Use of UAV oblique imaging for detection of individual trees in residential environments
Urban Forestry and Urban Greening 14(2), pp 404-412, 2015/4/15
Yi Lin; Miao Jiang; Yunjun Yao; Lifu Zhang; Jiayuan Lin
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Oblique imaging and unmanned aerial vehicles (UAV) are two state-of-the-art remote sensing (RS) techniques that are undergoing explosive development. While their synthesis means more possibilities for applications such as urban forestry and urban greening, the related methods for data processing and information extraction, e.g. individual tree detection, are still in short supply. In order to help fill this technical gap, this study focused on developing a new method applicable for detection of individual trees in UAV oblique images. The planned algorithm is composed of three steps: 1) classification based on K-means clustering and RGB-based vegetation index derivation to acquire vegetation cover maps, 2) suggestion of new feature parameters by synthesizing texture and color parameters to identify vegetation distribution, 3) individual tree detection based on marker-controlled watershed segmentation and shape analysis. The evaluations based on the images within residential environments indicated that the commission and omission errors are less than 32% and 26%, respectively. The results have basically validated the proposed method.
An Analysis of Shadow Effects on Spectral Vegetation Indexes Using a Ground-Based Imaging Spectrometer
IEEE Geoscience and Remote Sensing Letters 12(11), pp 2188-2192, 2015/11/02
Lifu Zhang, Xuejian Sun, Taixia Wu, and Hongming Zhang
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Abstract—Sunlit vegetation and shaded vegetation are inseparable parts for most remotely sensed images, and the presence of shadows affects high spatial resolution remote sensing and multiangle remote sensing data. Shadows can lead to either a reduction
in or a total loss of information in an image. This can potentially lead to the corruption of biophysical parameters derived from pixel values, such as vegetation indexes (VIs). VIs are widely used in remote sensing inversion applications. If the effects of shadows are not properly accounted for, retrieval may be uncertain when using a VI to calculate vegetation parameters. One of the major reasons that the effects of shadows are easy to be ignored in
remote sensing is the spatial resolution of the measurement. High spatial and spectral resolutions are typically difficult to achieve simultaneously, and images that have one tend to not have the
other. A ground-based imaging spectrometer brings a turning point to solve this problem as it can obtain both high spatial and high spectral resolutions to obtain feature and shadow images simultaneously. The resolution of the system used here was 1 mm at a height of 1 m, and the spectral resolution was better than 5 nm. For each pixel, the spectral curve of the image was almost a pure-pixel spectral curve, which allowed the differentiation of sunlit and shaded areas. To investigate the effects of shadows on
different indexes, 14 hyperspectral VIs were calculated.Moreover, the vegetation fractional coverage calculated using the same 14 VIs was compared. The results show that shadows affect not only each narrowband of a VI but also vegetation parameters.
Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types
Remote Sensing 10(7), pp 13485-13506, 2015/10/01
Xiaojun She, Lifu Zhang, Yi Cen, Taixia Wu, Changping Huang and Muhammad Hasan Ali Baig
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Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.
An Abundance Characteristic-Based Independent Component Analysis for Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing 53(1), pp 416-428, 2015/1
*Wang, Nan; Du, Bo; Zhang, Liangpei; Zhang, Lifu
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Independent component analysis (ICA) has been recently applied into hyperspectral unmixing as a result of its low computation time and its ability to perform without prior information. However, when applying ICA for hyperspectral unmixing, the independence assumption in the ICA model conflicts with the abundance sum-to-one constraint and the abundance nonnegative constraint in the linear mixture model, which affects the hyperspectral unmixing accuracy. In this paper, we consider an abundance matrix composed of Np-dimensional variables, and we propose a new hyperspectral unmixing approach with an abundance characteristic-based ICA model. Two characteristics of the abundance variables are explored, and the model is constructed by these characteristics. A corresponding gradient descent algorithm is also proposed to solve the proposed objective function. Both the synthetic and real experimental results demonstrate that the proposed method performs better than the other state-of-the-art methods in abundance and endmember extraction.
Increase in flood and drought disasters during 1500-2000 in southeast China
Natural Hazards 77(3), pp 1853-1861, 2015/07/01
Yuhe Ji；Guangsheng Zhou；Shudong Wang；Lixia Wang
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Climate change has had a huge impact on Southwest China, where extreme weather events have taken on an increasing trend. The region lies in the transition zone of three monsoons (East Asian, Indian, and Tibetan Plateau). We analyzed long-term trends of drought and flood disasters using sequential data on China’s drought and flood incidence over 500 years (1501–2000), as well as precipitation records from 45 weather stations during 1961–2010. Results show that both types of disaster became more frequent. During 1501–2000, drought frequency rose from 2.12 to 4.15 per century to 9.03 and flood frequency from 2.06 to 5.09 per century to 11.94. For the 10 decades during 1900–2000, droughts exhibited a significant increasing trend (R2 = 0.4491), while floods showed a
slight increasing trend (R2 = 0.0346). For extreme floods and droughts in 1961–2010, precipitation records revealed that most (2/3) areas in the region had increasing drought or flood frequencies. Generally, both frequency and intensity of these eather hazards conspicuously increased in Southwest China during 1501–2000.
Sophisticated Vegetation Classification Based on Feature Band Set Using Hyperspectral Image
Spectroscopy and Spectral Analysis 35(6), pp 1669-1676, 2015/06/11
Shang Kung；Zhang Xia；Sun Yanli；Zhang Lifu；Wang Shudong； Zhuang Zhi
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Ｔｈｅｒｅ ａｒｅ ｔｗｏ ｍａｊｏｒ ｐｒｏｂｌｅｍｓ ｏｆ ｓｏｐｈｉｓｔｉｃａｔｅｄ ｖｅｇｅｔａｔｉｏｎ ｃｌａｓｓｉｆｉｃａｔｉｏｎ（ＳＶＣ）ｕｓｉｎｇ ｈｙｐｅｒｓｐｅｃｔｒａｌ ｉｍａｇｅ．Ｃｌａｓｓｉｆｉｃａｔｉｏｎ ｒｅｓｕｌｔｓ ｕｓｉｎｇ ｏｎｌｙ ｓｐｅｃｔｒａｌ ｉｎｆｏｒｍａｔｉｏｎ ｃａｎ ｈａｒｄｌｙ ｍｅｅｔ ｔｈｅ ａｐｐｌｉｃａｔｉｏｎ ｒｅｑｕｉｒｅｍｅｎｔｓ ｗｉｔｈ ｔｈｅ ｎｅｅｄｅｄ ｖｅｇｅｔａｔｉｏｎ ｔｙｐｅ ｂｅｃｏｍｉｎｇ ｍｏｒｅ ｓｏｐｈｉｓｔｉｃａｔｅｄ．Ａｎｄ ａｐｐｌｉｃａｔｉｏｎｓ ｏｆ ｃｌａｓｓｉｆｉｃａｔｉｏｎ ｉｍａｇｅ ａｒｅ ａｌｓｏ ｌｉｍｉｔｅｄ ｄｕｅ ｔｏ ｓａｌｔ ａｎｄ ｐｅｐｐｅｒ ｎｏｉｓｅ．Ｔｈｅｒｅｆｏｒｅ ｔｈｅ ＳＶＣ ｓｔｒａｔｅｇｙ ｂａｓｅｄ ｏｎ ｃｏｎｓｔｒｕｃｔｉｏｎ ａｎｄ ｏｐｔｉｍｉｚａｔｉｏｎ ｏｆ ｖｅｇｅｔａｔｉｏｎ
ｆｅａｔｕｒｅ ｂａｎｄ ｓｅｔ（ＦＢＳ）ｉｓ ｐｒｏｐｏｓｅｄ．Ｂｅｓｉｄｅｓ ｓｐｅｃｔｒａｌ ａｎｄ ｔｅｘｔｕｒｅ ｆｅａｔｕｒｅｓ ｏｆ ｏｒｉｇｉｎａｌ ｉｍａｇｅ，３０ｓｐｅｃｔｒａｌ ｉｎｄｉｃｅｓ
ｗｈｉｃｈ ａｒｅ ｓｅｎｓｉｔｉｖｅ ｔｏ ｂｉｏｌｏｇｉｃａｌ ｐａｒａｍｅｔｅｒｓ ｏｆ ｖｅｇｅｔａｔｉｏｎ ａｒｅ ａｄｄｅｄ ｉｎｔｏ ＦＢＳ ｉｎ ｏｒｄｅｒ ｔｏ ｉｍｐｒｏｖｅ ｔｈｅ ｓｅｐａｒａｂｉｌｉｔｙ
ｂｅｔｗｅｅｎ ｄｉｆｆｅｒｅｎｔ ｋｉｎｄｓ ｏｆ ｖｅｇｅｔａｔｉｏｎ．Ａｎｄ ｔｏ ａｃｈｉｅｖｅ ｔｈｅ ｓａｍｅ ｇｏａｌ ａ ｓｐｅｃｔｒａｌ－ｄｉｍｅｎｓｉｏｎ ｏｐｔｉｍｉｚａｔｉｏｎ
ａｌｇｏｒｉｔｈｍ ｏｆ ＦＢＳ ｂａｓｅｄ ｏｎ ｃｌａｓｓ－ｐａｉｒ ｓｅｐａｒａｂｉｌｉｔｙ（ＣＰＳ）ｉｓ ａｌｓｏ ｐｒｏｐｏｓｅｄ．Ａ ｓｐａｔｉａｌ－ｄｉｍｅｎｓｉｏｎ ｏｐｔｉｍｉｚａｔｉｏｎ ａｌｇｏｒｉｔｈｍ
ｏｆ ＦＢＳ ｂａｓｅｄ ｏｎ ｎｅｉｇｈｂｏｒｈｏｏｄ ｐｉｘｅｌｓ’ｓｐｅｃｔｒａｌ ａｎｇｌｅ ｄｉｓｔａｎｃｅ（ＮＰＳＡＤ）ｉｓ ｐｒｏｐｏｓｅｄ ｓｏ ｔｈａｔ ｄｅｔａｉｌｅｄ
ｉｎｆｏｒｍａｔｉｏｎ ｃａｎ ｂｅ ｋｅｐｔ ｄｕｒｉｎｇ ｔｈｅ ｉｍａｇｅ ｓｍｏｏｔｈｉｎｇ ｐｒｏｃｅｓｓ．Ｔｈｅ ｒｅｓｕｌｔｓ ｏｆ ＳＶＣ ｅｘｐｅｒｉｍｅｎｔｓ ｂａｓｅｄ ｏｎ ａｉｒｂｏｒｎｅ
ｈｙｐｅｒｓｐｅｃｔｒａｌ ｉｍａｇｅ ｓｈｏｗ ｔｈａｔ ｔｈｅ ｐｒｏｐｏｓｅｄ ｍｅｔｈｏｄ ｃａｎ ｓｉｇｎｉｆｉｃａｎｔｌｙ ｉｍｐｒｏｖｅ ｔｈｅ ａｃｃｕｒａｃｙ ｏｆ ＳＶＣ ｓｏ
ｔｈａｔ ｓｏｍｅ ｗｉｄｅｓｐｒｅａｄ ａｐｐｌｉｃａｔｉｏｎ ｐｒｏｓｐｅｃｔｓ ｌｉｋｅ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｏｆ ｃｒｏｐ ｓｐｅｃｉｅｓ，ｍｏｎｉｔｏｒｉｎｇ ｏｆ ｉｎｖａｓｉｖｅ ｓｐｅｃｉｅｓ
ａｎｄ ｐｒｅｃｉｓｉｏｎ ａｇｒｉｃｕｌｔｕｒｅ ａｒｅ ｅｘｐｅｃｔａｂｌｅ．
Hyperspectral Feature Extraction Based on the Reference Spectral Background Removal Method
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(6), pp 2832-2844, 2015/06/01
Hengqian Zhao; *Lifu Zhang; Xia Zhang; Jia Liu; Taixia Wu; Shudong Wang
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In spectral analysis, diagnostic absorption features
can indicate the existence of specific materials. Absorption parameters such as absorption center, absorption width, and absorption depth can be used in not only identification and quantitative analysis
of minerals, but also in retrieval of surface physical properties. Continuum removal (CR) is commonly used to extract absorption features. However, for a band range containing more than one
absorption contribution factors, the feature extracted by CR could be a result of comprehensive effect of different factors. In this paper, a new spectral feature extraction method named reference
spectral background removal (RSBR) is proposed. Given the reference spectral background, RSBR can eliminate the influence of unwanted contribution factor, and extract the absorption feature of target contribution factor. Using RSBR, the basic absorption feature parameters including the absorption center, absorption
width, and absorption depth are extracted. The results are compared with those obtained from the CR. It is shown that RSBR can effectively extract pure absorption features of target material, while more accurate absorption parameters can also be achieved.
Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(5), pp 2198-2211, 2015/05/01
Xuejian Sun; Lifu Zhang; Hang Yang; Taixia Wu; Yi Cen; Yi Guo
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Hyperspectral (HS) remote sensing has an important
role in a wide variety of fields. However, its rapid progress has been
constrained due to the narrow swath of HS images. This paper
proposes a spectral resolution enhancement method (SREM) for
remotely sensed multispectral (MS) image, to generate wide swath
HS images using auxiliary multi/hyper-spectral data. Firstly, a set
number of spectra of different materials are extracted from both
the MS and HS data. Secondly, the approach makes use of the
linear relationships between multi and hyper-spectra of specific
materials to generate a set of transformation matrices. Then, a
spectral angle weighted minimum distance (SAWMD) matching
method is used to select a suitable matrix to create HS vectors
from the original MS image, pixel by pixel. The final result image
data has the same spectral resolution as the original HS data that
used and the spatial resolution and swath were also the same as
for the original MS data. The derived transformation matrices
can also be used to generate multitemporal HS data from MS
data for different periods. The approach was tested with three
image datasets, and the spectra-enhanced and real HS data were
compared by visual interpretation, statistical analysis, and classification
to evaluate the performance. The experimental results
demonstrated that SREM produces good image data, which will
not only greatly improve the range of applications for HS data but
also encourage more utilization of MS data.
Spatiotemporal Variation in Surface Urban Heat Island Intensity and Associated Determinants across Major Chinese Cities
Remote Sensing 7(4), pp 3670-3689, 2015/04/01
Juan Wang, Bo Huang, Dongjie Fu and Peter M. Atkinson
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Urban heat islands (UHIs) created through urbanization can have negative
impacts on the lives of people living in cities. They may also vary spatially and temporally
over a city. There is, thus, a need for greater understanding of these patterns and their causes.
While previous UHI studies focused on only a few cities and/or several explanatory
variables, this research provides a comprehensive and comparative characterization of the
diurnal and seasonal variation in surface UHI intensities (SUHIIs) across 67 major Chinese
cities. The factors associated with the SUHII were assessed by considering a variety of
related social, economic and natural factors using a regression tree model. Obvious seasonal
variation was observed for the daytime SUHII, and the diurnal variation in SUHII variedseasonally across China. Interestingly, the SUHII varied significantly in character between
northern and southern China. Southern China experienced more intense daytime SUHIIs,
while the opposite was true for nighttime SUHIIs. Vegetation had the greatest effect in the
day time in northern China. In southern China, annual electricity consumption and the
number of public buses were found to be important. These results have important theoretical
significance and may be of use to mitigate UHI effects.
Combining Satellite and GIS Data to Analyze Changes in Tropical Forests on Central Hainan Island in Response to the National Logging Ban and Economic Development
Fresenius Environmental Bulletin 24(1), pp 394-404, 2015/01/01
Shudong Wang, Yuhe Ji, Zhiyun Ouyang, Lifu Zhang and Liu Jia
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Changes in natural forest cover in tropical areas have
attracted international attention. Rubber and pulp plantations
threaten Hainan Island’s natural tropical forests.
Remote sensing provides a crucial tool for understanding
how forests change in response to forest protection strategies
and economic development. China’s government has
adopted protective measures designed to balance forest
protection and economic development on Hainan; however,
the effect of both management and economic development
on natural tropical forest remains unclear. To identify
changes in forest types, object-oriented decision-tree identification
techniques were developed using Landsat TM
images to identify causes of forest change. GIS techniques
allowed analysis of the forest’s spatial shift using
elevation, slope, transportation corridors, natural reserves,
and farmlands linked to three different periods of forestry
policy and economic development from 1988 to 2008. The
analysis shows: (1) Total tropical forest area increased
from 1988 to 2008, while natural tropical forest area increased
slightly from 1988 to 1998, but decreased significantly
from 1998 to 2008, despite implementation of the
"Natural Forest Protection Project." Meanwhile, economic
forests, mainly rubber and pulp plantations, expanded from
1988 to 2008. (2) Spatial changes occurred. Natural tropical forest shifted from the lower piedmont to higher mountaintops, and economic forests shifted to higher elevations under the complex effects of multiple factors. (3) The observed changes in forest cover could be related to protective measures and economic development, with economic
development seemingly having the strongest influence on
the condition of the forests. Elevation, slope, transportation corridors, and farmlands also affected the shift of tropical forests.
Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 7(1), pp 70-91, 2014/1/1
Tong Qingxi; Xue Yongqi; *zhang lifu
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This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hyperspectral imaging systems ranging from ground to airborne and satellite platforms have been designed, built, and operated. These include the field imaging spectrometer system (FISS), the Modular Airborne Imaging Spectrometer (MAIS), and the Chang'E-I Interferometer Spectrometer (IIM). In addition to developing sensors, Chinese scientists have proposed various novel image processing techniques. Applications of hyperspectral imaging in China have been also performed including mineral exploration in the Qilian Mountains and oil exploration in Xinjiang province. To promote the development of HRS, many generic and professional software tools have been developed. These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained in HRS data, allowing them to meet the demands of both common users and researchers in the scientific community.
Spatially explicit estimation of soil-water resources by coupling of an eco-hydrological model with remote sensing data in the Weihe River Basin of China
Journal of Applied Remote Sensing 8(1), pp 11-19, 2014
Wang, Shudong; *Wang, Yujuan; Yang, Shengtian; Wang, Mingcheng; Zhang, Lifu; Liu, Jia
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Soil-water resources are key components for agriculture and have great potential. Strategic and significant efforts are, therefore, required to make full use of soil-water resources, especially in dry or semidry areas.We coupled a soil-water model with remotely sensed data and associated techniques to analyze the spatial-temporal dynamics of soil-water resources in the Weihe River Basin in China. The moderate-resolution imaging spectroradiometer (MODIS), Chinese meteorological satellite precipitation estimation data (FY-2), and global land data assimilation system (GLDAS) products were used for spatial land surface characteristics interpretation and model parameters derivation. The modeling results were compared and validated using data from a nearby observation site. The average soil-water resources of the Weihe River Basin vary between 40 and 100 mm during the simulation period from January to December, with a maximum of 99 mm appearing in August and a minimum of 38 mm in December. Forest land was characterized by large soil-water resources, with an average annual rate of 1094.7 mm. Farmland and grassland exhibited low values, with average annual rates of 986.7 and 893.5 mm, respectively. The results could be taken into consideration for soil-water resources management.
The effects of solar irradiance spectra on calculation of narrow band top-of-atmosphere reflectance
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(1), pp 49-58, 2014
Zhang, Lifu; *Hu, Shunshi; Yang, Hang; Wu, Taixia; Tong, Qingxi; Zhang, Feizhou
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Extraterrestrial solar irradiance spectra detail the solar energy distribution over wavelengths, and numerous solar irradiance models are available within the remote sensing community. However, reference spectra may differ widely owing to differences in solar activity, measurement instruments and calibration. Six widely referenced solar spectra were selected in this work to examine their differences and the impacts of these differences on calculations of narrow band top-of-atmosphere reflectance using MERIS and Hyperion hyperspectral sensor spectral configurations. Mean solar exoatmospheric irradiance (MSEI) was computed using the different solar irradiance models and spectral response functions of the MERIS and Hyperion hyperspectral sensors. Then, the effects of MSEI on top-of-atmosphere (TOA) reflectance and the normalized difference vegetation index (NDVI) and atmospherically resistant vegetation index (ARVI) were investigated. The results show that the six selected solar irradiance models have significant differences from 350 to 2500 nm, which in turn result in differences in the MSEI derived from MERIS and Hyperion observations. These differences have a less significant effect on the TOA reflectance in the visible and near-infrared bands and on NDVI. However, the differences result in large differences in TOA reflectance in the infrared bands and in ARVI.
A spectral angle distance-weighting reconstruction method for filled pixels of the MODIS land surface temperature product
IEEE Geoscience and Remote Sensing Letters 11(9), pp 1514-1518, 2014
Shuai, Tong; Zhang, Xia; Wang, Shudong; Zhang, Lifu; Shang, Kun; Chen, Xiaoping; Wang, Jinnian
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Land surface temperature (LST) is an important parameter in the physics of land surface processes, but a large number of pixels are often filled as zero due to cloud, heavy aerosols, and so on in the Moderate-resolution Imaging Spectroradiometer (MODIS) LST product. This letter presents the spectral angle distance (SAD)-weighting reconstruction (SADWER) method of reconstruction of zero-filled pixels of the MODIS LST product. It relies on the hypothesis that pixels with the same land-cover type have nearly the same LST in a localized area. SAD can measure the similarity of land-cover types of different pixels, and pixels with higher land-cover similarity can contribute more to the reconstruction using the weighting method. The result shows that the reconstruction ratio could be as high as 95% using only the SADWER method and nearly 100% after spatial filter postprocessing. The reconstruction accuracy is validated using artificially generated 20-, 50-, and 80-km-diameter concentrically filled areas in both forest and crop land-cover types. The statistical result shows that the standard deviations of the reconstruction errors are less than 2 Kelvin.
Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance
Remote Sensing Letters 5(5), pp 423-431, 2014
Baig, Muhammad Hasan Ali; *Zhang, Lifu; Shuai, Tong; Tong, Qingxi
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The tasselled cap transformation (TCT) is a useful tool for compressing spectral data into a few bands associated with physical scene characteristics with minimal information loss. TCT was originally evolved from the Landsat multi-spectral scanner (MSS) launched in 1972 and is widely adapted to modern sensors. In this study, we derived the TCT coefficients for the newly launched (2013) operational land imager (OLI) sensor on-board Landsat 8 for at-satellite reflectance. A newly developed standardized mechanism was used to transform the principal component analysis (PCA)-based rotated axes through Procrustes rotation (PR) conformation according to the Landsat thematic mapper (TM)-based tasselled cap space. Firstly, OLI data were transformed into TM TCT space directly and considered as a dummy target. Then, PCA was applied on the original scene. Finally, PR was applied to get the transformation results in the best conformation to the target image. New coefficients were analysed in detail to confirm Landsat 8-based TCT as a continuity of the original tasselled cap idea. Results show that newly derived set of coefficients for Landsat OLI is in continuation of its predecessors and hence provide data continuity through TCT since 1972 for remote sensing of surface features such as vegetation, albedo and water. The newly derived TCT for OLI will also be very useful for studying biomass estimation and primary production for future studies.
Using a remote sensing driven model to analyze effect of land use on soil moisture in the weihe river basin, China
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(9), pp 3892-3902, 2014
Wang, Yujuan; *Wang, Shudong; Yang, Shentian; Zhang, Lifu; Zeng, Hongjuan; Zheng, Donghai
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In-depth study of the soil moisture mechanisms and understanding of the soil moisture transport law has an important practical significance for regional water resources management and the challenge of the water resources scarcity. Using traditional methods of soil moisture monitoring, deep soil layers can be monitored, but continuous monitoring of soil moisture at the regional level cannot be achieved. Although remote sensing simulation models can meet regional scale needs, these models are confined to the surface soil layer, and research on deep soil moisture inversion is still lacking. This paper focuses on these two issues, and investigates a remote sensing-driven soil moisture monitoring model for the Weihe River Basin. Considering water resource management needs in the Weihe River Basin, we improved the structure of the soil moisture balance model and optimized model parameters to build the remote sensing driven soil moisture balance model (RS-SWBM). Based on soil moisture modeling, the effect of vegetation on soil moisture in the Weihe River Basin was analyzed. The RS-SWBM developed for the Weihe River Basin was validated with observational data and Global Land Data Assimilation System (GLDAS) soil moisture data products. Based on the correlation analysis, correlation coefficients were all above 0.80, reflecting the effectiveness of the model. The effects of different vegetation types on soil moisture dynamics and consumption efficiency were analyzed. The results indicated that different vegetation types experienced different seasonal variations, vertical patterns, and consumption efficiencies, with strong correlations existing between these parameters and land use as well as precipitation.
Hyperspectral imagery denoising based on oblique subspace projection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(6), pp 2468-2480, 2014
Wang, Qian; Zhang, Lifu; Tong, Qingxi; *Zhang, Feizhou
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This paper presents a hyperspectral imagery denoising algorithm based on oblique subspace projection (DOBSP), which considers the correlation between noise and signal. The algorithm first estimates the signal and noise through segmentation Gaussian filtering which can reduce more influence of the image texture than traditional Gaussian filtering. Then, signal and noise estimates are fed into principal component analysis (PCA) to identify their respective subspace basis vectors. Finally, these basis vectors are used to compute matrices of oblique subspace projection (OBSP), and the signal and noise are extracted from the original image through OBSP. We assessed the DOBSP algorithm using both simulated and real Hyperion images. The orthogonal subspace projection (OSP) which assumes that noise is independent on signal and the subspace-based striping noise reduction (SBSR) algorithm which uses polynomial model to describe the relationship between noise and signal were introduced for comparison. Compared with signal and noise results by OSP and SBSR, both signal and noise extracted by DOBSP on the simulated image are closer to the original simulated signal and noise, and the noise image obtained by DOBSP on the Hyperion image has fewer textures.
Evaluation of multiple spring phenological indicators of yearly GPP and NEP at three Canadian forest sites
Remote Sensing 6(3), pp 1991-2007, 2014
Wang, Qian; *Zhang, Lifu; Wu, Taixia; Cen, Yi; Huang, Changping; Tong, Qingxi
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Phenological shifts in events such as flowering and bud break are important indicators of ecosystem processes, and are therefore of particular significance for carbon (C) cycle research. Using long-term flux data from three contrasting plant functional type (evergreen and deciduous) boreal forest sites, we evaluated and compared the responses of annual C fluxes to multiple spring phenological indicators, including the C-uptake period onset (CUP onset), spring temperature (average value from March to May), and satellite-derived enhanced vegetation index (EVI) (average value from March to May). We found that the CUP onset was negatively correlated with annual gross primary production (GPP) for all three sites, but that its predictive strength for annual net ecosystem production (NEP) differed substantially among plant functional types. Spring temperature demonstrated particularly good potential for predicting both annual GPP and NEP for the evergreen sites, but not for the deciduous site. Spring EVI was demonstrated to have potential for predicting annual NEP for all sites. However, both plant functional types confounded the correlation of annual NEP with annual GPP. Although none of these phenological indicators provided consistent insight into annual C fluxes, using various currently available datasets our results remain potentially useful for the assessment of forest C cycling with future climate change. Previous analyses using only a single phenological metric should be considered with caution.
Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements
Remote Sensing of Environment 141(0), pp 90-104, 2014
Fu, Dongjie; Chen, Baozhang; Zhang, Huifang; Wang, Juan; Black, T. Andy; Amiro, Brian; Bohrer, Gil; Bolstad, Paul; Coulter, Richard; Rahman, Faiz; Dunn, Allison; Harry, McCaughey; Meyers, Tilden; Verma, Shashi
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More accurate estimation of the carbon dioxide flux depends on the improved scientific understanding of the terrestrial carbon cycle. Remote-sensing-based approaches to continental-scale estimation of net ecosystem exchange (NEE) have been developed but coarse spatial resolution is a source of errors. Here we demonstrate a satellite-based method of estimating NEE using Landsat TM/ETM+data and an upscaling framework. The upscaling framework contains flux-footprint climatology modeling, modified regression tree (MRT) analysis and image fusion. By scaling NEE measured at flux towers to landscape and regional scales, this satellite-based method can improve NEE estimation at high spatial-temporal resolution at the landscape scale relative to methods based on MODIS data with coarser spatial-temporal resolution. This method was applied to sixteen flux sites from the Canadian Carbon Program and AmeriFlux networks located in North America, covering forest, grass, and cropland biomes. Compared to a similar method using MODIS data, our estimation is more effective for diagnosing landscape NEE with the same temporal resolution and higher spatial resolution (30m versus 1km) (r2=0.7548 vs. 0.5868, RMSE=1.3979 vs. 1.7497gCm-2day-1, average error=0.8950 vs. 1.0178gCm-2day-1, relative error=0.47 vs. 0.54 for fused Landsat and MODIS imagery, respectively). We also compared the regional NEE estimations using Carbon Tracker, our method and eddy-covariance observations. This study demonstrates that the data-driven satellite-based NEE diagnosed model can be used to upscale eddy-flux observations to landscape scales with high spatial-temporal resolutions.
Discrimination of corn varieties using Visible/Near infrared Imaging Spectrometer System
Infrared and Laser Engineering 42(9), pp 2437-2441, 2013/9/25
Hang Yang, Zhang, Lifu
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Hyperspectral images of five corn varieties were acquired using Field Imaging Spectrometer System (FISS). After reflectance retrieved, noise removal and First-order differential, stepwise discrimination analysis based on the minimization of Wilks' lambda was employed to select the feature bands of corn spectral, and then discrimination model was built. The results of Least-one-out Cross-validation (loocv) showed that: (1) average discrimination accuracy was 91.6%, in which, discrimination accuracy of High-oil corn NO.115 was 87%, and discrimination accuracy of the other varieties was over 90%; (2) If discrimination method, band number and the size of samples of each variety are fixed, discrimination accuracy is effected by variety number and separable; (3)the effect of selected band number on discrimination accuracy was analysed and result showed that discrimination accuracy increased with the increasing of band number. Therefore, Field Imaging Spectrometer System has an important application value in corn-variety discrimination and quality examination.
Hyperspectral Unstructured Background Target Detection Approach Based on Object-Oriented Analysis
Spectroscopy and Spectral Analysis 33(6), pp 1653-1657, 2013/6
Liu Kai; *Zhang Li-fu; Yang Hang; Zhu Hai-tao; Jiang Hai-ling; Li Yao
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In order to reduce the limitation in background statistics estimation of unstructured background detector, a small target detection algorithm based on object-oriented analysis was proposed. After segmenting the whole imagery into many fairly homogenous regions using adaptive iterative method, multivariate normality test was applied to choose several optimal object sets which obey the law of normal distribution well. Then, the selected objects would be combined with GLR to perform target detection. This method could make the local background well fit a normal distribution and effectively separate the target signal from background, and meanwhile avoid the contamination effect through the selection of optimal objects. A simulation experiment was conducted on real OMIS data to validate the effectiveness of the proposed algorithm. The detection results were compared with those detected by the unstructured background detector GLR and improved GLR which incorporated K Means clustering. The results show that the proposed algorithm has better detection performance and lower false alarm probability than other detection algorithms.
Polarized Spectral Measurement and Analysis of Sedum Spectabile Boreau Using a Field Imaging Spectrometer System
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(2), pp 724-730, 2013/4
*Wu, Taixia; Zhang, Lifu; Cen, Yi; Huang, Changping; Sun, Xuejian; Zhao, Hengqian; Tong, Qingxi
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Polarized hyperspectral imaging is a new remote sensing method that combines the benefits of polarization and hyperspectral characteristics. Based on a new self-developed polarized field imaging spectrometer system (FISS-P), we collected polarized hyperspectral images of leaves of Sedum spectabile Boreau. Polarization analysis of the diffuse reflectance standard white plate indicated that the FISS-P produced accurate polarization measurements. Ten related polarization parameters (I, Q, U, DoLP, AoP, R-0, R-60, R-120, R, R-1) were analyzed in this study. The angle of polarization (AoP) spectral curves of the S. spectabile leaf had no unique spectral features. The degree of linear polarization (DoLP) spectral curves displayed distinct spectral characteristics. However, the DoLP and spectral reflectance curves of the leaf displayed contrasting trends. Different parts of the same leaf, or different S. spectabile leaves, produced different spectral curve shapes. Analysis of the five reflectance parameters demonstrated that R-0, R-60, R-120, R-I, and R were consistent for all spectral and spatial aspects.
A Radiometric Calibration Model for the Field Imaging Spectrometer System
IEEE Transactions on Geoscience and Remote Sensing 51(4), pp 2465-2475, 2013/4
Huang, Changping; *Zhang, Lifu; Fang, Junyong; Tong, Qingxi
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Using the field imaging spectrometer system (FISS) recently developed by us, a new operational radiometric calibration (RC) model that takes into account three main adjustable sensor system settings, including the integration time (t), the aperture (F), and the detector temperature (T), is proposed. To better understand the influence of a single setting on the RC model, controlled experiments with one variable and two fixed settings were conducted and analyzed using a well-calibrated integrating sphere. Subsequently, a new variable was constructed with the ratio of t and F-2 to determine the system-setting-based RC model, where the radiometric offset was derived from system noise estimated by keeping the FISS entrance slit from a light source in a dark environment. Finally, the model was evaluated using experimental calibration results from the integrating-sphere data and real vegetation data. The results indicated that standard and calculated radiances were consistent over most spectral wavelengths. The proposed RC model could be effectively applied not only for the FISS and other ground-based sensors but also for future Chinese-developed intelligent remote sensing satellite systems that can automatically modify imaging settings in line with specific requirements.
Temperature and Emissivity Separation from Thermal Airborne Hyperspectral Imager (TASI) Data
Photogrammetric Engineering and Remote Sensing 79(12), pp 1099-1107, 2013/12
Yang Hang; *Zhang Lifu; Gao Yingqian; Hu Ghunshi; Li Xueke; Zhang Genzhong; Tong Qingxi
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The Thermal Airborne Hyperspectral Imager (TASI) acquires 32 bands to provide continuous spectral coverage in the wavelength range of 8 to 11.5 mu m. The instrument was used during a field campaign in the City of Shiliazhuang, Hebei Province, China, in 2010. Land surface temperature and emissivity were measured near simultaneous with the airborne campaign for calibration and validation of the instrument. Radiance calibration was performed band-by-band using calibration coefficients, and atmospheric correction was performed using data from in situ measurements and the MODTRAN model. Surface temperature and emissivity separation were determined using the ASTER temperature-emissivity separation (ASTER_TES) and iterative spectral smooth temperature and emissivity separation (ISSTES) methods. The ASTER_TES method resulted in satisfactory agreement with ground data, with root mean square error (RMSE) values of 2.2 K for temperature and 0.0460 for broad-emissivity. The ISSTES method provided better ground validation results, with a RMSE for temperature of 1.8 K and a RMSE for broad-emissivity of 0.0394. The emissivity shapes acquired by the two methods were very similar. The results have relevance to studies of global climate change, environmental monitoring, classification, feature mining, and target recognition.
Research on the characteristics of strong linearly related bands based on derivative of ratio spectroscopy
Journal of Infrared and Millimeter Waves 32(6), pp 563-568, 2013/12
Zhao Heng-Qian; *Zhang Li-Fu; Cen Yi; Wu Tai-Xia; Wang Jin-Nian
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The spectra of mineral powder and mineral rock were compared and analyzed. A quantitative inversion based on DRS was performed on powder mixtures of plaster and allochite to extract the strong linear bands of each component separately. Combining with the absorption characteristics of endmember minerals, the distribution characteristics of strong linear bands on the reflectance spectra, the ratio spectra and the derivative of ratio spectra were discussed.
Mapping global lunar abundance of plagioclase, clinopyroxene and olivine with Interference Imaging Spectrometer hyperspectral data considering space weathering effect
Icarus 222(1), pp 401-410, 2013/1
Shuai, Tong; *Zhang, Xia; Zhang, Lifu; Wang, Jinnian
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Mineral mapping of the lunar surface is critical to understanding the Moon's geological diversity and history, yet the global lunar abundance of minerals has not been mapped using hyperspectral data. The Interference Imaging Spectrometer (IIM) of Chang'E-1 mission obtained hyperspectral data of the global lunar surface within the wavelength of 480-960 nm in which major minerals can be discriminated by faint differences in 32 contiguous hyperspectral bands. The effect of space weathering produces multiple endmembers of lunar minerals by obscuring the pure spectra of minerals in different levels. In this study, the distributions of plagioclase, clinopyroxene and olivine on the global lunar surface were mapped with IIM hyperspectral data based on the modified Multiple Endmember Spectral Mixture Analysis (MESMA) method considering the space weathering effect. The distribution of lunar space weathering levels was retrieved as a byproduct of mineral mapping. The mineral mapping results were compared with recent mapping results. Although the wavelength of IIM is limited, it shows that our results are basically consistent with the recent research at both global and local scales. The distribution of space weathering levels is also consistent with the map of optical maturity parameter (OMAT) in most parts of the global lunar surface, especially in the highlands. This study demonstrates that the modified MESMA method is an effective approach to quantitative mapping of the lunar minerals and space weathering levels using hyperspectral data. In the future, more minerals can be mapped with higher accuracy if hyperspectral data with a wider spectral range are used based on the method proposed in this study.
Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data
International Journal of Remote Sensing 34(6), pp 2200-2215, 2013
Chen, Xiaoping; *Zhang, Lifu; Zhang, Xia; Liu, Bo
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In previous studies of the universal pattern decomposition method (UPDM), spectral shifts, which are very common in hyperspectral imaging spectrometers, were not taken into account when calculating standard spectral pattern vectors. This study evaluated the effect of spectral shifts on the sensor dependence of the vegetation index based on the UPDM (VIUPD) and 11 other vegetation indices (VIs). Spectral shifts were calculated using Gao';s spectrum-matching method. The influences of smoothing techniques (moving average and Savitzky-Golay filters) on the consistency of these VIs were also evaluated and compared. Data from the typical narrowband imaging spectrometers, Hyperion and the Compact High Resolution Imaging Spectrometer (CHRIS), were chosen for the study. For all VIs, both smoothing and spectral calibration changed the consistency between Hyperion and CHRIS. Spectral calibration had a positive effect on the majority of VIs, whereas smoothing improved the performance of some VIs but decreased the consistency of others. When compared with spectral calibration and Savitzky-Golay smoothing, moving average generated greater variations within the results. Among the smoothing techniques employed, moving average smoothing exhibited a larger distortion of VI sensor dependency than that of Savitzky-Golay smoothing of the same order. VIUPD based on narrowband hyperspectral data was sensitive to spectral operations (spectral calibration and smoothing). For VIUPD, spectral calibration increased its sensor independence, whereas smoothing had a negative effect. After spectral calibration, VIUPD was more sensor independent than any other VI examined in this study.
Examining the satellite-detected urban land use spatial patterns using multidimensional fractal dimension indices
Remote Sensing 5(10), pp 5152-5172, 2013
Wu, Hao; Sun, Yurong; *Shi, Wenzhong; Chen, Xiaoling; Fu, Dongjie
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Understanding the spatial patterns of urban land use at both the macro and the micro levels is a central issue in global change studies. Due to the nonlinear features associated with land use spatial patterns, it is currently necessary to provide some distinct analysis methods to analyze them across a range of remote sensing imagery resolutions. The objective of our study is to quantify urban land use patterns from various perspectives using multidimensional fractal methods. Three commonly used fractal dimensions, i.e., the boundary dimension, the radius dimension, and the information entropy dimension, are introduced as the typical indices to examine the complexity, centrality and balance of land use spatial patterns, respectively. Moreover, a new lacunarity dimension for describing the degree of self-organization of urban land use at the macro level is presented. A cloud-free Landsat ETM+ image acquired on 17 September 2010 was used to extract land use information in Wuhan, China. The results show that there are significant linear relationships represented by good statistical fitness related to these four indices. The results indicate that rapid urbanization has substantially affected the urban landscape pattern, and different land use types show different spatial patterns in response. This analysis reveals that multiple fractal/nonfractal indices provides a more comprehensive understanding of the spatial heterogeneity of urban land use spatial patterns than any single fractal dimension index. These findings can help us to gain deeper insight into the complex spatial patterns of urban land use.
Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring
IEEE Geoscience and Remote Sensing Letters 10(5), pp 1011-1015, 2013
Huang, Bo; Wang, Juan; Song, Huihui; Fu, Dongjie; Wong, Kwankit
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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.
Characterizing spatial representativeness of flux tower eddy-covariance measurements across the Canadian Carbon Program Network using remote sensing and footprint analysis
Remote Sensing of Environment 124(0), pp 742-755, 2012/9
*Chen, Baozhang; Coops, Nicholas C.; Fu, Dongjie; Margolis, Hank A.; Amiro, Brian D.; Black, T. Andrew; Arain, M. Altaf; Barr, Alan G.; Bourque, Charles P. -A.; Flanagan, Lawrence B.; Lafleur, Peter M.; McCaughey, J. Harry; Wofsy, Steven C.
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We describe a pragmatic approach for evaluating the spatial representativeness of flux tower measurements based on footprint climatology modeling analyses of land cover and remotely sensed vegetation indices. The approach was applied to the twelve flux sites of the Canadian Carbon Program (CCP) that include grassland, wetland, and temperate and boreal forests across an east-west continental gradient. The spatial variation within the footprint area was evaluated by examining the spatial structure of Normalized Difference Vegetation Index (NDVI) and land cover using geostatistical analyses of frequency distribution, variogram and window size. The results show that at most sites (i) the percentages of the target vegetation functional type (dominant land cover) observed by the CCP towers were higher than 60%; (ii) to some extent, most of the CCP sites presented anisotropically distributed patterns of NDVI in the 90% annual footprint climatology area; and (iii) the land surface heterogeneity within the flux footprint area differed among sites. Overall, the forest sites had larger fine-scale spatial variation than the grassland and wetland sites. The coniferous boreal forest sites had greater spatial variability than the two wetland sites and a coniferous temperate forest site. We conclude that the combination of footprint modeling, semivariogram and window size techniques, together with moderate spatial resolution remotely-sensed image data, is a pragmatic approach for assessing the spatial representativeness of flux tower measurements.
An Approach to Generate Synthetic Hyperspectral Data Used for Evaluating Linear Spectral Unmixing Algorithms
Remote Sensing Technology and Application 27(5), pp 0-0, 2012
Zhang Ming;Zhang Xia; Zhao Dong; Huang Changping; Zhang Lifu
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Linear spectral unmixing has been intensively studied for more than ten years, now it is a com- monly accepted approach to spectral unmixing, and a number of algorithms have been developed, but the objective evaluation of these algorithms is the foundation to the wide use of them. As the acquisition of reli- able ground-truth data is difficult and expensive, the use of real scenarios is limited, the simulated data have been used widely in the evaluation of these algorithms. The approach based on Dirichlet distribution is one of the common used methods for the simulation of hyperspectral data,it is more flexible and the Dirichlet density is suited to model abundance fractions. However,the parameters of the Dirichlet distribution affect the simulated results ,and improper parameters will cause the simulated data to lose the property that the hyperspectral data should preserve. This paper analyses the influence of the Dirichlet parameters on simula- ted results, and discusses the restrictions of the parameters value, and provides the recommended parame- ters range (0,1.5).
Thermal Infrared Target Recognition Using Multi-scale Fractal Model
Geomatics and Information Science of Wuhan University 37(3), pp 0-0, 2012
Zhang Lifu. Hang Yang; Fang CongHui; Pan Mao
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Using the thermal infrared image acquired by thermal airborne hyperspectral imager（TASI）,temperature and emissivity are separated.And target fine recognition based on temperature image using multi-scale fractal model was studied.Experimental results show that there is a positive correlation between fractal dimension and complexity of temperature spatial heterogeneity.Spatial distribution of temperature has a character of multi-scale fractal.Multi-scale fractal method was used to extract the thresholds,and extract the edge feature of building successfully.This method has an important value on classification,information extraction,and scale effect.
Assessment of the information quality of hyperspectral data from interference imaging spectrometer
Journal of Infrared and Millimeter Waves 31(2), pp 143-147, 2012
Zhang, Xia; *Shuai, Tong; Zhao, Dong
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The information quality of the IIM hyperspectral data has been assessed from the aspects of signal-to-noise ratio and comentropy for the further applications of the IIM data. As the assessment results show, the information of the IIM data has a good quality as a whole. The quality of the information at the middle bands are much better than that at both ends of the bands. Corresponding to reflectance of plagioclase and the pyroxene, the peak in the spectrum centered around 750 nm exhibits high value of SNR and comentropy. This result is of great importance for retrieving elements and minerals on the lunar surface. However, the absorption valley, centered around 930 nm, exhibits poor information quality. Although the information quality of the first nine bands has an obvious trend of rising, the noise should be reduced carefully before use because of the low quality of them as a whole. Moreover, the information quality of the first band and the last one is too poor to use.
Laboratory Calibration of a Field Imaging Spectrometer System
Sensors 11(3), pp 2408-2425, 2011/3
Zhang, Lifu; Huang, Changping; Wu, Taixia; *Zhang, Feizhou; Tong, Qingxi
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A new Field Imaging Spectrometer System (FISS) based on a cooling area CCD was developed. This paper describes the imaging principle, structural design, and main parameters of the FISS sensor. The FISS was spectrally calibrated with a double grating monochromator to determine the center wavelength and FWHM of each band. Calibration results showed that the spectral range of the FISS system is 437-902 nm, the number of channels is 344 and the spectral resolution of each channel is better than 5 nm. An integrating sphere was used to achieve absolute radiometric calibration of the FISS with less than 5% calibration error for each band. There are 215 channels with signal to noise ratios (SNRs) greater than 500 (62.5% of the bands). The results demonstrated that the FISS has achieved high performance that assures the feasibility of its practical use in various fields.
Algorithm of emissivity spectrum and temperature separation based on TASI data
Journal of Remote Sensing 0(6), pp 1242-1254, 2011/11/25
Hang Yang; Lifu Zhang;Fang CongHui; Qingxi Tong
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The retrieval and application of emissivity spectrum and temperature are key issues in thermal infrared remote sensing.Thermal airborne hyperspectral imager has 32 bands from 8 μm to 11.5 μm,which can provide abundant useful information for the retrieval of emissivity spectrum and temperature.This paper establishes regression between MMD and β min using 274 laboratory reflectance and field emissivity spectra,analyzes its accuracy using the data field measuring,and evaluates urban surface diurnal temperature...
Assessing eddy-covariance flux tower location bias across the Fluxnet-Canada Research Network based on remote sensing and footprint modelling
Agricultural and Forest Meteorology 151(1), pp 87-100, 2011/1/15
*Chen, Baozhang; Coops, Nicholas C.; Fu, Dongjie; Margolis, Hank A.; Amiro, Brian D.; Barr, Alan G.; Black, T. Andrew; Arain, M. Altaf; Bourque, Charles P-A; Flanagan, Lawrence B.; Lafleur, Peter M.; McCaughey, J. Harry; Wofsy, Steven C.
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We describe an approach for evaluating the representativeness of eddy covariance flux measurements and assessing sensor location bias (SLB) based on footprint modelling and remote sensing This approach was applied to the 12 main sites of the Fluxnet-Canada Research Network (FCRN)/Canadian Carbon Program (CCP) located along an east-west continental-scale transect covering grassland forest and wetland biomes For each site monthly and annual footprint climatologies e monthly or annual cumulative footprints) were calculated using the Simple Analytical Footprint model on Eulerian coordinates (SAFE) The resulting footprint climatologies were then overlaid on to Images of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from LANDSAT Thematic Mapper (TM) imagery which were used as surrogates of land surface fluxes to estimate SLB Results indicate that (i) the sizes of annual footprint climatology Increased exponentially with increasing cumulative footprint percentages and for a given percentage of footprint climatology the footprint areas were significantly different among the sites Typically the 90% annual footprint climatology areas varied from 1 1 km(2) to 5 0 km(2) (ii) using either NDVI or EVI as the flux surrogate the SLB was less than 5% for most sites with respect to the reference area of interest (A(r)) at 90% annual footprint climatology (scenario A) and a circular area with radius of 1 km centred at the individual tower (scenario B) with several exceptions (iii) the SLB decreased with increasing size of footprint climatology for all sites for both scenarios A and B (iv) out of 12 eight flux towers represented most of the ecosystem surrounding the towers for an area of 0 3 km(2) up to 10 km(2) with a satisfactorily low bias of <5% whereas four towers represented areas ranging from only 0 75 to 4 km(2) and (v) the seasonal differences in monthly SLB using NDVI as a flux surrogate were about 1-4% for most sites for both scenarios A and B (C) 2010 Elsevier B V All rights reserved
Study on Discrimination of Varieties of Milk Based on FISS Imaging Spectral Data
Spectroscopy and Spectral Analysis 31(1), pp 214-218, 2011/1
Changping Huang ; Lifu Zhang; Xiaohong Zhang; Zheng Lanfen; Qingxi Tong
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Using a self-developed field imaging spectrometer system (FISS), hyperspectral images of 14 typical kinds of milk were acquired, based on which the discrimination of varieties of milk was studied. Firstly, removing 2 abnormal samples, the remaining 12 kinds of milk were randomly sampled, a total of 1 200 pixel samples. To eliminating high-frequency random noises and baseline offset and decrease the multi-collinearity, all samples were preprocessed by smooth-moving average and first derivative. Secondly, multiple discriminant analysis models for milk were built using characteristic wavelengths selected by the step-wise method. Results demonstrated that the overall identification accuracy for 1 200 spectral samples put together reached 95. 5%, of which the overall distinguishing rate of Mengniu, Yili and Guangming acidophilous milk was 88. 3%. The discriminant models for the three kinds of acidophilous milk subset, 300 spectral samples in all, were built, with the overall distinguishing rate of 88. 7%. This explicated that FISS would be useful for discriminating milk varieties, and to accomplish specific discrimination of milk varieties, it would be best for milk of the same type from different manufacturers to form a subset, which may not only reduce the model variables, improving operational efficiency and the stability of the model, but improve their overall discriminant accuracy.
Laboratory calibration of a field imaging spectrometer system.
Sensors 11(3), pp 2408-2425, 2011
Zhang, Lifu; Huang, Changping; Wu, Taixia; Zhang, Feizhou; Tong, Qingxi
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A new Field Imaging Spectrometer System (FISS) based on a cooling area CCD was developed. This paper describes the imaging principle, structural design, and main parameters of the FISS sensor. The FISS was spectrally calibrated with a double grating monochromator to determine the center wavelength and FWHM of each band. Calibration results showed that the spectral range of the FISS system is 437-902 nm, the number of channels is 344 and the spectral resolution of each channel is better than 5 nm. An integrating sphere was used to achieve absolute radiometric calibration of the FISS with less than 5% calibration error for each band. There are 215 channels with signal to noise ratios (SNRs) greater than 500 (62.5% of the bands). The results demonstrated that the FISS has achieved high performance that assures the feasibility of its practical use in various fields.
The Optimal Choice of Edge-Radiation-Distortion Correction Methods for OMIS-II Hyperspectral Images
Remote Sensing for Land & Resources 0(2), pp 17-21, 2010/6/15
Hang Yang; Xia Zhang; Hehai Xia; Lifu Zhang; Qingxi Tong
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Owing to the effects of the bigger Field-of-View(FOV),atmospheric attenuation,non-Lambertian reflectance of ground objects and the sun-object-sensor geometry,the radiance of airborne hyperspectral imaging spectrometer image tends to be distorted along the scanning lines,and hence it is difficult to use these data directly for quantitative or semi-quantitative applications.This study tried to correct the radiometric distortion of hyperspectral images of OMIS-II(Operative Modular Imaging Spectrometer) by usin...
A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling
Biogeosciences 7(9), pp 2943-2958, 2010
*Chen, B.; Ge, Q.; Fu, D.; Yu, G.; Sun, X.; Wang, S.; Wang, H.
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In order to use the global available eddy-covariance (EC) flux dataset and remote-sensing measurements to provide estimates of gross primary productivity (GPP) at landscape (10(1)-10(2) km(2)), regional (10(3)-10(6) km(2)) and global land surface scales, we developed a satellite-based GPP algorithm using LANDSAT data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index EVI, Land Surface Water Index - LSWI). The upscalling framework involves flux footprint climatology modelling and data-model fusion. This approach was first applied to an evergreen coniferous stand in the subtropical monsoon climatic zone of south China. The EC measurements at Qian Yan Zhou tower site (26 degrees 44'48 '' N, 115 degrees 04'13 '' E), which belongs to the China flux network and the LANDSAT and MODIS imagery data for this region in 2004 were used in this study. A consecutive series of LANDSAT-like images of the surface reflectance at an 8-day interval were predicted by blending the LANDSAT and MODIS images using an existing algorithm (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model). The seasonal dynamics of GPP were then predicted by the satellite-based algorithm. MODIS products explained 60% of observed variations of GPP and underestimated the measured annual GPP (= 1879 g Cm(-2)) by 25-30%; while the satellite-based algorithm with default static parameters explained 88% of observed variations of GPP but overestimated GPP during the growing seasonal by about 20-25%. The optimization of the satellite-based algorithm using a data-model fusion technique with the assistance of EC flux tower footprint modelling reduced the biases in daily GPP estimations from about 2.24 gCm(-2) day(-1) (non-optimized, similar to 43.5% of mean measured daily value) to 1.18 gCm(-2) day(-1) (optimized, similar to 22.9% of mean measured daily value). The remotely sensed GPP using the optimized algorithm can explain 92% of the seasonal variations of EC observed GPP. These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modelling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle.
An evaluation of the effect of the spectral response function of satellite sensors on the precision of the universal pattern decomposition method
International Journal of Remote Sensing 31(8), pp 2083-2090, 2010
*Zhang, Lifu; Liu, Bo; Zhang, Bing; Tong, Qingxi
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In previous studies of the universal pattern decomposition method (UPDM), the band width has been used to calculate standard spectral pattern vectors, without consideration of the effect of spectral response functions (SRFs). This study revised the UPDM to further reduce sensor dependence, by taking into account the effect of SRFs. Both the UPDM and the revised UPDM (RUPDM) were applied to MODIS and ETM+ data acquired over the Three Gorges region of China. The reconstruction accuracy was significantly greater when the RUPDM was used, with a relative decrease in the mean chi(2) of more than 14%. Using the new method, the dependence of the decomposition coefficients and vegetation index (VIUPD) on the sensor also decreased, with their linear regression factors approximately equal to one. These increases in accuracy indicate that the RUPDM further reduces sensor dependence and hence can outperform the UPDM in data retrieval.
Sensor-independent analysis method for hyperspectral data based on the pattern decomposition method
International Journal of Remote Sensing 27(21), pp 4899-4910, 2006/11/10
*Zhang, Lifu; Furumi, S.; Muramatsu, K.; Fujiwara, N.; Daigo, M.; Zhang, Liangpei
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This paper describes a modified pattern decomposition method with a supplementary pattern. The proposed approach can be regarded either as a type of spectral mixing analysis or as a kind of multivariate analysis; the later explanation is more suitable considering the presence of the additional supplementary patterns. The sensor-independent method developed herein uses the same normalized spectral patterns for any sensor: fixed multi-band (1260 bands) spectra serve as the universal standard spectral patterns. The resulting pattern decomposition coefficients showed sensor independence. That is, regardless of sensor, the three coefficients had nearly the same values for the same samples. The estimation errors for pattern decomposition coefficients depended on the sensor used. The estimation errors for Landsat/MSS and ALOS/AVNIR-2 were larger than those of Landsat/TM (ETM+), Terra/MODIS and ADEOS-II/GLI. The latter three sensors had negligibly small errors.
The bidirectional polarized reflectance model of soil
IEEE Transactions on Geoscience and Remote Sensing 43(12), pp 2854-2859, 2005/12
*Wu, TX; Zhao, YS
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Soil albedo is a critical parameter affecting the Earth\'s climate and environment. In remote sensing data, analysis of the soil bidirectional reflectance distribution function (BRDF) has to be known. Several models for bidirectional reflectance over soil have been developed. The Hapke bidirectional reflectance model has been widely used for soil modeling. Polarization of radiation reflected by soil carries important information of soil properties. The polarized light always goes with the bidirectional reflectance. Therefore, polarization reflectance of a ground target carries equivalent important information as bidirectional reflectance. Detecting multiangle polarization information of soil becomes a new method in quantitative remote sensing., In this paper, we analyzed the existence of polarization on the soil surface in a 2 pi space and compared the bidirectional reflectance with the bidirectional polarized reflectance. We then developed a new polarized BRDF model of soil as the bidirectional polarization distribution function (BPDF) model. The BPDF model helps to improve classification and quantitative analysis of soil.
