Vegetation recovery monitoring and assessment at landslides caused by earthquake in Central

www.elsevier.com/locate/foreco

Forest Ecology and Management 210 (2005) 55–66

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, Taiwanb 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, Taiwand 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 Wu-

Chi 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 magni-

tude 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.

W.-T. Lin et al. / Forest Ecology and Management 210 (2005) 55–6656

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 Da-

Chia River basin, the Jou-Jou Mountain area in the

Wu-Chi River basin, at Tasoling near the border

between Yunlin and Chiai counties, and Chiufener-

shan 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 addi-

tional 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 monitor-

ing 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 near-

infrared 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 Choudh-

ury, 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 con-

vective 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. Multi-

temporal 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 algo-

rithms for acquiring land-cover data (Giannetti et al.,

2001; Boles et al., 2004). However, for the classifica-

tion of multiple image dates, several change detection

logics are used to precisely extract the change

detection information. The image differencing algo-

rithm (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 gen-

erate a third image composed of the numerical

differences between the pairs of pixels (Ridd and Liu,


Fig. 2. SPOT satellite images at the Jou-Jou Mountain area.

Fig. 3. Flowchart of vegetation recovery assessment at landslides in this study.


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 radio-

metrically 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 pre-

quake 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 Septem-

ber 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

maxðNDVIcÞ � minðNDVIcÞ� 100% (2)

where T is the suggested threshold; NDVIc is the

NDVI difference between pre-quake and post-quake

image; max, is the maximum difference; min, is

minimum difference. The extracted landslides were

compared with the post-quake ancillary data, includ-

ing 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 vegeta-

tion sites. The VRR formula can be written as:

VRRð%Þ ¼ NDVI2 � NDVI1

NDVI0 � NDVI1� 100% (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 Sep-

tember 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 distribu-

tion throughout the year and periodic typhoons, occur-

ring in July, August and September of each year, the

change monitoring for multi-temporal VRR were

compared with the climate data obtained from Tai-

wan’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 statis-

tics, 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


Fig. 4. Normalized NDVI images at the Jou-Jou Mountain area at different stages.

Fig. 5. Landslide distribution at Jou-Jou Mountain area.

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


spatial distribution. The formula for computing

collapse ratio can be written as:

CRð%Þ ¼ LA

TA� 100% (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-

Fig. 6. Frequency distribution of N

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. Ridge-

lines can be determined by flow accumulation

concepts based on the O’Callaghan and Mark

(1984) algorithm. In a raster DEM, a flow accumula-

tion grid is tabulated for each cell and the number of

DVI in the denudation sites.


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 compo-

nents (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 Y = �0.028+0.871X 0.967a

2000/10/29 Y = �0.011+0.745X 0.953a

2001/03/05 Y = �0.069+1.005X 0.942a

2001/07/20 Y = �0.056+0.783X 0.903a

2001/12/03 Y = �0.089+0.710X 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.

Fig. 7. The change of NDVI with prec

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.

ipitation in the denudation sites.


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

Fig. 8. Spatial distribution of classifie

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/

d VRR in the denudation sites.


Table 2

Distribution of classified vegetation recovery rate in different dates

Category VRR (%) Distribution by date

2000/10/29 2001/3/5 2001/7/20 2001/12/3

Area in hectare (%)

Excellent >100 4.73 (0.57) 11.03 (1.33) 67.11 (8.09) 6.64 (0.80)

Very good 75–100 32.13 (3.87) 55.03 (6.63) 204.17 (24.61) 44.48 (5.36)

Good 50–75 140.72 (16.96) 157.83 (19.03) 271.02 (32.67) 172.88 (20.84)

Average 25–50 282.73 (34.08) 235.48 (28.39) 183.81 (22.16) 269.27 (32.46)

Poor 0–25 242.23 (29.20) 212.80 (25.65) 84.06 (10.13) 172.31 (20.77)

Very poor <0 126.95 (15.30) 157.33 (18.97) 19.33 (2.33) 163.92 (19.76)

Total 829.50 (100)

Average VRR (%) 28.21 29.15 58.93 28.53

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 (%)

100–200 583.2 3.16 0.54

200–300 1039.52 28.48 2.74

300–400 1272.48 90.20 7.09

400–500 784.48 253.59 32.33

500–600 461.92 275.75 59.70

600–700 218.56 152.89 69.95

700–800 35.84 25.42 70.93

Table 4

Distribution of collapse ratio to slope classification

Slope

(%)

Total category

area (ha) (TA)

Landslide

area (ha) (LA)

Collapse ratio,

CR = LA/TA (%)

<5 509.44 2.61 0.51

5–15 402.08 15.44 3.84

15–30 779.52 66.67 8.55

30–40 660.32 75.48 11.43

40–55 801.28 141.88 17.71

55–100 1026.24 389.08 37.91

>100 217.12 138.34 63.72

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 there-

fore 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.

3.3. Landslide characteristics and vegetation

recovery placement analysis

The collapse ratio to various terrain factor

calculations are listed in Tables 3–5. The collapse

ratio to elevation indicates that the higher the

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


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 350.88 66.69 19.01

East 566.88 127.11 22.42

Southeast 509.92 89.33 17.52

South 889.60 164.89 18.54

Southwest 539.04 78.22 14.51

West 603.52 112.61 18.66

Northwest 347.04 68.08 19.62

North 589.12 122.58 20.81

Table 6

Analysis of excellent vegetation recovery placement on slope

classification

Slope

(%)

Distribution by date

2000/10/29 2001/3/5 2001/7/20 2001/12/3

Area in hectare (%)

<5 0.08 (1.65) 0.14 (1.27) 0.28 (0.42) 0.02 (0.24)

5–15 0.13 (2.64) 0.36 (3.26) 0.97 (1.44) 0.09 (1.41)

15–30 0.47 (9.90) 0.83 (7.51) 3.66 (5.45) 0.48 (7.29)

30–40 0.66 (13.86) 1.52 (13.74) 5.73 (8.54) 0.83 (12.47)

40–55 1.03 (21.78) 2.19 (19.83) 10.88 (16.20) 1.17 (17.65)

55–100 1.92 (40.59) 5.14 (46.60) 31.94 (47.59) 2.91 (43.76)

>100 0.45 (9.57) 0.86 (7.79) 13.66 (20.35) 1.14 (17.18)

Total 4.73 (100) 11.03 (100) 67.11 (100) 6.64 (100)

Fig. 10. Earthquake-induced landslide and the surviving Arundo

formosana on steep slopes (1999/10).

investigation (Fig. 9), the vegetation recovery condi-

tions 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 denuda-

tion 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


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-B-

451-001]

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Vegetation recovery monitoring and assessment at landslides caused by earthquake in Central

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