Citation: Nasser, I.A.; Adam, E. Urbanisation in Sub-Saharan Cities and the Implications for Urban Agriculture: Evidence-Based Remote Sensing from Niamey, Niger. Urban Sci. 2024, 8, 5. https://doi.org/ 10.3390/urbansci8010005 Academic Editor: Thomas Blaschke Received: 15 August 2023 Revised: 13 October 2023 Accepted: 24 October 2023 Published: 4 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Urbanisation in Sub-Saharan Cities and the Implications for Urban Agriculture: Evidence-Based Remote Sensing from Niamey, Niger Ibrahim Abdoul Nasser and Elhadi Adam * School of Geography, Archaeology and Environmental Studies, University of The Witwatersrand, Johannesburg 2050, South Africa; 1462908@students.wits.ac.za * Correspondence: elhadi.adam@wits.ac.za; Tel.: +27-117-176-532 Abstract: Urbanisation is the process whereby cities are transformed into large sprawling areas. Urbanisation combined with a continuous increase in population makes food security crucial for sustainable development. Urbanisation poses a threat to agricultural land use within built-up and peri-urban areas. It has resulted in the rapid disappearance and/or total change of agricultural farmland in urban and peri-urban areas. To monitor the changes in agricultural farmland, an understanding of changes in the urban landscape is becoming increasingly important. In this study, multi-temporal Landsat imagery were used to analyse the impact of urbanisation on urban agriculture in the city of Niamey. Changes in the urban landscape were determined using the support vector machine (machine learning) algorithm. Results of this study showed a decrease in land with crops from 3428 ha to 648 ha and an increase in built-up areas from 1352 ha to 11,596 ha between 1975 and 2020. Urbanisation and population growth are the main drivers of urban landscape change in Niamey. There was also a decrease in bare land, rock and vegetation classes, while a small increase in rice and water body classes, comparing the 1975 and 2020 values. This study demonstrates the importance of remote sensing in showing the implications of urbanisation on urban agriculture. These results can assist city planners and resource managers in decision-making and adoption of sustainable mitigation measures which are crucial for urban development. Keywords: urbanisation; urban agriculture; urban landscape; Landsat; change detection 1. Introduction Urbanisation is a megatrend that has been experienced by most sub-Saharan countries this century. The demographic definition of urbanisation is the increasing share of a nation’s population living in urban areas [1]. This results in a physical expansion of the built environment to house the urban population and their activities [2]. Rapid urbanisation and city growth are caused by several different factors, including rural–urban migration, natural population increase and annexation [3]. However, studies have shown that in most countries around the world, the built environment in urban areas is expanding faster than urban populations [4]. Whereas urban populations were expected to almost double from 2.6 billion in 2000 to 5 billion in 2030 [5,6], urban areas are forecast to triple in extent between 2000 and 2030 [7]. Studies have shown that urban areas offer economies of scale, richer market structures and social development [3,8]. Despite the high rates of urban poverty associated with the cities in developing countries, urban populations enjoy better basic public services such as access to education, health care, electricity, water and sanitation. However, urbanisation in developing countries is causing many challenges such as degradation of the urban environment, habitat damage, social instability and substantial reduction of cultivable lands [9]. The rapid growth of the urban population outstrips most cities’ capacity and has increased the demand for urban land, which in turn results in higher land values [9,10]. The increase in urban land value and rent wages are key factors Urban Sci. 2024, 8, 5. https://doi.org/10.3390/urbansci8010005 https://www.mdpi.com/journal/urbansci https://doi.org/10.3390/urbansci8010005 https://doi.org/10.3390/urbansci8010005 https://creativecommons.org/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://www.mdpi.com/journal/urbansci https://www.mdpi.com https://orcid.org/0000-0003-3626-5839 https://doi.org/10.3390/urbansci8010005 https://www.mdpi.com/journal/urbansci https://www.mdpi.com/article/10.3390/urbansci8010005?type=check_update&version=1 Urban Sci. 2024, 8, 5 2 of 15 that contribute to the conversion in less developed and developing countries of urban green spaces and urban agricultural lands into new urban development and peri-urban zones [11,12]. Urban and peri-urban agriculture is defined as the production, processing and dis- tribution of food and other products (plant crops and/or livestock) raised in and around cities to provide urban food security [13]. Urban agricultural lands create buffer zones between cities and natural habitats that provide food, fibre, clean air, soil and water to urban areas. This buffer also reduces the impact of urban systems on the environment and wildlife [14,15]. Studies have estimated that (mainly in developing countries) 200 million urban residents produce food for the urban market, providing 15% to 20% of the world’s supply [16]. Despite the importance of this contribution, urban agriculture faces ongoing land insecurity owing to competitive land use [13,15] and unsustainable development of settlements [17]. This is because (particularly in developing countries), city authorities pay little attention to integrating agriculture into the city land use planning and zoning processes. Instead, authorities have emphasised land uses with high bid rents to promote the ‘highest and best use’ [18]. Owing to their spatial proximity to urban areas, urban agri- cultural lands are the first ecosystem affected adversely by urban sprawl. Globally, urban agricultural lands have been increasingly transformed into continuous and discontinuous urban growth with the uneven development of infrastructure and settlements [15,19]. In sub-Saharan Africa, it is widely recognised that urbanisation has progressively altered contemporary urban agriculture, negatively influencing ecosystem processes and services, fragmenting or depleting agriculture land systems [15,18–20]. Niger is a sub-Saharan African country covering about 1.27 million square miles of landlocked area. It is one of the world’s poorest countries and is recurrently affected by famine and drought periods [21]. The country’s capital, Niamey, has been experiencing a high population growth rate of 5.3% per year owing to the high birth rate and large numbers migrating from rural areas into the city [21]. The in-migration to urban areas is contributed to by factors including low soil fertility, erratic rainfall and poor infrastructure that result in low food production and hence poor food security [21]. The city was first settled on the left bank of the Niger River but has progressively expanded on both riverbanks, thus encroaching upon agricultural lands and enticing rural people away from farming [21]. Owing to the increasing demand for growth of the city and expansion of built-up areas, the land in Niamey has become economically attractive, especially along the banks of the Niger River. This has also increased pressure on fertile areas and led to the conversion of arable to building land. The spatial and temporal expansion of Niamey on urban agriculture are still poorly understood [15]. Comprehensive understanding of peri-urban and urban farming and its dynamics over time and space are increasingly required to anticipate possible future trends and contribute to developing effective urban planning policies [20]. The capability of urban farming to feed the growing urban population, especially in developing countries, will depend on detailed spatio-temporal information to enable sustainable urban planning and management [22]. In Niamey, there is still a lack of reliable information on the extent and change of urban and peri-urban land use and land cover (LULC) classes that can be used to promote sustainable urban agriculture [21]. In previous years, costly and time-consuming ground-based mapping techniques were used to capture data on changes in urban LULC classes [23]. However, remote sensing data such as satellite imagery have been a valuable resource in assessing the spatio-temporal dynamics of urban and peri-urban agriculture [24]. Satellite imagery provides cost-effective, up-to-date, accurate and detailed information on LULC classes [25]. The analysis of remote sensing data using robust and reliable change detection algorithms helps analyse the pattern, growth and extent of urbanisation which can enable stakeholders to provide decisions that assist in reducing the negative impacts of urbanisation on the environment [26]. Most previous studies on the impact of urbanisation on urban agriculture were con- ducted in developed countries [27–30] and a few in Africa [31,32]. However, much of the research conducted in Africa up to now has been descriptive and conducted using costly Urban Sci. 2024, 8, 5 3 of 15 survey methods. Previous studies conducted in Niamey have indicated the importance of urban agriculture to the residents [21,33]. According to Bernholt, Kehlenbeck, Gebauer and Buerkert [21], the plant species richness in Niamey changed from 115 species in the cold season, to 110 and 77 species in the hot and rainy seasons, respectively. Graefe, Schlecht and Buerkert [33] highlighted the spatial distribution of urban agriculture activities using ground-based techniques, namely semi-structured interviews. The results from the study indicated that agricultural activities were conducted mainly along the Niger River. How- ever, these previous studies could not cost-effectively analyse the spatio-temporal changes in urban agricultural lands. Past studies in Niamey reported on how urban agriculture has been changing over the years and severely negatively impacted by urbanisation. While it has been demonstrated in other studies that satellite imagery and remote sensing tech- niques can provide vital information for urban planning and environmental development programmes for achieving sustainable urban agriculture, no studies have been carried out to date to analyse the impact of urbanisation on urban agriculture in Niamey. Hence this study analyses the impact of urbanisation on urban agriculture using remote sensing techniques in Niamey over 45 years. 2. Material and Methods 2.1. Study Area Niamey, the capital city of Niger, lies on both banks of the Niger River (Figure 1). The city covers a total area of 239 km2, mostly falling on the northern bank of the Niger River [33]. The population of the city is approximately 1,131,882 [34]. The annual average rainfall is around 540 mm per annum, with average temperatures of 33 ◦C during the hot season and 27 ◦C in the cold season [35]. Intensive horticulture and millet cropping, as well as milk, meat, rice and egg production are the most common agricultural activities in Niamey [21,33]. The Niger River is the main water source for the irrigation of horticultural crops in the city [21]. Urban Sci. 2023, 7, x FOR PEER REVIEW  4  of  16      Figure 1. (a) Administrative regions of Niger, (b) Tillabery region and the city of Niamey, and (c)  Basemap showing the city of Niamey.  2.2. Remote Sensing Data Acquisition  Ten cloud‐free Landsat  images  for  intervals ranging between  three and  five years  from 1975to 2020 (periods shown in Table 1) were used in this study. The images were  obtained  from  the  United  States  Geological  Survey  (USGS)  data  interface  (http://www.usgs.gov/, accessed on 26 May 2023). The Landsat datasets were chosen for  this study based on the success of previous studies that used them for urban agriculture  mapping. They are furthermore freely accessible and provide rich historical data that is  suitable for short‐ and long‐term landscape monitoring [36]. The images were acquired  during the wet seasons, during which there was high vegetation vigour that enhances the  spectral variability between crops and other LULC classes such as built‐up, rock and bare  land areas. We considered the time interval between the 11 Landsat images adequate to  show the changes in urban agriculture.  Table 1. Specifications of the Landsat imageries used in this study.  Satellite  Acquisition Date  Path/Row  Spatial Resolution  Landsat 2 (MSS)  29 September 1975  207/051  60 m  Landsat 2 (MSS)  1 November 1979  207/051  60 m  Landsat 5 (TM)  11 September 1984  193/051  30 m  Landsat 4 (TM)  30 October 1987  193/051  30 m  Landsat 4 (TM)  25 September 1992  193/051  30 m  Landsat 5 (TM)  21 November 1998  193/051  30 m  Landsat 7 (ETM+)  20 August 2002  193/051  30 m  Landsat 5 (TM)  10 August 2007  193/051  30 m  Landsat 7 (ETM+)  18 October 2012  193/051  30 m  Figure 1. (a) Administrative regions of Niger, (b) Tillabery region and the city of Niamey, and (c) Basemap showing the city of Niamey. Urban Sci. 2024, 8, 5 4 of 15 2.2. Remote Sensing Data Acquisition Ten cloud-free Landsat images for intervals ranging between three and five years from 1975to 2020 (periods shown in Table 1) were used in this study. The images were obtained from the United States Geological Survey (USGS) data interface (http://www.usgs.gov/, accessed on 26 May 2023). The Landsat datasets were chosen for this study based on the success of previous studies that used them for urban agriculture mapping. They are furthermore freely accessible and provide rich historical data that is suitable for short- and long-term landscape monitoring [36]. The images were acquired during the wet seasons, during which there was high vegetation vigour that enhances the spectral variability between crops and other LULC classes such as built-up, rock and bare land areas. We considered the time interval between the 11 Landsat images adequate to show the changes in urban agriculture. Table 1. Specifications of the Landsat imageries used in this study. Satellite Acquisition Date Path/Row Spatial Resolution Landsat 2 (MSS) 29 September 1975 207/051 60 m Landsat 2 (MSS) 1 November 1979 207/051 60 m Landsat 5 (TM) 11 September 1984 193/051 30 m Landsat 4 (TM) 30 October 1987 193/051 30 m Landsat 4 (TM) 25 September 1992 193/051 30 m Landsat 5 (TM) 21 November 1998 193/051 30 m Landsat 7 (ETM+) 20 August 2002 193/051 30 m Landsat 5 (TM) 10 August 2007 193/051 30 m Landsat 7 (ETM+) 18 October 2012 193/051 30 m Landsat 8 (OLI/TIRS) 6 September 2017 193/051 30 m Landsat 8 (OLI/TIRS) 16 October 2020 193/051 30 m 2.3. Image Preprocessing Preprocessing of satellite imagery, whether air- or space-borne, is fundamental to ensuring the accurate spatial location of datasets on the surface of the Earth [37]. The Landsat images were preprocessed to remove the effects that may have arisen from, inter alia, solar zenith angle effects, Earth-Sun distance, topography and temporal changes in target features [38]. Because the images used in this study were captured in different years, their solar radiation differs, which might adversely affect the LULC change detection model. To overcome this issue, the top-of-atmosphere reflectance method which uses the Earth-Sun geometry to adjust for differences in solar irradiance and eliminate solar zenith angle effects was used [39]. Atmospheric correction for all the Landsat images was done using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) model in ENVI 5.4 software. The images for 1975 and 1979 were resampled from a spatial resolution of 60 m to 30 m using the nearest neighbourhood resampling method to match the ones for 1984, 1987, 1992, 1998, 2002, 2007, 2012, 2017 and 2020 imageries. The images were geometrically corrected to a root mean square error (RMSE) of less than 0.5, which is in line with the recommendation by Jensen [40]. A destripe function in ENVI 5.4 software was used to reduce the scan pattern that was caused by scan line shifts on the Landsat 7 (ETM+) imagery for 2012. All the images from 1975 to 2017 were geometrically corrected to the 2020 image using the image-to-image registration tool in ENVI 5.4 software. The image projection used in this study was WGS84/Universal Transverse Mercator (UTM) Zone 31 North. 2.4. Urban LULC Classes and Reference Data Collection The training and reference data for all the maps used in this study were collected from Google historical maps and panchromatic images corresponding to the date the Landsat imageries were acquired. The total data samples per year were split into 70% (training samples) and 30% (test samples) as shown in Table 2. The training samples used to http://www.usgs.gov/ Urban Sci. 2024, 8, 5 5 of 15 classify the images and test samples were used to validate the accuracy of the classification model [41,42]. Table 2. The number of training (TR) and test samples (TS) used in this study. The urban LULC classes are bare land (BL), built-up (BU), other crops (OC), rice (RC), rock (RK), vegetation (VG) and waterbody (WB). Year 1975 1979 1984 1987 1992 1998 2002 2007 2012 2017 2020 Class TR TS TR TS TR TS TR TS TR TS TR TS TR TS TR TS TR TS TR TS TR TS BL 154 66 124 53 154 66 205 88 212 91 261 112 161 69 439 188 182 78 103 44 123 52 BU 75 32 196 84 63 27 72 31 103 44 91 39 266 114 98 42 152 65 198 85 243 103 OC 135 58 91 39 107 46 84 36 147 63 107 46 51 22 187 80 168 72 189 81 84 36 RC 110 47 98 42 152 65 201 86 152 65 156 67 93 40 376 161 75 32 86 37 102 43 RK 107 46 189 81 86 37 138 59 86 37 84 36 205 88 68 29 159 68 77 33 66 27 VG 219 94 105 45 79 34 175 75 131 56 126 54 96 41 128 55 138 59 226 97 80 33 WB 182 78 98 42 168 72 147 63 189 81 264 113 91 39 217 93 133 57 107 46 77 32 2.5. Image Classification A supervised classifier was used to classify and validate the urban LULC classes. The support vector machine (SVM) algorithm, first proposed by Vapnik [43] was used to classify all the images used in this study. The SVM classifier is non-parametric and maximises the margin surrounding the hyperplane that separates the points into different classes [44]. Support vectors are the points that constrain the width of the margin [45]. The hyperplane is found using the formula: yi(w × xi + b) ≥ 1 − ξi (1) in which w stands for the coefficient vector that determines the orientation of the hyperplane in the feature space [43]. The origin’s offset of the hyperplane is represented by b; and ξi stands for the positive slack variables [43]. The optimal hyperplane is determined by solving the optimisation problem as follows: Minimise n ∑ i=1 αi − 1 2 n ∑ i−1 n ∑ j=1 αiαjyiyj ( xixj ) (2) subject to n ∑ i=1 αiyj = 0, 0 ≤ αi ≤ C (3) in which αi stands for the Lagrange multiplier and C represents the penalty [43]. For the classification of linear data, the decision function is applied as follows: g(x) = sign ( n ∑ i=1 yiαixi + b ) (4) If the dataset is non-linear, the decision function as shown in Equation (4) is rewritten as follows: g(x) = sign ( n ∑ i=1 yiαiK(xi , xj ) + b ) (5) In the classification of non-linear data, the dataset is transformed into a higher di- mensional space using a kernel function (K). Four K types are commonly used in LULC classification studies, and these are sigmoid, radial basis function, linear and polyno- mial [46,47]. However, several studies have proven that the radial basis function is superior to other K’s in data classification [46–48]. The radial basis function kernel requires the tuning of the ‘cost’ (C) and the ‘gamma’ (γ) parameters, which can affect the classification accuracy [46]. The optimal C and γ parameters are selected using a comprehensive search method using a large number of data samples [49]. In this study, the 10-fold cross-validation technique was used to search for the optimal C and γ parameters. The optimal C and γ parameters used in this study were 120 and 1, respectively. Urban Sci. 2024, 8, 5 6 of 15 2.6. Classification Accuracy Confusion matrices were used to assess the classification accuracies of the Landsat imageries used in this study. The matrices compare the true classes with the ones allocated by the SVM classifier on the resultant maps [37]. The classified maps were assessed using Google historical maps and respective panchromatic images. Confusion matrices using the test data samples were then used to compute the kappa statistic, overall, producer’s and user’s accuracies. The kappa statistic evaluates if there is an agreement between the classifier and the reference data [50]. Overall accuracy is calculated by averaging the correctly classified TS among all the classes [50]. The producer’s accuracy determines the possibility of a data sample on the ground is correctly classified, while the user’s accuracy ascertains the possibility that a data sample belongs to a particular class and the classifier correctly assigned to it [51]. 2.7. Change Detection Change detection analysis in remote sensing studies is a broad process used to identify and quantify differences between two satellite images of the same scene at different times. The univariate image differencing method was applied to all the Landsat imageries in this study. In this method, spatially registered imagesries of time t_2 subtracts t_1, to produce an image that shows changes that happened between the time period. The method is mathematically calculated as follows: Dxk ij = xk ij(t2)− xk ij(t1) + C (6) where t1 is the first time, t2 is the second time, xk ij stands for the pixel value for band k. The pixel numbers on the image are represented by i and j. The constant to produce positive digital numbers in the calculation process is represented by C. In this study, the change detection statistics were tabulated showing the changes in images from 1975–1979; 1979–1984; 1984–1987; 1987–1992; 1992–1998; 1998–2002; 2002–2007; 2007–2012; 2012–2017; and 2017–2020. The changes for the LULC classes were expressed in hectares (ha). 3. Results 3.1. Urban Landscape Change The classification results showed that most urban farmlands are along the Nile River and built-up structures were mainly situated at the centre of the study area (Figure 2). Limited areas of rice crops, rocks and water bodies were also found in the study area (Figure 2). There was an increase in built-up area from 1353.34 ha to 20,687 ha and a decrease in area for the other crops category from 2791.55 to 752 ha between 1975 and 2020 (Figure 3). The was an increase in bare land from 18,038 ha to 20,870 ha, while the area covered by vegetation decreased from 5345 ha to 3160 ha from 2017 to 2020 (Figure 3). The urban landscape change in the study area between 1975 and 2020 is shown in Figure 3. Urban Sci. 2024, 8, 5 7 of 15 Urban Sci. 2023, 7, x FOR PEER REVIEW  7  of  16    produce  an  image  that  shows  changes  that  happened  between  the  time  period.  The  method is mathematically calculated as follows:  𝐷𝑥 𝑥 𝑡 𝑥 𝑡 𝐶  (6) where  𝑡   is the first time,  𝑡   is the second time,  𝑥   stands for the pixel value for band k.  The pixel numbers on the image are represented by i and j. The constant to produce posi‐ tive digital numbers  in  the  calculation process  is  represented by C.  In  this  study,  the  change detection statistics were tabulated showing the changes  in  images from 1975 to  1979; 1979 to 1984; 1984 to 1987; 1987 to 1992; 1992 to 1998; 1998 to 2002; 2002 to 2007; 2007  to 2012; 2012 to 2017; and 2017 to 2020. The changes for the LULC classes were expressed  in hectares (ha).    3. Results  3.1. Urban Landscape Change  The classification results showed that most urban farmlands are along the Nile River  and built‐up structures were mainly situated at the centre of the study area (Figure 2).  Limited areas of  rice crops,  rocks and water bodies were also  found  in  the study area  (Figure 2).      Figure 2. Urban LULC maps for Niamey from 1975 to 2020.  There was an increase in built‐up area from 1353.34 ha to 20,687 ha and a decrease in  area for the other crops category from 2791.55 to 752 ha between 1975 and 2020 (Figure 3).  The was an increase in bare land from 18,038 ha to 20,870 ha, while the area covered by  vegetation decreased  from 5345 ha  to 3160 ha  from 2017  to 2020  (Figure 3). The urban  landscape change in the study area between 1975 and 2020 is shown in Figure 3.  Figure 2. Urban LULC maps for Niamey from 1975 to 2020. Urban Sci. 2023, 7, x FOR PEER REVIEW  8  of  16      Figure 3. Urban LULC distribution for the city of Niamey between 1975 to 2020. The LULC classes  are bare land (BL), built‐up (BU), other crops (OC), rice (RC), rock (RK), vegetation (VG) and water‐ body (WB).  3.2. Inter‐Annual Urban Landscape Change  The highest urban landscape change was from vegetation to bare land, with a value  of 15 888 ha between 2002 and 2007, while the lowest changes were from water bodies to  built‐up, other crops and the rock class  in the year ranges 1975–1979, 1984–1987, 2007– 2012, 2012–2017 and 2017–2020 (Table 3). The detailed inter‐annual and interclass urban  landscape variability over the years of study are provided in Table 3. The urban landscape  per class in the years of study is shown in Figure 4. The highest urban landscape change  (−402%) was  for  the  rock class between 2002 and 2007  (Figure 4). There were positive  changes for the built‐up class in all the years of study with the highest value of 88% in  2020 (Figure 4). There was an increase in built‐up areas and a reduction in urban agricul‐ ture (Figure 5). The highest value  (11,596 ha) was for  the built‐up class and  the  lowest  value (648 ha) was for urban agriculture in 2020 (Figure 5). The changes in urban agricul‐ ture and built‐up over the years in Niamey are shown in Figure 5.    Table 3. Change detection statistics of urban LULC classes for Niamey between 1975 and 2020. The  LULC classes are bare land (BL), built‐up (BU), other crops (OC), rice (RC), rock (RK), vegetation  (VG) and waterbody (WB).  CLASS  BL  BU  OC  RC  RK  VG  WB  1975–1979  BL  19,332  1002  235  185  1549  10,206  144  BU  307  886  18  2  58  81  0  OC  212  64  287  265  250  30  3  RC  33  1  76  670  232  0  8  RK  5481  117  9  16  440  400  2  VG  4966  122  139  644  2600  1415  40  WB  85  21  21  18  63  0  1311  1979–1984  Figure 3. Urban LULC distribution for the city of Niamey between 1975 to 2020. The LULC classes are bare land (BL), built-up (BU), other crops (OC), rice (RC), rock (RK), vegetation (VG) and waterbody (WB). Urban Sci. 2024, 8, 5 8 of 15 3.2. Inter-Annual Urban Landscape Change The highest urban landscape change was from vegetation to bare land, with a value of 15 888 ha between 2002 and 2007, while the lowest changes were from water bod- ies to built-up, other crops and the rock class in the year ranges 1975–1979, 1984–1987, 2007–2012, 2012–2017 and 2017–2020 (Table 3). The detailed inter-annual and interclass urban landscape variability over the years of study are provided in Table 3. The urban landscape per class in the years of study is shown in Figure 4. The highest urban landscape change (−402%) was for the rock class between 2002 and 2007 (Figure 4). There were positive changes for the built-up class in all the years of study with the highest value of 88% in 2020 (Figure 4). There was an increase in built-up areas and a reduction in urban agriculture (Figure 5). The highest value (11,596 ha) was for the built-up class and the lowest value (648 ha) was for urban agriculture in 2020 (Figure 5). The changes in urban agriculture and built-up over the years in Niamey are shown in Figure 5. Table 3. Change detection statistics of urban LULC classes for Niamey between 1975 and 2020. The LULC classes are bare land (BL), built-up (BU), other crops (OC), rice (RC), rock (RK), vegetation (VG) and waterbody (WB). CLASS BL BU OC RC RK VG WB 1975–1979 BL 19,332 1002 235 185 1549 10,206 144 BU 307 886 18 2 58 81 0 OC 212 64 287 265 250 30 3 RC 33 1 76 670 232 0 8 RK 5481 117 9 16 440 400 2 VG 4966 122 139 644 2600 1415 40 WB 85 21 21 18 63 0 1311 1979–1984 BL 21,578 1074 219 43 4266 3055 180 BU 657 1010 39 11 17 433 46 OC 308 5 156 59 14 119 126 RC 536 13 59 817 17 119 242 RK 2434 105 233 333 905 880 303 VG 10,915 172 50 1 153 835 1 WB 23 2 1 4 0 7 1471 1984–1987 BL 28,022 543 944 236 1480 5202 25 BU 814 1327 34 8 3 196 1 OC 125 56 129 13 2 427 0 RC 347 66 229 395 11 210 4 RK 1346 30 20 7 3434 539 0 VG 1547 454 233 49 1295 1869 4 WB 151 21 30 320 2 77 1769 1987–1992 BL 17,316 1 895 1124 569 1123 10,183 141 BU 636 1512 70 83 97 66 32 OC 766 14 440 355 6 37 2 RC 116 8 25 680 1 142 55 RK 2219 38 12 14 3578 355 10 VG 6200 262 582 386 448 581 63 WB 17 1 1 48 3 200 1534 Urban Sci. 2024, 8, 5 9 of 15 Table 3. Cont. CLASS BL BU OC RC RK VG WB 1992–1998 BL 15,993 1510 1072 185 966 7432 111 BU 492 2520 130 10 148 397 34 OC 1118 122 505 117 20 347 26 RC 238 44 410 922 65 231 222 RK 730 158 8 8 3274 1079 1 VG 2875 279 83 44 765 7141 376 WB 9 6 6 14 3 70 1730 1998–2002 BL 16,223 906 1035 71 2075 1135 9 BU 393 3414 233 31 415 152 2 OC 353 383 589 200 95 589 5 RC 56 67 164 598 10 397 7 RK 1274 287 480 14 2192 995 2 VG 13,538 480 247 287 1776 299 68 WB 99 78 43 612 16 75 1577 2002–2007 BL 12,387 967 2168 250 203 15,888 38 BU 393 3814 462 179 6 773 5 OC 323 258 544 304 183 1194 3 RC 350 12 56 1210 0 45 152 RK 3178 646 359 24 283 2083 2 VG 232 126 398 835 637 1398 11 WB 127 0 2 38 0 10 1490 2007–2012 BL 3426 1592 76 118 2650 8711 412 BU 493 4916 15 61 21 276 19 OC 1963 544 609 386 13 380 74 RC 183 160 3 1212 10 274 987 RK 203 18 0 5 1147 33 0 VG 13,294 2081 322 527 880 4203 67 WB 2 8 0 10 0 37 1625 2012–2017 BL 6290 87 249 61 460 9921 14 BU 22 7593 952 873 8 312 70 OC 8843 1 475 338 103 888 2198 35 RC 11 33 361 692 5 79 55 RK 231 90 24 2 3344 940 0 VG 255 2167 377 56 412 23 WB 216 142 128 1 18 523 2026 2017–2020 BL 4290 2 984 1698 45 352 5823 52 BU 5765 10,525 425 1063 106 215 12 OC 1362 265 555 61 25 563 56 RC 11 44 153 691 5 85 5 RK 587 152 12 3 3758 26 0 VG 214 5469 1087 55 326 2603 55 WB 116 124 128 1 18 52 2025 Urban Sci. 2024, 8, 5 10 of 15 Urban Sci. 2023, 7, x FOR PEER REVIEW  10  of  16    RC  183  160  3  1212  10  274  987  RK  203  18  0  5  1147  33  0  VG  13,294  2081  322  527  880  4203  67  WB  2  8  0  10  0  37  1625  2012–2017  BL  6290  87  249  61  460  9921  14  BU  22  7593  952  873  8  312  70  OC  8843  1 475  338  103  888  2198  35  RC  11  33  361  692  5  79  55  RK  231  90  24  2  3344  940  0  VG  255  2167  377  56  412      23  WB  216  142  128  1  18  523  2026  2017–2020  BL  4290  2 984  1698  45  352  5823  52  BU  5765  10,525  425  1063  106  215  12  OC  1362  265  555  61  25  563  56  RC  11  44  153  691  5  85  5  RK  587  152  12  3  3758  26  0  VG  214  5469  1087  55  326  2603  55  WB  116  124  128  1  18  52  2025    Figure 4. Changes in LULC classes between 1975 and 2020 in Niamey. The LULC classes are (BL =  Bare  land, BU = Built‐up, OC = Other crops, RC = Rice, RK = Rock, VG = Vegetation, and WB =  Waterbody).  -500 -400 -300 -200 -100 0 100 200 BL BU OC RC RK VG WB C h an ge in p er ce n ta ge ( % ) 1975-1979 1979-1984 1984-1987 1987-1992 1992-1997 1997-2002 2002-2007 2007-2012 2012-2017 2017-2020 Figure 4. Changes in LULC classes between 1975 and 2020 in Niamey. The LULC classes are (BL = Bare land, BU = Built-up, OC = Other crops, RC = Rice, RK = Rock, VG = Vegetation, and WB = Waterbody). Urban Sci. 2023, 7, x FOR PEER REVIEW  11  of  16      Figure 5. Changes in the extent of built‐up and urban agriculture from 1975 to 2020 in hectares  (ha).  3.3. Validation  The overall accuracies for all the maps were greater than 85%, which is a threshold  value recommended by Anderson [52] in LULC classification, as shown in Table 3. The  kappa coefficients for all the classified maps were all above 80%, with the highest value  of 96% for 2020, and the lowest value of 84% for 1975 (Table 4). The user’s accuracies had  the lowest value of 59% for the built‐up class in 1975 (Table 4). The user’s accuracies for  the water body class were all ≥ 95%  (Table 4). The producer’s accuracies produced the  lowest value of 59% for the built‐up class in 1975, while the values for the waterbody class  were all ≥99% (Table 3).    Table 4. Confusion matrices showing overall accuracy (OA), producer’s accuracy (PA) and Kappa  coefficient value for 1975, 1979, 1984, 1987, 1992, 1998, 2002, 2007, 2012, 2017 and 2020. The LULC  classes are (BL = Bare land, BU = Built‐up, OC = Other crops, RC = Rice, RK = Rock, VG = Vegetation,  and WB = Waterbody).    Class    BL  BU  OC  RC  RK  VG  WB    Type    UA  PA  UA  PA  UA  PA  UA  PA  UA  PA  UA  PA  UA  PA  Year  OA  Kappa                              1975  87%  84  89  83  59  59  78  85  98  98  91  83  85  85  96  100  1979  88%  85  86  93  94  88  90  69  84  91  83  86  78  84  100  100  1984  93%  91  97  96  83  93  86  80  100  100  97  95  70  77  100  99  1987  95%  94  99  100  91  94  83  83  99  98  95  88  87  91  100  100  1992  93%  91  90  98  95  84  91  78  100  100  94  92  80  90  98  100  1998  94%  92  97  96  83  90  79  89  98  93  95  97  92  82  98  99  2002  92%  90  100  100  96  99  48  46  93  95  99  94  68  68  100  100  Figure 5. Changes in the extent of built-up and urban agriculture from 1975 to 2020 in hectares (ha). Urban Sci. 2024, 8, 5 11 of 15 3.3. Validation The overall accuracies for all the maps were greater than 85%, which is a threshold value recommended by Anderson [52] in LULC classification, as shown in Table 3. The kappa coefficients for all the classified maps were all above 80%, with the highest value of 96% for 2020, and the lowest value of 84% for 1975 (Table 4). The user’s accuracies had the lowest value of 59% for the built-up class in 1975 (Table 4). The user’s accuracies for the water body class were all ≥ 95% (Table 4). The producer’s accuracies produced the lowest value of 59% for the built-up class in 1975, while the values for the waterbody class were all ≥ 99% (Table 3). Table 4. Confusion matrices showing overall accuracy (OA), producer’s accuracy (PA) and Kappa coefficient value for 1975, 1979, 1984, 1987, 1992, 1998, 2002, 2007, 2012, 2017 and 2020. The LULC classes are (BL = Bare land, BU = Built-up, OC = Other crops, RC = Rice, RK = Rock, VG = Vegetation, and WB = Waterbody). Class BL BU OC RC RK VG WB Type UA PA UA PA UA PA UA PA UA PA UA PA UA PA Year OA Kappa 1975 87% 84 89 83 59 59 78 85 98 98 91 83 85 85 96 100 1979 88% 85 86 93 94 88 90 69 84 91 83 86 78 84 100 100 1984 93% 91 97 96 83 93 86 80 100 100 97 95 70 77 100 99 1987 95% 94 99 100 91 94 83 83 99 98 95 88 87 91 100 100 1992 93% 91 90 98 95 84 91 78 100 100 94 92 80 90 98 100 1998 94% 92 97 96 83 90 79 89 98 93 95 97 92 82 98 99 2002 92% 90 100 100 96 99 48 46 93 95 99 94 68 68 100 100 2007 91% 89 99 88 80 98 92 83 98 99 67 90 67 73 95 99 2012 91% 89 95 95 90 95 91 79 83 91 94 93 79 83 100 100 2017 93% 92 98 98 99 94 85 89 94 84 92 100 90 91 100 100 2020 96% 96 96 98 98 96 94 94 93 98 96 96 94 91 100 100 4. Discussion The multi-temporal urban landscape change analysis on the eleven Landsat imageries shows that there have been LULC changes in Niamey (Figure 3). There was an increase in the built-up areas and a decrease in areas with rice, as well as with other crops (Figure 3). This is due to population increase where agricultural land is cleared for built-up structures. This is in line with previous studies [53–55] which note that there has been an increase in settlements owing to population growth. This has resulted in changes in other LULC classes such as urban agriculture land. The population growth in Niamey is supported by Statista [56] which highlights that the city is one of the fastest-growing cities in Africa with an estimated growth rate of 101%, which has an impact on its economy and agricultural activities. The decrease in the land covered by rice, as well as the category of other crops, is in line with Balineau, et al. [57] who noted that there has been a reduction in urban agriculture in Niamey owing to competition between agricultural land and other productive sectors and residential uses. The major noticeable change was from vegetation to bare land—with a value of 15 888 ha between 2002 and 2007 (Table 3 and Figure 4). The loss of vegetation cover in the study area between 2003 and 2007 was caused by low rainfall and urbanisation (where trees were cut to clear the land for built-up structures) [58]. The loss of land by 23%, 306% and 79% for bare land, other crops, and vegetation, respectively (Figure 4), is attributed to urbanisation in the city. This is supported by Salamatou, Abdoulaye, Boubacar, Abou Soufianou, Ali and Mahamane [54] who stated that the growing spatial demand from the rising urban population in Niamey has led to a decrease in the area covered by crops, bare soil and vegetation. The rise in population has also been due to the in-migration of people from rural areas in Niger because of repeated droughts and food crises [33]. There are high fertility rates and declining mortality rates in Niamey which has created pressure on the Urban Sci. 2024, 8, 5 12 of 15 city because of the increase in built-up structures [59]. The construction of buildings on the outskirts of the city has also resulted in the loss of land for crop cultivation [59]. There was massive vegetation loss in Niamey between 1997 and 2002 (Figure 4). The loss of this vegetation was due to agricultural land clearing, grazing, climate change and urban fuel demand [60]. This was the result of a high rate of in-migration stemming from environmentally induced economic movement leading to deforestation to clear land for agriculture and settlements [61]. The clearing of land was mainly done along the Niger River where agricultural fields are mainly located and this has resulted in exacerbation of flooding events owing to increased runoff [62]. There was a loss of vegetation (−123%) between 1997 and 2002, as shown in Figure 4, which was a result of repeated droughts [55]. However, there were increases in the extent of vegetation during the years 1975–1979, 1984–1987, 2002–2007 and 2012–2017. The increase in vegetation is due to tree planting and management techniques. Some of the tree planting programmes such as Operation Sahel Vert (Operation Green Sahel) and Fête nationale de l’Arbre (National Tree Day) launched by Seyni Kountché in 1975 encouraged youth people to plant trees across Niger to reduce desertification and extend his rule across all the country’s territories and populations [63]. This led to an increase in vegetation across the city, and over 60 million trees were planted across the country [55]. There was an inverse change in the area covered by built-up structures and crops (Figure 5). The decrease in the area with crops has been one of the main issues in Niamey and it has resulted in food insecurity in the city. The National Adaptation Programme Action (NAPA) and Plan National de l’Environnement pour un Développement Durable (PNEDD) formed to reduce desertification and improve the daily lives of people in Niger identified that the decrease in land with crops is due to climate hazards such as droughts, extreme temperatures, strong winds, flooding dust storms and insect infestations [59]. These climate hazards have hurt the economy with a decrease in fishery productivity, groundwater depletion, formation of dunes and increased death of livestock. The increase in the population in Niamey is due to the in-migration of many young men into the city to work for their families; they move to the city in the hope of finding employment, and to ward off poverty and food insecurity [64]. The results of this study correspond to the United Nations [65] study which noted that the population in Niamey grew from 198,099 in 1975 to approximately 1.3 million residents in 2020. The OA results for all the classified maps in this study were high, with all of them greater than 85% (Table 3). The high accuracies were due to the SVM classifier’s strength in handling numerical data without assuming data distribution, and its ability to derive sets of valuable features from all the classes to characterise the urban environment [26,66]. The user’s and producer’s accuracies for the maps were high with maximum values of 100% (Table 3). This was due to the classifier’s ability to deal with noisy data where outliers effects were encountered because of atmospheric and topographic distortions. On the other hand, the lowest user’s and producer’s accuracies (59%) were for the built-up class in 1975. This was because the 1975 image was resampled from 60 m to 30 m pixel size using the nearest neighbourhood resampling method, which reduces the image quality and the image’s geolocation accuracy [67]. The results of this study show the importance of remote sensing in showing the effects of urbanisation on urban agriculture over the years. This can assist city planners, farmers, economists, policymakers and resource managers in making effective decisions for sustainable urban development in the rapidly changing city. This study was done at the city level, and it would be of greater value if scaled up to the district or national level to analyse the effects of urbanisation on the cities which can be used to create an urban farming inventory. The urban farming inventory can be used to reduce food insecurities and poverty in the country. The SVM classifier and the free Landsat images used in this study can provide planners, farmers and the government with a powerful toolset to analyse assess and evaluate the spatio-temporal changes of the urban landscape at the local level Urban Sci. 2024, 8, 5 13 of 15 or elsewhere. They can also assist roleplayers to understand the drivers responsible for the changes. 5. Conclusions This study contributes to the growing scientific literature on the use of low-cost remote sensing methods in analysing the impacts of urbanisation on urban agriculture over a long period. In this study, a detailed analysis of the impact of urbanisation in Niamey (Niger) over 45 years was performed and the results were presented in urban LULC maps. This study shows that urbanisation has caused significant changes in the landscape from 1975 to 2020. Specifically, there was a decline in bare land, rice, other crops, rocks, vegetation and water bodies and an increase in the built-up areas. The use of remote sensing methods and ancillary datasets is important in understanding the spatio-temporal urban LULC changes. Research into such changes at the city level is of paramount importance as it forms a critical basis for decision-making, policy formulation and administration for sustainable urban development. Author Contributions: Conceptualization, I.A.N. and E.A.; methodology, I.A.N. and E.A.; software, I.A.N.; validation, I.A.N., and E.A.; formal analysis, I.A.N.; data curation, I.A.N.; writing—original draft preparation, I.A.N.; writing—review and editing, I.A.N. and E.A.; visualization, E.A.; supervi- sion, E.A. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The data will be available upon request. 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MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. https://doi.org/10.1080/01431160903505302 https://doi.org/10.1080/23311886.2020.1754146 https://doi.org/10.1145/380995.380999 https://doi.org/10.1016/j.rse.2019.111232 https://doi.org/10.3390/rs70100153 https://doi.org/10.1007/s12518-021-00358-3 https://doi.org/10.3390/rs9090916 https://doi.org/10.1016/j.jag.2009.06.002 https://doi.org/10.1016/0034-4257(91)90048-B https://doi.org/10.1177/001316446002000104 https://doi.org/10.3390/urbansci3020063 https://doi.org/10.5897/JGRP2015.0491 https://doi.org/10.1080/19376812.2016.1226909 https://www.statista.com/statistics/1234653/africa-s-fastest-growing-cities/ https://www.statista.com/statistics/1234653/africa-s-fastest-growing-cities/ https://doi.org/10.1016/j.rse.2017.01.014 https://daraint.org/wp-content/uploads/2013/12/rri-niger.pdf https://doi.org/10.1046/j.1365-2699.1999.00146.x https://doi.org/10.1029/2007WR006785 https://doi.org/10.1007/s43545-021-00150-5 https://population.un.org/wpp https://doi.org/10.1016/j.isprsjprs.2014.07.016 Introduction Material and Methods Study Area Remote Sensing Data Acquisition Image Preprocessing Urban LULC Classes and Reference Data Collection Image Classification Classification Accuracy Change Detection Results Urban Landscape Change Inter-Annual Urban Landscape Change Validation Discussion Conclusions References