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Mr. George Chege
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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Research Keywords & Expertise

0 soil erosion
0 soil erosion modelling
0 Remote Sensing for archaeology,
0 GIS and applications
0 agricultural biodiversity

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Journal article
Published: 16 January 2021 in Sustainability
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The Kenya Great Rift Valley (KGRV) region unique landscape comprises of mountainous terrain, large valley-floor lakes, and agricultural lands bordered by extensive Arid and Semi-Arid Lands (ASALs). The East Africa (EA) region has received high amounts of rainfall in the recent past as evidenced by the rising lake levels in the GRV lakes. In Kenya, few studies have quantified soil loss at national scales and erosion rates information on these GRV lakes’ regional basins within the ASALs is lacking. This study used the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates between 1990 and 2015 in the Great Rift Valley region of Kenya which is approximately 84.5% ASAL. The mean erosion rates for both periods was estimated to be tolerable (6.26 t ha−1 yr−1 and 7.14 t ha−1 yr−1 in 1990 and 2015 respectively) resulting in total soil loss of 116 Mt yr−1 and 132 Mt yr−1 in 1990 and 2015 respectively. Approximately 83% and 81% of the erosive lands in KGRV fell under the low risk category (−1 yr−1) in 1990 and 2015 respectively while about 10% were classified under the top three conservation priority levels in 2015. Lake Nakuru basin had the highest erosion rate net change (4.19 t ha−1 yr−1) among the GRV lake basins with Lake Bogoria-Baringo recording annual soil loss rates >10 t ha−1 yr−1 in both years. The mountainous central parts of the KGRV with Andosol/Nitisols soils and high rainfall experienced a large change of land uses to croplands thus had highest soil loss net change (4.34 t ha−1 yr−1). In both years, forests recorded the lowest annual soil loss rates (−1 yr−1) while most of the ASAL districts presented erosion rates (−1 yr−1). Only 34% of all the protected areas were found to have erosion rates −1 yr−1 highlighting the need for effective anti-erosive measures.

ACS Style

George Watene; Lijun Yu; Yueping Nie; Jianfeng Zhu; Thomas Ngigi; Jean De Dieu Nambajimana; Benson Kenduiywo. Water Erosion Risk Assessment in the Kenya Great Rift Valley Region. Sustainability 2021, 13, 844 .

AMA Style

George Watene, Lijun Yu, Yueping Nie, Jianfeng Zhu, Thomas Ngigi, Jean De Dieu Nambajimana, Benson Kenduiywo. Water Erosion Risk Assessment in the Kenya Great Rift Valley Region. Sustainability. 2021; 13 (2):844.

Chicago/Turabian Style

George Watene; Lijun Yu; Yueping Nie; Jianfeng Zhu; Thomas Ngigi; Jean De Dieu Nambajimana; Benson Kenduiywo. 2021. "Water Erosion Risk Assessment in the Kenya Great Rift Valley Region." Sustainability 13, no. 2: 844.

Journal article
Published: 17 April 2017 in Remote Sensing
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Buried archeological features show up as crop marks that are mostly visible using high-resolution image data. Such data are costly and restricted to small regions and time domains. However, a time series of freely available medium resolution imagery can be employed to detect crop growth changes to reveal subtle surface marks in large areas. This paper aims to study the classical Chinese settlements of Taosi and Erlitou over large areas using Landsat NDVI time series crop phenology to determine the optimum periods for detection and monitoring of crop anomalies. Burial areas (such as the palace area and the sacrificial area) were selected as the research area while the surrounding empty fields with a low density of ancient features were used as reference regions. Landsat NDVI covering two years’ growth periods of wheat and maize were computed and analyzed using Pearson’s correlation coefficient and Euclidean distance. Similarities or disparities between the burial areas and their empty areas were computed using the Hausdorff distance. Based on the phenology of crop growth, the time series NDVI images of winter wheat and summer maize were generated to analyze crop anomalies in the archeological sites. Results show that the Hausdorff distance was high during the critical stages of water for both crops and that the images of high Hausdorff distance can provide more obvious subsurface archeological information.

ACS Style

Yuqing Pan; Yueping Nie; Chege Watene; Jianfeng Zhu; Fang Liu. Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series. Remote Sensing 2017, 9, 374 .

AMA Style

Yuqing Pan, Yueping Nie, Chege Watene, Jianfeng Zhu, Fang Liu. Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series. Remote Sensing. 2017; 9 (4):374.

Chicago/Turabian Style

Yuqing Pan; Yueping Nie; Chege Watene; Jianfeng Zhu; Fang Liu. 2017. "Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series." Remote Sensing 9, no. 4: 374.