)adsorption by soil minerals and microaggregate contents increased in the 0–20 and 20–40 cm soil layers(P<0.05).Although varying with vegetation types,SOC,HFOC,and ROC contents increased significantly with the content of clay and silt particles, MB adsorption by soil minerals,and microaggregate content(P<0.05),suggesting that soil texture,the MB adsorption by soil minerals,and microaggregate abundance might be important factors influencing the spatial heterogeneity of carbon contents in soils of the southern Hulun Buir region.

基于不确定性迭代优化的山地植被遥感制图

山地因其较高的异质性和特殊的环境特征给遥感科学及其应用带来了诸多问题和挑战。为实现山地植被信息的精准提取,本研究选择部分滇西北山地区域作为研究区开展方法实验,利用高分辨率遥感影像数据和数字高程模型,结合分区分层感知思想,提出一种基于不确定性理论的山地植被型组分类制图方法。首先结合地形对研究区影像进行多尺度分割制作图斑;然后根据图斑特征使用随机森林方法进行分类,将分类结果与对应类别样本间的相似性作为优化目标,并构建混合熵模型定量计算图斑推测类型的不确定性,据此进行针对性的样本补充和分类模型的迭代优化。实验总体分类精度达90.8%,较迭代前提升了29.4%,Kappa系数达到0.875。在高不确定性区域,该方法相比使用一次性补样和随机补样方法的分类结果,精度分别提高了17%和13%。研究结果表明,通过人机交互的方式,基于不确定性理论为样本库融入增量信息的迭代优化方法能够有效提高植被型组分类的精度,相较于传统的样本选择方法具有更高的效率和更低的不确定性。

A unified canopy bidirectional reflectance (BRDF) model for row ceops

Row sowing is a basic crop sowing method in China,and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological parameters.Because of the macroscopically geometric difference,the row crop is usually regarded as a transition between continuous and discrete vegetation in previous studies.Were row treated as the unit for calculating the four components in the Geometric Optical model (GO model),the formula would be too complex and difficult to retrieve.This study focuses on the microscopic structure of row crops.Regarding the row crop as a result of leaves clumped at canopy scale,we apply clumping index to link continuous vegetation and row crops.Meanwhile,the formula of clumping index is deduced theoretically.Then taking leaf as the basic unit,we calculate the four components of the GO model and develop a BRDF model for continuous vegetation,which is gradually extended to the unified BRDF model for row crops.It is of great importance to introduce clumping index into BRDF model.In order to evaluate the performance of the unified BRDF model,the canopy BRDF data collected in field experiment,"Watershed Allied Telemetry Experiment Research (WATER)",from May 30th to July 1st,2008 are used as the validation dataset for the simulated values.The results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row crops.In addition,the model is simple and easy to retrieve.In general,there is no irreconcilable conflict between continuous and discrete vegetation,so understanding their common and individual characteristics is advantageous for simulating canopy BRDF.It is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.