Spectroscopic detection of forest diseases:a review (1970-2020)

Sustainable forest management is essential to confront the detrimental impacts of diseases on forest eco-systems.This review highlights the potential of vegetation spectroscopy in improving the feasibility of assessing forest disturbances induced by diseases in a timely and cost-effec-tive manner.The basic concepts of vegetation spectroscopy and its application in phytopathology are first outlined then the literature on the topic is discussed.Using several opti-cal sensors from leaf to landscape-level,a number of for-est diseases characterized by variable pathogenic processes have been detected,identified and quantified in many coun-try sites worldwide.Overall,these reviewed studies have pointed out the green and red regions of the visible spec-trum,the red-edge and the early near-infrared as the spectral regions most sensitive to the disease development as they are mostly related to chlorophyll changes and symptom develop-ment.Late disease conditions particularly affect the short-wave-infrared region,mostly related to water content.This review also highlights some major issues to be addressed such as the need to explore other major forest diseases and geographic areas,to further develop hyperspectral sensors for early detection and discrimination of forest disturbances,to improve devices for remote sensing,to implement long-term monitoring,and to advance algorithms for exploitation of spectral data.Achieving of these goals will enhance the capability of vegetation spectroscopy in early detection of forest stress and in managing forest diseases.     

应用付里叶变换近红外漫反射光谱法测定谷子中的粗脂肪含量

本文介绍了我们用FTNIRDRS方法对谷物籽粒中蛋白质含量的定量分析的研究工作。预测(谷子、玉米、小麦)方程的相关系数R谷子为0.986,玉米为0.964,小麦为0.972,标准偏差SD,谷子为±0.325,玉米为±0.39,小麦为±0.45。     

近红外技术在牧草方面的应用

通过对近红外光谱法的基本原理、特点、近红外光谱仪的发展及其在国外牧草应用上的研究情况作一阐述,来推动近红外技术在我国牧草研究中的应用.     

棉花叶面积指数冠层反射率光谱响应及其反演

【目的】研究棉花冠层光谱对不同叶面积指数（LAI）的响应，建立棉花LAI光谱反演模型。【方法】利用2003～2004年采集的棉花光谱与LAI的246组数据，分析LAI与冠层反射率光谱和反射率一阶微分光谱间的定量关系。【结果】当LAI大于2.5后不同LAI棉花群体光谱反射率在可见光波段趋于饱和；LAI与可见光波段和短波红外波段（水分吸收带除外）光谱反射率呈显著负相关，与近红外波段高光谱反射率呈显著正相关；LAI与棉花反射率一阶微分光谱主要在蓝边（523～531 nm）、黄边（570～576 nm）、红边（700～755 nm）形成3个相关系数高台区，均达极显著水平，其中红边区的相关性最高。棉花红边位置固定，分别在718 nm和723 nm，且以 723 nm处对LAI更敏感。在反演棉花LAI的高光谱参数中VI (660、800)、VI (550、800)、VI (500、800)、VI (670、800)、Sdy (570～573 nm)、SDr (714～755 nm)、D723、Dr 估算LAI相对误差低于30%，RSME小于0.6，其中VI (600、800)、VI(550、800)两个参数估算水平最高，相对误差分别为21.7%与21.0%，RMSE分别为0.416与0.419；利用SDr与SDr/SDb分别对LAI大于1.0 与小于1.0 的棉花群体反演，能显著提高LAI的估算水平。【结论】应用高光谱分析方法能够提取棉花冠层特征光谱信息，构建LAI高光谱反演参数，建立估算模型，并且利用包含不同光谱参数的分段模型可以进一步提高LAI反演精度。     

玉米群体辐射传输特征

实验观测分析了玉米群体总辐射的透过率、截获率、反射率和消光系数,结果表明玉米群体总辐射透过率、反射率和消光系数具有明显日变化特征;玉米雄穗开花前后其群体透过率和截获率有明显变化;日际反射率随玉米生育期而异,初期达0.23,之后逐渐下降至0.14左右;生育期平均反射率约为0.19;总辐射和净辐射的消光系数分别为0.51和0.41。提出了总体消光系数的概念,该系数在观测期较稳定,约为0.26,依此利用入射总辐射可算出作物群体冠层辐射截获量。     

玉米和大豆LAI高光谱遥感估算模型研究

以ASD FieldSpec光谱仪实测了不同生长季的大田玉米、大豆的冠层高光谱与作物的叶面积指数LAI。采用单变量线性与非线性拟合和逐步回归分析的方式,建立了玉米、大豆LAI高光谱遥感估算模型,并对模型的估算结果进行了初步分析。分析结果表明,绿光波段反射峰区、红光波段以及近红外区的单波段反射率与作物的LAI有较强的相关性,而其他波段的反射率与作物的LAI的相关性相对较弱;以高光谱的窄波段构造的NDVI和RVI与作物的LAI的相关程度高,回归模型的预测水平高;而以多波段逐步回归方式构造的统计模型的预测效果最好。     

Estimating Rice Leaf Greenness (SPAD) Using Fixed-Point Continuous Observations of Visible Red and Near Infrared Narrow-Band Digital Images

Abstract

A narrow-band dual camera system demonstrated a new close-range sensing technique to seasonally track trends in leaf greenness in rice paddies. A weatherproof digital imaging system for the visible red (RED, 620?650 nm) and near infrared band (NIR, 820?900 nm) was positioned 12 m above a 600-m² rice field. During the 2009 and 2010 paddyrice seasons, the system automatically logged images at 10-min intervals throughout the day. Radiometric corrections for the images utilized solar irradiance sensors and prior calibration to calculate 0900-1500 JST daily-averaged reflectance factors (DARF). The DARF in RED (DARF-RED) and NIR (DARF-NIR) values were transformed to provide a daily-averaged normalizeddifference vegetation index (DA-NDVI). The DA-NDVI increased more rapidly in the vegetative growth period, and reached an asymptotic plateau earlier than the DARF-NIR. From transplanting to harvest, leaf greenness values (measured by the SPAD index) were measured for the central part of the uppermost leaves of targeted canopies weekly with a chlorophyll meter. We developed a leaf greenness index (LGI), the ratio of DA-NDVI to DARF-NIR, and a simple calculation method for area means to reduce the background effect. The modified area means of LGI followed the seasonal trend in SPAD value well; its patternwas inherently different from the patterns of any of the original three parameters: DARF-RED, DARF-NIR or DA-NDVI. Throughout the paddy seasons in the two years, a regression equation for estimating SPAD values using the LGI, daily solar radiation, the cosine of angle between the view and the meridian directions and the cosine of culmination solar zenith angle performed favorably (R²=0.815). The nitrogen concentration per dry plant hill (g kg-¹) had a close relation to the SPAD values estimated using the equation.     

作物胁迫早期症状监测的新图像技术

传统的作物胁迫早期症状诊断主要运用生化和分子方法对样品进行破坏性检测实现。计算机图像技术能在作物出现明显症状和不可逆转伤害前及时检测出作物的胁迫情况。尤其是在通过检测作物叶片散发光图像中与胁迫相关的变化量方面,热红外图像、荧光图像、反射多光谱和高光谱图像等新技术显示出较大的潜力。介绍和讨论了这些新图像技术在作物胁迫监测中的运用情况,并展望运用多种图像技术融合是作物综合胁迫早期监测诊断的发展趋势。     

付里叶变换近红外漫反射光谱分析仪器参数的确定

研究了FTNIRDRSA对FT光谱仪能量与分辩率的要求,也讨论了用于FTNIRDRSA的主要仪器参数(包括每个样品的扫描次数,动镜移动速度,每个光谱图的数据点与变换点数目,文件尺寸等)。     

Design of a hyperspectral nitrogen sensing system for orange leaves

The orange (Citrus sinensis) is one of the most important agricultural crops in Florida. Heavy reliance on agricultural chemicals and low fertilizer use efficiencies in citrus production have raised environmental and economic concerns. In this study, a nitrogen sensor was developed to predict nitrogen concentrations in orange leaves. Four design criteria were chosen to maximize the sensing efficiency and reliability. They were: (1) coverage of the spectral N sensing range, (2) no moving parts, (3) single leaf detection, and (4) diffuse reflectance measurement. Based on chlorophyll and protein spectral absorption bands, the sensor's wavelength ranges were chosen to be 620–950 nm and 1400–2500 nm. A reflectance housing was designed to block environmental noise and to ensure single leaf measurement. A halogen light source, two detector arrays, two linear variable filters, and data acquisition cards with 16-bit analog-to-digital converters were used to collect data. The designed N sensor had a spectral resolution less than 30 nm. Test results showed that the nitrogen sensor had good linearity (r > 0.99) and stability. With averaged signal-to-noise ratio (SNR) of 299, the system was able to predict N content with a root mean square difference (RMSD) of l.69 g kg⁻¹ for the validation data set. Using the N sensor, unknown leaf samples could be classified into low, medium and high N levels with 70% accuracy.