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Improving change vector analysis in multitemporal space to detect land cover changes by using cross-
correlogram spectral matching algorithm
Yuanyuan Zhao, Chunyang He, Yang yang
Beijing Normal University, Beijing, China, 100875
Email : [email protected]
2011 IEEE International Geoscience and Remote Sensing Symposium
Outline
Introduction
Methods
Case study
Effectiveness analysis of the new method
Accuracy assessment
Conclusions and discussion
Land cover change detection is of great significance
Land cover plays an important role in energy balance as well as biogeochemical and hydrological cycles in the Earth system (Avissar and Pielke, 1989; Lunetta et al., 2006).
Timely and accurate detection of land cover changes can a) provide essential information to
enhance our understanding of the mechanisms that drive the spatial-temporal processes of land cover change.
b) support the simulation and evaluation of the associated environmental impacts.
Traditional change vector analysis (TCVA)
TCVA has then been widely adopted in land cover change detection using VI data (Lambin and Strahler, 1994).
TCVA is sensitive to temporal fluctuations in VI values, which greatly limits the method’s accuracy. It may overestimate the actual changes. It is not able to determine whether the results represent land
cover conversion or simply VI variation of the same type of land cover.
Land cover conversion growth vigor changes
comparable change magnitude
?
The CCSM algorithm has demonstrated its merits in estimating the similarity of two VI profile curves
A cross-correlogram can be constructed for each pair of VI profiles and a goodness-of-match value can be calculated accordingly.
Advantage : it is able to capture the shape similarity of two VI profiles even if there is a time lag between the two.
Objectives
Proposing a new approach that improves TCVA with an
adapted CCSM analysis.
The proposed approach was applied and validated
through a case study of land cover conversion detection
in the Beijing–Tianjin–Tangshan urban agglomeration
district (BTT-UAD), China, using the MODIS Enhanced
VI data (EVI) for 2000–2008.
Outline
Introduction
Methods
Case study
Effectiveness analysis of the new method
Accuracy assessment
Conclusions and discussion
Traditional change vector analysis
1 2T
nR r ,r , ,r
1 2T
nS s ,s , ,s
nn sr
sr
sr
SRV
22
11
The VI time series data in the period R :
2222
211 )()()( nn srsrsrV
A greater M indicates a higher possibility of land cover change for pixel i.
A specific threshold is used to distinguish change pixels from no-change pixels ( Lambin and Strahler, 1994a )。
The VI time series data in the period S :
VI
0
0.2
0.4
0.6
0 5 10 15 20 25 30时间
As comparable magnitude
values of change vectors may
also result from phenological
variation of a vegetative type
of land cover or change in its
growth vigor, a threshold is
not always effective in
extracting land cover
conversions.
VI
0
0.2
0.4
0.6
0 5 10 15 20 25 30时间
VI
0
0.2
0.4
0.6
0 5 10 15 20 25 30时间
VI
0
0.2
0.4
0.6
0 5 10 15 20 25 30时间
VI
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30时间
VI
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30时间
VI
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30时间
VI
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30时间
VI
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30时间
VI
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30时间
( e ) No-change M =0.13
( d ) Vegetation growth status change M =0.77
( c ) Phenology change M =0.78
( b ) From ‘winter wheat-summer maize’ to ‘spring maize’
( a ) From cropland to built-up
M=0.78
M=0.82
Improved change vector analysis(ICVA)
Three step: • TCVA is employed to preliminarily detect land cover changes.• The CCSM approach is used to identify and eliminate areas in which the land cover type did not really change but only experienced some degree of VI variation. • The type of land cover conversion (e.g., from cropland to built-up area) is determined by further analyzing the change vectors for the remaining pixels of interest.
Preliminary detection of land cover change using traditional change vector analysis
Determination of land cover change types
Identification and elimination of land cover modifications using cross-correlogram spectral
matching analysis
Time series data in time r
Time series data in time s
Flow chart
Preliminary land cover change detection using TCVA
The change magnitude of VI time series was calculated using the
TCVA.
An optimal threshold was determined to extract the preliminary
change information.
A semi-automatic method called Double-windows Flexible Pace
Search method (DFPS) (Chen et al., 2003).
VI time series in time r
VI time series in time s
Change magnitude
Change information
TCVA DFPS
Identifying and eliminating pseudo-conversion by CCSM analysis
The correlation coefficients (Rm) of the two VI profile curves
between time r and s at different match positions (m) are calculated.
222 2
r s r sm
r r s s
nR
n n
where λs and λr are VI profile curve values for period r and s, respectively. m is the match position. n is the number of overlapping positions.
λs
λr
Rm
time
λr
time
λs
Eliminating land cover modification using the CCSM algorithm
The maximum correlation coefficient (Rmax) is chosen as the
shape similarity index of the two curves (Wang et al., 2009).
max 1 0 1max , , ,m mR R R R R R
where Rmax ranges from 0 to 1. The Rmax is equal to 1 when the shape of the VI profile curves between period r and s are completely the same. A larger Rmax indicates a smaller difference between the two shapes of the VI profile curves.
Time r Time sEVI
0
0. 2
0. 4
0. 6
0. 8
1
0 5 10 15 20 25 30时间
| V△ |= 0.78
Time
Rm
-0.4-0.2
00.20.40.60.8
1
-5 -4 -3 -2 -1 0 1 2 3 4 5
匹配位移(m)
Rmax=0.996
Match position (m)
The land cover modification is eliminated by an optimal threshold
for Rmax using a manual trial-and-error procedure .
Selecting sample areas with the help of the ancillary data.
Assessing the effectiveness of eliminating land cover modification for
different thresholds.
Assigning the optimal threshold for Rmax to the value at which the eliminating
effect is best.
Threshold
t1
…
tn
change 1
……
change n
reference
compare
compare
Kappa 1
……
Kappa n
Kappa kmax
tk
The optimal threshold
Change information
Eliminating land cover modification using the CCSM algorithm
Discriminating the land cover conversion type
Unsupervised clustering approach (Bruzzone and Prieto, 2000)。 Having no requirement for training data Partitioning remotely sensed data with multi-spectral or multi-
temporal information Transforming the partitioned classes into a thematic map of
interest by a posteriori
The unsupervised clustering method is adopted in this study
to the actual land cover conversion types with the support of
some ancillary data.
Change vector image
Class map
Unsupervised clustering
Change Typemap
Ancillary data
Outline
Introduction
Methods
Case study
Effectiveness analysis of the new method
Accuracy assessment
Conclusions and discussion
Study area
Latitude: 38°28′ N - 41°05′ N
Longitude: 115°25′ E -119°53′ E
Total area: 55774.5 km2
Climate: Sub-humid and temperate
monsoon climate
Main land cover type: cropland, built-up, forest
Over the past several decades, significant land cover changes have taken place in the BTT-UAD, mainly driven by rapid economic development and unprecedented urbanization (Tan et al., 2005).
Beijing–Tianjin–Tangshan urban agglomeration district
(BTT-UAD), China
Data
MODIS_EVI data (specifically MOD 13Q data
version 004)
The spatial resolution is 250m
The time spans from 2000 to 2008
They were downloaded from the Earth Resources
Observation Science Center of United States
Geological Survey (USGS EROS) Landsat ETM+ data:
123/32 20 August 2000 and 11 September 2008 122/33 10 June 2000 and 3 August 2008 They were downloaded from EROS data center
Other data : The land use/cover data in 2000
Field survey data
Images obtained from Google Earth
2000
2008
MODIS_EVI
MODIS_EVI data preprocessing
Image mosaicing The four tiles (h26v04 、 h26v05 、 h27v04 、 h27v05)
covering the study area were mosaiced to a complete EVI image covering the study area.
Projection converting The mosaiced images were converted to the map
projection format commonly used in China, the Albers Conical Equal Area format.
Noise removing The Harmonic Analysis of Time Series (HANTS) was
performed on the image time series.
Image clipping
Image mosaicking
Projectionconverting
Noise removing
Image clipping
1 2 3 4
MODIS_EVI of the study area
Extracting preliminary pixels of land cover change
Change magnitude image of the study area, 2000-2008
Preliminary extraction of land cover change (2000-2008) in the study area
EVI time series in 2000
EVI time series in 2008
TCVACalculating
Change magnitude
Preliminary change
informationDFPS
Land cover conversion in the study area, 2000-2008
Rmax calculated by CCSM using the EVI profile curves in 2000 and 2008
Eliminating land cover modification in the study area using the CCSM algorithm
The preliminary
change information
EVI time series in 2000
EVI time series in 2008
Calculating the shape similarity
index Rmax
Manual trial-and-error procedure
land cover conversion
Obtaining the land cover conversion map
The land cover conversion map was obtained by classifying the change vector image of land cover using an unsupervised classification technique.
(a) from water to cropland ; (b) from cropland to built-up ; (c) from water to built-up
2000 ETM+ 2008 ETM+
Outline
Introduction
Methods
Case study
Effectiveness analysis of the new method
Accuracy assessment
Conclusions and discussion
Effectiveness analysis of the ICVA
The TCVA could not distinguish the land cover modification from land cover conversion accurately only by selecting the threshold for the change magnitude.
The ICVA can effectively eliminate partial land cover modification information by thoroughly use of the shape variation of the EVI profile curves and determining an
optimal threshold for Rmax.
1
EVI
0
0.2
0.4
0.6
0 5 10 15 20 25 30Date order of 10-day
2000年2008年
R m
0
0.2
0.4
0.6
0.8
1
-5 -4 -3 -2 -1 0 1 2 3 4 5
Match position (m ) 2
3
EVI
0
0.2
0.4
0.6
0 5 10 15 20 25 30Date order of 10-day
R m
0
0.2
0.4
0.6
0.8
1
-5 -4 -3 -2 -1 0 1 2 3 4 5
Match position (m ) 4
5
EVI
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30Date order of 10-day
R m
-0.4-0.2
00.20.40.60.8
1
-5 -4 -3 -2 -1 0 1 2 3 4 5
Match position (m ) 6
7
EVI
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30Date order of 10-day
R m
0
0.2
0.4
0.6
0.8
1
-5 -4 -3 -2 -1 0 1 2 3 4 5
Match position (m ) 8
Rmax=0.991
M=0.77
Rmax=0.725
M=0.78
Rmax=0.569 M=0.82
Rmax=0.996
M=0.78
(a)From cropland to built-up
(b)From ‘winter wheat-summer maize’ to ‘spring maize’
(c)Advance of phenological period
(d)Vegetation growth status change
Outline
Introduction
Methods
Case study
Effectiveness analysis of the new method
Accuracy assessment
Conclusions and discussion
Visually comparing
(a) Differences in the case
of vegetation vigor change.
(b) Differences in the case
of phenological change.
The TCVA misinterpret
the vegetation vigor
change and phenological
change as land cover
conversion, while the
ICVA eliminate the two
types of changes.
The ICVA performed better than the TCVA in detecting the land cover conversion in the study area
The TCVA achieved a kappa coefficient of 0.29 and an overall accuracy of 60.40%, whereas the ICVA achieved a kappa coefficient of 0.42 and an overall accuracy of 71.20%.
Reference data (pixels) Reference data (pixels)
ICVAM Change No-change
Row
total
TCVAM Change No-change Row total
Change 118 132 250 Change 119 187 306
No-change 12 238 250 No-change 11 183 194 Results
(pixels) Column
total 130 370 500
Results
(pixels) Column
total 130 370 500
Total accuracy =71.20% Kappa coefficient = 0.42 Total accuracy = 60.40% Kappa coefficient = 0.29
Omission error Omission error
Change = 9.23% Change = 8.46%
No-change =35.68% No-change = 50.54%
Commission error Commission error
Change = 52.80% Change = 61.11%
No-change = 4.80% No-change = 5.67%
1
Outline
Introduction
Methods
Case study
Effectiveness analysis of the new method
Accuracy assessment
Conclusions and discussion
Conclusions
We have proposed a new approach, named ICVA, that improves TCVA with an adapted use of cross-correlogram spectral matching (CCSM) analysis.
ICVA was applied to detect land cover conversion in BTT-UAD, China from two time series of MODIS EVI data for 2000 and 2008. The results showed that ICVA is able to map land cover conversion with a significantly higher accuracy (71.20%, kappa = 0.42) than TCVA (60.40%, kappa = 0.29).
The higher accuracy has been achieved by analyzing the multi-temporal VI information with the consideration of not only change magnitude but also profile similarity.
Discussion
The application of ICVA has some limitations:
The approach is best used in distinguishing land cover modifications resulting from phenological and/or growth vigor changes.
More complex types of land cover changes, such as the cultivation pattern change of double cropping land to single cropping land, pose challenge to the proposed approach.
Thank you very much!