多花黑麦草氮素营养及生长状况的高光谱监测

以多花黑麦草为试验材料,开展了不同施氮水平多花黑麦草反射光谱特征及产量品质的比较研究,同时分析了多花黑麦草产草量、植株氮含量与光谱反射率间的相关性,构建了多花黑麦草产草量和植株氮含量的高光谱监测模型,结果表明:施氮对多花黑麦草可见光区光谱反射率具有显著的影响,随着施氮水平增加,多花黑麦草在可见光区反射率逐渐降低。不同施氮水平多花黑麦草在450、550、680、760nm处的光谱反射率以处理N200最低,显著低于处理N150、N100、N50和N0(P<0.05)。不同施氮处理多花黑麦草产草量与R680间的相关性最高,植株氮含量与R760间的相关性最高,以R680和R760构建了多花黑麦草产草量和植株氮含量的高光谱监测模型,经检验,模型估测值与实测值相关性达极显著水平(P<0.01),表明本研究建立的多花黑麦草产草量和植株氮含量的监测模型是准确可靠的。

基于高光谱遥感的小麦黄花叶病害等级监测研究

为了构建小麦黄花叶病的遥感监测技术,在小麦返青期、拔节前期和拔节后期测定了不同黄花叶病等级下的冠层反射率,并同步调查与病害等级相关的小麦株高、含水量、氮含量、色素含量等农学参数,筛选出适宜监测小麦黄花叶病的植被指数,并构建病害等级监测模型。结果表明,小麦黄花叶病的反射光谱敏感波段在返青期和拔节前期集中于560~720nm范围,而拔节后期则集中于800~900nm区域。随病害等级的增加,光谱反射率在可见光波段逐渐增加,而在近红外波段区域降低。植被指数与病害等级相关性在不同生育时期间存在显著差异,整体上以拔节前期最好,决定系数(r^(2))为0.72~0.82,而拔节后期模型精度急剧下降(r^(2)=0.26~0.72)。在植被指数中,整体上以表征色素变化的mND705模型预测精度最好,r^(2)和RMSE分别为0.59~0.68和0.79~0.98。采用偏最小二乘回归(PLSR)建立黄花叶病害分级模型,三个时期的模型精度均高于植被指数模型,且整体上以返青期和拔节期前期估算效果较好,模型验证r^(2)为0.93~0.97,RMSE为0.24~0.32。因此,利用PLSR模型可以准确评价返青至拔节期前期小麦黄花叶病害等级。

高光谱遥感监测冬小麦条锈病的研究进展(综述)

从高光谱遥感监测冬小麦条锈病的原理、特点、关键技术路线及存在的难点等方面出发综述利用遥感这一高技术手段来大面积监测农业上的重要病害之一—条锈病的方法以及把遥感的空间数据、影像与地面数据模型结合起来监测作物病害的可行性。同时展望基于GIS辅助的遥感技术在监测作物病害等方面的发展前景。

灌浆期水稻叶片铜含量变化的高光谱遥感定量监测研究

为了利用高光谱遥感有效地监测农作物叶片中的重金属含量变化,在化学分析和农作物叶片对铜元素含量增加的敏感性基础上,利用光谱植被指数定量监测作物叶片铜元素含量变化,为大面积、快速、准确、无损地监测农田水稻叶片重金属含量变化提供技术支持。以张家港市为研究区域,实地采集水稻叶片样品21个。采用便携式高光谱地物波谱仪,获取灌浆期水稻植株叶片的光谱反射率并提取光谱指数,室内测定叶片重金属铜含量,并分析水稻叶片重金属铜含量与不同类型光谱指数的相关性。结果表明,高光谱数据对叶片铜含量变化的敏感性较好,其中,红边位置(REP)、绿波段归一化差异指数(GNDVI)、比值植被指数(RVI)、Vogelmann红边指数(VOGI)和地面叶绿素指数(MTCI)可分别作为估测叶片铜含量的敏感光谱指数,其乘幂和指数回归模型能够较好地反演水稻叶片铜含量;叶片铜含量的敏感光谱波段参数在原始光谱中主要集中于420~670nm范围内,最小负相关系数的波长是646、647、648nm;而一阶微分和二阶微分光谱中在蓝边、黄边、红边和近红外区域均有分布,最大正相关系数的波长分别是660、715nm;水稻叶片铜含量估测的最佳模型是基于二阶微分敏感光谱参数构建的偏最小二乘回归模型,该模型预测2的铜含量值与实测值的拟合度较好(R=0.56)。研究结果证明可以利用高光谱生物遥感技术有效地监测农田水稻叶片中重金属含量的变化,判断作物中重金属浓度是否超标,为高光谱遥感立体、快速和大面积地监测农田作物铜含量的变化提供参考,也为评价水稻的食用安全提供科学方法。

张家港市农田土壤重金属含量高光谱遥感监测模型构建

以张家港农田土壤作为研究对象,在实验室测定土壤重金属元素As、Cd、Cr、Cu、Zn、Ni、Pb、Hg的含量,并与土壤可见近红外高光谱数据建立土壤重金属含量的定量估测模型,以快速获取研究区农田的土壤重金属含量。为保证模型预测的精度和稳定性,首先,对原始高光谱数据进行平滑处理,并进行一阶导数、倒数一阶导数、倒数的对数一阶导数、平方根一阶导数和连续统去除等形式的光谱变换;然后,提取不同变换光谱的特征波段进行相关性分析;最后,通过逐步回归法建立重金属含量的定量估算模型。结果表明:张家港市农田土壤中Cd、Hg、Cu、Zn存在一定的污染风险。在高光谱的不同变换形式中,一阶导数和连续统去除与重金属含量的相关系数高于其他变换形式。基于8种土壤重金属含量与高光谱数据建立的定量估算模型具有良好的预测精度。Cd、Hg、Cr、As、Cu、Zn、Ni、Pb估算模型的实际值与验证值的拟合度分别为0.874、0.879、0.800、0.646、0.513、0.655、0.603和0.542,可用于预测张家港市的农田土壤重金属含量。

高分卫星监测下的文山州植被生长环境与时空演变研究

云南省文山州早年石漠化严重,生态环境脆弱敏感,经过多年治理已呈现良好的发展态势,但缺乏采用多源遥感手段对其进行长时序广面域的动态监测的研究.为探索国产卫星在植被监测方面的应用潜力,研究结合高分一号(GF-1)、高分五号(GF-5)、高分六号(GF-6)数据优势,对2013—2020年文山州植被分布情况与生长环境进行反演.首先利用旱季的GF-5数据进行高光谱植被监测,将可见短波红外高级高光谱成像仪(Advanced Hyperspectral Imager,AHSI)、全谱段光谱成像仪(Visual and Infrared Multispectral Sensor,VIMS)数据叠加进行遥感生态指数分析(Remote Sensing Ecological Index,RSEI);然后利用GF-1、GF-6进行植被覆盖度反演时序性监测.结果表明,位于砚山、广南、丘北三县交界处的实验区RSEI平均值为0.70,生态环境质量处于良状态(0.60~0.80).砚山西部和丘北南部农作物胁迫指数高、易燃性高,文山市北部、丘北西南部以及砚山中部的林木健康等级低.2013—2020年,文山州平均覆盖度均值处于较高植被覆盖度区间,平均覆盖度及高植被覆盖区面积占比呈先下降后上升的趋势.

干旱胁迫对3种冷季型草坪草光谱反射率及生理特征的影响

为探讨高光谱分析技术在草坪草水分监测上的应用,以草地早熟禾、高羊茅、多年生黑麦草为试验材料,测定了干旱胁迫下3种冷季型草坪草生理指标及光谱反射率。结果表明:随着干旱胁迫程度的加深,供试草坪草叶片相对含水量和叶绿素含量显著下降(P<0.05),丙二醛含量、脯氨酸含量及敏感波段的(550和760 nm)的光谱反射率显著上升(P<0.05),3种草坪草抗旱性能的强弱顺序为:高羊茅>多年生黑麦草>草地早熟禾。3种草坪草敏感波段光谱反射率(R_(550 nm),R_(760 nm))及高光谱参数(Rg,R0,GNDVI)与生理指标间呈显著或极显著相关,以R_(550 nm)与生理指标间的相关系数绝对值最大。同时R_(550 nm)构建了供试草坪草生理指标的估测模型,经检验,模型估测值与实测值相关性达显著或极显著水平。

盐胁迫对作物生长的影响与监测技术研究

为明确盐胁迫对作物生长发育的影响及相应的监测技术,以盐土区作物为研究对象,综述盐胁迫对作物形态、生理性状以及产量及品质等方面的影响,以及常用的监测技术,如高光谱技术,无人机监测技术以及卫星遥感监测技术等,分析盐土区作物研究存在抗盐碱的作物品种相对缺乏,盐碱地的改良技术有待完善,监测所用传感器比较单一,监测手段不够丰富等问题,并对未来的发展应用进行展望。

Hyperspectral Intelligent Monitoring System of Major Soil Nutrients Based on ArcGIS Engine

Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.

Yield Estimation Model of Citrus Based on Spectral Data and Agronomic Parameters

With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.

Hyper-spectrum models for monitoring water quality in Dianshan Lake,China

The correlation between water quality parameters and hyper-spectral reflectance is studied with models established for each parameter and applied in Dianshan Lake, in the upstream of the Huangpu River running through Shanghai, China. Models are for dissolved oxygen (DO in mg/L): R720/R680 = 20.362×(R720/R680)2?31.438×(R720/R680)+19.156; for turbidity (NTU): R*714.5 = 206.07× (R*714.5)2?582.5×R*714.5 + 423.24; and for total phosphorus (TP in mg/L): R*509.5 = 16.661× (R*509.5)2?32.646×R*509.5+16.116. The R2 values are 0.770 8, 0.660 4 and 0.738 7, respectively, showing strong positive relationships. The models were then applied to assess water quality of different times. Results are quite satisfactory for some samples.

Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area.

Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China

Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.

基于Logistic、IBk以及Randomcommittee方法的条锈病潜育期小麦冠层光谱的定性识别

为寻求在小麦条锈病潜育期能探知和监测病害的简单便捷方法,通过人工接种不同品种小麦诱发条锈病,在小麦条锈病菌尚处于潜育期时,采集小麦冠层光谱数据,并利用双重Real-time PCR分子生物学技术检测条锈病菌潜育菌量,基于Logistic、IBK以及Randomcommittee三种方法,在不同建模比、不同参数变换下建立可识别潜育期小麦条锈病的数学模型。结果表明,在全波段范围内(325~1 075 nm),3种方法所建模型模拟识别潜育期小麦条锈病是可行的,但识别效果有一定差异,基于Logistic、IBK以及Randomcommittee方法所建模型的平均准确率分别为83.95%~84.51%、87.72%~88.98%、93.19%~93.46%。因此,基于Randomcommittee方法所建模型的识别准确率最高,效果最好,更适合小麦条锈病潜育期的定性识别。