多云雾地区高时空分辨率植被覆盖度构建方法研究

针对多云雾地区高时空分辨率数据缺乏现状,提出了一套区域尺度高时空分辨率植被覆盖度数据构建方法.首先,通过时空适应反射率融合模型(STARFM)有效地将TM 的较高空间分辨率与MODIS的高时间分辨率融合在一起,构建了研究区植被生长峰值阶段的NDVI数据;然后,以植被生长峰值阶段的NDVI为输入,基于地表覆被类型,综合应用等密度和非密度亚像元模型对研究区的植被覆盖度进行估算.结果表明:①即使数据源存在大量的云雾,且存在一定的时相差异,研究区植被覆盖度的估算结果过渡自然,不存在明显的不接边效应;②以植被生长峰值阶段的NDVI数据为输入进行植被覆盖度估算,有效拉开了同一地表覆被类型不同覆盖度像元的NDVI梯度,提高了亚像元估算模型对输入数据的抗扰动性;③基于地表覆被类型,应用亚像元混合模型,能够提高植被覆盖度的估算精度.经野外实测数据验证,总体约８５％的估算精度表明,针对高时空分辨率遥感数据缺乏的多云雾区域,本研究提出的方法能够实现区域尺度植被覆盖度数据的构建.     

基于多时相Landsat8 OLI影像的作物种植结构提取

针对基于多时相遥感影像、多种特征量提取多种作物种植结构在我国研究较少的现状,利用多时相Landsat8OLI影像数据,根据温宿县不同作物的农事历,通过分析主要地物的光谱特征和归一化植被指数的时间变化信息,构建不同作物种植结构提取的决策树模型,实现了对温宿县多种作物种植结构信息的提取。结果表明:1水稻的最佳识别依据是5月20日影像的近红外波段和7月23日影像的NDVI值;棉花和春玉米的最佳识别依据是5月20日~9月9日影像的NDVI变化值;冬小麦—夏玉米和林果的最佳识别依据是5月20日~7月23日影像的NDVI变化值;2与单时相监督分类相比,多时相决策树法对多种作物种植结构的提取效果更理想,总体精度提高了7.90%,Kappa系数提高了0.10;3Landsat8OLI影像数据分辨率高、成本低、获取方便,是农作物遥感的良好数据源。     

Sentinel-2 Satellite Image Fusion Methodand Quality Evaluation Analysis

Sentinel-2 satellite sensors acquire three kinds of optical remote sensing images with different spatial resolutions.How to improve the spatial resolution of lower spatial resolution bands by fusion method is one of the problems faced by Sentinel-2 applications.Taking the Sentinel\|2B image as the data source,a high spatial resolution band was generated or selected from the four 10m spatial resolution bands by four methods:the maximum correlation coefficient,the central wavelength nearest neighbor,the pixel maximum and the principal component analysis.We fused the one high spatial resolution band produced and six multispectral bands with 20 m spatial resolution by the five fusion methods of PCA,HPF,WT,GS and Pansharp to produce six multispectral bands with 10 m spatial resolution and the fusion results were evaluated from three aspects:qualitative and quantitative (information entropy,average gradient,spectral correlation coefficient,root mean square error and general image quality index) and classification accuracy of fused images.Results show that the fusion quality of Pansharp with the maximum correlation coefficient is better than other fusion methods,and the classification accuracy is slightly lower than the GS with the pixel maximum of the highest classification accuracy and far higher than the original four multispectral image with 10 m spatial resolution.According to the classification accuracy of experimental data,different fusion methods have different advantages in extraction of different ground objects.In application,appropriate schemes should be selected according to actual research needs.This research can provide reference for Sentinel-2 satellite and similar satellite data processing and application.     

Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series

Spatiotemporal fusion (STF) technologies are commonly used to acquire high spatiotemporal resolution remote sensing observations. However, most STF technologies fail to consider the nonlinear variation in vegetation in the time domain. Based on the Best Linear Unbiased Estimator (BLUE), this paper proposed a novel STF algorithm (referred to BLUE) which accounts for the phenological characteristics of vegetation. First, annual time series of normalized difference vegetation index (NDVI) data with high spatial resolution but low temporal resolution is fitted using a double logistic function and used as the background field. Then, NDVI data with low spatial resolution but high temporal resolution is used as the observation field. The information in the background and observation fields is fused using the BLUE to obtain high spatiotemporal resolution NDVI data. The proposed algorithm was used to produce dense time series of 30 m resolution NDVI data for a 10 km × 10 km experimental area in 2014. The experimental results demonstrate that the accuracy of fusion results from the proposed BLUE method are higher than those from the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Linear Mixing Growth Model (LMGM), especially when the temporal component of surface heterogeneity is dominant. The proposed algorithm has broad prospects in vegetation monitoring at high spatiotemporal resolution.     

基于多时相遥感观测的板栗林分布提取研究

板栗林在欧亚、北美等地广泛分布,具有良好的生态价值和经济效益。我国板栗产量居世界首位,是重要的经济树种。使用遥感影像建立板栗林空间分布提取方法能够为其科学管理和高效经营提供定量数据,但树种分类是遥感分类的难点,并且针对板栗林的遥感提取研究较少。以河北省宽城满族自治县为研究区,结合MODIS高时间分辨率特征和Landsat数据较高空间分辨率的特征,研究板栗林提取的最佳时相以及分类特征,并采用多时相观测基于支持向量机算法实现板栗林的提取。结果表明：①4月至6月各地类光谱差异最大,是板栗林提取的关键物候期;②蓝、绿、红、近红外和短波红外波段地表反射率是分类的有效波段,NDI、NDVI、NDWI、RSI和RVI等植被指数增强了植被信息,是板栗林提取的有效分类特征;③单一时相板栗林分类中,生长季前期6月精度最高,生长季后期9月次之,非生长季1月分类结果较差;④结合生长季6月、9月和非生长季1月遥感影像的分类精度最佳,板栗林制图和用户精度分别为89.90%和87.25%。与林业局板栗林面积统计数据相比,精度可达93.45%。     

基于影像融合和面向对象技术的植被信息提取研究

高分辨率影像具有丰富的光谱信息和空间信息。采用不同的图像融合技术融合GeoEye影像全色波段和多光谱波段,用建立的参考多边形和对应多边形残差法评价分割质量,以确定研究区各地物类型的最优分割参数组合,选择目标地物分类特征,建立分类规则,在此基础上实现研究区内不同地物类型的面向对象信息提取。结果表明:Gram-Schmidt(GS)融合法具有最优的融合效果,所选特征能够很好地实现目标地物信息提取,并且具有明确的地学意义,面向对象信息提取总体精度达到90.3%,Kappa系数为0.86,该研究为高精度植被信息的提取提供了有效的方法。     

遥感技术在毒草识别中的研究进展

毒草的滋生蔓延严重破坏草地生境,制约草地畜牧业的发展。遥感技术作为牧场管理的一种重要的技术手段,其传感器自身的空间分辨率和光谱分辨率的高低是决定毒草识别成功与否的关键。于毒草独特的物候特征出现时获取影像数据能帮助提高分类识别的精度。回顾了3种遥感技术在毒草识别中的研究进展。航空摄影成本高、数据处理复杂,难于得到推广 ;多光谱卫星遥感大多空间分辨率低,仅在识别大面积滋生、密度较大的毒草方面展现出了一定的潜力 ;高光谱遥感的出现改善了对植被分类识别的精度,是未来毒草识别的主要依据。由于高光谱数据的冗余性和复杂性,数据处理技术和分类方法的选择也是影响毒草识别精度的重要因素。     

基于MODIS NDVI的LandsatTM影像地形阴影区光谱信息恢复方法研究

遥感为获取山区生态环境与资源信息提供了重要的观测手段。然而受地形遮蔽影响,山区光学影像大量的地形阴影给山区土地覆被解译以及生态参量的遥感反演带来了巨大困难。针对地形阴影光谱信息的恢复,提出了一种基于MODIS NDVI的Landsat TM影像地形阴影区光谱信息恢复方法。该方法首先利用MODIS上午、下午星(Terra和Aqua)不同时间过境能够对地形阴影区信息实现互补的特点,采用最大值合成法合成MODIS上、下午星16dNDVI产品(MOD13Q1和MYD13Q1),获得低空间分辨率影像上的阴影区光谱信息;在此基础上,考虑MODIS与Landsat的观测角度、光谱差异,设置滑动窗口及筛选规则提取MODIS与TM影像相匹配的同质纯像元;基于中、低空间分辨率影像中均匀同质像元存在一定统计关系的假设,进一步建立同质区域中TM影像光照区域与对应MODIS NDVI的回归树模型,利用该统计关系和阴影区MODIS的NDVI信息推导得出地形阴影区的光谱信息。将阴影光谱信息恢复后的影像与SCS+C校正后的影像进行比较和分析,结果表明该方法恢复得到的地形阴影的光谱信息能够更好地反映阴影区信息,同时光谱保真程度较好。随着越来越多的中高空间分辨率卫星影像的发展,采用多源卫星数据进行山地地形阴影区信息恢复将成为一个新的发展趋势,该方法以期为同类影像处理提供参考。     

Monitoring rubber plantation distribution on Hainan Island using Landsat OLI imagery

Rubber expansion has been widely explored at various scales due to its increasing environmental and socio-economic impact and the global demand for natural rubber products. However, accurate and up-to-date maps and large-scale monitoring of rubber plantation spatial distribution are not yet available. In this article, we developed a simple algorithm for rapidly and accurately mapping rubber plantations on Hainan Island, China, by combining survey samples and Landsat 8 Operational Land Imager data from 2014 in order to understand their spatial distribution. The results showed that rubber plantations are distinguishable from other land-cover types by band value changes, vegetation index changes, and phenological phase changes (defoliation and foliation) and can be accurately extracted from multi-temporal Landsat images using a decision tree method and Google Earth Engine. This method results in a high overall classification accuracy of 92.17% with a corresponding κ of 84.33%. Rubber plantations are concentrated in the northwest of Hainan Island and gradually decrease to the south and east. Most plantations are found in the 50–500-m elevation range, with few found outside this range. We believe that the proposed approach will have significant implications for mapping and monitoring rubber spatial distribution at regional scales.     

结合地籍数据的高密度城区面向对象遥感分类 

利用高分辨率遥感影像和GIS辅助数据,对高密度城区进行面向对象的土地利用覆被分类研究。使用NAIP高分辨率航空遥感影像,在多尺度影像分割的基础上,针对特定地物选择合适的影像分割参数。采用决策树方法建立高密度城市地区的分类规则,并结合该地区地籍图数据作为辅助数据,逐步进行高密度城市地区地物信息提取。利用辅助数据进行面向对象的遥感分类效果优于单纯依靠遥感影像进行的分类,且有效提取了道路和复杂的房屋等信息,得到了理想的分类结果,其总分类精度从常规面向对象方法的84.08%提高到89.79%。利用辅助数据进行遥感分类提高了高分辨率遥感影像的分类精度,说明了利用辅助数据进行遥感分类方法的有效性。     

面向对象的黑河下游河岸林植被覆盖信息分类!

地表植被覆盖是描述区域生态系统的基础数据,也是全球及区域陆面过程、生态与水文众多模型中所需的重要地表参数。对于黑河下游额济纳绿洲,以Landsat 30m分辨率为主的遥感影像难以真实提取下游绿洲河岸林植被覆盖信息,而高分辨率影像目标地物轮廓清晰、空间细节信息丰富,有利于干旱背景下景观破碎、异质性强的植被覆盖信息分类。基于黑河下游额济纳绿洲QuickBird影像,通过面向对象的分类方法提取耕地、胡杨、柽柳、草地和裸地等主要植被覆盖类型,分类总体精度和Kappa系数分别为84.71%和0.7986。结果表明:利用面向对象分类方法对高分辨率影像进行植被覆盖信息分类,分类结果较好,能够满足精度要求。     

Land Cover Extraction in the Ejina Oasis by Hyperspectral Remote Sensing

The extraction of land surface coverage is the basis of ecological environment evaluation,vegetation change analysis and regional ecological and hydrological processes.Aerial hyperspectral remote sensing has great advantage in land surface coverage extraction,such as flexible,wide coverage,high spatial resolution and high spectral resolution.Research area has landscape characteristics of vegetation,landscape fragmentation and heterogeneity in Ejina Poplar Forest National Nature Reserve.Comparison and analysis of two methods of dimension reduction based on minimum noise transform and principal component analysis,three supervised classification methods based on maximum likelihood method,support vector machine and object\|oriented classification.Land surface coverage is extracted by NDVI threshold segmentation,minimum noise transform dimensionality reduction method and maximum likelihood classification method according to the characteristics of landscape fragmentation,heterogeneity and high redundancy of hyperspectral data based on the Airborne Hyperspectral Data of Ejina oasis in the lower reaches of Heihe.The land surface coverage results overall accuracy and Kappa coefficient are 87.95% and 0.885 by random sampling based on airborne remote sensing data.The results show that the classification results of high accuracy can provide effective parameters for ecological research.     

成像光谱技术在土地利用动态遥感监测中的应用研究

随着数字化调查技术的发展，国土资源管理对土地利用动态遥感监测提出了更高的要求，目前主要采用的多光谱数据由于受光谱分辨率限制以及“同谱异物，同物异谱”现象的影响，难以满足管理需要。成像光谱数据具有较高光谱分辨率。在类别细分方面具有一定的优势，在当前土地利用动态遥感监测中具有一定的应用潜质。该文针对成像光谱数据特点，探索了与成像光谱数据相适应的土地利用动态遥感监测方法，提出了异常光谱检测法，该方法在试验区应用中取得了良好效果。     

顾及面积和位置差异的高分遥感影像分割质量评价方法

高分遥感影像分割质量对面向对象分类精度有着重要的影响,良好的影像分割质量有利于得到较高的分类精度。对高分遥感影像分割质量进行评价,从而找到最优的分割结果就显得十分重要。通过对比参考对象和分割对象之间的面积和位置差异,提出了一种新的高分遥感影像分割质量评价方法。将该评价方法应用于GeoEye-1高分遥感影像分割质量评价,试验结果表明:该评价方法能客观地评价影像分割质量,所得到的最优分割结果与参考对象边界匹配程度高,有利于影像后续的分类。     

低空间分辨率多源遥感数据的空间一致性分析和相对几何校正

多源低空间分辨率遥感数据在空间上的一致性对于其在全球变化研究中的集成使用有非常重要的意义。对此,以公认几何精度较高的MODIS数据为基准,对NOAA/AVHRR、FY-3/VIRR、FY-3/MERSI、FY-2/VISSR这4类国内外常用的低空间分辨率传感器的L1B数据进行了一系列相对几何精度评价和多项式相对几何校正的实验。相对几何精度评价的结果表明:MODIS数据与这4类L1B数据在几何精度上的偏差都比较大。在此基础上,选取少量均匀分布的控制点并采用不同阶数的多项式几何校正模型对多源数据进行空间一致性校正。校正结果表明:低阶的多项式几何校正模型就能对各种待校正数据的几何精度有显著的提升,使其与基准数据在空间上达到一致,满足全球变化研究对低分辨率多源遥感数据在空间一致性上的需求。     

遥感图像时空融合综述

高时空分辨率的遥感图像大数据在遥感领域发挥着重要作用.然而,由于技术上和预算上的限制等原因,目前单一的卫星传感器无法获取同时具有高空间分辨率和高时间分辨率的遥感影像.因此遥感图像时空融合技术被认为是解决时间分辨率和空间分辨率折衷问题的有效途径之一.随着深度学习在各领域的广泛应用,深度学习技术已经被证实是解决图像问题非常...     

基于TM影像的典型内陆淡水湿地水体提取研究

水是维系湿地生态系统稳定和健康的决定性因子,利用卫星遥感影像快速、准确地提取湿地水体信息已经成为湿地调查、研究与保护的重要手段。鉴于TM遥感影像具有较高的空间分辨率、波谱分辨率、极为丰富的信息量、较高的定位精度和相对较低的价格,其必然成为近一段时期湿地调查、研究与保护的重要数据源之一。研究基于TM遥感影像,运用多种方法针对典型内陆淡水湿地的水体信息进行了提取实验,通过对实验结果的分析得出：在面积的准确性、提取的准确度以及视觉效果3种指标下,光谱分类法较其它方法效果要好,其次为单波段阈值分析法与植被指数法,较差的是多波段谱间关系法与水体指数法;影响提取效果的主要原因是湿地水体提取不够完全,这是由影像的分辨率及湿地特殊的水文条件所造成的,采用像元分解及多源遥感数据融合技术将成为提高水体提取精度的重要手段。     

HJ-1A NDVI与MODIS NDVI时间序列提取植被物候特征对比研究

以HJ-1A和MODIS为数据源,通过动态阈值法提取物候特征参数,对HJ-1A NDVI和MODIS NDVI时间序列进行植被物候特征提取进行定性和定量比较,通过比较结果,提出HJ-1A NDVI数据在该应用中存在的问题,促进国产中空间高时间分辨率影像数据在植被物候信息提取研究中的应用,提高其在生态系统研究中的应用价值。结果表明:在SOS、EOS和LOS以及TOMS几个主要的物候时间点上,MODIS NDVI时间序列的标准差较小,所得物候数据更为集中,偏离度较小,所得物候数据较稳定;而HJ-1A NDVI时间序列所得物候数据的标准差较大,数据偏离程度较大,而在POS、BOS和AOS等表征植被生命周期中生长幅度数据上,其标准差较小,离散程度小。     

LiDAR点云支持下地物精细分类的实现方法

在遥感数据分类中,获取精细的地物类别无疑能够传递更加丰富的信息量,进一步加深对遥感数据的理解和解译。在机载LiDAR点云高程数据的支持下,提出并实现了遥感影像上地物精细分类的方法。为保证高精度地同种地物再划分,综合考虑配准、辅助数据源、首次回波、点云密度及影像空间分辨率4种因素,并重点解决了点云密度与影像空间分辨率不匹配的问题,利用决策树显著地提高了影像上建筑物、植被的分类数量,使点云与影像联合分类的优势得到体现,达到了分类精度与地物类别数量相统一的目的。     

Land-cover classification using multi-temporal GF-1 wide field view data

ABSTRACT

Vegetation is an important land-cover type and its growth characteristics have potential for improving land-cover classification accuracy using remote-sensing data. However, due to lack of suitable remote-sensing data, temporal features are difficult to acquire for high spatial resolution land-cover classification. Several studies have extracted temporal features by fusing time-series Moderate Resolution Imaging Spectroradiometer data and Landsat data. Nevertheless, this method needs assumption of no land-cover change occurring during the period of blended data and the fusion results also present certain errors influencing temporal features extraction. Therefore, time-series high spatial resolution data from a single sensor are ideal for land-cover classification using temporal features. The Chinese GF-1 satellite wide field view (WFV) sensor has realized the ability of acquiring multispectral data with decametric spatial resolution, high temporal resolution and wide coverage, which contain abundant temporal information for improving land-cover classification accuracy. Therefore, it is of important significance to investigate the performance of GF-1 WFV data on land-cover classification. Time-series GF-1 WFV data covering the vegetation growth period were collected and temporal features reflecting the dynamic change characteristics of ground-objects were extracted. Then, Support Vector Machine classifier was used to land-cover classification based on the spectral features and their combination with temporal features. The validation results indicated that temporal features could effectively reflect the growth characteristics of different vegetation and finally improved classification accuracy of approximately 7%, reaching 92.89% with vegetation type identification accuracy greatly improved. The study confirmed that GF-1 WFV data had good performances on land-cover classification, which could provide reliable high spatial resolution land-cover data for related applications.