lab for remote sensing hydrology and spatial modeling dept. of bioenvironmental systems engineering,...
TRANSCRIPT
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
衛星遙測在變遷偵測之應用Satellite Remote Sensing for
Land-Use/Land-CoverChange Detection
鄭 克 聲 台灣大學生物環境系統工程學系
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Applications of Remote Sensing Change Detection
Land-use/land-cover changesForest or vegetation changeForest mortality, defoliation and damage assessmentDeforestation, regeneration and selective loggingWetland changeForest fireLandscape changeUrban changeEnvironmental changeOther applications such as crop monitoring, changes
in glacier mass, etc.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Factors Affecting Accuracy ofChange Detection
precise geometric registration between multi-temporal images
calibration or normalization between multi-temporal images
availability of quality ground truth datathe complexity of landscape and environments of
the study areachange detection methods or algorithms usedclassification and change detection schemesanalyst’s skills and experienceknowledge and familiarity of the study areatime and cost restrictions.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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
conditionsgeomorphological 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 variabilitythe greenhouse effect caused by human
activities.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Before implementing change detection analysis, the following conditions must be satisfied: precise registration of multi-temporal
imagesprecise 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Major Methods of Change Detection
Post-classification methodsImage-differencing methodsPrincipal component analysis methods
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Statistical Perspective of Change Detection
Uncertainties involvedA statistical-test perspective
Null and alternative hypothesesTest statisticLevel of significance
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Sources of Uncertainties inRemote Sensing Change Detection
Spatial and/or temporal variations in atmospheric conditionssoil moisture conditionsvegetation growth conditionsorographic conditions
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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?
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Determining Threshold for Change DetectionMultiple 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
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Bivariate Scatter Plot of Multi-temporal Images
Red band
01/10/1999 vs 21/09/2002
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Change Detection Using Bivariate
Probability Contours95% probability contour
X2
X1
: detected changes
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Conditional Prob. Distribution
Bivariate Joint Probability Distribution and Conditional
Probability Distribution X2
X1
Joint Prob. Distribution
)|( 12| 112xXf xXX
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Class-specific Temporal Correlation
X2
X1
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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 .
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
01/10/1999
IR R G
water
vegetation
soil
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
21/09/2001
IR R G
water
vegetation
Soil
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
SPOT Image of the Study Area
Sept. 21, 2001Oct. 1, 1999
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Confidence level 50%, 75%, 90%
Water
VegetationSoil
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Detected Changes (R Band)
90% confidence region 95% confidence region
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Detected Changes (IR/G)
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Examples of Detected Changes
21/09/2001Changed sites 01/10/1999
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 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 distributionsUncertainty in parameters estimationDifficulty in deriving conditional
probability distributions.
台灣大學生物環境系統工程學系 Lab for Remote Sensing Hydrology and Spatial Modeling遙測水文及空間模式研究室 Dept. of Bioenvironmental Systems Engineering, NTU
Thanks for your attention.