重庆市县域农村贫困化空间分异及影响因素探析

以重庆市为研究区,使用重庆市涉及扶贫开发任务的33个区县2014—2018年县域农村贫困数据,采用探索性空间数据分析(ESDA),刻画农村贫困化空间关联格局,构建影响县域农村贫困化空间分异的指标体系,并运用地理加权回归模型对县域农村贫困化的空间分异影响因素进行回归分析和探讨.研究结果表明:①总体来看,研究时段内重庆市县域农村贫困化热点区域整体空间格局稳定,高值区主要分布于巫溪、城口、酉阳和彭水等渝东北和渝东南区域的相关区县,而主城区和渝西地区的区县贫困发生率相对较低,呈现显著的空间差异性;从时间序列上来看,研究时段内各区县农村贫困发生率明显呈现下降态势,区县间的差距在缩小;②重庆市2014-2018年全局Moran’s I指数均达到显著的空间正相关,县域农村贫困化呈现出明显的空间集聚现象;③通过GWR模型进行回归分析发现,县域农村贫困化空间分异影响因素中,海拔高度、人均耕地资源面积、 25°以上耕地面积比重和区位水平、未通客运班车村比重、农村居民人均纯收入、文盲人口比、因残致贫人口比重等8个显著的解释变量对县域农村贫困化起到了直接或间接的影响,且各影响因素在不同区域上表现出的影响力不尽相同.     

Modeling crop yield potential of Eastern Anatolia by using geographically weighted regression

Wheat is a very important cereal crop in Eastern Anatolia in terms of acreage and production. It is a staple food and plays an important role in people's livelihood. Drought often occurs in the region and it dramatically influences wheat yield. The yield also depends upon prevailing climatic conditions, rainfall, temperature and humidity. Crop-modeling studies to forecast crop yield are important not only for people now but for planning studies and precautionary measures for the future. Traditional decision support systems based on crop simulation models are normally site-specific. In order to address the effect of spatial variability of weather variables on crop production, we modeled wheat yield potential on certain climatic conditions by using Geographically Weighted Regression and Geographical Information Systems in Eastern Anatolia in Turkey.     

基于光谱分析的温室番茄叶片叶绿素含量预测

通过分析叶片光谱反射率与其叶绿素含量的相关关系表明,温室番茄叶片的光谱反射率与其叶绿素含量的敏感波段为510～625 nm和690～710 nm,最佳波长为526 nm、609 nm和697 nm.利用最佳波长处的光谱反射率与多种建模方法如多元回归分析MLR(Multilinear Regression)、偏最小二乘回归分析PLSR(Partial Least Square Regression)建立了预测叶片叶绿素含量的模型,建模相关系数很相近,均大于0.740,但是PLSR在一定程度上消除了多重相关性的影响,模型验证时的相关系数(0.768)大于MLR模型(0.740);本研究在NDVI的基础上提出了一个新的光谱指数NDCI,并基于NDCI建立了叶片叶绿素含量的预测模型(Rc=0.753),获得了比较好的预测效果(Rv=0.761),为作物长势检测仪的开发奠定了基础.     

Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing

Airborne hyperspectral remote sensing was adapted to establish a general-purpose model for quantifying nitrogen content of rice plants at the heading stage using three years of data. There was a difference in dry mass and nitrogen concentration due to the difference in the accumulated daily radiation (ADR) and effective cumulative temperature (ECT). Because of these environmental differences, there was also a significant difference in nitrogen content among the three years. In the multiple linear regression (MLR) analysis, the accuracy (coefficient of determination: R², root mean square of error: RMSE and relative error: RE) of two-year models was better than that of single-year models as shown by R² ≥ 0.693, RMSE ≤ 1.405 g m⁻² and RE ≤ 9.136%. The accuracy of the three-year model was R² = 0.893, RMSE = 1.092 g m⁻² and RE = 8.550% with eight variables. When each model was verified using the other data, the range of RE for two-year models was similar or increased compared with that for single-year models. In the partial least square regression (PLSR) model for the validation, the accuracy of two-year models was also better than that of single-year models as R² ≥ 0.699, RMSE ≤ 1.611 g m⁻² and RE ≤ 13.36%. The accuracy of the three-year model was R² = 0.837, RMSE = 1.401 g m⁻² and RE = 11.23% with four latent variables. When each model was verified, the range of RE for two-year models was similar or decreased compared with that for single-year models. The similarities and differences of loading weights for each latent variable depending on hyperspectral reflectance might have affected the regression coefficients and the accuracy of each prediction model. The accuracy of the single-year MLR models was better than that of the single-year PLSR models. However, accuracy of the multi-year PLSR models was better than that of the multi-year MLR models. Therefore, PLSR model might be more suitable than MLR model to predict the nitrogen contents at the heading stage using the hyperspectral reflectance because PLSR models have more sensitive than MLR models for the inhomogeneous results. Although there were differences in the environmental variables (ADR and ECT), it is possible to establish a general-purpose prediction model for nitrogen content at the heading stage using airborne hyperspectral remote sensing.     

Inhibition of in vitro lymphocyte blastogenesis by chicken alpha-fetoprotein

Chicken alpha-fetoprotein (ch-AFP), purified from fetal chicken serum and embryo extracts, respectively, was examined for its immunomodulatory effect in vitro. Significant (P less than 0.05) suppression of the allogeneic mixed lymphocyte reaction (MLR) was observed, when these preparations were added to one-way mixed lymphocyte cultures (MLC) in quantities of 62.5-1000 micrograms/ml. Suppression of the MLR was depending on the presence of ch-AFP for at least 16 h after initiation of the MLC, suggesting that this fetal protein was acting mainly in the early phase of lymphoblastogenesis. Serum of chicken embryos (12th and 15th day of incubation), day-old chickens, and of 10-week-old chickens of four different inbred lines were also found to exert suppression of the MLR. From these data, it is hypothesized that ch-AFP plays an immunoregulatory role by maintaining a certain stage of self tolerance during differentiation of the avian immune system.     

Spatial modeling of the carbon stock of forest trees in Heilongjiang Province,China

Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.     

基于GWR模型的福建省绿色空间景观格局演变影响因素及其空间差异

由耕地、林地、草地、湿地及水域共同构成的绿色空间有利于提升城市生态环境,维护生物多样性和社会经济的可持续发展,理清福建省绿色空间的景观结构组成以及空间配置演变情况对构建生态屏障、打造全国生态示范省具有重要意义。以福建省绿色空间为对象,分析其景观格局在2000-2020年的演变,并采用地理加权回归（geographically weighted regression,GWR）构建回归模型,分析福建省绿色空间景观格局演变的影响因素及其空间差异性。结果表明:1)2000-2020年,整体绿色空间景观格局的时空变化特征较明显。福建省整体绿色空间破碎化程度上升,总体连通性降低和多样性降低。空间分布上,整体绿色格局破碎化、连通性和多样性变化随着空间位置不同而变化。2)GWR模型在空间位置层面对不同时期的绿色空间格局演变影响因素的解释优于普通最小二乘法（ordinary least squares,OLS）模型。GWR模型结果表明,社会经济影响因素对绿色空间景观格局破碎化、连通性、多样性的影响以负相关为主,且具有较显著的空间差异。自然因素中,地形、温度、降水对绿色空间景观格局的影响也具空间差异。分析揭示了福建省绿色空间景观格局的时空演变特征,并在空间层面上量化影响因素对绿色空间景观格局的影响特征,有助于进一步优化福建省绿色空间格局并构建福建省生态安全格局。     

基于空间模型的白河林业局天然红松分布

根据长白山地区白河林业局的772块固定标准地调查数据,分别建立以最小二乘法为基础的全局模型(Logistic和Poisson)和以地理加权回归模型(GWR)为基础的局域模型(GWLR和GWPR)来预估该局天然红松的分布情况。结果表明:天然红松分布受坡度和小班内树木平均胸径的影响最为显著,主要分布在东部和西南部地区,在北部的部分地区也有分布,但数量相对较少。通过比较全局模型和局域模型的AIC值和模型残差的空间相关性指数发现:GWR模型的AIC值明显小于全局模型,并且能够产生更为理想的模型残差,即模型残差的空间相关性明显减小,因此,GWR模型可以有效解决样地间空间异质性问题,有利于提高红松分布的预测精度。本研究将为大区域森林经营中的天然红松分布及其株数估测提供理论依据。      

Urban Green infrastructures: How much did they affect property prices in Singapore?

In 2015, 17 Sustainable Development Goals were outlined within the 2030 Agenda by the United Nations. Objective 11.7 aims to provide universal access to safe, inclusive, and accessible green and public spaces, highlighting the strategic role of these areas in cities. In the international literature, the theory of Ecosystem Services (ESs) has been developed to raise public awareness of the need to preserve biodiversity, enhance community cohesion, civic identity, and quality of life. In this study, we explored the economic value of Singapore's Urban Green Infrastructures (UGI) by investigating the private condominium and apartment unit market through the Hedonic Pricing Method (HPM). The HPM was integrated with Geographic Information System (GIS) to spatialize housing units, measure the distance of different green spaces, and consider the dependence effects among housing unit transactions. The results highlighted a positive effect on values of the proximity to the natural green areas, as well as to the regional, city parks, and small nature areas. The annual benefits of UGIs on households within 800 m range from 10,305,275 USD for nature areas to 59,723,703 USD for the city parks and 74,011,689 USD for the regional parks. Estimates showed the small contribution of park connectors (PCN) and neighborhood parks. The HPM results are a useful aid to understanding the amenity value of UGIs. The modeling outcomes could be used to inform policymakers and municipal green managers about UGIs preservation and new allocation starting from households’ preferences.     

土壤水分的高光谱响应特性及定量监测研究

为及时、准确地监测土壤含水量（SMC），采用室内条件下人工模拟不同SMC环境的方法，通过测定各时间段的SMC及其对应的土壤光谱反射率，运用连续投影算法（SPA）提取土壤水分的特征波长，结合多元线性回归（MLR）方法，构建SMC的高光谱定量监测模型。结果表明，光谱反射率随着样本SMC的增加而逐渐降低，二者存在明显的负相关关系；采用SPA方法提取的光谱特征波段为422、629、817、976、1121、1258、1359、1448、1830和2022nm；构建的SPA-MLR土壤水分高光谱监测模型表现出良好的预测效果（校正集的R2=0.930、RMSE=8.845、RPD=3.794，验证集的R2=0.927、RMSE=8.799、RPD=3.581）。研究结果可为土壤水分的高效精准监测提供一定的实践探索和理论参考。