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
Establishing continuous spatial scaling model of NDVI on fractal theory and five-index estimation system
- 2015年19卷第1期 页码:116-125
纸质出版日期: 2015
DOI: 10.11834/jrs.20153340
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纸质出版日期: 2015 ,
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[1]栾海军,田庆久,余涛,顾行发,黄彦,胡新礼,杨闫君.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].遥感学报,2015,19(01):116-125.
LUAN Haijun, TIAN Qingjiu, YU Tao, et al. Establishing continuous spatial scaling model of NDVI on fractal theory and five-index estimation system[J]. Journal of Remote Sensing, 2015,19(1):116-125.
针对目前基于统计或物理模型方法的升尺度转换研究中存在的不足
以归一化差分植被指数NDVI为研究对象
基于分形理论提出一种连续空间升尺度转换模型CSSM(Continuous Spatial Scaling Model)构建方法。所构建的模型尺度适用范围更广
且具有一定的物理意义。针对已有研究尚未解决的模型构建的最合理尺度层级确定问题
结合原有的统计学四指标评价体系(r、p、rlo、rup)
融入了真实性检验应用效能评价指标(Max
o
f
a
bs(Error))
建立了一个基于五指标评价体系的模型构建最合理尺度层级确定方法。以北海市沙田半岛Landsat ETM+影像为实验影像
设定r≥0.8
p
<
0.05
rlo≥r≤rup及Max
o
f
a
bs(Error)≤0.05为评价体系的边界条件
从追求模型尺度适用范围更大的角度考虑
确定出该影像模型构建的最合理尺度层级Level=267
则该模型最高可对30 m×267即8 km分辨率遥感影像进行NDVI验证。通过动态调整此评价体系的边界条件
实现了最合理尺度层级取值的敏感性分析。这些工作使得基于分形理论的NDVI’s CSSM构建研究更为系统。
Spatial scale transformation is one of the basic and important scientific problems in quantitative remote sensing field.Spatial up-scaling has particularly drawn much attention
as it can effectively help solve difficult problems
e. g.
validation of quantitative remote sensing products. However
some issues remain concerning spatial up-scaling research.( 1) The transformation formula established by statistical methods has no explicit physical meaning and its available range is limited.( 2) The lack of reasonable retrieved physical models hampers the development of up-scaling based on these models. As an important retrieval method
the up-scaling of NDVI also faces these two issues. To address these problems using statistical and physical methods
continuous spatial scaling model( CSSM) of NDVI on the basis of fractal theory was established. The CSSM exhibits a wide available scale range and partial physical meanings. However
the means of determining the most reasonable Level( scale hierarchies) for establishing the model remains an important problem
which is studied in this research.In this research
a precise and rigorous method of determining the most reasonable Level was developed based on a five-index estimation system. The system integrates statistical estimation indices( r
p
rlo
and rup) and an availability-in-validation index [largest error in validation
Max
o
f
a
bs( Error) ]. It was computed as follows. First
the NDVI CSSM of an image was established on each of the different Levels. Second
the indices( r
p
rlo
and rup) on each Level were compared and analyzed. Third
the most reasonable Level could be computed based on the defined Max
o
f
a
bs( Error) to establish the widest scale CSSM.Shatian Byland( Beihai City
Guangxi Zhuang Autonomous Region) was selected as the experimental area because of its variety of ground objects and high spatial heterogeneity. Taking the values( r≥0. 8
p
<
0. 05
rlo≥r≤rup and Max
o
f
a
bs( Error) ≤0. 05) as estimation system
the most reasonable Level( Level = 267) was computed. On that Level
the model was log2 NDVI =1- 0. 0347log2 1/scale-1.1296 and its scale range was from 30 m to 8010 m. Within the range
validating the NDVI image on any scaleup-scale( corresponding to the integral multiple of the 30 m resolution of ETM + image) could be implemented by the model.Furthermore
the sensitivity of the Level to values of the estimation system was analyzed. The Level would dynamically change when the threshold values of the five-index estimation system were different and the application purpose changed
which meant that the method in the research was steady and rigorous.In this research
the method of determining the most reasonable Level for establishing the CSSM of NDVI was developed based on a five-index [r
p
rlo
rup
and Max
o
f
a
bs( Error) ] estimation system. This model quantitatively described the transformation relationships of NDVI on continuous scales. On the basis of this result
NDVI validation of different low-resolution images could be implemented rapidly and effectively. This work results in a more systematic research on modeling the CSSM of NDVI.
NDVI空间升尺度转换连续空间尺度转换模型分形五指标评价体系
NDVIspatial up-scalingcontinuous spatial scaling modelFractalfive-index estimation system
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