integrated assessment of land use and cover changes in the malagarasi river catchment in tanzania

Physics and Chemistry of the Earth 35 (2010) 730–741

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Physics and Chemistry of the Earth

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Integrated assessment of land use and cover changes in the Malagarasiriver catchment in Tanzania

J.J. Kashaigili *, A.M. MajaliwaDepartment of Forest Mensuration and Management, Sokoine University of Agriculture, P.O. Box 3013, Chuo Kikuu, Morogoro, Tanzania

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

Article history:Received 4 February 2010Received in revised form 12 July 2010Accepted 24 July 2010Available online 6 August 2010

Keywords:Land use land cover changesMalagarasi river catchmentMuyovozi wetlandPerceptionsRemote sensing

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* Corresponding author. Tel.: +255 754207117; faxE-mail address: [email protected] (J.J. Kasha

Malagarasi river catchment represents one of the largest and most significant transboundary natural eco-systems in Africa. The catchment constitutes about one third of the catchment area of Lake Tanganyikaand contains ecosystems of both national and international importance (i.e. Muyovozi Wetland Ramsarsite). It has been increasingly said that increased anthropogenic activities have had negative impacts onthe Muyovozi wetland in particular and other catchment resources. Nevertheless, these beliefs are littlesupported by quantitative data. A study on the dynamics of land use and cover in the Malagarasi rivercatchment therefore investigated long-term and seasonal changes that have occurred as a result ofhuman activities in the area for the periods between 1984 and 2001. Landsat TM and ETM+ images wereused to locate and quantify the changes. Perceptions of local people on historical changes and drivers forthe changes were also collected and integrated in the assessment. The study revealed a significant changein land use and cover within a period of 18 year. Between 1984 and 2001, the woodland and wetland veg-etation covers declined by 0.09% and 2.51% per year. Areas with settlements and cultivation increased by1.05% annually while bushed grassland increased at 1.93% annually. The perceived principal drivers forthe changes were found to include fire, cultivation along rivers and lake shores, overgrazing, poor lawenforcement, insufficient knowledge on environmental issues, increasing poverty, deforestation and pop-ulation growth. The human population growth rate stands at 4.8% against a national figure of 2.9%. Themost perceived environmental problems include drying of streams and rivers, change in rainfall, loss ofsoil fertility, soil erosion and reduced crop yield. The study concludes that, there has been significantchanges in land use and cover in the catchment and these require concerted actions to reverse thechanges. The study highlights the importance of integrating remote sensing and local knowledge inunderstanding the dynamics catchment resources and generating information that could be used to over-come the catchment management problems.

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1. Introduction

Human activities in many parts of the world have greatly chan-ged the natural land cover. Large tracts of natural vegetation coverhave been converted into croplands or deserts, and natural wetlandshave been drained and filled in order to feed and shelter expandingpopulation (WCED, 1987; Monela and Solberg, 1998). The concernsabout land use and land cover change globally emerged due to real-ization that changes of the land surface influences climate and im-pact on ecosystem goods and services (Lambin et al., 2003). It ishowever important to realize the differences between the two ter-minologies. The terms land use and land cover are not synonymousand literature draws attention to their proper use in studies. Accord-ing to Di Gregorio and Jansen (2000), land cover describes the phys-ical states of the land surface including cropland, forest, wetlands,

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: +255 232604648.igili).

pastures roads and urban areas, whereas land use relates to the man-ner in which these biophysical assets are used by humans (Cihlarand Jansen, 2001). Since use depends largely on the land character-istics, there is a close relationship between land cover and land use.However, land cover observation does not automatically mean landuse, because land cover and land use, though interrelated, are notidentical. The land use choices made will vary in space and timeand so will the resulting land cover (Cihlar and Jansen, 2001). Oneof the important land use changes is that the world’s forest, grass-lands and woodlands have declined and cropped land areas have ex-panded (Skole and Tucker, 1993; Slayback, 2003).

To understand the magnitude, and pattern of change, carryingout land use and land cover change analysis using remotely senseddata is imperative. According to Lambin (1997), land use and landcover change analysis is an important tool to assess global changeat various spatial–temporal scales. Lopez et al. (2001) affirmed thatit reflects the dimension of human activities on a given environment.According to Zhou et al. (2008) land cover change often reflects the

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most significant impact on the environment due to human activitiesor natural forces and that remote sensing can be an appropriate toolfor getting wide impression on land cover change. It is now widelyaccepted that information generated from remotely sensed data isuseful for planning, and decision making. For example, Dewan andYamaguchi (2009) quantified the patterns of land use and land coverchange for the last 45 year for Dhaka Metropolitan that formed valu-able resources for urban planners and decision makers to devise sus-tainable land use and environmental planning. According toKennedy et al. (2009), for the resource manager, a particular attrac-tion of satellite remote sensing technology is the ability to provideconsistent measurements of landscape condition, allowing detec-tion of both abrupt changes and slow trends over time. Detectionand characterization of change in key resource attributes allows re-source managers to monitor landscape dynamics over large areas,including those areas where access is difficult or hazardous, andfacilitates extrapolation of expensive ground measurements or stra-tegic deployment of more expensive resources for monitoring ormanagement (Li et al., 2003; Schuck et al., 2003). In addition, long-term change detection results can provide insight into the stressorsand drivers of change, potentially allowing for management strate-gies targeted toward cause rather than simply the symptoms of thecause (Kennedy et al., 2009). According to MacLeod and Congalton(1998), change detection on land cover focuses mainly on four as-pects, namely; (a) detecting if a change has occurred, (b) identifyingthe nature of the change, (c) measuring the areal extent of thechange, and (d) assessing the spatial pattern of the change. Withthe growing use of remote sensing, some studies have embarkedon assessing and improving the methods for change detection (e.g.Fraser et al., 2009, 2005), while others are looking at the accuracy(e.g. Stehman et al., 2009; Foody, 2002; Zhang and Foody, 2009).

The assessment of spatial patterns of land cover changes over along period using images of multi-temporal coverage is now possi-ble considering the accumulation of remotely sensed images overthe past decades; as such making it possible to generate an under-standing of the drivers for the changes. Tanzania’s ecosystems havebeen affected by agriculture, pasture, logging, charcoal making andmining (Kidegesho, 2001; Ogungo and Njuguna, 2004). Like manyother developing countries, most of the population in Tanzanialives in rural areas and depends directly on the land for their live-lihoods. This rural population is causing resource degradationbrought about by the decrease in the area under natural vegetationand its conversion into other types of land use and land cover thatare human-managed systems.

This paper presents an assessment of land use and land coverchanges in the Malagarasi river catchment in Tanzania. Malagarasiriver catchment represents one of the largest and most significanttransboundary natural ecosystems in Africa. The catchment consti-tutes about one third of the catchment area of Lake Tanganyika andcontains ecosystems of both national and international importance(i.e. Muyovozi Wetland Ramsar site) and game reserves (Moyowosi/Kigozi and Ugalla Game Reserves). It has been increasingly said thatincreased anthropogenic activities have had negative impacts on theMuyovozi wetland in particular and other catchment resources.Nevertheless, these beliefs are little supported by quantitative data.Therefore this study investigated long-term and seasonal changesthat have occurred as a result of human activities in the area forthe periods between 1984 and 2002.

2. Materials and methods

2.1. Study areas

The Malagarasi river catchment lies within longitudes 3�15́S–6�10́S and latitudes 30�40́E–32�30́E (Fig. 1). The climate of the areais tropical in nature characterized by the interaction of the south-

ern monsoon winds as well as the southeast and northeast tradewinds. The area receives bimodal rains, namely November toDecember (short rains) and April to May (main rainy season).The average annual rainfall is about 800–1000 mm/year. Tempera-ture ranges from 12 �C to 20 �C in July/August and to as high as32 �C to 35 �C in August and September (main dry season). Altituderanges between 800 and 1600 m above mean sea level with anaverage of 1200 m asl.

A large part of the Malagarasi Catchment is occupied by thewetland (Malagarasi–Muyovozi – Ramsar site) and game reserves(Moyowosi/Kigozi and Ugalla Game Reserves). The major land usesare nature and game reserves conservation, recreational and tour-ist hunting including other licensed activities that are fishing andhoney gathering. Other land uses include cultivation for subsis-tence (food crops-primarily cassava) and tobacco growing (forcommercial purpose). In the surrounding areas (general lands),the principal land use are settlement, subsistence cultivation,fishing and seasonal grazing (Jones and Hill, 1994; NEMC/IUCN,1994; Lyaluu, 1996). Woodlands and grasslands are the dominantland cover within the catchment.

Kigoma District has one of the fastest growing population inTanzania. According to the 2002 census, the population of KigomaDistrict was 490,816 of which 225,002 are males. The averagehousehold size is 6.8 persons against the national average of 4.9persons. The annual average population growth for the period of1998–2002 was 4.1% against the national average of 2.9%. This highgrowth has been attributed to the influx of refugees from DRC,Burundi and Rwanda (URT, 2003).

2.2. Methods

The study applied contemporary techniques namely remotesensing and GIS to assess the land use and land cover changes inthe study area. Furthermore, interviews using a checklist, semi-structured questionnaire and focus group discussion were con-ducted to get perceptions about the changes in the catchment.

2.3. Socio-economic data collection and analysis

Socio-economic data were collected from four purposefully se-lected villages. These include Malagarasi, Kasisi, Ilalangulu andMtegowanoti. The selection of households for interviews wasbased on a simple random sampling technique. A total of 120household (i.e. 30 households from each village) were interviewed.A semi-structured questionnaire with both open-ended and close-ended questions was used to elicit information from the commu-nity. In addition, guiding questions were asked in focus group dis-cussion and key informants interviews to capture in-depthunderstanding of historical resources use pattern in the area. Vi-sual observations through transect walk were made along selectedroutes to ‘‘ground truthing” preliminary assumptions and maps.The transect walks (1 km long and 500 m apart) were used to iden-tify the various human activities and different land use/cover atspecific sample points. The waypoints were marked using a globalpositioning system (GPS) and used in map verification exercise, inwhich classes in imagery base map were correlated with actualground data. Descriptive statistics were summarized and tabula-tion was employed to report all quantitative information. Frequen-cies and percentage were calculated to facilitate the drawing up ofinferences related to socio-economic findings.

2.4. Remotely sensed data, processing and change detection

2.4.1. Image selection and acquisitionThe images used in the study are summarized in Table 1. The

target was images acquired during the dry season (July–October)

Fig. 1. A map of study area. The inset map (upper left) shows the location of Malagarasi river catchment in relation to Tanzania.

732 J.J. Kashaigili, A.M. Majaliwa / Physics and Chemistry of the Earth 35 (2010) 730–741

with minimum cloud cover. Nevertheless, the required images forthe entire catchment were not readily available. As a consequence,three images acquired during the transition period from wet seasonto dry season were used. As such, these might have implications ofthe change detection due to seasonal effect. Nevertheless, detailedtraining of the site had to be done and GIS tools such as area of inter-est (AOI) were applied using visual analysis, reference data and localknowledge to minimize the seasonality effect.

2.4.2. Image pre-processingThe methods for the images analysis combined both visual and

digital image processing. The processing involved image rectifica-tion/georeferencing and co-registration and image enhancement.

Prior to image processing, images layers/bands were importedand layer stacked to full scene. All image processing and subse-quent image analysis were carried out using ERDAS Imagine Soft-ware Version 9.2.

2.4.3. Image rectificationImage rectification was carried to correct for distortions or deg-

radation resulting from the image acquisition process. To ensureaccurate identification of temporal changes and geometric compat-ibility with other sources of information, the image were coded tothe co-ordinate and mapping system of the national topographicmaps, i.e. UTM co-ordinate zone 36 South, Spheroid Clarke 1880,Datum Arc 1960, based on a previous georeferenced Landsat image

Table 1Remotely sensed data used in the analysis of land use/cover change.

Image Path/row Acquisition date Season

Landsat TM 171/63 13th June 1984 DryLandsat ETM+ 171/63 16th May 2000 WetLandsat TM 171/64 13th June 1984 DryLandsat ETM+ 171/64 22nd May 2002 WetLandsat TM 172/63 12th July 1986 DryLandsat ETM+ 172/63 01st October 2001 DryLandsat TM 172/64 12th July 1986 DryLandsat ETM+ 172/64 01st October 2001 DryLandsat TM 170/64 31st August 1986 DryLandsat ETM+ 170/64 18th January 2000 Wet

Note: TM = thematic mapper; ETM+ = enhanced thematic mapper plus.


of 3rd August and 4th September 1994. Since the available satelliteimages had been already corrected for radiometric distortions andhad no apparent noise, the created sub-scene was only subjectedto geometric correction. The geometric correction allows to com-pensate for various distortion introduced by several factors includ-ing earth rotation effects, panoramic distortion (with field of viewof some sensor), curvature of the earth, atmospheric refraction, reliefdisplacement, variations in platform altitude, attitude and velocityand panoramic effects related to the imaging geometry (Lillesandand Kiefer, 1987; Richards, 1993). At least 60 well distributedground control points (GCPs) were used in the rectification process.The root mean square error (RMSE) varied from 0.3 to 0.5 pixels. Afirst order polynomial fit was applied and all the data were resam-pled to a spatial resolution of 30 m using nearest neighbour method.

2.4.4. Image enhancementEnhancement usually reinforces the visual interpretation of the

images (Kashaigili, 2006). In order to reinforce visual interpretabil-ity of images, a colour composite (Landsat TM bands 4, 5 and 3)was prepared and its contrast was stretched using Gaussian distri-bution function. The 3 � 3 high pass filters was applied to the col-our composite to further enhance visual interpretation of linearfeatures, e.g. rivers and vegetation features.

2.4.5. Ground truthingGround truthing was done in order to verify and modify land

covers described in the preliminary image interpretation. GPSwas used to locate sampled land cover observations while digitalcamera recorded photos on physical features about the areas. Allsampled GPS points were booked as way points on a booking paperand photograph numbered. Key informants were also involved togive some information on land cover and land use particularlyfor the past years. The exercise was done during the dry seasonto enable access to all the areas which is not possible during thewet season.

2.4.6. Image classificationSupervised classification, using Maximum Likelihood Classifier

(MLC), was utilized. Supervised classification process involvedselection of training sites on the image, which represent specificland classes to be mapped. Training sites are sites of pixels thatrepresent specific land classes to be mapped (ERDAS, 1999). Theyare pixels that represent what is recognized as a discernable pat-tern, or potential land cover class. The training sites were gener-ated by on-screen digitizing of selected areas for each land coverclass identified on colour composite. Training was an iterative pro-cess, whereby the selected training pixels were evaluated by per-forming an estimated classification (ALARM command). Theimage alarm performs a quick ‘‘pre-classification” of the imagedata and indicates where potential confusion among classes mayoccur. Basically it is a visual tool that gives an overview of where

the classes will be assigned in the image and whether additionalclasses are required. Based on the inspection of results, trainingsamples were refined until a satisfactory result was obtained.The objective was to produce thematic classes that resemble orcan be related to actual land cover types on the earth’s surface.The advantage of digital image classification is that it can provideefficient, consistent and repeatable routines for mapping largeareas (Kashaigili, 2006). Visual interpretation involved the use ofimage characteristics such as texture, pattern and colour to trans-late image into land covers. The enhanced image colour compositewas used in this operation.

2.4.7. Post-processing of classified imagesClassified images were recoded to respective classes (i.e. wet-

land vegetation, forest, water, bareland, woodland, bushland,grassland, settlement, cloud and cloud shadow). Following therecoding, images were filtered using a 3 � 3 majority-neighbour-hood filter. The classified images were filtered in order to eliminatepatches smaller than a specified value and replace them with thevalue that is most common among the neighbouring pixels. A mo-saic operation was performed to multiple classified images to pro-duce one map for the entire study area (Fig. 2). The imagemosaicking involved the joining of geo-referenced images to-gether. The input images contained the same map and projectioninformation with the same number of layers. After mosaicking,sub-setting was performed in order to breaking out a portion of alarge image file into one or more smaller files. Often, image filescontain areas much larger than a particular study area. In thesecases, it is helpful to reduce the size of the image file to includeonly the area of interest (AOI). This not only eliminates the extra-neous data in the file, but it speeds up processing due to the smal-ler amount of data to process (ERDAS, 1999).

2.5. Change detection

Change detection is a very common and powerful application ofsatellite based remote sensing. Change detection analysis entailsfindings the type, amount and location of land use changes thatare taking place (Yeh et al., 1996). Various algorithms are availablefor change detection analysis and they can be grouped into twocategories namely (a) pixel-to-pixel comparison of multi-temporalimages before image classification, and (b) post-classification com-parison (Jensen, 1996). In this study, a post-classification compar-ison method was used to asses land use and cover changes. It is themost common approach (Jensen, 1996; Mundia and Aniya, 2006)for comparing data from different sources and dates. The advan-tage of post-classification comparison is that it bypasses the diffi-culties associated with the analysis of images acquired atdifferent times of the year and/or by different sensors (Yuanet al., 2005; Coppin et al., 2004; Alphan, 2003). The method hasbeen found to be the most suitable for detecting land coverchanges (Wickware and Howarth, 1981); as this enables estima-tion of the amount, location, and nature of change. The only pitfallis that the accuracy of the change maps depends on the accuracy ofindividual classifications and subject to error propagation (Yuanet al., 2005; Zhang et al., 2002). The approach identifies changesby comparing independently classified multi-date images onpixel-by-pixel basis using a change detection matrix (Yuan andElvidge, 1998). The matrix produces a thematic layer that containsa separate class for every coincidence of classes in multi-datedataset.

2.6. Assessment of the rate of cover change

The estimation for the rate of change for the different coverswas computed based on the following formula:

Fig. 2. A mosaic map of different image scenes used in land cover change.

Fig. 3. Land use and land cover map 1984/1986.



% Cover change ¼ Areai year x � Areai year xþ1Pn

i¼1Areai year x� 100% ð1Þ

Annual rate of change ¼ Areai year x � Areai year xþ1

tyears� 100% ð2Þ

% Annual rate of change ¼ Areai year x � Areai year xþ1

Areai year x � tyears� 100%

ð3Þ

where Areaiyearx = area of cover i at the first date, Areaiyearx+1 = areaof cover i at the second date,

Pni¼1Areaiyearx = total cover area at the

first and tyears = period in years between the first and second sceneacquisition dates.

Fig. 4. Land use and land c

3. Results and discussion

3.1. Land cover maps

The land cover maps for 1984/1986 and 2000/2002 are pre-sented in Figs. 3 and 4 respectively. Generally, the maps showthe variation in cover coverage between the two periods underconsideration.

3.2. Change detection results

Table 2 presents the coverage of each land cover/use class in the1980s and 2000s including the area and percentage area changebetween the two periods for the Malagarasi river catchment while

over map 2000/2002.

Table 2Cover area, changed area and the rate of change between 1984/1986 and 2000/2002.

Cover class 1984/1986 2000/2002 Change area (ha) % Change Annual rate ofchange (ha/year)

% Annual rate ofchange (%/year)

Coverarea (ha)

% Covercoverage

Coverarea (ha)

% Covercoverage

FO 52535.7 0.7 83171.8 1.0 30636.1 +0.4 1702.0 3.2WD 4054761.7 50.1 3989114.2 49.3 �65647.4 �0.8 �3647.1 �0.1W 54548.0 0.7 83472.3 1.0 28924.4 +0.4 1606.9 3.0BS 851002.5 10.5 1144393.3 14.1 293390.9 +3.6 16299.5 1.9GR 1402296.7 17.3 1889554.2 23.4 487257.5 +6.0 27069.9 1.9WET 363505.1 4.5 199101.3 2.5 �164403.8 �2.0 �9133.5 �2.5SET + C 429120.0 5.3 510155.5 6.3 81035.6 +1.0 4502.0 1.1BR 789515.0 9.8 137526.4 1.7 �651988.6 �8.1 �36221.6 �4.6CL 14099.8 0.2 39827.0 0.5 25727.2 +0.32CLS 81917.1 1.0 16985.3 0.2 �64931.8 �0.80

Total 8093301.0 100.0 8093301.0 100.0

Note: FO = forest; WD = woodland; W = water; BS = bushland; GR = grassland; WET = wetland vegetation/marsh; SET + C = settlement/cultivation; BR = bareland/burnscars;CL = clouds; CLS = cloud shadow.


Figs. 5–7 present the change in woodland, forest and wetland veg-etation covers respectively. Considering the subset area of80,93,301 ha, the results (Table 2) indicate that in the year 1984/1986 the woodland covered 50.1% of the area followed by grass-land 17.3%, bushland 10.5%, bareland 9.8%, settlement/cultivation5.3%, wetland/marsh 4.5%, water 0.7% and forest 0.6% while theremaining are areas appeared covered by clouds and cloud sha-dow. The area under forest which occupied 52535.7 ha (0.6%) in1984/1986, increased to 83171.8 ha (1.0%) in 2000/2002, indicat-ing an increase in forested area of about 0.4%. Bushland increasedfrom 10.5% to 14.1%, while grassland increased from 17.3% to 23.4%and settlement/cultivation increased from 5.3% to 6.3% indicatingan increase of +3.6%, +6.1% and +1.0% respectively between thetwo periods under consideration. The wetland/marsh area declinedby 164403.8 ha (�2.0% of the subset area), while the woodlandarea declined by 65647.4 ha (�0.8%) between the two periods.The total area of water which occupied 54547.95 ha in 1980s, in-creased to 83472.3 ha (+0.4%) in 2000s, indicating an increase ofabout 28924.4 ha.

As revealed from Table 2, the forest cover increased at a rate of+1702.0 ha/year (+3.2%/year) over an average period of 18 year(i.e.1984/1986 and 2000/2002) assuming a linear increase. Thewoodlands and wetland vegetation/marsh decreased consistentlyat a rate of �3647.1 ha/year (�0.1%/year) and �9133.5 ha/year(�2.5%/year) respectively. It is possible that the increase in forestcover is attributable to reduced forest disturbance following thedesignation of Malagarasi–Muyovozi Ramsar site in 2000 includingregeneration (Fig. 6). Bushland and grassland cover increased at arate of 16299.5 ha/year (+1.9%/year) and 27069.9 ha/year (+1.9%/year) respectively. This rapid increase might be due to clear fellingof trees mainly woodlands for firewood, and increased settlementand agricultural activities (tobacco farms and subsistence farming).This has also been accentuated by local people during the inter-views and about 55% of the respondent reported cutting trees. Fireburning has been a serious problem in recent years. It is clear fromTable 2 that the woodland area decreased consistently over the18 year while the settlement and cultivation increased at a rateof +4502 ha/year (1.1%/year). The expansion in settlement and cul-tivated areas reflects on the land use transformation in thecatchment.

3.3. Land covers transformation/flow

Table 3 presents the land covers transition matrix between landcover classes in 1984/1986 and 2000/2002. The numbers in brack-ets indicates the cover area which remained unchanged between1984/1986 and 2000/2002, while others indicate the flow of covers

or covers that changed to another cover category. It is important tonote that all land cover categories changed but with varyingmagnitudes. For example, 272514.5 ha of woodland was trans-formed to bushland, 844317.1 ha to grassland, 147483.1 ha tosettlement and cultivation, 36154.7 ha to bareland/burnt area,while 7905.0 ha and 47427.9 ha appeared to be in water and wet-land respectively, and 10949.1 ha were covered by clouds while5263.5 ha were under cloud shadow.

Table 4 presents the detected changes in selected cover for theperiod of 1984/1986 and 2000/2002 deduced from the changedetection matrix. The arrow in each row indicates a conversion‘‘from. . .to”. As presented in Table 4; 52579.2 ha (1.3%) of total areaunder woodland in 1984/1986 changed to forest in 2000/2002,1116831.6 ha (27.5%) changed to bushland and grassland,47427.9 ha (1.2%) changed to wetland vegetation, 147483.1 ha(3.6%) to settlement and cultivation; and 36154.71 ha (0.7%) chan-ged to bareland/burnscars. There has also been significant changeof 155799.4 ha (42.9%) from wetland vegetation/swamp to bush-land and grassland, implying encroachment of wetland vegetationby other swamp vegetations species and 5073.4 ha (1.4%) to settle-ment/cultivation. The increased area under dry season farmingalong the wetlands, river and lakes banks which has also been re-vealed during interviews and confirmed during ground truthing.

Table 5 presents a summary on changed and unchanged coverareas between 1984/1986 and 2000/2002. The percentage changedindicates the percentage area of a particular cover which changedto other covers while the percentage unchanged represents thepercentage area of the original area of a particular cover which re-mained unchanged for a given period.

From Table 5, the forest cover changed to other forms by 66.3%,while bushland changed by 69.1%, grassland by 75.7%, settlementand cultivation by 56.2%. The woodland and wetland vegetationchanged by 35.1% and 79.7% respectively between the periods un-der consideration. Nevertheless, some cover areas remained un-changed between 1984/1986 and 2000/2002 (Table 5).

3.4. Variations on detected changes, interpretations and limitations

Variations on results from change detection analysis are inevi-table and these could impair the interpretability for the detectedchanges (Kashaigili, 2006). In this study some variations on the de-tected changes have been noted. By scrutinizing the change detec-tion matrix (Table 3), one identifies that some of the changes wereunrealistic (e.g. a change from forest to wetland vegetation coverand change from water to woodland and forest cover). It is highlyacknowledged that ecosystem dynamics response is not linear anddepends on many factors but most arguably the variation in rain-

Fig. 5. Land cover change for woodland between 1984/1986 and 2000/2002.


fall patter and distribution (Kashaigili et al., 2006). Trend analysisof rainfall in the Malagarasi Catchment revealed that there wasno significant increase in rainfall amount between 1984/1986and 2000/2002 (Majaliwa, 2009). It is possible that the variationsare due to plant phenological effects and spectral resolutions.The different plant phenological effects are related to which seasonan image is acquired on the ground. Studies have shown that dryseason is the most desirable period for image change analysis. Asnoted by Burns and Joyce (1981) selecting the driest period ofthe year for change analysis will enhance spectral similarity dueto excessive wetness prevailing during other periods of the year.The wet season spectral separability, which is responsible for classassignment, becomes somewhat difficult and may result in mis-classification. This is likely to be the source of variation in detectedchanges as images used for this study were obtained in both wet

and dry season (Table 1). The ground truthing fieldwork was doneat the peak of the dry season while some of the available and usedimages were acquired in the wet season. In any circumstance, suchseasonal differences could affect their use during class verification.

3.5. Perceptions on the causes for the changes, and problems resultingfrom changes in land use and land cover in Malagarasi river catchment

People’s perception on environmental change has been veryimportant in setting a clear view of what the stakeholders perceiveon utilization of natural resources (Rohr, 2002). Table 6 presentsthe local peoples’ perception on various identified causes for theland use and land cover changes, while Table 7 presents the per-ceptions on problems as a result of land use and cover changesin the Malagarasi river catchment. Majority of respondents indi-

Fig. 6. Land cover change for forest between 1984/1986 and 2000/2002.


cated fire (99%) and deforestation (96%) to be the main causes forthe land cover changes in the area. The main factors mentioned ascontributing to fire were beekeeping and hunting activities, whilefor deforestation are commercial logging, charcoals production,population growth, expansion of commercial farming and foodcrops production. Other identified causes for the changes includefarming along rivers and lake shores, overgrazing, poor lawenforcement, insufficient knowledge on environmental issues andincreasing poverty. The majority local people are poor with verylow income. The immediate source of livelihood available in thecatchment is based on direct exploitation of the catchment re-sources in forests, fisheries or most importantly land resourcesfor agricultural production. Majaliwa (2009) revealed a very lowrate of literacy in the study area, with majority not having attendedschool. According to Mahinya (2005) education promotes bettermanagement of household resources and reduces pressure on eas-ily accessible natural resources like water, grazing land, forest cov-er in the catchment, and is an ingredient for sustainable naturalresources management. It is among the factors that influence anindividual perception for decision making. Therefore, increasedresource extraction is impacting on the resources and increasingdeforestation in the area.

In many areas of the catchment, particularly along the Malagar-asi river banks and the lake shores, riparian areas were reported tobe highly degraded as a result of excessive livestock numbers.

Although there was no reliable data on carrying capacity and num-bers of livestock in the area, the majority of respondents were inagreement that the livestock numbers have increased, and over-grazing may become a serious problem in the future. A study byNkotagu and Simon (2004) indicated livestock grazing to be uncon-trolled and among the overwhelming problem creating conflict(land and water) between farmers and pastoralist; and in additionthreatening the catchment ecosystem. The increase in number oflivestock in the study area was found to be attributable to in-creased droughts in neighbouring areas such Shinyanga, Ufipaand Urambo during 2005/2006 that caused the agro-pastoraliststo migrate into the area. Studies by Yanda and Shishira (1999)and DANIDA (1999), observed that the lower catchment ofMalagarasi has been under severe overgrazing pressure over time.Increased grazing pressure along riverbanks and lake shores pre-vents natural regeneration of cover. Livestock affect both the soilstructure and the land cover of herbaceous plants. Removal of landcover exposes soil to erosion (wind and water), which when com-bined with soil disturbances by livestock, speed the erosive pro-cesses leading to reduction in infiltration and increased runoff(Lal, 2001).

Considering the increased pressure on the catchment resources,numerous problems are now evident (Table 7). Some of the envi-ronmental problems identified include drying of streams and riv-ers, loss of soil fertility, soil erosion and reduced crop yield. As

Fig. 7. Land cover change for wetland from 1984/1986 to 2000/2002.


regard to rainfall, respondents pointed out that rainy seasons in thepre-1970s were consistent and dependable, whereas today, it is nolonger easy to tell when the rainy season will start. Past studies inthe catchment has shown that rainfall regime in the area has animpact on both the size and depth of the lakes/river (Nkotaguand Simon, 2004). For water flows, it was stressed that the riverflows has been much reduced in recent years when compared tothe past 20 year. About 63% of the respondents reported reducedflow as an increasing problem and requested for strong measures

and immediate actions by the government to save the river andthe lakes (Majaliwa, 2009). Evidence from elderly respondents’memories revealed that in the past, water in the rivers and lakesused to flood and inundate some parts of the villages. In contrast,today the river do not flood, except during the wettest month ofthe year, mostly in April. Previous studies over the area by DANIDA(1999) revealed that competing and unsustainable uses of land-based resources in the upper catchment areas have affected down-stream water flow into the Malagarasi river catchment.

Table 4Detected changes in percentage for some selected cover for the period 1984/1986–2000/2002.

Change (from–to) 1984/1986–2000/2002area (ha)

% of the cover

FO ? WET 2656.9 5.1FO ? SET + C 904.1 1.7FO ? BR 588.0 1.1WD ? FO 52579.2 1.3WD ? BR + GR 1116831.6 27.5WD ? WET 47427.9 1.2WD ? SET + C 147483.1 3.6WD ? BR 36154.7 0.7WET ? BS + GR 155779.4 42.9WET ? SET + C 5073.4 1.4

Note: FO = forest; WD = woodland; BS = bushland; GR = grassland; WET = wetlandvegetation/marsh; SET + C = settlement/cultivation; BR = bareland/burnscars.

Table 5Percentage changes of individual cover between 1984/1986 and 2000/2002.

Cover type Unchanged Changed % Coverunchanged

% Coverchanged

Forest 17695.4 34840.5 33.7 66.3Woodland 2630167.7 1424594.1 64.9 35.1Water 34428.9 20119.2 63.1 36.9Bushland 263214.3 587788.3 30.9 69.1Grassland 340913.1 1061383.6 24.3 75.7Wetland vegetation/

marsh73726.0 289779.3 20.3 79.7

Settlement/cultivation 187852.2 241267.7 43.8 56.2Bare land/burnscar 29642.7 759872.3 3.8 96.2

Table 6Local peoples’ perceptions on the causes for the land use and cover.

Causes for the change Percentage response (%) (n = 120)

Agree Undecided Disagree

Deforestation 96 3 1Cultivation along river/lakes 30 31 39Overgrazing 72 23 5Fire 99 1 0Poverty 87 8 5Poor law enforcement 85 10 5Insufficient knowledge

on environmental issues83 9 8

Table 3Changes detection matrix in different land use/cover coverage (ha) between 1984/1986 and 2000/2002.

Cover in1984/1986 (ha)

Cover in 2000/2002 (ha)

FO WD W BS GR WET SET + C BR CL CLS Total

FO (17695.4) 19625.8 186.7 4788.8 5926.6 2656.9 904.1 588.0 54.8 108.8 52535.7WD 52579.2 (2630167.7) 7905.0 272514.5 844317.1 47427.9 147483.1 36154.7 10949.1 5263.5 4054761.7W 76.5 882.2 (34428.9) 1346.6 721.3 16252.2 109.3 264.9 165.0 301.2 54548.0BS 1907.2 248280.4 6267.0 (263214.3) 242252.3 23902.3 17177.7 33153.2 11388.2 3460.0 851002.5GR 3203.6 671781.3 10820.8 227721.1 (340913.1) 17678.7 101161.8 24051.6 3502.2 1462.5 1402296.7WET 5084.4 81557.5 18583.6 107697.8 48081.6 (73726.0) 5073.4 10144.0 9172.6 4384.4 363505.1SET + C 89.0 85047.7 103.2 38344.9 114769.1 1004.9 (187852.2) 1086.5 521.1 301.3 429120.0BR 1162.3 222482.7 4677.2 195174.5 272263.3 14391.9 44250.4 (29642.7) 4031.6 1438.4 789515.0CL 83.1 3428.1 26.7 5576.0 3600.8 128.3 908.2 333.9 (8.1) 6.5 14099.8CLS 1291.2 25861.1 473.5 28014.8 16709.0 1932.3 5235.4 2106.9 34.3 (258.8) 81917.1Total 83171.8 3989114.2 83472.3 1144393.3 1889554.2 199101.3 510155.5 137526.4 39827.0 16985.3 8093301.0

Note: FO = forest; WD = woodland; W = water; BS = bushland; GR = grassland; WET = wetland vegetation/marsh; SET + C = settlement/cultivation; BR = bareland/burnscars;CL = clouds; CLS = cloud shadow; numbers in brackets indicate cover areas that remained unchanged between the two periods.

Table 7Local peoples’ perceptions on problems as a result of land use and cover changes.

Type of problems Percentage response (%) (n = 120)

Veryserious

Serious Moderate Notserious

Availability of firewood 24 45 23 8Drying of streams and

rivers24 26 25 25

Changes in rainfall 61 20 15 4Loss of soil fertility 91 8 1 0Soil erosion 8 29 18 45Reduced crop yield 91 8 1 0


The population in Malagarasi Catchment has increased fromabout 24,800 in 1981 to about 57,698 respectively in 2002, withan average household 5.6 against the national average of 4.9

(URT, 2003; Nkotagu and Simon, 2004). Furthermore; the presenceof refugee camps hosting about 280,000 refugees at various loca-tions around the boundary of the site has led to intensified coverchanges surrounding the catchment (Majaliwa, 2009). The major-ity of refugees have recently repatriated and some naturalised.Although the exact number of repatriated refugees is not known,the residual effect on the environment as a result of tree cuttingfor firewood and housing is still noticeable as revealed on the field.A study by (Mung’ong’o, 1995) revealed that a household size is animportant variable in determining sustainability of natural re-sources in an ecosystem. Similarly, findings from baseline studiesconducted in Malagarasi–Muyovozi Ramsar site by IRA (2002)pointed out that there is a strong relationship between householdsize and environmental degradation in the area. This is due to thefact that large household tend to over-exploit their resources in or-der to meet their needs while so doing undermine their source oflivelihood.

4. Conclusion

This study investigated the land use and cover changes in theMalagarasi river catchment and the perceptions of people aboutthese changes. This was an integrated assessment combining vari-ous methodological approaches in understanding the land re-sources dynamics and the resulting environmental problems asrevealed by respondents. The findings have revealed that the studyarea has undergone notable changes in terms of land use and landcover for the period 1984/1986–2000/2002. The woodland areaswere found to be highly impacted, notably by the increasedanthropogenic activities likewise for the wetland vegetation. Thesettlement and cultivated land was found to have consistently in-creased between the two periods under investigation as well as thebare land area. Local knowledge revealed various factors associatedto land use and cover change that include fire, cultivation along


rivers and lake shores, overgrazing, poor law enforcement, insuffi-cient knowledge on environmental issues, increasing poverty anddeforestation. The main factors mentioned as contributing to firewere beekeeping and hunting activities, while for deforestation in-clude commercial logging, charcoals production, populationgrowth, expansion of commercial farming and food crops produc-tion. The most perceived environmental problems include dryingof streams and rivers, assumed change in rainfall, loss of soil fertil-ity, soil erosion and reduced crop yield. The study concludes that,there has been significant changes in land use and cover in thecatchment. The study highlights the importance of integrating re-mote sensing and local knowledge in understanding the catchmentresources dynamics and generating information that could be usedto overcome the catchment problems for the sustainability of thecatchment resources.

Acknowledgements

The authors highly acknowledge the financial support from Bel-gium Technical Cooperation and the all people consulted.

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