Vegetation condition prediction for drought monitoring in pastoralist areas: a case study in Ethiopia

Demisse, Getachew B.; Tadesse, Tsegaye; Bayissa, Yared A.; Atnafu, Solomon; Argaw, Mekuria; Nedaw, Dessie

doi:10.1080/01431161.2017.1421797

Cited by 16 publications

(9 citation statements)

References 29 publications

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“…The indigo colour cluster has the work by [88] in 2014 as the first publication and cited by [89][90][91]. The research aimed at developing an experimental drought monitoring tool that predicts the vegetation condition (vegetation outlook).…”

Section: Direct Citation Analysismentioning

confidence: 99%

“…Studies in this cluster are centred on agricultural drought prediction. Their methods include regression-tree technique [88], classification and regression tree (CART) modelling technique [89], ML, and general additive model (GAM) technique [90], and Vegout-UBN model [91]. Among their findings is the ex-ante drought early warning systems capable of offering drought forecasts with enough lead times.…”

Section: Direct Citation Analysismentioning

confidence: 99%

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Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa

et al. 2020

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The African continent has a long history of rainfall fluctuations of varying duration and intensities. This has led to varying degrees of drought conditions, triggering research interest across the continent. The research presented here is a bibliometric analysis of scientific articles on drought monitoring and prediction published in Africa. Scientific data analysis was carried out based on bibliometric mapping techniques applied to 332 scientific publications (1980 to 2020) retrieved from the Web of Science (WoS) and Scopus databases. In addition, time series of Standardized Precipitation Evapotranspiration Index for the previous 6 months (SPEI-6) over six regions in the continent was analysed giving the relative comparison of drought occurrences to the annual distribution of the scientific publications. The results revealed that agricultural and hydrological drought studies contributed about 75% of the total publications, while the remaining 25% was shared among socioeconomic and meteorological studies. Countries in the southern, western, and eastern regions of Africa led in terms of scientific publications during the period under review. The results further indicated that the continent experienced drought conditions in the years 1984, 1989, 1992, and 1997, thereby inducing an increase in the number of scientific publications on drought studies. The results show that the tools of analysis have also changed from simple statistics to the use of geospatial tools such as Remote Sensing (RS) and Geographical Information System (GIS) models, and recently Machine Learning (ML). The ML, particularly, contributed about 11% of the total scientific publications, while RS and GIS models, and basic statistical analysis account for about 44%, 20%, and 25% respectively. The integration of spatial technologies and ML are pivotal to the development of robust drought monitoring and drought prediction systems, especially in Africa, which is considered as a drought-prone continent. The research gaps presented in this study can help prospective researchers to respond to the continental and regional drought research needs.

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Section: Direct Citation Analysismentioning

confidence: 99%

Section: Direct Citation Analysismentioning

confidence: 99%

Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa

et al. 2020

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“…Particularmente, dentre os satélites da série MSG, o Meteosat-10 está localizado a 0° de latitude e longitude cobrindo a Europa, África, oeste do Oriente Médio e nordeste da América e apresenta alta resolução temporal (15 minutos) e baixa resolução espacial (3 km) (Schmetz et al, 2002). Este satélite mostra-se eficiente para o monitoramento da superfície em regiões da África, quando comparado com dados de satélites polares (Yan et al, 2016), sendo utilizado na análise e comportamento do estresse da vegetação (Demisse et al, 2018) e estimativas de fenologia (Julien et al, 2018).…”

Section: Introductionunclassified

Monitoramento da superfície do solo usando indicadores ambientais provenientes de dados de satélite em órbita geoestacionária

Silveira

Ferreira

Silva

2020

Rev. Bras. Geog. Fis.

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O satélite de órbita geoestacionária Meteosat-10 fornece indicadores ambientais com alta resolução temporal e baixa resolução espacial, permitindo o monitoramento da fenologia da paisagem. Porém, devido ao alto ângulo de visada do Meteosat-10, pouco tem sido utilizado para o Brasil. Indicadores como NDVI (Normalized Difference Vegetation Index), eficiente para o monitoramento da vegetação devido à simplicidade e estreita relação com a produtividade da vegetação, e o NDWI (Normalized Difference Water Index), que indica a umidade da superfície do solo combinando o solo e a vegetação expostos, ajudam a compreender a sazonalidade ambiental e ocupação das bacias hidrográficas. Esse artigo tem como objetivo avaliar a capacidade de indicadores biofísicos, provindos do satélite de órbita geoestacionária Meteosat-10, para o monitoramento ambiental da superfície do solo, tendo como área de estudo bacias hidrográficas, Ottobacias nível 5, do estado de Goiás, incluindo o Distrito Federal, no período 2013 a 2015. Para tanto, foram avaliados o NDVI, NDWI e a precipitação com resolução temporal de 12 dias e mensal. Fez-se o cálculo dos indicadores normalizados SPI (Standard Precipitation Index), SVI (Standard Vegetation Index) e SWI (Standard Water Index) para comparações entre os anos e obtenção da tendência de cada indicador. Como resultado pode-se identificar e compreender padrões sazonais das bacias hidrográficas em diferentes regiões e classes de uso, demonstrando o potencial de indicadores de vegetação provenientes de satélites geoestacionários, para o monitoramento da cobertura do solo em bacias hidrográficas, bem como identificar a maior sensibilidade do NDWI para o monitoramento de mudanças ocorridas na superfície. Monitoring of soil surface using environmental indicators satellite data monitor from geostationary orbit A B S T R A C TThe geostationary orbit satellite Meteosat-10, has spectral bands that allow create that provides environmental indicators with high temporal resolution and low spatial resolution, allowing the monitoring of landscape phenology. However, for Brazil little has been used due to the high viewing angle of Meteosat-10 (45 a 80º). Indicators like NDVI (Normalized Difference Vegetation Index), efficient for monitoring vegetation due to the simplicity and close relationship with the productivity of the vegetation, and the NDWI (Normalized Difference Water Index), which indicates the moisture of the soil surface combining the soil and the exposed vegetation, help to understand the environmental seasonality and occupation of watersheds. This article aims to evaluate the capacity of biophysical indicators, coming from the geostationary orbit satellite Meteosat-10, for the environmental monitoring of the soil surface, with hydrographic basins, Ottobasin level 5, from the state of Goiás, as the study area. Distrito Federal, from 2013 to 2015. For this purpose, NDVI, NDWI and precipitation with 12-day and monthly resolution were evaluated. Standardized indicators SPI (Standard Precipitation Index), SVI (Standard Vegetation Index) and SWI (Standard Water Index) were calculated for comparisons between years and obtaining the trend of each indicator. As a result, it is possible to identify and understand seasonal patterns of hydrographic basins in different regions and classes of use, demonstrating the potential of vegetation indicators from geostationary satellites, for monitoring soil cover in hydrographic basins, as well identify the highest sensitivity NDWI for monitoring surface changes.Keywords: NDVI, NDWI, Meteosat-10, environmental monitoring.

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“…Yang et al (2018) evaluated the health situation of Poyang Lake using vegetation-based indices. Demisse et al (2018) and Nanzad et al (2019) applied vegetation indices to evaluate the drought conditions. Sarkar and Kafatos (2004) studied on the Indian subcontinent for several years to study the variability of vegetation and found the dominance of local climate anomaly in determining the vegetation.…”

Section: Introductionmentioning

confidence: 99%

Intra-annual variations of vegetation status in a sub-tropical deciduous forest-dominated area using geospatial approach: a case study of Sali watershed, Bankura, West Bengal, India

Malik

Pal

Das

et al. 2019

Geology, Ecology, and Landscapes

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Intra-annual dynamics of vegetation status is very important to understand the spatial ecological environment. In this study, pixel wise temporal variation of vegetation status has been assessed using normalized differential vegetation index (NDVI) from Landsat 8 OLI data of 2014. Month-wise vegetation statuses were employed to understand the status of vegetation dynamics for the Sali River watershed. We have found that vegetation status (VS) of this study area varied with respect to space and time. We have identified two dominant natures of NDVI fluctuation, one with a single peak coming out of Sal forest and another with multiple peaks which is from paddy fields. NDVI curve of natural vegetation follows a rainfall pattern and depicts a single peak during the rainy season with the moderate standard deviation (S.D.) and coefficient of variation (C.V.). On the other hand, the area associated with population pressure and agricultural fields shows multiple peaks as well as a high degree of S.D. and C. V. of NDVI during the months of April, August and December.

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Vegetation condition prediction for drought monitoring in pastoralist areas: a case study in Ethiopia

Cited by 16 publications

References 29 publications

Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa

Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa

Monitoramento da superfície do solo usando indicadores ambientais provenientes de dados de satélite em órbita geoestacionária

Intra-annual variations of vegetation status in a sub-tropical deciduous forest-dominated area using geospatial approach: a case study of Sali watershed, Bankura, West Bengal, India

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