排土场不同草本种植模式和接种AMF对植被生长和土壤改良的作用

为研究不同草本种植模式和接种丛枝菌根真菌(AMF)对露天矿区排土场的生态效应,本试验以内蒙古锡林浩特市胜利露天矿为研究对象,通过设定2种不同豆科：禾本科比例种植方式,2种接菌处理：接种AMF和无接菌对照,1个自然恢复对照,共5个试验小区;分别进行了植被调查与土壤性质分析。结果表明：植物地上生物量在接菌区均显著高于对照区和自然恢复区,且最大生物量出现在1：2接菌区;硝态氮和铵态氮在1：2比例下最高,且接菌区高于相应对照和自然恢复区;土壤硝态氮、铵态氮与植物的地上生物量和植被盖度均呈极显著正相关(P＜0.01)。接菌处理对不同种植模式下植物的生长和土壤氮累积均有促进作用,不同草本比例的生态效应也有差异。本研究通过以上试验,研究不同豆科：禾本科种植模式对土壤的改良效应以及接种AMF对植物的促生效果,以期探寻适合本试验区域生态恢复的微生物-植物联合修复的最佳模式,为矿区生态恢复提供科学参考。     

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS) fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI) and photochemical reflectance index(PRI). NDVI is useful for indicating crop growth/phenology, whereas PRI was developed for observing physiological conditions. Thus, the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system. However, capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions, such as solar irradiance and precipitation. Further, unlike growth/phenology, the physiological condition has diurnal changes as well as seasonal characteristics. This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice, barley, and garlic. First, the measurement accuracy of SRSs was compared with handheld spectrometers, and the R~2 values between the two devices were 0.96 and 0.81 for NDVI and PRI, respectively. Second, the experimental study of threshold criteria with respect to meteorological variables(i.e., insolation, cloudiness, sunshine duration, and precipitation) was conducted, and sunshine duration was the most useful one for excluding distorted values of the vegetation indices. After data processing based on sunshine duration, the R2 values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI) and 0.065–0.298(PRI) on the three crops, and the seasonal signals of vegetation indices became noticeably improved. This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.     

协同冠层SIF和PRI光谱指数的构建及其在小麦条锈病监测中的应用

【目的】利用反射率光谱在作物生物物理方面的优势和日光诱导叶绿素荧光(solar-induced chlorophyll fluorescence,SIF)、光化学反射率指数(photochemical reflectance index,PRI)在光合生理方面的优势,构建协同冠层SIF和PRI光谱指数(synergistic spectral index of SIF and PRI,SISP),旨在提高作物病害遥感探测精度。【方法】基于3FLD(three bands fraunhofer line discrimination)算法,估测小麦条锈病在不同病情严重度下的单波段SIF强度,利用对作物冠层几何结构敏感的归一化植被指数(normalized difference vegetation index,NDVI)和重归一化植被指数(re-normalized vegetation index,RDVI)对SIF和PRI进行处理,再利用处理后的SIF和PRI数据构建SISP指数,通过建立传统的光谱指数和SIF、PRI及其组合对小麦条锈病的遥感监测模型,以病情指数(disease index,DI)实测值与预测值之间的决定系数(R²)、均方根误差(RMSE)和相对分析误差(RPD)评价模型精度,进而与SISP指数建立的模型进行比较,分析SISP指数对作物病害遥感监测的有效性。【结果】（1）综合利用SIF和PRI数据能够提高对小麦条锈病的遥感探测精度,3组验证样本数据集中,以PRI和SIF的简单组合PRI+SIF为自变量构建的小麦条锈病监测模型,预测DI值与实测DI值间的R²比单一PRI和SIF至少提高14.0%和1.7%,RMSE至少降低7.1%和3.7%。（2）利用反射率光谱指数NDVI和RDVI处理后的SIF和PRI构建的SISP指数,对小麦条锈病DI的预测精度优于直接利用PRI和SIF组合的各种指数,验证样本数据集中预测DI值与实测DI值间的R²至少提高3.7%,RMSE至少降低9%。（3）以SISP和反射率光谱指数为自变量构建的小麦条锈病多元线性回归(multiple linear regression,MLR)和径向基神经网络(radial basis function neural network,RBFN)模型的精度,高于仅利用反射率光谱指数构建的模型精度,其预测DI值与实测DI值间的R²分别较反射率光谱指数提高13.42%和5.72%,RMSE分别减少29.93%和19.24%,RPD分别提高44.53%和29.80%。【结论】利用NDVI和RDVI处理后的SIF和PRI构建SISP指数,能够减弱作物群体生物量对冠层SIF和PRI信号的影响,提高小麦条锈病的遥感监测精度。     

Canopy reflectance, thermal stress, and apparent soil electrical conductivity as predictors of within-field variability in grain yield and grain protein of malting barley

Three consecutive crops of malting barley grown during 2002–2004 on clay-loam on a Swedish farm (59°74’ N, 17°00’ E) were monitored for canopy reflectance at growth stages GS32 (second node detectable) and GS69 (anthesis complete), and the crops were sampled for above ground dry matter and nitrogen content. GPS-positioned unfertilised plots were established and used for soil sampling. At harvest, plots of 0.25 m² were cut in both fertilised and unfertilised plots, and 24 m² areas were also harvested from fertilised barley. The correlations between nine different vegetation indices (VIs) from each growth stage and yield and grain protein were tested. All indices were significantly correlated (at 5% level) with grain yield (GY), and protein when sampled at GS69 but only four when sampled at GS32. Three variables (the best-correlated vegetation index sampled at GS32; an index for accumulated elevated daily maximum temperatures for the grain filling period, and normalised apparent electrical conductivity (ECa) of the soil) were sufficient input in the final regressions. Using these three variables, it was possible to make either one multivariate (PLS) regression model or two linear multiple regression models for grain yield (GY) and grain protein, with correlation coefficients of 0.90 and 0.73 for yield and protein, respectively.     

Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis

Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal infection levels in rice (Oryza sativa L.) panicles. Four infection levels in rice panicles were used in the study: no infection condition, light and moderate infection caused by rice glume blight disease, and serious infection caused by rice false smut disease. Hyperspectral reflectance of rice panicles was measured through the wavelength range from 350 to 2500 nm with a portable spectroradiometer in the laboratory. The spectral response characteristics of rice panicles were analyzed, and principal component analysis (PCA) was performed to obtain the principal components (PCs) derived from different spectra processing methods, namely raw, inverse logarithmic, first, and second derivative reflectance. A learning vector quantization (LVQ) neural network classifier was employed to classify healthy, light, moderate, and serious infection levels. Classification accuracy was evaluated using overall accuracy and Kappa coefficient. The overall accuracies of LVQ with PCA derived from the raw, inverse logarithmic, first, and second derivative reflectance spectra for the validation dataset were 91.6%, 86.4%, 95.5%, and 100% respectively, and the corresponding Kappa coefficients were 0.887, 0.818, 0.939 and 1. Our results indicated that it is possible to discriminate different fungal infection levels of rice panicles under laboratory conditions using hyperspectral remote sensing data.     

黄土区露天煤矿排土场刺槐×油松复垦模式17~22年间群落生长动态

为了解矿区复垦土地人工重建植被的群落生长动态,基于2010年和2015年对安太堡露天煤矿排土场0.8 hm~2"刺槐(Robinia pseudoacacia)×油松(Pinus tabuliformis)"复垦模式动态监测样地的两次调查数据,从物种组成、数量特征、径级结构等方面分析了人工植被复垦17~22年间群落生长动态特征。结果表明:5年间,群落的树种组成较为稳定,优势种仍为刺槐,但其重要值大幅下降,入侵种榆树的重要值增长较快;草本层植物由44种减少到35种,在优势成分上呈现出由1年生或1~2年生向多年生、旱中生向中生演替的趋势;样地内胸径≥1 cm的独立个体数由1530株增加到2854株,其中死亡194株,新增1518株,每年死亡率和每年增补率分别为2.71%和15.18%;死亡量最大的树种是刺槐,增补量最大的是榆树;群落总的胸高断面积由10.99 m~2·hm~(-2)增加到14.19 m~2·hm~(-2),其中因死亡而减少的胸高断面积为0.65 m~2·hm~(-2),新增的胸高断面积为3.85 m~2·hm~(-2),以刺槐的损失量和新增量为最多;刺槐和油松的平均胸径增加,榆树的平均胸径减少;小径级个体死亡量较大,大径级个体死亡量较小;不同树种的死亡个体径级分布基本类似于2010年该树种的径级分布;刺槐和油松的种群大小变化率分别为-2.88%和-0.24%,均呈小幅度负增长,榆树的种群大小变化率(33.37%)在5%以上,属于快速增长的种群。从群落5年间的物种组成和结构变化来看,不同树种的死亡率和增补率各异,群落的物种成分和结构变化较大,复垦生态结构尚不稳定。