On the Use of FOSS4G in Land Cover Fraction Estimation with Unmixing Algorithms

Abstract:

The popularity and usage of FOSS4G (FOSS for Geoinformatics) has increased drastically in the last two decades with increasing benefits that facilitate spatial data analysis, image processing, graphics and map production, spatial modeling and visualization. The objective of this paper is to use FOSS4G to implement and perform a quantitative analysis of three different unmixing algorithms: Constraint Least-Square (CLS), Unconstraint Least-Square, and Orthogonal Subspace Projection to estimate land cover (LC) fraction estimates from RS data. The LC fractions obtained by unmixing of mixed pixels represent mixture of more than one class per pixel rendering more accurate LC abundance estimates. The algorithms were implemented in C++ programming language with OpenCV package (http://opencv.org/) and boost C++ libraries (www.boost.org) in the NASA Earth Exchange at the NASA Advanced Supercomputing Facility. GRASS GIS was used for visualization of results and statistical analysis was carried in R in a Linux system environment.

A set of global endmembers for substrate, vegetation and dark objects were used to unmix the data using the three algorithms and were compared with Singular Value decomposition unmixed outputs available in ENVI image processing software. First, computer simulated data of different signal to noise ratio were used to evaluate the algorithms. The second set of experiments was carried out in an agricultural set-up with a spectrally diverse collection of 11 Landsat-5 scenes (acquired in 2008) for an agricultural setup in Frenso, California and the ground data were collected on those specific dates when the satellite passed through the site. Finally, in the third set of experiments, a pair of coincident clear sky Landsat and World View 2 data for an urbanized area of San Francisco were used to assess the algorithm. Validation of the results using descriptive statistics, correlation coefficient (cc), RMSE, boxplot and bivariate distribution function indicated that with the computer simulated data, CLS was better than other techniques. With the real world data of an agricultural landscape, CLS was superior to other techniques with a mean absolute error for all four methods close to 7.3%. For the urban setup, CLS demonstrated highest average cc of 0.64 and lowest average RMSE of 0.19 for all the endmembers.