Next Article in Journal
International Severe Weather and Flash Flood Hazard Early Warning Systems—Leveraging Coordination, Cooperation, and Partnerships through a Hydrometeorological Project in Southern Africa
Next Article in Special Issue
Impacts of Salinity on Saint-Augustin Lake, Canada: Remediation Measures at Watershed Scale
Previous Article in Journal
Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization
Previous Article in Special Issue
Assessment of Risk Due to Chemicals Transferred in a Watershed: A Case of an Aquifer Storage Transfer and Recovery Site
Open AccessArticle

Prioritization of Watersheds across Mali Using Remote Sensing Data and GIS Techniques for Agricultural Development Planning

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502324, India
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Bamako BP320, Mali
Author to whom correspondence should be addressed.
Academic Editors: Joan M. Brehm and Brian W. Eisenhauer
Water 2016, 8(6), 260;
Received: 26 April 2016 / Revised: 9 June 2016 / Accepted: 13 June 2016 / Published: 18 June 2016
(This article belongs to the Special Issue Watershed Protection and Management)


Implementing agricultural water management programs over appropriate spatial extents can have positive effects on water access and erosion management. Lack of access to water for domestic and agricultural uses represents a major constraint on agricultural productivity and perpetuates poverty and hunger in sub-Saharan Africa (SSA). This lack of access is the result of erratic precipitation, poor water management, limited knowledge of hydrological systems, and inadequate investment in water infrastructure. Water management programs should be made by multi-disciplinary teams that consider the interrelationship between hydraulic and anthropogenic factors. This paper proposes a method to prioritize watersheds for water management and agricultural development across Mali (Western Africa) using remote sensing data and GIS tools. The method involves deriving a set of relevant thematic layers from satellite imagery. Satellite images from Landsat ETM+ were used to generate thematic layers such as land use/land cover. Slope and drainage density maps were derived from Shuttle RADAR Topography Mission (SRTM) Digital Elevation Model (DEM) at 90 m spatial resolution. Population grids were available from the Global rural-urban mapping project (GRUMP) database for the year 2000 and mean rainfall maps were extracted from Tropical rainfall measuring mission (TRMM) grids for each year between 1988 and 2014. Each thematic layer was divided into classes that were assigned a rank for agriculture and livelihoods development provided by experts in the relevant field (e.g., Soil scientist ranking the soil classes) and published literature on those themes. Zones of priority were delineated based on the combination of high scoring ranks from each thematic layer. Five categories of priority zones ranging from “very high” to “very low” were determined based on total score percentages. Field verification was then undertaken in selected categories to check the priority assigned to each class using a random sampling method. Watershed boundaries were prepared at 1000 ha scale and overlaid on the priority map to identify watersheds that were in a very high priority zone. The importance and efficiency of using remote sensing to prioritize watershed interventions across countries is critical due to the limited technical and financial resources available in sub-Saharan Africa (SSA).
Keywords: watersheds; prioritization; spatial data layers; scores; Mali; land use/land cover; suitability watersheds; prioritization; spatial data layers; scores; Mali; land use/land cover; suitability

1. Introduction, Rationale and Background

The growth of the global population requires effective utilization of dwindling natural resources, especially for agricultural and livelihood needs. Natural resource development programs are generally applied on a watershed level [1]. Watersheds, catchments and sub-catchments are the fundamental units for the management of land and water resources [2]. In sub-Saharan Africa (SSA), despite existing inter-country agreements for sharing water of large river basins, small watershed programs for soil and water conservation and equitable domestic distribution were not a focus until recently. Poverty is the main focus of developmental programs in SSA that examine the reasons behind income disparities. Since two thirds of the population in SSA practice subsistence agriculture, sustaining a strong natural resource base will not only increase the productivity of land, it will also provide better livelihood opportunities and improve income. For development programs to be successfully implemented, watersheds need to be assessed as holistic units as a part of a larger river basin and containing a varied resource base. Identifying the natural resources within watersheds and appropriate streams that need immediate attention to sustain the population enables technological interventions such as improved crops, management practices, and coping mechanisms to issues like climate change to be implemented. Using interdisciplinary approaches to provide solutions to major problems, including management of water and other natural resources, is well recognized as being an effective way to address anthropogenic and natural factors in resource management. Although Mali has abundant water resources, they are poorly utilized due to lack of appropriate approaches and properly tested methods. The failure of watershed management programs, as concluded by the FAO workshop, is due to non-participatory nature and non-people centric goals [3].
Characterization of natural resources is possible with multi-disciplinary investigations that bring together a wide array of individuals and organizations with varied interests, technical expertise, and priorities. In this multi-disciplinary setting, prioritization of areas based on different bio-physical and social parameters such as population, soil conditions, rainfall, land scape and land use/land cover, are important. The land resource management concept identifies the inter relationship between social and biophysical factors [4,5,6,7].
Prioritizing the watersheds of appropriate scale has been mostly based on the morphometric characteristics and quantitative measurements. Attention has also been focused on the natural resources (such as soil and water) based conservation of watersheds. The human dimension and the interplay of these two was given a blind eye, despite its importance in successful implementation of natural resource plans. Many studies have shown that integration of multi-thematic maps, using remote sensing and GIS, is useful for identifying accurate groundwater potential zones for the exploration, development and management of groundwater resources [8,9,10,11,12]. A number of studies have been carried out to illustrate the capability of remote sensing and GIS technologies in natural resource studies and development planning [10,13,14,15,16]. The first pilot study in India using remote sensing and GIS was done in Karnataka state [17]. Javed and others [18] prioritized sub-watersheds in the Kanera watershed of Madhya Pradesh, India by using morphometric and land use analysis. Sadeghi [19] also did a similar analysis by giving more importance to land use in the watershed. Vemu and Pinnamanesni [20] used sediment yield estimation using USLE to prioritize the watersheds in the Indravathi basin of Andhra Pradesh in India. Li et al. [21] studied the impact of deforestation and overgrazing on erosion and water yield in the Niger and Lake Chad basins, and also identified a threshold effect of land cover type. In the Atankwidi sub-watershed of the Volta River basin in northern Ghana and southern Burkina Faso, a map of irrigated areas by shallow groundwater in was prepared using a similar approach [22]. In the present study, an attempt has been made to prioritize watersheds for proper natural resources management in Mali.
The main objective of this study was to prioritize watersheds across Mali for productivity enhancement and livelihood improvement. The specific objectives of the study using RS-GIS spatial analysis were to: (1) prepare critical spatial data layers needed for such analysis using remote sensing and GIS; (2) assign weights to classes in each spatial data layer based on expert knowledge; and (3) develop spatial model that will identify priority watersheds and provide answers to relevant questions for implementing development programs.
The study described is intended to contribute and build upon on the available databases to help identify watersheds with highest priority at a range of scales across Mali for agricultural and livelihood development. The method uses of standard hydrologic functions in GIS software to derive slope, drainage density and satellite image processing tools to derive land use map.

2. Study Area

This study focuses on Mali, which is the largest country among the Western African nations. It is bordered by seven nations: Algeria lies to the north and northeast, Niger to the east, Burkina Faso to the southeast, and Guinea to the southwest, with the Ivory Coast to the south along with Senegal and Mauritania to the west. In the southwest are low mountains deeply notched by valleys formed by the coursing of water. The climate ranges from subtropical in the south to arid in the north (Figure 1). In Mali, 22% of the country is semi-arid, 7.2% is dry sub-humid, and the remaining majority is arid (Table 1). Flooding of the Niger River occurs regularly in the rainy season (June to November) washing away soil nutrients and causing soil erosion. Four bioclimatic zones characterize the Malian landscape. The Sahara Zone is hyper-arid and desertic with water as the main constraint. Rainfall is low (0–250 mm) erratic and uncertain. The soils are sandy and skeletal based on the origin of material with poor water holding capacity. The Sahelian zone is characterized by long dry spells of 9–12 months. The Sudan zone is semi-arid to sub-humid with rainfall ranging from 550 mm to 1100 mm. The major crops in Mali are sorghum, pearl millet, cotton, maize and rice. The weather is usually sunny and dry and rains occur from July to November.
Mali is one of the nine countries drained by the River Niger, which runs its longest course in middle and southern areas. Being one of the two major water resources consumer countries of River Niger, Mali’s 0.454 million square kilometers land is flooded providing irrigation to 0.122 million square kilometers of cotton and large tracts of rice and sugarcane. The drier reaches of the river are mostly rainfed and supports crops like corn, sorghum, millet and groundnut. These drier regions with diverse cropping patterns need the most attention for improving the livelihoods of the small holder farmers [23].

3. Methods and Approaches

Determining priority watersheds (Figure 2) for agriculture and livelihoods development was achieved through weighted integration of multiple thematic layers. Relevant thematic layers dictating the agro-ecology and socio-economic conditions prevalent are prepared using different tools and techniques in remote sensing and GIS. In the absence of scientifically evaluated suitability criteria for priority setting, it was necessary to develop a method of spatial analysis based on the relevance and importance of information necessary for planning and development [12]. Expert knowledge of all the variables was obtained from relevant scientists to rank the values in each variable. Using a multi-criteria decision rule, priority classes were created.
The thematic layers like Land use/Land Cover were derived from the satellite imagery (Landsat ETM+). Rainfall was derived from the Tropical Rainfall Measuring Mission (TRMM) sensors. Similarly other thematic layers were derived from available public domain sources. Each thematic layer was classified into appropriate number of classes with meaningful range (i.e., 1 to 5). Weights or ranks were assigned for each class in a theme from a high numeric value to a lowest of 1 based on highest value of quantity or quality in the theme.

3.1. Criteria and Determining Factors

A set of relevant thematic layers such as soils, slope, land use/land cover, rainfall, population and their importance to development of natural resources were considered in the analysis. The relationships between the selected thematic layers based on the weights allocated determined homogeneous zones within Mali. The criteria to determine a prioritization category was a logical combination of weights of the thematic layers [24]. A high prioritization category was determined by criteria where each of the themes exhibits marginality, stress or poor resource base. Similar scoring of the thematic classes and combinations will determine other prioritization categories.

3.2. Input Data and Deriving Analysis Maps

3.2.1. Generation of Watersheds Using DEM

The most important input data in this study was the Space Shuttle Radar Topography Mission (SRTM) DEM of the world at 90 m horizontal resolution, which captured the varying topography of Mali (elevation range 15–1057 m). This is a gap filled DEM and made available through the Consortium for Spatial Information (CSI) web portal ( The SRTM DEM was used to delineate stream networks and slope. The drainage system was also delineated using SRTM DEM in sequence of steps as described below, in ArcGIS (ESRI 2009).
Filling sinks: When delineating stream networks form DEMs, it is necessary to fill sinks. A sink is a cell or set of spatially connected cells whose flow direction cannot be assigned to one of the eight valid values in a flow direction grid. This can occur when all neighboring cells are higher than the processing cell, or when two cells flow into each other creating a two-cell loop (ESRI 2009). Sinks in the DEM were filled up with the FILL function. It is an iterative process that goes to each cell and fills the sinks by comparing the value of neighboring cells until all the sinks are filled. Even though creating a depression less DEM was the goal, sinks were minimized to 0.1 million cells from 3.6 million.
Generation of flow direction: The direction of flow was determined by finding the direction of steepest descent from each cell. This was calculated as: maximize drop = (change in z-value)/(distance) × 100. The distance is determined between cell centers. Therefore, if the cell size is 1, the distance between two orthogonal cells is 1 and the distance between two diagonal cells is 1.414. If the descent to all adjacent cells is the same, the neighborhood is enlarged until a steepest descent is found (ESRI 2009). The function FLOWDIRECTION was used to calculate the direction of flow of each cell.
Generation of flow accumulation: Flow accumulation represents the accumulated flow in each grid cell. It was calculated by using flow direction and by counting the number of cells flowing to a particular cell. Thus, flow accumulation represents the number of upstream cells of any cell in an area. The FLOWACCUMULATION function was used to calculate this automatically while it takes the flow direction grid as input.
Generation of stream network: A set of thresholds of 10, 100 and 1000 pixels were used to generate stream network. All the cells in the flow accumulation grid that were above or equal to those threshold values were identified to generate raster linear networks. The output grids were then vectorized using the STREAMLINE function of ArcGIS, which takes raster linear networks and flow direction raster as input to produce linear vectors that also show the direction of flow (Figure 3). Once the streams were accurately derived, the watersheds (sub-basins) were delineated using available pour point.
Generation of watersheds: Pour points were generated to derive the fourth order stream network for the entire study area. Strahler’s stream ordering method was used to categorize streams in to different orders based on the location of stream from stream head to tail of the watershed.

3.2.2. Population

The spatial distribution of population in Mali has a natural division between the Northern (Sahara) and Southern (sub-Sahara) parts. With a population of 16.4 million (July 2014 estimate) Mali has human development index ranking of 176 out of 187 countries. Half the population is under the age of 15. With a growth rate of 3%, the population of Mali has increased steadily from 6 million in 1976 to 14.5 million in 2012. The sex ratio is tilted towards females at 95 males/100 females. At current growth rates, the Malian population will reach about 30 million people in 20 years (5 times the population of 1976) with a density of 12.54 inhabitants per km² with implications such as pressure on natural resources, urbanization/migration, and rapid growth of social spending. The rural population is around 63.8% in 2013 according to World Bank estimates. Population was divided into 5 distinct classes, clearly indicating high rural population and a high density around the urban agglomerations (Figure 4a,b). These classes were assigned weights based on the scaling of total population. Higher population (in rural areas) was given a higher ranking relatively to prioritize the watersheds for a sustainable development and also stop migration to urban areas [25,26,27].

3.2.3. Land Use/Land Cover

Land use/land cover patterns were mapped and their areas were estimated (Figure 5, Table 2) using Landsat ETM+ 30 m spatial resolution satellite imagery. Landsat and MODIS MFDC were then classified using unsupervised ISOCLASS clustering K-means [28,29]. Land use classes were mapped based on ground data and land cover classes inferred from Google Earth high resolution imagery [30]. Irrigated land was assigned a score of five because it is mostly associated with flood plains and buried channels, which are very good recharge zones, as indicated by field derived information in the Upper East Region [31]. One of the dominant land use/land cover categories in the area is Class 4, Savannas: grasslands, shrub lands, and woodlands mixed with rainfed agriculture. In areas where there is high slope and thin soil cover, the groundwater prospects are considered to be poor and a score of one was assigned. Similarly, weights have been assigned subjectively to each of the categories of the land use/land cover pattern according to their influence on infiltration and runoff. The land use/land cover such as high gradient hill areas and settlements which have poor water holding capacity were given a score of one while savanna grass lands, irrigated areas and wetlands which are high water holding capacity were given a score of five (Table 2).

3.2.4. Slope

The SRTM DEM data was used to derive a slope map (in percent). Slope s one of the factors that directly influences the infiltration of rainfall in that steeper slopes generate large runoff during rainfall events, whereas gentle slopes allow sufficient time to infiltrate the surface [12]. Slope plays an important role in creating/arresting runoff and also ascertaining the land capabilities and suitability for different land uses and soil moisture. Slope was classified into thirteen categories [12] along with their areal extent (Figure 6, Table 2) and weights shown in Table 2. Weights were assigned according to the slope. A score of five was given to the plain region with lower slope because low runoff contributes to higher recharge.

3.2.5. Soils

Soils are characterized by climate and physiography of the location and play an important role in prioritization. The water holding capacity of an area depends upon the soil types and their permeability. Soil types were classified into nineteen categories along with their aerial extent (Figure 7, Table 2) and weightages shown in Table 2. Field verification in the identified soil units were conducted and confirmed. Weights were assigned subjectively to each soil unit after taking into account the type of soil, specific yield and its water holding capacity. The soils that have poor water holding capacity have been given a weight of 5 and those with high water holding capacity were given a weight of 3.5 (Table 2).

3.2.6. Rainfall

Mean annual rainfall data for a 10-year period (1995–2005) was adopted from worldclim ( Mali was divided into six rainfall zones (Figure 8) ranging from <100 mm to 1250 mm along with their areas shown (Table 2) and weightages shown (Table 3). An area receiving less than 100 mm of rainfall a year was given a score of one assuming a poor water availability zone, which are mainly located in northern Sahara region, while an area receiving greater than 1000 mm of rainfall was assigned a score of three assuming very good water availability in the southern region of Mali (Figure 8).

3.3. Determining Thematic Layer Weights

Suitable weights were assigned to the five themes and their individual classes after understanding their importance in setting priorities to watersheds in Mali. The weights of the individual themes were assigned based on expert knowledge and published literature [5,10,12,32,33] in Table 2. The assigning of weights to each of the thematic layers was purely based on the merits of the layer in arriving at a priority rank to a watershed. The importance of each theme was determined based on previous literature [12]. The percent importance assigned to each themes are as follows: population—17%; slope—21%; rainfall—26%; land use/land cover—18%; and soils—18%. Therefore, the higher the weight, the more influence a particular factor will have in the watershed prioritization model. In this study, we defined the maximum weight as five and the minimum weight as one.

3.4. Integration of Thematic Layers and Spatial Model

The first step in the spatial model development was to devise a scheme of classification for each thematic data layer and the ranks assigned to each class. (Table 2). ERDAS spatial modeler was used to derive and apply this model. All reclassified themes were integrated in weighted overlay analysis using Equation (1).
where WSPP is the Watershed priority score of pixel score in model output; TWS is the selected watershed priority theme; and FW is the Weightage factor of theme.

4. Results and Discussion

4.1. Prioritization of Watersheds

After rescaling of the thematic maps to five classes using the scores, the integration process resulted in a prioritized area map with the following categorization. On the basis of the WSPP value, watershed priorities were classified as: (i) very high priority; (ii) high priority; (iii) medium priority; (iv) low priority; and (v) very low priority across the study area (Figure 9; Table 3). Highly scored thematic classes and their combination were categorized as a high priority zone and vice versa. It was determined that very high priority should be given to watersheds in central and southern Segou, the northern and eastern part of Sikasso, and the southern and southeastern part of Koulikourou regions. With an area of 4.99 Mha, in the very high priority zone in Segou (1.86 Mha), Sikasso (1.83 Mha) and Koulikoro (1.11 Mha) regions, there is good potential for developing the rainfed cropland/shrub lands where water scarcity is a major constraint. High priority should be given to watersheds in southern and western parts of Sikasso (4.39 Mha), northern parts of Kayes (4.94 Mha), Koulikoro (6.22 Mha), Segou (3.82 Mha), southern Timbouktou (2.52 Mha) and most of Mopti (5.60 Mha) regions covering 29.1 Mha. Very low priority was given to the southern part of Kayes region. A moderate priority zone was delineated across the central region of Mali covering the southern parts of the Timbouctou and Gao regions. The central and northern parts of Timbouctou and Gao regions were categorized as low and very low due to extreme weather and low natural resource base. The total area under first priority was 4.9 Mha, which accounts for 4% of the total geographical area. The second priority zone is 29 Mha (23%); third priority zone was 14.8 Mha (12%); fourth priority zone was 51.6 Mha (41%); and very low priority zone was 25.9 Mha (21%) (Figure 9 and Table 4).
The spatial resolution of each thematic layer determined the amount information in that layer i.e., the higher the resolution, the more classes were resolved to assign appropriate weights to each class. Finer classes in each theme will increase the accuracy of prioritization, which will be useful for targeting interventions in smaller watersheds. Coarse resolution thematic layers generalize classes and less information over certain diverse landscapes may cause undue weightage to a particular class resulting in inappropriate priority rating. Hence, it was necessary to decide on the resolution of the thematic layers based on the extent of area to be prioritized.

4.2. Development of Spatial Model

Two models were developed: one of which was developed taking the Equal weights and variable scores [24].
Pixel score in model output = weightage of layer 1 × weightages of classes within layer 1 + weightage of layer 2 × weightages of classes within layer 2 +…+ weightage of layer n × weightages of classes within layer n.
High priority watersheds were selected based on this method in Koutiala and Bougouni districts in southern region of Mali. Four sub-watersheds were selected in different parts of Koutiala and Bougouni districts each to introduce best management practices based interventions for sustainable agriculture. Coincidentally, it was found that these watersheds were part of the Africa RISING project where several CGIAR centers and local partners are implementing development interventions for improving water availability and increasing productivity. One of the important inputs in this method was the accurate mapping of LULC using remote sensing imagery. This served the dual purpose of mapping as well as conducting farmer interviews to understand constraints, which in turn will help us in assigning appropriate weights for prioritization.

4.3. Validation with Ground Survey Data

Ground survey data was collected for 495 locations during 3–13 August 2015. Local agriculture experts accompanied the lead author during the field visit and farmer interviews and local experts provided detailed information at each location. Each location was selected based on representativeness of land use/land cover including major crop types. The 180 locations that had detailed ground information were collected, which was used for class identification and remaining 315 locations were used for validation of land use/land cover map and prioritization map.
The nine class land use/land cover map produced from satellite imagery was validated with 315 ground data points and an error matrix was produced (Table 5). For nine LULC classes 266 out of 315 ground points matched with the derived class, resulting in an accuracy of 84.44% and Kappa value of 0.796.
The five-class prioritization map produced was validated with 315 ground data points and an error matrix was produced (Table 6). For all five classes, 253 out of 315 ground points matched with the derived priority class, resulting in an accuracy of 80.32% and Kappa value of 0.689.

5. Conclusions

This research illustrates the development of a spatial modeling approach for prioritization of watersheds across Mali for agricultural and livelihood development. The process involved: (a) identifying and developing harmonized spatial database; (b) allocating weights to spatial data layers and classes within each data layers based on expert knowledge and previous literature; and (c) developing spatial relationships between layers by ranking the combination of weights and established priority zones. The model provided the various levels of development priority zones, percentage areas, and precise location of these areas. This method prioritizes watersheds in Mali for agricultural development but does not include action plan for each watershed. However, some watersheds that were selected under a different project for implementing agricultural development interventions are found to coincide with the priority category derived using the proposed method indicating the usefulness of this type of prioritization.
The study highlights priority zones across Mali and identified watersheds that are predominantly agricultural and need appropriate intervention to improve productivity. The outcome of this methodology paper also highlights the utility of spatial modeling, and the importance of spatial databases at different scales and resolutions for mapping prioritization of watersheds for development planning. When the sub-basins and small watersheds are selected for implementation of development activities, it is necessary to use higher spatial resolution thematic layers and ancillary data.
The accuracies of land use/land cover map and prioritization map were assessed based on intensive ground survey data. The overall accuracy of five prioritization classes was 80%. However, high and very high priority class accuracy exceeded 85% and also previously selected Africa RISING research study sites coincide with these classes. Mapping prioritization of watersheds is the first step in implementation of agricultural interventions for sustainable development and livelihoods. This approach was appropriate for planning and disseminating technologies. We suggest that this methodology can be improved and adopted for prioritizing watersheds in other countries in sub-Saharan Africa where productivity of land can be increased using improved technologies. This research makes a broader contribution to methods and products of the group on Earth observation for sustainable agriculture development and supporting future food security.


This work was supported by Africa Research in Sustainable Intensification for the Next Generation (Africa RISING) program in Mali and CGIAR research programs (Dryland systems and Water land and Ecosystems). Authors greatly appreciate the financial support provided by United States Agency for Development (USAID) through the International Institute of Tropical Agriculture (IITA). The authors thank Thenkabail Prasad for his valuable feedback on early versions of the Prioritization map. The authors would like to thank Adam Oliphant, Scientist, USGS and Amit Chakravarthi, Science editor, ICRISAT, for the support provided in the final editing of manuscript. Authors would like thanks to internal reviewers.

Author Contributions

Birhanu Zemadim Birhanu proposed and designed this study. Murali Krishna Gumma carried out analysis, results and discussions. Birhanu Zemadim Birhanu provided secondary information. Introduction and literature survey was provided by Birhanu Zemadim Birhanu and Irshad A. Mohammed. All authors drafted the respective contributions and draft manuscript was given to language and technical editor.

Conflicts of Interest

The authors declared no conflict of interest.


  1. Khan, M.A.; Gupta, V.P.; Moharana, P.C. Watershed prioritization using remote sensing and geographical information system: A case study from Guhiya, India. J. Arid Environ. 2001, 49, 465–475. [Google Scholar] [CrossRef]
  2. Jewitt, G. Can integrated water resources management sustain the provision of ecosystem goods and services? Phys. Chem. Earth A/B/C 2002, 27, 887–895. [Google Scholar] [CrossRef]
  3. Swallow, B.; Okono, N.; Achouri, M.; Tennyson, L. Watershed Management and Sustainable Mountain Development Working Paper No. 8. In Preparing for the Next Generation of Watershed Management Programmes and Projects, Proceedings of the African Workshop, Nairobi, Kenya, 8–10 October 2003; FAO: Rome, Italy, 2005. [Google Scholar]
  4. Iqbal, M.; Sajjad, H. Prioritization based on morphometric analysis if Dudhganga catchment, Kashmir valley, Inida, using remote sensing and geographical information system. Afr. J. Geo-Sci. Res. 2014, 2, 01–06. [Google Scholar]
  5. Moore, I.D.; Grayson, R.; Ladson, A. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrol. Process. 1991, 5, 3–30. [Google Scholar] [CrossRef]
  6. Panhalkar, S.; Pawar, C. Watershed Development Prioritization by Applying WERM Model and GIS Techniques in Vedaganga basin (India). ARPN J. Agric. Biol. Sci. 2011, 6, 38–44. [Google Scholar]
  7. Vittala, S.S.; Govindaiah, S.; Gowda, H.H. Prioritization of sub-watersheds for sustainable development and management of natural resources: An integrated approach using remote sensing, GIS and socio-economic data. Curr. Sci. 2008, 95, 345–354. [Google Scholar]
  8. Kamaraju, M.V.V.; Bhattacharya, A.; Sreenivasa, R.; Chandrasekhar, R.; Murthy, G.S.; Malleswara Rao, T.C.H. Ground-water potential evaluation of West Godavari District, Andhra Pradesh state, India—A GIS approach. Ground Water 1996, 34, 318–325. [Google Scholar] [CrossRef]
  9. Mattikalli, H.M.; Devereux, B.J.; Richards, K.S. Integration of remote sensedsatellite images with a geographical information system. Comput. Geosci. 1995, 21, 947–956. [Google Scholar] [CrossRef]
  10. Murthy, K.S.R. Groundwater potential in a semi-arid region of Andhra Pradesh—A geographical information system approach. Int. J. Remote Sens. 2000, 21, 1867–1884. [Google Scholar] [CrossRef]
  11. Sidhu, R.S.; Mehta, R.S. Delineation of groundwater potential zones in Kushawati river watershed a tributary of zauri river in goa, using remotely sensed data. In Proceedings of National Symposium on Engineering Applications of Remote Sensing and Recent Advantages; Indian Society of Remote Sensing: Dehradun, India, 1989; pp. 41–46. [Google Scholar]
  12. Gumma, M.K.; Pavelic, P. Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling. Environ. Monit. Assess. 2013, 185, 3561–3579. [Google Scholar] [CrossRef] [PubMed]
  13. Hellden, U.; Olsson, L.; Stern, M. Approaches to desertification monitoring in Sudan. In Satellite Remote Sensing in Developing Counties; Lery, L.G., Ed.; European Space Agency: Paris, France, 1982; pp. 131–144. [Google Scholar]
  14. Kushwaha, S.P.S. Application of Remote Sensing in Shifting Cultivation Areas; Technical Report; Abteilung Luftbildmessung and Fernerkundung, Universitat Freiburg: Freiburg, Germany, 1993; pp. 23–28. [Google Scholar]
  15. Smith, A.Y.; Blackwell, R.J. Development of an information data base for watershed monitoring. Photogramm. Eng. Remote Sens. 1980, 46, 1027–1038. [Google Scholar]
  16. Trotter, C.M. Remotely sensed data as information source for geographical information system in natural resources management: A review. Int. J. Remote Sens. 1991, 5, 225–239. [Google Scholar] [CrossRef]
  17. NRSA. Methodology Manual of Ground Water Prospective Zone Maps; Rajiv Gandhi National Rural Drinking Water Mission, Technical Guidelines For Preparation Of Ground Water Prospects Maps; Department of Space: Hyderabad, India, 2000; pp. 17–18. [Google Scholar]
  18. Javed, A.; Khanday, M.Y.; Ahmed, R. Prioritization of sub-watersheds based on morphometric and land use analysis using remote sensing and GIS techniques. J. Indian Soc. Remote Sens. 2009, 37, 261–274. [Google Scholar] [CrossRef]
  19. Sadeghi, S.; Jalili, K.; Nikkami, D. Land use optimization in watershed scale. Land Use Policy 2009, 26, 186–193. [Google Scholar] [CrossRef]
  20. Vemu, S.; Pinnamaneni, U.B. Sediment yield estimation and prioritization of watershed using remote sensing and GIS. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, 1, 529–533. [Google Scholar] [CrossRef]
  21. Li, K.; Coe, M.; Ramankutty, N.; De Jong, R. Modeling the hydrological impact of land-use change in West Africa. J. Hydrol. 2007, 337, 258–268. [Google Scholar] [CrossRef]
  22. Gumma, M.K.; Thenkabail, P.S.; Barry, B. Delineating shallow groundwater irrigated areas in the atankwidi watershed (Northern Ghana, Burkina Faso) using quickbird 0.61–2.44 meter data. Afr. J. Environ. Sci. Technol. 2010, 4, 455–664. [Google Scholar]
  23. Andersen, I.; Golitzen, K.G. The Niger River Basin: A Vision for Sustainable Management; The World Bank: Washington, DC, USA, 2005. [Google Scholar]
  24. Gumma, M.K.; Thenkabail, P.S.; Fujii, H.; Namara, R. Spatial models for selecting the most suitable areas of rice cultivation in the inland valley wetlands of Ghana using remote sensing and geographic information systems. J. Appl. Remote Sens. 2009, 3, 033537. [Google Scholar]
  25. Bah, M.; Cissé, S.; Diyamett, B.; Diallo, G.; Lerise, F.; Okali, D.; Okpara, E.; Olawoye, J.; Tacoli, C. Changing rural–urban linkages in Mali, Nigeria and Tanzania. Environ. Urban. 2003, 15, 13–24. [Google Scholar]
  26. Page, D.; Patterson, M.W.; Reeve, K. Modern migration in Ghana and Mali: A comparison of urban migration patterns. J. Glob. Initiat. Policy Pedagog. Perspect. 2010, 1, 4. [Google Scholar]
  27. Farvacque-Vitkovic, C.; Casalis, A.; Diop, M.; Eghoff, C. Development of the Cities of Mali—Challenges and Priorities; Africa Region Working Paper Series Number 104 a; The World Bank: Washington, DC, USA, 2007. [Google Scholar]
  28. Tou, J.T.; Gonzalez, R.C. Pattern Recognition Principles; Addison-Wesley Publishing Company: Reading, MA, USA, 1975. [Google Scholar]
  29. Thenkabail, P.S.; Enclona, E.A.; Ashton, M.S.; Legg, C.; De Dieu, M.J. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of african rainforests. Remote Sens. Environ. 2004, 90, 23–43. [Google Scholar] [CrossRef]
  30. Gumma, M.; Thenkabail, P.; Teluguntla, P.; Rao, M.; Mohammed, I.; Whitbread, A. Mapping rice fallow cropland areas for short season grain legumes intensification in south Asia using MODIS 250 m time-series data. Int. J. Digit. Earth 2016. [Google Scholar] [CrossRef][Green Version]
  31. Gumma, M.K.; Thenkabail, P.S.; Hideto, F.; Nelson, A.; Dheeravath, V.; Busia, D.; Rala, A. Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data. Remote Sens. 2011, 3, 816–835. [Google Scholar] [CrossRef]
  32. Murthy, K.S.R.; Mamo, A.G. Multi-criteria decision evaluation in groundwater zones identification in moyale-teltele subbasin, south Ethiopia. Int. J. Remote Sens. 2009, 30, 2729–2740. [Google Scholar] [CrossRef]
  33. Moran, M.S.; Peters-Lidard, C.D.; Watts, J.M.; McElroy, S. Estimating soil moisture at the watershed scale with satellite-based radar and land surface models. Can. J. Remote Sens. 2004, 30, 805–826. [Google Scholar] [CrossRef]
Figure 1. Location map of Mali with major rivers, Ground survey data and climate zones.
Figure 1. Location map of Mali with major rivers, Ground survey data and climate zones.
Water 08 00260 g001
Figure 2. Overview of the methodology for watershed prioritization using integrated remote sensing and GIS techniques.
Figure 2. Overview of the methodology for watershed prioritization using integrated remote sensing and GIS techniques.
Water 08 00260 g002
Figure 3. Fourth order watersheds in Mali derived using SRTM data: (a) fourth order watersheds for whole Mali; and (b) stream order network for selected area.
Figure 3. Fourth order watersheds in Mali derived using SRTM data: (a) fourth order watersheds for whole Mali; and (b) stream order network for selected area.
Water 08 00260 g003
Figure 4. Spatial distribution of Population in Mali: (a) population count; and (b) priority category.
Figure 4. Spatial distribution of Population in Mali: (a) population count; and (b) priority category.
Water 08 00260 g004
Figure 5. Land use/land cover classes in Mali, during 2000: (a) land use/land cover; and (b) priority category.
Figure 5. Land use/land cover classes in Mali, during 2000: (a) land use/land cover; and (b) priority category.
Water 08 00260 g005
Figure 6. Slope map of Mali (extracted from SRTM DEM: (a) slope class; and (b) priority category.
Figure 6. Slope map of Mali (extracted from SRTM DEM: (a) slope class; and (b) priority category.
Water 08 00260 g006
Figure 7. Soils of Mali (DSMW, FAO, 1995): (a) soil type; and (b) priority category.
Figure 7. Soils of Mali (DSMW, FAO, 1995): (a) soil type; and (b) priority category.
Water 08 00260 g007
Figure 8. Mean Annual rainfall in Mali (taken from (a) normal annual rainfall; and (b) priority category.
Figure 8. Mean Annual rainfall in Mali (taken from (a) normal annual rainfall; and (b) priority category.
Water 08 00260 g008
Figure 9. Priority zones for watershed based development across Mali. Test sites selected before this study (inset).
Figure 9. Priority zones for watershed based development across Mali. Test sites selected before this study (inset).
Water 08 00260 g009
Table 1. Climate zones in Mali.
Table 1. Climate zones in Mali.
Climate ZoneArea (ha)% of Total Area
Dry sub-humid9,128,1227
Hyper arid39,352,63731
Table 2. Spatial distribution of the various parameters/themes, identified units within each theme and their associated areal extent within the study area.
Table 2. Spatial distribution of the various parameters/themes, identified units within each theme and their associated areal extent within the study area.
Parameter/ThemeIdentified Units/ScoreArea (ha)% of Total Area (%)Priority ClassScores AssignedWeightage
PopulationPopulation Range (No. of people)3
10–10001,013,63080Very low1
510,000–15,00063341Very high5
6150,00–20,0002880.02Very high5
7>20,00015060.12Very high5
Slope 90 mSlope distribution (%)3
1<1 (level to nearly level)156,33812Very high5
2>1 and ≤2 (gentle slope)348,31228High5
3>2 and ≤3 (gentle slope)376,17930Moderate4
4>3 and ≤4 (gentle slope)258,61720Moderate3
5>4 and ≤5 (moderate slope)62,1985Low2
6>5 and ≤6 (moderate slope)22,3192Low2
7>6 and ≤7(moderate slope)11,3751Very low1
8>7 and ≤8 (steep slope)29,5412Very low1
Rainfall 0.5 degreesAnnual rainfall (mm)5
10–100133,90211Very low1
2100–250493,78539Very low1
6>100043,3673Very high5
Land use 30 mLand use/land cover classes3.5
1Rainfed-cropland/rangeland124,60310Very high5
2Rainfed-croplands/shrublands108,4389Very high5
3Irrigated-croplands65411Very high5
5Grasslands with shrubs64,8435High4
6Sandy desert and dunes706,37156Very low1
7Forests/shrublands101,1918Very low1
9Urban lands1200Very low1
SoilsSoil type (Source: FAO)4
1Cambic Arenosols96331Moderate3
2Chromic Vertisols10,9851Moderate3
3Dystric Nitosols80211High4
4Eutric Cambisols22670High4
5Eutric Fluvisols6100Very high5
6Eutric Gleysols124,97310Low2
7Eutric Nitosols12,8591High4
8Ferric Acrisols11770High4
9Ferric Luvisols57970Moderate3
10FLUVISOLS153,78612Very high5
11Gleyic Luvisols53,5724Moderate3
13Gypsic Yermosols14,7771Low2
14Haplic Yermosols103,3528Low2
15LITHOSOLS153,08812Very low1
16Luvic Arenosols182,20714Moderate3
17Pellic Vertisols440Moderate3
18Plinthic Acrisols17870High4
19Saltbeds201,95316Very low1
20Solodic Planosols3050Moderate3
21Takyric Solonchaks1740Low2
22Vertic Cambisols41850High4
23Water bodies13080Very low1
Table 3. Prioritization of watersheds across Mali with % extent.
Table 3. Prioritization of watersheds across Mali with % extent.
Groundwater Potential ClassTotal Score (%)Area (ha)% in Total Area
1st priority (very high)100–854,985,6464
2nd priority (high)85–7029,105,96823
3rd Priority (medium)70–6014,817,79512
4th priority (low)60–4551,617,54841
5th priority (very low)≤4525,960,89121
Total areas126,487,848100
Table 4. Potential areas for watershed interventions.
Table 4. Potential areas for watershed interventions.
RegionPotential Area (Mha)
1st Priority (Very High)2nd Priority (High)3rd Priority (Medium)4th Priority (Low)5th Priority (Very Low)
Table 5. Accuracy assessment of land use/land cover map using field-plot data using error matrix method.
Table 5. Accuracy assessment of land use/land cover map using field-plot data using error matrix method.
Classified DataReference Data (Classes)Accuracy
010203040506070809Row TotalsProducers AccuracyUsers Accuracy
Landsat derived classification01. Rainfed-croplands/Mix with shrubs960029010010893%89%
02. Rainfed-croplands/Plantation1823450130110996%75%
03. Irrigated-croplands00160000001684%100%
04. Grasslands00014010001570%93%
05. Grasslands with shrubs00001400001450%100%
06. Sandy desert and dunes000009000990%100%
07. Forests/shrublands63000034004371%79%
08. Water0000000000--
09. Urbanlands000000001150%100%
Column Total10385192028104802315
Note: Overall Classification Accuracy = 84.44%; Overall Kappa Statistics = 0.796.
Table 6. Accuracy assessment of prioritization map using field-plot data using error matrix method.
Table 6. Accuracy assessment of prioritization map using field-plot data using error matrix method.
Classified DataReference Data (Priority Classes)Row TotalsProducers AccuracyUsers Accuracy
01. Very High02. High03. Moderate04. Low05. Very Low
Prioritization map01. Very high8076009390%86%
02. High8139126016587%84%
03. Moderate01119203250%59%
04. Low1311352362%57%
05. Very low00002229%100%
Column Total8916038217315
Note: Overall Classification Accuracy = 80.32%; Overall Kappa Statistics = 0.6892.
Back to TopTop