Reducing landscape heterogeneity for improved land use and land cover (LULC) classification across the large and complex Ethiopian highlands

Kassawmar, Tibebu; Eckert, Sandra; Hurni, Kaspar; Zeleke, Gete; Hurni, Hans

doi:10.1080/10106049.2016.1222637

Cited by 51 publications

(36 citation statements)

References 41 publications

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“…Other high resolution land use maps are only available for small watersheds, such as the land use maps from the Water and Land Resource Centre (WLRC) [55]. Kassawmar et al [56] produced a land cover dataset for the Ethiopian Highlands with a resolution of 30 m. Due to cloud and haze cover, it was not possible to use images from only one specific year. The applicability of such data sets for a similar purpose was explained in the Economics of Land Degradation (ELD) Ethiopia Case Study [57].…”

Section: Topographical and Land Use Datamentioning

confidence: 99%

Spatial and Temporal Variability in Hydrological Responses of the Upper Blue Nile basin, Ethiopia

Lemann

Roth

Zeleke

et al. 2018

Water

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To assess the spatial and temporal availability of blue and green water for up- and downstream stakeholders, the hydrological responses of the upper Blue Nile basin in the Ethiopian Highlands was modelled and analysed with newly generated input data, such as soil and land use maps. To consider variations in the seasonal climate, topography, soil, land use, and land management, the upper Blue Nile basin was modelled in seven major sub-basins. The modelling showed significant spatial and temporal differences in the hydrological responses of different sub-basins and years. The long-term mean annual drainage ratios of the watersheds range from <0.1 to >0.65, and the annual drainage ratio of one sub-basin can vary from 0.22 to 0.49. Steep slopes, shallow soils, and cultivated areas increase the drainage ratios due to high surface runoff, low soil moisture content, and a smaller share of evapotranspiration. Various climate change scenarios predict more precipitation, and land use change scenarios foresee a higher share of cultivated areas due to population growth. In view of these trends, results from our study suggest that drainage ratios will increase and more available blue water can be expected for downstream stakeholders.

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Section: Topographical and Land Use Datamentioning

confidence: 99%

Spatial and Temporal Variability in Hydrological Responses of the Upper Blue Nile basin, Ethiopia

Lemann

Roth

Zeleke

et al. 2018

Water

Self Cite

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“…Table 4 A c c e p t e d M a n u s c r i p t Some indices such as BLFEI, BAEI and VgNIR-BI showed better results over others by using overlapping histogram and SDI; nevertheless, the Otsu method is more specifically suited to the BLFEI, BCI and IBI approaches, since each side (peak) of their histograms has a better symmetry with respect to the other. Moreover, as mentioned in the introduction Kassawmar et al (2018) implemented a new method to increase the accuracy of classification based on increasing number of classes. In the first method based on the SDI separability and overlapping histograms, the classes are four, but for the second method (Otsu's method) the classes are only two, which means that the first method is more precise.…”

Section: Results From the Extraction Of Built-up Areas Using Otsu's Omentioning

confidence: 99%

“…The results show that the built-up lands are well separated from vegetation but they are poorly isolated from bare soil and water (Piyoosh & Ghosh 2018) because the calculated indices misclassify a quantity of barren and water regions as built-up areas due to heterogeneity of complex urban areas. Moreover, Kassawmar et al (2018) have implemented a method to reduce this heterogeneity and improve the classification of LU/LC features. Kawamura et al (1996) have proposed the Urban Index (UI) using TM7 and TM4 bands from Landsat Thematic Mapper (TM) sensor.…”

Section: Introductionmentioning

confidence: 99%

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Bouhennache

Bouden

Taleb-Ahmed

et al. 2018

Geocarto International

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“…The preferred ANN method, Cellular automata simulation technique and use of Markov Chain approach in transition matrices could be affected by the increase in modeling success. In addition, the success of the classification has increased the success of the model (Kassawmar et al, 2018). For this reason, along with main roads, village and forest roads have been added as spatial variables.…”

Section: Cernementioning

confidence: 99%

Modeling and Simulating Land Use/Cover Change Using Artificial Neural Network From Remotely Sensing Data

Buğday

2019

CERNE

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BUĞDAY, E.; ERKAN BUĞDAY, S. Modeling and simulating land use/cover change using artificial neural network from remotely sensing data. CERNE, v. 25, n. 2, p.246-254, 2019. HIGHLIGHTSApplicability of decision support systems in landscape planning.To reveal the spatio-temporal land use and land cover changes.Estimation of land use and cover change by human population movements. ABSTRACTIncreasing population, mobility and requirements of human beings have significant effects on the dynamics of land use and land cover. Today, these impacts need to be understood and analyzed for the applicability of decision support systems, which are an important tool in the management of natural resources, urban and rural areas. The aim of this study is to detect the temporal and spatial changes of land cover and human population, in northwest Turkey. For this purpose, using satellite images of 1997-2007 and 2017 years' land cover was estimated for 2027 by ANN (Artificial Neural Network) approach. Kappa values are 93%, 87% and 95% for 1997, 2007 and 2017 respectively. As a result, learning success was 80.6%, and correctness validation value was 90.1% for 2027 simulation. In parallel, the spatial analysis of the population was conducted for 2000-2007-2017. Using the exponential rate of change; the population was predicted to increase by concentrating on the urban area and the rural areas surrounding the urban (with a rate of 2.019%) for 2027. According to the results; rural population, urban population, forest, and built-up areas is estimated to increase by 4.14%, 5.58%, 2.72%, and 0.77% respectively from 2017 to 2027, while the agricultural and water area is estimated to decrease by 3.47% and 0.02% respectively. Consequently, the observation of population movements and the use of the ANN approach in simulations could be suggested for the success of planning in forest and land management.v.25 n.

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Reducing landscape heterogeneity for improved land use and land cover (LULC) classification across the large and complex Ethiopian highlands

Cited by 51 publications

References 41 publications

Spatial and Temporal Variability in Hydrological Responses of the Upper Blue Nile basin, Ethiopia

Spatial and Temporal Variability in Hydrological Responses of the Upper Blue Nile basin, Ethiopia

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Modeling and Simulating Land Use/Cover Change Using Artificial Neural Network From Remotely Sensing Data

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