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1.
松材线虫是松树的毁灭性病害,松树占我国森林资源的1/4,松林的存亡直接关乎林业的兴衰。本研究以松材线虫入侵林地内的土壤因子为主要研究对象,意图通过对得到的土壤样品理化性质的分析,探究受到该病害入侵的不同生态系统中土壤因子的差异性。结果表明,在研究受松材线虫病入侵的生态系统的土壤因子时,P含量、全N量和有机质含量这三个指标是非常重要的。  相似文献   

2.
模拟酸雨对水稻叶片反射光谱特性影响的初步研究   总被引:5,自引:0,他引:5  
李德成  张崇静 《环境遥感》1996,11(4):241-247
本文研究了模拟酸雨对水稻叶片反射光谱特性的影响。结果表明:模拟酸雨会引起水稻叶片反射光谱的可见光区和中红外区的反射率升高,近红外区的反射率降低,相应的反射率比值也随之变化,一阶和二阶微分光谱蓝移,且上述变化的程度与酸雨的酸度,水稻的品种和生育期有关。这一结果也表明遥感技术监测酸  相似文献   

3.
凭借着高分辨率、可控性强和性价比高的特点,无人机遥感技术在森林研究中得到了迅速的发展与应用。对无人机遥感成像平台的发展和国内外利用无人机遥感技术开展森林调查的情况进行介绍,针对单木和林分两种森林资源调查对象,总结了目前利用无人机遥感技术提取森林参数的前沿方法。重点分析和讨论了基于无人机平台的多光谱、高光谱和激光雷达传感器获取森林参数的算法,对比了其优越性、局限性并分析其最佳应用场景。此外,介绍了无人机遥感在森林树种分类和病虫害监测方面的应用情况。最后,对无人机遥感技术在森林监测方面的应用前景进行了展望,可为今后基于无人机遥感的森林资源监管领域的研究提供理论依据与技术支持。  相似文献   

4.
滏阳河两岸农田土壤Fe、Zn、Se元素光谱响应研究   总被引:16,自引:0,他引:16  
为了探索遥感技术快速定量化监测土壤元素含量的可行性,本通过对滏阳河两岸农田51个土壤表层样品的室内光谱反射率及其Fe、Zn、Se含量关系的研究,探索了反射光谱快速预测土壤元素含量的技术途径。结果发现预测Fe的最佳光谱间隔为16nm。Zn和Se的为8nm,这说明在使用经验方法预测没有光谱特征的成分时,光谱分辨率不是一个必要条件;土壤中的Fe、Zn、Se元素与土壤的反射光谱存在较好的相关性,各元素含量与土壤平均反射率负复相关系数(R^2)均可达到0.49以上,而与相应TM各波段的平均光谱反射率也都具有较好的负相关关系,与TM7波段的复相关系数最大,Fe、Zn为0.58,Se元素为0.550本研究结果为今后利用高光谱遥感技术定量监测土壤Fe、Zn、Se元素含量提供了一种新的方法和技术途径,对土地质量变化的快速定量监测具有重要的科学意义和应用前景。  相似文献   

5.
松材线虫病(Buraphelenchusmucronatus)是发生在松树上的一种毁灭性病害 ,目前已在日本、中国、美国、加拿大和韩国等分布 ,并在日本和中国造成巨大的损失。在北美洲 ,松材线虫分布也很广。针叶树中还有一些形态与松材线虫非常相近的伞滑刃线虫 ,统称松材线虫复合种(PWNC) ,其中最常见的是拟松材线虫。拟松材线虫在东亚、美洲、欧洲都有 ,分布极广泛。杂交试验和DNA分析证实松材线虫和拟松材线虫有密切的关系 ,能用RAPD和RFLP技术进行区分 ,拟松材线虫的DNA有东亚和欧洲两种类型 ,前者主要分…  相似文献   

6.
滨海盐土重金属含量高光谱遥感研究   总被引:11,自引:0,他引:11       下载免费PDF全文
高光谱遥感凭借其极高的光谱分辨率在获取有机质、矿物质等土壤组分定量信息的研究中表现出非凡的潜力。以如东县洋口镇为研究区,通过对土壤反射光谱的测量和同步的土壤化学分析,研究了土壤重金属Cr、Cu、Ni与土壤粘土矿物、铁锰氧化物以及碳酸盐之间的赋存关系。利用光谱一阶微分、倒数对数和连续统去除法对土壤光谱的处理,获得了土壤成分的特征波段,通过土壤重金属与土壤光谱变量的相关分析,并利用逐步回归分析方法,确立了3种重金属元素的最佳遥感模型。结果表明,研究区3种重金属与波长429 nm、470 nm、490 nm、1 430 nm、2 398 nm、2 455 nm处光谱变量具有很好的相关性,在所建立的逐步回归模型中,以一阶微分处理的模型精度最高。研究结果可以为高光谱遥感技术反演土壤重金属含量,进一步应用空间或航空遥感进行大尺度环境污染遥感、遥测信息提取和反演提供技术支撑。  相似文献   

7.
在综合80年代以来国内外地植物遥感技术领域最新研究成果的基础上,总结出地植物遥感找矿的基本原理,阐述了目前已知的植物光谱反射特征以及植物受压后所产生的反射率变化.影象色调异常,并介绍了近年来国内外在这一领域的十一个有代表性的研究实例。指出利用地植物遥感技术找矿的两个关键是:①揭示植物光谱反射特性的变化、植被覆盖区遥感影象色调异常与植物受压后产生的变异之间的关系;②确定这些变异是否是由烃类、重金属等矿产有关的“压力”引起的。最后,预测了地植物遥感技术发展的三种趋势:①进一步根据植物的光谱特性研究植物内部的生物化学过程以及压力对植物内部生物化学过程的影响;②开展以地植物遥感信息为主的多源信息综合研究,进行多种技术结合的找矿勘探;③研制新一代的窄光谱、超多波段的高光谱、高空间分辨力的地植物研究专用遥感器。  相似文献   

8.
森林病虫害是影响森林健康的主要因素之一,全面、准确、迅速地对森林病虫害进行监测管理必须依靠先进的技术手段。利用光谱特征研究落叶松受害情况及叶绿素浓度变化情况,将落叶松的受害程度分为4个等级,选取了11组不同受害程度叶片的叶绿素、类胡萝卜素的浓度及相应的光谱反射率数据进行分析。结果表明,不同健康程度的光谱反射率有4个明显差别之处,分别在绿峰、吸收谷、“红边”位置及水分吸收带;随着受害程度的加重,“红边”位置“蓝移”,叶绿素反射峰“红移”明显。不同健康程度的落叶松叶片的“红边”拐点波长位置、吸收谷与其叶绿素浓度之间具有较强的相关性,为高光谱数据研究森林病虫害提供了方法和途径。  相似文献   

9.
基于光谱曲线特性和波谱角分类的赤潮监测方法   总被引:1,自引:1,他引:0  
吴文瑾 《遥感信息》2009,(4):50-54,61
遥感技术已经成为赤潮监测的重要手段之一,目前,国内外已有多项应用遥感技术对赤潮成功监测的实例。现有的监测方法主要依据海水水色图、叶绿素浓度图和海洋表面温度图,但这些专题图在反应赤潮发生情况时都不可避免地存在一些局限性。而基于光谱曲线特性和波谱角分类的赤潮监测方法不但可以对各种不同赤潮监测均有很好的可靠性,同时还具有可以识别赤潮优势种等诸多优点。该文章对这种监测方法的原理和过程进行了详细的阐述,并使用实际数据进行了实验分析。  相似文献   

10.
遥感技术在环境污染监测中的应用   总被引:24,自引:3,他引:21  
环境污染遥感监测技术具有监测范围广、速度快、成本低,且便于进行长期的动态监测等优
点,是实现宏观、快速、连续、动态地监测环境污染的有效高新技术手段。介绍了应用于环境污染监
测的可见光、反射红外遥感技术、热红外遥感技术、高光谱技术以及微波遥感监测技术,并着重阐述
了遥感监测技术在水环境污染、大气环境污染中的应用。最后,指出了我国环境污染遥感监测技术
存在的问题和发展趋势,建议尽快发展我国的环境污染遥感监测技术,以满足我国环境污染监测的
需要。  相似文献   

11.
森林病虫害等扰动类型造成的亚健康林木监测预警工作不能及时到位,导致防治工作长期处于灾后救灾的被动局面。基于2019年5~9月份的多时相GF-1 WFV数据,应用比值植被指数和红绿植被指数,准实时地监测逆生长、叶冠胁迫或失色等“灾害”信息。结果表明:虽然树木叶片枯黄、萎蔫等叶绿素降解并逐渐转化成叶黄素和叶红素需要一定的过程,或“灾害症状”有时具有滞后性,但高频次遥感动态监测结果对于指导森林灾害地面踏查,提高监测覆盖率和科学性,防范大面积灾害,具有积极作用。国产GF-1和GF-6 WFV遥感数据的高重访周期能为月度森林资源生长过程的监测提供坚实的数据保障,满足公顷级树叶长势退化预警监测的需要。  相似文献   

12.

The ever-wet tropics are under threat from ENSO events and there is a need for a monitoring system to analyse and describe their responses to such events. This letter explores the relative value of using NOAA AVHRR middle infrared (MIR) reflectance data and NDVI data for the monitoring of ENSO-related drought stress of a tropical forest ecosystem in Sabah, Malaysia. Relationships between rainfall and MIR reflectance were examined. Correlation coefficients are generally large and significant (at 0.1 level) while those between rainfall and NDVI were small and insignificant. This letter concludes that there is potential in using MIR reflectance for monitoring the effects of ENSO-induced drought stress on these forests and this has a bearing on how NOAA AVHRR data may be used to further our knowledge on the impacts of ENSO events on tropical forest environments.  相似文献   

13.
This study applies remote sensing techniques for monitoring non-ferrous metal smelting impacts in the extreme environment of northern Siberia. Ground and at-satellite reflectance and normalized difference vegetation index (NDVI) values for different vegetation types have been compared and a hybrid supervised-unsupervised classification of Landsat TM data performed, based on field and ancillary data. This has allowed us to distinguish several degrees of vegetation damage in tundra and forests. However, it was difficult to differentiate between some significant classes, such as damaged grass tundra and sparse dead larch forests with a grass understorey. We suggest possible refinement of our results, including the combination of images taken at different phenological stages and from different sensors. However, it should be noted that the north-Siberian environment presents unusually severe limitations of optical-infrared satellite observation possibilities and problems in imagery interpretation. Standard indicators of vegetation vigour, such as NDVI, widely applied at lower latitudes, become less informative in highly variable tundra and pre-tundra ecosystems.  相似文献   

14.
Spectral discrimination of vegetation types in a coastal wetland   总被引:2,自引:0,他引:2  
Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery for airborne, as well as space-borne, systems. This paper investigates whether vegetation associations can be differentiated using hyperspectral reflectance in the visible to shortwave infrared spectral range, and how well species can be separated based on their spectra. For this purpose, the field reflectance spectra of 27 saltmarsh vegetation types of the Dutch Waddenzee wetland were analysed in three steps. Prior to analysis, the spectra were smoothed with an innovative wavelet approach.In the first stage of the analysis, the reflectance spectra of the vegetation types were tested for differences between type classes. It was found that the reflectance spectra of saltmarsh vegetation types are statistically significantly different for various spectral regions.Secondly, it was tested whether this statistical difference could be enhanced by using continuum removal as a normalisation technique. For vegetation spectra, continuum removal improves the statistical difference between vegetation types in the visible spectrum, but weakens the statistical difference of the spectra in the near-infrared and shortwave infrared part of the spectrum.Thirdly, after statistical differences were found, it was determined how distant in spectral space the vegetation type classes were from each other, using the Bhattacharyya (BH) and the Jeffries-Matusita (JM) distance measures. We selected six wavelengths for this, based on the statistical analysis of the first step. The potential of correct classification of the saltmarsh vegetation types using hyperspectral remote sensing is predicted by these distance measures.It is concluded that the reflectance of vegetation types is statistically different. With high quality radiometric calibration of hyperspectral imagery, it is anticipated that vegetation species may be identified from imagery using spectral libraries that were measured in the field during the time of image acquisition.  相似文献   

15.
Reliable monitoring of seasonality in the forest canopy leaf area index (LAI) in Siberian forests is required to advance the understanding of climate-forest interactions under global environmental change and to develop a forest phenology model within ecosystem modeling. Here, we compare multi-satellite (AVHRR, MODIS, and SPOT/VEGETATION) reflectance, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and LAI with aircraft-based spectral reflectance data and field-measured forest data acquired from April to June in 2000 in a larch forest near Yakutsk, Russia. Field data in a 30 × 30-m study site and aircraft data observed around the field site were used. Larch is a dominant forest type in eastern Siberia, but comparison studies that consider multi-satellite data, aircraft-based reflectance, and field-based measurement data are rarely conducted. Three-dimensional canopy radiative transfer calculations, which are based on Antyufeev and Marshak's [Antyufeev, V.S., & Marshak, A.L. (1990). Monte Carlo method and transport equation in plant canopies, Remote Sensing of Environment, 31, 183-191] Monte Carlo photon transport method combined with North's [North, P.R. (1996). Three-dimensional forest light interaction model using a Monte Carlo method, IEEE Transactions on Geoscience and Remote Sensing, 34(4), 946-956] geometric-optical hybrid forest canopy scene, helped elucidate the relationship between canopy reflectance and forest structural parameters, including several forest floor conditions. Aircraft-based spectral measurements and the spectral response functions of all satellite sensors confirmed that biases in reflectance seasonality caused by differences in spectral response functions among sensors were small. However, some reflectance biases occur among the near infrared (NIR) reflectance data from satellite products; these biases were potentially caused by absolute calibration errors or cloud/cloud shadow contamination. In addition, reflectance seasonality in AVHRR-based NIR data was very small compared to other datasets, which was partially due to the spring-to-summer increase in the amount of atmospheric water vapor. Radiative transfer simulations suggest that bi-directional reflectance effects were small for the study site and observation period; however, changes in tree density and forest floor conditions affect the absolute value of NIR reflectance, even if the canopy leaf area condition does not change. Reliable monitoring of canopy LAI is achieved by minimizing these effects through the use of NIR reflectance difference, i.e., the difference in reflectance on the observation day from the reflectance on a snow-free/pre-foliation day. This may yield useful and robust parameters for multi-satellite monitoring of the larch canopy LAI with less error from intersensor biases and forest structure/floor differences. Further validation with field data and combined use of other index (e.g. normalized difference water index, NDWI) data will enable an extension of these findings to all Siberian deciduous forests.  相似文献   

16.
Crop diseases and pests are the first natural biological hazards that threaten food production and quality.The investigation and sampling in field of plant protection department can’t meet demand of the accurate,non-destructive and efficient monitoring and warning.Currently,remote sensing which can monitor dynamically in real time provides the possibility for the rapid acquisition of continuous surface information,and is also the main development direction monitoring and prediction of crop diseases and pests in the future.Research status of three main directions,including classification of different stresses,severity estimation and stress forecasting,are summarized,and the methods of feature extraction,feature selection,and algorithms are expounded.Then,the application of diseases and pests of three major foodsby remote sensing was analyzed by means of domestic retrieval platforms.On this basis,the existing problems and future development trend of monitoring and forecasting of crop diseases and pests by remote sensing are discussed to promotethe long-term mechanism of agricultural sustainable development.  相似文献   

17.
Light Detection and Ranging(LiDAR) Remote Sensing(RS) can map the 3D structures of objects,and polarization RS can implement information retrieval enhancement via strengthening the weak reflectance and weakening the strong reflectance that are inverse scenarios in traditional optical RS.Their combination,theoretically,can open a possible way for developing the next-generation multi-dimensional active optical RS technologies.However,the literature review suggested that the concept of polarization LiDAR was earlier proposed,while its applications mainly occurred in the field of atmospheric monitoring.The focuses were on study of laser backscatter depolarization techniques,e.g.,sensing water content in the air based on the metrics of polarization diversity so as to distinguish the structures and orientations between water vapor and ice crystal clouds in the atmosphere,and further,on investigation of aerosol content in the troposphere and improvement of the related methods.But in other fields,the applications of polarization LiDAR RS have been almost blank.Now,it is time to extend the applications of polarization LiDAR for RS of complex objects.This paper analyzed the mechanisms of the two component modules in terms of their RS principles and listed the fundamental works necessitated for comprising the polarization LiDAR RS technology.Finally,this study proposed the ranges of possibly applying polarization LiDAR RS,such as agronomy,environment,ocean and space exploration.  相似文献   

18.
A new semi-physical forest reflectance model, PARAS, is presented in the paper. PARAS is a simple parameterization model for taking into account the effect of within-shoot scattering on coniferous canopy reflectance. Multiple scattering at the small scale represented by a shoot is a conifer-specific characteristic which causes the spectral signature of coniferous forests to differ from that of broadleaved forests. This has for long led to problems in remote sensing of canopy structural variables in coniferous dominated regions. The PARAS model uses a relationship between photon recollision probability and leaf area index (LAI) for simulating forest reflectance. The recollision probability is a measurable, wavelength independent variable which is defined as the probability with which a photon scattered in the canopy interacts with a phytoelement again. In this study, we present application results using PARAS in simulating reflectance of coniferous forests for approximately 800 Scots pine and Norway spruce dominated stands. The results of this study clearly indicate that a major improvement in simulating canopy reflectance in near-infrared (NIR) is achieved by simply accounting for the within-shoot scattering. In other words, the low NIR reflectance observed in coniferous areas is mainly due to within-shoot scattering. In the red wavelength the effect of within-shoot scattering was not pronounced due to the high level of needle absorption in the red range. To conclude the paper, further application possibilities of the presented parameterization model are discussed.  相似文献   

19.
Insect outbreaks cause significant tree mortality across western North America, including in high-elevation whitebark pine forests. These forests are under several threats, which include attack by insects and white pine blister rust, as well as conversion to other tree species as a result of fire suppression. Mapping tree mortality is critical to determining the status of whitebark pine as a species. Satellite remote sensing builds upon existing aerial surveys by using objective, repeatable methods that can result in high spatial resolution monitoring. Past studies concentrated on level terrain and only forest vegetation type. The objective of this study was to develop a means of classifying whitebark pine mortality caused by a mountain pine beetle infestation in rugged, remote terrain using high spatial resolution satellite imagery. We overcame three challenges of mapping mortality in this mountainous region: (1) separating non-vegetated cover types, green and brown herbaceous cover, green (live) tree cover, and red-attack (dead) tree cover; (2) variations in illumination as a result of variations in slope and aspect related to the mountainous terrain of the study site; and (3) the difficulty of georegistering the imagery for use in comparing field measurements. Quickbird multi-spectral imagery (2.4 m spatial resolution) was used, together with a maximum likelihood classification method, to classify vegetation cover types over a 6400 ha area. To train the classifier, we selected pixels in each cover class from the imagery guided by our knowledge of the study site. Variables used in the maximum likelihood classifier included the ratio of red reflectance to green reflectance as well as green reflectance. These variables were stratified by solar incidence angle to account for illumination variability. We evaluated the results of the classified image using a reserved set of image-derived class members and field measurements of live and dead trees. Classification results yielded high overall accuracy (86% and 91% using image-derived class members and field measurements respectively) and kappa statistics (0.82 and 0.82) and low commission (0.9% and 1.5%) and omission (6.5% and 15.9%) errors for the red-attack tree class. Across the scene, 700 ha or 31% of the forest was identified as in the red-attack stage. Severity (percent mortality by canopy cover) varied from nearly 100% for some areas to regions with little mortality. These results suggest that high spatial resolution satellite imagery can provide valuable information for mapping and monitoring tree mortality even in rugged, mountainous terrain.  相似文献   

20.
Subpixel mapping of snow cover in forests by optical remote sensing   总被引:1,自引:0,他引:1  
Forest represents a challenging problem for snow-cover mapping by optical satellite remote sensing. To investigate reflectance variability and to improve the mapping of snow in forested areas, a method for subpixel mapping of snow cover in forests (SnowFrac) has been developed. The SnowFrac method is based on linear spectral mixing modelling of snow, trees and snow-free ground. The focus has been on developing a physically based reflectance model which uses a forest-cover map as prior information. The method was tested in flat terrain covered by spruce, pine and birch forests, close to the Jotunheimen region of South Norway. Experiments were carried out using a completely snow-covered Landsat Thematic Mapper (TM) scene, aerial photos and in situ reflectance measurements. A detailed forest model was photogrammetrically derived from the aerial photos. Modelled and observed TM reflectances were compared. In the given situation, the results demonstrate that snow and individual tree species, in addition to cast shadows on the snow surface from single trees, are the most influencing factors on visible and near-infrared reflectance. Modelling of diffuse radiation reduced by surrounding trees slightly improve the results, indicating that this effect is less important. The best results are obtained for pine forest and mixed pine and birch forest. Future work will focus on deriving a simplified reflectance model suitable for operational snow-cover mapping in forests.  相似文献   

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