Application of GIS and remote sensing techniques in frost risk mapping for mitigating agricultural losses in the Aberdare ecosystem, Kenya

Kotikot, Susan M.; Onywere, S. M.

doi:10.1080/10106049.2014.965758

Cited by 17 publications

(10 citation statements)

References 9 publications

(9 reference statements)

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“…Susan Malaso, (2015) has developed a Frost risk mapping model using pixel based classification of satellite images in manipulating the minimum temperature [23]. It is a highly evident mode of satellite image interpretation where the extracted temperature values are analysed for tracing the previous change outcome or for future prediction.…”

Section: Methodsmentioning

confidence: 99%

An Overview of Technological Revolution in Satellite Image Analysis

Jenice¹,

Raimond²

2016

JESTR

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The satellite image based applications are highly utilized nowadays from simple purposes like vehicle navigation to complex surveillance and virtual environment modeling projects. On increased population rate, the depletion of natural resources is highly unavoidable and it leads to increased threats on natural hazards. In order to protect and overcome the physical losses on devastation of properties, the risk mapping models such as weather forecasts, drought modeling and other hazard assessment models are in need. Though many intelligent risk mapping models do exist, the high level of intelligence needed to predict the real time events is still unresolved. And it leads to increased research efforts on designing intelligent assessment models using more advanced machine learning (ML) schemes for better satellite image interpretation. This paper focuses on bringing a clear understanding on satellite image interpretation methods right from the traditional statistical models to the more recent ML methods through stating the gradual revolution in satellite image analysis.

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Section: Methodsmentioning

confidence: 99%

An Overview of Technological Revolution in Satellite Image Analysis

Jenice¹,

Raimond²

2016

JESTR

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“…Frost is physically determined by the presence of minimum temperature below 0°C influenced by terrain aspect, i.e., slope and elevation (Kotikot and Onywere 2014;Wang et al, 2016). Hansen et al (2014) describes another factor that has a strong influence on frost is land cover condition.…”

Section: Research Proceduresmentioning

confidence: 99%

Frost Hazard Assessment on Agricultural Land to Achieve Resilient Agriculture in Dieng Volcanic Highland, Central Java

Pradana

Mardiana

Lestari

et al. 2019

ipas

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Dieng Volcanic Highland is one of the most intensive potato agricultural land in Indonesia, as well as frost disaster prone area. Frost indicated by appearance of frozen dew on the ground or vegetation surface due to cold temperatures during dry season. Frost causes damage to leaf tissue in potato plants, resulting crop failure and losses of up to tens of millions. Disaster management needs to be assess in order to achieve Disaster Risk Reduction (DRR) on agricultural land. This research aims to identify frost hazard areas based on local physical characteristics, analyze frost impacts on agricultural land, and provide preparedness recommendation to reduce the impact of frost disasters in Dieng Volcanic Highland. Research was conducted in Dieng Village, Wonosobo and Dieng Kulon Village, Banjarnegara. Method to assess hazard level was performed by spatial mapping technology using ArcGIS and comprehensive analysis using frost assessment through combinations of geomorphology, land use, proximity to water bodies and weather aspects. Dieng Volcanic Highland has a 125.59 hectare frost hazard areas, as many as 58.4 hectares of hazard areas are dominated by high level, while 24.84 hectares are moderate level and 42.95 hectares are low level. Cropland dominated by potato commodity has the highest hazard level, frost incident causing agricultural commodities to wither to death so that farmers experience losses. Frost losses in Dieng have a range from 800 thousand rupiah to over 155 million rupiah and only destructive on potato farm. In order to encourage agricultural resilience and reduce the loss of frost disasters, effort of preparedness can be done by passively and actively methods. Passive methods includes site selection, early warning system, shifting commodities, cropland modification, and appropriate calendar planting. Active methods includes frost modification using heaters, blower and sprinkle irrigation, and cropland covering using mulch, plastic or nets.

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“…East Africa is particularly vulnerable to the effects of rainfall and temperature extremes, as much of the population depends on rainfed agriculture for their income: 79% in Ethiopia, 77% in Tanzania, and 61% in Kenya (FAO 2018). Crops and livestock can be affected by heat stress, drought conditions (Herrero et al 2010), and frosts (Kotikot and Onywere 2015), and the prevalence of disease is also influenced by both rainfall and temperature (Bandyopadhyay et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Skill of Seasonal Rainfall and Temperature Forecasts for East Africa

Young

Klingaman

2020

Weather and Forecasting

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Skillful seasonal forecasts can provide useful information for decision-makers, particularly in regions heavily dependent on agriculture, such as East Africa. We analyze prediction skill for seasonal East African rainfall and temperature one to four months ahead from two seasonal forecasting systems: the U.S. National Centers for Environmental Prediction (NCEP) Coupled Forecast System Model, version 2 (CFSv2), and the Met Office (UKMO) Global Seasonal Forecast System, version 5 (GloSea5). We focus on skill for low or high temperature and rainfall, below the 25th or above the 75th percentile, respectively, as these events can have damaging effects in this region. We find skill one month ahead for both low and high rainfall from CFSv2 for December–February in Tanzania, and from GloSea5 for September–November in Kenya. Both models have higher skill for temperature than for rainfall across Ethiopia, Kenya, and Tanzania, with skill two months ahead in some cases. Performance for rainfall and temperature change in the two models during certain El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) phases, the impacts of which vary by country, season, and sometimes by model. While most changes in performance are within the range of uncertainty due to the relatively small sample size in each phase, they are significant in some cases. For example, La Niña lowers performance for Kenya September–November rainfall in CFSv2 but does not affect skill in GloSea5.

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Application of GIS and remote sensing techniques in frost risk mapping for mitigating agricultural losses in the Aberdare ecosystem, Kenya

Cited by 17 publications

References 9 publications

An Overview of Technological Revolution in Satellite Image Analysis

An Overview of Technological Revolution in Satellite Image Analysis

Frost Hazard Assessment on Agricultural Land to Achieve Resilient Agriculture in Dieng Volcanic Highland, Central Java

Skill of Seasonal Rainfall and Temperature Forecasts for East Africa

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