Quantification of visual soil erosion indicators in Gikuuri catchment in the central highlands of Kenya

Okoba, Barrack; Sterk, G.

doi:10.1016/j.geoderma.2005.08.013

Cited by 31 publications

(11 citation statements)

References 19 publications

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“…Pilot studies at farm level Local · Enables a grass roots view on the severity of degradation and its causes · Relying on fi eld indicators of degradation can be subjective · Can be costly depending on area coverage Dendrogeomorphological analysis using exposed shrub roots to estimate soil erosion rates in rangelands (Chartier et al, 2009). Quantifi cation of visual soil erosion indicators (splash pedestals, sheetwash, rills, sedimentation, red color and stoniness) to evaluate soil loss in the Gikuuri catchment, Kenya (Okoba and Sterk, 2006).…”

Section: Productivity Changesmentioning

confidence: 99%

Remote Sensing of Land Degradation: Experiences from Latin America and the Caribbean

Metternicht

Zinck²,

Blanco

et al. 2010

J of Env Quality

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Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

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Section: Productivity Changesmentioning

confidence: 99%

Remote Sensing of Land Degradation: Experiences from Latin America and the Caribbean

Metternicht

Zinck²,

Blanco

et al. 2010

J of Env Quality

View full text Add to dashboard Cite

show abstract

“…However, the field investigations usually do not cover the all land as the soil conservation regulations require. Therefore, the research activities cannot supply practical information for land management and soil conservation (Okoba and Sterk, 2006). In addition, sampling sites are often too few to represent characteristics of soil erosion for large area.…”

Section: Methods Of Studying Relations Between Soil Erosion and Land Usementioning

confidence: 99%

Soil Erosion Features by Land Use and Land Cover in Hilly Agricultural Watersheds in Central Sichuan Province, China

Yin

Zuo

2011

Computer and Computing Technologies in Agriculture IV

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Abstract. The study assessed soil erosion features in hilly agricultural watersheds in central Sichuan Province, China. Relationships among area percentages of land covers, soil erosion modulus, soil loss rate, and area percentage of erosion in the watersheds were examined by multivariate regression analysis. The first two regression models were constructed with area percentages of land covers as independent variables and soil erosion modulus or soil loss rate as dependent variables. The third one was built with soil erosion modulus as dependent variable and area percentage of the four erosion intensity classes as independent variables. Results showed that for flat and slope fields, forests, shrubs, abandoned fields, and other land covers, percentage of soil loss were -6%, 89%, -4%, 11%, 12%, and -3%, and area percentage of erosion were 1%, 72%, 0%, 18%, 9%, and 0%. The slope fields were the main source of soil loss. In contrast, flat crop fields, forests and other land covers could conserve soil from eroding. Changing land covers and reducing area of slope fields to 7% and of abandoned fields to 0%, were proposed as a management strategy for soil conservation in the watersheds. As completion of the land cover conversion, percentages of the land covers should be 35%, 7%, 5%, 45%, 0%, and 8% for flat and slope fields, forests, shrubs, abandoned fields, and other land covers. The study suggested that statistical models could be successfully used for processing inventory data for make decisions in soil conservation.

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“…The applied research methods mainly include remote sensing, field observation, and isotope tracer. Therefore, the research activities cannot supply practical information for land management and soil conservation (Okoba and Sterk, 2006). For instance, remote sensing techniques are appropriate for assessing large area soil erosion.…”

Section: Methods Of Studying Relations Between Soil Erosion and Land Usementioning

confidence: 99%

Computer and Computing Technologies in Agriculture IV

2011

IFIP Advances in Information and Communication Technology

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Quantification of visual soil erosion indicators in Gikuuri catchment in the central highlands of Kenya

Cited by 31 publications

References 19 publications

Remote Sensing of Land Degradation: Experiences from Latin America and the Caribbean

Remote Sensing of Land Degradation: Experiences from Latin America and the Caribbean

Soil Erosion Features by Land Use and Land Cover in Hilly Agricultural Watersheds in Central Sichuan Province, China

Computer and Computing Technologies in Agriculture IV

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