Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis

Small, Christopher; Lu, Jacqueline W.T.

doi:10.1016/j.rse.2005.10.023

Cited by 154 publications

(77 citation statements)

References 28 publications

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“…Theoretically, the constraint equation specifies that the sum of the fractions should equal 1 and the unmixing process should make the RMS error small. Linear spectral mixture modeling is described in detail in [7]. The selection of substrate, vegetation and dark endmembers is consistent with this research [40].…”

Section: Spectra Mixture Analysissupporting

confidence: 54%

“…Therefore, accurate and detailed spatiotemporal vegetation phenology is critical to model climatic, hydrological and ecological processes [7].…”

Section: Introductionmentioning

confidence: 99%

“…Landsat TM and ETM+ at 30-m resolution also have been used to identify vegetation phenology [19,20]. Spectral mixture analysis (SMA) could be used to map urban land cover and provide vegetation fraction that could be compared to other measures of vegetation cover [7,21]. In this study, spectral mixture analysis (Substrate-Vegetation-Dark endmembers) has been used to extract vegetation abundance time series curves that are compared with phenological endmembers from EVI, to calibrate the phenology patterns [7,22].…”

Section: Introductionmentioning

confidence: 99%

“…Spectral mixture analysis (SMA) could be used to map urban land cover and provide vegetation fraction that could be compared to other measures of vegetation cover [7,21]. In this study, spectral mixture analysis (Substrate-Vegetation-Dark endmembers) has been used to extract vegetation abundance time series curves that are compared with phenological endmembers from EVI, to calibrate the phenology patterns [7,22]. Finally, spatial vegetation phenology maps based on field survey [23][24][25] are compared with vegetation phenology map derived from the EOF and TUA methods.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China

2015

Sustainability

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Abstract:Vegetation plays an important role in atmospheric, hydrologic and biochemical cycles and is an important indicator of the impact of climate and human factors on the environment. In this paper, a method, which combines the empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) methods, is applied to monitor the phenological characteristcs and spatial distribution of vegetation phenology in the middle part of the Huai River region. Based on the variance and EOF curves, the EOF provides the number of phenology modes, information which is the basis for an accurate temporal unmixing model. The TUA describes the temporal vegetation phenological details and spatial distribution. Importantly, this approach does not require assumptions, prior information or pre-defined thresholds. The vegetation phenology curves derived from the MODIS EVI data using the combined EOF and TUA methods display much more detail than the curves from Landsat TM using spectral mixture analysis (SMA). Additionally, the vegetation phenology spatial distribution from MODIS EVI is consistent with the field survey data. The combination method of EOF and TUA can be used to monitor vegetation phenology spatiotemporal change in a large area from time series of MODIS EVI data.

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Section: Spectra Mixture Analysissupporting

confidence: 54%

“…Therefore, accurate and detailed spatiotemporal vegetation phenology is critical to model climatic, hydrological and ecological processes [7].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China

2015

Sustainability

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“…Sometimes, if both pervious and impervious surfaces are exposed and illuminated, then this leads to ambiguity, a tricky classification in arid and semi arid regions. Small and Lu (2006) suggests to use vegetation fraction as a proxy for fractional pervious surface because vegetation cannot thrive on impervious surface, so presence of vegetation implies presence of some amount of pervious surface. Therefore, using detectable vegetation as an indicator of permeable surface can account for the range of natural and built surfaces.…”

Section: An Urban Scenariomentioning

confidence: 99%

Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data

Kumar

Milesi

Nemani

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we explore the possibility of sparse regression, a new direction in unmixing, for vegetation and urban area classification. SUnSAL (Sparse unmixing via variable splitting and augmented Lagrangian) in both unconstrained and constrained forms (with the abundance non-negativity and abundance sum-to-one constraints) were used with a set of global endmembers (substrate, vegetation and dark objects) to unmix a set of computer simulated noise-free and noisy data (with Gaussian noise of different signal-to-noise ratio) in order to judge the robustness of the algorithm. The error in the fractional estimate was examined for varying noise power (variance): 2, 4, 8, 16, 32, 64, 128 and 256. In the second set of experiments, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data representing an agricultural setup in Fresno, California, USA were used. The corresponding ground data for validation were collected on the same days of satellite overpass. Finally in the third set of experiments, a clear sky Landsat-5 TM data for an area near the Golden Gate Bridge, San Francisco (an urbanized landscape), California, USA were used to assess the algorithm. The fractional estimates of the 30 m Landsat-5 TM data were compared with the fractional estimates of a high-resolution World View-2 data (2 m spatial resolution) obtained using a fully constrained least squares algorithm. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success and bivariate distribution function, which showed that constrained model was better than unconstrained form.

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Characterizing Urban Subpixel Composition Using Spectral Mixture Analysis

Powell

2011

Urban Remote Sensing

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Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis

Cited by 154 publications

References 28 publications

Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China

Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China

Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data

Characterizing Urban Subpixel Composition Using Spectral Mixture Analysis

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