Governing agricultural drought: Monitoring using the vegetation condition index

Gebrehiwot, Tagel; Veen, A. van der; Maathuis, B.H.P.

doi:10.4314/ejesm.v9i3.9

Cited by 17 publications

(9 citation statements)

References 42 publications

(41 reference statements)

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“…On the other hand, the years 2001,2005,2006,2007,2013,2016, and 2018 reflected near-normal NDVI throughout most of the rain-fed agriculture regions. These results are coherent with the findings of previous studies in indicating the onset, spatial, and temporal dynamics of agricultural drought in Ethiopia [18]. Pixel-based trend analysis showed that a significant precipitation decrease in the central areas is accompanied by a significant increase in LST.…”

Section: Discussionsupporting

confidence: 92%

“…Thus, NDWI is defined in a similar way to NDVI but uses the near-infrared channel to monitor the water content of the vegetation canopy. Fluctuations in the vegetation canopy are indicators of drought stress [18]. Besides NIR and SWIR, two spectral regions of the electromagnetic spectrum have been found to be useful for the detection of plant water content: the green and red channels.…”

Section: Datasetsmentioning

confidence: 99%

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Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts

Liou

Mulualem

2019

Remote Sensing

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The recent droughts that have occurred in different parts of Ethiopia are generally linked to fluctuations in atmospheric and ocean circulations. Understanding these large-scale phenomena that play a crucial role in vegetation productivity in Ethiopia is important. In view of this, several techniques and datasets were analyzed to study the spatio-temporal variability of vegetation in response to a changing climate. In this study, 18 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) of Moderate Resolution Imaging Spectroscopy (MODIS) Terra/Aqua, normalized difference vegetation index (NDVI), land surface temperature (LST), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) daily precipitation, and the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) soil moisture datasets were processed. Pixel-based Mann-Kendall trend analysis and the Vegetation Condition Index (VCI) were used to assess the drought patterns during the cropping season. Results indicate that the central highlands and northwestern part of Ethiopia, which have land cover dominated by cropland, had experienced decreasing precipitation and NDVI trends. About 52.8% of the pixels showed a decreasing precipitation trend, of which the significant decreasing trends focused on the central and low land areas. Also, 41.67% of the pixels showed a decreasing NDVI trend, especially in major parts of the northwestern region of Ethiopia. Based on the trend test and VCI analysis, significant countrywide droughts occurred during the El Niño 2009 and 2015 years. Furthermore, the Pearson correlation coefficient analysis assures that the low NDVI was mainly attributed to the low precipitation and water availability in the soils. This study provides valuable information in identifying the locations with the potential concern of drought and planning for immediate action of relief measures. Furthermore, this paper presents the results of the first attempt to apply a recently developed index, the Normalized Difference Latent Heat Index (NDLI), to monitor drought conditions. The results show that the NDLI has a high correlation with NDVI (r = 0.96), precipitation (r = 0.81), soil moisture (r = 0.73), and LST (r = −0.67). NDLI successfully captures the historical droughts and shows a notable correlation with the climatic variables. The analysis shows that using the radiances of green, red, and short wave infrared (SWIR), a simplified crop monitoring model with satisfactory accuracy and easiness can be developed.

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Section: Discussionsupporting

confidence: 92%

Section: Datasetsmentioning

confidence: 99%

Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts

Liou

Mulualem

2019

Remote Sensing

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“…Many drought studies have focused on assessment of both meteorological and agricultural droughts in Ethiopia. While Gebrehiwot et al (2016) undertook a determination of spatiotemporal seasonal agricultural drought in Ethiopia during the 1998 to 2013 cropping seasons; Suryabhagavan (2017) characterized meteorological droughts and climate variability in Ethiopia over three decades (1983 to 2012). The study of Qu et al (2019) on impacts of drought, includes other countries aside Ethiopia and covering the Horn of Africa, while that of Gidey et al (2018) characterized the occurrence of drought in northern Ethiopia for 15 years (2001 to 2015).…”

Section: The Commonly Used Meteorological Drought Indicators Include mentioning

confidence: 99%

“…The study of Gebrehiwot et al (2016), classi es Tigray as one the 'most severely affected (regions) during well-known famines', where a slight rainfall-de ciency can result in crop failure, with a direct impact on food security of households. Crop failure has been attributed to the paltry amount of water stored in the soil during a prolonged dry spell (Marteau et al, 2011;Mugalavai et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Yield and yield-reduction in recent drought years in southern Tigray, northern Ethiopia: Implications on food security

Eze

Okolo

Negash

2020

Preprint

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The consequences of prolonged precipitation-deficient periods are primarily substantial water deficit, with the spatial characteristics of a place being (semi)arid, and various socioeconomic factors worsening its impacts and deepening poverty among agrarian communities.This study utilizes a combination of climate, remote sensing and field survey data from farmers to obtain a first-hand information on the impacts of recent (2015 and 2017) droughts on crop yield in Raya Azebo and Endamehoni woredas in Ethiopia. Annual rainfall, kiremt rainfall, annual NDVI and Dev-NDVI, kiremt season Dev-NDVI, monthly Dev-NDVI (for June to October), and monthly SPI-1, SPI-3 and SPI-12 (for June to October) were considered as likely factors that could relate with yield and yield loss in the area. Correlation and multiple linear stepwise regression statistical techniques were used to determine drought-yield relationships, and identify more accurate predictors of yield and yield losses in each of the drought years.Results obtained show droughts as having spatiotemporal variations and impacts, with its primary and common reflection being reduced vegetation amount, translating to crop failures, food shortages and reduced income of smallholders.Spate irrigation should be further popularized in the low-lying areas of Raya Azebo to augment for future deficiencies in the kiremt rainfall.

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“…No obstante, se ha seleccionado únicamente desde 2001, haciendo coincidir así los años de las series temporales de precipitación y NVDI. La decisión de emplear los mismos años se ha basado en estudios similares a este donde se opta por emplear series temporales de la misma longitud temporal (Boschetti et al, 2013;Gebrehiwot et al, 2016;Njery, 2016).…”

Section: Materialesunclassified

Análisis de series temporales de precipitación y vegetación para la detección de anomalías en la producción de alimentos en el Cuerno de África. El caso de Lower Shabelle (Somalia)

Belenguer-Plomer

2016

Rev. Teledetec.

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Resumen: El Cuerno de África es uno de los lugares con mayor inseguridad alimentaria de todo el mundo debido al continuo incremento de su población y a la práctica de la agricultura de subsistencia. Esto provoca que gran parte de la población no pueda cubrir las necesidades nutricionales mínimas para llevar una vida saludable. Además, esta situación de vulnerabilidad alimentaria puede verse seriamente afectada en los próximos años debido a los efectos del cambio climático. El objetivo de este trabajo es combinar la información acerca del estado de la vegetación que ofrece el NDVI con datos de precipitación para detectar anomalías negativas en la producción de alimentos. Para ello, se han empleado los productos mensuales de NDVI MOD13A3 de MODIS y de precipitación estimada TAMSAT, ambos durante el periodo 2001-2015. Con dichos productos se ha calculado el promedio de todo el periodo temporal seleccionado y se han detectado los años cuyos valores de NDVI más se alejan de dicho promedio, siendo estos 2010, 2011 y 2014. Una vez detectados los años con mayores anomalías en el NDVI, se ha realizado un análisis mensual exclusivo de dichos años, donde se ha analizado de forma estadística la relación existente entre el valor de NDVI y de precipitación mensual, obteniéndose una relación directa entre ambos valores. Asimismo, se ha empleado el calendario de cosechas para centrar el análisis en los meses de producción agrícola, hallando así que la principal causa de las anomalías en la vegetación es una disminución en el registro de precipitaciones durante los meses de producción agrícola. Dicho motivo explica el origen de la escasez alimentaria sucedida en 2010 y 2011, que generó una enorme crisis humanitaria en esta zona.Palabras clave: NDVI, sequías, Cuerno de África, MODIS, TAMSAT. Time series analysis of precipitation and vegetation to detect food production anomalies in the Horn of Africa. The case of Lower Shabelle (Somalia)Abstract: The Horn of Africa is one of the most food-insecure locations around the world due to the continuous increase of its population and the practice of the subsistence agriculture. This causes that much of the population cannot take the minimum nutritional needs for a healthy life. Moreover, this situation of food vulnerability may be seriously affected in the coming years due to the effects of climate change. The aim of this work is combine the information about the state of the vegetation that offers the NDVI with rainfall data to detect negative anomalies in food production. This work has been used the monthly products of NDVI MOD13A3 of MODIS and the rainfall estimation product TAMSAT, both during the period 2001-2015. With these products we have calculated the average of the entire time period selected and we have detected the years whose NDVI values were further away from the average, being these 2010, 2011 and 2014. Once detected the years with major anomalies in NDVI, there has been an exclusive monthly analysis CASO PRÁCTICO

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Governing agricultural drought: Monitoring using the vegetation condition index

Cited by 17 publications

References 42 publications

Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts

Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts

Yield and yield-reduction in recent drought years in southern Tigray, northern Ethiopia: Implications on food security

Análisis de series temporales de precipitación y vegetación para la detección de anomalías en la producción de alimentos en el Cuerno de África. El caso de Lower Shabelle (Somalia)

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