Forest Ecology and Management 210 (2005) 55–66
www.elsevier.com/locate/foreco
Vegetation recovery monitoring and assessment at landslides
caused by earthquake in Central Taiwan
Wen-Tzu Lin a, Wen-Chieh Chou b,*, Chao-Yuan Lin c,
Pi-Hui Huang d, Jing-Shyan Tsai e
a
Graduate Institute of Environmental Planning and Design, Ming Dao University, Changhua County 523, Taiwan
b
Department of Civil Engineering, Chung Hua University, Hsinchu City 300, Taiwan
c
Department of Soil and Water Conservation, National Chung Hsing University, Taichung City 402, Taiwan
d
Graduate Institute of Civil and Hydraulic Engineering, Feng Chia University, Taichung City 407, Taiwan
e
Department of Landscape Architecture, Chung Hua University, Hsinchu City 300, Taiwan
Received 29 August 2003; received in revised form 8 December 2004; accepted 7 February 2005
Abstract
Massive landslides, caused by the catastrophic Chi-Chi earthquake in 1999, occurred at the Jou-Jou Mountain area in the WuChi basin, Taiwan. Multi-temporal satellite images and digital elevation models coupled with GIS were used to process the
vegetation index analysis for identifying landslide sites and calculating the vegetation recovery rate (VRR). Topographic
information for these areas was extracted. Eight hundred twenty-nine hectares of landslide area was extracted from multi-date
NDVI images by combining the image differencing method with the change detection threshold. Over 2 years of monitoring and
assessing, the vegetation recovery rate reached 58.93% original vegetation regeneration in the landslide areas. Soil moisture is
one of the most important environmental factors for vegetation recovery in the landslide sites. The analyzed results provide very
useful information for decision making and policy planning in the landslide area.
# 2005 Published by Elsevier B.V.
Keywords: Digital elevation model; Vegetation recovery rate; Landslide characteristics
1. Introduction
A catastrophic earthquake with a Richter magnitude of 7.3 occurred at Chi-Chi and the Sun-Moon
Lake area of Nantou County in the early morning
(01:47 local time) on September 21, 1999. There were
* Corresponding author. Tel.: +886 93 2288965;
fax: +886 3 5372188.
E-mail address: [email protected] (W.-C. Chou).
0378-1127/$ – see front matter # 2005 Published by Elsevier B.V.
doi:10.1016/j.foreco.2005.02.026
heavy casualties and extensive damage to buildings
and property losses. A large number of landslides also
occurred in Central Taiwan. According to airborne
photo interpretation coupled with field surveys
obtained from Taiwan’s Soil and Water Conservation
Bureau, Council of Agriculture (COA) in 2000, there
were more than 20,000 sites with a total area of
15,977 ha of landslides identified as a result of this
quake. Chang (2000) found that most landslides
occurred at the outer edge or inner side of the terraces.
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Both of these areas are adjacent to steep slopes that are
prone to collapse. Lin et al. (2001) and Wang et al.
(2000) pointed out that the Chi-Chi earthquake
induced several large-scale landslides such as the
slope-land along the East-West Expressway in the DaChia River basin, the Jou-Jou Mountain area in the
Wu-Chi River basin, at Tasoling near the border
between Yunlin and Chiai counties, and Chiufenershan in Nantou county. The landslide at the Jou-Jou
Mountain area in the Wu-Chi basin was especially
serious. Lin et al. (2001) indicated that during the
typhoon season, a tremendous amount of loose earth
and stones accumulated on the surface of the slopes
increasing the possibility of debris flows and additional landslides. This action will deteriorate the
revegetation problem even worse.
Because of severe denudation on the surface of
these slopes, the Jou-Jou Mountain area was proposed
as a Nature Reserve Area by the Taiwan Forestry
Bureau to restore the natural landscape and ecosystem.
In the 3 years since the catastrophic earthquake, many
researchers have assessed and monitored landslide
area vegetation recovery using the field surveys or a
variety of measuring equipment. However, these
attempts failed to effectively evaluate the scope of
such tremendous landslides due to their scattered
distribution. Recently, with the fast growing progress
in technologies, remotely sensed data can be rapidly
acquired and widely used for monitoring the earth’s
resources (Lillesand and Kiefer, 2000). Numerous
researches have applied satellite images for monitoring natural disasters such as fire potential assessment,
flood damage estimation and drought detection
(Burgan et al., 1998; Dhakal et al., 2002; Perters
et al., 2002). The normalized difference vegetation
index (NDVI) is one of the most popular methods for
vegetation monitoring (Teillet et al., 1997). The NDVI
is calculated as:
NDVI ¼
NIR RED
NIR þ RED
(1)
where NIR is the reflectance radiated in the nearinfrared waveband and RED is the reflectance radiated
in the visible red waveband of the satellite radiometer
(Justice et al., 1985). Higher NDVI indicates a greater
level of photosynthetic activity (Sellers, 1985). It has
been demonstrated that multi-temporal NDVI derived
from AVHRR data is useful for monitoring vegetation
dynamics on a regional and continental scale (Goward
et al., 1985; Justice et al., 1985; Tucker and Choudhury, 1987; Eidenshink and Hass, 1992).
Due to the scattered distribution of the large-scale
landslides in the Jou-Jou Mountain area, an effective
evaluation approach, vegetation recovery rate (VRR),
was developed to aid in making appropriate and timely
decisions in response to vegetation recovery from
landslides. In this study, multi-temporal satellite
images and digital elevation models (DEMs) were
used to process the vegetation index analysis for
identifying landslide sites and extract topographic
information from the denudation areas. A system
coupled with GIS was developed in this study and
employed to monitor and assess the vegetation
recovery rate for the landslide areas.
2. Materials and methods
2.1. Study area
The Jou-Jou Mountain area, 4396 ha, 134–776 m
altitude and 42% average slope, is located along the
northern Wu-Chi River (Fig. 1), the administrative
border between Taichung and Nantou counties. The
climate data obtained from Taiwan’s Central Climate
Bureau shows rain about 120 days a year with average
precipitation 1684 mm, mainly concentrated from
February to September. The rainfall types are convective precipitation as thunderstorms and orographic
precipitation for topographic reasons. The geological
data from Taiwan’s Central Geological Service shows
that the rock formation occurring in the target area is the
Tou-Ke-Shan stratifications, chiefly formed by high
percentage of gravel, rock and minor sandstone. Over
time, the slopes adjacent to the active stream channel
were eroded by torrential water flows from the Wu-Chi
River. A unique cliff terrain resulted from this
geomorphic activity. Grass species and herbaceous
species fully dominated the stable slopes, especially the
Formosan giantreed, Arundo formosana Hack. On the
lower slopes and ridgelines, unique broadleaf forests,
occasionally mixed with pine stands, existed. The
earthquake changed the previous terrain and eliminated
much of the standing vegetation, creating extremely
harsh and unstable lands in the Jou-Jou Mountain area
right after the earthquake. Recent investigation shows,
W.-T. Lin et al. / Forest Ecology and Management 210 (2005) 55–66
57
Fig. 1. Illustration of study area for the Jou-Jou Mountain.
in the cliff areas, the vegetation is still simple and
composed mainly of Formosan giantreed as before the
earthquake. In the hillside bases, the Camphor tree,
Cinnamomum camphora (L.) Sieb., Taiwan Red Pine,
Pinus taiwanensis Hayata, and Taiwan Short-leaf Pine,
Pinus morrisonicola Hayata, are the major trees. The
species of plants in this study area are not much
different comparing with the stage prior to the
earthquake.
2.2. Methods
Six SPOT satellite images were used to extract
landslides induced soon after the earthquake. Multitemporal post-quake vegetation recovery rate (VRR)
was used for monitoring the succession and progress
of natural regeneration in the landslide area (Fig. 2).
Imagery was taken before the earthquake on April 1,
1999. The imagery soon after the earthquake was on
September 27, 1999. Other images were taken from
October 2000 through December 2001, over 1 year
after the earthquake. Fig. 3 illustrates the flowchart for
this study.
2.3. Landslide image analysis
Both supervised and unsupervised classification
methods are the most used image-processing algorithms for acquiring land-cover data (Giannetti et al.,
2001; Boles et al., 2004). However, for the classification of multiple image dates, several change detection
logics are used to precisely extract the change
detection information. The image differencing algorithm (Jensen and Toll, 1982), one of the most
appropriate methods for acquiring change detection
information, is suitable for extracting multi-temporal
land-cover features. Image differencing is based on a
pair of coregistered images of the same area collected
at different times. The process simply subtracts one
digital image, pixel-by-pixel, from another, to generate a third image composed of the numerical
differences between the pairs of pixels (Ridd and Liu,
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Fig. 2. SPOT satellite images at the Jou-Jou Mountain area.
Fig. 3. Flowchart of vegetation recovery assessment at landslides in this study.
W.-T. Lin et al. / Forest Ecology and Management 210 (2005) 55–66
1998). However, some errors, such as varying sun
angles, atmospheric and soil moisture conditions,
seasonal changes and topographic effects must be
rectified while processing multiple image dates
(Jensen, 1995). A common method used to radiometrically correct or adjust multiple-date images is to
normalize the data so that these effects can be
minimized or eliminated (Eckhardt et al., 1990; Hall
et al., 1991). The normalization procedures can be
referred to methods by Eckhardt et al. (1990) and
Jensen (1995). Procedures include selecting the
unchanged sites from pre-quake and post-quake
images, establishing the linear regression model,
and calibrating post-quake NDVI image. In addition,
ratio transformations of the remotely sensed data such
as NDVI, a normalized ratio, could be applied to
reduce the effects of such environmental conditions, as
indicated in Avery and Berlin (1992). In this research,
the procedures for landslides extraction were: (1)
calculate the NDVI of all images; (2) normalize the
post-quake NDVI images by referencing the prequake NDVI image; and (3) extract the landslides by
applying the image differencing method coupled with
a change detection threshold based on the change
percentage in the differencing image.
This research began by calculating the NDVI for all
images. The post-quake images, taken after September
27, 1999, were normalized with the pre-quake image,
taken on April 1, 1999. The post-quake images were
assigned with a total of 477 radiometric control points.
According to suggestion by Eckhardt et al. (1990), the
radiometric control points can be selected by multiple,
uniform, unchanged, and small image sample sites.
Those control points were determined by data from
GPS field investigation and aerial photo in this study.
Landslides that occurred on September 21, 1999 were
extracted by subtracting the NDVI image of September 21, 1999 from that of April 1, 1999 and calculating
the change area from the differencing NDVI image
using a 25% change detection threshold. The landslide
area change detection threshold is affected by different
image. The change detection threshold calculation
proposed in this study can be determined as:
Tð%Þ ¼
NDVIc
100%
maxðNDVIc Þ minðNDVIc Þ
(2)
where T is the suggested threshold; NDVIc is the
NDVI difference between pre-quake and post-quake
59
image; max, is the maximum difference; min, is
minimum difference. The extracted landslides were
compared with the post-quake ancillary data, including the field surveys and aerial photos from the
Agricultural and Forestry Aerial Survey Institute,
Taiwan Forestry Bureau.
The VRR, calculated from multi-temporal NDVI
images, is a useful index that can be rapidly used to
assess and monitor the vegetation recovery condition
and the rate and progress of natural regeneration on
landslides for further analysis of aggravated vegetation sites. The VRR formula can be written as:
VRRð%Þ ¼
NDVI2 NDVI1
100%
NDVI0 NDVI1
(3)
where NDVI0 is the NDVI of an image taken before
the earthquake, such as the one on April 1, 1999.
NDVI1 is the normalized NDVI of an image taken
soon after the earthquake, such as the one on September 27, 1999. NDVI2 is the normalized NDVI of an
evaluated image taken after the earthquake, such as
those from October 2000 through December 2001. If
the VRR value is less than 0, the vegetation recovery
condition of the landslide is aggravated. If the VRR
value ranges from 0 to 100, the vegetation recovery
condition of the landslide is gradually enriched. If the
VRR value is greater than 100, the vegetation recovery
condition of the landslide is superior to that before the
earthquake. Due to the uneven precipitation distribution throughout the year and periodic typhoons, occurring in July, August and September of each year, the
change monitoring for multi-temporal VRR were
compared with the climate data obtained from Taiwan’s Central Climate Bureau from October 2000
through December 2001.
2.4. Landslide characteristic analysis
As indicated from Chang (2000), most landslides
are closely related to the surrounding terrain, such as
the areas adjacent to steep slopes, unsteady toe-slopes
and ridgelines, and along rivers. The calculated
information includes spatial distribution, area statistics, elevation collapse ratio and the aggravated
vegetation sites along the ridgelines in the landslide
area. The elevation was obtained from a raster DEM,
which is generated by the Agricultural and Forestry
Aerial Survey Institute, Taiwan Forestry Bureau. For
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Fig. 4. Normalized NDVI images at the Jou-Jou Mountain area at different stages.
computing the slope and aspect, this research selected
Horn’s algorithm (1981), which is the best estimating
method (Skidmore, 1989) and currently used in
ArcInfo and ArcView (ESRI, 1998). This algorithm
uses a 3 3 moving window to estimate the slope and
aspect of the center cell. The weight applied to each
cell differs. To analyze the collapse ratio to elevation,
slope and aspect, the respective values were grouped
into certain classes according in the study area. The
elevation values were classified into seven classes,
varying in the range from 100 to 800 m, with a fixed
step of 100 m. The slope values were grouped into
seven classes (<5%, 5–15%, 15–30%, 30–40%, 40–
55%, 55–100% and >100%) in accordance with the
slope classification in the Soil and Water Technical
Regulations published by the Council of Agriculture,
Taiwan government. The aspect values were classified
into eight principal directions (north, northeast, east,
southeast, south, southwest, west and northwest). The
collapse ratio to elevation, slope and aspect can assist
in measuring the landslide characteristics in the terrain
Fig. 5. Landslide distribution at Jou-Jou Mountain area.
W.-T. Lin et al. / Forest Ecology and Management 210 (2005) 55–66
spatial distribution. The formula for computing
collapse ratio can be written as:
CRð%Þ ¼
LA
100%
TA
(4)
where LA is the area of the landslide at a certain
elevation, slope or aspect distribution. TA is the
total area at a certain elevation, slope or aspect
class distribution in the study area. The larger the
calculated collapse ratio, the more serious the land-
61
slide at a certain elevation, slope or aspect class
distribution.
The aggravated vegetation sites extracted from
VRR analysis were compared with field surveys for
evaluating the slope, aspect and elevation factors,
especially for sites distributed on ridgelines. Ridgelines can be determined by flow accumulation
concepts based on the O’Callaghan and Mark
(1984) algorithm. In a raster DEM, a flow accumulation grid is tabulated for each cell and the number of
Fig. 6. Frequency distribution of NDVI in the denudation sites.
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cells that will flow into it. Cells having high
accumulation values generally correspond to stream
channels, whereas cells having an accumulation value
of zero generally correspond to ridgelines.
2.5. System architecture
This project developed the WinGrid spatial
analysis software to compute the VRR and calculate
the topographic information of the landslide areas. In
the WinGrid system, the basic data storage unit can be
represented as a single layer in a map that contains
information about the location features. The WinGrid
system consists of several separate program components (e.g. GRIDDING, SPATIAL, WATERSHED,
MODULES, UTILITY, DISPLAY and IMPORT/
Table 1
The normalization equations for the post-quake NDVI images
Date
Equation
R2
1999/09/27
2000/10/29
2001/03/05
2001/07/20
2001/12/03
Y = 0.028+0.871X
Y = 0.011+0.745X
Y = 0.069+1.005X
Y = 0.056+0.783X
Y = 0.089+0.710X
0.967a
0.953a
0.942a
0.903a
0.965a
X, post-quake NDVI images before normalization; Y, post-quake
NDVI images after normalization.
a
All regression equations were significant at the 0.001 level.
EXPORT). Each component performs a separate
task. In this research, the TERRAIN and MODULES
components provided the menu interface to process
most of tasks from data sets. The former function
includes terrain analysis such as calculation and
statistics for aspect, slope, and ridgeline extraction.
The latter function includes the analysis and
calculation for the vegetation recovery rate of the
landslides.
3. Results and discussion
3.1. Image normalization and landslide extraction
Image normalization for each individual date was
achieved by applying regression equations, listed in
Table 1. The normalized NDVI images are illustrated
in Fig. 4. The dark color represents landslides or poor
vegetation sites. The bright color represents excellent
vegetation areas. The size and extent of scattered
landslides can be roughly estimated. Fig. 5 illustrates
the extracted landslides. Eight hundred twenty-nine
hectares of landslides occurred in the Jou-Jou
Mountain area in this quake. The landslide extraction
indicates that the landslide areas are widely distributed
throughout the study area.
Fig. 7. The change of NDVI with precipitation in the denudation sites.
W.-T. Lin et al. / Forest Ecology and Management 210 (2005) 55–66
3.2. Vegetation recovery rate assessment
Fig. 6 illustrates the NDVI frequency distribution in
the denudation sites before and after the Chi-Chi
earthquake. The pre-quake vegetation condition
located at the landslide sites was excellent with an
average NDVI value of 0.4. In the initial earthquake
stage, the average landslide NDVI value declined to
0.081. From the subsequent landslide monitoring, the
change in average NDVI value on four assessment
dates rose gradually from 0.171 on October 29, 2000
63
to 0.269 on July 20, 2001. There was a sudden decline
to 0.172 on December 3, 2001. This result was
compared to the typhoon records and precipitation
data from January 1999 through December 2001, as
shown in Fig. 7. The normalized NDVI value was
greater in October 2000 than September 1999 because
no typhoons struck Taiwan during the typhoon season
and there was above average monthly rainfall for
vegetation growth after February 2000. In the recent
10 years data from Taiwan’s Water Resources Agency
from 1992 to 2001, the average rainfall was 132 mm/
Fig. 8. Spatial distribution of classified VRR in the denudation sites.
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W.-T. Lin et al. / Forest Ecology and Management 210 (2005) 55–66
Table 2
Distribution of classified vegetation recovery rate in different dates
Category
Area in hectare (%)
Excellent
Very good
Good
Average
Poor
Very poor
Total
Average VRR (%)
VRR (%)
>100
75–100
50–75
25–50
0–25
<0
Distribution by date
2000/10/29
2001/3/5
2001/7/20
2001/12/3
4.73
32.13
140.72
282.73
242.23
126.95
11.03
55.03
157.83
235.48
212.80
157.33
67.11
204.17
271.02
183.81
84.06
19.33
6.64
44.48
172.88
269.27
172.31
163.92
(0.57)
(3.87)
(16.96)
(34.08)
(29.20)
(15.30)
829.50 (100)
28.21
(1.33)
(6.63)
(19.03)
(28.39)
(25.65)
(18.97)
29.15
(8.09)
(24.61)
(32.67)
(22.16)
(10.13)
(2.33)
58.93
(0.80)
(5.36)
(20.84)
(32.46)
(20.77)
(19.76)
28.53
month for this basin. Similar to the first year after the
quake, the average NDVI value was greater in July
2001 than March 2001. However, typhoon Toraji,
which struck the Central and Eastern regions of
Taiwan at the end of August 2001 enlarged the
previous landslides. The average NDVI value therefore declined significantly to the value calculated in
October 2000. From the above vegetation recovery
monitoring, the natural vegetation regeneration ability
for landslides is related to the precipitation and
typhoons. Four VRR assessment periods were derived
from the post-quake average NDVI value. Similar to
the above change, the average VRR for the landslides
calculated on October 29, 2000 is 28.21%. The
subsequent change in vegetation recovery on the
landslide surface changed from 29.15% on March 5,
2001 to 58.93% on July 20, 2001, and then declined to
28.53% on December 3, 2001. The VRR spatial
distribution and area statistics for the landslides is
illustrated in Fig. 8 and listed in Table 2. In the four
assessment periods, the percentage of excellent
vegetation recovery sites ranges from 0.57 to
8.09%. At very poor vegetation recovery sites, the
area percentage was 15.30% on October 29, 2000 and
declined to 2.33% on July 20, 2001. However, from
July 20, 2001 through December 3, 2001, those sites
were enlarged due to typhoon Toraji.
distributed landslide elevation, the greater the collapse
ratio percentage obtained. A similar trend can be
found in the slope to collapse ratio. However, the
collapse to aspect ratio is different, which has quite
even distribution in each category. In the vegetative
recovery placement analysis, more than 40% of the
excellent vegetation recovery sites are concentrated on
slopes between 55 and 100% for all dates, as listed in
Table 6. This result shows that steep slopes are not the
primary restriction factor on vegetation recovery in the
landslide area. It can be observed by the field
Slope
(%)
Total category
area (ha) (TA)
Landslide
area (ha) (LA)
Collapse ratio,
CR = LA/TA (%)
3.3. Landslide characteristics and vegetation
recovery placement analysis
<5
5–15
15–30
30–40
40–55
55–100
>100
509.44
402.08
779.52
660.32
801.28
1026.24
217.12
2.61
15.44
66.67
75.48
141.88
389.08
138.34
0.51
3.84
8.55
11.43
17.71
37.91
63.72
The collapse ratio to various terrain factor
calculations are listed in Tables 3–5. The collapse
ratio to elevation indicates that the higher the
Table 3
Distribution of collapse ratio to elevation classification
Elevation
(m)
Total category
area (ha) (TA)
Landslide
area (ha) (LA)
Collapse ratio,
CR = LA/TA (%)
100–200
200–300
300–400
400–500
500–600
600–700
700–800
583.2
1039.52
1272.48
784.48
461.92
218.56
35.84
3.16
28.48
90.20
253.59
275.75
152.89
25.42
0.54
2.74
7.09
32.33
59.70
69.95
70.93
Table 4
Distribution of collapse ratio to slope classification
W.-T. Lin et al. / Forest Ecology and Management 210 (2005) 55–66
65
Table 5
Distribution of collapse ratio to aspect classification
Aspect
Total category
area (ha) (TA)
Landslide
area (ha) (LA)
Collapse ratio,
CR = LA/TA (%)
Northeast
East
Southeast
South
Southwest
West
Northwest
North
350.88
566.88
509.92
889.60
539.04
603.52
347.04
589.12
66.69
127.11
89.33
164.89
78.22
112.61
68.08
122.58
19.01
22.42
17.52
18.54
14.51
18.66
19.62
20.81
Fig. 10. Earthquake-induced landslide and the surviving Arundo
formosana on steep slopes (1999/10).
Table 6
Analysis of excellent vegetation recovery placement on slope
classification
Slope
(%)
<5
5–15
15–30
30–40
40–55
55–100
>100
Total
Distribution by date
2000/10/29
2001/3/5
Area in hectare
0.08 (1.65)
0.13 (2.64)
0.47 (9.90)
0.66 (13.86)
1.03 (21.78)
1.92 (40.59)
0.45 (9.57)
(%)
0.14
0.36
0.83
1.52
2.19
5.14
0.86
4.73 (100)
2001/7/20
2001/12/3
(1.27)
0.28 (0.42) 0.02
(3.26)
0.97 (1.44) 0.09
(7.51)
3.66 (5.45) 0.48
(13.74) 5.73 (8.54) 0.83
(19.83) 10.88 (16.20) 1.17
(46.60) 31.94 (47.59) 2.91
(7.79) 13.66 (20.35) 1.14
11.03 (100)
67.11 (100)
(0.24)
(1.41)
(7.29)
(12.47)
(17.65)
(43.76)
(17.18)
6.64 (100)
investigation (Fig. 9), the vegetation recovery conditions on steep slopes are much better than ridgelines. It
also shows that the soil moisture is more important for
plant growth than slope factor in this study area.
Fig. 10 illustrates the actual vegetation situation on
slopes obtained from the field survey soon after the
earthquake. Although the earthquake caused massive
Fig. 9. The vegetation recovery conditions on steep slopes and
ridgelines (2001/6).
landslides and eliminated much of the standing
vegetation, there were still a number of grass species
that survived on steep slopes, especially Arundo
formosana, one of the native grass species with robust
vitality in Taiwan. Once adequate rainfall was
supplied to the area, the surviving vegetation on the
steep slopes rapidly restored itself. The analyzed result
is checked by the field survey, as shown in Fig. 9. The
difficulty in preserving water on ridgeline surfaces
influences the vegetation growing at those sites. Data
from Taiwan’s Central Geological Survey shows the
local hydrological property is a uniform gravel layer
consisting of massive conglomerate and merges
laterally into alternating sand and clay beds with
high hydraulic conductivity. How soil holding/storing
the water for the plants in this study area is critical to
vegetation recovery.
4. Conclusions
Remotely sensed data coupled with a GIS for
massive landslide identification, is very effective and
rapid. The VRR calculation proposed in this study
provided a quantitative method for monitoring and
assessing vegetation recovery at the Jou-Jou Mountain
landslides. However, the vegetation succession cannot
be accomplished in such a short time. The improved
VRR from NDVI calculation revealed a stable plant
growth and vegetation recovery tendency for denudation sites. From 2 years of vegetation recovery
monitoring, the highest average VRR in the landslide
area reached 58.93% without any human intervention.
This result shows that nature has a robust ability to
regenerate vegetation on landslides. In accordance
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W.-T. Lin et al. / Forest Ecology and Management 210 (2005) 55–66
with the climate data, multi-temporal satellite images,
landslide characteristics, and field survey analysis,
most of the aggravated vegetation sites were
distributed on toppled toe-slopes and ridgelines.
The analyzed results also show that soil moisture is
a critical factor for vegetation recovery in the landslide
area.
Acknowledgment
This research was supported by a grant from the
National Science Council, R.O.C. [NSC 92-2313-B451-001]
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