General Regression Neural Network and Radial Basis Neural Network for the Estimation of Crop Variables of Lady Finger

Estimation of crop variables is necessary for crop type monitoring as well as crop yield forecast. At the present era artificial neural network methodology are widely used to the remote sensing domain for numerous applications like crop yield forecasting and crop type classification. In the present work, two neural network models namely general regression neural network (GRNN) and radial basis function neural network (RBFNN) are used to estimate crop variables: leaf area index (LAI), biomass (BM), plant height (PH) and soil moisture (SM) by using ground based X-band scatterometer data. The both networks are trained and tested with X-band scatterometer data. The performance of the GRNN and RBFNN networks are found that the radial basis approach is more suitable for crop variable estimation in comparison to the GRNN approach. This work presents the applicability of neural network as an estimator and method employed could be useful to estimate the crop variables of other crops.     

一种修正的星载散射计反演海面风场的场方式反演算法

由于卫星散射计探视雷达回波的各向异性的双调和性质,同时由于散向散射物理模型函数的非线性及信号中存在噪声,使得常规点方式风场反演哕向有多至４个解的多解存在。给出了一种改进的场方式钣演方法,利用该人卫星散射计测量的后向散射强度的数据中唯一反演出大尺度海洋风场。通过数值模拟和实际算例计算表明反演过程在风向、风速上都与真解是吻合的。从结果可以看到,所采用的改进的场方式反演方法对模拟数据或真实散射计探测海面     

利用高光谱对生物变量进行估计

该文分析了非光合背景物质—土壤光谱信号的特点 ,认为利用对光谱信号进行一阶导数的运算就能对混合光谱中的土壤光谱信号进行压缩 ,由此而得到的生物指数如LAI、SApar等的估计值更能客观地反映实际 ,这对于利用成象光谱进行环境应用有很大的实用价值。     

基于无人机影像的农作物净初级生产力估算

基于无人机影像对不同农作物植被在不同生长期的净初级生产力进行估算.本文对试验田区域的可见光植被指数(GRRI、GBRI、NGRDI、NGBDI、VDVI及RGBVI)与NDVI指数进行相关性分析,发现可见光波段差异指数(VDVI)与NDVI指数呈显著相关性.在此基础上,将无人机影像的VDVI指数代入CASA模型,对试验...     

对建立遥感估产模式的几点初步认识

本文从分析遥感光谱参数的生物学意义着手，论证了正确建立遥感估产模型的可能途径。对几种有代表性的遥感估产模型作了分析，作者认为把可见光、近红外波段的遥感信息与热红外信息有机结合是解决遥感估产模型的最佳方案。对ＮＯＡＡ－ＡＶＨＲＲ的第１通道与第２通道光谱数值进行非朗伯体特性的纠正是必要的。遥感估产模型不仅可以使估产的空间尺度大大缩小而且参数数目亦可大大减小，更有利于实际运行。     

基于虚拟空间场景的现地用图训练方法

从现地用图训练现状出发,分析虚拟空间场景的特点和性能,设计了现地用图虚拟训练方法,模拟野外现地用图中的地貌认知、地图对照和按图行进,建立计算机量化评测机制。研究表明,利用虚拟空间场景进行现地用图训练,提升了训练信息化程度和训练效率。     

Improvement in Precision of Crop Acreage Estimation by Remote Sensing Using Frequency Distribution Based Stratification

A study was conducted to improve precision of crop acreage adopting stratified random sampling approach. Remotely sensed data was used to classify mustard crop for the states of Rajasthan, Madhya Pradesh, Uttar Pradesh, Gujarat and Haryana covering 81% of mustard area of India. A grid of size 5 × 5 km was super-imposed on classified image of study area and proportion of mustard crop within the grid was ascertained. Crop proportion was used to determine strata. Stratification was done based on equal interval of proportion, equal sample number and cumulative square root of frequency method. Cumulative square root of frequency method gave highest precision in all the cases.     

用太阳光谱遥测大气污染气体NO_2的含量

利用地面可见光 (4 30— 45 0nm)波段的太阳光谱和大气上界的参考太阳光谱 ,反演出大气中NO2 的柱总含量 ,得到了该地区上空NO2 含量及其变化 ,其变化与当地环境监测站用常规法测量的NO2 浓度的变化基本一致。该文介绍了测量仪器、测量原理和部分测量结果     

基于ADS80航空影像的像控点布设方案研究

以数码推扫式航空影像为基础,选用具体的试验区,采用空三精度验证的方式,对ADS80影像覆盖区域不同的控制点布设方案产生的空三结果进行对比分析.在分析试验结果的基础上,提出了在保证成图精度的情况下,以减少外业工作量为目的的最佳像控点布设方案.     

用太阳光谱遥测大气污染气体NO2的含量

利用地面可见光（430－450m)波段的太阳光谱和大气上界的参考太际光谱，反演出大气中NO2的柱总含量，得到了该地区上空NO2含量及其变化，其变化与当地环境监测站用常规法测量的NO2浓度的变化基本一致，该文介绍了测量仪器，测量原理和部分测量结果。     

基于遥感的区域尺度森林地上生物量估算研究

森林是陆地生态系统最大的碳库,精确估算森林生物量是陆地碳循环研究的关键。首先从机载LiDAR数据中提取高度和密度统计量,采用逐步回归模型进行典型样区生物量估算;然后利用机载LiDAR数据估算的生物量作为样本数据,与多光谱遥感数据Landsat8 OLI的波段反射率及植被指数建立回归模型,实现区域尺度森林地上生物量估算。实验结果显示,机载LiDAR数据估算的鼎湖山样区生物量与地面实测生物量的相关性R2达0.81,生物量RMSE为40.85 t/ha,说明机载LiDAR点云数据的高度和密度统计量与生物量存在较高的相关性。以机载LiDAR数据估算的生物量为样本数据,结合多光谱遥感数据Landsat8 OLI估算粤西北地区的森林地上生物量,精度验证结果为:R2为0.58,RMSE为36.9 t/ha;针叶林、阔叶林和针阔叶混交林等3种不同森林类型生物量的估算结果为:R2分别为0.51(n=251)、0.58(n=235)和0.56(n=241),生物量RMSE分别为24.1 t/ha、31.3 t/ha和29.9 t/ha,估算精度相差不大。总体上看,利用遥感数据可以开展区域尺度的森林地上生物量估算,为森林固碳监测提供有力的参考数据。     

Use of Statistical Method to Remote Sensing Data for In-season Crop Growth Assessment

Farming is a risky business but, from the food security point of view, it is important that farmers continue to grow crops so that people get food to eat. Although natural calamities cannot be eliminated, its impact can be reduced through implementation of pro-active and pro-poor risk management policy programs. Remote sensing, with capabilities of synoptic coverage, multi-spectral and multi-temporal observations, is ideally suited for in-season monitoring the progress of crop. Normalized Differential Vegetation Index (NDVI) is the primary index for monitoring vegetation status and its temporal behavior captures the dynamic response of vegetation cover to prevailing physical conditions. The present study offers a methodology for making multiple inseason assessment of the crop growth vis-a-vis its normal performance. This is treated by use of conditional distribution. Present analysis reports the performance in deriving spectral maxima (Gmax) from complete profile of validation season and one which was derived from conditional mean approach. It was observed that in more than 90 % of the cases, the difference in Gmax was less than 3 %. Thus the performance of methodology can be termed as very good.     

遥感影像预分类精度对地物面积空间抽样估算的敏感性分析

传统的地物面积测量受精度和效率制约,为此引入了结合遥感影像的空间分层抽样方法.首先以遥感影像的预分类结果作为模拟地物的真实分布,在地物外沿等概率随机添加不同比例的错分像元,从而获得准真实地物区的摸拟预分类结果,并依此设定各层等比例取样的样本人层标志,指导地物样本的选取,然后以抽中样本地物的准真实值之和按比例推算出总量.通过比较分析各水平含量的地物类别、不同预分类精度、层内随机和系统抽样下的多次总量估计精度及其稳定性变化情况,结果表明:该方法不需要背景数据库等先验知识,在预分类达到一定精度之上时,依分类区域设立层标志的分层抽样方法所获得的总量估计精度及标准差均好于无分类支持的随机和系统抽样;当预分类精度达到50%以上时,具有较高的成本效率比,其中在60%时,各类地物在0.5%抽样率、95%的置信度下可以保证估计量精度在92%以上.     

基于识别变量的粗差探测Bayes方法

从一个新的角度出发,对应每个观测值引入一个识别变量,基于识别变量的后验概率提出一种新的粗差定位的Bayes方法,并构造相应的均值漂移模型给出粗差估算的Bayes方法.由于识别变量的后验分布往往是复杂的、非标准形式的,为此设计一种MCMC(Markov Chain Monte Carlo)抽样方法以计算识别变量的后验概率值.最后对一边角网进行了计算和分析.试验表明,本文给出的探测粗差的Bayes方法不仅充分利用了先验信息,而且克服了以往粗差定位方法的模糊性以及探测标准选择的问题,同时计算简便.     

测量噪声污染模型下的动态定位Bayes算法

应用Bayes定理,给出了测量噪声污染模型下一类动态模型状态的抗差Bayes估计,并同时给出了一种污染率未知时的滤波算法。模拟算例结果证明了该算法的有效性和可靠性。     

空地一体化测绘在城市更新中的应用

在大范围测图中,传统的单点测量模式效率低下,很难满足实际需要。随着近些年无人机倾斜摄影与三维激光扫描技术的快速发展与广泛应用,这个难题在一定程度上得到了解决,不过单项技术独立用于测图仍然存在一些不足。本文分析这两种技术的优缺点,用三维激光扫描辅助无人机倾斜摄影进行空地一体化测绘,可以全方位采集数据,提高测量精度和测图效率,并成功用于城市更新基础数据调查。     

Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ⁰). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R²) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area.     

一种融合测高仪与地面GPS数据的单站电离层反演方法

电离层层析成像技术非常适用于检测电离层电子密度的大尺度空间分布及其扰动。利用地面单站的GPSTEC值和另一站的数字测高仪观测数据,结合国际参考电离层（IRI）,利用MART算法反演得到测站上空电子密度的垂直分布。利用白天和夜间的实测数据进行了CIT反演,结果表明了该方法的可靠性。     

徐州体育中心田径场的验收测量

随着体育事业的发展，各大中城市体育场地的建设也在不断兴起，就其等级、质量如何评定验收，是我们面临的又一新课题，本文以徐州市体育中心为例，介绍了田径场主要环节的验收测量标准，并对该田径场的各主要指标进行了验收测量和详细介绍，供同行们在以后的工作中参考和进一步的探讨。