栾海军
, 田庆久
Luan Haijun, Tian Qingjiu
中图分类号: P237;TP701
文献标识码: A
文章编号: 1001-8166(2018)05-0483-10
收稿日期: 2017-12-15
修回日期: 2018-04-2
网络出版日期: 2018-05-20
版权声明: 2018 地球科学进展 编辑部
基金资助:
作者简介:
First author:Luan Haijun (1984-), male, Luyi County, Henan Province, Lecturer. Research areas include scale effects research of quantitative remote sensing. E-mail:luanhaijun@xmut.edu.cn
作者简介:栾海军(1984-),男,河南鹿邑人,讲师,主要从事定量遥感尺度效应研究.E-mail:luanhaijun@xmut.edu.cn
展开
摘要
尺度效应是定量遥感重要而基础的问题之一,学者们利用尺度转换模型定量描述尺度效应。重点以归一化差分植被指数(NDVI)为例,对其尺度转换研究现状进行分析,进而对定量遥感地表参数尺度转换研究趋势进行探讨。认为:① 融合地物类别信息的升尺度转换模型建立将成为遥感地表参数升尺度转换研究的一种新趋势;② 利用分形理论与方法尝试揭示尺度转换动力学过程也是遥感地表参数降尺度转换研究的一个新的发展趋势;③ 时空尺度转换耦合研究将继续成为未来遥感地表参数尺度转换研究的新主题,对利用多重分形方法建立时空尺度转换耦合模型的可能性进行分析,展示了该方法的潜在研究价值;④ 定量遥感尺度转换与遥感影像地类自动识别结合研究将成为新趋势,2个研究领域可相辅相成,在今后的研究中取得新的成果。
关键词：
Abstract
Scale effect is a crucial scientific problem in quantitative remote sensing, and scholars attempt to solve it with scales transformation models. As a significant land surface parameter, NDVI’s scaling has been studied for a long time. Therefore, we took NDVI as a main example. Its development of scaling research was described and analyzed in the paper, and the development trends were discussed for land surface parameters in quantitative remote sensing. Our opinions are as follows: ① It will be the new trend to establish upscaling models fused with ground objects classification information for land surface parameters in quantitative remote sensing; ② It will be the new trend to establish downscaling models based on fractal for land surface parameters in quantitative remote sensing; ③ It is still the hotspot to establish temporal-spatial coupled scaling models for land surface parameters in quantitative remote sensing in the future. The multi-fractal scaling methodology was proposed and its availability was analyzed in the paper, which presented significant potential; ④ It will be the novel trend to combine scales transformation in quantitative remote sensing presented automatic ground objects recognition in remote sensing images. It is proposed that the two research fields can help each other and both can make much progress in the future.
Keywords：
空间尺度问题是定量遥感重要而基础的问题之一[1,2,3]。学者们对不同地表参数的尺度效应进行研究。研究尺度效应有利于协同利用不同时空尺度遥感数据,解决“海量”遥感影像无法充分利用的问题,具有重要的应用潜力与科研价值[3]。鉴于地物具备时空特性,遥感地表参数不仅具有空间尺度效应,而且具有时间尺度效应。学者们对地表参数尺度效应进行广泛而深入的研究,这包含了尺度效应的发生机理、表现形式、效应分析、解决方法等方面,作者此前曾对其做过具体论述[4]。在上述诸多研究方面中,尺度转换作为尺度效应的解决方法受到重视。尺度转换模型可以定量描述尺度效应,但是目前寻找一种适用于实际混合地表的高精度、具备一定物理意义的尺度转换方法仍存在困难。本文亦将重点讨论尺度效应中的尺度转换研究进展。
在诸多遥感地表生物物理参数中,归一化差分植被指数(Normalized Difference Vegetation Index,NDVI)作为植被生长状态及植被覆盖度的最佳指示因子,与植被覆盖度(Fraction of Vegetation Cover,FVC)[5]、叶面积指数(Leaf Area Index,LAI)[6]、光合作用光利用效率[7,8]、绿色生物量[9]、植被生产力[10,11]和景观物候学参数[12,13]等关系密切,被广泛应用于环境(气候)变化和农作物估产等领域。因此,其空间尺度效应得到特别关注与研究。论文将着重对其尺度转换研究现状进行分析,进而对遥感地表参数尺度转换研究趋势进行探讨。
空间升尺度转换可用于解决如反演产品真实性检验[14,15]等重要问题,因此受到广泛关注。学者们曾对地表反照率[16,17]、二向性反射率分布函数(Bidirectional Reflectance Distribution Function, BRDF)[18],LAI[19,20,21,22]、总初级生产力(Gross Primary Productivity, GPP)[10]、光合作用光利用效率[7,8]和景观物候学参数[12,13]等多种遥感地表参数的升尺度转换进行研究。
一些学者利用统计学方法进行遥感地表参数(升)尺度转换研究。以NDVI为例,Aman等[23]运用数量统计方法,发现高空间分辨率上NDVI平均值与低空间分辨率上相应位置的NDVI值基本呈线性关系。Bian等[24]研究了空间尺度对植被生物量与地形因子之间关系的影响,分析了植被指数与高程、坡度和坡向等地形因子之间的相关性,并用半方差和分形方法来描述空间尺度的依赖范围。研究结果表明:地形因子与植被生物量之间的关系随空间尺度的变化存在明显的差异。Friedl等[25]采用模拟数据和地面数据分析了LAI在亚像元尺度上的空间异质性对LAI、光合有效辐射比率和NDVI等地表参数的影响,对传感器进行正则化处理,最后得出结论:NDVI是尺度不变的,而LAI和光合有效辐射比率之间的关系随尺度呈现非线性关系,LAI和NDVI之间的关系则近似线性。Van Der Meer等[26]基于模拟的不同分辨率中分辨率成像光谱仪(MEdium Resolution Imaging Spectrometer, MERIS)数据,分析了基于不同空间分辨率遥感数据中计算的各种植被指数和地面生物量的尺度效应。Gu等[11]基于分段回归的方法建立生长季中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer, MODIS)与Landsat NDVI间的统计关系。虽然统计学方法可以窥探部分尺度转换规律、呈现其一定外在表现形态,可以在实际中较好地解决具体问题,但是该方法基于特定实际成像参数的特点也决定其存在较显著的缺陷:转换关系的建立需要大量样本数据,且所得转换模型物理意义不明确,模型不适宜推广使用。
还有一些学者基于物理模型进行地表反射率或发射率等基础遥感地表参数的尺度转换研究[27,28,29,30,31],或者进行LAI等高级别产品的尺度转换研究[20,22]。但总体来说,由于物理模型数量及精度发展的限制,其研究无法满足需求。
另外还有一些学者利用数学解析的方法进行地表参数(升)尺度转换研究。基于泰勒级数展开的尺度转换模型被认为是一种高精度的普适性尺度转换方法。Hu等[32]基于此模型设计了“尺不变”算法的框架,并初步分析了NDVI的尺度转换算法。Li等[30,31]基于此模型推演出像元尺度的普朗克定律。Zhang等[5]基于此方法构建NDVI的空间尺度校正模型,并进一步提出一种“尺不变”的FVC模型,研究表明该模型在实际应用中反演精度更高。之后,吴骅等[33]将此模型应用于LAI尺度效应刻画中,认为利用此方法对低分辨率LAI尺度校正后的相对误差小于1%。而刘艳等[34]进一步基于此模型建立了“点”观测数据到低分辨率遥感产品的尺度转换模型及误差评估方法,并以LAI为例进行实验验证。刘良云[6]则从反射率的层次进行LAI泰勒级数展开,提高了尺度转换模型的精度。这些研究验证了该方法的有效性。
Hu和Islam的“尺不变”算法框架[35]为:
式中:PD,PL分别代表以“分布(distributed)”和“聚合(lumped)”2种方法所得低分辨率大尺度像元的地表参数,其差代表尺度效应显著程度,由高空间分辨率地表参数影像的统计参数(方差、协方差等)确定。但是,进一步可知,虽然地表参数泰勒级数展开模型在地表参数类别、展开阶数、误差估计、精度提高等方面皆有较大发展,但是根据公式(1)发现:传统的模型从本质上仍然利用影像的基本纹理信息(均值、方差、协方差),在以往研究文献中此类模型往往用于均一地类。而参考Chen[35]的观点,复合纹理信息(如地物类别)可以直观反映地表空间异质性,研究尺度效应时融入地物类别信息更为合理[35]:
式中:PD,PL的含义与前式相同,不同的是尺度效应的大小则由引起尺度效应的本质因素(地表空间异质性,即式中各地类组分)确定,这一理念更为合理,且Chen[35]和Zhang等[5]通过实验研究证明了此观点的科学性。故可考虑把直观反映地表空间异质性的地物类别信息融入NDVI泰勒级数展开模型,建立适用混合地类的高精度NDVI升尺度转换模型,这一思想具有重要研究价值。下面对其实现方式进行了探讨。
如何融合地物类别信息建立NDVI泰勒级数升尺度转换模型?这一问题包含下面的要点:首先,对于NDVI,如何确定其尺度效应的重要影响地类、进而确定遥感影像合适的分类体系;其次,如何将地物类别信息融入NDVI泰勒级数展开模型,实现NDVI升尺度转换;最后,如何对尺度转换结果进行误差分析。针对这一问题可通过如下步骤初步解决。
(1) 确定敏感因子,确定分类体系
根据已有研究结论:Chen[35]和Zhang等[5]研究了NDVI的空间尺度转换特性,认为NDVI具有尺度效应,在像元内包含水体时这一效应更为显著。故确定对NDVI尺度效应影响较大的一种典型地物是水体,同时为了研究便利,考虑将地物分为水体和陆地两大类,进而将2种地物类别信息融合NDVI泰勒级数展开方法进行NDVI尺度转换模型构建。
(2) 模型建立
根据Chen[35]等的观点,相同区域大像元NDVI与其中陆地、水体两地类小像元NDVI间存在如下关系:
式中:NDVItotal代表低空间分辨率大像元的NDVI值;NDVIland和NDVIwater分别代表相同区域陆地、水体的NDVI值;Sland和Swater分别对应于两地类在大像元内所占的面积比。
对于陆地、水体均一地表,以F(p)作为NDVIland和NDVIwater的共同函数代表,则根据刘良云[6]和刘艳等[34]的研究成果,可方便确定对于陆地、水体单一地类的大小尺度间转换的差异Fland(p)-Fland(
则对于陆地、水体混合地类而言,NDVI尺度转换模型为:
Ftotal(p)-Ftotal(
=Sland(Fland(p)-Fland(
-Fwater(
≈
式中:kland=F″land(
Vland=
Vwater=
F″(p)是陆地或水体F(p)函数的二阶偏导数,
(3) 误差估计
可参考刘艳等[34]的模型误差估计方差,各均一地类的误差可方便评估。则对于陆地、水体混合地类而言,模型总误差为2种地类误差之和:
式中:errtotal为包含混合地类的像元尺度转换总误差;errland和errwater为此混合像元内陆地、水体分别带来的误差。
需要明确的是,虽然现有文献已呈现多种地表参数一元或者二元的泰勒级数展开模型,但是泰勒级数展开模型自一元向二元过渡时,其展开形式及误差估计模型皆有较大变化,现有二元展开模型的数学严密程度有待进一步论证。这也是今后研究的一个关注点。此外,需要注意的是,泰勒级数展开方法亦有适用范围:反演函数连续可导,否则将无法利用该方法。根据前文表述及公式(3)~(5)可知,融入地类类别信息的NDVI泰勒级数展开模型从本质上与应用于均一地类的该模型是一致的,区别在于本文的方法是针对异质性下垫面内不同地类分别基于泰勒级数展开方法进行NDVI尺度转换建模,进而根据不同地类所占面积权重对各地类的尺度转换结果做组合,尝试实现异质性地表的更高精度的NDVI尺度转换模型构建。不同的遥感地表参数,反演原理与算法可能不同,当反演函数不符合上述泰勒级数展开方法适用条件时,此时需要考虑其他普适性尺度转换方法,如计算几何模型[33]和趋势面[3,36]方法都有重要的应用潜力。
通常,对于NDVI等地表参数,因为不同地类的计算模型没有大的差别,且水体是其尺度效应的关键影响因子[35,5],上述(融合精确地物类别信息的)尺度转换思想的必要性无法充分体现。但是对于计算模型依赖于地物类别的地表参数而言(如LAI等),其必要性可得到完整展现:因为当利用统计模型计算不同地类LAI等地表参数时模型不同,对这些地表参数上推尺度需要严格按照上述步骤进行。Shi等[37]融合地物信息进行LAI升尺度转换建模,但其所考虑地类较为简单(植被与非植被2类),未考虑植被不同类别对LAI反演模型的重要影响,有待改进。
遥感地类信息融入地表参数尺度转换的重要性不仅体现于升尺度转换中,在地表参数降尺度转换,如精细亚像元制图中也得到充分体现。Wang等[38,39,40,41]和Shi等[42]通过光谱约束(混合像元分解)确定亚像元尺度上地物类别,并进一步通过空间约束定位上述地类位置,获取精细亚像元制图结果,在此过程中地物类别的物理属性和几何位置属性得到最大程度挖掘。
以上论述表明,将地物类别信息融入升尺度转换正在成为研究趋势,值得进一步深入研究。
虽然基于泰勒级数展开的地表参数升尺度转换方法更为精确,但是它却无法描述尺度转换的动力学过程、物理意义不明显。基于分形IFS函数的地表参数降尺度转换研究可以弥补此方面的不足。
梁顺林[1]曾对当前的一些降尺度转换方法做过综述,包括:线性分解方法和非线性统计分解方法、产生连续区域的方法、NDVI时间序列分解、多分辨率数据融合及全球气候模型(Global Climate Model,GCM)产品的统计降尺度方法等。进一步,Gao等[43]、Zhu等[44]和Huang等[45,46]在时空融合地表反射率降尺度方面做了系统而有成效的工作,成为研究热点。而Wang等[38,39,40,41]和Shi等[42]融合光谱空间特征在亚像元制图方面也取得了很好的成果。但是在这些研究中,从动力学角度考量尺度转换过程的很少,多是基于分形迭代函数系统(Iterated Function System, IFS)的地表参数降尺度转换对此进行的关注与研究。
作为数学分支的分形几何学,因为具有完整、严谨的理论体系,可针对自然现象的多尺度特性的表现、本质及产生原因进行系统研究。在分形几何理论体系中,除了大家所熟悉的分形现象描述与分形量测以外,数学分形产生的内在原因或者动力学过程(相互作用、反馈和迭代,以IFS为代表)、统计分形产生的物理原因(如临界或突变)亦是分形几何的重要研究内容,分形几何学已成为非线性动力学研究的一部分[47]。虽然当前分形动力学的研究刚刚起步,尚有许多问题等待解决,但不可否认其在动力学研究中的潜在价值与意义。
在定量遥感研究中,分形方法较多地应用于主动雷达影像、雪地和海洋影像等地表形态(空间结构)的刻画[48],但是其在尺度转换研究中亦有重要应用,且被进一步深化与拓展。利用分形进行地表参数尺度转换建模通常包含2个重要的研究内容:① 分形特征的表现,即分形度量,也就是研究对象的分维数。如Zhang等[49,50]利用信息维方法进行LAI尺度转换分维特性描述,栾海军等[51,52]和Wu等[53]分别利用相似维方法对NDVI和LAI升尺度转换分维特性进行了一系列研究。② 分形现象的内在本质,即产生的动力学原因,这是地表多因素作用的综合效果。分形产生的数学基础为IFS,Kim等[54]融合土壤水分尺度转换的动力学因子(土壤含沙量、植被含水量)构建了r函数,进而建立了描述土壤水分降尺度的IFS,转换效果良好。所建立模型可描述土壤水分尺度转换的动力学过程,具备物理意义,展示了基于分形IFS函数进行地表参数降尺度转换的优势。总体上,目前对于分形的动力学原因探究较少。数学中的分形IFS是以研究对象整体为单位进行连续迭代计算的[47],而遥感地表参数影像是以各局部像素为单位进行的。这就决定了数学中的IFS垂直转换因子(r函数)通常为常量[55],而遥感地表参数(如土壤水分)的垂直转换因子则是根据各个像元处的物理要素(如土壤含沙量、植被含水量)的空间、时间变化而动态变化[54]。这是IFS函数可以描述地表参数的尺度转换动力学过程、模型具备一定物理意义的原因。垂直转换因子用于描述地表参数值的尺度间转换方式,是确定IFS函数的关键。而不同地表参数由于空间分布和尺度转换的影响因素(或者动力学因子)不同,垂直转换因子(r函数)所包含的变量类型及函数形式也不相同。如何确定r函数是确定IFS函数的难点,这也是IFS函数在定量遥感地表参数尺度转换描述中应用较少的重要原因。故可考虑基于分形IFS函数建立NDVI降尺度转换模型,以描述其尺度转换动力学过程,这一研究具有较大的研究空间、且具有重要意义。下面对其实现方式进行初步探讨。
如何基于分形IFS函数建立NDVI降尺度转换模型?这一问题包含以下要点:首先,针对NDVI,如何确定影响NDVI空间分布和尺度效应的敏感因子;其次,如何利用此敏感因子建立IFS中垂直尺度转换因子r函数,并进而确定IFS函数,实现NDVI降尺度转换;最后,如何评估降尺度转换结果。针对这些要点,其解决方法描述如下。
(1) 确定敏感因子
根据前文描述可知,水体为影响NDVI空间分布与尺度效应的重要参数,故可确定像元水体参数为NDVI尺度转换的重要动力学因子之一。此外,Wen等[56]给出了一种小尺度影像到大尺度影像反照率转换的方法,并利用像元地形影响因子对转换后结果进行校正,结果证明了该方法在崎岖地形下反照率尺度转换时的有效性。考虑到地表反射率与地表反照率的密切关系、且地表反射率是计算NDVI的基本参量,故可确定地形因子参数作为NDVI尺度转换的重要动力学因子之一。故确定NDVI空间分布与尺度转换中的重要动力学因子是像元水体参数、地形因子。
(2) 确定垂直转换因子r函数,建立IFS
参考Kim等[54]论文,得到如下进行大尺度地表参数像元降尺度的IFS公式(6)、水平变换公式(7)和垂直变换公式(8),利用IFS公式逐像元滑动计算,可得整幅影像降尺度结果:
IFSi,j|n,m(xi,yj,sij)=(pn(xi),qm(yj),In,m(xi,yj,sij)), (6)
In,m(xi,yj,sij)=ij(en,mxi+fn,myj+gn,mxiyj +r1(xi,yj)sij+kn,m)×r2(xi,yj),(8)
式中:IFSi,j|n,m(xi,yj,sij)为地表参数大尺度像元降尺度至n×m维小尺度影像时第(i,j)处像元的地表参数;xi,yj,sij分别对应于该像素三维数据的x方向坐标pn(xi)、y方向坐标qm(yj)、地表参数值In,m(xi,yj,sij);
下面重点阐述针对NDVI对上述垂直变换公式所做的改进,即r函数的确定(式中,r1(xi,yj)与r2(xi,yj)函数形式相同,但自变量系数有差异):
基于上述敏感因子,可构建垂直转换因子r函数:
式中:Swater代表像元水体参数,s代表地形信息,考虑到r函数的数量级,将分别以NDWI(归一化水体指数)、坡度(由DEM影像计算)代表像元中水体作用、地形影响,γ和β分别为两参数的系数,δ代表调节常数。2种不同数量级的r函数如下:
r函数构建完成后,结合其他已知条件则公式(6)~(8)可解算,NDVI降尺度转换可实现。
(3) 降尺度结果评估
为了获取更为精确的降尺度结果,若低分辨率影像与目标分辨率影像分辨率相差太大(如由250 m MODIS NDVI降尺度至30 m NDVI),将采取分层次降尺度的方法,即先由低分辨率地表参数影像降尺度至某一中间分辨率影像,继而由此中间分辨率影像进一步降尺度至目标分辨率影像,这样可以在很大程度上保障结果的精度。
参考Kim等[54]的研究,将使用最大值(max)、最小值(min)、方差(var)和标准差(std)等统计指标评估降尺度转换结果(相比较中高分辨率NDVI影像)的准确度。
综上所述,虽然分形IFS在遥感地表参数降尺度转换模型建立方面应用的广度与深度仍显不足,但是该方法内在的物理意义及动力过程表达优势使其具备较大的应用潜力,有待进一步挖掘。该方法有望成为定量遥感地表参数降尺度转换一种新的普适性方法,为其提供新的研究手段。
时相是遥感影像的一种重要特性。当时相发生变化时,影像内地物光谱随之发生变化。继而,基于光谱信息计算得到的参量亦将发生变化,如地表反射率和NDVI等。遥感地表参数的时相响应将进一步反映在其空间尺度转换模型的变化上,即空间尺度效应的时相特性。
为定量刻画空间尺度效应的时相特性,即建立时空尺度转换耦合模型(或称为时空融合模型),学者们融合低空间分辨率影像高时相分辨率特点与中等空间分辨率影像分辨率较高的优势,对地表反射率[57]、地表温度[58,59]、植被指数[60]和LAI[61]等地表参数时空尺度融合进行了一系列的研究,呈现出体系化的理论与应用成果,黄波等[62]对此做了综述。从时空融合的理论基础(时相变化模型的空间尺度一致性、空间降尺度模型的时间一致性)到时空融合算法的类型划分(基于地物组分的时空融合、基于地表空间信息的时空融合、基于地物时相变化的时空融合和组合性的时空融合),再到现有研究所遇到的关键问题和挑战(多源遥感影像的成像几何与辐射特性差异、混合像元模型的复杂性和地物时相变化模型的复杂性等),以及未来的可能发展趋势(算法的通用性和鲁棒性提升),他做了细致深入的阐释,使我们对目前时空融合的发展有了较全面的认识。实际上,除了此方法,多重分形方法也有解决上述问题的重要潜力[35,54]。下面以NDVI为例进行分析,阐述如何基于多重分形理论与方法建立其时空尺度转换耦合模型(或时空融合模型)。
NDVI作为反映植被生长状态及植被覆盖度的最佳指示因子,具有典型的物候学特征。这意味着:在地表覆被类型不变的同一地区,在植物不同的生长期,其生理特性及外在形态皆可发生显著变化,而这种变化将直接反映在影像内地表覆被光谱、NDVI的变化上。进而,基于不同生长期(即不同时相)的遥感影像所构建的NDVI空间尺度转换模型亦将发生变化。如何有效地反映遥感影像时相特性对此模型构建的影响,进而构建出可以融合地表覆被物候学特征、更为普适的全生长期NDVI空间尺度转换模型,即NDVI时空尺度转换耦合模型?这一问题具有重要的研究价值。Kim等[54]曾提出利用多重分形(Multi-fractal)方法建立多时相遥感土壤水分空间降尺度转换模型的思想,以描述土壤水分空间降尺度转换的时相特性,但是未做具体研究。参考已有知识,这里给出NDVI时空尺度转换耦合模型建立的具体方法:分析研究区下垫面状况,确立研究区主体覆被的类型,并根据其物候学知识选取尽可能多的、可细致代表植被整个生长期各重要“节点”的低空间、中高空间分辨率影像;基于分形方法分别构建不同生长期“节点”的NDVI空间降尺度转换模型;根据多重分形理论与方法,把时相作为分维数算法中的一个因子,将对应于各生长期的模型进行“融合”,获得统一的、全生长期NDVI尺度转换模型(即NDVI时空尺度转换耦合模型)。此时,时相(即不同生长期)已作为一个参量体现在模型中,此模型相比较基于单一时相影像构建得到的降尺度转换模型也更普适。若要获取植被生长期间某一时相的中高空间分辨率NDVI影像,将对应的时相及该时相低空间分辨率NDVI影像带入模型计算即可。当然,这一方法要求研究对象具有较显著的时相规律或时间周期性,此时建立的时空尺度转换耦合模型才更准确。虽然多重分形在理论上具备建立遥感地表参数时空尺度转换耦合模型的优势,但是该方法的理论与实施更为复杂,目前的研究实例很少。该方法有望成为遥感时空尺度转换耦合(时空融合)的一种新方法,值得进一步深入研究。
定量遥感研究可突破原有的应用领域与范围,尝试与遥感的另外一个重要研究领域——影像地类自动识别相结合。比如,在城市遥感地类自动识别中,影像空间分辨率一般更高,“异物同谱”、“同物异谱”现象显著,除去传统的辅以地物纹理特征、形状特征、时相特征等属性外,也可考虑挖掘使用地表温度等特征,以此区分比热等物理性质差异大而光谱等特征相似的地物(如阴影与水体)。
以融入地表温度影像优化地类自动识别结果为例进行细致分析。将遥感地表参数(如地表温度)融入中高空间分辨率影像地类自动识别中所遇到的一个重要问题是“尺度不匹配”:遥感地表温度影像相比较分类影像通常空间分辨率更低,如何实现上述“融合”,可以从3个层次逐步推进。一方面,可以将地表温度影像重采样至分类影像的空间分辨率,利用温度的自身特性,对于光谱纹理特征相似的地类(如水体和阴影、部分道路和滩涂)辅助区分,提高影像整体分类精度;由于温度在空间范围内是连续的、渐变的,同一温度扩散形成“不规则面状区域”,这在一定程度上削弱了地表温度“重采样”带来的误差。另一方面,为进一步削弱地表温度“重采样”带来的误差,可利用面向对象方法进行地类自动识别,它可以将分类影像从“像素尺度”提升到基于“特征”的“块尺度”,这一尺度与上述地表温度的空间分布特性更吻合;这样,融入地表温度时其“重采样”带来的误差将进一步减弱。再一方面,在后续研究中,将引入遥感地表温度的空间降尺度技术,以获取与中高分辨率分类影像更为接近的空间分辨率,以便进一步削弱地表温度影像简单“重采样”的不利影响;这方面可借鉴遥感地表温度影像时空融合研究成果[58,59],生成高时空分辨率地表温度影像,为影像地类自动识别优化提供持续、有效的辅助特征。
通过上述分析可见,遥感地表参数及其尺度转换融入遥感地类自动识别具有较大意义和重大潜力,值得进一步深入研究。
另一方面,从第1节的分析可见,融入地物类别信息有助于研究尺度转换模型在异质性地表的适用性问题,具有重要意义。对于NDVI而言,水体的存在影响重大,将影像分为水体与陆地2类对于研究NDVI尺度效应较充分,尚无法充分体现精细地类融入尺度转换的效果;但是当遥感地表参数的尺度效应对于多种地类比较敏感时,精细地类融入尺度转换的优势也将得到更充分的展现。
地物类别信息是地表空间异质性最直观的反映,而后者是遥感地表参数尺度效应存在的根本原因。将地物类别信息融入地表参数尺度转换研究具有重要的理论和实际意义,值得继续深入研究。
可以预见,定量遥感尺度转换研究成果将为遥感影像地类自动识别的发展带来新的机遇;而地物类别信息的融入也将为定量遥感尺度转换研究的发展提供重要推动作用。定量遥感尺度转换与遥感影像地类识别的结合研究将相辅相成,有望成为一种新的发展方向。
文中对遥感地表参数的尺度转换发展趋势进行分析,认为未来的遥感地表参数尺度转换研究可能在以下4个方面重点发展:
(1)在遥感地表参数升尺度转换研究中,将进一步结合精细地物类别信息或其衍生信息,以充分融合定量化的地表空间异质性信息,获取高精度转换模型。
(2)在遥感地表参数降尺度转换研究中,基于分形IFS函数的降尺度转换具有重要的研究价值与研究空间,可进一步深入研究。
(3)在遥感地表参数时空尺度转换耦合模型研究中,多重分形理论与方法仍具有重要价值及潜力,有待进一步深入研究。
(4)定量遥感尺度转换将与遥感影像地类自动识别相结合,相辅相成,有望取得新的成果。
致 谢:感谢南京大学国际地球系统科学研究所耿君博士对本文撰写提出的重要建议与意见!
The authors have declared that no competing interests exist.
| [1] |
|
| [2] |
Soil hydraulic property estimation using remote sensing: A review [J].
A review of recent developments related to soil hydraulic property estimation using remote sensing is presented. Several soil hydraulic parameter estimation techniques using proximal-, air-, or satellite-based remotely sensed soil moisture, land surface temperature, and/or evapotranspiration time series have evolved over the past decades. In particular, microwave remote sensing of near-surface soil moisture has played a key role in this respect. Inverse modeling, regression techniques, data assimilation methods by utilizing soil鈥搗egetation鈥揳tmosphere transfer models, genetic algorithm based optimization, and uncertainty quantification using ensemble based approaches under synthetic and field conditions have been developed and adapted over these years. In this context multiple approaches for estimating effective soil hydraulic parameters at different spatial scales (e.g., point to remote sensing footprints) by various scaling methods are also summarized. Methods reviewed include traditional bottom-up approaches, such as homogenization of local scale properties up to footprint scale, as well as newer top-down approaches, such as use of inverse modeling of remotely sensed soil moisture or temperature using genetic algorithms, Markov Chain Monte Carlo simulations with data assimilation, and multi-scale Bayesian neural networks. While new remote sensing platforms provide powerful tools and techniques for noninvasive soil hydraulic property estimation of the vadose zone at multiple scales, current limitations including vision for future developments are also summarized.
|
| [3] |
Prospects on future developments of quantitative remote sensing [J].定量遥感尺度效应刍议 [J]. |
| [4] |
Review of up-scaling of quantitative remote sensing [J].定量遥感升尺度转换研究综述 [J].
尺度转换问题是定量遥感领域基础而重要的科学问题之一。重点针对升尺度转换研究现状，从现象描述、尺度效应产生原因分析、尺度转换方法归纳及尺度转换效果评价4个方面进行细致论述。认为目前的研究主要存在3个问题：①基于离散的多传感器影像进行的反演量尺度转换研究，受到不同传感器间成像参数归一化精度的影响；②反演量物理模型发展有限，基于这些模型的反演量连续空间尺度转换研究仍不成熟；③基于分形理论等数学方法的反演量连续空间尺度转换研究取得了一定的进展，但仍受限于尺度上推理论与技术的发展水平。对于定量遥感升尺度转换研究的发展趋势，做出如下展望：①随着成像参数归一化技术的进步，问题①将得到更有效的处理，这将有助于实际应用问题的解决；②连续空间尺度转换模型构建是升尺度转换研究发展的重要趋势。随着多学科知识的融入，定量遥感反演理论的发展及尺度上推理论与技术的进步，问题②与③亦将得到较好的解决，这将有益于揭示遥感反演量真正意义上的尺度转换规律。
|
| [5] |
Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model [J].
Vegetation index (VI) is an important variable for retrieving the vegetation biophysical parameters. With different kinds of remote sensing data sets, it is easy to get the VI at different spatial and temporal resolutions. However, the main concern is whether the relationship existing at some scale between the VI and biophysical parameters is still applicable to other scales. This paper first presents a method to correct the spatial scaling effect of NDVI by mathematic analysis, and then analyses NDVI scale sensitivity with data from a spectral database. The result shows that the NDVI obtained by reflectance up‐scaling is larger than the up‐scaled NDVI from NDVI itself in most situations. The NDVI scaling effect is more significant when water exists in a pixel, and increases with the increase in the difference of the sum of visible reflectance and near‐infrared (NIR) reflectance between the vegetation and soil. Finally, a method is proposed to estimate the fraction of vegetation cover (FVC) on the basis of the NDVI spatial scaling correction model. The method is accurate enough to assess the FVC taking into account the scaling effect.
|
| [6] |
Simulation and correction of spatial scaling effects for leaf area index [J].叶面积指数遥感尺度效应与尺度纠正 [J]. |
| [7] |
Remote sensing of photosynthetic light-use efficiency across two forested biomes: Spatial scaling [J].
Eddy covariance (EC) measurements have greatly advanced our knowledge of carbon exchange in terrestrial ecosystems. However, appropriate techniques are required to upscale these spatially discrete findings globally. Satellite remote sensing provides unique opportunities in this respect, but remote sensing of the photosynthetic light-use efficiency ( 蔚), one of the key components of Gross Primary Production, is challenging. Some progress has been made in recent years using the photochemical reflectance index, a narrow waveband index centered at 531 and 570 nm. The high sensitivity of this index to various extraneous effects such as canopy structure, and the view observer geometry has so far prevented its use at landscape and global scales. One critical aspect of upscaling PRI is the development of generic algorithms to account for structural differences in vegetation. Building on previous work, this study compares the differences in the PRI: 蓻 relationship between a coastal Douglas-fir forest located on Vancouver Island, British Columbia, and a mature Aspen stand located in central Saskatchewan, Canada. Using continuous, tower-based observations acquired from an automated multi-angular spectro-radiometer (AMSPEC II) installed at each site, we demonstrate that PRI can be used to measure 蓻 throughout the vegetation season at the DF-49 stand (r 2 = 0.91, p < 0.00) as well as the deciduous site (r 2 = 0.88, p < 0.00). It is further shown that this PRI signal can be also observed from space at both sites using daily observations from the Moderate Resolution Imaging Spectro-radiometer (MODIS) and a multi-angular implementation of atmospheric correction (MAIAC) (r 2 = 0.54 DF-49; r 2 = 0.63 SOA; p < 0.00). By implementing a simple hillshade model derived from airborne light detection and ranging (LiDAR) to approximate canopy shadow fractions (伪 s), it is further demonstrated that the differences observed in the relationship between PRI and 蔚 at DF-49 and SOA can be attributed largely to differences in 伪 s. The findings of this study suggest that algorithms used to separate physiological from extraneous effects in PRI reflectance may be more broadly applicable and portable across these two climatically and structurally different biome types, when the differences in canopy structure are known.
|
| [8] |
Application of the photosynthetic light-use efficiency model in a northern Great Plains grassland [J].
The light-use efficiency (LUE) model of photosynthesis is widely used to estimate ecosystem photosynthesis and net primary production from remote sensing measurements. The fraction of absorbed photosynthetically active radiation ( f APAR ) is a dominant term in this model, and it is fundamentally important for model calculations of ecosystem productivity across large areas. The LUE term is sometimes considered a constant, but may be best represented as a variable scalar under stress conditions. The main objective of this study was to better understand factors influencing f APAR , its relationship with seasonal variation in canopy greenness (Normalized Difference Vegetation Index (NDVI)), and the consequences of potential seasonal changes in the NDVI- f APAR relationship for LUE model calculations of ecosystem photosynthesis in a semi-arid grassland. We used two approaches to determine f APAR , (i) direct incoming and outgoing radiation measurements above and below the canopy, and (ii) an inversion approach based on incident photosynthetically active radiation and the light response curve of net ecosystem productivity measured by eddy covariance at low light levels. The two approaches resulted in f APAR values that were very strongly correlated during the initial development of the canopy until peak leaf area index (LAI) was reached. During this time, a strong linear relationship also occurred between f APAR and NDVI calculated from spectral reflectance measurements of the grassland canopy. After peak LAI, there was hysteresis in the NDVI鈥 f APAR relationship, and the two f APAR estimates diverged. Light-use efficiency model calculations of ecosystem photosynthesis made using f APAR values were strongly correlated with chamber CO 2 exchange measurements during the initial development of the canopy leaf area. After peak LAI, a stress function, based on either soil moisture or vapour pressure deficit (VPD) measurements, was necessary to reduce quantum yield and model calculations of ecosystem photosynthesis during periods of relatively low soil moisture and higher VPD later in the growing season. Both stress functions were similarly effective in improving the correlation between modeled and measured ecosystem photosynthesis values, and indicated reduced LUE under late season conditions. Modulating LUE based on the Photochemical Reflectance Index or the Water Band Index (both proposed as possible indicators of LUE) was not effective to improve the correlation between modeled and measured ecosystem photosynthesis values in this ecosystem.
|
| [9] |
The vegetation greenness trend in Canada and US Alaska from 1984-2012 Landsat data [J].
61Discrepancy between Landsat 5 & 7 NDVI was corrected.61Wall-to-wall mapping of Landsat NDVI trend (1984–2012) across northern North America61Landsat data well capture the fire disturbances in boreal forests.61Landsat reveals more extensive greening in northeastern Canada compared to AVHRR.61But much less extensive greening in northern Alaska compared to AVHRR
|
| [10] |
Scaling and assessment of GPP from MODIS using a combination of airborne lidar and eddy covariance measurements over jack pine forests [J].
Understanding the influence of within-pixel land cover heterogeneity is essential for the extrapolation of measured and modeled CO 2 fluxes from the canopy to regional scales using remote sensing. Airborne light detection and ranging (lidar) was used to estimate spatial and temporal variations of gross primary production (GPP) across a jack pine chronosequence of four sites in Saskatchewan, Canada for comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product. This study utilizes high resolution canopy structural information obtained from airborne lidar to bridge gaps in spatial representation between plot, eddy covariance (EC), and MODIS estimates of vegetation GPP. First we investigate linkages between canopy structure obtained from measurements and light response curves at a jack pine chronosequence during the growing season of 2004. Second, we use the measured canopy height and foliage cover inputs to create a structure-based GPP model (GPP Landsberg) which was tested in 2005. The GPP model is then run using lidar data (GPP Lidar) and compared with eight-day cumulative MODIS GPP (GPP MODIS) and EC observations (GPP EC). Finally, we apply the lidar GPP model at spatial resolutions of 1聽m to 1000聽m to examine the influence of within-pixel heterogeneity and scaling (or pixel aggregation) on GPP Lidar. When compared over eight-day cumulative periods throughout the 2005 growing season, the standard deviation of differences between GPP lidar and GPP MODIS were less than differences between either of them and GPP EC at all sites. As might be expected, the differences between pixel aggregated GPP estimates are most pronounced at sites with the highest levels of spatial canopy heterogeneity. The results of this study demonstrate one method for using lidar to scale between eddy covariance flux towers and coarse resolution remote sensing pixels using a structure-based Landsberg light curve model.
|
| [11] |
Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations [J].
61A rule-based piecewise regression growing season NDVI (GSN) model was developed.61The GSN model was based on the 250-m MODIS GSN and 30-m 2-Landsat path/row data.61Strong correlation between the predicted GSN and the MODIS actual GSN (r=0.91)61A 30-m grassland productivity estimation map for central Nebraska was developed.61The 30-m grassland production map provides detailed ecological features of a site.
|
| [12] |
Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest [J].
Phenology is a key component of monitoring terrestrial ecosystem variations in response to global climate change. Satellite-measured land surface phenology (LSP) has been widely used to assess large scale phenological patterns and processes. However, the accuracy of LSP is rarely validated with spatially compatible field data due to the significant spatiotemporal scale mismatch. In this study, we employ intensive field observations specifically designed to address this deficiency. High density/frequency spring phenological observations were collected in a mixed seasonal forest during 2008 and 2009. A landscape up-scaling approach was used to derive landscape phenology (LP) indices from plot-level observations in order to validate Moderate-resolution Imaging Spectroradiometer (MODIS) based LSP. Results show that the MODIS Enhanced Vegetation Index (EVI) derived start of spring season (SOS) measure was able to predict LP full bud burst date with absolute errors less than two days. In addition, LSP derived SOS captured inter-annual variations and spatial differences that agreed with ground observations. Comparison of complete time series of LP and LSP revealed that fundamental differences exist between the two observation means, e.g., LP development had increased influence on LSP during the course of spring onset. Therefore, inferring continuous LP processes directly from LSP measures could be problematic. However, using LSP derived from techniques such as logistic curve modeling for extracting seasonal markers appears more promising. This study contributes to a more explicit understanding of the linkages between remotely sensed phenology and traditionally observed (ground-based) phenology.
|
| [13] |
Matching the phenology of net ecosystem exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations [J].
61MODIS VI's show a higher correlation with SGSNEEcompared toin-situVI's.61The MODIS NDVI matches best with SGSNEE.61Specific VI's can be applied improving a SGSNEEestimate specific for a PFT.
|
| [14] |
Key methods and experiment verification for the validation of quantitative remote sensing products [J].陆地定量遥感产品的真实性检验关键技术与试验验证 [J].
遥感产品真实性检验是评价遥感产品质量、可靠性和适用性的唯一手段,是提高遥感产品精度、改善遥感产品质量的主要依据,更是推动遥感产品应用范围和应用水平的重要保障。主要介绍国家高技术研究发展计划地球观测与导航技术领域“星机地综合定量遥感系统与应用示范（一期）”项目在“遥感产品真实性检验关键技术及其试验验证”方面取得的主要进展：研制了一系列国家标准,包括陆地定量遥感产品真实性检验通用方法,遥感产品真实性检验地面观测场的选场和布设规范,以及24个遥感产品真实性检验的单项国家标准;研建了遥感产品真实性检验的完整技术流程体系,发展和完善了真实性检验过程中的空间优化采样—尺度上推—检验策略的关键技术方法;通过星机地同步试验获取多尺度配套观测数据集,系统实证了遥感产品真实性检验标准与技术体系;构建了遥感产品真实性检验网,开展核心观测场的多模式联网观测实践,初步形成全国真实性检验网的原型体系和运行机制,为我国遥感产品真实性检验的业务化运行奠定了坚实的基础。
|
| [15] |
Review on validation of remotely sensed land surface temperature [J].卫星遥感地表温度的真实性检验研究进展 [J].
地表温度是多种地表过程模型的输入参数,遥感反演地表温度是估算区域及全球尺度上地表辐射平衡和能量收支的关键手段.对遥感地表温度开展真实性检验有利于客观评价其精度和稳定性,对遥感地表温度的反演及应用都具有重要意义.简单回顾了通过遥感手段反演地表温度的基本原理和常用方法.回顾并分析了基于实测地表温度的检验方法、基于辐亮度的检验方法、交叉比较以及时间序列分析4种典型地表温度真实性检验方法的优缺点.在此基础上,重点总结了地表温度直接检验方法中地面观测数据获取方法、检验对象,分析了直接检验中的不确定来源.最后,对地表温度真实性检验中存在的问题进行了讨论.
|
| [16] |
Numerical experiments on the spatial scaling of land surface albedo and leaf area index [J].
Understanding the spatial scaling of land surface albedo and leaf area index (LAI) over heterogeneous landscapes has the practical implications to algorithm development, validation and applications of satellite products. In this study, we designed the numerical experiments to evaluate whether the retrieved LAI values from coarse‐resolution satellite imagery are equivalent to the ground “true” values, and to test whether the spectral BRDF and albedos can be linearly scaled from the fine resolutions to the coarse resolutions. These numerical experiments were conducted by using both three‐dimensional atmospheric and canopy radiative transfer models. The results based on the summer Landsat Thematic Mapper data over the US Department of Agriculture (USDA) Beltsville Agricultural Research Center (BARC) Remote Sensing Validation Site showed that the BRDF and albedo scaling is quite linear, and the retrieved LAI values could be quite different from the true LAI values if the surface is highly heterogeneous.
|
| [17] |
Validating MODIS land surface reflectance and albedo products: Methods and preliminary results [J].
This paper presents the general methods and some preliminary results of validating Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface reflectance and albedo products using ground measurements and Enhanced Thematic Mapper Plus (ETM+) imagery. Since ground 鈥減oint鈥 measurements are not suitable for direct comparisons with MODIS pixels of about 1 km over heterogeneous landscapes, upscaling based on high-resolution remotely sensed imagery is critical. In this study, ground measurements at Beltsville, MD were used to calibrate land surface reflectance and albedo products derived from ETM+ imagery at 30 m, which were then aggregated to the MODIS resolution for determining the accuracy of the following land surface products: (1) bidirectional reflectance from atmospheric correction, (2) bidirectional reflectance distribution function (BRDF), (3) broadband albedos, and (4) nadir BRDF-adjusted reflectance. The initial validation results from ground measurements and two ETM+ images acquired on October 2 and November 3, 2000 showed that these products are reasonably accurate, with typically less than 5% absolute error. Final conclusions on their accuracy depend on more validation results.
|
| [18] |
Variability in surface BRDF at different spatial scales (30~500 m) over a mixed agricultural landscape as retrieved from airborne and satellite spectral measurements [J].
78 We created a new surface BRDF retrieval scheme using CAR multiangle airborne data. 78 We analyzed the interaction between surface BRDF and sensor spatial resolution. 78 We also examined the use of a priori knowledge in kernel-driven BRDF models. 78 Results provide new empirical evidence concerning the role of scale in the BRDF. 78 Results offer new insights into the directional nature of agricultural landscapes.
|
| [19] |
Multiscale analysis and validation of the MODIS LAI product: Ⅰ. Uncertainty assessment [J]. |
| [20] |
The spatial scaling effect of continuous canopy Leaves Area Index retrieved by remote sensing [J].
Leave Area Index (LAI) is one of the most basic parameters to describe the geometric structure of plant canopies. It is also important input data for climatic model and interaction model between Earth surface and atmosphere, and some other things. The spatial scaling of retrieved LAI has been widely studied in recent years. Based on the new canopy reflectance model, the mechanism of the scaling effect of con- tinuous canopy Leaf Area Index is studied, and the scaling transform formula among different scales is found. Both the numerical simulation and the field validation show that the scale transform formula is reliable.
|
| [21] |
Scale effect and error analysis of crop LAI inversion [J].作物LAI的遥感尺度效应与误差分析 [J].
以黑河中游盈科绿洲为研究区, 利用Hyperion高光谱数据, 采用双层冠层反射率模型(ACRM)迭代运算反演LAI; 通过LAI的均值化(LAImean)以及Hyperion数据反射率线性累加反演LAI(LAIp), 定量分析LAI反演的尺度效应; 从模型的非线性和地表景观结构的空间异质性2个方面分析引起反演误差的原因, 并在LAI-NDVI回归方程的基础上利用泰勒展开的方法对低分辨率数据反演结果进行了误差纠正。结果表明, 地表景观结构的空间异质性是造成多尺度LAI反演误差的关键因素, 通过泰勒展开式能很好地实现大尺度数据LAI反演结果的误差纠正。
|
| [22] |
The spatial scaling effect of the discrete-canopy effective Leaf Area Index retrieved by remote sensing [J].
The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.
|
| [23] |
Upscale integration of normalized difference vegetation index: The problem of spatial heterogeneity [J].
The correspondence between the normalized difference vegetation index (NDVI) calculated from average reflectances, M/sub NDVI/, and NDVI integrated from individual NDVIs, I/sub NDVI/, by simulating AVHRR data from high spatial resolution SPOT 1 Haute Resolution Visible radiometer and Landsat Thematic Mapper data is analyzed. For the considered sites, located in tropical West Africa and temperate France, and the scales analyzed, 300-1000 m, a strong correlation exists between the two types of index. The relationship is almost perfectly linear, with a slope depending slightly on the variability of the vegetation cover. Effecting the scale change using M/sub NDVI/ instead of I/sub NDVI/ does not introduce significant errors.
|
| [24] |
Scale dependencies of vegetation and topography in a mountainous environment of Montana [J].
Abstract This research examines the effects of spatial scale on estimating the relationship between vegetation biomass and topography within a portion of Glacier National Park, Montana. The Reflectance/Absorptance vegetation index, developed from processed Landsat Thematic Mapper digital data, is related to three topographic variables obtained through processed Digital Elevation Models: elevation, slope angle, and slope aspect. R 2 values between the vegetation index and the topographic variables are obtained from regression analyses at a series of aggregated spatial scales. The effective range of spatial scales within which the two sets of variables are spatially dependent and the degree of the spatial dependencies are characterized through semivariance and fractal analyses.
|
| [25] |
Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE [J].
Biophysical inversion of remotely sensed data is constrained by the complexity of the remote sensing process. Variations in sensor response associated with solar and sensor geometries, surface directional reflectance, topography, atmospheric absorption and scattering, and sensor electrical-optical engineering interact in complex manners that are difficult to deconvolve and quantify in individual images or in time series of images. We have developed a model of the remote sensing process to allow systematic examination of these factors. The model is composed of three main components, including a ground scene model, an atmospheric model, and a sensor model, and may be used to simulate imagery produced by instruments such as the Landsat Thematic Mapper and the Advanced Very High Resolution Radiometer. Using this model, we examine the effect of subpixel variance in leaf area index (LAI) on relationships among LAI, the fraction of absorbed photosynthetically active radiation (FPAR), and the normalized difference vegetation index (NDVI). To do this, we use data from the first ISLSCP Field Experiment (FIFE) to parameterize ground scene properties within the model. Our results demonstrate interactions between sensor spatial resolution and spatial autocorrelation in ground scenes that produce a variety of effects in the relationship between both LAI and FPAR and NDVI. Specifically, sensor regularization, nonlinearity in the relationship between LAI and NDVI, and scaling the NDVI all influence the range, variance, and uncertainty associated with estimates of LAI and FPAR inverted from simulated NDVI data. These results have important implications for parameterization of land surface process models using biophysical variables such as LAI and FPAR estimated from remotely sensed data.
|
| [26] |
. Spatial scale variations in vegetation indices and above-ground biomass estimates: Implications for MERIS [J].
The Medium Resolution Imaging Spectrometer (MERIS) is one of the sensors carried by Envisat. MERIS is a fully programmable imaging spectrometer, however a standard 15-channel band set will be transmitted for each 300 m pixel (over land while over the ocean the pixels will be aggregated to 1200 m spatial resolution) covering visible and near-infrared wavelengths. Since MERIS is a multidisciplinary sensor providing data that can be input into ecosystem models at various scales, we studied MERIS''s performance relative to the scale of observation using simulated datasets degraded to various spatial resolutions in the range of 6-300 m. Algorithms to simulate MERIS data using airborne imaging spectrometer datasets were presented, including a case study from DAIS (i.e. Digital Airborne Imaging Spectrometer) 79-channel imaging spectrometer data acquired on 8 July 1997 over the Le Peyne test site in southern France. For selected target endmembers garrigue, maquis, mixed oak forest, pine forest and bare agricultural field, regions-of-interest (ROI) were defined in the DAIS scene. For each of the endmembers, the vegetation index values in the corresponding ROI is calculated for the MERIS data at the spatial resolutions ranging from 6 to 300 m. We applied the NDVI, PVI, WDVI, SAVI, MSAVI, MSAVI2 and GEMI vegetation indices. Above-ground biomass (AGB) was estimated in the field and derived from the DAIS image and the MERIS datasets (6-300 m spatial resolution). The vegetation indices are shown to be constant with the spatial scale of observation. The strongest correlation between the MERIS and DAIS NDVI is obtained when using a linear model with an offset of 0.15 ( r =0.31). A Pearson correlation matrix between AGB measured in the field and each spectral band reveals a modest but significant ( p <0.05) correlation for most spectral bands. When mathematical functions are fitted through the NDVI and biomass data, an exponential fit shows the extinction and saturation at larger vegetation biomass values. The correlation between biomass and NDVI for DAIS as well as for the MERIS simulated dataset is modest. Further research is required to analyse the scale effects that limit the correlation between field and image AGB estimates.
|
| [27] |
Surface temperature and emissivity at various scales: Definition, measurement, and related problems [J].
中国科学院机构知识库(CAS IR GRID)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。
|
| [28] |
Comments on reciprocity in the directional reflectance modeling [J]. |
| [29] |
The definition of effective emissivity of land surface at the scale of remote sensing pixels [J].
Remote sensing (RS) of land surface temperature (LST) is a very challenging problem at the present development stage of RS science. Tremendous efforts have been devoted to atmosphere correction and temperature emissivity separation (TES) of new LST product algorithms. However, the mechanism of directionality of thermal emission from land surface remains unknown, and even worse, there are confusions on the definition of the effective emissivity of land surface at the scale of RS pixels. The mechanism of directionality of thermal emission for isothermal pixels and non-isothermal pixels is different. For non-isothermal pixels (case in most canopy/soil structures), the directionality of their thermal emission is determined by both bidirectional reflectance distribution function (BRDF) derived emissivity and distribution patterns of temperature differences. A new definition is suggested to take into account material mixture, multiple scattering, and temperature variation within thermal infrared (TIR) RS pixels.
|
| [30] |
Scale effect of Planck’s law over nonisothermal blackbody surface [J].
Many physical laws, principles, models, measurement methods, etc., are applicable only to either a point on surface or a homogeneous surface. However, remote sensing deals with pixels which may range from meters to kilometers. Therefore scale effects of these laws and measurements are inevitable problems which must be faced. As an example, the spatial scale effect of Planck Law over nonisothermal blackbody surface is considered.
|
| [31] |
Scale effects and scaling-up by geometric-optical model [J].
This is a follow-up paper to our “Scale effect of Planck’s law over nonisothermal blackbody surface”. More examples are used to describe the scale effect in detail, and the scaling-up of Planck law over blackbody surface is further extended to three-dimension nonisothermal surface. This scaling-up results in a conceptual model for the directionality and spectral signature of thermal radiation at the scale of remote sensing pixels. This new model is also an improvement of Li-Strahler-Friedl conceptual model in a sense that the new model needs only statistic parameters at the pixel scale, without request of sub-pixel scale parameters as the LSF model does.
|
| [32] |
A framework for analyzing and designing scale invariant remote sensing algorithms [J].
The land surface exhibits heterogeneity across a range of spatial scales. Remote sensors provide integrated information at the pixel scale, however, there is important spatial variability at scales smaller than the scale of the sensor. On the other hand, large scale models that use remotely sensed data do not require them at the same spatial resolution at which remote sensors are required to operate. In this paper, a framework for testing aggregation-disaggregation properties of remote sensing algorithms is presented. The proposed framework provides a systematic approach for parameterizing the land surface heterogeneity effects. For the estimation of the pixel scale response, the lumped response should be modified by the variance and covariance terms. This representation of land surface heterogeneity could lead to substantial savings in remote sensing data storage and management. Using simulated land and vegetation scenarios, the authors have successfully parameterized subpixel scale heterogeneity effects for the estimation of vegetation index, by modeling the variances and covariance terms with the pixel scale values
|
| [33] |
Comparison and analysis of two general scaling methods for remotely sensed information [J].两种普适性尺度转换方法比较与分析研究 [J].
着重阐述了地表空间信息尺度转换的必要性和方法.首先从尺度转换成因分析入手,介绍了两种普适性的尺度转换模型,即泰勒级数展开模型和计算几何模型,并对这两个模型的适用性进行了分析.借助叶面积指数的反演,针对林地、农田、水体3种不同下垫面的试验样区进行了模型的分析比较.结果表明,在拥有小尺度(高分辨率)数据时,泰勒级数模型能够很好的刻画尺度效应,使得尺度效应改正后的相对误差小于1%,获取更为准确的地表参数反演值.遥感尺度转换方法、技术为获取不同尺度的地学信息,为资源调查和环境灾害监测等相关领域的应用提供真实可靠的多尺度数据支持.
|
| [34] |
Upscaling approach for validation of LAI products derived from remote sensing observation [J].用地面点测量数据验证LAI产品中的尺度转换方法 [J]. |
| [35] |
Spatial scaling of a remotely sensed surface parameter by contexture [J]. |
| [36] |
Universal scaling methodology in remote sensing science by constructing geographic trend surface [J].构造地理要素趋势面的尺度转换普适性方法探讨 [J]. |
| [37] |
An upscaling algorithm to obtain the representative ground truth of LAI time series in heterogeneous land surface [J].
Upscaling in situ leaf area index (LAI) measurements to the footprint scale is important for the validation of medium resolution remote sensing products. However, surface heterogeneity and temporal variation of vegetation make this difficult. In this study, a two-step upscaling algorithm was developed to obtain the representative ground truth of LAI time series in heterogeneous surfaces based on in situ LAI data measured by the wireless sensor network (WSN) observation system. Since heterogeneity within a site usually arises from the mixture of vegetation and non-vegetation surfaces, the spatial heterogeneity of vegetation and land cover types were separately considered. Representative LAI time series of vegetation surfaces were obtained by upscaling in situ measurements using an optimal weighted combination method, incorporating the expectation maximum (EM) algorithm to derive the weights. The ground truth of LAI over the whole site could then be determined using area weighted combination of representative LAIs of different land cover types. The algorithm was evaluated using a dataset collected in Heihe Watershed Allied Telemetry Experimental Research (HiWater) experiment. The proposed algorithm can effectively obtain the representative ground truth of LAI time series in heterogeneous cropland areas. Using the normal method of an average LAI measurement to represent the heterogeneous surface produced a root mean square error (RMSE) of 0.69, whereas the proposed algorithm provided RMSE = 0.032 using 23 sampling points. The proposed ground truth derived method was implemented to validate four major LAI products.
|
| [38] |
Allocating classes for soft-then-hard subpixel mapping algorithms in units of class [J].
There is a type of algorithm for subpixel mapping (SPM), namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at the subpixel scale level and then allocates classes (i.e., hard attribute values) for subpixels according to the soft attribute values. This paper presents a novel class allocation approach for STHSPM algorithms, which allocates classes in units of class (UOC). First, a visiting order for all classes is predetermined, and the number of subpixels belonging to each class is calculated using coarse fraction data. Then, according to the visiting order, the subpixels belonging to the being visited class are determined by comparing the soft attribute values of this class, and the remaining subpixels are used for the allocation of the next class. The process is terminated when each subpixel is allocated to a class. UOC was tested on three remote sensing images with five STHSPM algorithms: back-propagation neural network, Hopfield neural network, subpixel/pixel spatial attraction model, kriging, and indicator cokriging. UOC was also compared with three existing allocation methods, i.e., linear optimization technique (LOT), sequential assignment in units of subpixel (UOS), and a method that assigns subpixels with highest soft attribute values first (HAVF). Results show that for all STHSPM algorithms, UOC is able to produce higher SPM accuracy than UOS and HAVF; compared with LOT, UOC is able to achieve at least comparable accuracy but needs much less computing time. Hence, UOC provides an effective and real-time class allocation method for STHSPM algorithms.
|
| [39] |
Downscaling MODIS images with area-to-point regression kriging [J].
The first seven bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) data have been used widely for global land-cover/land-use (LCLU) monitoring (e.g., deforestation over the Amazon basin). However, the spatial resolution of MODIS bands 3–7 (i.e., 50002m) is coarser than that of bands 1 and 2 (i.e., 25002m), and may be too coarse for a large number of applications. In this paper, a new geostatistical approach based on area-to-point regression kriging (ATPRK) is proposed for downscaling coarse spatial resolution bands 3–7 such as to produce a complete set of MODIS images at 25002m. ATPRK takes advantages of the fine spatial resolution information in bands 1 and 2 by regression modeling, and uses area-to-point kriging to downscale the coarse residuals from the regression. ATPRK was compared to four existing methods, including the principal component analysis, wavelets, high-pass filter and kriging with external drift (KED) methods for downscaling in two experiments on MODIS data from the Brazilian Amazon. Both visual and quantitative evaluations (in terms of the root mean square error, correlation coefficient, relative global-dimensional synthesis error, universal image quality index, spectral angle mapper and spectral information divergence) showed that ATPRK produced sharpened images with the greatest quality. In addition, ATPRK perfectly preserved the spectral properties of the original coarse data and was faster than KED. The results reveal the great potential of ATPRK applied to MODIS data for a wide variety of applications, including global monitoring of deforestation. The ATPRK proposed in this paper is an entirely new image fusion approach based on a new conceptualization.
|
| [40] |
Indicator cokriging-based subpixel mapping without prior spatial structure information [J].
Indicator cokriging (ICK) has been shown to be an effective subpixel mapping (SPM) algorithm. It is noniterative and involves few parameters. The original ICK-based SPM method, however, requires the semivariogram of land cover classes from prior information, usually in the form of fine spatial resolution training images. In reality, training images are not always available, or laborious work is needed to acquire them. This paper aims to seek spatial structure information for ICK when such prior land cover information is not obtainable. Specifically, the fine spatial resolution semivariogram of each class is estimated by the deconvolution process, taking the coarse spatial resolution semivariogram extracted from the class proportion image as input. The obtained fine spatial resolution semivariogram is then used to estimate class occurrence probability at each subpixel with the ICK method. Experiments demonstrated the feasibility of the proposed ICK with the deconvolution approach. It obtains comparable SPM accuracy to ICK that requires semivariogram estimated from fine spatial resolution training images. The proposed method extends ICK to cases where the prior spatial structure information is unavailable.
|
| [41] |
Fast sub-pixel mapping algorithms for sub-pixel resolution change detection [J].
Due to rapid changes on the Earth's surface, it is important to perform land cover change detection (CD) at a fine spatial and fine temporal resolution. However, remote sensing images with both fine spatial and temporal resolutions are commonly not available or, where available, may be expensive to obtain. This paper attempts to achieve fine spatial and temporal resolution land cover CD with a new computer technology based on subpixel mapping (SPM): The fine spatial resolution land cover maps (FRMs) are first predicted through SPM of the coarse spatial but fine temporal resolution images, and then, subpixel resolution CD is performed by comparison of class labels in the SPM results. For the first time, five fast SPM algorithms, including bilinear interpolation, bicubic interpolation, subpixel/pixel spatial attraction model, Kriging, and radial basis function interpolation methods, are proposed for subpixel resolution CD. The auxiliary information from the known FRM on one date is incorporated in SPM of coarse images on other dates to increase the CD accuracy. Based on the five fast SPM algorithms and the availability of the FRM, subpixels for each class are predicted by comparison of the estimated soft class values at the target fine spatial resolution and borrowing information from the FRM. Experiments demonstrate the feasibility of the five SPM algorithms using FRM in subpixel resolution CD. They are fast methods to achieve subpixel resolution CD.
|
| [42] |
Soft-then-hard sub-pixel mapping with multiple shifted images [J].
The soft-then-hard sub-pixel mapping (STHSPM) algorithm is a type of sub-pixel mapping (SPM) algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. In this article, four fast STHSPM algorithms (i.e. bilinear, bicubic, kriging, and radial basis function interpolation) were enhanced by using multiple shifted images (MSIs). The proportion images of the MSIs were first downscaled to the desired fine spatial resolution and then the multiple downscaled images were integrated for each class, followed by the class allocation process. Three remote-sensing images were used to test the proposed methods, and the results showed that MSIs can help to increase the SPM accuracy of the four STHSPM algorithms. The approach to incorporating MSIs into the STHSPM algorithms is non-iterative and fast.
|
| [43] |
On the Blending of the landsat and MODIS surface reflectance: Predicting daily landsat surface reflectance [J].
The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes.
|
| [44] |
An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions [J].
Due to technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result not one sensor provides both high spatial resolution and high temporal resolution. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. One approach is to 鈥渂lend鈥 the radiometry from daily, global data (e.g. MODIS, MERIS, SPOT-Vegetation) with data from high-resolution sensors with less frequent coverage (e.g. Landsat, CBERS, ResourceSat). Unfortunately, existing algorithms for blending multi-source data have some shortcomings, particularly in accurately predicting the surface reflectance of heterogeneous landscapes. This study has developed an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm, and has tested it with both simulated and actual satellite data. Results show that ESTARFM improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes, and preserves spatial details. Taking the NIR band as an example, for homogeneous regions the prediction of the ESTARFM is slightly better than the STARFM (average absolute difference [ AAD] 0.0106 vs. 0.0129 reflectance units). But for a complex, heterogeneous landscape, the prediction accuracy of ESTARFM is improved even more compared with STARFM ( AAD 0.0135 vs. 0.0194). This improved fusion algorithm will support new investigations into how global landscapes are changing across both seasonal and interannual timescales.
|
| [45] |
Unified fusion of remote sensing imagery: Generating simultaneously high-resolution synthetic spatial-temporal spectral earth observations [J].
Current satellite remote-sensing systems compromise between spatial resolution and spectral and/or temporal resolution, which potentially limits the use of remotely sensed data in various applications. Image fusion processes, including spatial and spectral fusion (SSF) and spatial and temporal fusion (STF), provide powerful tools for addressing these technological limitations. Although SSF and STF have been extensively studied separately, they have not yet been integrated into a unified framework to generate synthetic satellite images with high spatial, temporal and spectral resolution. By formulating these two types of fusion into one general problem, i.e. super resolving a low spatial resolution image with a high spatial resolution image acquired under different conditions (e.g. at different times and/or in different acquisition bands), this letter proposes a notion of unified fusion that can accomplish both SSF and STF in one process. A Bayesian framework is subsequently developed to implement SSF, STF and unified fusion to generate ‘virtual sensor’ data, characterized by high spatial, temporal and spectral resolution simultaneously. The proposed method was then applied to the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images of the Hong Kong area, with the average spatial correlation coefficient exceeding 0.9 for near infrared–red–green bands between the fused result and the input Landsat image and with good preservation of the MODIS spectral properties.
|
| [46] |
Spatio-temporal reflectance fusion via unmixing: Accounting for both phenological and land-cover changes [J].
Owing to technical limitations the acquisition of fine spatial resolution images (e.g. Landsat data) with frequent (e.g. daily) coverage remains a challenge. One approach is to generate frequent Landsat surface reflectances through blending with coarse spatial resolution images (e.g. Moderate Resolution Imaging Spectroradiometer, MODIS). Existing implementations for data blending, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM), have their shortcomings, particularly in predicting the surface reflectance characterized by land-cover-type changes. This article proposes a novel blending model, namely the Unmixing-based Spatio-Temporal Reflectance Fusion Model (U-STFM), to estimate the reflectance change trend without reference to the change type, i.e. phenological change (e.g. seasonal change in vegetation) or land-cover change (e.g. conversion of a vegetated area to a built-up area). It is based on homogeneous change regions (HCRs) that are delineated by segmenting the Landsat reflectance difference images. The proposed model was tested on both simulated and actual data sets featuring phenological and land-cover changes. It proved more capable of capturing both types of change compared to STARFM and ESTARFM. The improvement was particularly observed on those areas characterized by land-cover-type changes. This improved fusion algorithm will thereby open new avenues for the application of spatio-temporal reflectance fusion.
|
| [47] |
|
| [48] |
Synthesis of fractal surfaces for remote-sensing applications [J].
A physical approach to synthesize fractal surfaces for a reliable and controllable use within remote-sensing applications is presented in this paper. In particular, the physical criteria to determine the minimum number of tones of the Weierstrass-Mandelbrot (WM) function needed to adequately synthesize realizations of fractional Brownian motion (fBm) processes are analytically derived. The presented rationale relies on considering, in an appropriate range of scales, the WM function as a spectral sampling of the fBm process, and linking the number of sampling functions to the width of the scale range and to the sampling rate; hence, it is shown how to set the lower and the upper scales according to a given remote-sensing problem. In this paper, the condition for determining the WM wavenumber sampling rate is analytically derived. The method is also applied to the efficient simulation of the synthetic aperture radar signal scattered by natural surfaces.
|
| [49] |
Spatial scaling and information fractal dimension of surface parameters used in quantitative remote sensing [J].
In this paper, a general formula has been modified, proving that the scaling difference of a surface parameter depends not only on the variance of the surface parameter itself but also on the function structure of the surface parameter. Through quantitatively describing the relationship between scaling differences and measuring scale, in terms of the concept of information fractal dimension and topological dimension, a definition of information fractal dimension used in remote sensing was proposed. By computing the information fractal dimension of Leaf Area Index and surface temperature, we found that the method describes not only the information on spatial texture and spatial structure of remotely sensed data as the traditional methods did, but also illustrates the connection between the scaling difference and measuring scale. Where the information fractal dimension of a surface parameter in some areas is known, the scaling difference can be obtained according to the measuring scale, then it can be eliminated and more accurate results could be achieved after scaling transform. At last, the problems about the relativity of true values of surface parameters were discussed.
|
| [50] |
Principles and methods for the validation of quantitative remote sensing products [J].
We first discuss the relativity of “true value and homogeneity” for quantitative remote sensing products (QRSPs), and then propose the definitions of “eigenaccuracy” and “eigenhomogeneity” under practical conditions. The eigenaccuracy and eigenhomogeneity for land surface crucial parameters such as albedo, leaf area index (LAI), and surface temperature are analyzed based on a series of experiments. Secondly, we point out the differences and similarities between the scale-free phenomena of the QRSPs and the measurements of the coastline length (1-dimensional) and the curved surface area (2-dimensional). An information fractal algorithm for the QRSPs is presented. In a case study for the LAI, when the fractal dimension is 2.16, the ratio of the LAI retrieval values obtained respectively from remote sensing data of 30 m and 6 km pixel resolution can actually reach as high as 2.86 for the same 6 km pixel using the same retrieval model. Finally, we propose an operational validation method “one test and two matches” and multipoint observation when the real situation does not allow carrying out scanning measurement without gap and overlap on the ground surface.
|
| [51] |
Establishing continuous scaling of NDVI based on fractal theory and GEOEYE-1 image [J].基于分形理论与GEOEYE-1影像的NDVI连续空间尺度转换模型构建及应用 [J].
基于GEOEYE-1多光谱影像、以归一化差分植被指数(NDVI)为研究对象继续就分形方法在高空间分辨率基础图影像中的适用性进行研究，并就分形模型构建最合理尺度层级的确定进行细致探讨.实验表明，基于分形理论的NDVI连续空间尺度转换模型构建方法适用于高空间分辨率遥感影像； 在给定的条件下，NDVI尺度转换分形模型构建时最合理尺度层级存在且可计算.研究使得分形方法所适用的反演量类型及基础图空间分辨率范围皆有大的扩展.
|
| [52] |
Establishing continuous spatial scaling model of NDVI on fractal theory and five-index estimation system [J].根据分形理论与五指标评价体系构建NDVI连续空间尺度转换模型 [J]. |
| [53] |
Spatial up-scaling correction for leaf area index based on the fractal theory [J].
The scaling effect correction of retrieved parameters is an essential and difficult issue in analysis and application of remote sensing information. Based on fractal theory, this paper developed a scaling transfer model to correct the scaling effect of the leaf area index (LAI) estimated from coarse spatial resolution image. As the key parameter of the proposed model, the information fractal dimension (D) of the up-scaling pixel was calculated by establishing the double logarithmic linear relationship between D-2 and the normalized difference vegetation index (NDVI) standard deviation (蟽NDVI) of the up-scaling pixel. Based on the calculated D and the fractal relationship between the exact LAI and the approximated LAI estimated from the coarse resolution pixel, a LAI scaling transfer model was established. Finally, the model accuracy in correcting the scaling effect was discussed. Results indicated that the D increases with increasing 蟽NDVI, and the D-2 was highly linearly correlated with 蟽NDVI on the double logarithmic coordinate axis. The scaling transfer model corrected the scaling effect of LAI with a maximum value of root-mean-square error (RMSE) of 0.011. The maximum absolute correction error (ACE) and relative correction error (RCE) were only 0.108% and 8.56%, respectively. The spatial heterogeneity was the primary cause resulting in the scaling effect and the key influencing factor of correction effect. The results indicated that the developed method based on fractal theory could effectively correct the scaling effect of LAI estimated from the heterogeneous pixels.
|
| [54] |
Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data [J].
A downscaling model is presented which consists of a modified fractal interpolation method based on contraction mapping. This methodology is different from other fractal interpolation schemes because it generates unique fractal surfaces. It is different from other contraction mapping models because it includes spatially and temporally varying scaling functions as opposed to single-valued scaling factors. The scaling functions are linear combinations of the spatial distributions of ancillary data. The model is demonstrated by downscaling soil moisture fields from 10 to 1 km resolution using remote-sensing data from the Southern Great Plains 1997 (SGP'97) field experiment.
|
| [55] |
The study on bivariate fractal interpolation functions and creation of fractal interpolated surfaces [J].
In this paper, the methods of construction of a fractalsurface are introduced, the principle of bivariate fractalinterpolation functions is discussed. The theorem of the uniquenessof an iterated function system of bivariate fractal interpolationfunctions is proved. Moreover, the theorem of fractal dimension offractal interpolated surface is derived. Based on these theorems, thefractal interpolated surfaces are created by using practical data.
|
| [56] |
Scale effect and scale correction of land-surface albedo in rugged terrain [J].
Influence of topography must be corrected when fine-scale remote sensing data are used to estimate surface albedo in rugged terrain. In the case of coarse-scale satellite remote sensing data, the topographic effect on albedo estimation is generally ignored because the influence to albedo estimation of overall slope in coarse-scale pixels is usually considered negligible; however, because of the scale effect in albedo, neglecting within-pixel topology may cause errors in albedo estimation from coarse-scale data. This paper investigates the scale effect on land-surface albedo estimations, particularly in the case of converting fine-scale albedo to coarse-scale albedo in rugged terrain. Starting from the definition of difference-scale albedo in rugged terrain, a method is presented to convert fine-scale surface albedo to coarse-scale albedo, then deriving a factor to correct coarse-scale land-surface albedo. The performance and accuracy analysis of the method are investigated by using a simulated digital elevation model (DEM) with different mean slopes, as well as real DEM and Thematic Mapper images. Results show that the method is effective for scale-effect correction of land-surface albedo in rugged terrain.
|
| [57] |
Assessing the accuracy of blending Landsat-MODIS surface reflectance in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection [J].
Blending algorithms model land cover change by using highly resolved spatial data from one sensor and highly resolved temporal data from another. Because the data are not usually observed concurrently, unaccounted spatial and temporal variances cause error in blending algorithms, yet, to date, there has been no definitive assessment of algorithm performance against spatial and temporal variances. Our objectives were to: (i) evaluate the accuracy of two advanced blending algorithms (STARFM and ESTARFM) and two simple benchmarking algorithms in two landscapes with contrasting spatial and temporal variances; and (ii) synthesise the spatial and temporal conditions under which the algorithms performed best. Landsat-like images were simulated on 27 dates in total using the nearest temporal cloud-free Landsat-MODIS pairs to the simulation date, one before and one after. RMSD, bias, and r(2) estimates between simulated and observed Landsat images were calculated, and overall variance of Landsat and MODIS datasets were partitioned into spatial and temporal components. Assessment was performed over the whole study site, and for specific land covers. Results addressing objective (i) were that: ESTARFM did not always produce lower errors than STARFM; STARFM and ESTARFM did not always produce lower errors than simple benchmarking algorithms; and land cover spatial and temporal variances were strongly associated with algorithm performance. Results addressing objective (ii) indicated ESTARFM was superior where/when spatial variance was dominant; and STARFM was superior where/when temporal variance was dominant. We proposed a framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components and suggested that comparing Landsat and MODIS spatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending. Crown Copyright (c) 2013 Published by Elsevier Inc. All rights reserved.
|
| [58] |
Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring [J].
Land surface temperature (LST) retrieved from Landsat thermal infrared bands has been proved to have the most suitable spatial resolution for urban thermal environment studies, i.e., 60 m for Enhanced Thematic Mapper Plus (ETM+) and 120 m for Thematic Mapper (TM). However, its long revisit cycle (or low temporal resolution) coupled with cloud contamination has largely limited its application in urban environments. This letter presents a spatiotemporal image fusion model to produce high spatiotemporal resolution LST data, by combining the high spatial resolution of Landsat images and the frequent coverage of Moderate Resolution Imaging Spectroradiometer (MODIS) images. Taking into consideration light reflection and refraction among ground objects and the continuity of LST in the temperature space in urban areas, a spatiotemporal image fusion model based on bilateral filtering has been proposed. The main contribution of this model is that it accounts for the warming and cooling effect of ground objects in urban areas and establishes a new weight function to account for the effect of neighboring pixels. The proposed method is tested using four pairs of LST from Landsat ETM+ and MODIS on February 15, March 19, October 13, and November 14 in 2002, covering the center of Beijing, and the results show that our method is capable of generating dense time-series LST data by combining the strengths of the MODIS and Landsat images. Our method is also compared with a state-of-the-art method, and the better performance of our system in generating high spatiotemporal resolution LST is demonstrated.
|
| [59] |
Evaluation and sensitivity testing of a coupled Landsat-MODIS downscaling method for land surface temperature and vegetation indices in semi-arid regions [J].
The current study investigates a method to provide land surface parameters [i.e., land surface temperature (LST) and normalized difference vegetation index (NDVI)] at a high spatial (藴30 and 60 m) and temporal (daily and 8-day) resolution by combining advantages from Landsat and moderate-resolution imaging spectroradiometer (MODIS) satellites. We adopt a previously developed subtraction method that merges the spatial detail of higher-resolution imagery (Landsat) with the temporal change observed in coarser or moderate-resolution imagery (MODIS). Applying the temporal difference between MODIS images observed at two different dates to a higher-resolution Landsat image allows prediction of a combined or fused image (Landsat+MODIS) at a future date. Evaluation of the resultant merged products is undertaken within the Southeastern Arizona region where data is available from a range of flux tower sites. The Landsat+MODIS fused products capture the raw Landsat values and also reflect the MODIS temporal variation. The predicted Landsat+MODIS LST improves mean absolute error around 5掳C at the more heterogeneous sites compared to the original satellite products. The fused Landsat+MODIS NDVI product also shows good correlation to ground-based data and is relatively consistent except during the acute (monsoon) growing season. The sensitivity of the fused product relative to temporal gaps in Landsat data appears to be more affected by uncertainty associated with regional precipitation and green-up, than the length of the gap associated with Landsat viewing, suggesting the potential to use a minimal number of original Landsat images during relatively stable land surface and climate conditions. Our extensive validation yields insight on the ability of the proposed method to integrate multiscale platforms and the potential for reducing costs associated with high-resolution satellite systems (e.g., SPOT, QuickBird, IKONOS).
|
| [60] |
Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed [J].
Grasslands form the dominant land cover in the upper reaches of the Yellow River and provide a reliable indicator by being strongly correlated with regional terrestrial ecological status. Remote sensing can provide information useful for vegetation quality assessments, but no single sensor can meet the needs for the high temporal鈥搒patial resolution required for such assessments on a watershed scale. To observe long-term grassland dynamics in the Longliu Watershed located in the upper reaches of the Yellow River, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images were integrated to obtain Normalized Difference Vegetation Index (NDVI) data. The MODIS images were used to identify patterns of monthly variation. With the temporal dynamics of NDVI provided by the MODIS images, an exponential regression model was obtained that described the relationship between Julian day and NDVI. Four time-series data sets from multi-spectral sensors were constructed to obtain regional grassland NDVI information from 1977 to 2006 in the Longliu Watershed. Using the daily NDVI correlation coefficient, NDVI values for different days were obtained from Landsat series images, standardised to the same day and integrated into TM format by using NDVI coefficients between the four different sensors. Thus, the NDVI data obtained from multi-sensors on different days were integrated into a comparable format. A regression analysis correlating the NDVI data from two sensors with fresh grass biomass showed that the integration procedure was reliable.
|
| [61] |
Reconstructing seasonal variation of landsat vegetation index related to leaf area index by fusing with modis data [J].
In the development of an empirical relationship between the leaf area index (LAI) and the vegetation index (VI), the infrequency of the medium resolution VI often makes it difficult, sometimes impossible, to find VI observations acquired close to the LAI measurement date. To overcome this dilemma, this paper presents a method, named reduced simple ratio (RSR), to reconstruct seasonal time series of a VI at the Landsat resolution. Each RSR time series is represented by a double logistic (D-L) curve with seven unknown parameters. The methodology solves these parameters using a multi-objective optimization method by blending frequent MODIS observations with Landsat observations acquired at a few dates (usually fewer than seven) in a year. We tested the reconstructing approach in a boreal forest in Canada and a cropland area in Australia. The reconstructed Landsat RSR compared well with the observed RSR even when only two Landsat images were used for reconstruction, and better accuracy was achieved when more Landsat images were used. Ground LAI measurements were taken at a date not coincident with any of the Landsat dates in the Canada study area. Results of LAI retrieval showed that the measured LAI had a higher correlation with the reconstructed RSR at the measurement date than with the observed Landsat RSR at the three acquisition dates.
|
| [62] |
Research status and prospect of spatiotemporal fusion of multi-source satellite remote sensign imagery [J].多源卫星遥感影像时空融合研究的现状及展望 [J]. |
甘公网安备62010202000687
/
| 〈 |
|
〉 |
