基于两个独立抽样框架的农作物种植面积遥感估算方法

通过分析遥感技术在中国农作物种植面积估算中所遇到的难点 ,针对运行化的农作物遥感估产系统对主要农作物种植面积估算的需求 ,提出在农作物种植结构区划的基础上 ,采用整群抽样和样条采样技术相结合的方法 ,进行农作物种植面积估算。整群抽样技术利用遥感影像估算农作物总种植成数 ,样条采样是一种适合中国农作物种植结构特征的采样技术 ,用于调查不同农作物类别在所有播种作物中的分类成数。在中国现有的耕地数据库基础上 ,根据两次抽样获得的成数 ,计算得到具体某一种农作物类别的种植面积。最后给出了 2 0 0 3年早稻种植面积估算的实例。     

农作物含水量的遥感反演

利用遥感手段,在短波角度斜率指数(shortwave angle slope index,SASI)的基础上,采用全局植被湿度指数(global vegetation moisture index,GVMI)获取单位面积的植被冠层水分含量(EWT<,canopy>),建立了农作物水分含量的相关统计模型,并对黄淮海平原农作物水分含量进行反演研究.结果表明,该角度指数模型与EWTcanopy之间有很强的相关性,由此模型计算得到的黄淮海平原农作物水分含量,与同期的土壤相对湿度有较强的一致性.     

ERDAS IMAGINE V8.6新功能介绍之一——用于土地利用量测的面积背景采样相关工具

背景采样(frame sampling)工具是利用多分辨率图像和采样统计技术对大区域进行土地覆盖评估的方法,它可以提高统计意义上的精度,降低成本。其原理是基于面积背景采样统计原理,即面积背景采样(area frame sampling,AFS)最终就是要分配、收集和标识区域背景中的各种采样值(samples).利用AFS方法,人们能够给出这样的报告:“在某个区域种植农作物的面积是Xhm²,误差为5%,95%的置信度水平”.具有理论根据地将可信水平设置到估计值上了。     

尺度变化对冬小麦种植面积遥感测量区域精度影响的研究

不同尺度遥感数据源的选取将直接影响到作物种植面积测量的精度,研究尺度因子在农作物面积遥感测量中的作用,尺度与面积测量精度的定性和定量关系是非常必要的.为此,本文利用SPOT5卫星数据,以尺度变化对农作物种植面积遥感测量精度影响的分析为主线,运用空间统计分析方法和多种精度评价指标,从不同空间分辨率、不同空间范围、不同农作物百分比等角度系统分析了农作物种植面积遥感测量中的尺度效应问题.为基于多尺度遥感数据复合的农作物种植面积测量业务化运行中的数据选择和精度保证问题提供理论与实验基础.     

农作物种植面积遥感估算的影响因素研究

针对不同的农作物种植结构区,研究影响遥感影像分类各因素与农作物种植面积估算精度的定性和定量关系是十分必要的。以Rapid Eye影像提取的早稻种植信息为研究对象,从农作物的种植成数、种植破碎度和地块形状指数3个角度进行了不同空间分辨率下各因素对农作物面积监测的影响研究。结果表明:随着农作物种植成数的降低,种植结构越来越破碎,种植地块趋于狭长分布,各分辨率下农作物面积估算精度均呈递减趋势;要达到85%以上的面积估算精度,当作物种植成数在50%以上时,可选取高于150 m分辨率的遥感数据;当作物种植较为破碎时,需要在提高影像空间分辨率的同时融入其他技术手段;当作物种植地块为狭长分布时,提高影像的空间分辨率并不能保证面积估算精度,必须通过其他技术手段达到精度要求;并最终得到了4种影响因素对面积估算精度的定量评估模型。研究结果为解决不同农作物种植结构区遥感数据的选择、面积估算精度的提高,以及在特定研究区和数据源条件下可达到的面积估算水平等问题提供了理论基础。     

基于高分二号卫星数据的农作物分类方法研究

针对目前高分二号卫星数据(GF-2)有较高的空间分辨率而在农业领域应用较少和农作物分类普遍存在"同谱异物"和"同物异谱"的现象,以辽宁省沈阳市苏家屯区以西的新开河村周边为试验基地,利用最佳波段组合指数法(OIF)对所选取的高分二号(GF-2)卫星数据的纹理特征和植被指数以及波段信息进行筛选,选取最佳的波段组合,以增加分类信息、减少数据冗余。最后,针对筛选后的数据,使用最大似然法进行分类,得到农作物的分类结果。结果表明,利用该方法对农作物进行分类,分类精度得到了一定程度的提高,为目前大规模农作物种植面积的精确、迅速统计提供了一套可行的方案。     

基于GF-1卫星数据的冬小麦种植面积估算模型

目前遥感技术越来越广泛地应用于农作物面积的估算,本文研究了基于高分辨率卫星影像的农作物面积测量技术,提出一种冬小麦种植面积估算模型。该模型采用最大似然法对遥感影像进行监督分类,利用耕地矢量数据优化分类结果,结合高分辨率样本村数据拟合估算冬小麦种植面积。以泗洪县为研究区,采用GF-1卫星数据完成了泗洪县冬小麦面积提取实验,验证了该模型的有效性。     

利用无人机遥感监测农作物种植面积

南方平原耕地具有地块破碎、农作物种植品种多且空间分布混杂程度高等特点,运用传统的遥感技术方法精确监测农作物面积较为困难。无人机航拍具有拍摄时间灵活、空间分辨率高、成本低等优势,为解决这一难题提供了有利途径。本文通过地面样地调查,获取杭州市余杭区瓶窑镇农作物样地的位置及种植品种数据,利用面向对象的多尺度分割方法与随机森林的分类方法对无人机航拍数据进行分割、分类,深入挖掘高分辨率遥感数据信息,用于提取农作物种植品种及其空间分布信息,实现高精度的农作物种植面积遥感监测,推进无人机遥感在农业中的深入应用,提高农业遥感应用效益。     

征地面积自动标注、统计的研究与实现

征地测量内业数据处理中,针对土地面积计算和用地面积手工统计速度慢、易出错等问题,本文研究计算机编程自动实现面积计算、标注和统计,提高征地资料编制效率,确保征地图的面积计算的正确性和面积的统计的准确性。     

基于Sentinel-2和MODIS数据的宣恩县水稻遥感估产研究

基于作物的波谱反射特征,利用公开的多源遥感数据和相关技术能够实现农作物种植面积提取和产量预估,为作物长势监测等农业需求提供科学决策依据。本文首先基于Sentinel-2卫星影像,结合基于人工目视解译的监督分类、基于规则的面向对象分类和基于专家知识的决策树分类3种影像分类方法综合确定县级研究区的水稻种植范围,再选取水稻生育物候期内的多时相多光谱MODIS13Q1影像产品,建立影像提取出的植被指数EVI与水稻年产量之间的多元统计回归模型并应用于年产量预估,估产结果精度均达94%以上,符合实际需求。该模型可用性较强,对县域农作物遥感估产应用具有一定的指导意义。     

陆面温度反演算法——劈窗算法的敏感度分析

首先给出ＣＯ２ 倍增下遥感光合作物产量的概念模型,之后分析未受ＣＯ２ 倍增的遥感光合作物产量估测模型;在考虑ＣＯ２ 倍增对作物产量的影响后,对影响干物质累积的作物光合速率的模型进行修正,进而修正遥感光合作物产量估测模型。建立ＣＯ２ 倍增下作物产量响应模型,求取各参数,并在ＣＯ２ 倍增下对我国华北地区冬小麦产量响应进行填图,表明模型的估测结果有良好的可比性。     

构建地块二维表征及CNN模型的作物遥感分类

本文旨在研究基于地块数据约束的深度学习模型的分类特征表示方法,以识别不同作物在不同时相上光谱差异从而对作物类型进行分类。通过Google Earth Engine平台获取作物生育期内全部Landsat 8影像,利用其质量评定波段完成研究区无云时相及区域上的地块统计,提取地块级别的各波段反射率均值按照时相顺序及波长进行排列,构建波谱、时相二维特征图作为该地块的抽象表示。通过构建相对最优的卷积神经网络CNN(Convolutional Neural Network)结构完成对特征图的分类,从而完成对地块的分类。构建CNN模型并不需要手工特征和预定义功能的需求,可完成提取特征并遵循端到端原则进行分类。将该模型的分类结果与其他最为常用机器学习分类器进行了比较,获得了优于常用遥感分类算法的分类精度。结果表明地块数据的加入可以有效的缩减计算规模并提供了准确的分类边界。所提出得方法在地块特征表示及作物分类中具有突出的应用潜力,应视为基于地块的多时相影像分类任务的优选方法。     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

洪涝灾害灾情分析和评估的遥感方法研究

探讨了基于遥感和地理信息系统手段进行洪涝灾害灾情分析和损失估计的途径，研究了利用灾后一定时期内的遥感影像再现淹没状况的方法。     

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.     

不同地表覆盖下SPOT-7影像大气校正方法研究

针对不同的地物覆盖类型,分析和评价了适用于SPOT-7卫星数据的大气校正方法,为其遥感定量研究和应用提供思路和参考.在河南省嵩山地区进行了同步观测实验,获取了SPOT-7卫星影像并进行大气校正处理,地面同步测量了大气光学特性和典型地物样区光谱,计算了地物样区在影像上的反射率和植被指数,分析了不同地物覆盖类型下大气校正模...     

面向对象与卷积神经网络模型的GF-6 WFV影像作物分类

GF-6WFV影像是中国首颗带有红边波段的中高分辨率8波段多光谱卫星的遥感影像,对于其影像及红边波段对作物分类影响的研究利用亟待展开.本文结合面向对象和深度学习提出一种适用于GF-6 WFV红边波段的卷积神经网络(RE-CNN)遥感影像作物分类方法.首先采用多尺度分割和ESP工具选择最佳分割参数完成影像分割,通过面向对...     

基于遥感影像的土地利用变化检测

随着遥感科学技术的发展、各种遥感数据的累加以及遥感变化检测研究的深入,基于遥感影像的土地利用变化检测手段也日新月异。本文主要阐述利用不同的遥感影像处理方法对不同影像之间的土地利用变化进行检测,同时利用影像融合对比法、影像差值法、主成分分析法(PCA法),以及影像分类法等遥感影像处理方法,对同一地区前后不同时相的土地利用变化检测效果进行精度对比,并总结出不同检测方法在不同条件下的检测效果。     

Direct delivery of remote sensing data to user computer systems using satellite-based data broadcast techniques

The remote sensing applications are growing very rapidly with the availability of high-resolution data from the state of the art satellites like IRS-1C/1D/P4. The advancement in computer hardware and software in the area of remote sensing also enhance the growth of remote sensing applications. IRS-1C/1D/P4 provides data with the resolution of 5.6m in panchromatic mode giving more information of the ground area covered. The remote sensing satellites with high-resolution sensors and wide coverage capabilities will provide the data with better resolution, coverage and revisit to meet the growing application needs. Many applications like crop acreage and yield estimation, draught monitoring and assessment, flood mapping, waste land mapping, mineral prospectus, forest resource survey etc., have become an integral part of the resources management system in the developing countries. These resource management systems need the data to be transferred in real time or near real time for processing. The transfer of data in real time or near real time calls for advanced data delivery techniques to deliver the data as quickly as possible. Processing of remote sensing data can be performed even on low cost personal computers, which in turn further increases the remote sensing applications enabling by setting up the processing centers even at grass root level i.e., at district, taluk or village level. Setting up of processing centers at grass root level demands for quick, cost effective and efficient data delivery mechanism to transfer remote sensing data with or without value added services. The digital revolution has reached broadcasting with the introduction of direct broadcasting of digital data. These modify the traditional data transfer techniques by separating the actual service from the transmission system, thus enabling the distribution of any kind of digital data to stationary, portable or mobile terminals. This allows remote sensing data to reach a large number of users simultaneously and independent of their location. This article highlights the concepts, possibilities, and implementation mechanisms to realize the remote sensing data transfer through direct broadcasting technique and enhance remote sensing applications.