Use of time series normalized difference vegetation index (NDVI) to monitor fall armyworm (Spodoptera frugiperda) damage on maize production systems in Africa

Figures & data

Figure 1. Study area map showing the study regions located in western and southern Africa: Centrale (Togo), Atlantique (Benin), Salima (Malawi), Chongwe (Zambia), and Limpopo (South Africa).

Table 1. Cropping Surface of Maize and Sorghum for the study area in hectares (ha).

Region, Country	Maize cropping surface ha	Sorghum cropping surface ha
Atlantique, Benin	19,000	7,000
Centrale, Togo	28,000	9,000
Salima, Malawi	43,000	6,400
Chongwe region in Zambia	19,000	6,900
Limpopo, South Africa	464,000	16,000

Source: FAO 2018. Statistical yearbook 2018. Retrieved from http://www.fao.org/state-of-food-security-nutrition/en/

Figure 2. Flowchart of the hierarchical methodology steps for assessing and quantifying the damage of fall armyworm on maize productivity using the normalized difference vegetation index (NDVI).

Table 2. Landsat 8 satellite images 11 spectral bands, which are used to capture different aspects of the Earth’s surface.

Band Number	Band Name	Wavelength (μm)	Resolution (m)
1	Coastal	0.44 − 0.48	30
2	Blue	0.45 − 0.51	30
3	Green	0.53 − 0.59	30
4	Red	0.64 − 0.67	30
5	NIR1	0.84 − 0.88	30
6	SWIR1	1.57 − 1.65	30
7	SWIR2	2.11 − 2.29	30
8	Panchromatic	0.50 − 0.68	15
9	Cirrus	1.36 − 1.38	30

Table 2 lists the spectral bands of a satellite sensor, along with their band numbers, names, wavelength ranges in micrometers (μm), and spatial resolutions in meters. The sensor has eight multispectral bands (bands 1-7 and 9) and one panchromatic band (band 8). The multispectral bands cover a wide range of wavelengths from the visible to the near-infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum. The spatial resolution of the multispectral bands is 30 meters, while the panchromatic band has a higher resolution of 15 meters. Additionally, the sensor has a Cirrus band (band 9) that is sensitive to high-altitude clouds.

Table 3. The threshold range of the normalized difference vegetation index (NDVI) values for the four vegetation density classes viz., water body, low, sparse, moderate, and dense vegetation for the five study regions.

Study region	Vegetation class	NDVI values range
Atlantique (Benin)	Low vegetation	−0.15 to 0.10
	Sparse vegetation	0.10 to 0.26
	Moderate vegetation	0.26 to 0.36
	Dense vegetation	0.36 to 0.67
Centrale (Togo)	Low vegetation	−0.10 to 0.39
	Sparse vegetation	0.36 to 0.47
	Moderate vegetation	0.47 to 0.54
	Dense vegetation	0.54 to 0.77
Salima (Malawi)	Low vegetation	−0.48 to −0.04
	Sparse vegetation	−0.04 to 0.22
	Moderate vegetation	0.22 to 0.31
	Dense vegetation	0.31 to 0.75
Chongwe (Zambia)	Low vegetation	−0.37 to 0.05
	Sparse vegetation	0.05 to 0.28
	Moderate vegetation	0.28 to 0.36
	Dense vegetation	0.36 to 0.77
Limpopo (South Africa)	Low vegetation	−0.67 to −0.00
	Sparse vegetation	−0.00 to 0.27
	Moderate vegetation	0.27 to 0.41
	Dense vegetation	0.41 to 0.84

The range of NDVI values for each vegetation class in five different study regions in table 3: Atlantique (Benin), Centrale (Togo), Salima (Malawi), Chongwe (Zambia), and Limpopo (South Africa). The Low vegetation class had the lowest NDVI values, ranging from -0.67 to -0.04, while the Dense vegetation class had the highest values, ranging from 0.31 to 0.84. The range of NDVI values for Sparse vegetation and Moderate vegetation classes varied between regions.

Figure 3. The classified normalized difference vegetation index (NDVI) for the years 2013 to 2020, for the regions of a) Atlantique (Benin), b) Centrale (Togo), c) Salima (Malawi), d) Limpopo (South Africa) and e) Chongwe (Zambia).

Figure 4. Annual average normalized difference vegetation indices (NDVI) from 2013 to 2020 for the regions of a) Atlantique (Benin), b) Centrale (Togo), c) Chongwe (Zambia), d) Salima (Malawi), and e) Limpopo (South Africa).

Table 4. Area and percentage of change in the normalized difference vegetation index (NDVI) classes between 2013 and 2016 for the Atlantique region of Benin (veg. = vegetation).

NDVI class	Area (ha)		Difference (ha)	Change (%)		Difference (%)
	2013	2016	Δ	2013	2016	Δ
Low veg.	14931.99	15647.91	715.92	4.86	5.09	0.23
Sparse veg.	94165.94	104702.21	10536.28	30.65	34.08	3.43
Moderate veg.	127876.97	126585.90	−1291.08	41.62	41.20	−0.42
Dense veg.	70267.69	60306.57	−9961.12	22.87	19.63	−3.24

Difference (ha) and Change (%) columns in the table 4 shows the change in area and percentage change, respectively, for each vegetation class between 2013 and 2016. A positive or negative sign indicates whether the area increased or decreased, respectively. 

Table 5. Area and percentage of change for the normalized difference vegetation index (NDVI) classes between 2013 and 2016 for the Centrale region of Togo (veg. = vegetation).

NDVI class	Area (ha)		Difference (ha)	Change (%)		Difference (%)
	2013	2016	Δ	2013	2016	Δ
Low veg.	165432.06	358454.01	193021.95	12.49	27.05	14.57
Sparse veg.	431605.47	491854.69	60249.22	32.58	37.12	4.55
Moderate veg	484141.68	342134.53	−142007.15	36.54	25.82	−10.71
Dense veg.	243706.11	132442.09	−111264.02	18.40	1.00	−8.40

Difference (ha) and Change (%) columns in the table 5 shows the change in area and percentage change, respectively, for each vegetation class between 2013 and 2016. A positive or negative sign indicates whether the area increased or decreased, respectively. 

Table 6. Area and percentage of change for the normalized difference vegetation index (NDVI) classes between 2013 and 2017 for the Chongwe region of Zambia (veg. = vegetation).

NDVI Class	Area (ha)		Difference (ha)	Change (%)		Difference (%)
	2013	2017	Δ	2013	2017	Δ
Low veg.	266.85	200410.83	200143.98	18.43	18.41	0.02
Sparse veg.	313848.90	429315.49	115466.59	28.87	39.50	10.60
Moderate veg.	539585.67	317554.72	−222029.95	49.64	29.20	−20.40
Dense veg.	233235.93	139655.31	−93580.62	21.46	12.85	−8.61

Difference (ha) and Change (%) columns in the table 6 shows the change in area and percentage change, respectively, for each vegetation class between 2013 and 2017. A positive or negative sign indicates whether the area increased or decreased, respectively. 

Table 7. Area and percentage of change for the normalized difference vegetation index (NDVI) classes between 2013 and 2017 for the Salima region of Malawi (veg. = vegetation).

	Area (ha)		Difference (ha)	Change (%)		Difference (%)
NDVI Class	2013	2017	Δ	2013	2017	Δ
Low veg.	97298.64	96534.19	−764.45	31.18	30.93	−0.24
Sparse veg.	65001.06	105596.58	40595.52	20.83	33.83	13.00
Moderate veg.	108690.06	93423.57	−15266.48	34.83	29.93	−4.89
Dense veg.	41105.16	16540.58	−24564.58	13.17	5.30	−7.87

Difference (ha) and Change (%) columns in the table 7 shows the change in area and percentage change, respectively, for each vegetation class between 2013 and 2017. A positive or negative sign indicates whether the area increased or decreased, respectively. 

Table 8. Area and percentage of change for the normalized difference vegetation index (NDVI) classes between 2013 and 2018 for Limpopo, South Africa (veg = vegetation).

NDVI class	Area (ha)		Difference (ha)	Change (%)		Difference (%)
	2013	2018	Δ	2013	2018	Δ
Low veg.	21195.16	4195000.91	4173805.75	0.17	34.02	33.85
Sparse veg.	6854595.74	5769171.76	−1085423.99	55.59	46.78	−8.80
Moderate veg.	4776531.85	1999759.36	−2776772.49	38.73	16.22	−22.51
Dense veg.	679377.82	367768.54	−311609.28	5.51	2.98	−2.53

Difference (ha) and Change (%) columns in the table 8 shows the change in area and percentage change, respectively, for each vegetation class between 2013 and 2018. A positive or negative sign indicates whether the area increased or decreased, respectively. 

Figure 5. Correlation between the normalized difference vegetation index (NDVI) and prevalence of fall armyworm (FAW) in five regions of sub-Saharan Africa, i.e. the regions of a) Atlantique (Benin), b) Centrale (Togo), c) Chongwe (Zambia), d) Salima (Malawi), and e) Limpopo (South Africa).

FAO. 2018. Integrated management of the Fall Armyworm on maize A guide for Farmer Field Schools in Africa; p. 119.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.