The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia

Abstract. Wetlands provide multiple ecosystem services such as storing and regulating water flows and water quality, providing unique habitats to flora and fauna, and regulating micro-climatic conditions. Conversion of wetlands for agricultural use is a widespread practice in Ethiopia, particularly in the southwestern part where wetlands cover large areas. Although there are many studies on land cover and land use changes in this region, comprehensive studies on wetlands are still missing. Hence, extent and rate of wetland loss at regional scales is unknown. The objective of this paper is to quantify wetland dynamics and estimate wetland loss in the Choke Mountain range (area covering 17 443 km2) in the Upper Blue Nile basin, a key headwater region of the river Nile. Therefore, satellite remote sensing imagery of the period 1986–2005 were considered. To create images of surface reflectance that are radiometrically consistent, a combination of cross-calibration and atmospheric correction (Vogelman-DOS3) methods was used. A hybrid supervised/unsupervised classification approach was used to classify the images. Overall accuracies of 94.1% and 93.5% and Kappa Coefficients of 0.908 and 0.913 for the 1986 and 2005 imageries, respectively were obtained. The results showed that 607 km2 of seasonal wetland with low moisture and 22.4 km2 of open water are lost in the study area during the period 1986 to 2005. The current situation in the wetlands of Choke Mountain is characterized by further degradation which calls for wetland conservation and rehabilitation efforts through incorporating wetlands into watershed management plans.

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The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-6243-2010 ◽

2010 ◽

Vol 7 (4) ◽

pp. 6243-6284 ◽

Cited By ~ 6

Author(s):

E. Teferi ◽

S. Uhlenbrook ◽

W. Bewket ◽

J. Wenninger ◽

B. Simane

Keyword(s):

Remote Sensing ◽

Regional Scale ◽

Atmospheric Correction ◽

Wetland Loss ◽

Climatic Conditions ◽

Mountain Range ◽

Nile Basin ◽

Blue Nile ◽

Blue Nile Basin ◽

Upper Blue Nile Basin

Abstract. Wetlands provide multiple ecosystem services such as storing and regulating water flows and water quality, providing unique habitats to flora and fauna, and regulating micro-climatic conditions. Conversion of wetlands for agricultural use is a widespread practice in Ethiopia, particularly in the southwestern part where wetlands cover large areas. Although there are many studies on land cover and land use changes in this region, comprehensive studies on wetlands are still missing. Hence, extent and rate of wetland loss at regional scale is unknown. The objective of this paper is to quantify wetland dynamics and estimate wetland loss in the Choke Mountain range (area covering 17 443 km2) in the Upper Blue Nile basin, a key headwater region of the river Nile. Therefore, satellite remote sensing images of the period 1986–2005 were considered. To create images of surface reflectance that are radiometrically consistent, a combination of cross-calibration and atmospheric correction (Vogelman-DOS3) methods was used. A hybrid supervised/unsupervised classification approach was used to classify the images. Overall accuracies of 94.1% and 93.5% and Kappa Coefficients of 0.908 and 0.913 for the 1986 and 2005 imageries, respectively were obtained. The results showed that 607 km2 of seasonal wetland with low moisture and 22.4 km2 of open water are lost in the study area during the period 1986 to 2005. The current situation in the wetlands of Choke Mountain is characterized by further degradation which calls for wetland conservation and rehabilitation efforts through incorporating wetlands into watershed management plans.

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Land use dynamics and base and peak flow responses in the Choke mountain range, Upper Blue Nile Basin, Ethiopia

International Journal of River Basin Management ◽

10.1080/15715124.2019.1672700 ◽

2019 ◽

pp. 1-13

Author(s):

Agenagnew A. Gessesse ◽

Assefa M. Melesse ◽

Anteneh Z. Abiy

Keyword(s):

Land Use ◽

Peak Flow ◽

Mountain Range ◽

Nile Basin ◽

Blue Nile ◽

Blue Nile Basin ◽

Land Use Dynamics ◽

Upper Blue Nile Basin

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Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

Remote Sensing ◽

10.3390/rs11020125 ◽

2019 ◽

Vol 11 (2) ◽

pp. 125 ◽

Cited By ~ 5

Author(s):

Getachew Ayehu ◽

Tsegaye Tadesse ◽

Berhan Gessesse ◽

Yibeltal Yigrem

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Prediction Model ◽

Residual Soil ◽

Nile Basin ◽

Blue Nile ◽

Volumetric Soil Moisture ◽

Blue Nile Basin ◽

Sar Data ◽

Upper Blue Nile Basin

In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.

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