Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin,Turkey

Land use and land cover (LULC) changes affect several natural environmental factors, including soil erosion, hydrological balance, biodiversity, and the climate, which ultimately impact societal well-being. Therefore, LULC changes are an important aspect of land management. One method used to analyze LULC changes is the mathematical modeling approach. In this study, Cellular Automata and Markov Chain (CA-MC) models were used to predict the LULC changes in the Seyhan Basin in Turkey that are likely to occur by 2036. Satellite multispectral imagery acquired in the years 1995, 2006, and 2016 were classified using the object-based classification method and used as the input data for the CA-MC model. Subsequently, the post-classification comparison technique was used to determine the parameters of the model to be simulated. The Markov Chain analyses and the multi-criteria evaluation (MCE) method were used to produce a transition probability matrix and land suitability maps, respectively. The model was validated using the Kappa index, which reached an overall level of 77%. Finally, the LULC changes were mapped for the year 2036 based on transition rules and a transition area matrix. The LULC prediction for the year 2036 showed a 50% increase in the built-up area class and a 7% decrease in the open spaces class compared to the LULC status of the reference year 2016. About an 8% increase in agricultural land is also likely to occur in 2036. About a 4% increase in shrub land and a 5% decrease in forest areas are also predicted.     

Determining the impact of urban components on land surface temperature of Istanbul by using remote sensing indices

For the past 60 years, Istanbul has been experiencing an accelerated urban expansion. This urban expansion is leading to the replacement of natural surfaces by various artificial materials. This situation has a critical impact on the environment due to the alteration of heat energy balance. In this study, the effect upon the urban heat island (UHI) of Istanbul was analyzed using 2009 dated Landsat 5 Thematic Mapper (TM) data. An Index Based Built-up Index (IBI) was used to derive artificial surfaces in the study area. To produce the IBI index, Soil-Adjusted Vegetation Index, Normalized Difference Built-up Index, and Modified Normalized Difference Water Index were calculated. Land surface temperature (LST) distribution was derived from Landsat 5 TM images using a mono-window algorithm. In addition, 24 transects were selected, and different regression models were applied to explore the correlation between LST and IBI index. The results show that artificial surfaces have a positive exponential relationship with LST rather than a simple linear one. An ecological evaluation index of the region was calculated to explore the impact of both the vegetated land and the artificial surfaces on the UHI. Therefore, the quantitative relationship of urban components (artificial surfaces, vegetation, and water) and LST was examined using multivariate statistical analysis, and the correlation coefficient was obtained as 0.829. This suggested that the areas with a high rate of urbanization will accelerate the rise of LST and UHI in Istanbul.     

Assessment of some remote sensing techniques used to detect land use/land cover changes in South-East Transilvania,Romania

This paper assesses the image differencing technique for the Normalized Difference Vegetation Index (NDVI), the second principal component (PC2), and the TM 4 band (TM 4), as well as the post-classification comparison (PCC) in order to analyze the land use/land cover changes in the South-East Transilvania, Romania. The analysis was performed using two frames from Landsat 5 TM satellite images acquired on August 5, 1993 and July 24, 2009. After applying the NDVI, PC2, and TM 4 image differencing techniques, the images obtained were transformed into change/no change maps. The thresholds identified to highlight the changes were set at 0.6 s for NDVI and 0.7 s for PC2 and TM 4. Before applying the PCC technique, the satellite images were classified through the supervised classification method. The overall accuracy obtained was 85.91 % and the kappa statistics 0.8249 for 1993, 88.18 % and 0.8497 for 2009, respectively. The assessment of the changes detection methods in the studied area shows that the first place is occupied by NDVI image differencing with an overall accuracy of 83.80 %, followed by PCC method with 83.20 %, PC2 difference with an overall accuracy of 81.60 %, and TM 4 difference with an overall accuracy of 79.40 %.     

Mapping aboveground woody biomass using forest inventory,remote sensing and geostatistical techniques

Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m?×?31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10–40, 40–70 and >70 % of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m³ ha^?1. The total growing stock of the forest was found to be 2,024,652.88 m³. The AGWB ranged from 143 to 421 Mgha^?1. Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE)?=?42.25 Mgha^?1) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha^?1 respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha^?1. The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.     

Identification of heavy metal pollutants using multivariate analysis and effects of land uses on their accumulation in urban soils in Beijing, China

In order to evaluate the current state of the environmental quality of soils in Beijing, we investigated contents of 14 metals in Beijing urban soils inside the 5th ring road by even grids sampling. Statistic analyses were conducted to identify possible heavy metal pollutants, as well as the effects of land uses on their accumulation. Our results revealed that the urban soils in Beijing were contaminated by Cd, Pb, Cu, and Zn. Land uses and urbanization ages affected the accumulation of the four heavy metals in soils significantly. Soils in industrial areas have the highest average Cu and Zn contents, while Pb contents in park areas and Cd in agricultural areas are the highest. The accumulations of Pb and Zn in urban soils increase significantly with sampling plots approaching the city center. And Pb, Cd, and Zn contents in soils in traffic areas also tend to increase in the city center. However, residential areas have the lowest contents of all the four heavy metals.     

Environmental modelling of Omerli catchment area in Istanbul, Turkey using remote sensing and GIS techniques

Omerli Reservoir is one of the major drinking water reservoirs of Greater Metropolis Istanbul, providing 40% of the overall water demand. Istanbul where is one of the greatest metropolitan areas of the world with a population over 10 million and a rate of population increase about twice that of Turkey. As a result of population growth and industrial development, Omerli watershed is highly affected by the wastewater discharges from the residential areas and industrial plants. The main objective of this study is to investigate the temporal assessment of the land-use/cover of the Omerli Watershed and the water quality changes in the Reservoir. It is not possible to adequately control urbanization and other pollution sources affecting the water quality. Responses of these detrimental effects are due to rapidly increasing population, unplanned and illegal housing, and irrelevant industries at the protection zones of the watershed, together with insufficient infrastructure. The study is focused on the assessment of urbanization in relation to land use and water quality using Remote Sensing (RS) and Geographic Information Systems (GIS) techniques for all the four protection zones of the Reservoir and a time variant analyzing model is obtained. IRS-1C LISS and IRS-1C PAN, LANDSAT-5 TM satellite data of 1997, 1998, 2000, 2001 and 2006 are analyzed by confirmation through the ground truth data. RS data have been transferred into UTM coordinate system and image enhancement and classification techniques were used. Raster data were converted to vector data that belongs to study area to analyze in GIS for the purpose of planning and decision-making on protected watersheds.     

Monitoring the urban green spaces and landscape fragmentation using remote sensing: a case study in Osmaniye,Turkey

Urban green spaces play a significant role in management of physical activity, psychological well-being, and public health of urban residents. With the expansion of urban areas in Turkey during the past decades, urban green spaces have been fragmented and dispersed causing impairment and environmental degradation. The purpose of this study is to model urban green space distribution by focusing on the landscape fragmentation in city of Osmaniye using remote sensing and geographic information system technology. Normalized difference vegetation index (NDVI) and urban landscape ratio (ULR) were used to assess the proximity and spatial arrangement of urban green spaces within the neighbor landscapes to quantify the urban land use effect. The geospatial analysis results showed that increase in total built-up area and population has significantly decreased the urban green space cover because of high levels of landscape fragmentation in urban city center. Also, due to high levels of landscape fragmentation, approximately 45% of the Osmaniye city is estimated to become urbanized by 2030. This study demonstrated the benefits of directional vegetation index application with geospatial analyses in characterizing the environmental quality for planning and management of urban green spaces. This approach could be used for determining the future urban land development scenarios correlating with regional planning procedures.     

An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems

This study was undertaken to incorporate geostatistics, remote sensing, and geographic information system (GIS) technologies to improve the qualitative land suitability assessment in arid and semiarid ecosystems of Arsanjan plain, southern Iran. The primary data were obtained from 85 soil samples collected from tree depths (0–30, 30–60, and 60–90 cm); the secondary information was acquired from the remotely sensed data from the linear imaging self-scanner (LISS-III) receiver of the IRS-P6 satellite. Ordinary kriging and simple kriging with varying local means (SKVLM) methods were used to identify the spatial dependency of soil important parameters. It was observed that using the data collected from the spectral values of band 1 of the LISS-III receiver as the secondary variable applying the SKVLM method resulted in the lowest mean square error for mapping the pH and electrical conductivity (ECe) in the 0–30-cm depth. On the other hand, the ordinary kriging method resulted in a reliable accuracy for the other soil properties with moderate to strong spatial dependency in the study area for interpolation in the unstamped points. The parametric land suitability evaluation method was applied on the density points (150 × 150 m²) instead of applying on the limited representative profiles conventionally, which were obtained by the kriging or SKVLM methods. Overlaying the information layers of the data was used with the GIS for preparing the final land suitability evaluation. Therefore, changes in land characteristics could be identified in the same soil uniform mapping units over a very short distance. In general, this new method can easily present the squares and limitation factors of the different land suitability classes with considerable accuracy in arbitrary land indices.     

Characterization of Solang valley watershed in western Himalaya for bio-resource conservation using remote sensing techniques

The development activities in mountainous region though provide comfort to the human being and enhance the socioeconomic status of the people but create pressure on the bio-resources. In this paper, the current status of land use/landcover and the vegetation communities of the Solang valley watershed in Himachal Pradesh of Indian western Himalaya has been mapped and presented using remote sensing. This watershed area was dominated by alpine and sub-alpine pastures (30.34%) followed by scree slopes (22.34%) and forests (21.06%). Many tree, shrub, and herb species identified in the study area are among the prioritized species for conservation in the Indian Himalayan Region. Thus, scientific interventions and preparation of action plans based on ecological survey are required for conservation of the Solang valley watershed.     

Monitoring global change: Comparison of forest cover estimates using remote sensing and inventory approaches

Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. In this study, forest cover estimates for the United States from a recently developed land surface cover map generated from satellite remote sensing data were compared to state-level inventory data from the U.S. National Resources Planning Act Timber Database. The land cover map was produced at the U.S. Geological Survey EROS Data Center and is based on imagery from the AVHRR sensor (spatial resolution 1.1 km). Vegetation type was classified using the temporal signal in the Normalized Difference Vegetation Index derived from AVHRR data. Comparisons revealed close agreement in the estimate of forest cover for extensively forested states with large polygons of relatively similar vegetation such as Oregon. Larger forest cover differences were observed in other states with some regional patterns in the level of agreement apparent.Comparisons in inventory- and remote sensing-based estimates of current forested area with potential vegetation maps indicated the magnitude of past land use change and the potential for future changes. The remote sensing approach appears to hold promise for conducting surveys of forest cover where inventory data are limited or where rates of vegetation change, due to human or climatic factors, are rapid.     

Detecting vegetation cover change on the summit of Cadillac Mountain using multi-temporal remote sensing datasets: 1979, 2001, and 2007

This study examines the efficacy of management strategies implemented in 2000 to reduce visitor-induced vegetation impact and enhance vegetation recovery at the summit loop trail on Cadillac Mountain at Acadia National Park, Maine. Using single-spectral high-resolution remote sensing datasets captured in 1979, 2001, and 2007, pre-classification change detection analysis techniques were applied to measure fractional vegetation cover changes between the time periods. This popular sub-alpine summit with low-lying vegetation and attractive granite outcroppings experiences dispersed visitor use away from the designated trail, so three pre-defined spatial scales (small, 0-30 m; medium, 0-60 m; and large, 0-90 m) were examined in the vicinity of the summit loop trail with visitor use (experimental site) and a site chosen nearby in a relatively pristine undisturbed area (control site) with similar spatial scales. Results reveal significant changes in terms of rates of vegetation impact between 1979 and 2001 extending out to 90 m from the summit loop trail with no management at the site. No significant differences were detected among three spatial zones (inner, 0-30 m; middle, 30-60 m; and outer, 60-90 m) at the experimental site, but all were significantly higher rates of impact compared to similar spatial scales at the control site (all p?< 0.001). In contrast, significant changes in rates of recovery between 2001 and 2007 were observed in the medium and large spatial scales at the experimental site under management as compared to the control site (all p?< 0.05). Also during this later period a higher rate of recovery was observed in the outer zone as compared to the inner zone at the experimental site (p?< 0.05). The overall study results suggest a trend in the desired direction for the site and visitor management strategies designed to reduce vegetation impact and enhance vegetation recovery at the summit loop trail of Cadillac Mountain since 2000. However, the vegetation recovery has been rather minimal and did not reach the level of cover observed during the 1979 time period. In addition, the advantages and some limitations of using remote sensing technologies are discussed in detecting vegetation change in this setting and potential application to other recreation settings.     

Mining and environmental change in Sierra Leone, West Africa: a remote sensing and hydrogeomorphological study

This paper evaluates the environmental changes in southwestern Sierra Leone, West Africa from rutile (titanium dioxide) between 1967 and 1995. Mining in peripheral parts of the world economy is a consequence of larger global economic interests. Historically, long-distance trade and export production of minerals and other natural resources primarily for the benefit of core countries are responsible for transforming the natural environment and landscapes of peripheral sectors of the world economy. Tracking environmental change in developing countries such as Sierra Leone is challenging because of financial and infrastructural constraints on the use of ground methods of evaluation and monitoring. Remote sensing data are invaluable in assessing the human dimensions of Land Use and Land Cover Change (LULCC) with implications for political ecology. Using available multi-date infrared Landsat images supplemented with field hydrological and biophysical data, we monitored the rapid temporal and spatial dynamic characteristic of mining areas in the study area with a focus on physical changes to the landscape. Reservoir construction for mining has caused flooding of alluvial lowlands, deforestation, and the creation of tailings and stockpiles over mined-out portions of the lease. Although the study was conducted at a local scale, it represents the broad, regional, past-to-present manner by which global economic interests exploit natural resources and impact the environment in distant places.     

Vegetation dynamics, and land use and land cover change in the Bale Mountains, Ethiopia

Shifts in biological communities are occurring at rapid rates as human activities induced global climate change increases. Understanding the effects of the change on biodiversity is important to reduce loss of biodiversity and mass extinction, and to insure the long-term persistence of natural resources and natures’ services. Especially in remote landscapes of developing countries, precise knowledge about on-going processes is scarce. Here we apply satellite imagery to assess spatio-temporal land use and land cover change (LULCC) in the Bale Mountains for a period of four decades. This study aims to identify the main drivers of change in vegetation patterns and to discuss the implications of LULCC on spatial arrangements and trajectories of floral communities. Remote sensing data acquired from Landsat MSS, Landsat ETM + and SPOT for four time steps (1973, 1987, 2000, and 2008) were analyzed using 11 LULC units defined based on the dominant plant taxa and cover types of the habitat. Change detection matrices revealed that over the last 40?years, the area has changed from a quite natural to a more cultural landscape. Within a representative subset of the study area (7,957.5?km^?2), agricultural fields have increased from 1.71% to 9.34% of the total study area since 1973. Natural habitats such as upper montane forest, afroalpine grasslands, afromontane dwarf shrubs and herbaceous formations, and water bodies also increased. Conversely, afromontane grasslands have decreased in size by more than half (going from 19.3% to 8.77%). Closed Erica forest also shrank from 15.0% to 12.37%, and isolated Erica shrubs have decreased from 6.86% to 5.55%, and afroalpine dwarf shrubs and herbaceous formations reduced from 5.2% to 1.56%. Despite fluctuations the afromontane rainforest (Harenna forest), located south of the Bale Mountains, has remained relatively stable. In conclusion this study documents a rapid and ecosystem-specific change of this biodiversity hotspot due to intensified human activities (e.g., deforestation, agriculture, infrastructure expansion). Specifically, the ecotone between the afromontane and the afroalpine area represent a “hotspot of biodiversity loss” today. Taking into consideration the projections of regional climate warming and modified precipitation regimes, LULCC can be expected to become even more intensive in the near future. This is likely to impose unprecedented pressures on the largely endemic biota of the area.     

Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924–1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km² (52.5 %), 56,661.1 km² (36.4 %), 51,642.3 km² (33.2 %), 49,773 km² (32 %) and 48,669.4 km² (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 %?year^?1 during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km²) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.     

Estimation of underwater visibility in coastal and inland waters using remote sensing data

An optical method is developed to estimate water transparency (or underwater visibility) in terms of Secchi depth (Z _sd ), which follows the remote sensing and contrast transmittance theory. The major factors governing the variation in Z _sd , namely, turbidity and length attenuation coefficient (1/(c + K _d ), c = beam attenuation coefficient; K _d  = diffuse attenuation coefficient at 531 nm), are obtained based on band rationing techniques. It was found that the band ratio of remote sensing reflectance (expressed as (R _rs (443) + R _rs (490))/(R _rs (555) + R _rs (670)) contains essential information about the water column optical properties and thereby positively correlates to turbidity. The beam attenuation coefficient (c) at 531 nm is obtained by a linear relationship with turbidity. To derive the vertical diffuse attenuation coefficient (K _d ) at 531 nm, K _d (490) is estimated as a function of reflectance ratio (R _rs (670)/R _rs (490)), which provides the bio-optical link between chlorophyll concentration and K _d (531). The present algorithm was applied to MODIS-Aqua images, and the results were evaluated by matchup comparisons between the remotely estimated Z _sd and in situ Z _sd in coastal waters off Point Calimere and its adjoining regions on the southeast coast of India. The results showed the pattern of increasing Z _sd from shallow turbid waters to deep clear waters. The statistical evaluation of the results showed that the percent mean relative error between the MODIS-Aqua-derived Z _sd and in situ Z _sd values was within ±25%. A close agreement achieved in spatial contours of MODIS-Aqua-derived Z _sd and in situ Z _sd for the month of January 2014 and August 2013 promises the model capability to yield accurate estimates of Z _sd in coastal, estuarine, and inland waters. The spatial contours have been included to provide the best data visualization of the measured, modeled (in situ), and satellite-derived Z _sd products. The modeled and satellite-derived Z _sd values were compared with measurement data which yielded RMSE = 0.079, MRE = ?0.016, and R ²  = 0.95 for the modeled Z _sd and RMSE = 0.075, MRE = 0.020, and R ²  = 0.95 for the satellite-derived Z _sd products.     

Water quality monitoring of Al-Habbaniyah Lake using remote sensing and in situ measurements

Environmental Monitoring and Assessment - A good quality drinking water is essential for human survival. However, a large percentage of the populations in most developing countries (Nigeria...     

Decadal time-scale monitoring of forest fires in Similipal Biosphere Reserve,India using remote sensing and GIS

Analyzing the spatial extent and distribution of forest fires is essential for sustainable forest resource management. There is no comprehensive data existing on forest fires on a regular basis in Biosphere Reserves of India. The present work have been carried out to locate and estimate the spatial extent of forest burnt areas using Resourcesat-1 data and fire frequency covering decadal fire events (2004–2013) in Similipal Biosphere Reserve. The anomalous quantity of forest burnt area was recorded during 2009 as 1,014.7 km². There was inconsistency in the fire susceptibility across the different vegetation types. The spatial analysis of burnt area shows that an area of 34.2 % of dry deciduous forests, followed by tree savannah, shrub savannah, and grasslands affected by fires in 2013. The analysis based on decadal time scale satellite data reveals that an area of 2,175.9 km² (59.6 % of total vegetation cover) has been affected by varied rate of frequency of forest fires. Fire density pattern indicates low count of burnt area patches in 2013 estimated at 1,017 and high count at 1,916 in 2004. An estimate of fire risk area over a decade identifies 12.2 km² is experiencing an annual fire damage. Summing the fire frequency data across the grids (each 1 km²) indicates 1,211 (26 %) grids are having very high disturbance regimes due to repeated fires in all the 10 years, followed by 711 grids in 9 years and 418 in 8 years and 382 in 7 years. The spatial database offers excellent opportunities to understand the ecological impact of fires on biodiversity and is helpful in formulating conservation action plans.     

The potential of remote sensing for monitoring land cover changes and effects on physical geography in the area of Kayisdagi Mountain and its surroundings (Istanbul)

The effect of land cover change, from natural to anthropogenic, on physical geography conditions has been studied in Kayisdagi Mountain. Land degradation is the most important environmental issue involved in this study. Most forms of land degradation are natural processes accelerated by human activity. Land degradation is a human induced or natural process that negatively affects the ability of land to function effectively within an ecosystem. Environmental degradation from human pressure and land use has become a major problem in the study area because of high population growth, urbanization rate, and the associated rapid depletion of natural resources. When studying the cost of land degradation, it is not possible to ignore the role of urbanization. In particular, a major cause of deforestation is conversion to urban land. The paper reviews the principles of current remote sensing techniques considered particularly suitable for monitoring Kayisdagi Mountain and its surrounding land cover changes and their effects on physical geography conditions. In addition, this paper addresses the problem of how spatially explicit information about degradation processes in the study area rangelands can be derived from different time series of satellite data. The monitoring approach comprises the time period between 1990 and 2005. Satellite remote sensing techniques have proven to be cost effective in widespread land cover changes. Physical geography and particularly natural geomorphologic processes like erosion, mass movement, physical weathering, and chemical weathering features etc. have faced significant unnatural variation.     

Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling

Groundwater development across much of sub-Saharan Africa is constrained by a lack of knowledge on the suitability of aquifers for borehole construction. The main objective of this study was to map groundwater potential at the country-scale for Ghana to identify locations for developing new supplies that could be used for a range of purposes. Groundwater potential zones were delineated using remote sensing and geographical information system (GIS) techniques drawing from a database that includes climate, geology, and satellite data. Subjective scores and weights were assigned to each of seven key spatial data layers and integrated to identify groundwater potential according to five categories ranging from very good to very poor derived from the total percentage score. From this analysis, areas of very good groundwater potential are estimated to cover 689,680 ha (2.9 % of the country), good potential 5,158,955 ha (21.6 %), moderate potential 10,898,140 ha (45.6 %), and poor/very poor potential 7,167,713 ha (30 %). The results were independently tested against borehole yield data (2,650 measurements) which conformed to the anticipated trend between groundwater potential and borehole yield. The satisfactory delineation of groundwater potential zones through spatial modeling suggests that groundwater development should first focus on areas of the highest potential. This study demonstrates the importance of remote sensing and GIS techniques in mapping groundwater potential at the country-scale and suggests that similar methods could be applied across other African countries and regions.