第29回日本情報地質学会講演会 講演要旨集 049-050頁 2018年

Surface Elevation Estimation Using Global DEMs andGeographically Weighted Regression
Le Van Bien*, Venkatesh Raghavan*, Tran Thi An**, Do Thi Hang** Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan.
** Faculty of Geography, University of Education, The University of Danang
459 Ton Duc Thang Street, Danang City, Vietnam.

Keywords : GWR, GDEM, SRTM, ALOS AW3D30, Surface Estimation

1. Introduction
Digital elevation model (DEM) has become oneof the most important and essential data sources
for various terrain related applications. Theavailable global DEMs including the Advanced
Land Observing Satellite World 3D-30m DEM(ALOS AW3D30 or ALOS-30), the Advanced
Spaceborne Thermal Emission and Reflection
Radiometer (ASTER) Global DEM (GDEM) andShuttle Radar Topographic Mission (SRTM)
DEM are being used widely. Although theseabove global DEMs are freely access and can beeffectively applied for many purposes, they stillcontain some height errors that affect theprocessing results significantly. This study
presented an advanced method to improve theaccuracy of global DEMs using Geographically
Weighted Regression (GWR).

2. Methodology

2.1 Data set
Study area is Danang city, located in middle ofCentral Vietnam, with the elevation range from0 to 1664m. Global free DEMs used in thisstudy including ASTER GDEM version 2(earthexplorer.usgs.gov),ALOS
AW3D30(eorc.jaxa.jp) and SRTM (srtm.csi.cgiar.org). Allof the global elevation sources are in 30m
resolution. The reference elevation data is spot
height elevation data with more than 130,000
elevation points, was developed by Danang citygovernment in 2009.
2.2. DEM Estimation Using Geographically
Weighted Regression (GWR)
GWR is a local form of linear regression which
is a method for exploring how the relationshipbetween a dependent variable (Y) andindependent variables (X). Subsequently, it will
predict the dependent variable based on
independent variables. In contrast to global
regression, instead of using same equation
coefficient for entire study area and one
independent variable, GWR can use multiple
independent variables and can build a separate
equation for local area in the dataset withingiven bandwidth (neighborhood size). Thatmeans GWR can consider geographicaldifferences in the estimation. The equation ofGWR model is based on Fotheringham et al.,
2002.
This study has explored the advantages of GWR
for estimation of a new DEM using three global
DEMs (ALOS AW3D30, ASTER GDEM, SRTM)
and referent elevation points. Firstly, reference
points were separated randomly into two groups.
One group was used for estimation DEM andthe remaining group was used for validation ofestimated DEM. This was done using ther.random function in GRASS GIS. Many caseswith different number of reference points wereapplied for running GWR in DEM estimation.
The different estimated DEMs were evaluatedin relation with slope, landform, and landuse. As
the results, estimated DEM used random 50.000
input points showed the best result. Therefore,
we selected random 50,000 reference points for
DEM estimation and the remaining reference
point for validation.
In GWR model, the term of bandwidth andkernel function have effect on the estimation
result. Various conditions of bandwidth andkernel were applied and bi-square function (1)50with fixed bandwidth size 50 showed the best
estimated DEM.
wp = (1 – (d/bw)2)2(1)
Where, bw is the bandwidth, d is the distancefrom a pixel to the current pixel, and wp is theweight assigned to a pixel.
GWR is available as module r.gwr in GRASS
GIS. As a result, the estimated DEM which
derived from three global DEMs and the numberof 50,000 reference elevation points wasgenerated.
2.3. Evaluation of Estimated DEM
The remaining group of reference elevation
points including 80000 points was used forevaluation of the GWR derived DEM. Theaccuracy of the estimated DEM can be
determined by statistical analysis such as Meanof Absolute Error (MAE), Root Mean SquareError (RMSE), Standard Deviation (SD) andcorrelation coefficient (R2). The MAE, RMSEand SD of the estimated DEM were much
improved compared to individual global DEMs.
The RMSE was reduced from 7.0m in ALOS-30,
8.3m in the GDEM and 6.4m in SRTM to 4.1min the GWR DEM. The MAE was also reducedfrom 5.2m in ALOS-30, 5.4m in GDEM and 4.6m
in SRTM to 1.9m in the estimated DEM (Table1).
The GWR DEM and reference data also shows
the significant correlation, with R2 = 0.9994(Table 1). It can be, therefore, concluded that theestimated DEM shows better correlation to thereference data, comparing to global DEMs.
Statistics about vertical accuracy of ALOS-30,
GDEM, SRTM and the estimated DEM is shownin Table 1. The MAE, RMSE and SD of theestimated DEM show much improvementcompared to ALOS, GDEM and SRTM before
estimation.
Table 1: Summary statistics for the error of global
DEMs and GWR DEM
DEM

R2MAE

RMSE

SD(m)

(m)

(m)

ALOS-30

5.2

7.0

5.90.9985

GDEM2

5.4

8.3

8.2

0.9973

SRTM

4.6

6.4

5.5

0.9987GWR DEM

1.9

4.1

4.1

0.9994The vertical elevation errors of estimated DEM
and the global DEMs were compared again inrelation to landform characteristics. The

classification of landform is based on Bien Le(2017). As a result, the RMSE of estimated DEM
shows really significant improvement than theindividual global DEMs (Table 2). In general,the vertical error in the mountainous areas
tends to be highly considerable than in the flatareas.
Table 2: RMSE of global DEMs and estimated DEM
for different landforms
Root Mean Square Error (m)(RMSE)

Landform

GWR

ALOS

GDEM

SRTM

Flat

5.5

5.0

4.4

1.6

Valley

9.1

12.4

9.7

6.9

Mountain

12.2

17.6

12.1

9.5

DEM

3. Conclusion
In this study, GWR model was applied
successfully in surface elevation estimation forDanang area, using the available global DEMs
and field survey elevation points. Results
indicate that the estimated DEM has improved
accuracy compared to individual global DEMs.
It means GWR for DEM estimation can provide
significant enhancement on quality of DEMs
derived from various sources.
In this study, GWR only used less than a half ofreference points to generate the combined DEM.
However, in comparison with three global DEMs,
the estimated DEM showed most accurate
results. Thus, estimation an elevation model
based on field survey-based and satellite-based
elevation data using GWR is effective for areawith limited number of survey point data.

References[1] Fotheringham, AS., Brunsdon, C. andCharlton, M., (2002) Geographically WeightedRegression:the analysis of spatially varying
relationships, Wiley, Chichester, pp. 13-15.
[2] Le Van Bien (2017), Accuracy Assessment of
Global DEM and Surface Elevation Estimation
Using Geographically Weighted Regression:
A Case Study in Danang City, Vietnam, Masterthesis, Graduate School for Creative Cities,
Osaka City University, Japan.