Linear spectral mixture modelling to estimate vegetation amount from optical spectral data

Garcı́a-Haro, Francisco Javier; Gilabert, María Amparo; Meliá, J.

doi:10.1080/01431169608949157

Cited by 138 publications

(65 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…SMA is a physically-based model that transforms radiance or reflectance values to physical variables that are linked to the subpixel abundances of surface components within each pixel [89,90]. SMA proved useful specifically for the mapping of salt and gypsum surfaces [13,91], but also in a wide range of other applications, such as vegetation (e.g., [92]), mineralogical and lithological mapping (e.g., [93,94]), and soil degradation assessment [95]. The SMA analysis assumes a linear mixing of the scene constituents in the sensor field-of-view and is particularly adapted to arid landscapes with aerial mixing.…”

Section: Eo-1 Hyperion Mineralogical Mappingmentioning

confidence: 99%

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

2017

View full text Add to dashboard Cite

This study combines spaceborne multitemporal and hyperspectral data to analyze the spatial distribution of surface evaporite minerals and changes in a semi-arid depositional environment associated with episodic flooding events, the Omongwa salt pan (Kalahari, Namibia). The dynamic of the surface crust is evaluated by a change-detection approach using the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) based on the Landsat archive imagery from 1984 to 2015. The results show that the salt pan is a highly dynamic and heterogeneous landform. A change gradient is observed from very stable pan border to a highly dynamic central pan. On the basis of hyperspectral EO-1 Hyperion images, the current distribution of surface evaporite minerals is characterized using Spectral Mixture Analysis (SMA). Assessment of field and image endmembers revealed that the pan surface can be categorized into three major crust types based on diagnostic absorption features and mineralogical ground truth data. The mineralogical crust types are related to different zones of surface change as well as pan morphology that influences brine flow during the pan inundation and desiccation cycles. These combined information are used to spatially map depositional environments where the more dynamic halite crust concentrates in lower areas although stable gypsum and calcite/sepiolite crusts appear in higher elevated areas.

show abstract

Section: Eo-1 Hyperion Mineralogical Mappingmentioning

confidence: 99%

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

2017

View full text Add to dashboard Cite

show abstract

“…Wide variety of methods has been developed for soft classification of satellite imagery. For instance, linear spectral unmixing (Foody and Cox, 1994;Garcia-Haro et al, 1996), softened maximum likelihood (Bastin, 1997), multi-layer perceptron , fuzzy c-means (Bezdek et al, 1984;Foody, 1996) and Support Vector Machines (SVM) (Brown et al, 1999) are the well-known methods in this area. The output of soft classifiers is fractional maps corresponding to each one of classification classes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Super Resolution Mapping of Multispectral Imagery Based on Pixel Swapping Technique

Niroumand-Jadidi

Safdarinezhad

Sahebi

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Mixed pixels could be considered as a major source of uncertainty through classification process of satellite imagery. In this regard, the use of soft classifiers is often inevitable in order to increase the accuracy of land cover estimates. Although soft classifiers provide detailed information for each pixel, spatial arrangement of sub-pixels remains unknown. Super Resolution Mapping (SRM) has opened up a new horizon to produce finer spatial resolution maps using the outputs of soft classifiers. Wide variety of SRM algorithms has been developed. In this way, spatial optimisation techniques are the most applicable ones. However, random allocation of sub-pixels and iterative procedure of optimisation are the main limitations of current methods (e.g. Hopfield Neural Network, Simulated Annealing). This research attempts to provide an optimisation approach based on the pixel swapping technique in order to simplify the concept and to reduce the iteration procedure. In this paper, a brief survey is conducted on spatial optimisation based techniques of SRM. SVM and SMACC are used cooperatively to produce fractional maps as an input of SRM algorithm. The initial allocation of the sub-pixels is performed nonrandomised based on the highest amounts of attractiveness. An optimisation procedure is proposed to transfer the multiple allocated sub-pixels to the non-allocated ones. This procedure usually stops with minimal iterations and is time effective. The proposed method is tested on multispectral imagery (Landsat ETM+ and Quickbird) and has demonstrated precise results particularly in boundary pixels.

show abstract

“…Variations in the algorithm arise from the choice of variable used to measure the similarity between the model and the measurement data. For example, when reflectance is directly used as a variable, the minimization process considers the difference (often defined as the root mean square error, RMSE) between the modeled and measured spectrum [8][9][10][11][12][13][14]21,22,24,28,[31][32][33][34][35]. Altering the reflectance into a spectral vegetation index (VI) produces another group of algorithms [1,4,7,25,26,[36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

“…In these algorithms, FVC is modeled using the weights of the representative spectra, known as endmembers. Because of its simplicity, LMMs with very few endmembers have been used frequently over the years [5,7,13,14,[20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Inter-Algorithm Relationships for the Estimation of the Fraction of Vegetation Cover Based on a Two Endmember Linear Mixture Model with the VI Constraint

Obata

Yoshioka

2010

Remote Sensing

View full text Add to dashboard Cite

Measurements of the fraction of vegetation cover (FVC), retrieved from remotely sensed reflectance spectra, serves as a useful measure of land cover changes on the regional and global scales. A linear mixture model (LMM) is frequently employed to analytically estimate the FVC using the spectral vegetation index (VI) as a constraint. Variations in the application of this algorithm arise due to differences in the choice of endmember spectra and VI model assumptions. As a result, the retrieved FVCs from a single spectrum depend on those choices. Therefore, the mechanism underlying this dependency must be understood fully to improve the interpretation of the results. The objective of this study is to clarify the relationships among algorithms based on the LMM. The relationships were derived analytically by limiting both the number of endmembers and the spectral wavelength band to two each. Numerical experiments were conducted to demonstrate and validate the derived relationships. It was found that the relationships between two algorithms of this kind could be characterized by a single parameter that was determined by the endmember spectra and the coefficients of a VI model equation used in the algorithms.

show abstract

Linear spectral mixture modelling to estimate vegetation amount from optical spectral data

Cited by 138 publications

References 24 publications

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

A Novel Approach to Super Resolution Mapping of Multispectral Imagery Based on Pixel Swapping Technique

Inter-Algorithm Relationships for the Estimation of the Fraction of Vegetation Cover Based on a Two Endmember Linear Mixture Model with the VI Constraint

Contact Info

Product

Resources

About