land use and land cover change on slope in qiandongnan prefecture of southwest china

J. Mt. Sci. (2014) 11(3): 762-773 e-mail: [email protected] http://jms.imde.ac.cn DOI: 10.1007/s11629-012-2570-8

762

Abstract: This study uses DEM (Digital Elevation

Model) data and remote sensing maps of the study

area in 1993, 1999, and 2009 to analyze the slope

gradient change of land use patterns in Qiandongnan

Prefecture, Guizhou province, China. The land use

data were classified into five types, forest, farmland,

grassland, water and built-up, the slope gradients

were divided into four grades. Indices for analyzing

land use features were defined by their proportions,

transformation matrixes, land use degree and changes.

The results showed that all land use types can be

found at every gradient. Generally, with the slope

degree increased, the area of forest being augmented

as well, while the area of the other land use types

(farmland, grassland and build-up) declined.

Moreover, a mass of farmland were shifted from other

land use types from 0° to 25°, while a quantity of

forest were transformed from the other land use types

on ＞25° from 1993 to 2009. In terms of land use

degree and changes, the area of farmland and build-

up land use types decreased when slope degree

increased. Finally, we calculated the five landscape

pattern metrics: Patch Density (PD) value, Largest

Patch Index (LPI), Shannon’s Diversity Index (SHDI),

Area-Weighted Mean Shape Index (AWMSI) and

Contagion Index (CONTAG). The results of metrics

analysis showed that PD values, SHDI values and

CONTA values had a similar variation trend, that is,

they decreased when slope degree increased. There

was no obvious variation trend on LPI value.

Keywords: Land use; Land cover change; Spatial

gradient slope; Landscape metrics; Qiandongnan

prefecture

Introduction

Land use/cover change is the result of natural

conditions and human disturbance. Human activity

is the force usually affecting spatial and temporal

changes in land use, but the underlying physical

structure of a landsape often constrains the use of

land (Fu et al. 2006). Slope is one of the most key

physical factors in geography and ecology which

may affect the quality of soil (Buyinza and

Nabalegwa 2011; Pierret et al. 2007; Aumtong and

Magid 2006; Moges et al. 2008). It can cause water

loss (Dorofki et al. 2011), and even landslides (Bai

et al. 2010; Nandi and Shakoor 2009; Poudel 1996).

Many researchers have pointed out that there is a

distinct relationship between land use and slope

(Fu et al. 2006; Zhang et al. 2009; Silva et al. 2007;

Ma et al. 2003; Shao et al. 2011; Wei et al. 2008). A Received: 23 October 2012 Accepted: 3 May 2013

Land Use and Land Cover Change on Slope in Qiandongnan

Prefecture of Southwest China

LU Liang1,2, GUO Luo1*, ZHAO Song-ting3

1 College of Life and Environmental Science, Minzu University of China, Beijing 100081, China

2 Geomatics Centre of Zhejiang, Hangzhou 310012, China

3 Beijing Institute of Landscape Architecture, Beijing 100102, China

*Corresponding author, e-mail: [email protected]; First author, e-mail: [email protected]

Citation: Lu L, Guo, L, Zhao ST (2014) Land use and land cover change on slope in Qiandongnan prefecture of southwest China. Journal of Mountain Science 11(3). DOI: 10.1007/s11629-012-2570-8

© Science Press and Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2014

J. Mt. Sci. (2014) 11(3): 762-773

763

thorough understanding of the relationship

between land use and slope will enhance our

capability to predict landscapes changes and devise

more effective landscape management strategies.

And with the increasing availablility and

effectiveness of remote sensing, land use data can

be acquired in a more timely manner. Meanwhile,

with the advance in geographic information system

techniques, it has become easier to handle large

databases describing the land use type

characteristics (Dorofki et al. 2011). Besides, we

can extract topographic variables via the Digital

Elevation Model (DEM) which is one of the

products of digital mapping technique. The

development of new technology has made the study

of diverse slope area more efficient.

1 Study Area

The study area is located in southeast Guizhou

Province, China, which covers an area of 30337.2

km2 between 107°17'20" N to 109°35'24" N and

25°19'20" E to 27°31'40" E. This prefecture

includes 16 counties and has a mountainous terrain

with elevation ranging from 137 m to 2179 m. The

population was 3.48 million in the 6th nationwide

census (2010), with the Miao and Dong being the

most populous ethnic groups taking up 42.08%

and 31.86% of the population, respectively. These

ethnic groups have their own unique ecological

value and special land use technique. For example,

they erect a rice-fish ecosystem in terrace on slope

to live with nature in harmony and keep high

coverage for worship of holy-forest even after the

national level deforestation. The area is

characterised by a northern subtropicical monsoon

climate. The annual mean rainfall ranges between

1035.6 mm and 1499.4 mm, of which 70% falls

within the rainy season. The annual mean

temperature of Qiandongnan area ranges from

14.6°C to 18.5°C. There are 2900 rivers in the area

with the Wuyang River, Duliu River and Qingshui

River being the main rivers, they all flow in a

roughly west-east direction. Qingshui River and

Wuyang River are upstream of the Yangtze River

watershed and Duliu River is upstream of the Pearl

River watershed. Also, the study area is one of the

most significant forested areas with a forest

coverage of 53.68%. The study area plays a vital

role in the soil and water conservation of the

Yangtze and Pearl River watershed.

2 Method

In this study, multi-temporal sets of land-use

maps were collected from remote sensing images at

3 occasions from 1993 to 2009. The remote sensing

images were Landsat 5 TM in 1993, Landsat 7

ETM+ in 1999, and Landsat 7 TM in 2009 at a

spatial resolution of 30 m per pixel. Before

interpretation of remote sensing images, a land use

reconnaissance survey was carried out to obtain a

general understanding of the land use situation of

the study area. The land use types were classified

into five categories: forest, farmland, grassland,

water and built-up area. All the bands and NDVI

were used in supervised classification after ortho-

rectifition and five land use types were mapped by

the software ERDAS Imagine 9.3. In the process of

classification, we used the maximum likelihood

classifier and selected more than 200 training

samples for each images whose separability

indicators were all above 1.8, to make sure that

different land use classes could be separated. After

supervised classificaion, some modification was

done according to the forestry survey data in 2008

and some pictures on land use from local

government.

To assess the classification accuracy, an

aligned systematic sampling approach, modified

from Ismail and Jusoff (Ismail and Jusoff 2008),

was applied. By using a grid of 20 km × 20 km

(Figure 1), a total of 201 ground reference points

were generated for the whole study area. Of the 201

ground reference points, visual interpretation

following Lung and Schaab (Lung and Schaab 2010;

Wang et al. 2011; Zhou and Wang 2011) was carried

out for 163 points using the original Landsat image

facilitated by the high resolution images from

Google Earth, and field verification was done in

early 2009 and later 2011 for 38 points where the

locations could be reached. Accuracy reports were

then generated from a confusion matrix for overall

accuracy, the Kappa coefficient. In this study, the

overall accuracy were 88.3654%, 88.6952%,

88.0290%, and the Kappa coefficient were 0.8489,

0.8550, 0.8411 in 1993, 1999, and 2009,

respectively. The detailed accuracy information

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was shown in Tables 1-3. From the tables, we can

see that water and built-up was easier to be

differentiated than forest, farmland and grassland.

This might be decided by the attributes of land use

types. Forest, farmland and grassland all include

diverse vegetation, so it was a little difficult to be

classified exactly. Therefore, we used the forestry

survey data in 2008 and some pictures on land use

from the local government to rectify some errors.

In total, this classification results can be acceptable

to analyze the land use and land cover change of

Qiandongnan Prefecture.

A slope map was obtained from 1:50,000 DEM

data (which was purchased from the Chinese

Ministry of Land and Resources) by surface

analysis operation via Arcgis 10.0. The slope was

reclassified into four categories: 0-7°, 7-15°, 15-25°,

＞25° according to the gravity principle and local

geographic trait. The largest area of slope gradient

falls between 15 and 25, this group took up 33.63%.

The next was the gradient of 7°-15° taking up

31.25%. Lastly, the slope gradients of 0°-7° and ＞

25° were both 17.56%. Then land use data of each

gradient were extracted from classifcation result

and they were used for subsequent calculation as

shown in Figures 2-4. These indices include

percents on land use constitution, transformation

matrix, land use degree and the changes and

landscape pattern metrics in landscape level.

To explain the constitution of land use, the

proportions of two kinds of land use features were

calculated. For each land use type within a

particular grade, we calculated the percentages in

relation to both land use type (P1) and grade (P2)

for the three time periods we examined. For

example, as Table 4 indicates, in 1993 forestlands with slope 0°-7° constituted 38.05% of lands with

that slope and 9.62% of forests.

100%i,a

1j

a

SP

TS= ×

100%i,a

2j

i

SP

TS= ×

In these equations, Si,a is the area of i land use

type on a gradient, TSa is the total area of a

gradient, TSi is the total area of i land use type.

It has been established that the degree and

changes in land use are not only dependent on the

nature of the land but also on human and societal

Figure 1 Distribution of the 201 reference points for classification accuracy assessment.

Table 1 Confusion matrix of the land use classification in 1993. The figures in the table are the number of the pixels.

Actual class

Forest Farmland Grassland WaterBuild-up

Total User’s accuracy (%)

Errors commission (%)

Predicted class

Forest 4091 293 314 79 0 4793 85.35 14.65Farmland 260 2791 156 160 97 3464 80.57 19.43Grassland 184 51 2108 14 18 2375 88.76 11.24 Water 0 0 0 2230 4 2234 80.57 0.18 Build-up 0 0 0 1 1289 1290 99.92 0.08 Total 4535 3135 2578 2484 1424 14,156 Producer’s Accuracy (%)

90.21 89.03 81.77 89.77 90.52 Overall accuracy = 88.3654%

Errors of Omission (%)

9.79 10.97 18.23 10.23 9.48 Kappa coefficient = 0.8489

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765

factors (Liu 1996). In order to reflect the non-

natural factor from an ecological standpoint, land

use types was given divided into four grades and

each gradeis given its own value in the calculation

(Table 5).

Land use degree index：

1

100n

i ii

L A C=

= × ×∑

Table 2 Confusion matrix of the land use classification in 1999 The figures in the table are the number of the pixels.

Actual class


TotalUser’s accuracy (%)

Errors of commission (%)

Predicted class

Forest 3913 328 257 18 5 4521 86.55 13.45 Farmland 271 2674 90 293 157 3485 76.73 23.27 Grassland 149 14 1918 4 0 2085 91.99 8.01 Water 0 0 0 2028 0 2028 100.00 0.00 Build-up 0 0 0 9 1981 1990 99.55 0.45 Total 4333 3016 2265 2352 2143 14,109 Producer’s accuracy(%)


Errors of omission(%)

9.69 11.34 15.32 13.78 7.56 Kappa coefficient=0.8550

Table 3 Confusion matrix of the land use classification in 2009. The figures in the table are the number of the pixels.

Actual class


Total User’s accuracy (%)

Errors of commission (%)

Predicted class

Forest 6855 516 388 322 5 8086 84.78 15.22 Farmland 410 4292 229 146 264 5341 80.36 19.64 Grassland 27 38 3008 3 1 3077 97.76 2.24 Water 162 0 0 2085 1 2248 92.75 7.25 Build-up 0 0 0 0 2232 2232 100.00 0.00 Total 7454 4846 3625 2556 2503 20,984 Producer’s accuracy (%)


Errors of omission (%)

8.04 11.43 17.02 18.43 10.83 Kappa coefficient = 0.8411

Table 4 Percentage of land use constitution on slope gradients from 1993 to 2009.

Year Degree Slope Forest Farmland Grassland Water Build-up

P1 P2 P1 P2 P1 P2 P1 P2 P1 P2

1993

1 0°-7° 38.05 9.62 41.51 25.94 18.36 18.10 1.53 51.17 0.55 55.482 7°-15° 53.53 28.78 27.62 36.70 18.27 38.28 0.42 30.08 0.16 34.123 15°-25° 65.84 38.09 19.96 28.54 13.99 31.56 0.17 13.27 0.04 9.314 >25° 77.79 23.50 11.82 8.83 10.25 12.06 0.14 5.48 0.01 1.10

1999

1 0°-7° 30.43 8.29 60.32 24.57 7.59 16.74 0.99 60.07 0.67 56.592 7°-15° 47.86 27.71 43.80 37.93 7.94 37.25 0.22 27.79 0.18 32.883 15°-25° 62.78 39.12 30.67 28.58 6.44 32.51 0.06 8.96 0.05 9.214 >25° 76.51 24.89 18.32 8.91 5.12 13.49 0.04 3.18 0.01 1.32

2009

1 0°-7° 32.85 9.90 60.51 20.60 4.45 14.12 1.28 57.15 0.91 58.532 7°-15° 45.29 29.02 49.20 35.61 4.99 33.64 0.29 27.59 0.23 31.003 15°-25° 54.35 37.48 40.71 31.70 4.78 34.72 0.10 10.15 0.06 8.904 >25° 65.55 23.60 29.71 12.08 4.62 17.52 0.10 5.11 0.02 1.56

Table 5 The hierarchy chart of land use types

Grades Land use type Value

unused land - 1 Forest, grassland,water

Forest, grassland, water

2

agriculture farmland 3 urban build-up 4

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where L is the land use degree index; Ai is

corresponding value of certain land use type; Ci is

the percent of certain land use type area in the total

area.

, ,1 1

,1

( ) ( )

( )

n n

i ib i iai i

n

i iai

A C A C

R

A C

= =

=

× − ×

=

×

∑ ∑

∑

where Ai is the corresponding value of certain land

use type; Ci,b and Ci,a are the percent of certain land

use type area in the total area on time b and time a,

respectively. If R＞0，then land use in this period

is in development, otherwise, it is in adjustment or

recession.

To capture some of the synoptic feature of

landscape pattern, several landscape-level metrics

were calculated using the raster version of

FRAGSTATS 3.3. We chose a small set of metrics

that were both sensitive to changes and numerically

reliable for depicting landscape patterns. Five

Figure 2 Land use distribution on diverse slope gradient in 1993.

J. Mt. Sci. (2014) 11(3): 762-773

767

metrics were used to quantify the landscape pattern

of Qiandongnan prefecture. As in most cases in the

existing literature, the term “landscape pattern” here

includes both the non-spatial composition (e.g., the

number and relative abundance of patch types,

patch size, and other related non-spatial measures)

and spatial configuration (e.g., patch shape,

juxtaposition, contrast, and boundary

characteristics). Specifically, we considered 3 indices

as compositional measurements: patch density (PD),

largest patch index (LPI) and Shannon’s diversity

index (SHDI). The remaining 2 indices we classified

as configurational measurements: area-weighted

mean shape index (AWMSI) and contagion

(CONTA). This dichotomy of compositional versus

configurational indices is an oversimplification, and

many of landscape pattern indices reflect both

aspects of landscape pattern to varing degree. We

adopted this simple classification scheme so as to

facilitate the organization and interpretation of


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results.

3 Result and Discussion

3.1 Land use features of diverse slope gradients

In the study period, it was found that there

was always an apparent difference between each of

the five land use types distributed on all grades and

their respective proportions (P1 in Table 4). On the

first gradient, farmland was the chief land use type

occupying 41.51%, while forest, grassland, water

and built-up accounted for 38.05%, 18.36%, 1.53%

and 0.55%, respectively. On the other gradients,

however, forest became the primary land use type,

with farmland becoming second. There were


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noteworthy regional differences on each land use

type proportion. To be specific, when the slope

degree increased, the area of forest gradually

increased as well, whereas the area of the other

land use types including farmland, grassland and

built-up decreased. From 1993 to 2009, forestry

area declined on every slope gradient, especially on

the fourth gradient which saw a decline from

77.79% to 65.55%. Additionally, the area of

grassland saw a heavy decline in the first gradient

from 18.36% to 4.45%, water area also saw a

decline during this time period. These losses

resulted in a great gain of increased farmland on

every gradient over all periods.

Our interpretation for the forest decreasing

was the consequence of chasing economic benefits

in the study periods especially from 1993 to 1999.

After the 1998 flood of the Yangtze River, the

government began implementing serious policies

to protect the forest. This included Natural Forest

Protection Programs and Grain for Green Project

to protect the forest. This was done in an effort to

increase the forest on the first gradient. Although

deforestation still existed on the other gradients

due to rising timber prices from 1999 to 2009, the

deforestation speed was much lower when

compared to that in 1993 to 1999. As farming was

the leading industry in Qiandongnan prefecture

and its economy developed rapidly during the

study period, farmland aggrandized heavily on

each gradient especially in the third gradient,

however, the increase speed from 1999 to 2009 was

much lower than that from 1993 to 1999. At the

same time, the need for more infrastructures

quickly became apparent to facilitate the growing

population, technology and economy of the area.

Many old roads were broadened and many new

roads were built to provide much more convenient

conditions for building the infrastructure to

facilitate people. These expansions even affected

people on some steep slope areas. This expansion

of farmland and built-up resulted in the decrease of

grassland which was vulnerable to change. Water

area was subject to precipitation human demand.

During the study period, water demand increased

as a result of both a growing living standard and

population as well as new development in the

farming industry. This coupled with the changes in

precipitation over years causing the water area to

fluctuate and eventually decline.

Additionally, land use types had a regular

distribution on diverse gradients (P2 in Table 4).

Forest and farmland primarily distributed on the

third gradient and the second gradient was the

second largest distribution. These distributions

accorded with the proportion of the second and

third gradients on area. However, there was a great

contrast between forest and farmland on the first

gradient and the fourth gradient which had the same

area. Specifically, on the first gradient, the

percentage of forest increased from 8.29% to 9.90%,

while the percentage of farmland increased from

20.60% to 25.94%, however, the value of forest

ranged from 24.89% to 23.50%, and the value of

farmland varied from 8.83% to 12.08%. This shows

that forest was fit for steep area and farmland was

inclined to distribute on gentle area. Meanwhile, the

fourth gradient owned the least farmland and

grassland, because most farmland was terrace and

terrace was usually built below the fourth gradient

for better soil and water conservation. In terms of

water and build-up, the first gradient was the largest

distribution area, the next was the second gradient

and the fourth gradient was the least. Due to the

gravity and the characteristics of water, gentle

sloped areas had the biggest distribution of water.

Since built-up areas require resources from other

areas to be made it was also distributed on a gentle

slope, as moving these resources onto areas with

steeper slopes was difficult.

3.2 Land use type shift analysis on diverse gradient

Several obvious land use shift trends have

been observed to arise on the gradients (Table 6).

On the first gradient, farmland was the most

primary objective shifted and large quantities of

farmland were transformed from 42% of forest,

59% of grassland, 37% of water and 23% of build-

up. Also, a quantity of forest was shifted from

farmland, grassland and water. At the same time,

an extremely drastic transformation emerged on

the second gradient. 98% of forest, 99% of

grassland, 99% of water and 97% of built-up were

shifted into farmland. On the third gradient, the

largest shift proportion occurred from grassland

and water to farmland. The proportion shift to

forest evidently increased when compared to the

first and second gradients. However, forest became

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the chief land use type to

be transformed to on the

fourth gradient and the

highest transformation

rate was 51% arising

from farmland to forest.

Generally, on the gentle

slopes (0°-25°),

transformation patterns

tended to be farmland

shifted from the other

types. While the highest

transformation rate was

forest shifted from

grassland and farmland

on the steep slopes (＞

25°), there were large

amount of land shifting

to farmland. And built-

up area kept much

higher percent than

water and grassland

through all gradients. In

summary, large amounts

of natural land use area

such as forest, grassland,

and water were

transformed to the man-

made land use type,

farmland and build-up.

3.3 Land use degree analysis on diverse gradients

There was an obvious degree of variation

concerning land use on different gradients (Table

7). Land use degree decreased as the slope

increased in each of the study periods. This

indicated that human beings prefered working on

areas with gentler slopes than areas with steep

ones. In terms of land use degree changes, all

values were greater than 0, so land use conditions

of all slope gradients in the three periods were in

development. Additionally, regular variation

occurred on diverse gradients from 1993 to 1999

and from 1999 to 2009. Values of land use degree

change decreased by approximately 60% when the

slope degree increased from 1993 to 1999, while it

increased almost 20 times when the slope degree

increased from 1999 to 2009. The strong change

between the two periods is good evidence to show

the influence of national government control. The

changes value on the first gradient from 1993 to

1999 was nearly 32 times as large as that from 1999

to 2009, and the decrease revealed that the

intensity of human force on land use had declined

heavily since 1999 on this gradient. However, there

were no distinct regular alterations from 1993 to

2009 which had highest value among all periods.

The largest value of 0.0953 emerged on the second

gradient, and the lowest value was 0.0813 on the

first gradient.

3.4 Landscape pattern analysis on diverse slope gradients

In this section, we first presented the

compositional and then the configurational

landscape-level metrics. We then combined all five

Table 6 Land use shift proportions (%) in Qiandongnan area

Year 2009

1993

Grade Slope Type ForestFarmlandGrassland Water Build-up

1 0°-7°

Forest 54 42 4 0 0Farmland17 79 2 1 1Grassland28 59. 12 0 1Water 5 37 1 56 1Build-up 0 23 0 2 75

2 7°-15°

Forest 2 98 0 0 0Farmland0 100 0 0 0Grassland1 99 0 0 0Water 0 99 0 1 0Build-up 0 97 0 0 3

3 15°-25°


4 >25°


Table 7 Land use degree and changes in slope gradients from 1993 to 2009

Grade SlopeL R

1993 1999 2009 1993-1999 1999-2009 1993-20091 0°-7° 242.6 261.7 262.3 0.0786 0.0025 0.08132 7°-15° 227.9 244.2 249.7 0.0712 0.0225 0.0953 3 15°-25°220.0 230.8 240.8 0.0488 0.0436 0.0945 4 >25° 211.8 218.4 229.8 0.0307 0.0522 0.0846

Note: L means the land use degree; R means the ratio of land use degree.

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771

land use types, to quantify the spatial pattern of

each slope gradient (Table 8). The PD value varied

from 141.23 to 49.87 in the study period, and there

was a noticeable regional difference. PD values

progressively declined when the slope degree

increased until the PD values on gradient of 0-7

were approximately twice as large as those on

gradient of ＞25°. From the standpoint of time

scale, the decrease in PD values indicated that the

average patch are was becoming bigger on every

gradient, moreover the decreasing speed from 1993

to 1999 was much quicker than the speed from

1999 to 2009. Change in the SHDI value was

similar to the variation of the PD value. The SHDI

value cut down when slope degree increased and it

declined from 1993 to 2009 except a small gain on

the third and fourth gradients from 1999 to 2009.

These two indices both suggested a high degree of

fragmentation and most likely occurred on the

gentle sloped area rather than steeper sloped areas.

This also indicates that the fragmentation degree

generally declined from 1993 to 2009. In contrast,

there was no distinct variation trend of LPI on

slope degree and time scale when compared to the

other non-spatial landscape metrics. Generally, the

first gradient always had larger LPI values. LPI

values generally decreased at higher gradients in

study period. The values of LPI were relatively low

and the largest value was only 2.28 on the first

gradient in 1999. This suggests the dominant land

use type may not distinguish from the others. The

curved variation of LPI values on diverse gradients

illustrated that the dominance of any land use type

was not stable.

AWMSI is an index to describe shape of

patches and CONTA is used to depict aggregation.

The values of AWMSI became larger when slope

degree increased within 0°-25°, while it declined

from the third to the fourth gradient. Moreover, on

all gradients, the values declined from 1993 to 1999,

then increased from 1999 to 2009. Overall, the

values experienced a curved increase from 1993 to

2009. This variation indicated the shape of patches

became more complicated the ecological processes

became more affected by edge effects within the

degree 0°-25°. In end the degree of complexity and

edge effect intensified from 1993 to 2009. There

exists an evident rule when using CONTA values

that must be taken into account when used on

diverse slope gradients; CONTA values increased

when the slope degree increase. This result

suggested there were many channels in dominant

land use type which was key in the interchange of

materials and energy in ecosystem on the ＞25°

gradient. On the contrary it was found that there

were many fragmentized and disordered patches

on the first gradient which were not suitable for

materials and energy interchange. In addition,

CONTA value increased when the slope degree

increased within 0°-25°, and, the changing rate

from 1993 to 1999 was much quicker than that

from 1999 to 2009. This index also showed that

human beings preferred to work on gentle slope

area rather than steeper areas and that the work

intensity was weakened after 1999, especially on

gradient of ＞25°.

4 Conclusion

In virtue of mathematical calculation for the

proportion, transform matrix, land use degree and

landscape pattern metrics, the state and trend of

LUCC (land use and land cover change) can be

deeply researched. Mathematical calculation were

developed to quantify the important aspects or

elements related to land change, trend and

variation. By means of the study of LUCC in the

recent 16 year of Qiandongnan area on diverse

slope gradients, the following conclusions were

obtained:

(1) In the study period, each land use type was

found at each gradient. Land use proportion varied

with both slope degree and time scale. On the first

gradient, farmland was the chief land use type,

Table 8 Landscape pattern indices in slope gradients.

Year Slope PD LI SI AI CI

1993

0°-7° 141.23 1.15 0.71 3.93 42.967°-15° 117.59 0.18 0.64 4.05 46.8115°-25° 93.52 0.32 0.55 5.42 54.24>25° 76.34 0.73 0.43 4.09 64.91

1999

0°-7° 92.37 2.28 0.59 5.45 57.637°-15° 70.67 0.53 0.58 5.41 56.2315°-25° 59.20 0.18 0.52 5.33 60.16>25° 55.64 0.38 0.42 4.01 68.01

2009

0°-7° 84.90 2.07 0.56 4.51 59.597°-15° 60.40 0.44 0.55 5.13 59.6015°-25° 50.24 0.29 0.53 4.89 61.05>25° 49.87 0.35 0.49 3.13 64.60

Notes: PD is Patch Density; LI is Largest Patch Index; SI is Shannon’s Diversity Index; AI is Area-Weighted Mean Shape Index; CI is Contagion Index.

J. Mt. Sci. (2014) 11(3): 762-773

772

while on the other gradients forest was the primary

land use type. Large amounts of natural land use

area such as forest, grassland, water were

transformed to man-made land use type including

farmland and built-up on all gradients. Moreover,

the decrease in land use degree in relation to

increasing slope revealed the limitations of working

on land with steeper slopes. The land use change

speed from 1999 to 2009 suggested influence of

human activities.

(2) In general, PD and SHDI had similar

variation trends based on slope in the study area.

They both suggested a high degree of

fragmentation was more likely to occur in the

gentle sloped areas rather than the steeper sloped

areas, and indicated that the degree of

fragmentation generally declined from 1993 to

2009. The changing LPI indicated that the

dominance of land use type was not stable.

Additionally, patch shape became more complex

within 0°-25°, and the degree of complexity

fluctuated, but generally increased from 1993 to

2009. Generally, aggregation degree gradually

strengthened with rising slopes. And, temporally,

the variation of aggregation degree showed that

human beings preferred to working on gentler

sloped areas rather than steep areas and the work

intensity weakened after 1999, especially on

gradient of ＞25°.

(3) Finally, the first gradient is a good example

of the effects of Natural Forest Protection Projects

and Grain for Green Project since 1998. The forest

percent in the time period from 1999-2009

remained relatively stable, while the percent of

other land use types declined. This, as well as the

fact that the rate of fragmentation was significantly

lower than what it was from 1993-1999 all point to

the effectiveness of the governments conservation

programs. The fourth gradient is an area that could

draw more attention in future studies. Although

the majority of land use type shifting was to forest,

the percent of forest still declined. The change rate

was much higher than other types and the diversity

and aggregation increased from 1999 to 2009, an

observation we did not expect. Also, since steep

areas with low forest coverage are often in danger

of landslides, further studies could be conducted in

order to find possible ways to increase water and

soil protection.

Acknowledgements

The work presented in this paper was

supported by the National Natural Science

Foundation of China (Grant No. 31370480), 111

Project (B08044) and Minzu University of China

(MUC98507-08).

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