基于典型叶片模板自动匹配的虫损叶面积测量

为了定量化评估农作物的虫害程度,提出了一种基于典型叶片模板自动匹配的叶片虫损面积测量新方法。先将叶片图像二值化并提取其外轮廓;再对提取的轮廓进行多边形近似,以多边形的顶点为端点将叶片外轮廓划分成若干子轮廓;然后采用形状上下文对完整叶片与虫损叶片之间的子轮廓进行自动配准,找出其间的相互映射关系;最后根据映射关系对虫损叶片进行重建,计算出虫损面积。对10类不同叶片的测量分析表明：该方法平均每叶片耗时0.962 s,最大相对误差为8.22%,平均相对误差为4.78%。其中,形状复杂度高的叶片平均相对误差为7.48%,复杂度中等的叶片为5.99%,复杂度低的叶片为1.84%。结果表明,该方法能准确而快速地测量虫损叶面积。     

A comparison of linear and exponential regression for estimating diffusive CH4 fluxes by closed-chambers in peatlands

The closed-chamber method is the most common approach to determine CH₄ fluxes in peatlands. The concentration change in the chamber is monitored over time, and the flux is usually calculated by the slope of a linear regression function. Theoretically, the gas exchange cannot be constant over time but has to decrease, when the concentration gradient between chamber headspace and soil air decreases. In this study, we test whether we can detect this non-linearity in the concentration change during the chamber closure with six air samples. We expect generally a low concentration gradient on dry sites (hummocks) and thus the occurrence of exponential concentration changes in the chamber due to a quick equilibrium of gas concentrations between peat and chamber headspace. On wet (flarks) and sedge-covered sites (lawns), we expect a high gradient and near-linear concentration changes in the chamber. To evaluate these model assumptions, we calculate both linear and exponential regressions for a test data set (n = 597) from a Finnish mire. We use the Akaike Information Criterion with small sample second order bias correction to select the best-fitted model. 13.6%, 19.2% and 9.8% of measurements on hummocks, lawns and flarks, respectively, were best fitted with an exponential regression model. A flux estimation derived from the slope of the exponential function at the beginning of the chamber closure can be significantly higher than using the slope of the linear regression function. Non-linear concentration-over-time curves occurred mostly during periods of changing water table. This could be due to either natural processes or chamber artefacts, e.g. initial pressure fluctuations during chamber deployment. To be able to exclude either natural processes or artefacts as cause of non-linearity, further information, e.g. CH₄ concentration profile measurements in the peat, would be needed. If this is not available, the range of uncertainty can be substantial. We suggest to use the range between the slopes of the exponential regression at the beginning and at the end of the closure time as an estimate of the overall uncertainty.     

Hyper-spectral estimation of wheat biomass after alleviating of soil effects on spectra by non-negative matrix factorization

Hyper-spectral technology has been proven to be an effective method for the fast and non-destructive monitoring of crop biomass. However, the biomass estimation accuracy of this method is limited due to the effects of background factors, such as soils and water. In this study, a spectral separation method, non-negative matrix factorization (NMF), was proposed to alleviate the effects of soil on spectra. With the application of the NMF method, pure vegetation spectra were extracted from the field-observed spectra of wheat canopy, which were collected in four growing seasons from the tillering to the booting stages of wheat. Then, prediction models of wheat biomass (WB) were established and validated using the extracted spectra with the partial least squares regression (PLSR) method. The results showed that the NMF method could effectively separate the vegetation spectra from the mixed canopy spectra. Based on the extracted vegetation spectra, the WB prediction accuracy could be greatly improved with an increase of 31.7% for the R²_p and an increase of 46.6% for the ratio of performance to deviation (RPD) as compared to the original spectra, indicating that the NMF method could significantly improve the performance of the WB prediction model. This method has potential application in the estimation of biomass using remote sensing technology.     

冬小麦不同株型品种光谱响应及株型识别方法研究

以直立和平展2种株型的冬小麦品种为材料，研究了它们的光谱响应以及田间植被覆盖度的差异，探讨了利用冠层光谱反射率、光谱特征参量NDVI及植被覆盖度识别小麦株型的方法。结果表明，（1）小麦不同株型品种在近红外波段（700～1300 nm）光谱反射率有明显差异，生育前期平展型品种高于直立型品种，并以拔节期的差异为最显著，随着生育进程差异逐渐变小。拔节期是进行株型识别的最佳时期，并且此期冠层的敏感波段680 nm和760～900 nm的反射率在2种株型品种之间差异明显。（2）小麦冠层叶面积指数（LAI）与归一化差异植被指数NDVI(680,890)呈正相关，并且不同生育阶段其相关程度有差异，这是利用NDVI和植被覆盖度(COV)识别不同株型的基础。（3）相同COV条件下，直立型品种的NDVI高于平展型品种的NDVI，并且随着COV的增加，差异逐渐变小，二者的变化关系体现了直立型品种株型紧凑和平展型品种株型披散的特点，利用NDVI和COV的关系可以对株型进行识别，以小麦拔节期为最佳识别阶段，此期2种株型品种的NDVI具有显著差异（P＜0.05）。     

小麦叶片氮含量与冠层反射光谱指数的定量关系

本文以3种蛋白质类型的小麦品种在不同施氮水平下的3年田间试验为基础，研究了小麦叶片氮含量与冠层反射光谱的定量关系。结果显示，不同试验中拔节后叶片氮含量均随施氮水平呈上升趋势，同时冠层光谱反射率在不同施氮水平下存在明显差异。对于低、中、高籽粒蛋白质含量的品种，叶片氮含量与冠层反射光谱的归一化植被指数NDVI (1 220, 710)和红边位置均有密切的定量关系，决定系数在0.80左右。对于不同品质类型小麦品种，均可利用统一的回归方程描述其叶片氮含量随反射光谱参数的变化，对于低蛋白类型品种，采用单独的回归系数即可提高叶片氮含量估测的准确性。本研究确立的小麦叶片氮含量与冠层反射光谱的定量关系可用于不同的小麦品种、生育时期和施氮水平，为小麦氮素营养的监测诊断与精确施肥等提供理论依据和技术途径。     

基于交流阻抗法的豆浆电特性检测

为实现豆浆品质快速检测和通电加热电源频率的优化,该文利用精密阻抗分析仪和DJS-10电导电极通过50 mV的激励电压在20 Hz～12 MHz频率范围内测量了不同温度（30～85℃）和不同固形物含量（1.01%～9.58%）的豆浆的交流阻抗。试验结果表明,豆浆阻抗模值与阻抗相位角随测量频率变化具有明显规律。豆浆的阻抗特性与温度以及固形物含量关系显著,且阻抗Nyquist 图存在明显差异。在全频段,豆浆阻抗模值随着温度和固形物含量的升高而减小;在低频段,豆浆阻抗相位角随温度以及固形物含量的升高而增大;在高频段,豆浆阻抗相位角随温度以及固形物含量的升高而减小。同时,研究提出了豆浆的电阻R、电容C、恒相位元件CPE（constant phase element）三元件等效电路模型,并用ZSimpWin软件进行拟合得到了试验条件下豆浆的等效电路元件参数。基于豆浆阻抗随频率变化规律及豆浆等效电路模型分析得出,豆浆通电加热电源的频率应在300 Hz～300 kHz范围内。豆浆的等效电阻R与温度以及固形物含量之间具有良好的负指数关系。CPE参数Q值随温度的升高而降低,当固形物含量超过3.5%后,Q值随固形物含量的升高迅速增加。CPE参数n值随温度的升高而升高,但在本研究中的固形物含量范围内n值并未随固形物含量的改变发生明显变化。本研究为豆浆通电加热电源的频率选择提供了参考依据,同时为基于豆浆电特性分析实现豆浆品质的快速检测奠定基础。     

Rainfall and tillage effects on soil structure after alfalfa conversion to maize on a clay loam soil in New York

Soil degradation is accelerated when perennial crops are converted to annual row crops, primarily due to increased soil disturbance from tillage. Subsequent heavy rainfall may induce soil settling, reduce macroporosity and increase hardsetting upon drying. An experiment involving plow and no-tillage and two simulated rainfall treatments (‘wet’ and ‘dry’) was conducted on Kingsbury clay loam soil in northern New York in 1992 and 1993 to study their effects on soil structure under maize (Zea mays L.) after conversion from alfalfa (Medicago sativa L.), and to evaluate the use of spectral analysis of micropenetrometer observations for studying soil aggregation. Undisturbed soil cores were collected from the row and trafficked and non-trafficked interrow positions at the 0.05 and 0.15 m depths and used for laboratory measurement of soil strength and pore system properties. These well-structured soils show a high contribution (up to 0.15 m³ m⁻³) of macropores to the total porosity of the soil. Soil strength was generally slightly higher for no-till (NT) than plow till (PT), although only significant in 1992. Soil strength in the surface layer did not change significantly with drying. Spectral density patterns did not show strong treatment effects, although distinct peaks reflect 3.0–3.5 mm stable structural units within macroaggregates. Simulated rainfall treatments and tillage treatments generally did not strongly affect measured soil properties, presumably due to stable soil structure. Structurally stable clay loam soils show little effect of tillage or settling on soil physical properties in the first years after alfalfa to maize conversion, and have good potential for long-term annual crop production if properly managed.     

以光谱反射曲线评估染色单板的耐光性

以杨木和桦木单板为试材,引入光谱反射曲线研究了室内自然光环境下染色单板的光变色过程,分析了树种、漂白处理、染料种类及浓度对染色单板耐光性的影响。结果表明,光辐射后染色单板K/S曲线呈下降趋势,光谱反射率曲线向黄光方向移动,染色单板发生黄化。漂白处理加速了染色单板的光变色。染料品种对染色单板耐光性影响显著,酸性蓝染色单板的光谱反射率曲线变化明显,而酸性大红的变化不大。利用光谱反射曲线可快速直观地评估染色单板耐光性的优劣。     

自动测量胸径和树高便携设备的研制与测量精度分析

胸径和树高测量是评价立地质量和林木生长状况的重要依据,该文以图像自动识别原理、摄影测量原理、相似三角形几何原理和三角函数原理为理论基础,研制了可测量胸径和树高的多功能便携式微型超站仪,该设备集成电荷藕合器件图像传感器、倾角传感器、激光测距传感器、中央处理器、存储器、液晶显示屏,测量时可获取测站点到待测点的倾角和距离2个参数值,并记录存储测量时的图像信息,通过仪器的嵌入式操作程序,实现胸径的自动测量、树高测量、任意处直径自动测量、基本测量等4项基本功能。通过试验验证,215株立木的单次胸径测量的平均绝对误差为0.369 cm,平均相对误差为2.08%。101株立木的单次树高测量的平均相对误差为1.44%。胸径测量精度达到97.92%,树高测量精度达到98.56%,符合国家森林资源连续清查中的测量精度要求。该设备为森林资源调查提供了参考。     

浙江省石灰土光谱特征及其自动识别分类技术研究

本文通过石灰土专题调查，室内土壤光谱测定及其特定及其特征分析，并在此基础上进行土壤光谱自动识别分类方法的对比试验。明确了浙江省石灰土的四个土类，六个土属的光谱特征分异规律；提出了一种参数少，运算量小，精度高的光谱自动识别新方法－分层提取判别法。