Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Satellite Remote Sensing for Land-Use/Land-Cover Change Detection

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Change Detection Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times. In general, change detection involves the application of multi-temporal datasets to quantitatively analyze the temporal effects of the phenomenon.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Because of the advantages of repetitive data acquisition, its synoptic view, and digital format suitable for computer processing, remotely sensed data have become the major data sources for different change detection applications during the past decades.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Applications of Remote Sensing Change Detection Land-use/land-cover changes Forest or vegetation change Forest mortality, defoliation and damage assessment Deforestation, regeneration and selective logging Wetland change Forest fire Landscape change Urban change Environmental change Other applications such as crop monitoring, changes in glacier mass, etc.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Factors Affecting Accuracy of Change Detection precise geometric registration between multi- temporal images calibration or normalization between multi- temporal images availability of quality ground truth data the complexity of landscape and environments of the study area change detection methods or algorithms used classification and change detection schemes analysts skills and experience knowledge and familiarity of the study area time and cost restrictions.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Lambin and Strahler (1994) listed five categories of causes that influenced land-cover change: long-term natural changes in climate conditions geomorphological and ecological processes such as soil erosion and vegetation succession human-induced alterations of vegetation cover and landscapes such as deforestation and land degradation inter-annual climate variability the greenhouse effect caused by human activities.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU When selecting remote sensing data for change detection applications, it is important to use the same sensor, same radiometric and spatial resolution data with anniversary or very near anniversary acquisition dates in order to eliminate the effects of external sources such as sun angle, seasonal and phenological differences.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Before implementing change detection analysis, the following conditions must be satisfied: precise registration of multi-temporal images precise radiometric and atmospheric calibration or normalization between multi-temporal images similar phenological states between multi- temporal images selection of the same spatial and spectral resolution images if possible.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Major Methods of Change Detection Post-classification methods Image-differencing methods Principal component analysis methods

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Statistical Perspective of Change Detection Uncertainties involved A statistical-test perspective Null and alternative hypotheses Test statistic Level of significance

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Sources of Uncertainties in Remote Sensing Change Detection Spatial and/or temporal variations in atmospheric conditions soil moisture conditions vegetation growth conditions orographic conditions

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU The Soil and Water Conservation Bureau (SWCB) implements a standard process to routinely monitor land-cover changes on slopeland. The process basically calculates grey level difference between two images and adopts a threshold value of grey level difference for land-cover change detection. Image differencing on single band or composite images is the most widely used approach of change detection.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Problems and challenges How should the threshold value be determined? How much confidence do we have on decision of change detection?

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Determining Threshold for Change Detection Multiple of standard deviation of DN difference. Nelson (1983): k = 0.5~1 Ridd and Liu (1998): k = 0.9~1.4 Sohl (1999): k = 2

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Thresholding of grey level difference is globally based. It does not consider the grey level correlation of multi-temporal images and grey level of the pixel under investigation. It is important to examine the bivariate scatter plot of multi-temporal images.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Bivariate Scatter Plot of Multi- temporal Images Red band 01/10/1999 vs 21/09/2002

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Pre- and post-images of the same spectral band are highly correlated. Bivariate scatter plot shows bivariate joint probability distribution.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Change Detection Using Bivariate Probability Contours 95% probability contour X 2 X 1 : detected changes

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Conditional Prob. Distribution Bivariate Joint Probability Distribution and Conditional Probability Distribution X 2 X 1 Joint Prob. Distribution

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Class-specific Temporal Correlation X 2 X 1

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Transforming Change Detection to Hypothesis Test Using conditional probability distribution, the work of change detection can be placed in the framework of hypothesis test. Null hypothesis Ho: no change.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Bivariate Normal Distribution Conditional normal distribution Parameters can be estimated using pixels associated with no change. Critical regions with respect to chosen level of significance can then be determined.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Major features (vegetation, soil and water) classification for individual images. Class-specific correlation analysis using pixel pairs that are not associated with change. Determining bivariate probability distribution for each class. Specifying class-specific critical regions for test at level of significance.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU 01/10/1999 IRRG water vegetation soil

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU 21/09/2001 IRRG water vegetation Soil

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU SPOT Image of the Study Area Sept. 21, 2001 Oct. 1, 1999

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Confidence level 50%, 75%, 90% Water Vegetation Soil

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Detected Changes (R Band) 90% confidence region95% confidence region

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Detected Changes (IR/G)

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Examples of Detected Changes 21/09/2001Changed sites01/10/1999

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Summary We have demonstrated that change detection can be placed in a hypothesis test framework. Preliminary results are promising. Several problems remain: Non-gaussian probability distributions Uncertainty in parameters estimation Difficulty in deriving conditional probability distributions.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Thanks for your attention.