NIGERIA
REMOTE SENSING ANALYSIS OF BORNO STATE
Exploring proxy measures of vulnerability in hard-to-reach areas
22 October 2020 This document was produced by REACH using primary data collected by REACH Nigeria and remote sensing analysis conducted by United Nations Institute for Training and Research - Operational Satellite Applications Programme (UNITAR-UNOSAT)
Production date: 22 October 2020 CONTENTS LIST OF FIGURES
Contents i Figure 1 - 2019 REACH MSNA Coverage 2 List of Acronyms ii Figure 2 - Cadre Harmonisé Food Insecurity Situation 3 Introduction 1 Figure 3 - Food Security & Livelihoods Living Standards Gap 4 Study Focus 3 Figure 4 - Key Findings from Hard to Reach Assessment 4 Methodology 5 Figure 5 - Location of Monguno and Bama study areas 5 Results 7 Figure 6 - FEWSNET Livelihood Zones for Borno State 6 Limitations 13 Figure 7 - Crop calendar for Borno State 6 Conclusions 13 Figure 8 - Agriculture area change from 2013 to 2019 7 Next Steps 14 Figure 9 - Borno State agricultural area 7 Figure 10 - Borno State annual NDVI change 8 Figure 11 - Average cropland extent by LGA 9 Figure 12 - Total agriculture area over study years 9 Figure 13 - Presence of agricultural land 2015 - 2019 9 Figure 14 - Changes in agricultural land by LGA 10 Figure 15 - Percent change in agricultural land by LGA 10 Figure 16 - Correlation between agriculture & conflict 11 Figure 17 - Location of conflict & decrease in agriculture 12 Figure 18 - Number of conflict events in Borno State 12
i LIST OF ACRONYMS
BAY Borno, Adamawa, & Yobe States IDP Internally displaced person MSNA Multi-sector needs assessment AoK Area of knowledge LGA Local government area KI Key informant HNO Humanitarian needs overview HRP Humanitarian response plan OCHA United Nations Office for the Coordination of Humanitarian Affairs FSL Food security and livelihoods IPC International Phase Classification NDVI Normalized difference vegetation index FEWSNET Famine Early Warning Systems Network SRTM Shuttle Radar Topography Mission SWIR Shortwave infrared VV Vertical-vertical polarization (radar) VH Vertical-horizontal polarization (radar)
ii Fields outside of Maiduguri, Nigeria (January, 2017) © Maxar / Digital Globe 5 INTRODUCTION Almost eight million people are affected by the humanitarian crisis in north-east Nigeria, and Multi-Sector Needs Assessment since the start of the conflict in 2010, more than 50,000 people have been killed and 1.6 The MSNA is a crisis-wide assessment to identify inter-sectoral humanitarian needs, which million displaced. In spite of the turmoil and the critical humanitarian conditions faced by the aims to provide a strong evidence base for the Humanitarian Needs Overview (HNO) and conflict-affected populations, significant proportions of the displaced have begun to return to Humanitarian Response Plan (HRP). Co-led by OCHA and facilitated by REACH; the MSNA their areas of origin. in particular is not meant to be sector-specific and strives to gain a better understanding of As the protracted crisis in north-east Nigeria progresses into its tenth year, and despite a cross-sectoral needs, dynamics and response. Following a robust methodology, household sustained number of humanitarian actors responding to the crisis, humanitarian needs in level data is representative at the LGA level with 90% confidence interval and 10% margin of Borno, Adamawa and Yobe (BAY) States remain dire and multi-faceted. The conflict has error of all accessible areas in the BAY States. The overall, inter-sectoral research question resulted in an estimated 7.1 million individuals in need of humanitarian assistance in 2019 – guiding this assessment is: What are the priority multi-sectoral humanitarian needs of the more than 50% of the entire estimated population of the three affected States.1 crisis-affected population, and how do these vary between geographical locations, population groups and household profiles? Moreover, over 80% of internally displaced persons (IDPs) were located in Borno State only, the epicentre of the protracted crisis, with a majority living in urban host communities, making it difficult for actors to reach them and to plan responses appropriate to urban contexts. In Assessment of Hard-to-Reach Areas addition, in 2020, an estimated 1 million people are located in hard-to-reach areas, with limited to no access to humanitarian assistance.2 Using its Area of Knowledge (AoK) methodology, REACH remotely monitors the situation in hard-to reach areas through monthly multi-sector interviews in accessible Local Government The humanitarian crisis has been exacerbated by mass population movements, a breakdown Area (LGA) capitals with the following typology of Key Informants (KIs): in basic infrastructure, multi-faceted poverty, and chronic long-term underdevelopment in the north-east. The fluid situation makes comprehensive, up-to-date data necessary to efficiently • KIs who are newly arrived internally displaced persons (IDPs) who have left a hard-to- and effectively respond to humanitarian needs of affected populations. Yet, humanitarian reach settlement in the last 3 months actors working in Nigeria continue to face significant information gaps lacking required • KIs who have had contact with someone living or having been in a hard-to-reach granular and comprehensive evidence to efficiently inform their response planning settlement in the last month (traders, migrants, family members, etc.) To address information gaps facing the humanitarian response, including a lack of consistent Selected KIs are purposively sampled and are interviewed on settlement-wide circumstances response-wide information on the needs, and vulnerabilities of crisis-affected populations in in hard-to-reach areas, rather than their individual experiences. Responses from KIs reporting north-east Nigeria, REACH has been conducting the following two recurring multi-sectoral on the same settlement are then aggregated to the settlement level. The most common data collection exercises since 2018: response provided by the greatest number of KIs is reported for each settlement. When no • annual multi-sector needs assessments (MSNAs) in the BAY states, and; most common response could be identified, the response is considered as ‘no consensus’. • monthly assessments of hard-to-reach areas in north-east Nigeria.
1 OCHA, Humanitarian Needs Overview 2019 Nigeria 1 2 OCHA, Global Humanitarian Overview 2020, Nigeria chapter Limitations of Existing Assessments Figure 1: 2019 REACH MSNA Coverage Despite the considerable scale of these two exercises, significant limitations remain, largely due to consistent patterns of insecurity and the resulting access barriers. The MSNA covers accessible areas of Adamawa, Borno and Yobe States only, the representative data drawn from the sample therefore does not inform needs in areas that could not be assessed, due to security constraints or lack of partners to conduct data collection. Due to severe access constraints in Borno State, the sampling is generally limited to urban centres (with the exception of South Borno), which affects data results compared to other areas in Adamawa and Yobe. The findings from assessments in hard to reach areas are only indicative of broader trends in assessed settlements, and are not statistically generalisable. Findings are only reported on LGAs where at least 5% of populated settlements and at least 5 settlements in the respective LGA have been assessed.
Problem Statement / Research Question With the above limitations in mind, this pilot exercise aims at evaluating whether remote sensing analysis can help to fill gaps in current primary data collection methodologies employed in Nigeria. More specifically, can remote sensing analysis: (a) complement ongoing assessments; (b) corroborate assessment findings; and/or (c) shed new insights on currently inaccessible areas?
2 STUDY FOCUS Figure 2: Cadre Harmonisé food insecurity situation in Borno State Considering the number of multi-sector indicators assessed and the overall geographic area of the BAY states, it was necesssary to narrow the focus of this exercise both thematically and spatially. Borno state serves as the primary geographic area of interest for this study since it has been the epicentre of the protraced crisis and hosts the majority of northeast Nigeria’s IDPs. Abadam After reviewing REACH assessment findings (see Figures 3 and 4 on the opposite page), it
was determined that Food Security and Livelihoods (FSL) should be the primary thematic Mobbar Kukawa focus for evaluating alignment with remote sensing analysis approaches. As there are Guzamala consistent reports of people having less land for cultivation as compared to previous years Gubio and high proportions of settlements reporting subsistence farming and cultivation as both Nganzai Monguno the main source of livelihood and food (Hard to Reach Assessment) as well as considerable Marte proportions of households with unmet needs in FSL (MSNA), the priority aim of this study is to Ngala look at the status of agricultural activity in Borno state using available satellite data. Magumeri Mafa Kala/Balge Secondary sources further support the thematic area selection, particularly the Cadre Jere Dikwa Maiduguri Harmonisé analysis of the food and nutrition insecurity situation for the BAY states, which Kaga classifies the food security situation in nearly all of Borno in crisis or emergency (see Figure Konduga 2). Notably, the Cadre Harmonisé indicates 4 LGAs were not analysed due to inadequate Bama evidence in the north-east of Borno State along the Lake Chad shore, further highlighting the difficulty of reporting on the situation in hard-to-reach areas. Gwoza Damboa As this is an initial investigation, the decision was made to look for trends over recent years as opposed to the entire duration of the crisis. Further, two smaller study areas, in Bama and Biu Chibok Monguno LGAs, were selected based on reports of declining agricultural activity. Askira/Uba Projected food and nutrition Kwaya Kusar insecurity situation by LGA (June- Hawul August 2020) Bayo Under pressure
Shani Crisis
Emergency
Not analysed
Data source: FAO
3 Borno - Food Security and Livelihoods (FSL) Borno - Food Security and Livelihoods (FSL) Assessment of Hard-to-Reach Areas in Northeast Nigeria April 2020 Assessment of Hard-to-Reach Areas in Northeast Nigeria April 2020 Figure 3: Food Security & Livelihoods Living Standards Gap (2019 MSNA) Figure 4: Key Findings from Hard to Reach Assessment (April 2020) Access to food and livelihoods Access to food and livelihoods PProportionroportion ooff aassessedssessed ssettlementsettlements rreportingeporting tthathat mmostost PProportionroportion ooff aassessedssessed ssettlementsettlements rreportingeporting mmostost PProportionrrooppoorrttiioonn oofoff aassessedasssseesssseedd ssettlementsseettttlleemeennttss rreportingreeppoorrttiinngg tthatthhaatt ppeoplepeeooppllee PProportionroportion ooff aassessedssessed ssettlementsettlements rreportingeporting tthathat mmostost PProportionroportion ooff aassessedssessed ssettlementsettlements rreportingeporting mmostostPProportion roportion ooff aassessedssessed ssettlementsettlements rreportingeporting tthathat ppeopleeople PProportionroportion ooff aassessedssessed ssettlementsettlements rreportingeporting tthathat mmostost PProportionroportion ooff aassessedssessed ssettlementsettlements rreportingeporting mmostost ppeopleeople eeatat oonn aaverageverage oonene mmealeal oorr llessess pperer dday:ay: ppeopleeople hhavingaving llessess llandand fforor ccultivationultivation aavailablevailable aass eeateeataatt wwildwildiilldd ffoodsffoodsooooddss tthattthathhaatt aareaarerree nnotnnotoott ppartppartaarrtt oofoofff ttheirttheirhheeiirr uusualuusualssuuaall ddiet:ddiet:iieett:: ppeopleeople eeatat oonn aaverageverage oonene mmealeal oorr llessess pperer dday:ay: ppeopleeople hhavingaving llessess llandand fforor ccultivationultivation aavailablevailable aaseeatsa t wwildild ffoodsoods tthathat aarere nnotot ppartart ooff ttheirheir uusualsual ddiet:iet: ppeopleeople eeatat oonn aaverageverage oonene mmealeal oorr llessess pperer dday:ay: ppeopleeople hhavingaving llessess llandand fforor ccultivationultivation aavailablevailable aass ccomparedccomparedoomppaarreedd ttottooo tthetthehhee ssamessameaamee ttimettimeiimee iiniinnn tthetthehhee yyearyyeareeaarr bbefore:bbefore:eeffoorree:: ccomparedompared ttoo tthehe ssameame ttimeime iinn tthehe yyearear bbefore:efore: LGA boundary LGA boundary LGA boundary LGA boundary Abadam LGA boundary Abadam LGA boundary Abadam LGA boundary LGA boundary LGA boundary 0% Abadam 0% Abadam 0% Abadam Abadam Abadam Abadam 0% 0% 0% 0% 0% 0% 1 - 20% 1 - 20% 1 - 20% 1 - 20% 1 - 20% 1 - 20% 1 - 20% 1 - 20% 1 - 20% 21 - 40% 21 - 40% 21 - 40% 21 - 40% 21 - 40% 21 - 40% 21 - 40% 21 - 40% 21 - 40% 41 - 60% 41 - 60% 41 - 60% 41 - 60% Kukawa 41 - 60% Kukawa 41 - 60% Kukawa 41 - 60% 41 - 60% 41 - 60% 61 - 80% 61 - 80% 61 - 80% Kukawa Kukawa Kukawa 61 - 80% Kukawa 61 - 80% Kukawa 61 - 80% Kukawa 61 - 80% 61 - 80% 61 - 80% 81 - 100% 81 - 100% 81 - 100% 81 - 100% Guzamala 81 - 100% Guzamala 81 - 100% Guzamala 81 - 100% 81 - 100% 81 - 100% Assessed Settlement Guzamala Assessed Settlement Guzamala Assessed Settlement Guzamala Guzamala Guzamala Guzamala LIVINGAssessed Settlement Assessed Settlement Assessed Settlement Assessed Settlement Assessed Settlement Assessed Settlement Monguno Monguno Monguno Nganzai Monguno Nganzai Monguno Nganzai Monguno Monguno Monguno Monguno Nganzai Marte Nganzai Marte Nganzai Marte Nganzai Nganzai Nganzai Marte Marte Marte Marte Marte Marte Ngala Ngala Ngala STANDARDS Ngala Ngala Ngala Ngala Ngala Ngala Kala/Balge Kala/Balge Kala/Balge Mafa Mafa Mafa Kala/Balge Kala/Balge Kala/Balge Jere Mafa Kala/Balge Jere Mafa Kala/Balge Jere Mafa Kala/Balge Mafa Mafa Mafa Jere Dikwa Jere Dikwa Jere Dikwa Jere Jere Jere GAP (LSG) – Dikwa Dikwa Dikwa Dikwa Dikwa Dikwa
Konduga Konduga Konduga Bama Bama Bama Konduga Konduga Konduga FOOD Konduga Bama Konduga Bama Konduga Bama Bama Bama Bama Gwoza Gwoza Gwoza Damboa Gwoza Damboa Gwoza Damboa Gwoza Gwoza Gwoza Gwoza Damboa Damboa Damboa Damboa Damboa Damboa
SECURITY & Madagali Madagali Madagali Chibok Chibok Chibok Chibok Madagali km Chibok Madagali km Chibok Madagali km Chibok Madagali Chibok Madagali Chibok Madagali 0 20 40 80km 0 20 40 80km 0 20 40 80km km km km LIVELIHOODS 0 20 40 80 0 20 40 80 0 20 40 80 0 20 40 80 0 20 40 80 0 20 40 80 Main sources of food reported by assessed Proportion of assessed settlements reporting Main livelihood sources reported by assessed Main sources of food reported by assessed Proportion of assessed settlements reporting Main livelihood sources reported by assessed settlements:settlements: thatthat atat leastleast oneone communitycommunity membermember plantedplanted andand settlementssettlements (multiple(multiple answersanswers perper settlementsettlement settlements: that at least one community member planted and settlements (multiple answers per settlement harvestedharvested inin thethe previousprevious rainyrainy season:season: possible):possible): harvested in the previous rainy season: possible):
Note: Within the MSNA analysis, the “Living Standard Gap” corresponds to the sectoral analysis (excluding nutrition which is a “well being” pillar component). For each sector, a composite indicator was designed and agreed upon with the sectors. After adding up scores for each indicators, HHs are then classified following a sectoral needs severity scale from “No/minimal” severity of needs, to “Extreme” severity of needs.
4 For more information on this factsheet please contact: Informing For more information on this factsheet please contact: Informing For more information on this factsheet please contact: Informing REACH more effective REACH more effective REACH more effective [email protected] humanitarian action [email protected] REACH humanitarian action [email protected] REACH humanitarian action METHODOLOGY In order to analyze how the crisis has affected agriculture in Borno State, a multi-tiered Bama and Monguno NDVI Analysis approach was developed based on available data sources. First, smaller areas of interest As the Bama and Monguno areas are quite small (100 km2 and 75 km2 respectively), the in Bama and Monguno LGAs were evaluated for agricultural activity and changes between main idea was to compare the difference of NDVI values during the highest NDVI activity 2013 and 2019. Then, a complete state-wide analysis of Borno was conducted, identifying period from 2013 to 2019. Following a literature review, it was concluded that the highest NDVI agricultural areas and vegetation changes comparing yearly normalized difference vegetation value should be between 1 September and 15 October. index (NDVI) values between 2016 and 2019. Note: NDVI is a routine measure in remote sensing used to monitor agriculture, capturing how much more near infrared light is reflected In order to provide an analysis as precise as possible, Landsat-8 was chosen for the years compared to visible red, which helps differentiate bare soil from grass or forest, detect plants 2013 to 2015 and Sentinel-2 for 2016 to 2019, sensors which have a spatial resolution under stress, and differentiate between crops and crop stages. Lastly, more detailed analysis of 30/15 meters and 10 meters respectively. For the analysis of agricultural areas, an was conducted to identify regional variations among Borno’s 27 LGAs. unsupervised classification was performed. This classification was based on pansharpend Landsat-8 and Sentinel-2 imagery. The Landsat-8 imagery was pansharpend in order to obtain For locations of Monguno and Bama study areas see Figure 5, which also displays the extent a similar spatial resolution as the Sentinel-2 imagery. of the Borno State. To obtain yearly NDVI data, the most suitable imagery from the period suggested by the literature was used. This resulted in 7 annual NDVI layers, which were later compared to one Figure 5: Location of Monguno and Bama study areas in Borno State another to get 6 yearly change layers. Borno State Borno State NDVI Analysis
Yobe Monguno For the state-wide NDVI analysis, Sentinel-2 imagery was filtered by date and area of interest. This subset of imagery had a cloud mask applied to reduce the influence of clouds on the
Bama NDVI results. NDVI was then calculated for each image in the filtered collection.The NDVI data was then split into two parts; one to represent the season with high NDVI values and the other for the season with low NDVI values. The period for the high NDVI is between 19 June Nigeria and 7 October, and the low 20 January to 31 March. The season when agriculture and NDVI Adamawa values are at its peak varies across the state, it tends to be earlier in the north than in the south. To capture the peak for all parts of the state, a wider timeframe was chosen compared to the timeframe capturing the lower season. Each seasonal subset was later split by year, to get the NDVI data for each year. The seasons were later reduced to a single layer by choosing the maximum value in the subset for the high NDVI seasons, and the mean for the lower NDVI season. To be able to detect the change of NDVI over the years, the following year maximum layer needed to be subtracted from the previous year maximum layer. This was done for all 4 years
5 Figure 7: Crop Calendar for Borno State and resulted in 3 yearly change layers. The change layers were reclassified to quantify areas of increase, decrease and no change (stable). Crop Type Abadam # LGAs Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Source To estimate total agricultural area in the state, a combination of the previously-created Present NDVI layers was used. A new layer was created Mobbarto identify areas with seasonal change by Cowpeas 26 Nigeria Farmers Group Kukawa Groundnuts calculating for each year the change between the seasonGuzamala with the lowest NDVI values and the 25 USDA Millet season with highest NDVI values. This layer was combined with the seasonal yearly layers to 18 FAO Gubio Maize 15 FAO extract vegetation. Finally, the yearly layers were added to create a single layer representing Rice (Main) 13 USDA Nganzai Monguno Marte agricultural areas from 2016 to 2019. Rice (Second) 13 USDA Ngala Magumeri Sorghum 13 FAO, USDA LGA Comparative Analysis Jere Soybeans 11 USDA Mafa Kala/Balge Dikwa Wheat 11 Legit To identify regional variations in the 27 Borno LGAs, the Maiduguriagriculture classification was Kaga improved by using more input data. The refined approach was modelled after a crop calendar Sowing Konduga Bama (see Figure 7) based on the crops found in Borno according to FEWSNET Livelihood Zones Growing 3 Harvesting for Nigeria (see Figure 6). To summarise and quantify the resultsGwoza from the agriculture classification for each LGA, cloud-masked surfaceDamboa reflectance imagery from Landsat-8 and
Biu Chibok Figure 6: FEWSNET Livelihood Zones for BornoAskira/Uba State Sentinel-2, radar imagery from Sentinel-1’s C-band, and SRTM elevation data were used. Due
Kwaya Kusar Hawul to the variation in spatial resolution of the data types, it was necessary to resample the data to Bayo LGA Boundary the cell size of Landsat-8, which is 30 meters. Furthermore, the data was merged into intervals Abadam Shani Livelihood Zone NG04 - North-east sahelian: millet, sesame, cowpeas, and livestock of 6 months from January 2015 to December 2019, on the basis of the median value each NG05 - Borno-Yobe-Bauchi millet, cowpeas, groundnuts, and sesame Mobbar NG07 - Komadugu-Yobe irrigated peppers with rice, millet, and vegetables Kukawa pixel in the period. The interval periods were designed based on the crop calendar for Borno NG08 - Lake Chad fishing, maize, wheat, cowpeas, and vegetables Guzamala NG09 - Chad Basin: masakwa flood-recession sorghum and wheat in Figure 7 and by taking into account the rainy season in northern Nigeria4. The first interval NG14 - Central sorghum, maize, groundnuts, cowpeas, and sesame Gubio NG15 - North-east maize dominant with rice, cowpeas, soybeans, and groundnuts spanned from 1 January to 30 June which aimed to capture the sowing season, and the Nganzai Monguno Marte NG34 - North-east cattle, small ruminants, and food crops with cross-border livestock trade second interval was from 1 July to 31 December which aimed to capture the growing period. Ngala Magumeri Jere Additionally, for each interval, an NDVI layer and a slope layer were calculated from the multi- Mafa Kala/Balge Dikwa Maiduguri Kaga spectral sensors and SRTM elevation data, respectively. Konduga Bama For the random forest classifications, two manually generated training sample datasets were
Gwoza used, one representing cropland present in all 5 years and the other representing areas with Damboa non-cropland for each year. These training samples together with the bands blue, green, red, Biu Chibok
Askira/Uba near infrared, SWIR1, SWIR2, VV, VH, NDVI, and slope were the inputs in a random forest Kwaya Kusar Hawul classifier with a limit of 10 classification trees. The classifier resulted in five raster datasets Bayo LGA Boundary Shani Livelihood Zone representing agricultural land for the years 2015, 2016, 2017, 2018 and 2019. NG04 - North-east sahelian: millet, sesame, cowpeas, and livestock NG05 - Borno-Yobe-Bauchi millet, cowpeas, groundnuts, and sesame NG07 - Komadugu-Yobe irrigated peppers with rice, millet, and vegetables NG08 - Lake Chad fishing, maize, wheat, cowpeas, and vegetables 3 https://fews.net/fews-data/335NG09 - Chad Basin: masakwa flood-recession sorghum and wheat NG14 - Central sorghum, maize, groundnuts, cowpeas, and sesame 6 4 NG15 - North-east maize dominant with rice, cowpeas, soybeans, and groundnuts https://fews.net/west-africa/nigeria/seasonal-calendar/december-2013NG34 - North-east cattle, small ruminants, and food crops with cross-border livestock trade RESULTS Figure 9: Borno State agricultural area 2016 - 2019 NDVI Analysis of Bama and Monguno Areas
In both Bama and Monguno, an overall decrease of agricultural land could be observed. Abadam Initially for Monguno, an increase was visible between the years 2014 and 2016, but between 2016 and 2017 an 88% decrease was detected. Moreover, the 2018 and 2019 results have Mobbar Kukawa to be considered as merely an indication of decrease since the classification of agricultural Guzamala land was not able to detect all fields within the analysis extent. For Bama, the most significant decrease was between 2013 and 2014, when a 59% decrease can be observed. The Gubio decrease continues after 2014 until 2016 but with less intensity, after that a positive trend is Nganzai Monguno Marte
detected for the areal extent of agricultural land. See Figure 8 for the change of agricultural Ngala Magumeri land area between 2013 and 2019. Jere Mafa Kala/Balge Dikwa Maiduguri Kaga Figure 8: Agriculture area change from 2013 to 2019 (Monguno and Bama) Konduga Bama
30,00 Gwoza Damboa
25,00 Biu Chibok
Askira/Uba LGA Boundary 20,00 Agricultural area Kwaya Kusar Hawul Bayo 15,00 Shani
10,00 Field Field area (km2)
5,00 It is important to note that these results only consider the field area detected using the 0,00 unsupervised classification, which extracts clearly visible, defined crop areas.Therefore, the 2013 2014 2015 2016 2017 2018 2019 classification is probably underestimated due to the fact that subtle area is not detected by the classification, yet remains visible on the NDVI difference layer. It should also be noted that BAMA MONGUNO NDVI provides a measure of all vegetation, and not just agricultural land, which should be kept in mind when interpreting state-wide NDVI results. In some instances, there is a clear over- estimation of cropland, notably around the Sambisa Forest area, south-east of Maiduguri.
7 Figure 10: Borno State Annual NDVI change between 2016 and 2019
Abadam Abadam Abadam
Mobbar Mobbar Mobbar Kukawa Kukawa Kukawa Guzamala Guzamala Guzamala
Gubio Gubio Gubio
Nganzai Monguno Marte Nganzai Monguno Marte Nganzai Monguno Marte
Ngala Ngala Ngala Magumeri Magumeri Magumeri Jere Jere Jere Mafa Kala/Balge Mafa Kala/Balge Mafa Kala/Balge Dikwa Dikwa Dikwa Maiduguri Maiduguri Maiduguri Kaga Kaga Kaga
Konduga Bama Konduga Bama Konduga Bama
Gwoza Gwoza Gwoza Damboa Damboa Damboa
LGA Boundary LGA Boundary LGA Boundary Biu Chibok Biu Chibok Biu Chibok NDVI Change 2016-2017 NDVI Change 2017-2018 NDVI Change 2018-2019 Askira/Uba Strong decrease Askira/Uba Strong decrease Askira/Uba Strong decrease Kwaya Kusar Hawul Decrease Kwaya Kusar Hawul Decrease Kwaya Kusar Hawul Decrease Bayo Stable Bayo Stable Bayo Stable Shani Increase Shani Increase Shani Increase Strong increase Strong Increase Strong increase
Borno State NDVI Analysis The analysis estimates that 28% of Borno comprises agricultural land, equaling 22,762 square However, these areas are only around 5% of the total area of the state. The decrease kilometers, most of which is located in the southern parts of the state (see Figure 9 for the observed between 2016 and 2017 is mainly notable in the Lake Chad area in the north- extent of agricultural land). eastern part of Borno. The same north-eastern part also has the strongest increase between 2017 and 2018, whereas the Lake Chad area has a decrease of NDVI for the same period. The highest NDVI values are found in the Lake Chad area and in the southern parts of the Between 2018 and 2019, the most notable increase can be seen in the eastern parts of Borno state. Around 90% of the state the NDVI values remain stable throughout the years 2016 state. Maps showing the areas with the strongest change in NDVI can be seen in Figure 10. to 2019. Even though, in general there is a notable positive increase of NDVI each year.
8 RESULTS Figure 13: Presence of agricultural land 2015 - 2019 LGA Comparative Analysis
Borno State has an area of 72,181 square kilometres and between 2015 and 2019, the state’s Abadam cropland extent varied between 22,529.4 km2 and 27066.9 km2, which is between 31% and 37% of the state’s total area. The LGA in Borno with the greatest cropland extent recorded for Mobbar Kukawa all the studied years was Magumeri, where the maximum extent was observed in 2016 with Guzamala a 51% coverage. Furthermore, the LGA with the relative highest agricultural extent is Jere in Gubio the Senatorial Districts of Borno Central, with an annual average of 64% and 539 km2. For the agricultural extent of all LGAs in Borno see Figure 11. Nganzai Monguno Marte Ngala Magumeri Jere Mafa Kala/Balge Dikwa Figure 11: Average cropland extent by LGA Maiduguri Average cropland extent Kaga 2500 70,0% Konduga Bama 60,0% 2000 50,0% 1500 40,0% Gwoza 1000 30,0% Damboa 20,0% 500 10,0% LGA Boundary Chibok 0 0,0% Biu Agricultural presence Biu Jere Kaga
Bayo 1 year
Mafa Askira/Uba Shani Bama Ngala Gubio Dikwa Marte Hawul Gwoza Chibok Kukawa Nganzai Mobbar Abadam Damboa
Konduga 2 years Monguno Guzamala Maiduguri Magumeri
Kala/Balge Kwaya Kusar Hawul Askira/Uba
Kwaya Kusar Kwaya 3 years Bayo 4 years Average extent in square kilometers Average extent in percent Shani 5 years
Figure 12: Total agriculture area over study years (in km2) Cropland Extent Between 2015 and 2019, Borno state has seen a 5% decrease in total agricultural land. The 30000 most remarkable change in agricultural land took place between 2016 and 2017, where there 28000 27066,8718 26275,9212 was a 17 % decrease in agricultural land. However, from 2017 to 2019 there was an overall 2 26000 24970,1256 increase 11%. For the variation in cropland extent see Figure 12. 23472,7038 24000 Area km 22529,412 The permanent presence of agricultural land in Borno is concentrated mainly to the central 22000 and northwestern parts of the state and particularly around the state’s capital Maiduguri. 20000 2015 2016 2017 2018 2019 Additionally, the LGA of Askira/Uba in the southern part shows a strong permanent presence Year of agriculture. For the distribution of cropland in Borno see Figure 13.
9 Figure 14: Changes in agricultural land by LGA 2015 - 2019, at the pixel level (left) and generalised to LGA (right)
A adam
o ar ka a amala
o
an a on no arte
ala a mer ere a a ala al e k a a d r a a
ond a ama
o a am oa
LGA Boundary A o ndar C ok Change of agricultural land C an e n a r lt ral land Decrease A k ra a tron n rea e Stable a a ar a l n rea e Increase a o ta le Non-agricultural land an e rea e tron de rea e
Figure 15: Percent change inAnnual area of cropland agricultural change land by LGA (2015 - 2019) The most significant increase of agriculture land in percent occurred in the LGAs Biu, Hawul, 500,0% Kwaya Kusar, and Shani, all located in southern Borno. For these LGAs most of the increase 400,0% 300,0% took place between 2015 and 2016. Decrease in agricultural land, on the other hand, is 200,0% mostly detected in central parts of Borno, where 11 LGAs have seen a decrease. Moreover, 100,0% a remarkable decrease is observed in the east in the LGA of Kala/Balge, where the most 0,0% dramatic decrease took place between 2016 and 2017. See Figures 14 and 15 for change in -100,0% Biu Jere Kaga Bayo Mafa Shani Bama Ngala Gubio Dikwa Marte Hawul
agriculture land per LGA. Gwoza Chibok Kukawa Nganzai Mobbar Abadam Damboa Konduga Monguno Guzamala Maiduguri Magumeri Kala/Balge Askira/Uba Kwaya Kusar Kwaya
2015 and 2016 2016 and 2017 2017 and 2018 2018 and 2019
10 Correlation with conflict Data from ACLED5 shows there has been a continuous series of violent events in Borno When looking at the correlation between conflict events and agriculture land at the LGA level, between the years 2015 and 2019, with 1,731 conflict events recorded.The years with most however, the relationship is not as evident as on the state level, with a coefficient value of conflict events are 2017 and 2018, with 376 and 378 events respectively. However, the 0.07. Nevertheless, the tendency is the same as for the whole state where a low number of months with the greatest number of conflict incidents are March 2015 with 58 events and conflict events correlates with a higher coverage of agricultural land.The same pattern is December 2019 with 61 events. The distribution of conflict events between the years 2015 and observed when entering more data points in the correlation with both year and agricultural 2019 can be seen in Figure18. extent per LGA, then the R2 value is 0.06. Between 2015 and 2019 conflict events occurred in all LGAs except in Bayo.There are three By spatially correlating the conflict events and the change in agricultural land, no strong LGAs where significantly more conflict events have been recorded than the others, these are relation can be observed, see Figure 17. Maiduguri, Guzamala, and Bama, with all over 150 events. On the contrary, the LGAs Kwaya Kusar and Shani both only have three events recorded in the same period By correlating overall agricultural extent and number of events for each year, a strong relation is detected with the coefficient value of 0.86, see Figure 16. This indicates that a year with a higher number of conflict events has less agricultural extent, and years with fewer events have a larger extent of agriculture land.
Figure 16: Correlation between total agricultureConflict events area and and number agricultural of conflict areaevents byin Bornoyear State 400 2017 2018 380 2019 360 2015 340 y = -0,0178x + 789,78 R² = 0,8631 320
300 2016
Nr of conflict events 280
260 22000 23000 24000 25000 26000 27000 28000 Agricultural area km2
5 ACLED. (2019). “Armed Conflict Location & Event Data Project (ACLED) Codebook, 2019” 11 Figure 17: Location of conflict events by year (left) and decrease in agricultural area during study period (right)
Abadam Abadam
obbar Mobbar uka a Kukawa u amala Guzamala
ubio Gubio
Ngan ai onguno arte Nganzai Monguno Marte
Ngala Ngala agumeri Magumeri ere Jere afa ala alge Mafa Kala/Balge ik a Dikwa aiduguri Maiduguri aga Kaga
onduga ama Konduga Bama
o a Gwoza amboa Damboa
LGA Boundary C ibok Chibok iu A oundary Biu Change of agricultural land Conflict incidents AC Askira ba Askira/Uba Decrease (2015-2019) ear aya usar a ul 2015 Kwaya Kusar Hawul ayo Bayo 2016 ani Shani 2017 2018 Count of Number 2019 Figure 18: Number of conflict events in Borno State from 2015 to 2019Conflict (ACLED) incidents in Borno 70 60 50 40 30 20 10 0 Numberconflict of events Jul Jul Jul Jul Jul Jan Jan Jan Jan Jan Jun Jun Jun Jun Jun Oct Oct Oct Oct Oct Apr Apr Apr Apr Apr Feb Sep Feb Sep Feb Sep Feb Sep Feb Sep Dec Dec Dec Dec Dec Aug Aug Aug Aug Aug Nov Nov Nov Nov Nov Mar Mar Mar Mar Mar May May May May May 2015 2016 2017 2018 2019 Years event_date 12 LIMITATIONS CONCLUSIONS The primary limitation of the method is of course the complete lack of locally collected field Returning to the research question highlighted in the introduction: data points for agricultural areas. Similarly, locally collected field data on crop types is also Can remote sensing analysis (a) complement ongoing assessments; (b) corroborate lacking. assessment findings; and/or (c) shed new insights on currently inaccessible areas? All estimates of agricultural areas are based on general models developed for this project, It becomes clear that the answer to all three components is at least a qualified yes. based on an assumed size and shape of agricultural fields. The high-resolution analysis did provide a good training set, yet, considering the lack of field data, findings should be viewed The changes observed and measured in agricultural area can clearly compliment ongoing with caution. In particular, given the spatial resolution of the Landsat and Sentinal data, assessments and monitoring efforts. The general trends witnessed appear to match reports many smaller agricultural fields were excluded from this analysis, while they may comprise a from survey respondents, indicating potential or partial corroboration of assessment findings. significant extent of agricultural land that would have impacted the results. This analysis also highlights how information can be extracted from areas with insufficient access or inadequate evidence from other sources. And new insights, particularly the Although NDVI is very commonly used for agriculture detection, it is not without limitations, correlation with conflict data, may prove useful. notably a lack of differentiation between vegetation types. However, it is also clear that much is still missing. At first glance, the results look less It should also be noted that a number of potential drivers for agricultural change were not severe than the situation reported from the people assessed through REACH assessments. evaluated in this study, from more regular variation in rainfall patterns to climatic shocks, such Therefore, generalizing findings to the LGA level may run the risk of hiding underlying as flooding or drought. It is very likely that there is a complex combination of factors involved, vulnerabilities within, at the community level. However, large parts of the population did not and isolating one or even a few potential contributing factors, out of possibly very many, have a great deal of agricultural land even before the crisis. So it is possible that when even a makes for a rather significant limitation. small proportion of that land is lost, it can have dramatic impacts on their livelihoods. Finally, while ACLED data is laudable, it is likewise limited due to it being based on media Current indicators assessed also do not fully align with what is being measured through and open source information, so events not reported on in some manner are unlikely to be remotes sensing analysis, making direct ‘apples to apples’ comparison difficult if not featured in ACLED data. Notably, incidents in the hardest-to-reach areas are least likely to be impossible. reported. Therefore, ACLED is exposed to the same risk of data gaps and bias resulting from inaccessibility as other actors. And while the observed correlation between total agricultural area and number of conflict incidents in Borno State is indeed an interesting finding, it should be treated with caution as the correlation was not observed at the LGA level, and of course, correlation does not imply causation. As this was an initial pilot study, some limitations were expected. As highlighted in the limitations section, there remains room to further refine the analysis approach. In the end, this study offered a cursory glance that shed light on an interesting pathway for future analysis. Hence, the current analysis might serve as a stepping stone; more work remains to be done to better pair remote sensing analysis with field assessment data, and to triangulate this with secondary information, to in turn support deeper understanding of the humanitarian situation in hard-to-reach areas.
13 CONCLUSIONS NEXT STEPS Develop better linkages between remote sensing and assessment data Incorporate other potential drivers of change Through this process it has become clear that current FSL indicators and survey questions There are a number of factors that this study overlooked that should be included in any further do not align directly with remote sensing analysis. A key question going forward will be analysis. Of particular importance are: centered on how this connection can be improved, whether within ongoing MSNA or hard • Environmental factors (e.g. precipitation); to reach assessments or in a more tailored assessment. It does appear that some existing survey questions could be modified to capture corresponding time periods assessed in remote • Displacement and settlement dynamics, conflict-related and otherwise; sensing analysis. But this will require a dialogue between FSL sector specialists and remote • Shocks or events (e.g. flooding, drought, climate change). sensing experts. All of which can be interrelated and can be linked to agriculture and insecurity, highlighting the complex interplay of potential factors leading to increased vulnerability. Expand agricultural analysis
There are a number of ways to expand upon this agriculture analysis that will be explored, Develop a more complete and nuanced picture of insecurity including: Given the limitations of existing conflict datasets, it will be worthwhile to establish links to other • Establishing a pre-crisis agricultural baseline - this would entail analysis of decades of forms of conflicict analysis, including remote detection of shelter damage or loss, triangulation pre-crisis archive imagery to identify the average extent; with key informants, participatory mapping exercises, and/or self-reported protection concerns, • Evaluating agricultural change for the entire crisis period to date - different trends may be to establish a more complete picture of insecurity. revealed going back to the start of the crisis; Existing conflict data can be leveraged futher and evaluated in different ways, particulary • Exploring more localized analysis - whether at the level of individual livelihood zones, looking at types and magnitude of events. It may be possible that key findings are missed hardest to reach LGAs and below the LGA level, at the community level, more detailed by only using frequency and not including a proxy for severity. Both frequency and mortality analysis is needed; based estimates of severity could be linked to disruption in planting for instance. In future, it may be worthwhile to develop corresponding categorizations based on deeper exploration of • Investigate opportunities for collecting data locally on agricultural areas as well as crop existing data. types (ground truthing); Further, it will be important to evaluate the timing of conflict events to the extent possible, both • Combing more localized analysis with ground truthed data to map crop types and degree on an annual basis and seasonally. It is unclear currently if conflict events have a greater of crop land loss; effect in the current year or in years following. And with more robust seasonal remote sensing • Looking more in depth at the seasonal calendar, evaluating sowing, growing and harvest analysis it may be possible to look at conflict and any related disruptions to sowing, growing periods individually to the extent possible. and harvest periods.
14 ABOUT REACH The REACH Initiative facilitates the development of information tools and products that enhance the capacity of aid actors to make evidence-based decisions in emergency, recovery and development contexts. The methodologies used by REACH include primary data collection and in-depth analysis, and all activities are conducted through inter-agency aid coordination mechanisms. REACH is a joint initiative of IMPACT Initiatives, ACTED and the United Nations Institute for Training and Research - Operational Satellite Applications Programme (UNITAR-UNOSAT).
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