VEGETATION植被指数数据预处理方法

VEGETATION数据的植被指数,受云雾影响,含有大量的噪声信号,使用之前必须进行去噪声处理.结合实例数据介绍了半中值滤波法、半均值滤波法、等间隔极大值法用于植被指数的去噪声处理,效果良好;该处理方法同样适用于NOAA和MODIS等数据的植被指数预处理.     

基于ETM＋的湖南省四水流域遥感调查

利用美国Landsat-7的ETM＋数据作为主要数据源，采用ERMAPPER7．0专用图像处理软件制作了湖南省四水流域和洞庭湖水域解译图，获取了湖南省水资源的空间分布信息。结果表明：湘、资、沅、澧四水和洞庭湖水域面积总和为6224．72km^2，其中洞庭湖水域面积为2710．13km^2，湘江省内长度为722．07km，资水省内长度为687．31km，沅水省内长度为597．35km，澧水省内长度为423．57km，说明利用遥感技术调查湖南省水资源具有快速、准确和高效的特点。     

基于ANN模型的森林景观研究

利用人工神经网络(ANN)模拟了天鹅山林场景观格局与森林景观类型所占面积百分比的关系,并对各景观类型面积所占百分比对景观格局的影响进行了预测,为景观优化对策提供了一条新的思路。结果表明人工神经网络方法非常适于研究森林景观格局驱动因素和森林景观格局的非线性对应关系,模型体现了较高的可靠性和操作性。     

遥感与GIS相结合的森林资源信息更新与制图方法研究

目前我国实行的森林经营单位水平上森林资源动态监测主要是通过森林资源规划设计调查 (简称二类调查 )来实现。重点是对森林资源动态、林业经营效果和森林资源监测的综合评价。通常 ,二类调查每 1 0 a复查 1次 ,这种方法所得到的森林资源调查结果可落实到地块 ,也是森林经营单位进行营林生产的重要基础数据 [1 ] 。但是这种常规调查方法也有许多不足之处 ,如 :(1 )复查间隔期长 ,无法及时满足森林经营单位对森林资源变化信息的需求 ;(2 )调查成果多是以报表、文字的形式提交 ,只能建立属性数据库 ,森林资源的空间信息较少通过计算机实现可视…     

为森林资源动态监测的TM影像标准化*

对ＴＭ卫星影像做综合大气校正，可获得地物反射率影像。大气参数是由当地气象记录的内插值导出，再以像幅内黑暗地物做调整。地形模型既可用来调整各参量随高程的变化，又用以校正辐射的地形效应。当无地形模型时，可由卫星影像本身提取入射角。校正后影像便于做资源信息的多时相比较，即动态监测。还可应用校正过程中生成的参数，做林地及其它再生资源的各生物指标的计算。     

基于遥感和GIS的城市绿地景观格局分析

运用景观生态学原理,借助遥感和GIS技术,选取了绿地景观构成、景观多样性、优势度、廊道密度和分维数等指数,分析了深圳特区城市绿地景观的空间格局。结果表明:整个研究区内的绿地类型以公园绿地和风景名胜区绿地为主,城市绿地廊道主要是道路交通绿地廊道,特区内绿地景观多样性相对丰富,所占比例相对均匀,分维数居于中等水平。     

基于多载荷无人机遥感的大豆地上鲜生物量反演

以多载荷无人机获取数据和地面实测的数据为基础,将大豆生殖生长期分段建模,采用植被指数和光谱参数相结合再加上农学参数株高,通过最小二乘法建立多元线性回规模型的方法,来估算大豆开花期和结荚期的鲜生物量,采用高光谱植被指数法估算大豆鼓粒期和成熟期的鲜生物量。结果表明:在大豆开花期和结荚期内,采用混合法构建生物量反演模型利用交叉验证法,验证结果的R~2和RMSE分别为0.714和0.393;在大豆鼓粒期和成熟期内,采用高光谱植被指数法构建生物量反演模型,利用交叉验证法,验证结果的R~2和RMSE分别为0.697和0.386;大豆开花结荚期构建的模型和鼓粒成熟期构建的模型都有比较高的精度和可靠性,利用这两种模型完成了高光谱影像鲜生物量的遥感空间制图,能反映当地当时大豆的真实长势情况。     

Study on a soil erosion sampling survey in the Pan-Third Pole region based on higher-resolution images

Soil erosion is one of the most severe global environmental problems, and soil erosion surveys are the scientific basis for planning soil conservation and ecological development. To improve soil erosion sampling survey methods and accurately and rapidly estimate the actual rates of soil erosion, a Pan-Third Pole region was taken as an example to study a methodology of soil erosion sampling survey based on high-spatial-resolution remote sensing images. The sampling units were designed using a stratified variable probability systematic sampling method. The spatiotemporal characteristics of soil erosion and conservation were taken into account, and finer-resolution freely available and accessible images in Google Earth were used. Through the visual interpretation of the free high-resolution remote sensing images, detailed information on land use and soil conservation measures was obtained. Then, combined with the regional soil erosion factor data products, such as rainfall-runoff erosivity factor (R), soil erodibility factor (K), and slope length and steepness factor (LS), the soil loss rates of some sampling units were calculated. The results show that, based on these high-resolution remote sensing images, the land use and soil conservation measures of the sampling units can be quickly and accurately extracted. The interpretation accuracy in 4 typical cross sections was more than 80%, and sampling accuracy, described by histogram similarity in 11 large sampling sites, show that the landuse of sampling uints can represent the structural characteristics of regional land use. Based on the interpretation of data from the sample survey and the regional soil erosion factor data products, the calculation of the soil erosion rate can be completed quickly. The calculation results can reflect the actual conditions of soil erosion better than the potential soil erosion rates calculated by using the coarse-resolution remote sensing method.     

The use of remote sensing to detect the consequences of erosion in gypsiferous soils

Tillage practices on sloping ground often result in unsustainable soil losses impairing soil functions such as crop productivity, water and nutrients storage, and soil organic carbon (SOC) sequestration. A sloping olive grove (10%) was planted in shallow gypsiferous soils in 2004. It was managed by minimum tillage; the most frequent management practice in central Spain. The consequences of erosion were studied in soil samples (at 0–10, 10–20, and 20–30 cm depths) by analyzing SOC, available water and gypsum content, and by detecting spectral signatures using an ASD FieldSpecPro® VIS/NIR-spectroradiometer. The Brightness index (BI), Shape index (FI), and Normalized Difference Vegetation Index (NDVI) were derived from the ASD spectral signatures and from remote sensing (Sentinel-2 image) data. The development of these young olive trees was estimated from the measured diameter of the trunks (17 ± 18 cm diameter). In 20–30 cm of the soil, the carbon stock (38 ± 18 Mg ha⁻¹) as well as the available water content (12 ± 6%) was scarce, affecting the productivity of the olive grove. The above-mentioned indices obtained from the laboratory samples and the pixels of the Sentinel-2 image were significantly (p < 0.01) correlated, with a correlation coefficient of around 0.4. The BI was related to the gypsum content and the slope of the plot. The FI was related to the carbon and water contents. The NDVI derived from the satellite image identified the influence of soil degradation on the trees and the carbon content. The spatial-temporal changes of the indices might help in tracking soil changes over time.     

一种改进深度卷积神经网络的海岛识别方法

受不规律潮汐的影响,现有的海岛地物类别自动识别方法存在精度低和时效性差等问题,通过改进深度卷积神经网络提出了一种基于遥感影像的海岛快速识别方法:(1)在深度卷积神经网络的卷积层中增设1×1的卷积核作为瓶颈单元,对多波段的遥感影像进行降维;(2)在池化层引入了重采样方法,基于灰度值对海量的遥感影像进行特征压缩。以300景Landsat-8遥感影像为源数据,分别采用CNN、RCNN和本文改进的深度卷积神经网络对遥感影像中的海岛进行识别,实验结果表明:(1)改进的深度卷积神经网络降低了海岛识别的计算耗时,其计算耗时仅为CNN的4.56%和RCNN的5.60%;(2)改进的深度卷积神经网络较CNN和RCNN提高了海岛识别的精度,识别精度分别为96.0%、93.3%和95.0%。结果说明,改进的深度卷积神经网络适用于面向遥感影像的海岛自动识别。