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Assessing rangeland degradation using multi temporal satellite images and grazingpressure surface model in Upper Mustang, Trans Himalaya, Nepal

Keshav Prasad Paudel ⁎, Peter Andersen

Department of Geography, University of Bergen, Fosswinkelsgate 6. N-5007, Bergen, Norway

a b s t r a c ta r t i c l e i n f o

Article history:

Received 4 November 2009

Received in revised form 17 March 2010Accepted 21 March 2010

Keywords:

Rangeland

Degradation

Remote sensing

Grazing pressure

Residual trend

Cost surface

Trans Himalaya

This studyaims to map and discriminate rangeland degradationfrom the effects of precipitation variability and

thereby identify the driving forces of degradation in the grazing areas of Ghiling in Upper Mustang, Nepal.Landsat MSS, TM,ETM, andSPOT imagescoveringthe years 1976–2008were analyzed.8 km resolutionNOAA

NDVI from 1981to 2006 were used to identify the long term interrelationship between vegetation greennessand precipitation variability. The use of time series residual of the NDVI/precipitation linear regression to

normalize the precipitation effect on vegetation productivity and identify the long term degradation wasextended at thelocalscale. A weighted grazing pressure surface model wasdeveloped combining informationfrom satellite images, topography, forage availability and detailed eld work data on points of livestock

concentration,herders' ranking of forage quality and grazing pattern in each pasture unit.The grazing pressure

of a given site was dened as the product of annual net stocking density and the inverse of the total frictionof livestock movement. While annual precipitation was found as the dominant factor for the interannualvegetation variability, degradation in Upper Mustang was the result of grazing induced change and some

localized natural processes.

© 2010 Elsevier Inc. All rights reserved.

1. Introduction

Temporal variation in rangeland productivity is a function of climatic and anthropogenic factors. Other natural factors like vegeta-tion ecosystem, topography and soil/hydrology characteristics can beassumed as relatively constant within annual to a few decadal time

scales (Di et al., 1994). Among the climatic variables in arid and semiarid environments, precipitation variability has been found to be theprimary determinant for rangeland vegetation dynamics (Le Houérouand Hoste, 1977; Le Houérou et al., 1988; Nicholson et al., 1990; Noy-

Meir, 1973; Wang et al., 2001; Whittaker, 1970). High interannualprecipitation variability in arid/semi arid environments represents anexternal disturbance to rangeland ecosystems resulting in high var-iation in vegetation cover.

Changes in vegetation cover or productivity derived from a timeseries based satellite images vegetation index (VI) have been widelyused to map, quantify and analyze vegetation change and rangelanddegradation (Anderson et al., 1993; Deering et al., 1975; Liu et al.,

2004; Lyon et al., 1998; Myneni et al., 1997; Pettorelli et al., 2005;Pickup et al., 1998; Singh, 1989; Tucker, 1979; Tueller, 1989). How-ever, because of the high interannual precipitation variability in semi/

arid environments and its effect on rangeland production trends, adescription of trends in the vegetation cover only is insuf cient to

characterize degradation in rangeland (Pickup et al., 1998). It isessential to distinguish between ‘degradation’ and ‘changes due to

uctuation of precipitation’. In the landscapes where precipitation isthe only determinant for biomass productivity, vegetation can benaturally restored following good rainfall/snowfall. Looking at suchreversible changes following precipitation, decreasing vegetation

productivity due to declining precipitation can be categorized as‘vegetation variability’ or what Bai et al. (2008) termed ‘false alarm’rather than degradation. In contrast, a decreasing response of veg-etation to precipitation over time indicates an inuence of some long

term localized processes, which reduce the ability of those landscapesto recover after the changes. Such long term reduction in rangelandproductivity systems implies decreasing ecosystem resilience and hasbeen dened as rangeland degradation (Pickup, 1996; Pickup et al.,

1998). Spatial variation of grazing is generally considered as a longterm process reducing the resilience of specic geographical units.In addition, active erosion processes in a land unit may also inuencethe spatial pattern of degradation. Hence, for a better understanding

of the underlying driving forces of rangeland degradation in dryenvironments, it is necessary to identify and normalize the effect of precipitation on the rangeland vegetation trends.

This study aims to discriminate long term degradation from pre-cipitation driven vegetation variability in a Trans Himalayan range-land. The Trans Himalayan rangelands of Nepal are not just a resourcefor livestock grazing, but have also been regarded as a storehouse

for biodiversity including many endangered species. The area alsoencompasses the headwaters of the major river systems of Nepal and

Remote Sensing of Environment 114 (2010) 1845–1855

⁎ Corresponding author. Tel.: +47 98683685; fax: +47 55583099.

E-mail address: [email protected] (K.P. Paudel).

0034-4257/$ – see front matter © 2010 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2010.03.011

Contents lists available at ScienceDirect

Remote Sensing of Environment

j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e

mailto:[email protected]://dx.doi.org/10.1016/j.rse.2010.03.011http://www.sciencedirect.com/science/journal/00344257http://www.sciencedirect.com/science/journal/00344257http://dx.doi.org/10.1016/j.rse.2010.03.011mailto:[email protected]



the Indiansubcontinent. It has been suggested that these high altituderangelands are severely threatened by rapid degradation due to over-grazing (NTNC, 2008:7–8). However, such general concepts advocat-ed by policy makers and development agencies often rely rather on

estimations than on empirical nding. Has the Trans Himalayanrangeland really degraded towards such a critical level?

To identify and normalize the effect of rainfall variability on veg-

etation productivity, residual trend method has been used recently

(Archer, 2004; Evans and Geerken, 2004; Herrmann et al., 2005;Wessels et al., 2007). The residual trend method is based on linearrelation between VI and accumulated precipitation. Residual(VIres), the

difference between observed and predicted VI, theoretically representsthe part of the observed VI which is not explained by precipitation.Thus, a signicant negative trend in time series residuals represents arangeland degradation excluding the precipitation effects. One majorproblem in this method is that the trend is measured against a linear

regression of all the data, including degraded, so the non-degradedrelationship with precipitation is, in fact, the average of non-and de-graded data. Thus in sites degraded before the time series started,the observed relationship between vegetation index and precipitation

will underestimate the expected production for a given amount of precipitation and consequent identied degradationmagnitude (Princeet al., 2007; Wessels et al., 2007). However, the calculated interannualtrends is not affected, so long as VIres is independent of rainfall which

indicates the xed reduction in vegetation production independentof precipitation due to degradation (Evans and Geerken, 2004; Wesselset al., 2007).

In dry Trans Himalayan rangelands, where seasonal transhumanceand rotational grazing are practiced, grazing and related phenomena

are concentrated around some point of livestock concentration (PLC)like encampment sites (sheds), water points, livestock trails andsettlements. In addition to these factors, topography and range qualityalso inuence the spatial pattern of livestock movement (Cingolani

et al., 2008; Röder et al., 2007). Röder et al. (2007) extended thegradient concept (Pickupand Chewings, 1994) to a cost surface model,incorporating most of the above factors which determine spatialdistribution patterns. However, the cost surface model still lacks the

grazing management variables such as livestock density, grazingpattern andtotal grazing days in a paddock etc. Without incorporatingnet annualstocking density, as the function of size of subpasture units,

forage availability, livestock population of each animal species andtheir respective total grazing days in a year (Holechek et al., 2001;Wehn, 2009), cost surface model alone cannot represent the grazingpressure. Similarly, only the function of demand and supply does not

represent the spatial pattern of grazing pressure within subpastureunits. This study also aims to develop a ‘weighted grazing pressuresurface model’ combining net annual stocking density with frictionsurface, for the assessment of grazing induced rangeland degradation.

This study aims at mapping andquantifyingthe extent of rangelanddegradation and identies thedriving forcesof degradation at thelocallevel. Thus it presents a methodology that could be used in support

of decision making at community level rangeland management inthe region. It also provides an example of extending existing GIS/remote sensing analysis framework, incorporating information fromoral history and eld observation.

2. Study area

The Trans Himalaya region (THR) of Nepal, the rain shadow of the

great Himalaya, lies between the Himalayan range and the Tibetanplateau. The rangelands of Ghiling, located approximately between28°57′ to 29°03′N and 83°47′ to 83°55′ E in Upper Mustang in theAnnapurna Conservation Area, have been selected as a test site for

this study (Fig. 1). It is situated between 3000 and 5300 m above sealevel (asl), has an alpine cold, arid to semi arid climate with mean

annual precipitation of 153 mm. The coef cient of variation of annual

precipitation during 1973 to 2008 amounts to 49%. Most of the pre-cipitation occurs during the monsoon (Jun–Sep) as rainfall and duringwinter (Dec–Feb) as snowfall. Mean maximum/minimum tempera-tures during1970–2008 were recorded as 20.8/11.9in monsoon, 15.6/

3.5 in post-monsoon (Oct–Nov), 11.4/−1.02 in winter and 17.1/4.8in pre-monsoon (Mar–May). The seasonal trends in mean tempera-ture indicate a warming in monsoon (0.017 °C y−1) and winter

(0.003 °C y−1) and cooling in pre-monsoon (−0.013 °C y−1) and

post-monsoon (−

0.016 °C y

−1

). However, these trends are based ononly two stations' last 38 year data, so caution needs to be takenfor further interpretation. Snow cover lasts for 4–5 months, from

November to March. Owing to snow cover and very low temperatureduring post-monsoon to the rst month of pre-monsoon, the highaltitude areas are characterized by a very short growing season. Oralhistory from local people evidenced a decline in snow fall and asubstantial decline in ice/snow cover area in the region. Snow-melt

and glacier fed irrigation canals are important sources of soil moisturefor agriculture and rangeland productivity.

Covering 40% of the total area, rangelandis themajor land resourcein the region. The vegetation cover exhibits a clear distinction of

belts across altitude. Areas below 4100 m asl comprise shrubberiesand herbs dominated by Caragana spp., Juniperus spp., Lonicera spp.,

Artemisia spp., Rosa spp.,and Stipa spp. between 4100 and4300 m asl amixed belt of alpine grassland with shrub and dwarf shrub occurs,above 4300 m asl there is an alpine grassland dominated by Kobresiaspp., Carex spp. andStipa spp. Because of suf cient moisture from snowmelt and mist, grass cover above 4300 m asl generally exceeds 85%,while below 4100 m asl vegetation is characterized by a windblown

Caragana Gerardiana steppe with a vegetation cover of generally less

than 60%.Ghiling was selected forthis study because it is the village with the

highest number of total livestock units including mountain goats,which is the main source of livestock income of the Upper Mustang

valley (NTNC-ACAP, 2005). Considering the importance of livestock intheir livelihood, the rangeland degradation has been of great concern.

Lulu cattle1, Jhopa2, mountain goat, horse and mule are the mainlivestock types reared in Ghiling (Fig. 1). Apart from livestock from

Ghiling, goats from Tange village graze in Dhowa pasture for threeand half months during winter (see Fig. 2a). Between Ghiling andChhusang a common pasture is located, called Ripima. Livestock of

both villages graze in this area during summer. Similarly a commonpasture exists between Ghiling and Tange, called Piri and livestockfrom both villages graze there during winter.

Local agro-pastoralists divide the rangeland into several smaller

units, called Ri (pasture), on the basis of spatial variationof vegetationgrowthpattern,plant community and use pattern, and landscape unit.Ghiling village comprises a total of 61 subpasture units (Fig. 2a). Localpeople have a clear distinction between winter, summer and inter-

mediate grazing pastures with a strictly regulated movement of live-stock according to an agro-pastoral calendar. Grazing in Ghiling, likein other THR, is based on a seasonal transhumance rotation practice,

as a mix of seasonal deferment, transhumance and rotational stockinggrazing management strategies (Paudel, 2006). Thus there are someencampment sites in summer and winter pasture areas, which actas the primary PLC (Fig. 2b). Animals are led to pastures in themorning and returned to their sheds in the evening. Not all agro-

pastoralists of Ghiling own a shed at pasture for their animals andbring their herd home in the evening. In addition, all animals arebrought to the village in the evening during 4 to 5 months in thesummer. Being a mountainous and dry region, there are few water

points where livestock activities are concentrated as well. In addition

1 Humpless dwarf cattle (Bos Taurus) found in Mustang district of Nepal (see

Kumiko et al. (2004) for detail).2

Male cross breeding of male yak and common cow.

1846 K.P. Paudel, P. Andersen / Remote Sensing of Environment 114 (2010) 1845–1855

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animal activities are also concentrated along major livestock trails(Fig. 2b). Livestock uses these major trails as primary routes of travelbetween settlement, shed and pasture. Thus, the distance to these

trails also represents decreasing livestock pressure and associatedactivities including shrub and scrub uprooting and re wood col-lection (Cingolani et al., 2008).

Fig. 1. Location of the Upper Mustang; the box outlines the study site Ghiling; the table at the bottom right shows the livestock population of Ghiling village.

Fig. 2. Pastureunits and points of livestockconcentrations(a) pasture unitboundary (b) points of livestockconcentrations (shed/ encampment site,water point, major livestocktrail

and settlement location).

1847K.P. Paudel, P. Andersen / Remote Sensing of Environment 114 (2010) 1845–1855



3. Methodology

3.1. Remote sensing data

The rangeland vegetation growth peak period in Upper Mustangoccurs during the rst half of September. However, this period oftencorresponds to high cloud covers and thus limits the selection of cloud

free images. All available Landsat-Multi Spectral Scanner (MSS),

Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+)-images in USGS archives were reviewed for the post-monsoon periodand cloud free (b10%) images were selected (Table 1). Because of

the lack of cloud free TM image for post-monsoon 2008, the yearwhen we conducted major eld work, SPOT 4 (XI), 20 m spatialresolution image, was used for this year. Though most of the ETM+data from 2003 showed a cloud cover of less than 10%, these werenot used for the trend analysis due to the ETM+SLC failure in 2003.

However, we used ETM+ images of May 2008 to analyze effective-ness of VIs compared with ground measured vegetation cover andof October 2008 to asses comparability of SPOT 4 (20 m resolution)image.

Twenty-six years (July 1981–December 2006) Normalized Differ-ence Vegetation Index (NDVI) images at 15 day intervals wereacquired from the global inventory modeling and mapping studies(GIMMS) AVHRR 8 km, bimonthly (1981–2006) data set, availableat bftp://ftp.glcf.umiacs.umd.edu/glcf/GIMMS/N (Pinzon et al., 2004;

Tucker et al., 2005).

3.2. Pre-processing

The Landsat ETM+ captured on 31 October 2001, which has goodvisibility and is almost cloud free, was rst rectied with 56 groundcontrol points of topographic map (1:50,000). All other images were

co-registered with this image with at least 38 control points as links.Using the information provided in the header le and followingthe equations and calibrating coef cients of Chander et al. (2009) forLandsat images and following Soudani et al. (2006) for SPOT image,

all images were converted to at-sensors radiance. Following Soudaniet al. (2006) we used the dark object subtraction (DOS) approach(Chavez, 1996; Schroeder et al., 2006) to minimize the noise due

to atmospheric effects on satellite radiance and calibrate at-sensorradiance to scaled surface reectance. After geometric, radiometricand atmospheric correction of high resolution images, all imageswere masked out by study area boundary for further analysis. Only

rangeland area, excluding area covered by the main village and KaliGandaki River, was selected for analysis.

The NDVI, which is most commonly used to monitor green veg-etation dynamic (Lyon et al., 1998; Shabanov and Myneni, 2001;

Tucker, 1979; Tucker and Sellers, 1986), was computed for each highresolution images. Very high correlation (0.907, n =50) was foundbetween the computed NDVI (May 2008) and visually estimated

percent green vegetation cover in the eld (Fig. 3).

3.3. Field data collection

Field work was conducted by the rst author during April– July in2008 and in April/May 2009. During the eld visits, data have been

collected by means of a detailed household survey of all 59 house-holds of Ghiling village, semi to unstructured interviews with keypersons in the village and group discussions with herders and local

agro-pastoralists. Questions included livestock population, composi-tion and trend, grazing patterns, rotation and management, uprootingof shrubs and processes of rangeland changes.

During the group discussion, to identify pasture unit boundaries

and major trails of livestock movements, participants were asked tomark relevant information in the maps, using digital display of GoogleEarth and printed topographic maps (1:50,000) as reference. The

herders were also asked to mark and divide pastures in to differentsubunit areas, according to their experience, particularly attractive, interms of perceived forage quality, for livestock. These subunits wereranked from 100 as the most attractive to 10 as the least attractive.The rst authordrew the pasture boundaries, subunits andtrails using

the drawing tools of Google Earth to record the information providedduring group discussion and the mapping exercise was instantlyveried by participants. Subsequently, these sketches were saved andexported to a GIS. Later these layers were nalized via verication

with DEM, GPS observation points and a topographic base map.Location of all water points and livestock shed were recorded with

the help of a GPS. Based on data from household survey and groupdiscussions, total livestock grazing in each pasture unit and grazing

duration for each livestock type was calculated. All livestock was

converted to sheep units (SU) based on the standard body weight

Table 1

Dates, sources and characteristics of satellite images used in this study.

Date Source Resolution (m) Path/row Cell center lat/Long (°) Sun elevation (°) Sun azimuth (°)

1976 Nov 16 Landsat MSS 60 153/040 28.6/83.5 34.0 143.71990 Nov 10 Landsat TM 30 142/040 28.9/84.1 37.0 145.0

1999 Oct 10 Landsat ETM+ 30 142/040 28.9/84.1 49.3 146.9

2000 Sep 26 Landsat ETM+ 30 142/040 28.9/84.1 52.8 140.5

2001 Oct 31 Landsat ETM+ 30 142/040 28.9/84.1 42.3 151.6

2006 Nov 22 Landat TM 30 142/040 28.9/84.0 37.3 155.9

2008 May 27 Landsat ETM+ 30 142/040 28.8/83.9 66.7 103.2

2008 Oct 18 Landsat ETM+ 30 142/040 28.9/83.9 46.1 148.5

2008 Oct 16 SPOT 4 20 – 28.9/83.7 48.0 152.4

Fig. 3. Regression relationship between vegetation cover (%) estimated on the groundand ETM+ derived NDVI. Field estimation was conducted during 19 to 28 of May 2008.


ftp://ftp.glcf.umiacs.umd.edu/glcf/GIMMS/ftp://ftp.glcf.umiacs.umd.edu/glcf/GIMMS/



(Ghimire, 1992; Joshi, 1992; Kumiko et al., 2004; Liseet al., 2006), whichdetermines:1 goat/sheep= 1 SU;1 horse/mule= 7 SU;1 Jhopa=6 SU;1 Lulu cattle= 5 SU.

3.4. Vegetation change analysis

In order to identify long term change in vegetation production in

the study area, the NDVI from high resolution images were regressed

over time (Fig. 4). We applied a linear regression analysis. Pixels witha negative slope of the regression thus indicate areas of decliningvegetation cover. Pixels exhibiting marginal decreases (i.e. b5%)

during the 32 year period (1976–2008) were excluded or consideredas stable. The T -test was computed to assess signicance of negativeslope.

3.5. Precipitation data preparation and identi cation of precipitation

signal

We collected available 13 meteorological stations (Mustang andManang district) daily precipitation data and two nearby stations'

(Thakmarpha and Jomsom) monthly temperature data of period 1970–2008 from the Department of Hydrology and Meteorology (DHM),the Government of Nepal. There is no meteorological station withinthestudy areabut twoprecipitationstations arelocatedvery closeto the

study area—Ghami in the northern side (about 1 km) and Samar Gaonin the southern side (about 2.5 km).

Following Evans and Geerken (2004) we computed the correlationbetween various amounts of precipitation with different time range

accumulation and NOAA NDVI in order to identify the relationshipbetween precipitation and vegetation production in THR of Nepal.The correlation analysis was performed using pairs of accumulatedprecipitation of each station with the pixel value of NOAA NDVI where

the station is located. Stations are well distributed, locating differ-ent NOAA NDVI pixels, from south to north altitude ranging from2384 m asl to 3705 m asl. For the correlation computation, all com-binations of precipitation accumulation length ranging from 15 days

to 14 months and lag time ranging up to 3 months (with 5 daysincrement) was done for each bimonthly NDVI value. 12 month ac-cumulation precipitation with 15 days lag time was found the bestprecipitation accumulation period for the vegetation production of

September to November. We also examined the correlation betweenseasonal total precipitation and NOAA NDVI maximum value duringSeptember to November.

The daily precipitation of each station was summed every 365 dayperiodwith 15 days lagtime from the captured date of high resolution

images. This accumulated precipitation of all available stations wasused for interpolation using the Inverse Distance Weighted (IDW)method (Hartkamp et al., 1999; Mitas and Mitasova, 1999). Eachinterpolated precipitation layers was then masked with study area

boundary. Using interpolated accumulated precipitation grids, wecalculated linear regression between NDVI (high resolution images)and accumulated precipitation for each pixel. Time series residual(NDVIres), i.e. observed NDVI—precipitation predicted NDVI, is con-sidered as non-precipitation-triggered time series vegetation produc-tion(Geerken and Ilaiwi, 2004). Computing linear regression betweenthese time series residual and time, the trend of non-precipitation

triggered vegetation was derived for each pixel. Declining trendsthrough time present in the residuals then indicates changes in veg-etation response other than precipitation effects. Pixels exhibitingmarginal decreases (i.e. b5%) in 1976–2008 were excluded or con-sidered as stable because they reect only uncertainties possibly

caused by difference of image dates within the season/month and theimage calibration processes. Signicance of the trend was assessed bythe T -test.

Fig. 4. Flowchart illustrating steps to identify driving forces of rangeland degradation.




3.6. Developing grazing pressure surface

Distance surface layers for each PLC (shed, water points, livestocktrails and settlement) were prepared calculating straight line distance

from each identied PLC in the study area. All subpasture units do nothave their own sheds and water points (Fig. 2) and during the stayin a given encampment site livestock graze in a group of pasture units.

A group of pasture units may have multiple sheds. To represent such

grazing pattern, we

rst created distance layers for a group of pas-ture units calculating distance from shed(s), from where livestockgraze in those pasture units. Finally, all subshed distance layers were

merged to create one distance layer for the whole study area. Sim-ilarly, subdistance layers of water point were also created for a groupof pasture units according to water points use pattern, and weremerged to prepare a single water point distance layer for the wholestudy area. Access to water points during grazing in a given pasture

unit is also determined by geographical barriers in a landscape likecliffs, and deep gullies. During calculation of distance from waterpoint, a barrier function was also included. For settlements andlivestock trails, we created distance layers for the whole study area.

Each pixel in distance layers represents the shortest distance from thegiven PLC. Four distance cost surface layers were calculated convert-ing each distance surface layers into cost surface using a simple lineartransfer function (Eq. (1)) as:

C idist = 1 + 99 ⁎ dist

i= Max

idist ð1Þ

where, C disti is the distance cost surface of each given PLC (i), that

is shed, water point, livestock trails and settlement, disti is the pixelvalue in distance layer of i and Maxdist

i is the maximum distance valuein distance layer of i. The value of distance cost surface for each PLC

ranges from pixel value 1 to 100, where pixel value 1 represents thecell where given PLC is located i.e. no effort is needed for livestockspatial movement and cost value 100 represents the maximum effortrequire to reach that pixel.

From the elevation contour with an interval of 20 m, we con-

structed a digital elevation model (DEM) with 30×30 m resolutionand obtained slope in degree. Slope was considered as a topographyfactor determining spatial movement of livestock. Slope values (in

degree) were converted to cost surface (0–100) as:

C slope = 100 ⁎ slope= 90ð Þ ð2Þ

where, C slope is the friction surfaces of topography based on slope,

where 90° slope corresponds to cost of 100 and relatively at surfacecorresponds to very low cost.

Based on the herders ranking (10–100) of range quality (r attract),we developed friction surface (C attract) of attractiveness as:

C attract = 100 – r attract: ð3Þ

All individual cost surfaces were combined by calculating theaverage value for eachpixel. The resultingsurface was dened as totalcost (tcost).

Considering the seasonal transhumance and rotational grazingpractices in THR, crude livestock density in a pasture unit cannotrepresent the actual annual livestock pressure. So, we computed

‘annual net grazing density’ as a function of total livestock units and

grazing days in a given pasture unit and total forage supply in thatpasture unit. Total forage production in a given pasture unit is de-termined by the percent grass cover in that site. If we assume theproportional relation between forage production and percent vegeta-

tion cover, a pasture unit having100% vegetation cover canhave equalforage production to twice as large a pasture unit having 50% vege-

tation cover. Hence, net vegetation cover area (vcov_a) can represent

the forage supply than only the area of pasture unit. Thus, annualnet grazing density (Gden) of each pasture unit was calculated as:

Gden = Σ SUgd= V cov a ð4Þ

where

SUgd x = SU x ⁎ gd x= 365 ð5Þ

vcov a = vcov ⁎ A pu= 100: ð6Þ

SUgd_ x is the annual grazing sheep unit (SU) days for livestocktype x (i.e. cattle, horse/mule, goat, Jhopa etc.); SU_ x is the product of total population of livestock type x in sheep unit in a given site and

gd_ x is the total number of days in a year they grazed in that givenpasture unit; Σ SUgd is the sum of annual grazing sheep unit of alllivestock type grazing in a given pasture unit, vcov_a is the netvegetation cover area; A_pu is the area of given pasture unit and vcov

is the average percent vegetation cover derived from time series NDVIof high resolution images used in this study. We used regressionequation (Fig. 3) derived from the relation between eld estimatedvegetation cover and NDVI to calculate vcov.

Finally the weighted grazing pressure surface model (GP) wasderived by multiplying the inverse of total cost surface with the netlivestock grazing density as:

GP = Gden ⁎ 1= tcost: ð7Þ

Identied degradation patches derived from residual trendmethodwere carefully examined with reference to grazing pressure surface.In addition, results were checked against the information from eldobservation and in-depth interview and group discussion.

4. Results

4.1. The relationship between precipitation and vegetation

In Upper Mustang we found a very strong relationship (r =0.823, pb0.001) between each 15 days NOAA NDVI between September andNovember (1981–2006) and the 12 months accumulation precipita-

tion ending 15 days before the date of each NDVI image. The cor-relation between seasonal precipitation and maximum NOAA NDVIvalues in Upper Mustang shows the strong inuence of monsoon, pre-monsoon and winter precipitation to the vegetation production of

September to November (Table 2). Fig. 5 highlights the relationshipbetweenaccumulated precipitation and maximum NOAA NDVI valuesbetween September and November in Ghami station, the observationstation nearest to the study area.

The correlation between accumulated precipitation and NDVIvalues derived from high resolution images reveals a strong positivecorrelation (r =0.6 to 0.99) for about 66% of the rangeland area in

Ghiling. However on south facing steep cliffs along Kali Gandaki andits' tributary Kolang Kolang river, an area of very low vegetation cover(b10%) and where soils have been eroded due to snowmelt waterand monsoon rainfall, a negative correlation was found ( Fig. 6).

Table 2

Correlation coef cients (r ) between NOAA NDVI maximum (September–November)

and seasonal precipitation.

Total precipitation

Monsoon Pre-monsoon Post-monsoon Winter

NOAA NDVI maximum

(September–November)

0.818⁎⁎ 0.801⁎⁎ 0.359⁎⁎ 0.741⁎⁎

⁎⁎ p

b0.01.




4.2. Long term vegetation change and rangeland degradation

As expected for a semiarid/arid environment, vegetation produc-tion uctuates strongly according to interannual precipitation

variability. A linear regression analysis of NDVI derived from thehigh resolution images from1976 to 2008 reveals that about 20.2%area of the total rangeland of Ghiling shows a declining trend of vegetation production (Fig. 7a). The pattern of declining NDVI is

patchy rather than continuous. The declining patches of vegetationwere found mainly in the southeastern pastures (winter grazing). Incontrast, the high altitude summer grazing pastures in the north-western belt, which benet from the effect of mist, showed mainly a

positive or stable trend of long term vegetation productivity. Fig. 7bshows the signicance of negative trend found in study area. As

expected, with areas showing negligible declining trends in NDVIcoincide with insignicant results.

According to the analysis some 2.4% of the study area was foundhaving worse response to the precipitation during 1976–2008(Fig. 8a). Fig. 8b highlights that the trends found in the residuals are

signicant over most of the area. A comparison of temporal trendsin NDVI with the temporal trends of NDVIres reveals that most of the

change in vegetation can be explained by precipitation variability(Figs. 7a and 8a). The correlation between residual NDVI and pre-cipitation was calculated for each pixel and no relationship foundbetween precipitation and NDVIres (Fig. 9). Some clusters of pixels

that were identied as stable or improving in NDVI trend were foundto decline in the residual trends. This indicates that although NDVIin those areas increased over time, their response to precipitationhas been declining over time. The spatial pattern of degradation is

generally sporadic however four clusters of degradation can be iden-tied: a) large cluster at north eastern part at Dhowa pasture b)patches of narrow linear clusters in the south at northern part of Kali Gandaki river at Rajmangwa pasture, c) near to Ghiling village

in north side and d) winter grazing pasture in eastern side at Ngailaand Shere pasture.

4.3. Weighted grazing pressure surface and driving factors of degradation

The weighted grazing pressure surfaceindex ranges from 0.35 verylowto 20.28 very high grazing pressure surface(Fig. 10a). As expecteda high grazing induceddegradation wasin identied degraded patchesafter removal of precipitation effect, the largest degraded cluster at

Dhowa pasture, and cluster near village and at Syangmochen highlycoincide with identied high grazing pressure clusters (Fig. 10b).However, thelinear degraded patches along theKaliGandakiRiverandsome of the sporadic degraded patches were found in low grazingpressure areas indicating other driving factors for degradation than

humanpressure or declining precipitation. When comparing degradedpatches with topography, the linear clusters of degradation patches inthesouthern belt along the Kali Gandaki river were found to be locatedon very steep slopes (slope N50°) (Fig. 11). Field observations suggest

a strong wind effect along the Kali Gandaki River and associated highwind erosion.

5. Discussion

Available soil moisture, a key determinant for actual evapotrans-piration in dry region is primarily a function of accumulated pre-cipitation over certain periods of time rather than instantaneous

precipitation amount. Farrar et al. (1994) found strong correlationbetween available accumulated precipitation and soil moisture in dry

rangelands. Different precipitation accumulation period, ranging from

Fig. 5. Relationship between maximum NOAA NDVI (September–November) and accumulated precipitation (12 months 15 days earlier to date of maximum NDVI) in Ghami.

Fig. 6. Correlation between High resolution NDVI and accumulated precipitation.




2 to15 months, have been reported in different regions (Geerken andIlaiwi, 2004; Nicholson et al., 1990; Nicholson et al., 1998; Richard

and Poccard, 1998; Wang et al., 2001; Yang et al., 1997 ). Reportedsuch variation in relation between NDVI and precipitation in differentregions highlights the importance of geo-climatic condition of the givenregion. 12 months summed precipitation with 15 days lag time as thebest precipitation accumulation period for maximum NDVI of Septem-

ber to November was found in upper Mustang. In addition to monsoonand pre-monsoon rainfall, a strong inuence of winter precipitation(r =0.741, pb0.01) for post-monsoon vegetation production highlightsthe importance of snow fall for vegetation production in the region.

Local people also have a very strong perception that snow fall is themajor determinant for rangeland vegetation.

In this study, rangeland degradation is dened as long term veg-

etation productivity decreases due to grazing and related phenomena

and localized natural processes. Dening degradation as the loss of productivity incorporates agro-pastoralists' perspectivesand concerns(Stocking and Murnaghan, 2001) together with commonly used bio-

physical indicators for rangeland degradation (Behnke and Scoones,1993).

According to some authors, shrub encroachment and consequentdecline of proportion of palatable species, have a great effect on

rangeland productivity ( Jeltsch et al., 1997; Todd and Hoffman, 1999).Local people and herders reported no signicant changes in palatableand non-palatable species composition. However, the process couldbe slow one. The study area is subject to a strictly controlled practice

of shrub and scrub uprooting for ensuring a sustained rewood col-lection. Such woody shrub uprooting in different pasture units maybe regarded as an indirect control mechanism against bush encroach-

ment. However, more detailed eld measurement and vegetation unit

Fig. 7. Overall negative trend in vegetation greenness based on high resolution NDVI (1976–2008). (a) Decreasing vegetation production. (b) Signicance of declining trends in the

high resolution NDVI. Trends are termed insignicant for pixels in which p N0.1.

Fig. 8. Decliningtrend in the residual NDVI basedon highresolution images (1976–2008) (a) Rangeland degradationafter reducing precipitation effects (b) Signicance of declining

trends in the residual NDVI. Trends are termed insignicant for pixels in which pN

0.1.




mapping (e.g. Cingolani et al., 2004) and change analysis is required

to derive conclusion.The strong impact of high ux of interannual precipitation

variability on NDVI is conrmed by the strong positive correlationand linear relation with strong determinant of coef cient between

accumulated precipitation and NDVI. In addition, similar to Wesselset al. (2007) no relation was found betweenprecipitation and residual(NDVIres) in the study sites which supports the use of residual trendmethod. Similarly, negligible (almost zero) relationship found

between predicted NDVI (from precipitation) and residuals (NDVIres)also conrms our basic assumption behind the use of a residual trendmethod to normalize the precipitation effects from time series NDVI(Archer, 2004). However, used residual trend method still has some

shortcomings like dif culties of detecting degradation that occur

within the rst or two years of time series (Wessels et al., 2007) andlack of proper accounting of the carryover effects of previous years'precipitation (Wiegand et al., 2004).

In grazing pressure model we attempted to incorporate the ma- jority of factors inuencing livestock distribution. This was achievedby combining the ‘cost surfaces’ and livestock net annual grazing

density. The livestock net annual grazing density is more robust thangenerally used ‘stocking density’ since the former also incorporatestotal grazing days in a particular pasture unit in a year and its' areaweighted by forage availability. The model has still some short

comings: rst, we assume forage being directly proportional topercent vegetation cover. Second, equal weighting has been given tothe components of cost. However, the effect of distance from PLCor forage amount may not be equal. The weighted grazing pressure

surface model may furthermore be improved by using differentweight of the components of cost, derived from multiple regres-sion analysis. The agreement and high correlation between identied

Fig. 9. The relation between residual NDVI and accumulated precipitation.

Fig.10. Weightedgrazingpressure surface (a) showingclear distinction of grazing pressureclusters (overlaid boundaryline shows thepastureunit) (b) degradedrangeland patches

and grazing pressure surface (the dark line shows the high agreement between degraded patches and high grazing pressure).

Fig. 11. Degradationpatcheslying in verysteepslope: thered boundaryshowsthe slope

greater than 50°.



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degradation patches and high grazing pressures areas indicates thatthe model is useful to identify and predict grazing induced rangelanddegradation.

The increasing trend of degradation in some patches in steeper

slope in southern belt, despite low grazing pressure, could be at-tributed to a changing pattern of soil water holding capacity andactual evapotranspiratoin due to increasing wind erosion processes

and declining snow accumulation in terms of thickness and duration.

The snow accumulation in highland pastures signi

cantly decreases,owing to irregularity, declining and shifting pattern (from Dec/Jantoward Feb/Mar) of snowfall, as commonly perceived by local people.

Due to lack of information regarding the form of precipitation, wecould not verify and quantify such shifting patterns of snowfall and itsimpact on vegetation productivity. However, a cooling trend in pre-monsoon (after winter) season and a warming trend during winterindirectly conrm the experiences of local people. Furthermore, rapid

drying out and snow melt is another feature of south facing slopes.Increasing wind effect in dry years, as perceived by local people,signicantly erodes the top soil surfaces of these steeper slopes. Inactive eroding sites, where natural processes of erosion occur, rates of

soil loss can be greater than rates of soil formation even with zerohuman inuence (Behnke andScoones, 1993). Suchdegraded patchesin very low vegetation cover in southern stiff cliff could be furtherextended since monsoon rainfall and snowmelt water exacerbate theongoing processes.

6. Conclusions

This research extends the use of NDVI residual trend method toidentify human induced land degradation to a high resolution at localscale, which was originally developed andused in coarse-resolution ata regional scale (cf. Bai et al., 2008; Wessels et al., 2007). Precipitation

variability greatly inuences the interannual variation in vegetationproduction in the high altitude cold arid region. The nding indicatesthat rangeland degradation in Upper Mustang is not only a responseto long and short term variation in rainfall as conceptualized in the

non-equilibrium theory (Ellis and Swift, 1988; Miller, 1997; Scoones,

1994) rather is the effects of precipitation, grazing and localizednatural processes. Through a combination of information from eldwork and oral history with the remote sensing and terrain data we

were able to map high grazing pressure areas where rangeland deg-radation has apparently been caused by human activities (Fig. 10b).Increasing evapotranspiration and wind erosion in the southernsteeper areas, as the inuence of strong wind and high radiation,

causes for increasing degradation trend within some patches of thevery low grazing pressure areas.

The extended grazing gradient concept and cost surface model to

“the weighted grazing pressure surface model” is useful to understand

the spatial dispersion of livestock pressure and to detect areas of highgrazing inuences. The methodology could also be used as a man-agement tool for identifying optimal grazing patterns and be very

effective tool for studying grazing induced rangeland degradation.Combining the residual trend method and weighted grazing pressuresurface model provides a tool to identify effects of precipitation onvegetation change and understand the causes of degradation other

than precipitation. Integration of information from qualitative in-depthinterview and group discussions within the analysis framework of GIS/remote sensing has proven to be extremely valuable for validation andexplanation of driving forces of degradation in different spatial units.

Acknowledgements

The research work was partiallyfunded by the Meltzer Foundation

and a grant from the Research Council of Norway during rst author'sstay in YSSP/IIASA, where some part of analysis was performed.

We are grateful for the support and advice of LUC/IIASA research staff

and for the comments and inputs of Sylvia Prieler. The anonymous

reviewer is thanked for valuable comments and suggestions thatgreatly helped to improve the manuscript.

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