Method for Forecasting of Changes in Land Use and Land Cover Using Satellite Remote Sensing Techniques
Abstract
In this investigation is proposed a method for forecasting of changes in land use and land cover using satellite remote sensing techniques. This study includes thefollowing twelve stages: 1) acquisition of remote sensing data, 2) collection of the reflectance image time series, 3) preliminary processing of reflectance image time series, 4) transformation of reflectance image to principal components, 5) modelling of PC1 statistical spatial prediction, 6) calibration of forecasting models for the PC1 SSPM coefficients, 7) calibration of PC1 SSPM, 8) validation of PC1 SSPM, 9) forecasting of PC1 SSPM coefficients and 10) calibration of CP1 SSPM with forecasted coefficients, 11) application of change detection techniques and 12) comparison of methods. Sixteensatellite images are acquired from the Landsat satellite in the period from 1986 to 2016. The study unit is the Pao river basin. The proposed method is a hybrid combination that includes three types of applied models that are based on time series of reflectance images in sequence as follows: the principal component analysis, the statistical spatial prediction models and forecasting models for time series. The current study proposes a method that contributes to introduce the temporal pattern of LULC changes captured by the statistical spatial prediction method coefficients and provides results characterized by a seasonality parameter; which is able to reproduce the spatio-temporal variation collected by the reception of the reflectance variable by satellite sensor. The statistics of error predictions indicate gradients of the predicted and observed function approximated to the unity as well as near to zero for the errors. The samples evaluated in the validation stage give correlation coefficient upper to 0.6; being a successful adjust between observed and predicted values. The forecasted changes in the Pao river basin for 2020 and 20130 vary from: 5.54 to 8. 14%, 5.52 to 8. 14%. These changes are equivalent to those observed from 2000 and 2016 of 5.13% as well as from 1990 to 2016 of 7.05 %.
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