metadata: authors: 0: author: Sedano,Fernando
1: author: Molini,Vasco
2: author: Azad,M Abul Kalam
count: Nigeria
docna: 0: docna: The State of Land Use in Northern Nigeria :
            A Landsat-Based Mapping Framework
docty: Policy Research Working Paper
subtopic: Food Security,Inequality,Hydrology,Climate Change and Agriculture,Water and Food Supply,Irrigation and Drainage,Agricultural Irrigation and Drainage
teratopic: Poverty Reduction,Water Resources,Agriculture
abstracts: cdata!: This study developed a land cover and
            land use mapping framework specifically designed for
            agricultural systems in the Sudan-Sahel region. The mapping
            approach extracts information from inter- and intra-annual
            vegetation dynamics from dense stacks of Landsat 8 images.
            The framework was applied to create a 30-meter spatial
            resolution land use map focusing on the 2015 agricultural
            landscapes in northern Nigeria. The map provides up-to-date
            information with a high level of spatial and thematic
            detail, resulting in a more precise characterization of
            agriculture in the region. The map reveals that agriculture
            is the main land use in the region. Arable land represents
            on average 52.5 percent of the area, which is higher than
            the reported national average for Nigeria (38.4 percent).
            Irrigated agriculture covers nearly 2.2 percent of the total
            area, reaching nearly 20 percent of the cultivated land when
            traditional floodplain agriculture systems are included,
            which is above the reported national average (0.63 percent).
            There is significant variability in land use in the region.
            This study demonstrates the feasibility of multitemporal
            medium-resolution remote sensing data to provide detailed,
            up-to-date information on agricultural systems in the arid
            and sub-arid landscapes of the Sahel region.
geo_reg_key: 517191,1927320
repnme: repnme: The State of Land Use in Northern Nigeria :
            A Landsat-Based Mapping Framework
display_title: The State of Land Use in Northern
            Nigeria : A Landsat-Based Mapping Framework
listing_relative_url: /research/2020/07/32276908/state-land-use-northern-nigeria-landsat-based-mapping-framework
topicv3: Agriculture and Food Security m1326205 435,Water m1326522 137,Poverty m1326687 695,Rainfed Agriculture m1327357 152,Irrigation and Drainage m1504475 3676
publishtoextweb_dt: 2020-07-27T00:00:00Z
historic_topic: Poverty Reduction,Water Resources,Agriculture
origu: EFI-AFR2-POV-Poverty and Equity (EAWPV)
no_of_pages: 23
author_keys: 0: author_key: 458815
1: author_key: 1228442
model_extractions: Policy Research Working Paper 9335 The State of Land Use in Northern Nigeria A Landsat-Based Mapping Framework _Fernando Sedano Vasco Molini M Abul Kalam Azad_ Poverty and Equity Global Practice July 2020
Policy Research Working Paper 9335

# **Abstract**

This study developed a land cover and land use mapping framework specifically designed for agricultural systems in the Sudan-Sahel region. The mapping approach extracts information from inter- and intra-annual vegetation dynamics from dense stacks of Landsat 8 images. The framework was applied to create a 30-meter spatial resolution land use map focusing on the 2015 agricultural landscapes in northern Nigeria. The map provides up-todate information with a high level of spatial and thematic detail, resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5 percent of the area, which is higher than the reported national average for Nigeria (38.4 percent). Irrigated agriculture covers nearly 2.2 percent of the total area, reaching nearly 20 percent of the cultivated land when traditional floodplain agriculture systems are included, which is above the reported national average (0.63 percent). There is significant variability in land use in the region. This study demonstrates the feasibility of multitemporal medium-resolution remote sensing data to provide detailed, up-to-date information on agricultural systems in the arid and sub-arid landscapes of the Sahel region.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at vmolini@worldbank.org or mazad@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The State of Land Use in Northern Nigeria: A Landsat-Based Mapping Framework

Fernando Sedano,<sup>*</sup> Vasco Molini,<sup>†</sup> and M Abul Kalam Azad<sup>‡</sup>

**Keywords:** land degradation, Landsat, medium resolution, land-cover/land-use change, agriculture, multitemporal, Sudan-Sahel region.

## **JEL Classification:** Q150, Q160

> * Department of Geographical Sciences, University of Maryland–College Park; fsedano@umd.edu

> † Poverty and Equity Global Practice, World Bank; vmolini@worldbank.com

> ‡ Poverty and Equity Global Practice, World Bank; mazad@worldbank.com
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# **Introduction**

<mark>Over the last 30 years, earth observation remote sensing for the monitoring of land use and agricultural development has grown significantly. Remote sensing-based cropland monitoring is rapidly becoming operational in different parts of the world (Gumma et al. 2014; Ozdogan 2010; Ozdogan and Gutman 2008; Thenkabail and Wu 2012). A number of agricultural monitoring systems started forecasting crop yields and production. But the advances have been geographically uneven, and the Sudan-Sahel region of Sub-Saharan Africa is one of them. Beginning with Tucker et al. (1985), a number of initiatives have studied land-cover and land-use (LCLU) trends in the region using Earth observation data (Brink and Eva 2009; CILSS 2016, 219; Fensholt et al. 2013; Herrmann, Anyamba, and Tucker 2005; Knauer et al. 2014; Spiekermann, Brandt, and Samimi 2015). But the use of remote sensing monitoring systems in the region has been sluggish. Availability and quality of land-use data have therefore been limited due to limited agricultural production, technical challenges, and slow adoption of remote sensing monitoring systems.</mark> Despite its relevance for human development and poverty alleviation, there is a lack of basic information on the distribution of cultivated land and the main land processes in the Sudan-Sahel. The region is expected to experience major changes in the near future, and land-use information remains crucial because agriculture represents the main source of livelihoods. The region’s fertility rates are among the highest in the developing world (Bertoni et al. 2016), and rural population growth will accelerate the expansion of cultivated land for subsistence farming. Likewise, the steady growth of the urban population will fuel the demand for agricultural products, which is a major driver of land-use change (DeFries et al. 2010). The consequences of climate change will likely disrupt regional agricultural practices and production. Expected temperature increases by 2050 will shorten crop-growing cycles, leading to severe yield reductions and threatening food production systems (Cervigni, Valentini, and Santini 2013; Jones and Thornton 2003; Porter et al. 2014). Coping strategies for the changing conditions may lead to progressive land degradation, further compromising rural livelihoods and increasing their vulnerability to future internal and external shocks (Leichenko and O'Brien 2008). For instance, as productivity decreases due to climate change, farmers may overexploit soil or expand cultivated lands into more marginal lands.

Global remote sensing–based LCLU change products include coarse-resolution land-use information for the Sudan-Sahel region (for example, MODIS MCS12Q1 and GlobCover 2005 and 2009). These products have been integrated with national statistics to generate a more robust outcome (Ramankutty et al. 2008). However, although coarse spatial resolution data meet the observational requirements for the world’s large agricultural regions, they are not well suited for monitoring crops in regions with highly heterogeneous agricultural landscapes dominated by smaller farms, such as those in the Sudan-Sahel region. Furthermore, the static nature of these products fails to capture changes over time (Leroux et al. 2014; Pittman et al. 2010). The opening of the Landsat archive in 2008–09 (Woodcock et al. 2008) and, more recently, the launch of new medium-resolution sensors (Landsat 8, Sentinel-2 Disaster Monitoring Constellation, and so forth) offers unprecedented opportunities to study LCLU change at higher spatial resolution. Several initiatives are already exploiting these new data with increasing processing capabilities to map agricultural landscapes with higher spatial detail at scales more relevant to African agricultural processes. For example, Gong et al. (2013) created a global LCLU map at 30-meter spatial
3 of 21 resolution, which included a cropland class. Xiong et al. (2017) produced a nominal 30-meter cropland extent map of continental Africa by using Sentinel-2 and Landsat 8 data (GFSAD30AFCE). From an African context, this product represents a major improvement for food and water security assessments and is a first step for exploring not only cropland extent but also crop type, intensity, and change. The European Space Agency released a prototype land-cover 20-meter map of Africa for 2016 with cropland as one of its classes.<sup>1</sup> However, these products still present some limitations that hamper the extraction of information on land-use dynamics for specific regions of Africa. Cropland systems across the continent are highly diverse and are often adapted to very specific environmental conditions. The phenological signatures of the different land-use types in a region can be very similar (Xiong et al. 2017). As a consequence, mapping croplands at the continental level requires large, up-to-date training and validating data sets (Xiong et al. 2017). Although high-resolution imagery and crowdsourcing (Fritz et al. 2015) are gaining ground as sources of training and data validation, these remain scarce in remote regions, constraining the precision of supervised learning algorithms. Consequently, continental and global medium-resolution products that implement a single mapping approach are not always flexible enough to accurately characterize land-use processes in specific regions of the continent.

Northern Nigeria presents a concrete example of a data-poor region where land-use information is crucial for human development. Poverty indicators over the last decade show a growing north-south divide (World Bank 2016). Although poverty rates are only 16 percent in the southern part of the country, an estimated 50.2 percent of the population lives below the poverty line in northern Nigeria, where up to 70 percent of households rely primarily on agriculture (World Bank 2016). Ongoing land-cover change and land-use processes compromise households’ main livelihood strategies, reduce their adaptation alternatives, and thus increase their vulnerability. Yet the lack of consistent and reliable land-use information hinders the accurate characterization of northern Nigeria’s agricultural sector. As the links between climate change, crop failures, poverty, migration, and conflict become more explicit in the research literature, it becomes urgent to monitor land-use processes in the region to inform decision makers and design and implement efficient policy interventions in agricultural development and poverty alleviation.

This work proposes a mapping approach specifically designed for the agricultural systems of the Sudan-Sahel region. It aims to overcome some of the limitations in the region of the global and continental remote sensing products. The approach makes extensive use of expert knowledge and exploits dense stacks of Landsat 8 imagery to capture inter- and intra-annual vegetation dynamics and better characterize the study region’s main land-use types. The proposed mapping framework is flexible and robust enough to operate with limited imagery, and can be easily and rapidly updated in successive years. We have applied this approach to produce a 30-meter spatial resolution LCLU map that emphasizes agricultural classes. This work aims to fill a gap of information and provide a precise, up-to-date assessment of agricultural systems in northern Nigeria.

> 1 For more information, see the European Space Agency’s “CCI Land Cover—S2 Prototype Land Cover 20m Map of Africa 2016,” http://2016africalandcover20m.esrin.esa.int.
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5 of 21 Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007), at 30-meter spatial resolution were used to define floodplains. Given its focus on agricultural landscapes, urban areas were not directly mapped. Instead, urban pixels were extracted from Landsat’s Global Human Built-up and Settlement Extent (HBASE) Dataset (Wang et al. 2017). The HBASE provides global 30-meter spatial resolution information on settlement extent for 2010.

Using these data, we developed an LCLU mapping framework adapted to the region’s agricultural landscapes to produce a 30-meter resolution map of northern Nigeria for the baseline year 2015. The framework implemented a knowledge-based expert system (KBES) that relied on dense stacks of Landsat 8 images and exploited inter- and intra-annual vegetation dynamics and contextual information to map the main components of the study region’s land surfaces (Srinivasan and Richards 1990). Recent research highlights the role of expert knowledge to advance remote sensing–based agricultural monitoring (Bégué et al. 2018), even if such experts lack remote sensing experience (for example, field extension agents). These systems constitute a useful alternative when the lack of consistent training and validation data limit the use of supervised learning systems. Expert knowledge is commonly stored as a set of rules in a knowledge base. Subsequently, the information is passed to an inference mechanism that interprets and assigns class memberships to pixels. A KBES has been successfully implemented in a number of applications, such as protected areas conservation (Szantoi et al. 2016), crop classification (Choudhury et al. 2012; Cohen and Shoshany 2002; El Hajj et al. 2009; Janssen and Middelkoop 1992), and urban mapping (Amarsaikhan et al. 2013).

We built a knowledge base that established production rules using spectral, temporal, and spatial constraints to identify the main agricultural systems and natural vegetation types in the study area (Cohen and Shoshany 2002). The production rules were not necessarily conclusive, but they provided a degree of evidence in favor of some class labels (Richards and Jia 2006, 342–55). These rules were defined from the analysis of the spectral profiles during the vegetative cycles of the different land-use types. Specific temporal windows were selected to maximize the separability between classes using expert knowledge in the seasonal dynamics of the land surfaces in the study area (figure 2, panel a). Similar strategies have been previously used for LCLU in tropical environments (Hansen et al. 2008; Sedano, Gong, and Ferrao 2005). The numerical values in the spectral rules were provided as thresholds that were empirically generated from observed data (Hodgson et al. 2003). These spectral thresholds were defined based on a training data set of 1,750 points of known land-use types randomly searched using visual interpretation of a combination of Landsat and very high-resolution imagery (Google Earth) for the period of study. The thresholds for each land-use class were calculated from the statistical distribution of pixel values in the training data set. The spectral rules were built based on Normalized Difference Vegetation Index (NDVI) images (Tucker 1985) calculated from each image in the original data set. The NDVI was chosen because, besides data compression, it facilitates the interpretation of land surface dynamics over time and the definition of spectral thresholds. The presence of burnt areas in natural vegetation surfaces within the three-year imagery epoch was interpreted as a sign of land-cover transition, supporting the labeling as nonstable natural vegetation pixels.
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8 of 21 The rules were applied to all available images during the defined temporal windows and imposed initial preconditions for class membership. The KBES inference mechanism analyzed the set of rules, resolved potential redundancies and inconsistencies, and made decisions about class membership using a majority rule criterion (Srinivasan and Richards 1990) (figure 3). This approach reduced the potential impact of individual aerosol-contaminated images in the mapping process.

An uncertainty flag was raised when the number of available observations during the temporal window was below a preestablished number due to cloud coverage (n=5). This was often the case for rain-fed agriculture based on observations during the rainy season. In these cases, we used an alternative approach and applied the rules of the knowledge base to an NDVI maximum value composite from all images available during the temporal window.

The map was validated against an independent data set of 754 ground points spread over the study region. Validation points were identified by visually interpreting images with very high resolution for the year 2015 (Google Earth) and combining these with known locations in true color Landsat 8 images. The analysis was carried out on a per-scene basis. Outputs from individual scenes were subsequently mosaicked in a final map at 30-meter resolution covering the whole study area. The implementation of the KBES and data processing was carried out in the Google Earth Engine cloud-based platform.
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**Figure 5. Examples of 30-meter resolution Landsat 8–based LCLU map for northern Nigeria**

_Note:_ Google Earth high resolution imagery (left side) and the corresponding 30-meter resolution Landsat 8- based land-cover and land-use (LCLU) map for northern Nigeria (right side). Dark green: stable natural vegetation; light green: nonstable natural vegetation; light yellow: rain-fed agriculture; red: irrigation agriculture.

# **Results**

We created a 30-meter spatial resolution LCLU map focusing on the agricultural landscapes of northern Nigeria for the year 2015. Validating the map against an independent data set of ground points resulted in an overall accuracy of 0.91 and a kappa coefficient of 0.89. Accuracies were consistent throughout the classes, with individual user and producer accuracies above 0.82 and 0.85, respectively (table 2).
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# **Conclusions**

This work presents a mapping approach specifically designed for the agricultural systems of northern Nigeria. This approach has the potential to be replicable in successive years and expanded to other locations within the Sudan-Sahel ecozone. The present work underscores the relevance of incorporating expert knowledge in the design-mapping strategies in regions where other data sources are scarce. It also highlights the importance of interannual land surface dynamics to achieve a robust characterization of land use in the study area. Likewise, it demonstrates the potential of dense stacks of medium-resolution imagery to capture these dynamics.

The mapping framework was applied to produce a 30-meter spatial resolution LCLU map focusing on the agricultural landscapes of northern Nigeria for 2015. The map provides up-to-date information at higher spatial resolution and better characterizes agriculture in a region with limited land-use information, where agriculture is the main livelihood strategy. The map shows a high farming intensity and a cropland area that is significantly higher than in the rest of the country. Rain-fed agriculture systems already occupy most of the landscapes—at the expense of natural vegetation—and the majority of floodplains suitable for irrigation agriculture are already under use. The map also identifies the landscape variations associated with the region’s welldefined north-south gradients.

In a region under increasing environmental and demographic stress, this work highlights the potential of multitemporal medium-resolution satellite data to generate detailed, up-to-date landuse information. This type of information is essential for designing efficient policy interventions, improving famine-related early warning and response systems, and understanding the links between land use and agricultural development, migration, and conflict.