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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
Table 2. Subsidence velocities of 1520 persistent scatters 
detected by Leveling and persistent scatter InNSAR 
respectively based on planar network and TDPDN 
  
Maximum Minimum Average 
Measurement type 
(mm/a) (mm/a) (mm/a) 
  
leveling 15.0 12.0 12.6 
InSAR with planar network 21.0 6.0 16.8 
InSAR with TDPDN 18.3 7.8 13.7 
  
In recent years, both precise leveling and GPS survey have 
been carried out to monitor subsidence in Shanghai by some 
authorities (Liu et al. 1998a, Liu, 2000b). The leveling (see 
Table 2) shows the subsidence rates from 1992 to 2002 in the 
study area range from 12.0 to15.0 mm/a, and the averaged 
subsidence rate reaches 12.6 mm/a (Yan et al. 2002). Table 2 
shows the annual subsidence rates estimated with INSAR based 
on TDPDN are in good agreement with the leveling subsidence 
results reported in some open literature (Liu et al. 19982, Liu, 
2000b). This indicates that INSAR with TDPDN is effective for 
detecting land subsidence in Shanghai. Furthermore, TDPDN is 
more advantageous than persistent scatter planar network in 
terms of accuracy and reliability of estimating subsidence rates 
at persistent scatters. For one thing, TDPDN has more 
connections (arcs) between adjacent persistent scatters than 
planar network in the study area which means the total number 
of redundant observations in TDPDN is larger than that in 
planar network. Hence the LS estimator for TDPDN is less 
disturbed by outliers. For another, also the most important, the 
adjacency established by TDPDN really reflects persistent 
scatter target geographic relationship that varies with terrain. 
The atmospheric delays in NDPs of adjacent persistent 
scatters derived from optimized TDPDN with 1km threshold is 
to be eliminated to an extreme extent. In other words, the 
accuracy and reliability of InSAR with TDPDN are 
significantly improved. 
Finally, the time series of subsidence was obtained as a 
sum of linear and nonlinear parts through InSAR with 
TDPDN. As examples, Figure 8 shows the temporal 
evolution of subsidence at 5 persistent scatters (see 
Figure 5) in the central part of the study area, where 
about 15-cm land sinking was accumulated from 1992 to 
2002. For visualization, a perspective view of the entire 
61 
subsidence field is shown in Figure 9, where the 
remarkable sinking parts can be better appreciated. 
Maximum and minimum subsidence values are -18 and 
-9 cm, respectively. The current land sinking is highly 
related to the large-scale urban construction and the 
overuse of groundwater. Especially from 1992 to 1995, 
the skyscrapers’ constructions are most remarkable (Liu 
et al. 1998a). It should be noted that the estimated 
vertical displacement may also contain the settlement of 
skyscrapers, and not purely the natural subsidence of the 
land surface. The annual subsidence rate is however 
much smaller than that occurring in the 1980's. This is 
primarily attributed to some mitigation strategies which 
include reducing groundwater withdrawal, increasing 
river water use, pumping water back into depleted 
aquifers, and utilizing light materials for construction. 
  
  
  
Subsidence (em) 
  
  
Js i L i i L eee 
Tos 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2 
Time (year) 
Figure 8. Time series of subsidence at 5 
PSs as marked in Figure 5 
  
wn 
in 
xU oem 
MIE poets iig 
  
  
Figure 9. Perspective view of the subsidence field 
accumulated between June 1992 and August 2002 
5. CONCLUSIONS 
In order to improve the accuracy of persistent scatter InSAR, 
the approach for constructing three-dimensional persistent 
scatter Delaunay network is promoted based on the algorithm 
for establishing three-dimensional GPS network. The TDPDN