Spectral unmixing

Quintano, Carmen; Fernández–Manso, Alfonso; Shimabukuro, Y. E.; Pereira, Gabriel

doi:10.1080/01431161.2012.661095

Cited by 148 publications

(86 citation statements)

References 148 publications

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“…Though different sub-pixel image analysis techniques exist, linear Spectral Mixture Analysis (SMA) is the most often utilized (Quintano et al, 2012). This technique deals with mixed pixels by effectively defining independent endmembers that are supposed to represent the most important terrain attributes.…”

Section: U N C O R R E C T E D P R O O Fmentioning

confidence: 99%

“…The endmember spectra must have recognizable characteristics in the scene and must be meaningful for the user, since they are regarded as abstractions of physical objects that have the same or similar properties (Quintano et al, 2012). There are two broad alternatives to select the adequate endmember: reference endmembers from spectral libraries or image endmembers from the image itself.…”

Section: Mesma Proceduresmentioning

confidence: 99%

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Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

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Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999-2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km 2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forests.

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Section: U N C O R R E C T E D P R O O Fmentioning

confidence: 99%

Section: Mesma Proceduresmentioning

confidence: 99%

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

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“…Therefore, the cells of the same object type cannot be grouped as in Equation (4). This phenomenon can be elucidated by Figure 1a,b; they have the same object distribution patterns as Figure 1c,d, respectively, and their target object proportions have the same value of 5/16.…”

Section: Mechanism Analysismentioning

confidence: 91%

“…More than one type of land cover occurs within mixed pixels, from which proportions of land covers are extracted by spectral unmixing. The approach has been widely applied to acquire information on natural resources, such as agriculture, forestry and minerals, and to monitor the ecological environment, including soil erosion, deforestation and forest fires [4].…”

Section: Introductionmentioning

confidence: 99%

“…The previous studies in this field can be mainly divided into two categories: models of spectral unmixing and the method of endmember selection, in which the model is further categorized as linear or nonlinear, and the method adopts different techniques depending on the presence of pure pixels [4,5]. Although great progress has been made in mixed pixel decomposition, most of the studies were conducted with the assumption that the spatial response of the sensor to the surface radiation, characterized as the spatial limitation and uniformity of the sensor response and described by a rectangular response function, was ideal [3,6].…”

Section: Introductionmentioning

confidence: 99%

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Simulation of the Impact of a Sensor’s PSF on Mixed Pixel Decomposition: 1. Nonuniformity Effect

Liu

Hou

2016

Remote Sensing

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Abstract:The nonuniformity of the spatial response to surface radiation is a fundamental characteristic of all airborne and spaceborne sensors that inevitably introduces uncertainty into the estimation of object proportions by the spectral unmixing of mixed pixels. Simulated data of the surface radiation distribution and a TM (thematic mapper) response matrix were developed and utilized to imitate the generation of mixed pixels and the extraction of the object proportion via a Monte Carlo simulation, and then, the nonuniformity effect of a sensor's PSF (point spread function) was explored. The following conclusions were drawn: (1) given a nonuniform spatial response of a sensor to a surface scene with a constant object proportion and various object distribution patterns, the mixed pixel DN (digital number) of a remotely-sensed image becomes a random variable, which causes a PSF nonuniform effect on the object proportion extraction; (2) for the estimated object proportion, the corresponding true object proportion appears with a random variation; its upper and lower bounds take on an asymmetrical spindle shape; and models of these bound curves at any probability level were established; (3) there exists a negative linear relationship between the bias of the spectral unmixing and the estimated proportion; the bias is zero at an estimated proportion of 50%, and when the estimated proportions are approximately 100% and 0%, the object proportion is overestimated by 0.78% and underestimated by 0.78%, respectively; (4) the relationship between the standard deviation of the spectral unmixing and the estimated proportion follows a symmetrical polynomial function opening downward; the standard deviation reaches a maximum of 4.4% at the estimated proportion of 50%, and when the estimated proportion is approximately 100% or 0%, the standard deviation is a minimum, 1.05%. The above findings contribute to a comprehensive understanding of the PSF nonuniformity effect, have the potential to compensate for the bias of proportion estimation and present its confidence interval at any probability level.

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Sparse Representation‐Based Hyperspectral Image Classification

et al. 2022

Advances in Hyperspectral Image Processing Techniques

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Spectral unmixing

Cited by 148 publications

References 148 publications

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Simulation of the Impact of a Sensor’s PSF on Mixed Pixel Decomposition: 1. Nonuniformity Effect

Sparse Representation‐Based Hyperspectral Image Classification

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