Open access peer-reviewed chapter

The Impact of Land Use and Land Cover Changes on the Nkula Dam in the Middle Shire River Catchment, Malawi

Written By

Maureen Kapute Mzuza, Weiguo Zhang, Fanuel Kapute and Xiaodao Wei

Submitted: 22 November 2018 Reviewed: 23 April 2019 Published: 27 June 2019

DOI: 10.5772/intechopen.86452

From the Edited Volume

Geospatial Analyses of Earth Observation (EO) data

Edited by Antonio Pepe and Qing Zhao

Chapter metrics overview

1,891 Chapter Downloads

View Full Metrics


Land use and land cover changes over a 26-year period for the middle Shire River catchment, Malawi, in southern Africa, were assessed using geographic information systems (GIS) and remote sensing techniques. The catchment area under study was divided into two sections, western and eastern sides of the Shire River. High rate of deforestation averaging 4.3% per annum was observed and more pronounced in the western side of the river. Rapid population growth and increase in gross domestic product (GDP) are identified as the major drivers of deforestation and forest degradation due to clearing of vast fields for agriculture, land expansion for urban settlement, and cutting down of trees for wood fuel energy. Deforestation in the middle Shire River catchment has resulted into increased soil loss through erosion causing huge accumulation of sediment at the Nkula B Hydroelectric Power Dam downstream and, consequently, causing serious problems with generation of hydroelectricity. Frequent droughts and floods in the area have drastically affected crop production forcing people into cutting down of trees for charcoal as a livelihood strategy. Combined techniques such as GIS, remote sensing, and socioeconomic factors used in this study could be applied in other places where similar challenges occur.


  • LUCC
  • GIS
  • remote sensing
  • soil
  • Malawi

1. Introduction

Land use and land cover changes have significant environmental consequences at local, regional, and global scales. These changes have intense implications at the regional and global scales for global loss of biodiversity, distresses in hydrological cycles, increase in soil erosion, and sediment loads [1]. At the local level, changes in the use of land and its cover affect watershed runoff, microclimatic resources, processes of land degradation and landscape-level biodiversity, soil erosion, and sediment loads [2]. All these have direct impacts on livelihoods of local societies.

The Shire River in Malawi, southern Africa, is among the areas where land use land cover change (LUCC) has become more prevalent in recent years resulting into severe soil erosion and causing heavy siltation downstream [3, 4, 5, 6, 7, 8, 9]. The river is an important source of livelihood to many people, using the water for agriculture, domestic purposes, and the generation of electricity [6, 8, 10]. One of the most important structures across the Shire River is the Nkula B Hydroelectric Power Station situated in the middle section of the river. The dam at Nkula Falls that supplies water into the power station has, in recent times, been threatened with massive siltation, some studies attributing this to increased human population and agricultural activities [5, 6, 8]. The conceptual setting of this study originates from a strong link that exists between land use change and soil erosion [8, 11, 12, 13, 14, 15]. Land use and management practices are important factors in determining the extent of soil erosion [8, 15]. Good vegetation cover promotes infiltration of water into the ground and soil retention, while deforestation results into increased runoff than infiltration occurring during periods of more precipitation [16, 17, 18]. Increased runoff consequently leads to stronger soil erosion usually in areas with poor vegetation cover [8, 19, 20]. Erosion of soil under continuous cultivation is the most serious form of resource degradation occurring in Malawi [3, 8, 19, 21, 22, 23]. The rate of soil loss in Malawi is currently estimated at 29 t/ha/year [24], which is higher than the previously reported 20 t/ha/year [21]. In the middle Shire River, estimated soil loss between the year 2000 and 2014 ranged from 0.1 to 21.1 t/ha/year [24, 25]. According to the Malawi Government Report (2015), the middle Shire River catchment has many bright spots (areas experiencing high soil loss but declining trends over time), for example, Neno and Ntcheu in the west and Zomba and Chiradzulu in the eastern side of the river.

The question regarding land use changes over time, and its driving forces in the middle Shire River catchment nevertheless remain unresolved [4, 6]. Such knowledge is critical to the development of policies and action plans necessary for changing current LUCC trends in the area as it has been observed in other places [26, 27, 28, 29, 30]. Furthermore, problems of LUCC are global and serious in many developing countries where increasing population has resulted into excessive pressure on natural resources [8, 30].

The study was carried out to understand the impact of land use and land cover changes on the Nkula Dam in the middle Shire River catchment, Malawi. The LUCC drivers analyzed in this study include biophysical changes (e.g., climate change) and human activities (e.g., population, poverty, land policies, and GDP growth) [3, 4, 6]. Climate and socioeconomic data were compiled to analyze the drivers of LUCC in the study area. Geographic information systems (GIS) and remote sensing techniques which are gaining increased recognition globally as rapid methods of acquiring and analyzing up-to-date information over a large geographical area were used in the study [30, 31, 32, 33].


2. Study area and methods

2.1 Description of the study area

The Shire River is the largest river in Malawi, originating from Lake Malawi which supports vast agricultural and socioeconomic activities in its catchment (Figure 1) [34]. The river is divided into three sections, namely, the upper, middle, and lower Shire [34, 35]. This study focused on the catchment of the middle section of the river which includes the Shire Plain which is bounded by mountains on both sides and the Nkula Dam downstream [34, 36]. The plain is more extensive to the west of the river than it is to the east (Figure 1). The middle section of the Shire River has eight administrative districts, supporting a population of about 5 million people (Figure 1).

Figure 1.

Map of Malawi (left) showing the middle Shire River and its catchment (right). Eight administrative districts are located in the study area.

Climate in the middle Shire River catchment area varies due to differences in altitude with annual average precipitation ranging from 750 to 2500 mm [35, 37]. Highlands receive more rain which begins in November and ends late in April [6, 37]. Annual average temperature of the area is around 23°C, with highlands in the east experiencing cooler temperatures than plains in the west [6, 35]. The rocks in the study area are mainly composed of Precambrian basement complex and igneous rocks [37]. Amphibolite and granulite facies are dominant in the western and eastern side of the Shire River, respectively, while soils in the river’s catchment are dominated by Cambisols [6, 24, 37].

2.2 Data collection procedure

The following procedures were followed in order to answer the study questions: firstly, six Landsat images for the dry seasons (to avoid cloud cover effects) of 1989, 1993, 2000, 2006, 2011, and 2015 were downloaded from the United States Geological Survey (USGS, at Level 1 T using different paths and rows (167/070, 167/071, 168/070, and 168/071). All images had a spatial resolution of 30 m which is large enough to visualize changes in land use [38] from Landsat 5, 7, and 8. Secondly, meteorological, topographical, and socioeconomic data from 1989 to 2015 were collected from the Malawi Department of Meteorological Services and Statistics [24, 36]. The third stage was the processing of the Landsat images and, finally, classification of land use which was followed by analysis of different land covers. Statistical analysis was done on data for the topography of the catchment area, temperature, rainfall, population, and GDP in order to determine drivers of LUCC.

2.2.1 Remote sensing image processing

Landsat images were processed using ENVI 5.1 Software to study information on the types of land use and their spatial patterns. To analyze these spatial patterns, the following steps were followed: firstly, relative radiometric correction was done on each band to eliminate errors arising from radiation caused by weather conditions; secondly, multiband combination of Landsat images was done in preparation for research spectral characteristics of various types of land use; thirdly, geometric correction of remote sensing images was done using Malawi DEM, Universal Transverse Mercator Projection, Arc 1960, and UTM Zone 36S, based on 1:50,000 topographic map scale so that it fits with the Landsat images [38, 39]. This helps to eliminate position errors of Landsat images which the terrain, position of the sun, and angle sensor may produce. A mosaic of required images was prepared and a single image generated. Atmospheric Landsat images were then corrected by ENVI 5.1 FLAASH module.

2.2.2 Land use classification

After processing the Landsat images, identification of different land use classes was done where some visual designs like texture, tone, and the effect zones were used [38]. The land in the study area was classified according to its use or description such as cultivated land, water, forest (indigenous and plantations were combined), etc. When identifying the training sites, the spectral signatures separability of all the eight land use classes presented in Table 1 were verified including control fields in situ that were also set for validation of each classified image [38]. Land use types were classified by supervised classification maximum likelihood method since it’s among the broadly used methods in the scientific literature in addition to it being the fastest and easy to use and giving a perfect interpretation of the outcomes [38, 39, 40, 41, 42, 43, 44]. In addition, the method is able to accommodate covarying data which is common with satellite image data [41, 45]. Representative zones for each desired class were located in the image with adequate number of pixels covering the known classes to reduce the image noise [38]. Secondly, training area number and percentage were identified in order to classify several training and test areas. These results were compared with supporting ground data so that the new training statistics could be derived. Thirdly, a statistical file known as spectral signature was created by the image processing software for each class because each and every pixel can only be assigned to one spectral class. Lastly, each pixel was allocated to the most likely class based on the maximum likelihood algorithm where each pixel is assigned to the spectral class that has the greatest probability density function for the multispectral values of the pixel. Maximum likelihood algorithm is the most commonly used algorithm in which a pixel is classified into the corresponding class [38, 43, 46]. Land cover types were then classified into the following eight main classes according to Anderson et al. [47]: (1) forest, (2) shrubland, (3) grassland, (4) cultivated land, (5) bare land, (6) water bodies, (7) wetland, and (8) artificial surfaces (Table 1).

No.Land cover classDescription
1ForestWoodland open general (15–65%) with herbaceous layer. Broadleaved deciduous trees, closed >(70–60)%. Vegetative cover is in balance with the abiotic and biotic forces of its biotope
2ShrublandClosed to open (thicket) (15–100%) scattered trees
3GrasslandHerbaceous closed vegetation (15–100%) with some trees, shrub Savannah, and permanent marsh
4Cultivated landAreas where the natural vegetation has been removed or modified and replaced by other types of vegetative cover of anthropogenic origin. All vegetation that is planted or cultivated with intent to harvest is included in this class
5Bare landBare rock and/or coarse fragments. Areas that do not have an artificial cover as a result of human activities. These areas include areas with less than 4% vegetative cover
6Water bodiesThis class refers to areas that are naturally covered by water, such as lakes, rivers, snow, or ice
7WetlandsAreas that are transitional between pure terrestrial and aquatic systems and where the water table is usually at or near the surface or the land is covered by shallow water
8Artificial surfacesAreas that have an artificial cover as a result of human activities, such as construction (cities, towns, and transportation), extraction (open mines and quarries), or waste disposal

Table 1.

Land cover classes considered and their description [71].

A total of 165 training sites (sampled portions of the scene, purposely selected, for the derivation of the training statistics) were chosen for each image to ensure that all spectral classes constituting each land use and land cover categories were adequately represented in the training statistics to classify the entire scene [48]. Classification was done using ground checkpoints, digital topographic maps, vegetation cover map, and the researchers’ knowledge of the study area [49, 50]. A total of 156 sampling points (GPS + photograph) were collected out of the 165 training sites during the dry season to avoid cloud cover effects which is more common in rainy season. Land use types at the sampling sites were evaluated according to field surveys (photographs + GPS) where photographs were taken using a camera and coordinates of the spot were taken using GPS. Accuracy of the supervised classification methods was checked by a confusion matrix of accuracy (Table 2) [38, 44, 51] to ensure that various measures, such as error-rate, accuracy, specificity, sensitivity, and precision, were checked.

Actual typeClassified type
ForestShrublandGrasslandCultivated landArtificial surfacesWetlandWater bodiesBare landActual sumAccuracy
Cultivated land0012110002391%
Artificial surfaces1112340003987%
Water bodies0100013203494%
Bare land0000000000
Classified sum101825253510321156

Table 2.

Confusion matrix of accuracy evaluation in middle Shire River catchment in 2015.

Landsat image classified type results were compared with the field survey results to evaluate their accuracy and then calculated using confusion matrix evaluation table (Table 2).

2.2.3 Statistical analysis

LUCC drivers were mainly analyzed using descriptive methods due to inavailability of spatial socioeconomic data from the government database. Pearson correlation coefficients between socioeconomic data and land use types were analyzed in SPSS for Windows version 10.


3. Results

3.1 Land use and land cover changes over the past 26 years

The overall classification accuracy ranged from 82 to 94% (Table 2). The western side of the Shire River covers an area of approximately 3353 km2, while the eastern side is 2770 km2 comprising 55 and 45% of the total area, respectively. Regions were defined by slope of less than 10o as plain/flat area. According to Table 3, total plain/flat area covers 2417 km2 which is lesser compared to highlands (with slope ranging from 10o to 90o) covering 3706 km2. Eastern and western plain/flat areas cover 988 and 1429 km2, representing 41 and 59% of the total plain/flat area of the study area, respectively (Table 3).

Area/coveragePlain (≤10°)Highlands (10–90°)
Area (km2)Percentage (%)Area (km2)Percentage (%)
Western side142959107529
Eastern side98841263171
Total catchment area24171003706100

Table 3.

Distribution of plains and highlands in eastern and western side of the middle Shire River.

The middle Shire River catchment is dominated by shrubland, grassland, cultivated land, and forestland, which accounted for 36, 28, 22, and 12% in 1989, respectively (Figure 2).

Figure 2.

Land use and land cover changes from 1989 to 2015.

Findings (Table 4) show significant land use and land cover changes in the middle Shire River catchment over the 26-year period.

Land cover typeYear
Area (km2)%Area (km2)%Area (km2)%Area (km2)%Area (km2)%Area (km2)%
Cultivated land136722.33153825.12174528.50181429.64206733.76207334.09
Artificial surfaces260.43280.45330.54370.60390.64430.71
Water bodies310.51300.49200.33300.49360.58220.44
Bare land40.0620.0390.1590.1590.1580.13

Table 4.

Area (km2) and percentages of different land cover types from the year 1989 to 2015.

Artificial and cultivated land increased by 65 and 52%, respectively, in the 26-year period, while forest cover, grass, and shrubland decreased by 35, 27, and 7%, respectively. Other land classes such as wetlands and water bodies show fluctuations (Figure 2 and Table 4). Spatially, in 1989, total cultivated land in the western side was 694 km2 which increased to 1226 km2 by the year 2015, representing 21 and 37% of the total land in the western side, respectively (Table 5).

Western sideBalaka335556627655688853
Total area6949469359999821226
Eastern sideBlantyre359264362381244278
Total area673591810816520862

Table 5.

Changes in cultivated land area (km2) in districts of the middle Shire River catchment.

This suggests an increase of 16% of cultivated land in the western side between 1989 and 2015. In the eastern side, cultivated land increased from 673 to 862 km2 within the same period, representing 24 and 31%, respectively, of the total land area indicating a 7% change. In 1989, the western side of the Shire River catchment mainly consisted of shrubland, grassland, and forestland which accounted for 35, 33, and 10%, respectively. In the eastern side, shrubland, grassland, and forestland accounted for 37, 22, and 15%, respectively. The western side (Balaka, Neno, and Ntcheu) and eastern side (Zomba) are the main districts where forest, shrubland, and grassland decreased the most. For example, in Balaka District, forest area reduced from 11% in 1989 to 2% in 2011 before increasing to 3% in 2015, while shrubland decreased from 38% in 1989 to 18% in 2011 and then increased to 23% in 2015. Forestland in Neno District decreased from 10% in 1989 to 1% in 2011 and then increased up to 5% in 2015, while shrubland decreased from 35% in 1989 to 19% in 2015 and grassland from 27% in 1989 to 17% in 2015 with some fluctuations in between the years. In Ntcheu District, grassland decreased from 35% in 1989 to 15% in 2015. Forest cover in Zomba district declined from 19% in 1989 to 7% in 2006 and then started to increase from 2011 reaching 12% in 2015. Shrubland decreased from 41% in 1989 to 27% in 2015 in the same district.

3.2 Changes in climate, population, and GDP

Results indicate some fluctuations in the amount of rainfall received in the area within the 26-year period that might be due to climate change as a result of land use and land cover changes due to human activities (Figure 3).

Figure 3.

Annual rainfall and temperature for the middle Shire River catchment from 1989 to 2015. Circles represent flood years, while rectangles represent drought years (Source: Malawi Meteorological Department).

Rainfall in the catchment area declined continuously from 1989 to 1993, culminating into the drought of 1992 and 1993 (Figure 3) [52, 53]. Malawi is regularly affected by drought and floods [53]. The country (including the study area) was affected by heavy floods in 1989, 1998, 2000, 2001, and 2015, destroying crops and displacing many people (Figure 3) [53]. Earlier studies indicate that rainy season in Malawi is dominated by tropical and extratropical influences with links to the El Niño-Southern Oscillation (ENSO) [54, 55]. Actually, this is reported for the whole of Southern Africa [56].

The population of Malawi which includes districts under study on the western (Mangochi, Balaka, Ntcheu, and Neno) and eastern sides of the middle Shire River (Blantyre, Zomba, Machinga, and Chiradzulu) has been increasing steadily since the 1980s (Figure 4).

Figure 4.

Population of districts in the middle Shire River catchment area and GDP (US$) for Malawi from 1989 to 2015 [53].

Increased population is more pronounced in urban areas. For example, in 2015, Blantyre and Zomba cities had 3006 and 2240 people per km2, respectively [34, 53, 57]. There has been a general increase in the GDP over the past 26 years especially between 2006 and 2011 and falling between 1993 and 2003 (Figure 4).


4. Discussion

4.1 Drivers of LUCC in the middle Shire River catchment

Rainfall affects LUCC in the middle Shire River catchment. Drought and floods in the western side of the river, therefore, have resulted into low crop yield. As a survival mechanism, people resort to cutting down of trees to earn income, causing forest degradation [58, 59]. This may, therefore, explain the concurrent low rainfall received against a sharp decline in forest areas between 2006 and 2011 (Figures 2 and 3). Results in this study agree with an earlier report for the upper Shire River catchment [60] indicating a direct link between poor rainfall (drought/floods) and cutting down of trees.

Rapid population growth is one of the drivers of LUCC in the western side of the middle Shire River earlier reported by [60, 61]. Population increase in the western part of the middle Shire River is mainly attributed to the influx of refugees fleeing the civil war from Mozambique from the 1990s. Population growth leads to urbanization, increase in cultivated land, and residential area [3, 8]. The high population density in Malawi with an estimated growth rate of 2.8% is putting increasing pressure on its natural resources, leading to expansion of farming on marginal lands and forests as well as encroachment into protected forest reserves/parks. Results in this study show a transition of land use from forest, shrubland, and grassland to cultivated land and buildup areas (Tables 4 and 5). These changes mainly occurred between 1989 and 2011 (Figure 2 and Table 4) probably due to increasing anthropogenic pressure on natural forests. Results also show a drastic change in forest/grassland/shrubland between 1989 and 2011 in three out of the four districts (Balaka, Neno, and Ntcheu) in the western side of the middle River Shire. Large proportion of shrubland, grassland, and forestland (84%) in the western part of the river were converted to cultivated land, buildup areas, and/or bare land. This confirms earlier assertion that increasing population results into a decrease in forest area (Figure 5).

Figure 5.

Changes in forest, cultivated land in the catchment area, and siltation volume in the Nkula Dam from 1989 to 2015.

The rate of forest decline experienced by Malawi [61] and the Shire River catchment in particular [59], due to heavy dependency on wood for energy, is alarming. Most people around the middle Shire River catchment rely on firewood and charcoal for their daily living [58, 62, 63]. Malawi’s forest cover loss is estimated at 2.6% per annum [64]. The middle Shire River catchment lost, on average, about 4.3% of its forest and shrubland annually between 1989 and 2011 (Table 4), suggesting a negative relationship between population increase and the decline in forest coverage (Figures 4 and 5). Results, nevertheless, showed a recovery in forest cover from 2011 to 2015 (Tables 4 and 5), likely attributed to interventions by the government of Malawi and nongovernmental organizations in strengthening natural resource management policies that started around 2008 up to date [5, 65].

Macroeconomic activities such as increase in manufacturing industries and other businesses which contribute to the growth of GDP often require large areas, which also contributed to the transition of forest/shrubland/grassland into buildup areas. Some of such economic activities include opening of new farms which also require clearing of forest areas (Figures 4 and 5).

National policies in the past have failed to effectively enforce ban of unabated harvesting of forest resources until recently with the introduction of community-based natural resource management groups and intervention of some nongovernmental organizations in afforestation programs. This may explain the increase in forest cover from 2011 to 2015 as earlier indicated (Figure 2 and Table 4). Globally, large expanses of forests are being converted into bare land for domestic purposes and, principally, due to harvesting of timber [66]. In a study carried out between 1989 and 2002 in the upper section of the Shire River, [60] reported impacts of LUCC on the river’s catchment hydrological regime which includes increase in soil erosion. It is reported that agricultural land increased by 18% between 1989 and 2002 [60]. In another LUCC assessment study for Likangala River catchment (a stream from Zomba Mountain which is also a source of several rivers draining into the eastern side of the middle Shire River), woodlands decreased from 135.3 km2 in 1984 to 15.5 km2 in 2013 [67]. These results agree with the present study confirming negative impacts of LUCC. Agriculture is the main source of employment to about 92% of the population in Malawi which lives in rural areas [61, 68]. Increase in agricultural activities leads to cultivated land expansion. Cash crops (e.g., tea, coffee, tobacco, and cotton), subsistence crops (e.g., maize and groundnuts), and animal rearing contribute to the increase in agricultural GDP. Results in the present study agree with a report for the region in which land use change (increase in farming activities) contributed to increase in GDP. Similar findings have also been reported correlating land use to increase in income [67]. The increase in cultivated land and artificial surfaces resulted into a decline in forest and shrubland (Tables 4 and 5).

Furthermore, the country loses about 1.7% of its GDP on average annually due to the combined effects of droughts and floods [69]. Heavy rains received during the 1989 season in the country (Figure 3) were associated with devastating floods that drastically affected the GDP due to crop failure and loss of property as well as human life in the same period but increased in the subsequent year (Figure 4). Although the devastating rainfall in the 1989 season played a role in influencing the GDP, other factors could also be at play due to the fact that drivers of economic growth are diverse and vary in the magnitude of influence. For example, in 1989, Malawi’s economy was associated with high fuel prices due to the war in Mozambique. All fuel transportation routes from the Indian Ocean ports in Mozambique were blocked, and consequently, there was a collapse in commodity prices [68]. Poor sales of tobacco which is the country’s major foreign exchange earner also affected the GDP in 1989 [68]. Increased GDP between 2005 and 2009 has been attributed to stabilization and enhanced income growth, which increased income per capita due to the new economic policies and a stable political environment in 2004 [68].

4.2 Consequences of forest decline

These study findings show a decline in forests and then an increase over the past 26 years (Figures 2 and 5 and Table 4). Clearing of forests from the catchment of the middle Shire River has subjected the bare soil to erosion which finds its way into the Shire River downstream to the Nkula Dam as a sink. This, thus, may explain the heavy siltation at the Dam which has reduced the volume of water causing problems with normal generation of electricity (Figures 4 and 5). The volume of the Dam at Nkula Falls, which was 3 million m3 at its construction in the 1980s, has recently dropped to nearly half of its original size due to massive siltation which consequently resulted in low production of hydroelectricity, now failing to meet the country’s demand for power. Nkula B Hydroelectric Power Station is the main electricity generation plant in Malawi producing about 124 MW of electricity [70]. The electricity-providing company—the Electricity Supply Commission of Malawi (ESCOM)—is now implementing involuntary power load shedding programs resulting into national frequent blackouts. Consumers now resort to excessive use of firewood/charcoal in place of electricity for cooking and other domestic chores creating a heavy dependency on forest resources.

High soil losses in Ntcheu and Neno Districts could be due to increased population as a result of the refugees’ long time settlement in these areas resulting into removal of forests. The expansion of cultivated land could thus be the cause for increased soil erosion and sediment transport downstream, which consequently accumulate in the Nkula Dam in the middle Shire River (Figure 5). These findings agree with a recent study [6] which confirmed that most of the sediments going into the Shire River and finally depositing at the Nkula Dam originate from the western side of the Shire River. Several studies elsewhere [20, 66] also report the same, linking increased population to deforestation and soil. Loss of forests coupled with agriculture are cause for rapid land use change resulting into increased soil erosion and siltation in the middle Shire River catchment [4, 6, 8] (Figure 5). Malawi, and the middle Shire River in particular, is therefore locked up in a cycle where anthropogenic activities in the river’s catchment meant for a survival alternative to lack of electricity have become a cause for soil erosion and siltation in the river, consequently hampering the generation of the needed electricity.


5. Conclusions

Findings in this study show significant land use and land cover changes that have occurred in the middle Shire River catchment over the past 26 years which have also affected the Nkula Dam. Forestland and shrubland have declined, while cultivated land and artificial surfaces have increased in the area, and deforestation appears to be more pronounced in the western side of the middle Shire River. Severe siltation downstream in the Nkula Dam appears to be strongly linked to increased soil erosion as a result of land use and land cover change. Notable drivers for LUCC include rapid population growth and GDP, macroeconomic activities occurring especially in the western part of the river such as manufacturing industries, and poor national policies that have failed to effectively enforce ban of uncontrolled harvesting of forest resources.

To solve these problems, there is a need to review and amend weak policies that encourage noncompliance to regulations of managing forests. For example, all policies that may encourage or result in soil erosion such as river bank cultivation must be amended. Powers should be invested in local authorities to take part in protecting the environment and/or in planting trees, and the government should be able to provide seedlings for the operation. This should be done in a competition manner that the village which will perform well should be given some incentives. There is also need to increase fertilizer use so that land expansion for farming is curbed and yields are improved. In addition to that, population growth can be controlled through increase use of family planning. Encouraging children to go to school to avoid early marriages might also help to reduce poverty which will help to avoid cutting down of trees careless. Deliberate programs should be instituted by the government to curb further effects of climate variability such as droughts and floods. Such programs may include good agricultural practices that conserve soil and protect it from water erosion, discourage river bank cultivation, intensify afforestation programs, and ban the burning of charcoal. Findings in this study and the combination of methods used (application of GIS, remote sensing, and analysis of socioeconomic factors) can possibly be applied in areas where similar environmental problems have occurred. It is preferable to include a conclusion(s) section which will summarize the content of the book chapter.



We thank the State Key Laboratory of Estuarine and Coastal Research and Graduate School of East China Normal University (ECNU) for supporting this study. We also appreciate the valuable comments provided by Professor Christo C.P. Van der Westhuizen of North West University (South Africa), Professor Fang Shen of East China Normal University (China), Dr. Mavuto Tembo of Mzuzu University (Malawi), Ms. Lostina S. Chapola of Catholic University (Malawi), Mr. Tanazio Kwenda from the Department of Surveys (Malawi), Mr. Patrick Jambo from Forestry Department of Mzuzu University (Malawi), Mr. Samuel Limbu of the University of Dar es Salaam (Tanzania), Dr. Naziha Mokadem of North West University (South Africa), and the anonymous reviewers who helped us to polish this manuscript.


Conflict of interest

No potential conflict of interest was reported by the authors.


  1. 1. Lambin EF, Geist HJ. Land use and land cover change: Local processes and global impacts. Environmental Sciences. 2006;1:1-8
  2. 2. Sultan RM. The impacts of agricultural expansion and interest groups on deforestation: An optimal forest control model. International Journal of Agricultural Resources, Governance and Ecology. 2016;12(2):137-154. DOI: 10.1504/IJARGE.2016.076926
  3. 3. Palamuleni LG. Land cover change and hydrological regimes in the Shire River Catchment, Malawi [doctoral dissertation]. Johannesburg: University of Johannesburg; 2009
  4. 4. Wiyo KA, Fiwa L, Mwase W. Solving deforestation, protecting and managing key water catchments in Malawi using smart public and private partnerships. Journal of Sustainable Development. 2015;8(8):251. DOI: 10.5539/jsd.v8n8p251
  5. 5. Mzuza MK, Chapola L, Kapute F, Chikopa I, Gondwe J. Analysis of the impact of aquatic weeds in the Shire River on generation of electricity in Malawi: A case of Nkula Falls hydro-electric power station in Mwanza District, Southern Malawi. International Journal of Geosciences. 2015;6(06):1-8
  6. 6. Mzuza MK, Weiguo Z, Chapola LS, Tembo M, Kapute F. Determining sources of sediments at Nkula Dam in the Middle Shire River, Malawi, using mineral magnetic approach. Journal of African Earth Sciences. 2017;126:23-32. DOI: 10.1016/j.jafrearsci.2016.11.023
  7. 7. Dulanya Z. A review of the geomorphotectonic evolution of the south Malawi rift. Journal of African Earth Sciences. 2017;129:728-738. DOI: 10.1016/j.jafrearsci.2017.02.016
  8. 8. Bell AR, Ward PS, Mapemba L, Nyirenda Z, Msukwa W, Kenamu E. Smart subsidies for catchment conservation in Malawi. Scientific Data. 2018;5:180113
  9. 9. Musakwa W, Wang S. Landscape change and its drivers: A Southern African perspective. Current Opinion in Environmental Sustainability. 2018;33:80-86. DOI: 10.1016/j.cosust.2018.05.001
  10. 10. Kaunda CS, Mtalo F. Impacts of environmental degradation and climate change on electricity generation in Malawi. International Journal of Energy & Environment. 2013;4(3):481-496
  11. 11. Xu HQ, Wang XQ, Xiao GR. A remote sensing and GIS integrated study on urbanization with its impact on arable lands: Fuqing City, Fujian Province, China. Land Degradation & Development. 2000;11(4):301-314. DOI: 10.1002/1099-145X(200007/08)11:4<301:AID-LDR392>3.0.CO;2-N
  12. 12. Meshesha TW, Tripathi SK, Khare D. Analyses of land use and land cover change dynamics using GIS and remote sensing during 1984 and 2015 in the Beressa Watershed Northern Central Highland of Ethiopia. Modeling Earth Systems and Environment. 2016;2(4):168
  13. 13. Worku T, Khare D, Tripathi SK. Modeling runoff–sediment response to land use/land cover changes using integrated GIS and SWAT model in the Beressa watershed. Environmental Earth Sciences. 2017;76(16):550
  14. 14. Yang K, Lu C. Evaluation of land-use change effects on runoff and soil erosion of a hilly basin—The Yanhe River in the Chinese Loess Plateau. Land Degradation & Development. 2018;29(4):1211-1221. DOI: 10.1002/ldr.2873
  15. 15. Eaton D. The Economics of Soil Erosion: A Model of Farm Decision-making. Londres: International Institute for Environment and Development; 1996. pp. 1-48
  16. 16. Greenough G, McGeehin M, Bernard SM, Trtanj J, Riad J, Engelberg D. The potential impacts of climate variability and change on health impacts of extreme weather events in the United States. Environmental Health Perspectives. 2001;109(Suppl 2):191. DOI: 10.1289/ehp.109-1240666
  17. 17. Ouellet C, Saint-Laurent D, Normand F. Flood events and flood risk assessment in relation to climate and land-use changes: Saint-François River, southern Québec, Canada. Hydrological Sciences Journal. 2012;57(2):313-325. DOI: 10.1080/02626667.2011.645475
  18. 18. Alexakis DD, Grillakis MG, Koutroulis AG, Agapiou A, Themistocleous K, Tsanis IK, et al. GIS and remote sensing techniques for the assessment of land use change impact on flood hydrology: The case study of Yialias basin in Cyprus. Natural Hazards and Earth System Sciences. 2014;14(2):413-426. DOI: 10.5194/nhessd-1-4833-2013
  19. 19. Davies GM, Pollard L, Mwenda MD. Perceptions of land-degradation, forest restoration and fire management: A case study from Malawi. Land Degradation & Development. 2010;21(6):546-556. DOI: 10.1002/ldr.995
  20. 20. Mohawesh Y, Taimeh A, Ziadat F. Effects of land use changes and soil conservation intervention on soil properties as indicators for land degradation under a Mediterranean climate. Solid Earth. 2015;6(3):857-868. DOI: 10.5194/se-6-857-2015
  21. 21. Bishop J. The Cost of Soil Erosion in Malawi. Washington, DC: Draft report for World Bank; 1990
  22. 22. Barbier EB, Burgess J. Agricultural pricing and environmental degradation. In: Background Paper for World Bank. World Development Report. 1992
  23. 23. Chavula G, Brezonik P, Bauer M. Land use and land cover change (LULC) in the Lake Malawi Drainage Basin, 1982-2005. International Journal of Geosciences. 2011;2(02):172. DOI: 10.4236/ijg.2011.22018
  24. 24. Malawi Government. Land Husbandry Department. Chitedze Agriculture Research Station Official Report; Lilongwe, Malawi. 2015
  25. 25. Coulibaly JY, Mbow C, Sileshi GW, Beedy T, Kundhlande G, Musau J. Mapping vulnerability to climate change in Malawi: Spatial and social differentiation in the Shire River Basin. American Journal of Climate Change. 2015;4(03):282. DOI: 10.4236/ajcc.2015.43023
  26. 26. Schäfer MP, Dietrich O, Mbilinyi B. Streamflow and lake water level changes and their attributed causes in Eastern and Southern Africa: State of the art review. International Journal of Water Resources Development. 2016;32(6):853-880. DOI: 10.1080/07900627.2015.1091289
  27. 27. Gessesse B, Bewket W, Bräuning A. Model-based characterization and monitoring of runoff and soil erosion in response to land use/land cover changes in the Modjo watershed, Ethiopia. Land Degradation & Development. 2015;26(7):711-724. DOI: 10.1002/ldr.2276
  28. 28. Ligonja PJ, Shrestha RP. Soil erosion assessment in kondoa eroded area in Tanzania using universal soil loss equation, geographic information systems and socioeconomic approach. Land Degradation & Development. 2015;26(4):367-379. DOI: 10.1002/ldr.2215
  29. 29. Zhang F, Tiyip T, Feng ZD, Kung HT, Johnson VC, Ding JL, et al. Spatio-temporal patterns of land use/cover changes over the past 20 years in the middle reaches of the Tarim River, Xinjiang, China. Land Degradation & Development. 2015;26(3):284-299. DOI: 10.1002/ldr.2206
  30. 30. Tsendbazar N, Herold M, Lesiv M, Fritz S. Copernicus Global Land Operations—Vegetation and Energy “CGLOPS-1”. 2018
  31. 31. Kapetsky JM, Aguilar-Manjarrez J. Geographic information systems, remote sensing and mapping for the development and management of marine aquaculture. In: Food & Agriculture Org. Technical Paper No. 458. 2007. p. 125
  32. 32. Simic A, Fernandes R, Wang S. Assessing the impact of leaf area index on evapotranspiration and groundwater recharge across a shallow water region for diverse land cover and soil properties. Journal of Water Resource and Hydraulic Engineering. 2014;3:60-73
  33. 33. Kaunda CS. Energy situation, potential and application status of small-scale hydropower systems in Malawi. Renewable and Sustainable Energy Reviews. 2013;26:1-9. DOI: 10.1016/j.rser.2013.05.034
  34. 34. Gamula GE, Hui L, Peng W. Development of renewable energy technologies in Malawi. International Journal of Renewable Energy Technology Research. 2013;2(2):44-52
  35. 35. Shela ON. Naturalisation of Lake Malawi levels and Shire River flows: Challenges of water resources research and sustainable utilisation of the Lake Malawi-Shire River system. In: Water Net Symposium: Sustainable Use of Water Resources. 2000. pp. 1-12
  36. 36. Malawi Meteorological Services. Climate of Malawi; 2013; Malawi. Lilongwe: 2013
  37. 37. Morel SW. Chemical mineralogy and geothermometry of the middle Shire granulites, Malawi. Journal of African Earth Sciences (and the Middle East). 1989;9(1):169-178. DOI: 10.1016/0899-5362(89)90018-3
  38. 38. Jiménez A, Vilchez F, González O, Flores S. Analysis of the land use and cover changes in the metropolitan area of Tepic-Xalisco (1973–2015) through landsat images. Sustainability. 2018;10(6):1860. DOI: 10.3390/su10061860
  39. 39. Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution. 2005;20(9):503-510. DOI: 10.1016/j.tree.2005.05.011
  40. 40. Foody GM, Campbell NA, Trodd NM, Wood TF. Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogrammetric Engineering and Remote Sensing. 1992;58(9):1335-1341
  41. 41. Bolstad P, Lillesand TM. Rapid maximum likelihood classification. Photogrammetric Engineering and Remote Sensing. 1991;57(1):67-74
  42. 42. Gupta S, Islam S, Hasan MM. Analysis of impervious land-cover expansion using remote sensing and GIS: A case study of Sylhet sadar upazila. Applied Geography. 2018;98:156-165. DOI: 10.1016/j.apgeog.2018.07.012
  43. 43. Halefom A, Teshome A, Sisay E, Ahmad I. Dynamics of land use and land cover change using remote sensing and GIS: A case study of Debre Tabor Town, South Gondar, Ethiopia. Journal of Geographic Information System. 2018;10(02):165-174. DOI: 10.4236/jgis.2018.102008
  44. 44. Ornetsmüller C, Heinimann A, Verburg PH. Operationalizing a land systems classification for Laos. Landscape and Urban Planning. 2018;169:229-240. DOI: 10.1016/j.landurbplan.2017.09.018
  45. 45. Payne C, Panda S, Prakash A. Remote sensing of river erosion on the Colville river, North Slope Alaska. Remote Sensing. 2018;10(3):397. DOI: 10.3390/rs10030397
  46. 46. Jensen J. Residual maximum likelihood estimation of (co)variance components in multivariate mixed linear models using average information. Journal of the Indian Society of Agricultural Statistics. 1997;49:215-236
  47. 47. Anderson JR. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. US: Government Printing Office; 1976;964
  48. 48. Weng Q. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management. 2002;64(3):273-284. DOI: 10.1006/jema.2001.0509
  49. 49. Foody GM. Status of land cover classification accuracy assessment. Remote Sensing of Environment. 2002;80(1):185-201. DOI: 10.1016/S0034-4257(01)00295-4
  50. 50. Foody GM. Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sensing of Environment. 2010;114(10):2271-2285. DOI: 10.1016/j.rse.2010.05.003
  51. 51. Stehman SV, Czaplewski RL. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment. 1998;64(3):331-344. DOI: 10.1016/S0034-4257(98)00010-8
  52. 52. Pauw K, Thurlow J, Bachu M, Van Seventer DE. The economic costs of extreme weather events: A hydrometeorological CGE analysis for Malawi. Environment and Development Economics. 2011;16(2):177-198. DOI: 10.1017/S1355770X10000471
  53. 53. NSO (National statistical Office). Malawi Statistics 2012-2017. Zomba: National Statistical Office. 2017
  54. 54. Nicholson SE, Klotter D, Chavula G. A detailed rainfall climatology for Malawi, Southern Africa. International Journal of Climatology. 2014;34(2):315-325. DOI: 10.1002/joc.3687
  55. 55. Kumbuyo CP, Yasuda H, Kitamura Y, Shimizu K. Fluctuation of rainfall time series in Malawi: An analysis of selected areas. Geofizika. 2014;31(1):13-28. DOI: 15233/gfz.2014.31.1
  56. 56. Clay E, Bohn L, de Armas EB, Kabambe S, Tchale H. Malawi and Southern Africa: Climatic variability and economic performance. Disaster Management Facility. Washington DC: World Bank Working Paper Series (No. 7); 2003
  57. 57. Kambewa P. Charcoal—The reality: A study of charcoal consumption, trade, and production in Malawi. Iied; 2007
  58. 58. Hudak AT, Wessman CA. Deforestation in Mwanza District, Malawi, from 1981 to 1992, as determined from Landsat MSS imagery. Applied Geography. 2000;20(2):155-175. DOI: 10.1016/S0143-6228(00)00002-3
  59. 59. Nanthambwe S. Policy Sector Review for incorporating Sustainable Land Management in the Shire River Basin and development of an institutional framework for sustainable land management. Final Report, Government of Malawi, Environmental Affairs Department, Ministry of Environment and Climate Change Management; Lilongwe; 2013
  60. 60. Palamuleni LG, Ndomba PM, Annegarn HJ. Evaluating land cover change and its impact on hydrological regime in Upper Shire river catchment, Malawi. Regional Environmental Change. 2011;11(4):845-855
  61. 61. Tobin RJ, Knausenberger WI. Dilemmas of development: Burley tobacco, the environment and economic growth in Malawi. Journal of Southern African Studies. 1998;24(2):405-424. DOI: 10.1080/03057079808708582
  62. 62. Mlotha MJ. Remote sensing and GIS linked to socio-analysis for land cover change assessment. In: Geoscience and Remote Sensing Symposium, 2001. IGARSS'01. IEEE 2001 International. IEEE; 2001. pp. 459-461. DOI: 10.1109/IGARSS.2001.976189
  63. 63. Taulo JL, Gondwe KJ, Sebitosi AB. Energy supply in Malawi: Options and issues. Journal of Energy in Southern Africa. 2015;26(2):19-32
  64. 64. Mwanakatwe P. African Economic Outlook. In: Malawi 2015. AfDB, OECD: UNDP; 2015
  65. 65. Zulu LC. The forbidden fuel: Charcoal, urban wood fuel demand and supply dynamics, community forest management and wood fuel policy in Malawi. Energy Policy. 2010;38(7):3717-3730. DOI: 10.1016/j.enpol.2010.02.050
  66. 66. Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW, et al. The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change. 2001;11(4):261-269. DOI: 10.1016/S0959-3780(01)00007-3
  67. 67. Pullanikkatil D, Palamuleni L, Ruhiiga T. Assessment of land use change in Likangala River catchment, Malawi: A remote sensing and DPSIR approach. Applied Geography. 2016;71:9-23. DOI: 10.1016/j.apgeog.2016.04.005
  68. 68. Ngwira AR, Aune JB, Mkwinda S. On-farm evaluation of yield and economic benefit of short term maize legume intercropping systems under conservation agriculture in Malawi. Field Crops Research. 2012;132:149-157. DOI: 10.1016/j.fcr.2011.12.014
  69. 69. Pauw K, Thurlow J, Van Seventer D. Droughts and Floods in Malawi. No. 962. Assessing the Economy Wide Effects. Malawi: Discussion Paper, International Food Policy Research Institute (IFPRI); 2010
  70. 70. ESCOM (Electricity Supply commission of Malawi). 2018. Available from: [Accessed: 2018-08-14]
  71. 71. FAO (The Food and Agriculture Organization). Global forest resources assessment 2015. Country Report, Malawi, 2015. Available from: [Accessed: 2018-11-20]

Written By

Maureen Kapute Mzuza, Weiguo Zhang, Fanuel Kapute and Xiaodao Wei

Submitted: 22 November 2018 Reviewed: 23 April 2019 Published: 27 June 2019