local knowledge coupled with gis and … sensed data, ... mekelle university, p.o. box 231 mekelle,...

Copyright © Journal of the Drylands 2017 ISSN 1817-3322 582

JOURNAL OF THE DRYLANDS 7(1): 582-597, 2017

LOCAL KNOWLEDGE COUPLED WITH GIS AND REMOTE SENSING IN LANDSCAPE

ANALYSIS FOR RE-GREENING AND REHABILITATION OF DEGRADED LANDS IN

SOUTH EASTERN ZONE OF TIGRAY, NORTHERN ETHIOPIA.

Nuraini Tahir,a Kiros M. Hadgu,

a,b Emiru Birhane,

c Ayele Almaw Fenta,

a,c Kindeya Gebrehiwot

c

Nuraini Tahir, Kiros M. Hadgu, Emiru Birhane, Ayele Almaw Fenta, Kindeya Gebrehiwot (2017): Local Knowledge

Coupled with GIS and Remote Sensing in Landscape Analysis for Re-Greening and Rehabilitation of Degraded Lands in South Eastern

Zone of Tigray, Northern Ethiopia. Journal of the Drlylands, 7(1): 582-597

The objective of this study is to analyze and evaluate the landscape pattern for re-greening and rehabilitating the

degraded landscapes of Hidmo-myhaydi Village, south eastern zone of Tigray. For this study, a combination of remotely sensed data, field observations and information from local people were analyzed and used. Landsat

imageries (1984-2009) of the study area were classified into five land use/cover (LULC) types using supervised

image classification method with Maximum Likelihood classifier algorithm. Analysis of LULC change was done through a post classification change detection method. A rapid reduction in forest land cover was observed during the

first (1984–2000) and second (1984–2009) transition periods by 4, and 2.5% respectively. Forest land increased in third transition period (2000–2009) by 1.6 %. Similarly, settlement land cover increased by 2.2%, 11.5% and 9.3%

during the first, second and third periods respectively. Bare land declined by10.7, 14.5%, and 3.8 during the three

periods, respectively. With the help of poly-scape, it becomes imperative to identify which trees to plant and where based on the capacity of a location to support plant growth. The analysis of local knowledge and prevailing

biophysical conditions helps draw implications that would assist policy makers to decrease the vulnerability of rural

farming communities to adverse impacts of LU/LC change and enabled to develop polyscape trade-off map.

Key words: Land use/cover, GIS, Remote sensing, Local knowledge, Polyscape

aInstitute of Geo-information and Earth Observation Sciences, Mekelle University, P.O. Box 231 Mekelle, Tigray,

Ethiopia. bWorld Agroforestry Center (ICRAF), ILRI Campus, P.O. Box 5689 Addis Ababa, Ethiopia cDepartment of Land Resources Management and Environmental Protection, College of Dryland Agriculture and

Natural Resources, Mekelle University, P.O. Box 231, Mekelle, Tigray, Ethiopia.

Corresponding author’s address: Emiru Birhane, Department of Land Resources Management and Environmental

Protection, College of Dryland Agriculture and Natural Resources, Mekelle University, P.O. Box 231, Mekelle, Tigray, Ethiopia. E-mail: [email protected]

Received: September 5, 2016; Accepted: May 11, 2017

INTRODUCTION

Land use/cover (LU/LC) change is a major issue of

global environmental change. Scientific research

community called for substantive study of land use

changes during the 1972 Stockholm Conference on

the Human Environment, and again 20 years later,

at the 1992 United Nations Conference on

Environment and Developm

ent (UNCED) (Prakasam, 2010). In addition,

LU/LC change influences climate and weather

conditions from local to global scales. LU/LC

change can have such impacts by affecting the

composition of the atmosphere and the exchange of

energy between continents and the atmosphere,

which can lead to global warming. Changes in

LU/LC, specifically from forest to cultivated

land, can also affect biological diversity,

contribute to forest fragmentation, lead to soil

erosion, alter ecosystem services, disrupt socio-

cultural practices, and increase natural disasters,

such as flooding (Mengistu et al., 2013). This calls

for global attention for continuous monitoring of

the changes. Up-to-date datasets on LU/LC provide

critical inputs to evaluate complex causes and

responses in order to project future trends better,

ranging from local, regional, to global scales

(Mengistu et al., 2013). Land use/land cover and

human/natural modifications have largely resulted

in deforestation, biodiversity loss, global warming

and increase of natural disaster such as flooding.

These environmental problems are often related to

changes in LU/LC such as from forest to cultivated

land. Therefore, available data on LU/LC changes

can provide critical input to decision-making of

environmental management and planning the future

(e.g., Selcuk, 2008; Nyssen et al., 2008; Munro et

al., 2008; Teka et al., 2013).

mailto:[email protected]


In light of this, Ethiopia has experienced huge

LU/LC dynamics including changes from natural

vegatation to farmland and human settlements. The

problem of land cover dynamics is more severe in

the highlands, which account nearly 44% of the

country’s landmass. These highlands have been

cultivated for millennia (Nyssen et al., 2004). Like

other parts of the world, the use and management

of natural resources, and returning the vast

degraded landscapes to protective and/or

productive systems, have substantial importance to

attain the goal of sustainable development.

Moreover, the roles of farmers’ indigenous

knowledge in LU/LC changes analysis in the

Ethiopian highlands draw implications that would

assist policy makers to decrease the vulnerability of

rural farming communities to adverse impacts of

LU/LC change (Mengistu et al., 2013).

Remotely sensed imageries, demographic and

housing data of an area provide a means of

understanding the key driving forces of landscape

change (Lindgren 1985; Jensen 2006). Hence,

satellite data can be used as an important tool to

evaluate and monitor LU/LC changes. This helps to

identify the evaluation of changes between two (or

more) dates of imaging thereby providing

quantitative analysis of the spatial distribution of

the population of interest. Lu et al. (2004) and

Singh (1989) proposed several procedures of

LULC change detection using remotely sensed

images which could aid in monitoring and

managing of natural resources. These methods

include comparison of land cover classifications

(Howarth and Wickware 1981), image

differencing/rationing (Jenson and Toll 1982; Jha

and Unni, 1994; Sohl, 1999), vegetation index

differencing (Howarth and Boason 1983; Nelson

1983; Townshend and Justice 1995), analysis of

principal components (Ingebritsen and Lyon, 1985;

Kwarteng and Chavez 1998), analysis of change

vector (Johnson and Kasischke,1998; Allen and

Kupfer, 2000) and post-classification comparison

(Brondı´zioet al.,1994; Dimyatiet al.,1996; Miller

et al.,199; Mas 1999; Foody 2001). Environmental

degradation problems are the main indicator of the

landscape in Northern highlands of Ethiopia. In

addition, Hintalo-Wajerat district is one of the four

districts of the south- eastern zone of Tigray region

and is one of the drought prone and chronic food

deficient districts in Tigray. In order to identify re-

greening intervention option which are the

plantation of trees and area exclosures to restore the

degraded area, GIS based landscape analysis is

needed. The overall objective of the study is to

identify and analyze degraded areas for re-greening

and rehabilitation in Hidmo-myhaydi village,

HintaloWejerat district using remote sensing and

GIS based analysis to produce trade- off map with

the help of poly-scape.

MATERIALS AND METHODS

Study area

The study was conducted in Hintalo-Wajerat

district, located in the South Eastern zone of

Tigray, Northern Ethiopia (Figure 1). It is found at

the eastern edge of the Ethiopian highlands at about

50 km south of Mekelle on the main highway from

Mekelle to Addis Ababa. Geographically, the

district is located at 12022’00’ and’12

054’00’’

latitude and at 39017’46’’ and 39043’00’’

longitude. The altitude of the district ranges from

1400 to 3050 m.a.s.l. and the average annual

rainfall in the district ranges from 500 to 600 mm

per year (ENMA, 2007).


Figure 1. Location map of the study area

According to reports from SAERT (1994) and

CECE (1999), Cambisol is the dominant soil type

in the study area that are found in valley floor and

hill terraces. Such soil typeis characterized by

weakly developed soil horizons and occurring in

wide range of topographic position, slopes and

parent materials. The second dominant soil type is

Vertisol, which contains more Mont-morillonitic

clay and is found on valley floor and plain with

almost flat topography. This soil type is

characterized by fine textures, dark in color and

cracks when dry. The third is Fluvisol, which is

developed from alluvial deposits; it is located in

flat or nearly flat land on alluvial deposits along the

belts of river and enclosed basin in the Highlands.

The type of land use varies with the topography or

landform. Most of the hilltops are occupied by the

churches and villages while the flat level areas are

used for agriculture and urbanization (Eseri, 2008).

Agriculture and livestock are the backbone of the

economy in the area. The farming system of the

study area is mixed crop with livestock farming

system. Agriculture is source of subsistence for the

majority of the population. The total population of

the district was 174,532 of which 86, 285 were

male and 88,247 female in the year 2013 (CSA

2007).

METHODS

Image processing

Present and past information on LU/LC for the

study area was generated from Landsat 5 TM

image for 22 December 1984, and Land-sat 7

ETM+ images for 5 January 2000 and 27

December 2009. These dates were selected based

on availability of cloud-free satellite images and for

the dry season to minimize seasonal illumination

differences of vegetation and cover conditions.

Precise geometrical registration and atmospheric

correction for multi-temporal images were

prerequisites for change detection.


The Landsat TM and ETM+ satellite images were

rectified with local geodetic datum of Adindan,

reference ellipsoid of Clark 1880 (Modified) and

projection type of UTM zone 37 North. This was

done using ground control points taken from

topographic map (scale 1:50000) and GPS

measurement collected from field.

For many applications involving image

classification and change detection, atmospheric

correction is unnecessary depending on the

information desired and analytical methods used to

extract the information (Lu et al., 2004). While

using a maximum likelihood classifier and if the

training data and the image to be classified are on

the same relative scale (corrected or uncorrected),

atmospheric correction has little effect on

classification accuracy (Potter 1974; Fraser et al.,

1977; Kawataet al., 1990). For this study, since the

images selected were cloud-free, image

classification was done independently and there

was no atmospheric data available; rigorous

atmospheric correction was not necessary (Lu et

al., 2004). A band combination of 4 (NIR), 3 (Red),

and 2 (Green) displayed the land-sat images in

standard false color composite and was most

effective for land use and vegetation mapping

(Jensen 1996).

Image classification

Five LU/LC classes were identified based on field

observation and the new land cover map of Africa

(Mayauxet al., 2004) with some modification. The

description of these land use/cover classes is as

follows: Cultivated land (CL) - areas of land

prepared for growing rain fed or irrigated crops.

This includes areas currently under crop, fallow,

and land under seedbed preparation. Forest land

(FL) - area with high density of trees (35-60 %)

which include eucalyptus and coniferous trees.

Grass land (GL) - all areas of grassland with less

than 10% tree and/or shrub canopy cover and

greater than 0.1% total vegetation cover.

Settlement land (SL) - consist of areas of

intensive use with much of the land covered by

structure such as housing, roads, school and health

centers. Bare land (BL) - is land of limited ability

to support life and in which less than one-third of

the area has vegetation or other cover. In general, it

is an area of thin soil, sand, or rock outcrops.

A pixel based supervised image classification

using the Gaussian distribution Maximum

Likelihood algorithm (Lillesand and Kiefer, 2000)

was done using the collected training samples

based on a prior unsupervised classification. The

maximum likelihood classifier is selected since

unlike other classifiers it considers the spectral

variation within each category and the overlap

covering the different classes. Accurate

classifications were imperative to ensure precise

change-detection results. Thus, accuracies of the

classified images (overall accuracy, producer’s

accuracy, user’s accuracy and Kappa coefficient)

were assessed using confusion matrix before

employing the classified images for decision-

making. A total of 300 points collected during field

survey out of which 150 points were used for

classification and the remaining 150 were used for

accuracy assessment for the 2009 image.

Land use/land cover change detection

The LU/LC change between the two time periods

(i.e., 1984 to 2000 and 2000 to 2009) were

quantified using a post classification comparison

change detection algorithm (Brondı´zio et al.,

1994; Dimyatiet al., 1996; Miller et al., 1998; Mas,

1999; Foody, 2001; Alphan, 2003; Braimoh, 2006;

Tsegaye et al., 2010). The advantage of post

classification comparison is that it minimizes

impacts of atmospheric, sensor and environmental

differences between multi-temporal images; it

provides a complete matrix of change information

(Lu et al., 2004; Richard et al., 2011). The output

of the post classification comparison is best

described by a matrix diagram in which the LU/LC

classes in the respective periods are shown across

the rows and columns of the matrix. The output

classes are assigned according to the coincidence of

any two input classes in the respective periods. If

there is no change of the land use, or land cover

during the respective time period, then values

appear only in the diagonal of the matrix. In

addition to analyzing the changes in the amount of

LU/LC change types, the temporal transitions

among the types were also important as they can

express the detailed information of the conversion

between different LU/LC change types. Thus, the

transition matrix of LU/LC change types for

different periods was analyzed to identify the

processes of LU/LC change. Further, net change

and net change-to-persistence ratio (Braimoh,

2006) were computed to show the resistance and

vulnerability of a given LU/LC change type.

Data sources

Remote sensing data

LU/LC change studies involve the use of multi-

temporal data sets to discriminate areas of LU/LC

between dates (1984, 2000 and 2009) of imaging

where drought has happened. Remote sensing

imageries offered unique possibilities for spatial

and temporal characterization of LU/LC change.

The basic requirement was the availability of

different dates of imageries which permitted

continuous monitoring of change and

environmental development over time. Thus, for

this study, Topographic map (scale 1:50000)

Landsat Thematic Mapper (TM) and Enhanced

Thematic Mapper plus (ETM+) imageries for the

above indicated years were used to create the

spatiotemporal database. Field observation was also


carried out to collect 300 ground truths/training

samples and socioeconomic data. The Landsat TM

and ETM+ (path 50, row 51) imageries were

acquired from the USGS Global Land Cover

Facility (GLCF). For both the TM and ETM+

bands 2, 3, and 4 represent electromagnetic

radiances at wave lengths 0.52–0.60 μm (green),

0.63–0.69 μm (red), and 0.76–0.90 μm (near

infrared) respectively (Table1).

Table 1: Characteristics of Landsat satellites.

Date of

acquisition

Name of

Satellites

Band combination Spatial resolution

(m)

Wavelength

(μm)

22/11/1984 Land-sat 5 (TM) 4-3-2 30 0.52 to 0.9

05/12/2000 Land-sat 7 (ETM+) 4-3-2 30 0.52 to 0.9

27/11/2009 Land-sat 7 (ETM+) 4-3-2 30 0.52 to 0.9

Local knowledge

Local knowledge was acquired using the Agro-

ecological Knowledge Toolkit (AKT) knowledge-

based systems methodology and software system

(Sinclair and Walker, 1998). This methodology

involves series of iterative cycles of eliciting

knowledge from a small purposive sample of

information from farmers related to the

phenomenon of interest, through semi-structured

interview, and then representation and evaluation

of the knowledge obtained using an explicit

knowledge-based systems approach. Each new

round of interviews is informed by the previous

evaluation cycle and the process is complete when

further interviews do not result in a change to the

knowledge base. The knowledge base remains a

durable and accessible record of the knowledge

acquired and is subjected to validation in a

generalization phase where a questionnaire is used

with a large random sample of informants to

explore the occurrence of knowledge amongst

people within the community (Walker and Sinclair,

1998).

The study area was divided into different sub-

villages. Only the representative farmers

(information rich) and model farmers were

selected, so the interviewed individuals included 20

(16 local farmers, and 4 trained professionals –

agricultural extension, city municipalities, and rural

social counsel).

The gathered data was supplemented by

repeated farm visit and observation and notes done

by the researcher and 16 farmers were selected

using a systematic random sampling method from

the lists available in the respective peasant

associations and were interviewed using semi -

structured questionnaire. Different literatures were

reviewed to identify plant names after vernacular

names of plants are recorded in a checklist in the

study site. Field observation was done to see the

existing situation and cross check people's opinion

which is information rich with real field condition

to validate the data.

RESULTS

Classification accuracy

The overall accuracy for the classified satellite

image of 2009 is about 95% and the Kappa

statistics was 0.93 respectively (Table 2).

Producer’s accuracies of individual classes of the

classified maps ranged from accuracies of

individual classes of classified maps for 2009

ranged from 77% (settlement lands in 2009) to

100% (forest in 2009), and 96.49 and 96% (bare

land and forest land 2009) to 100% (grassland

2009) was attained. Based on Congalton (2001),

the accuracy assessment results showed strong

agreement between the reference data and the

classified classes.

Table 2: Accuracy Assessments of Landsat 2009.

Accuracy Assessment of the Landsat 2009 (ETM+)

LULC Cultivated Settlement Bare Forest Grass Total PA%

Cultivated 98 0 0 0 0 98 96%

Settlement 0 23 0 0 0 23 77%

Bare 0 0 55 0 0 55 96%

Forest 0 0 0 44 0 44 100%

Total 98 23 55 44 0 220 100%

For 2009 Overall accuracy=94.92%, Kappa statistic=0.93




Land use/cover change

The LU/LC change classes for 1984, 2000 and

2009 showed shifts in cover types (Table 3).

Spatial variation of each LU/LC classes was

overtime as visible throughout the study period

(Figure 3-5). Although small change took place

between 1984 and 2000, the amount of cultivated

land, settlement area, and grass land increased by

the amount of 127.7 ha (12.2%), 22.47 ha (2%),

and 5.8 ha (0.55%) respectively. The amount of

bare land and forest land decreased by 113.7 ha

(10.8%) and 42.2ha (4%) respectively (Table 4).

Land under settlement land use type has

increased from 231.57 ha in 1984 to 351.87 ha in

2009 for the study area which showed an increase

of 1.49 ha yr-1

(Table 4), cultivated land and grass

land areas have also increased by 2.2 ha yr-1

and

1.65 ha yr-1

respectively between 1984 and 2009.

On the other hand, a decrease in forest land and

bare land by 1.7 ha yr-1

and 10.2 ha yr-1

respectively

were observed between 1984 and 2009 (Table 3).

Whereas cultivated land, settlement land and grass

land areas increased by 2.25 ha yr-1

, 8 ha yr-1

and

1.65 ha yr-1

respectively during the same period. A

comparison among settlement land, cultivated land

and grass land classes increased between 1984 and

2009 which accounts 120.3 ha, 33.77 ha and 24.87

ha respectively, whereas a reduction which was

detected for forest land was 25.76 ha and bare land

153 ha (Table 4). Hence, Hidmo-mayhaydi village

experienced an increasing rate of LU/LC changes

in the span of 25 years from 1984 to 2009 (Table

4).

Table 3: Land use/ land cover in 1984, 2000 and 2009.

Land-use/cover Absolute area cover (ha) Cover change between periods (%)*

1984(ha) 2000(ha) 2009(ha) 1984-2000

(ha)

2000-2009

(ha)

1984-2009

(ha) CL 351 478.68 384.77 127.7 -93.91 33.17

SL 231.57 254.04 351.87 22.47 97.83 120.3

BL 224.62 110.88 71.6 -113.74 -39.28 -153.02

FL 118.68 76 .51 92.92 -42.17 16.41 -25.76

GL 124.13 129.89 148.9 5.76 19 24.77

Total 1050 1050 1050

* Cover change between periods was calculated as 100× (Afinal year/Ainitial year)-100, where A = area of the LU/LC

type.

Table 4: LU/LC transition matrix showing major changes in the landscape (%), 1984-2009.

To final State 2009

CL SL BL FL GL Total 1984 Loss (%)

From initial state 1984

CL 4.65 14.69 0.73 3.53 3.82 27.42 22.77

SL 12 5.2 0.94 5.2 3.2 26.54 21.34

BL 4.56 2.11 2.1 2.44 0.55 11.76 9.66

FL 7 6.3 2.72 6.8 6.51 29.33 22.53

GL 2 1.44 1.22 1.07 0.31 6.04 5.73

19.06a

Summary

Total 2009 30.21 29.74 7.71 19.04 14.39

Gain 25.56 24.54 5.61 12.24 14.08

Net changeb 2.79 3.2 -4.05 -10.29 8.35

Net persistence (Np)c 0.6 0.61 -1.9 -1.51 26

Bold diagonal elements represent proportions of each LU/LCclass that were static (persisted) between 1984 and

2009. The loss column and gain row indicate the proportion of the landscape that experienced gross loss and

gain in each class, respectively.All the figures in the table are in percent except Np, which is a ratio.a The shaded

figure is the sum of diagonals and represents the overall persistence (i.e., the landscape that did not change).b

Net change = gain−loss. cNp refers to net change to persistence ratio (i.e., net change/diagonals of each class).


Figure 2. General view of farmers about the causes and effects of tree cover decline in the landscape.

The bare land category showed decreasing

pattern of change by 113.7 ha during 1984 to 2009

and the annual rate was 7.58 ha yr-1

. The area of

bare land in 1984 was 224.62 ha that was 21.3% of

the total study area. However, this decreased to

71.6 ha i.e. a reduction by 6.8 % in 2009. At initial

stage, comparison of the 1984 to 2009, grassland

category in 1984 covered a total area of 124.13 ha

which is 11.8%. However, the grassland increased

to 148.9 ha (14%) in 2009. There was also

grassland which was changed to cultivated land and

settlement land and accounted for about 2.12% and

1.44 % respectively. The land use, or land cover

classification for 1984 from TM satellite image

(Figure 3) showed that majority of the study area

was under cultivation and settlement which

accounted for about 351 ha (33.43 %) and 231.57

ha (22.05 %) respectively, while grassland, forest

land, and bare land accounted for about 124.13ha

(11.82 %),118.68 ha (11.3%), and 224.62 ha (21.4

%) respectively. The major driving forces for these

changes were population growth, over

intensification of land use, increase in cultivated

land size and policies on land uses.


Figure 3: Land use/cover map of 1984.

Figure 4: Land use/cover map of 2000

The land use, or land cover classification for 2000

from ETM+ satellite image (Figure 4) showed that

cultivated land and settlement areas accounted for

478.68 ha (46%) and 254.04 ha (24.1%)

respectively, while bare land, forest land and

grassland areas accounted for about 110.88 ha

(10.5%), 76.51 ha (7.2%), and 129.9ha (1.2%)

respectively. The land use land, or cover

classification for 2009 from ETM+ satellite image

(Figure 5) showed that cultivated land and

settlement land accounted for 384.77 ha (36.64%)

and 351.9 ha (33.5%), respectively. Though there

was a drought period in Ethiopia in 1984, it was

taken as a reference, where a rapid reduction in

forest land from 11.3%in to 8.84 % in 2009 and

bare land from 21.3% to 6.8% cover took place

between 1984 and 2009 in the landscape (Table 3).

The average rate of reduction for these LU/LC

classes were 1.7 ha yr-1

, 10.2 ha yr-1

respectively

during 25 years period. The proportion of


cultivated land, settlement land and grasslands

increased by 3.2%, 11.5% and 2.2% respectively

over the same period. On the other hand, the

average rate of change for these land class were 2.3

ha yr-1

for cultivated land, 8 ha yr-1

for settlement

area and 1.65 ha yr-1

for grass land (Table 4).

Figure 5. Land use /Land cover map of 2009.

Local knowledge

Drivers of land use/land cover change

Farmers responded that the main drivers for LU/LC

were settlement policy, infrastructural

development, town establishment and immigration

of people from other areas. Therefore, the degraded

land of the study area was the result of natural and

human pressure in the landscape. These include

afforestation, land tenure change from communal

to private, growing grass in their settlement for

their livestock, expansion of new agricultural land

into marginal areas, establishment of new

settlement for protection of their farm (Table 5).

Increased demand for firewood, increased growing

human population, agriculture and free grazing

have also significantly impacted on the mode and

rate of transformation of forest areas (Table 5).

Farmer’s choice of tree in their landscape

The landscapes in the study area were classified

into three main groups depending on the degree and

risk of degradation involved (Table 6). In the area,

there were highly degraded and already abandoned

land, farm area, which has been still under

agricultural production but under high risk of

erosion and areas which would soon be under

shifting cultivation. Yet, there were some woodland

during the study time.

Different tree species were suggested for each

landscape situation based on their growth rate,

resistance and ultimate goal for reclamation (Table

7). Farmers prefer to plant tree species such as

Faihderbia albida, Opuntia ficus-indica, and

Eucalyptus species on a highly degraded areas and

tree species such as Faihderbia albida that do not

compete with cropping in their farmland, and

Opuntia ficus-indica on areas which are not used

for farming purpose (Table 7).

Polyscape trade-off map

Based on the result of the polyscape analysis

(Figure 6), out of the total area, the red color was

non-negotiable which included forest areas,

settlement land and fertile cultivate lands. The

yellow color shows exclosures which can be

possible to re-greening after negotiation with the

existing land users; these areas included least fertile

cultivated land and cultivated land with slope

greater than 5 % (Figure 6). The green color

indicates areas which are having opportunity to be

converted to exclosures through re-greening and

rehabilitation; these areas include bare land areas

(Figure 6). There are small areas where tree

planting meets all criteria (shown as green); areas

where a single ecosystem is good and other

ecosystem are neutral (shown as yellow). The areas

colored red show areas covered with trees either

not desirable or would require large incentives to

promote rehabilitation (Figure 6).


Figure 6.Polyscape trade-off map.

DISCUSSIONS

The poor vegetation cover observed in 2009 (Table

3) compared to 1984 indicated that most of the area

was deforested particularly in the hills and

boundary. The increment of settlement land due to

population growth from 231.57 ha in 1984 to

351.87 ha in 2009 showed that there was massive

deforestation during this period. The area of land

deforested during 25 years period was 25.8 ha (2.45

%); this was mainly because of the increased

demand of fuel wood which is the main source of

energy in the study area. The change detection

matrix of 1984 to 2009 LU/LC indicates that

decrease in bare land was due to conversion into

cultivated land and settlement land which was

amounted (4.56%) and (2.11%) respectively, This

is because of both human expansion of cultivated

land and settlement land, and bare land which is

found at suitable area for agricultural activity was

converted to cultivated land. Land degradation that

includes the degradation of vegetation cover, soil

and nutrient depletion is a major ecological and

socioeconomic problem in Ethiopia (Haileslassie et

al., 2005). Tigray, the Northern region of Ethiopia,

suffers from extreme land degradation. Steep

slopes have been cultivated for many centuries and

have been a subject to serious soil erosion

problems. High population growth, combined with

poor agricultural productivity has resulted in

serious land use conflicts, particularly between the

agricultural and forestry sector (Tewolde Berhan,

1989). To compensate for the low agricultural

productivity, forest clearing for arable land has

been the principal form of land use conversion in

Tigray; it has resulted in accelerated soil erosion

and deterioration of soil nutrient status (Nyssen et

al., 2004).

Change detection matrix showed that most of

changes occurred between grassland and cultivated

land that was the bare land received about (6.5%)

and cultivated land received (7%) from forest land.

The decreased in forest land coverage is

compensated by the corresponding increase in

cultivated and grassland. This shows that there was

high deforestation in the area and expansion of new

land for cultivation and grassland. Land

degradation is a major problem in the Ethiopian

highlands. It is aggravated when the anthropogenic

influences are combined with the effect of

unreliable and extreme events of rain fall (Nyssen

et al., 2005). Different program set to reverse

degradation were not successful due to

inappropriate approaches and low community

involvement in the planning, designing and

implementation of the programs (Kebede et al.,

2013).

Demand for additional land for settlement,

grazing and farming was perceived to contribute a

great deal to the decline in vegetation cover in the

landscapes. Farmers’ perception and explanation of

complex processes by which this decline could

cause harm to the environment and the livelihoods

of the local people (Figure 2).

From the analysis of local knowledge (LK)

(Table 5), it was identified that soil type affects

fertility and susceptibility to erosion. Local

Legend

Tradeoff

all layers

Nonnegotiable

Negotiable

opportunity for change


knowledge was used to inform specifications for

Polyscape. Thus, LK was used not only to

document what local people understand but also to

develop locally specific and explicit specifications.

Different species have different characteristics in

the landscape. Farmers act at farm scale, and its

impacts of their decision are shared between all

individuals in the landscape. This can relate to

standard economic theory that assumes individuals

maximize their own welfare (Boumaet al., 2008).

From the specifications generated through the

analysis of LK and prevailing biophysical

conditions, polyscape trade-off map was developed

(Figure 6). Areas in the landscape which can

change with discussions with local people

(negotiable) which is yellow color includes areas

with least fertile soil type, slope greater than 5%, so

in these type of landscape it can be possible to plant

tree species that can grow healthy in the area. The

other areas which were red color locations have

shown considerable change that includes areas with

forest cover, settlements and fertile farm lands. The

bare areas were easy for intervention (high

opportunity for change) with green color and can

be possible to plant tree species that can be suitable

with the existing soil types. In the green color area,

tree species like Opuntia ficus-indica, Sesbania

sesban, Lucenea lucocephala, Acacia etbaica,

Acacia seyal, Acacia senegal and Faihderbia

albida can be planted. According to Betru et al.,

(2005), the implementation of SWC activities on a

large scale started through food for work projects

by the help of world food program (WFP) in the

mid-1970s. In 1980, WFP’s relatively small-scale,

fragmented projects were consolidated under one

support program called "Rehabilitation of forest,

grazing and agricultural lands”. The beginning of

this project marked the beginning of large-scale

soil and water conservation and land rehabilitation

programs in the country (Kifle, 2012). Such

programs ultimately failed within a short period of

time as being entirely followed top down approach

with poor community involvement in planning,

designing and implementation in addition to the

technical problems as it failed to consider the

diversity of the farming systems and agro

ecological conditions of the country into account

(Kifle, 2012; Kebede et al., 2013). Hot spot areas

that require urgent treatment, and options of

conservation measures were made fully by the

experts with no people participation (MERET,

2013; Kebede et al., 2013). Therefore, it is

important to recognize the interactions between

different elements and consider the site conditions

in the landscape prior to implementing any

conservation options to avert environmental

degradation (Lal et al., 1989).

Decisions are made to maximize individual

benefits from each land use types. To understand

this dynamics, spatially oriented approaches are

needed. Where are landscape locations of

degradation hotspots? What are major management

decisions that lead to those outcomes? How is the

spatial distribution of landscape services? Where is

information gathered to answer these questions;

interventions that address the problem is likely to

be formulated. After visualizing landscape

locations with the help of polyscape with maximum

opportunities for change through the specifications

developed in previous sections, it becomes

imperative to identify the appropriate places for

each plant species. The capacity of a location to

support plant growth depends on the degree of

degradation and growth conditions it provides.

CONCLUSIONS

The LU/LC pattern of change of different

categories shows variation during the three periods.

LU/LC classes like cultivated land, settlement land

showed an increase and some classes like forest

land and bare land showed a decrease by (2.45%)

and (14.5%) respectively. The general trend

observed in the study area implies loss of forest

land and woodland cover and an increase in

settlement land areas. The present tendency may

lead to more land degradation if no assisted

restoration is made. Continued LU/LC change,

coupled with a drier climate, greatly affects

people’s livelihoods. This is because of both human

induced and environmental process. The major

driving forces for these changes were population

growth, over intensification of land use, increase in

cultivated land size and policies on land uses.

Therefore, addressing these issues to improve

farmers’ knowledge and perception on LU/LC

change and access them with wider choices;

adaptation options can significantly help them

tailor their management practices to warmer and

drier conditions. It would have a significant

potential to increase and sustain their productivity

even under changing LU/LC.

ACKNOWLEDGMENTS

We thank Mekelle University for funding the

research through its NORAD III project (I-

GEOS/MU-UMB/01/2012). We are very grateful to

farmers in the study area for their hospitality during

field work. We are grateful to the anonymous

referees for constructive comments on an earlier

version of this manuscript.

REFERENCES

Allen, T.R, and Kupfer, J.A. (2000). Application of

spherical statistics to change vector analysis of

Land-sat data: southern Appalachian spruce-fir

forests. Remote Sensing of Environment 74:

482–493.

Alphan, H. (2003).Land-use change and

urbanization of Adana, Turkey.Land

Degradation and Development 14:575 - 586


Betru, N., Jawad, A., and Ingrid, N. (2005).

Exploring ecological and socio-economic

issues for the improvement of area enclosure

management: A case study from Ethiopia. The

dry lands coordination group (DCG) report ,

38, 2-18.

Braimoh, A.K. (2006). Random and systematic

land-cover transitions in northern Ghana.

Agr.Ecosyst. Environ. 113: 254–263.

Brondi´Zio, E.S., Moran, E.F., Mausel, P., Wu, Y.

(1994). Land use change in the Amazon

Estuary: patterns of Caboclo settlement and

landscape management. Human Ecology 22:

249–278.

Congalton, R.G., 2001. Accuracy assessment and

validation of remotely sensed and other spatial

information. Int. J. Wildland Fire. 10(4), 321–

328.

Dimyati, M., Mizuno, K., Kobayashi, S. Kitamura,

T. (1996). An analysis of land use/cover

change using the combination of MSS Land-

sat and land use map—a case study in

Yogyakarta, Indonesia. International Journal of

Remote Sensing 17: 931–944.

Foody, G.M. (2001).Monitoring the magnitude of

land-cover change around the southern limits

of the Sahara. Photogrammetric Engineering

and Remote Sensing 67: 841–847.

Fraser, R. S., Bahethi, O. P., and Al-Abbas, A. H.

(1977). The effect of the atmosphere on the

classification of satellite observation to

identify surface features. Remote Sens.

Environ. 6:229–249.

Haileslassie, A., Priess, J., Veldkamp, E., Teketay,

D., and Lensschen, J.P. (2005). Assessment of

soil nutrient depletion and its spatial

variability on smallholders’ mixed farming

systems in Ethiopia using partial versus full

nutrient balances. Agriculture, Ecosystems and

Environment 108: 1–16

Howarth, P.J. and Wickware, G.M.

(1981).Procedures for change detection using

Landsat digital data. Int. J. Remote Sensing

Environ. 2:277-291

Howarth, J.P., Boasson, E. (1983). Landsat digital

enhancements forchange detection in urban

environment. Remote Sensing of Environ13:

149-160.

Ingebritsen, S.E., Lyon, R.J.P. (1985). Principal

component analysis of multitemporal image

pairs. International Journal of Remote Sensing

6: 687–696.Jensen, J.R. (2006). Introductory

Digital Image Processing: A Remote Sensing

Perspective 3rd Edition. Upper Saddle River,

NY, Prentice Hall, 525 p.

Jensen, J.R. (1996). Introductory Digital Image

Processing: A Remote Sensing Perspective

(2nd edn). Upper Saddle River, NJ: Prentice

Hall.

Jenson, R., Toll, D.L. (1982). Detecting residential

land use development at the urban fringe.

Photogrammetric Eng. Remote Sensing 48:

629-643.

Jha, C.S., Unni, N.V.M. (1994). Digital change

detection of forest conversion of dry tropical

forest region. International Journal of Remote

Sensing 15: 2543–2552.

Johnson, R.D., Kasischke, E.S. (1998). Change

vector analysis: a technique for the multi-

temporal monitoring of land cover and

condition. International Journal of Remote

Sensing 19: 411–426.

Kebede, W., Awdenegest, M., and Fantaw, Y.

(2013). Farmers’ perception of the effects of

soil and water conservation structures on crop

production: The case of Bokole watershed,

Southern Ethiopia. Journal of Environmental

science and Technology of Africa, 7: 990-

1000.

Kifle, W.W. (2012). Cost and benefit of catchment

management and re greening in Tigray,

Ethiopia. Addis Ababa, Ethiopia.,Kwarteng,

A.Y., Chavez, P.S.J.R. (1998). Change

detection study of Kuwait city and environs

using multitemporal Land sat Thematic

Mapper data. International Journal of Remote

Sensing 19: 1651–1661.

Lal, R., Hall, G., and Miller, F. (1989). Soil

Degradation: Basic Processes. Journal of Land

Degradation and Rehabilitation 1: 51-69.

Lillesand, T.M., Kiefer, R.W. (2000). Remote

Sensing and ImageInterpretation, 4th

edition.New York, John Wiley and Sons, Inc.,

p.724.

Lindgren, D.T. (1985). Land Use Planning and

Remote Sensing. Dordrecht, Netherlands: M.

Nijhoff Publishers.

Lu, D., Mausel, P., Brondizios, E., Moran, E.

(2004). Change detection techniques,

International Journal of Remote Sensing 25:

2365-2407.

Mas, J.F. (1999).Monitoring land-cover changes: a

comparison of change detection techniques.


139–152.

Mayaux, P., Bartholome, E., Fritz, S., Belward, A.

(2004).A new land-cover map of Africa for the

year 2000. J. Biogeogr. 31: 861–877.

MERET (2013). Managing Environmental

Resources to Enable Transitions to more

sustainable livelihoods. Scaling up an

integrated watershed management approach

through social protection programmes in

Ethiopia: The MERET and PSNP schemes.

Addis Ababa, Ethiopia.

Miller, A.B., Bryant, E.S., Birnie, R.W. (1998). An

analysis of land cover changes in the northern

forest of New England using multitemporal


Land-sat MSS data. International Journal of

Remote Sensing 19: 245–265.

Mengiste, K., Schneider, T., Teketay, D., Knoke, T.

(2013). Land Use/Land Cover Change

Analysis Using Object-Based Classification

Approach in Munessa-Shashemene Landscape

of the Ethiopian Highlands. Remote sensing 5:

2411-2435.

Nelson, R.F. (1983). Detecting forest canopy

change due to insect activity using Landsat

MSS. Photogram.Eng. Remote Sensing 49:

1303-1314.

Nyssen, J., Mitiku, H., Moeyersons, J., Poesen, J.,

and Deckers, J. (2004). Environmental policy

in Ethiopia: a rejoinder to Keeley and Scones,

Cambridge University Press, United Kingdom.

Journal of Modern African studies, 42: 137–

147.

Nyssen, J., Nigussie, H., Descheemaeker, K.,

Desta, G., and Vancampenhout, K. (2005).

Modelling the effect of soil and water

conservation practices in Tigray, Ethiopia.

Journal of Agriculture, Ecosystem and

Environment, 105: 29-40.

Potter, J.F. (1974). Haze and sun angle effects on

automatic classification of satellite data-

simulation and correction. Proc. Soc. Photo-

Opt. Instrum. Eng. 51: 73–83.

Prakasam, C. (2010). Land use and land cover

change detection through remote sensing

approach: A case study of Kodaikanal taluk,

Tamil Nadu, International Journal of

Geomatics and Geosciences 1: 104-117

Richard, J.R., Omar, A.K., Badrinath, R. (2005).

Image change detection algorithms: A

systematic survey. IEEE T. Image Process.

14: 294-307.

Selçuk, R. (2008). Analyzing Land Use/Land

Cover Changes Using Remote Sensing and

GIS in Rize, North-East Turkey. Sensors 8:

6188-6202. doi:10.3390/s8106188

Sinclair, F.L., Walker, D.H. (1998).Acquiring

Qualitative Knowledge about Complex Agro

ecosystems. Part 1: Representation as Natural

Language. Agricultural Systems 56:341-363.

Singh, A. (1989). Digital Change detection

techniques using remotely sensing data. Int J

Rem Sens.10: 989-1003.

Sohl, T. (1999). Change analysis in the United

Arab Emirates: an investigation of techniques.

Photogrammetric Engineering and Remote

Sensing 65: 475–484.

Tewolde Berhan, G. (1989). The Environmental

variables which led to the ecological crisis in

Ethiopia. Conenoses 4: 61-67.Townshend,

J.R.G., Justice, C.O. (1995). Spatial variability

of images and the monitoring of changes in the

normalized difference vegetation index.


2187–2195.

Tsegaye, D., Moe, S.R., Vedeld, P. Aynekulu, E.

(2010). LU/LC dynamics in Northern Afar

rangelands, Ethiopia. Agriculture, Ecosystems

and Environment 139: 174–180.

Walker, D., Sinclair, F. (1998).Acquiring

qualitative knowledge about complex agro

ecosystems. Part 2: formal representation.

Agricultural Systems 56: 365–386.

http://dx.doi.org/10.3390/s8106188


Table 5: Linkage between major LU/LC change transitions and drivers as perceived by the local people

Transition (from 1984 to 2009) Immediate causes Drivers

Farmland to settlement bare,

forest and grassland,

Minimize their farm size, soil loss, new regenerate tree and

,Grass and bare land replaces when crop production is

abandoned

Settlementpolicy, infrastructural development, town establishment Immigration of

people from other areas Infrastructural development such as roads and town

establishment Settlement policy,replantation.

Settlement to farm, forest, bare

and grass

Farm near side of their settlement

Exploitation of shrubs for firewood near settlements

Planting tree in their settlement area

Increased forest in their settlement and, Land tenure change from communal to

private,

Growing grass in their settlement for their live stock

Bare to farm,

settlement, forest

and grassland

Some bare areas were turned to bush land and scrubland

when left untouched

Overgrazing reduces grass cover and gives way to bush

cover (i.e. scrubland)

Conversion to agriculture in the alluvial plains reduces

bushy grassland, In some areas secondary succession leads

to more vegetation cover and in other areas disturbance

leads to bare land or cultivated fields

Expansion of new agricultural land in to marginal areas ,establish new settlement

for protection of their farm,

Natural succession to more vegetation cover in good years, increase rear of

animals

Forest to farm,

bare, settlement and grass

Overgrazing and firewood exploitation leads to less

vegetation cover

Increased demand for firewood

Absence of burning as a management tool Establishment of new

town(urbanization)

Increased livestock with the growing human population

Grass to farm, forest, bare and

settlement

Soil erosion, free grazing in farmland area and

urbanization,

Land tenure change from communal to private

use right, free grazing


Table 6: Farmers’ knowledge about soil fertility, soil type, uses of forest and characteristics of Species

Type of knowledge Shared Unique Contradictory

1. Walka soil type depth is shallowest (12), walka soil type water holding capacity is highest (12) X

2. Baekel soil type depth is deepest (10), Baekel soil type water holding capacity is highest (12) X

3. Mekayih soil type depth is medium (11), Mekayih soil type water holding capacity is medium (12)

4. An increase in size of population reduces forest cover(10), an increase in demand of settlement

minimizes land holding size (10)

5. Establishment of town an increase in demand of settlement (10) ,and causes vegetation amount is decline(9) X

6.An increase in size of population makes land per-capita is small (4) ,and causes fragmentation of land (3) X

7. A decrease in amount of forest land result amount vegetation decline (6) X

8. The encroachment of forest is high if vegetation amount is decline (8) X

9. If tenure arrangement of forest is poor then an increase in level of frees grazing (7) X.

10. Vegetation amount decline if an increase in amount of bare land (9) X

11. An increase in amount of bare land increases in amount of barrier to runoff speed (9) X

12. A decrease in amount of barrier to runoff if an increase in rate of erosion of soil (12) X

13. The intercropping quality of eucalyptus is low (12), the intercropping quality of cactus is low (12) X

14. The intercropping quality of sesbania is high (10) planting sesbaniais good in degraded area (10) X

15. The intercropping quality of Acacia is high (9), planting Acacia is good in degraded area (11) X

16. The intercropping quality of Faidherbiaalbida is high (11) X

17. Falling leaves of tree species good for soil increase (12) X

18 . Forest cover increases soil fertility capacity (10) X

19. Planting tree is good for shading (6) X

20. Fuel demand increases tree product demand (10) X

21.Tree product increases clearing tree (9) X

22. Run-off concentration increases soil erosion rate (8) X

23. Soil erosion rate increases gully formation (9) X

Note

This is another new table

X means the number of interviewed farmers either to shared ,to answer unique or contradict each other (indication)

The number indicate number of interviewed farmers


Table7: Topics selected from the farmers’ knowledge compilation stage to be asked in the generalization

Soil fertility 80% of farmers and 90% experts responded that walka soil is the most fertile and Mekayih soil type is the least fertile

Water holding capacity of soil 86% of farmers and 72% experts responded that Baekel soil type water holding capacity is highest and walka was the poorest

Drivers of forest land change to other land

uses

90% of farmers and 70% experts responded that urbanization, free grazing and new settlement

Source of energy 92% of farmers and 94% experts responded that wood is the main source of energy

Uses of tree in landscape 84% of farmers and 94% experts responded more of for source of fuel

Which tree good for intercropping 80% of farmers and 68% of expert responded that Sesbania, Fa8hderbia, Acacia senegal and Parkisonia good for intercropping

Which tree is not good for intercropping 90% of farmers and 94% of expert responded that cactus, and Eucalyptuss are not good for intercropping

Trees planted in highly degraded area 93% of farmers and 85% of expert responded that it is good to plant tree species mono, cactus, olive and eucalyptus

Trees good for soil fertility 67% of farmers and 82% of expert responded that Faihderbia, Elephant grass, and eucalyptus are good for soil fertility

Which tree species is good for shading Acacia etbaica, Eucalyptus, and Melliaazedrach and olive are good for shading

Which tree species should plant in water

scarcity area

60% of farmers and 69% of expert responded that cactus ,olive, Eucalyptus , Acacia are good for intercropping

Which tree are riparian trees 60% of farmers responded that cactus, elephant grass are riparian trees

Mechanism of soil and water conservation 70% of farmers and 79% of expert responded that making terraces and planting trees

Existing tree species in the landscape 92% of farmers and 89% of expert Sesbania, Elephant grass, olive, Faihderbia, Acacia, are existing tree species

Challenges not to plant tree in their

landscape

85% of farmers responded that scarcity of water, free grazing, lack of knowledge how to manage trees and how to get benefits

from the landscape

Trees prefer in their landscape to plant 80% of farmers responded that Faihderbiaalbida and fruit trees,

local knowledge coupled with gis and … sensed data, ... mekelle university, p.o. box 231 mekelle,...

Documents