THE TRUE FACES BETWEEN LANDSLIDES AND MOUNTAIN
DEVELOPMENT-INTEGRATING GEOSPATIAL STATISTICS AND
GEODATABASE
1
YUNG-CHUNG MATT CHUANG, 2YI-SHIANG SHIU
1,2
Department of Urban Planning and Spatial Information,
Feng Chia University, Taichung 40724, Taiwan.
E-mail: [email protected], [email protected]
Abstract— Development in mountainous areas in high population density countries is inevitable, but the true faces between
development and landslides are still uncertain.Clarifying keycurrent factors or ancient factors resulting in landslidescan be
important for hazard prevention and mitigation.This study tookShihmen Reservoir Catchment in Taiwan as a study area. Two
different types of explanatory variable combinations in 5 periods (1946, 1971, 2001, 2004, and 2012) collected from
geodatabase and digital achieves were applied in investigation with discretelogistic regression analysis.It is clear in results
that the landslide increased dramatically from 1946 to 2012 in the catchment area. The proximity and overlapping of human
development with landslides arose. However, the logistic regression results indicated that the landslide susceptibility
variables were all natural with the exception of historical deforestation and newly constructed road systems.For this reason,
well-recovered ancient woodland sites might be landslide prone area now. We suggested that the accumulated ancient events
should be considered as explanatory variables in future landslide prediction analysis.
Keywords— Landslide, Human Development, Discretelogistic Regression Analysis.
are dependent, the bivariate analysis tends to lead to
unpredictable influence on the evaluation result, and
therefore, the multivariate analysis is preferred for
landslide susceptibility analysis. For the logistic
regression of the multivariate analysis, immeasurable
type factors are considered and therefore it has been
adopted in studies of sociology for a very long time,
but it was not widely used in the study of landslides
until recently. (Atkinson & Massari, 1998)started
employing the logistic regression in the landslide
study in Apennine of central Italy to develop the
relationship between landslides, susceptibility factors
and calculate the susceptibility values and produce
the landslide susceptibility maps. (Chang et al.,
2007)selected typhoon Herb and Chichi Earthquake
as the events of study for the river basin of Chenyulan
River of central Taiwan and used the logistic
regression to establish the landslide models triggered
by earthquake and typhoon and plot the landslide
susceptibility map. (Yang et al., 2011)used the
logistic regression for the landslide susceptibility for
Nantou County of central Taiwan and predict
dangerous sections of mountain roads. These studies
above all suggest that the logistic regression is a
model that is accepted and widely applied for
landslide prediction.
As mentioned above, historic cases can help robust
the prediction models and thus the historic data
become more and more important for land use/cover
(LULC) studies. Historic LULC data usually derives
from historic maps or remote sensing imagery.
Remote sensing data is especially a useful tool for
land use/cover monitoring for it captures the real
world images with wide range. However, most
remote sensing images opened for academic use were
acquired in the forty years. Obtaining LULC data
I. INTRODUCTION
Development in hillside areas in high population
density countries is inevitable. The constant increase
of use and development of hill slopes make mountain
ecosystems experience extensive land use changes.
The replacement of forests by agriculture and
settlements makes severe erosion or landslides (Glade,
2003; Soini, 2005).
Clarifying key variables resulting in landslide in
different study areas can be important for social
justice. For example, some disadvantaged or
aboriginal people tend to live in remote regions, such
as hillside areas, because they cannot afford to the
living cost in urban regions. In order to subsist in
remote regions, disturbing the original ecosystem is
inevitable. Therefore, people in hillside areas are
easily to blame for the main causes for mountain
ecosystem changes and unstable. In order to moderate
the degradation of ecosystems, the governors and
authorities formulate legislation and increase the
limitations on development of hillside areas. The
limitations decrease not only the damage on hillside
ecosystems but also the living areas for
disadvantaged or aboriginal people. The debate is
how to trade conservations off against people’s living.
Therefore, defining the key factors specifically and
locate landslide susceptibility areas accurately are
important for hillside area management.
Among all these methods, the statistical analysis is
the most commonly used, and the results of different
periods of time can be incorporated and integrated
easily. Therefore, it is frequently used in the
event-type landslide susceptibility analysis. In the
statistical analysis, the bivariate and multivariate
analyses are the dominant approaches. When factors
Proceedings of ISER 42nd International Conference, Tokyo, Japan, 11th -12th November 2016, ISBN: 978-93-86291-31-8
1
The True Faces Between Landslides and Mountain Development-Integrating Geospatial Statistics and Geodatabase
earlier than forty years is limited because of immature
techniques and military restrictions. Recently, several
kinds of aerial photos and satellite images for military
use previously have been released and digitized as
open data, such as US Army aerial photos and
CORONA satellite images. The release of historic
remote sensing data provides mid- to long-term
factors related to LULC change for LULC simulation
models (Clarke et al., 2007; Tappan et al., 2000;
Woomer et al., 2004). Researchers can prove if the
previous factors influence the present cases or factors.
In this study, we examine the factors affecting
landslide in the catchment area of Shihmen Reservoir
in northern Taiwan. The main questions of this study
were:
1. Does the development of settlements and roads
dominate the occurrence of landslide events?
2. Does previous factors dominate the occurrence
of landslide events more than the present ones?
Logistic regression was introduced to examine the
extent of spatial correlation between human activities
and landslides. The results were compared for the
reference of catchment area managers for disaster
prevention and mitigation.
improve the interpreting accuracy.Besides, high
resolution digital terrain model (40m*40m) and
geological survey map (1:10000) were also included
in generating explanatory variables such as slope and
geological type.
Fig. 1. Location map of Shihmen Reservoir Catchment (red
dots are weather stations)
II. STUDY AREA AND WORKFLOW
A. Study area and materials
The study area is Shihmen ReservoirCatchment in the
northern Taiwan (Fig. 1). Itlocated inupper reaches of
Tahan River and covers an area of 75924ha. The
land-useand land cover are mainly conifer and
broadleaved forest,interspersed with bamboos
andhuman-developed farmland, orchards and
roads.Shihmen Reservoir Catchment is also
atraditional area of Atayal indigenous communities.
Inthe past, indigenouspeople mainly lived on hunting,
dryfarming, supplemented by ramie, bamboo,
fruittrees, and other crops, but the land-use type
gradually transferred intolarger-area farming, logging,
plantation ofhighland vegetables and fruits in 20th
century due to the affection of Chinese
farmingtechnology, but with the limitation of steep
slope, small river terraces,and high terrain complexity,
settlements
andhuman
developments
are
scattered-distributed.
The
original
materials were
the
digital
archivedmilitary photographsin 1946, Corona satellite
images in 1971, and ortho-images in 2001, 2004 and
2012 (Fig. 2).Moreover, contents in topographic
maps such as streams, main roads, andadministrative
boundaries were adopted as areference for land-use
interpretations. The total area of catchment was
categorized into six major land-useand land cover
types, including human development, forest, roads,
water, and landslide. The results weremanually
digitized from the ortho-image usingERDAS Leica
Photogrammetric Suite (LPS) and ArcGIS 10.1.For
landslide areas in 2001, 2004 and 2012, stereo
interpretation with 1:1000 scales wasconducted to
Fig.2 The digital achieved photographs of this study (A)1946;
(B)1971; (C)2001; (D)2004; (E)2012
B. Methods and workflow
The main purpose of this study was to explore the
significant recent or past factors related to landslides
in every specific period.The landslide eventstaken
place in 1946, 1971, 2001, 2004, and 2012 were
considered as dependent variables.13variables were
selected as potential explanatory variables of
landslidesbased on previous experiences:elevation
(continuous variable, derived from digital terrain
model), slope (continuous variable, derived from
digital terrain model), aspect (categorical explanatory
variable, derived from digital terrain model,was
classified into 9 categoriesviz., N, NE, E, SE, S, SW,
W, NW, flat), terrain curvature (continuous variables,
derived from digital terrain model), topographic
index (continuous variables, derived from digital
terrain model), contributing area (continuous variable,
derived from digital terrain model), profile curvature
(continuous variable, derived from digital terrain
model), road location in different periods (nominal
explanatory variable, digitized from ortho-images),
annual precipitation (mm)(continuous variable),
maximum 3-days precipitation (mm) (continuous
variable), locations of human development of
Proceedings of ISER 42nd International Conference, Tokyo, Japan, 11th -12th November 2016, ISBN: 978-93-86291-31-8
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The True Faces Between Landslides and Mountain Development-Integrating Geospatial Statistics and Geodatabase
different periods (nominal explanatory variable,
digitized from ortho-images), and geology
(categorical explanatory variable, derived from
geological survey map,was classified into 8
categories). The precipitation data of each weather
station were interpolated into 40m*40m grid data
using Kriging approach in ArcGIS 10.1 and the
precipitation
distribution
maps
werealso
developed.Besides, all the independent variables were
normalized between 0 and 1 before introducing them
in the model. The natural log transformation was
done for the continuous variables. For the categorical
explanatory variables, the dummy variable
transformation was applied.
The logistic regression modeling analysis was set as
the main operating method in this study. Not only the
unrevealed
relationships
between
human
development and landslides in different periods
would be discovered, but also the impact degree of
ancient events (e.g., deforestation in 1947 and 1971)
on recent landslides would also be verified.We
applied SPSS software for calculating logistic
regression model here, and all input row data (.txt)
were generated from original layer files (40m*40m
grid data) by using the conversion tools of ArcGIS
10.1 software.
As for the reasonable model construction,explanatory
variables in different periods derived from
geodatabase were integrated together into logistic
regression modeling analysis, and the stepwise
method was used to explore the best-fitted model
among the two basic types of predictor sets in each
period (Predictor set-1: only current year explanatory
variables were included; Predictor set-2: All
cumulative historical related events, viz., both current
year explanatory variables and ancient explanatory
variables, were added into the model).
The determination for best-fitted model and predictor
sets was based on accuracy assessment of landslides.
We compared the landslidemapsof each period
withlandslide predicting results and calculate the areas
classified correctly and incorrectly. Indices including
producer’s accuracy (PA), user’s accuracy (UA),
overall accuracy (OA) were then applied to evaluate
the logistic regression modeling results. Finally, we
can find the influence of historical events and ancient
events in landslide prediction.
coefficients were given statisticalsignificance that a
positive coefficient meansan increase in the
probability of landslides and anegative sign, a
decrease in the probability.
Thecommon statistically significant variables at the
95%level were maximum 3-days precipitation,
elevation, topographic index and slope. While many
of thesigns on the estimated coefficients were as
expected, most of the signs on the coefficients
remainconsistent
over
time
and
meet
expectationseven thoughthe power of explanation of
the models were unstable: the higherthe slope is, the
higher probability of landslides;the lowertopographic
index value is, the lesslikelihood of landslides; the
more maximum 3-days precipitation is, the greater
the probability of landslides.
As for the difference for model set-1 and set-2 in each
period, most of the significant variables were the
same. The coefficient on annual rainfall, and
lithologylocated in hard-rock terraneswere negative
and statistically significant inboth models. Other
results showed thatthe landslides are more likely to
occur onsteep slope,fragile lithology, and high plane
curvature. It is worth noticing that insignificant or
significant negative correlation in spatial distribution
was observed between human development and
landslides
during
2001~2012,
but
humandevelopmentlocation in 1946 and 1971
appeared to have significant proportional relationship
with the presence of landslides. Since deforestation
accounted for large percentage of human
development area before 1991, in other words, it can
be considered that historical deforestation events in
1946 and 1971 seemed to play an important role in
landslideprediction
models.Furthermore,
road
development and ancient landslides were also
significant susceptibility variables for the influence
on landslide occurrence in some specific periods such
as 1946 and 2001.In other words, despite of human
development in 2001, 2004, and 2012, destructive
forest harvesting, roads construction and ancient
landslidesincreased the probability of landslides.
The analyze results clearly showed forest harvesting
events (include clear cutting, partial cutting, selection
cutting)in 1946 and 1971 had significant statistical
correlation not only with landslides happened in 1946
and 1971, but also affected the slope stability in 2001,
2004, and 2012. Although almost all b coefficients
were between 0~1 and demonstrated limited effects,
however, it meant the ancient land-use type may
impact the slope stability after many years passing,
even more than half a century. While such a
phenomenon
may
sound
unusual
and
counterintuitive,the ancient woodland sites would
become the landslide prone area in this study,
whatever the forest land cover already recovered or
not. In order to find out the real reason, we
interviewed at least 30 indigenous people lived in this
area, and gradually pieced together what happened to
the forest and slope. The interview records showed
III. RESULTS AND DISCUSSION
Different sets of logisticmodels (Predictor set-1 and
Predictor set-2) for each of the timeperiods (1946,
1971, 2001, 2004, and 2012) were performed forall of
the catchment area.A separate model was estimatedto
compare the coefficients, model fit, and prediction
accuracy from the differenttime periods, and to test if
the effects of the variables are consistent over time.
Collinearity was also avoided by diagnosingthe
correlation between all explanatory variables.As this
is a discrete choice model, thesigns on the
Proceedings of ISER 42nd International Conference, Tokyo, Japan, 11th -12th November 2016, ISBN: 978-93-86291-31-8
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The True Faces Between Landslides and Mountain Development-Integrating Geospatial Statistics and Geodatabase
most indigenous people left root part of primeval
forests after clear cutting or partial cutting. The
decrease of crown interception led the increase of
surface soils erosion, but the slopewasremainingfully
reinforced with tree root cohesion. Then the forest
recovered with secondary forestin the succeeding few
years, but the interconnected root-web matrix of
clear-cut primeval forests had rotted away, adding the
slope weight and root cohesion loss rate. For this
reason, the present well-covered forest tended to
become rapidly unstable when disturbed, or subjected
to increased hydrological influences such as heavy
rainfall or typhoons, and many landslides occurred.
variablesin future landslide prediction analysis.
Besides, digital achieve technology would be a very
powerful tool for researchers and governors to
recover theunknown secrets, and to make good
connection to discrete statistical models
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CONCLUSIONS
Discrete logistic regression models were applied to
find the related variables of hillslope landslides in
Shihman Reservoir Catchment, and the exploration
ofthe correlation between landslides and human
development was also carried out. It is clear in results
that landslide areas extended dramaticallyfrom 1946
to 2012 in both number and size, and the increase was
particularly obvious in number of landslides
occurring close to human developments. However,
the logistic regression results indicated that the
landslide susceptibility variables were almost natural
with the exception of large-scale ancient forest
harvestings, road development and historical
landslide events.For this reason, many people have
misconceptions on existing and legitimate hillside
development, wrong hazard prevention and
mitigation policies such as narrowing development
interests of mountain habitats, ignoring the landslide
probability of recovered logging area,such as
non-interfered national forest land, would be
proposed. We suggested that the accumulated ancient
events should be considered as explanatory
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