Abstract
Abstract: A challenge for the use of medium spatial resolution imagery for land use change detection consists of the reduced availability of ground reference data for previous dates. This study aims to obtain invariant training points using the backdating process for supervised classification of images that have no field data available. The study area comprises 1,353 km² in Santa Catarina, southern Brazil. We compared the accuracy performance of invariant area sets (binary change maps) generated by using three methods (IR-MAD - Iteratively Reweighted Multivariate Alteration Detection, CVA - Change Vector Analysis and SGD - Spectral Gradient Difference) for two periods (2017-2011 and 2011-2006). The classification of the Landsat-5 TM image of 2006 was performed using as training data the sets of points indicated as invariant in the binary maps resulted from the three abovementioned methods. The accuracies for seven land-use classes were computed. The overall accuracy was greater (80,5% and 80,2%) when using training areas achieved by CVA and SGD, respectively than IR-MAD (76%). Were obtained accuracies greater than 80% for the forest class. The results stress that the combination of the IR-MAD and SGD is preferable since the CVA is more time consuming due to the subjective application of thresholds.
Highlights
Land Use and Land Cover Change (LUCC) detection is an important tool for several applications, such as land use surveys, as monitoring of wildfires, reforestation, forest regeneration, agricultural and forest growth, crop forecasting and landscape dynamics features (Banskota et al 2014)
We focus on the invariant pixel accuracy because of their importance for the backdating of training areas
Regarding the user’s accuracies of invariant pixels, we may assert that the IR-MAD method was superior with 100%
Summary
Land Use and Land Cover Change (LUCC) detection is an important tool for several applications, such as land use surveys (especially deforestation), as monitoring of wildfires, reforestation, forest regeneration, agricultural and forest growth, crop forecasting and landscape dynamics features (Banskota et al 2014). Employed analysis techniques are the generation of two-date difference images, based on surface reflectance data, vegetation indices, or independent (LUCC) classification of multi-temporal time series of satellite images. Based on this information it is possible to model the land use dynamic with change detection algorithms (Phiri and Morgenroth 2017). Atmospheric correction and radiometric normalization, as topographic correction, are essential pre-processing steps for many of these techniques These corrections attenuate disturbance cause by the atmosphere and by the geometry of illumination (BRDF effects - bidirectional reflectance distribution function). The in-situ collection becomes unrealistic being replaced by remotely sensed data with higher spatial or spectral resolution than the original time series of images (Olofsson et al 2014; Muller-Warrant et al 2015)
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