Why does accuracy assessment and validation of multi-resolution-based satellite image classification matter? A methodological discourse

This study presents a methodological discourse about how to validate the reliability of thematic maps derived from multi-resolution satellite-based image classification. Besides, the paper examines unbiased estimates of accuracy assessment using known sampling units. Landsat and spot images were used for lulc thematic layer extraction. These thematic layers together with reference data extracted from panchromatic aerial photo interpretation and ground survey were used as input datasets for accuracy assessment and validation analysis. For each lulc unit, a minimum of 50 reference samples were derived using a stratified random sampling scheme. Consequently, error matrices were generated to validate the quality of the 1973, 1995 and 2007 lulc maps. To improve sampling biases introduced due to the stratified random sampling reference data collection scheme, accuracy assessment indices including the producer’s, user’s and overall accuracy as well as Kappa coefficient of agreement were adjusted to the known areal proportion of map categories. The computed overall accuracy, corrected for bias using known marginal proportions of the 1973, 1995 and 2007 thematic layers were 88.12%, 89.95% and 92.27%, respectively. Also, 81.20%, 82.17% and 83.11% of Kappa coefficient of agreement were achieved from the 1972, 1995 and 2007 classifications, respectively. The findings show that high resolution aerial photos are good sources of  reference datasets in the absence of historical ground truth data for accuracy assessment analysis and the lulc classifications fulfilled the minimum of lulc classification standards of overall accuracy and Kappa coefficient of agreement. Consequently, all the lulc classifications could be used as an input for policy options for integrated land resource management practices in the watershed studied

Characterizing the spatiotemporal distribution of meteorological drought as a response to climate variability: The case of rift valley lakes basin of Ethiopia

Climate variability and recurrent meteorological droughts frequently affect the rain-dependent Ethiopian agriculture, where the rift valley lakes basin is one of the most drought-prone regions in the country. The aim of this study was to evaluate climate variability and characterize the spatiotemporal distribution of meteorological droughts using a merged satellite-gauge rainfall across the major agroecological zones (AEZs) of the rift valley lakes basin. To this end, coefficient of variation (CV) and standardized rainfall anomaly (SRA) were used to evaluate rainfall variability; Mann-Kendell test was used to examine trends of temperature and rainfall; and a grid-rainfall based standardized precipitation index (SPI) was used to assess the spatiotemporal distribution and severity of meteorological droughts. The SPI was computed for 37 years over 1981–2017 at 3-month and 4-month timescales for the bimodal rainy seasons. Finally, a higher inter-annual and spatial variability of rainfall and frequent meteorological droughts were found across the basin. Compared to the nationally documented historical drought years in the country, more frequent drought events were found in this basin, signifying its higher vulnerability to climate variability. As a result, between 1981 and 2017, the basin has partially experienced at least a moderate drought intensity on average every 1.68 and 1.76 years during the 'Belg' and 'Kiremt' season, respectively. Drought frequency was higher at the 'Kolla' AEZ, characterized by the highest CV of rainfall. Furthermore, these frequent droughts were accompanied by significant rising trends in monthly temperature. Such a warming trend, in this inherently warm area, coupled with expected global climate change scenarios could further aggravate drought conditions in the future. Moreover, the spatiotemporal distribution of drought events was found to be variable between and within AEZs in the basin so that more localized drought adaptation strategies could help to alleviate potential impacts. Thus, the drought history of each agroecological zone and the spatiotemporal distributions of recent droughts, this study has delivered, could enhance the awareness of concerned decision makers in tracing frequently affected locations, which could in turn enable them to design and implement improved water management techniques as a means of drought mitigation strategy. Keywords: Climate variability, Drought, Mann-Kendall test, Merged satellite-gauge rainfall, Rift valley lakes basin, SP

MaxEnt-based modeling of suitable habitat for rehabilitation of Podocarpus forest at landscape-scale

AbstractModeling the current distribution and predicting suitable habitats of threatened species support proper planning processes for conservation and restoration. The aim of this study was thus to model the actual distribution and predict environmentally suitable habitats for Podocarpus falcatus, a locally threatened native tree species in Ethiopia. To realize this objective, species' presence samples, BIOCLIMATIC, and topographic predictors were combined to run a MaxEnt model. Finally, a model-generated habitat suitability map was produced with AUC accuracy of 0.783. Among the variables used for modeling, elevation range was found to be a key predictor of Podocarpus distribution, followed by precipitation of the driest quarter and isothermality. An extensive area (> 48%) of the studied landscape has been predicted to be environmentally suitable for the target species. However, only a small portion open-land area is practically available for rehabilitation since the area has been intensively cultivated to support the densely inhabited population. Therefore, potential areas for a small-scale plantation of Podocarpus trees remain to be pocket sites in religious places and around farmers' homesteads. So far, many farmers in this area have demonstrated a successful experience of growing this degraded native tree species. Thus, encouraging privately owned small-scale plantations could enhance rehabilitation and more sustainable conservation of the locally threatened native tree species

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at various scales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite rainfall estimates could be used as a sole product (e.g., in areas with no (or poor) ground observations) or through integrating with rain gauge measurements. In this study, the potential of a newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall product has been evaluated in comparison to rain gauge data over the Upper Blue Nile basin in Ethiopia for the period of 2000 to 2015. In addition, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT 3) and the African Rainfall Climatology (ARC 2) products have been used as a benchmark and compared with CHIRPS. From the overall analysis at dekadal (10 days) and monthly temporal scale, CHIRPS exhibited better performance in comparison to TAMSAT 3 and ARC 2 products. An evaluation based on categorical/volumetric and continuous statistics indicated that CHIRPS has the greatest skills in detecting rainfall events (POD D0.99, 1.00) and measure of volumetric rainfall (VHID1.00, 1.00), the highest correlation coefficients (r D0.81, 0.88), better bias values (0.96, 0.96), and the lowest RMSE (28.45mmdekad 1, 59.03mmmonth 1) than TAMSAT 3 and ARC 2 products at dekadal and monthly analysis, respectively. CHIRPS overestimates the frequency of rainfall occurrence (up to 31% at dekadal scale), although the volume of rainfall recorded during those events was very small. Indeed, TAMSAT 3 has shown a comparable performance with that of the CHIRPS product, mainly with regard to bias. The ARC 2 product was found to have the weakest performance underestimating rain gauge observed rainfall by about 24 %. In addition, the skill of CHIRPS is less affected by variation in elevation in comparison to TAMSAT 3 and ARC 2 products. CHIRPS resulted in average biases of 1.11, 0.99, and 1.00 at lower (\u3c 1000ma.s.l.), medium (1000 to 2000ma.s.l.), and higher elevation (\u3e 2000ma.s.l.), respectively. Overall, the finding of this validation study shows the potentials of the CHIRPS product to be used for various operational applications such as rainfall pattern and

Monitoring Residual Soil Moisture and Its Association to the Long-Term Variability of Rainfall over the Upper Blue Nile Basin in Ethiopia

Monitoring soil moisture and its association with rainfall variability is important to comprehend the hydrological processes and to set proper agricultural water use management to maximize crop growth and productivity. In this study, the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture product was applied to assess the dynamics of residual soil moisture in autumn (September to November) and its response to the long-term variability of rainfall in the Upper Blue Nile Basin (UBNB) of Ethiopia from 1992 to 2017. The basin was found to have autumn soil moisture (ASM) ranging from 0.09–0.38 m3/m3, with an average of 0.26 m3/m3. The ASM time series resulted in the coe_cient of variation (CV) ranging from 2.8%–28% and classified as low-to-medium variability. In general, the monotonic trend analysis for ASM revealed that the UBNB had experienced a wetting trend for the past 26 years (1992–2017) at a rate of 0.00024 m3/m3 per year. A significant wetting trend ranging from 0.001 to 0.006 m3/m3 per year for the autumn season was found. This trend was mainly showed across the northwest region of the basin and covers about 18% of the total basin area. The spatial patterns and variability of rainfall and ASM were also found to be similar, which implies the strong relationship between rainfall and soil moisture in autumn. The spring and autumn season rainfall explained a considerable portion of ASM in the basin. The analyses also signified that the rainfall amount and distribution impacted by the topography and land cover classes of the basin showed a significant influence on the characteristics of the ASM. Further, the result verified that the behavior of ASM could be controlled by the loss of soil moisture through evapotranspiration and the gain from rainfall, although changes in rainfall were found to be the primary driver of ASM variability over the UBNB

Hydro-geomorphological characterization of Dhidhessa River Basin, Ethiopia

Dhidhessa River Basin is physio-graphically and hydrologically important in the Blue Nile basin, however, its morphometry and hydrology are not well known. This study aimed to characterize hydro-geomorphology of the basin via basin morphometry analysis. SRTM DEM, geological and hydrological maps of the area were used in ArcGIS 10.3 environment for this analysis. Results showed that a 33,468 km total stream length of all orders was found distributed within 28,637 km2 drainage area in a dendritic pattern. According to morphometric parameter classification, total stream length and stream order of the basin were high whereas stream length ratio, bifurcation ratio and hydrologic storage coefficient were low. Furthermore, drainage area was large, drainage frequency was coarse, basin shape was more elongated, drainage density was medium, infiltration number was low, overland flow was long and constant of channel maintenance was high. Moreover, the basin's relief, relief ratio, ruggedness number, gradient ratio and the slope was high. In general, the study asserted that the basin was underlain by uniform resistant rocks, less prone to flooding, with high water resources potential and susceptible to soil erosion. The morphometric analysis approach pursued in this study was cost- and time-effective for basin characterization. Keywords: Dhidhessa River Basin, Hydro-geomorphology, Hydrological processes, Morphometric parameters, Water resource potentia

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

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., a = 0.01, a = 0.05, and a = 0.1). Thus, the AWS based SCA model with a = 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 a 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

Monitoring Residual Soil Moisture and Its Association to the Long-Term Variability of Rainfall over the Upper Blue Nile Basin in Ethiopia

Monitoring soil moisture and its association with rainfall variability is important to comprehend the hydrological processes and to set proper agricultural water use management to maximize crop growth and productivity. In this study, the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture product was applied to assess the dynamics of residual soil moisture in autumn (September to November) and its response to the long-term variability of rainfall in the Upper Blue Nile Basin (UBNB) of Ethiopia from 1992 to 2017. The basin was found to have autumn soil moisture (ASM) ranging from 0.09–0.38 m3/m3, with an average of 0.26 m3/m3. The ASM time series resulted in the coe_cient of variation (CV) ranging from 2.8%–28% and classified as low-to-medium variability. In general, the monotonic trend analysis for ASM revealed that the UBNB had experienced a wetting trend for the past 26 years (1992–2017) at a rate of 0.00024 m3/m3 per year. A significant wetting trend ranging from 0.001 to 0.006 m3/m3 per year for the autumn season was found. This trend was mainly showed across the northwest region of the basin and covers about 18% of the total basin area. The spatial patterns and variability of rainfall and ASM were also found to be similar, which implies the strong relationship between rainfall and soil moisture in autumn. The spring and autumn season rainfall explained a considerable portion of ASM in the basin. The analyses also signified that the rainfall amount and distribution impacted by the topography and land cover classes of the basin showed a significant influence on the characteristics of the ASM. Further, the result verified that the behavior of ASM could be controlled by the loss of soil moisture through evapotranspiration and the gain from rainfall, although changes in rainfall were found to be the primary driver of ASM variability over the UBNB

Determinants of farmers' tree-planting investment decisions as a degraded landscape management strategy in the central highlands of Ethiopia

Land degradation due to lack of sustainable land management practices is one of the critical challenges in many developing countries including Ethiopia. This study explored the major determinants of farm-level tree-planting decisions as a land management strategy in a typical farming and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and a binary logistic regression model. The model significantly predicted farmers' tree-planting decisions (χ2 =  37.29, df  =  15, P < 0.001). Besides, the computed significant value of the model revealed that all the considered predictor variables jointly influenced the farmers' decisions to plant trees as a land management strategy. The findings of the study demonstrated that the adoption of tree-growing decisions by local land users was a function of a wide range of biophysical, institutional, socioeconomic and household-level factors. In this regard, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system had a critical influence on tree-growing investment decisions in the study watershed. Eventually, the processes of land-use conversion and land degradation were serious, which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, the study recommended that devising and implementing sustainable land management policy options would enhance ecological restoration and livelihood sustainability in the study watershed

Spatiotemporal patterns of water hyacinth dynamics as a response to seasonal climate variability in Lake Tana, Ethiopia

Abstract Lake Tana, which is the largest Lake in Ethiopia, has been invaded by water hyacinths since 2011. Although the government and the community have devoted considerable time and energy over a long period to removing the invasive weed mechanically and manually, the weed has been increasing significantly. Accurate, reliable, and timely information on the spatiotemporal distribution and extent of water hyacinth is crucial to determine its evolution, propagation, and potentially vulnerable areas of the Lake. Therefore, comprehensive information on the spatial distribution of water hyacinths and their annual and seasonal variability is essential for Lake Tana’s water resource planning, development, and management. This study aims to evaluate the spatiotemporal pattern of water hyacinth and its dynamics with seasonal climate variability and impact on evapotranspiration. Landsat 7 ETM+, Landsat 8 OLI, and Sentinel 2 and meteorological datasets were employed. Supervised and manual digitization image classification methods were applied to prepare Land-use/ Land-cover in the Lake. The Mann–Kendall trend test and Pearson correlation coefficient were used to evaluate the trend of water hyacinth and the impact of climate variability on water hyacinth distribution respectively. Besides, the evapotranspiration and water losses were estimated using the FAO-56 Penman–Monteith method. The surface extent of the water hyacinth in Lake Tana has increased by 96% in 2019 from 2011. However, the surface area of the Lake has declined. That means 1603 ha of water surface area has been changed to land surface from 2011 to 2019. The average volume of water loss in Lake Tana was 0.21% of the volume of the Lake from September 2016 to December 2018