Effects of land-use land-cover data resolution and classification methods on SWAT model flow predictive reliability

2017 ◽  
Vol 7 (1) ◽  
pp. 39 ◽  
Author(s):  
Kwasi Asante ◽  
Mansoor D. Leh ◽  
Jackson D. Cothren ◽  
Mauro Di Luzio ◽  
John Van Brahana
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Swat Model ◽  
Land Use Land Cover ◽  
Classification Methods ◽  
Land Cover Data ◽  
Model Flow ◽  
Data Resolution ◽  
Effects Of Land Use

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

scholarly journals Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China

2013 ◽  
Vol 8 (1) ◽  
pp. 084596 ◽  
Author(s):  
Zhongchang Sun ◽  
Xinwu Li ◽  
Wenxue Fu ◽  
Yingkui Li ◽  
Dongsheng Tang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Surface Runoff ◽  
Urban Areas ◽  
Land Use Land Cover ◽  
Long Term Effects ◽  
Effects Of Land Use ◽  
Beijing China

scholarly journals Hydrological Responses to Land Use Land Cover Changes in the Fincha’a Watershed, Ethiopia

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 916
Author(s):  
Urgessa Kenea ◽  
Dereje Adeba ◽  
Motuma Shiferaw Regasa ◽  
Michael Nones
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Local Governments ◽  
Swat Model ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Hydrological Response ◽  
African Countries ◽  
Hydrological Responses ◽  
Basin Scale

Land use land cover (LULC) changes are highly pronounced in African countries, as they are characterized by an agriculture-based economy and a rapidly growing population. Understanding how land use/cover changes (LULCC) influence watershed hydrology will enable local governments and policymakers to formulate and implement effective and appropriate response strategies to minimize the undesirable effects of future land use/cover change or modification and sustain the local socio-economic situation. The hydrological response of the Ethiopia Fincha’a watershed to LULCC that happened during 25 years was investigated, comparing the situation in three reference years: 1994, 2004, and 2018. The information was derived from Landsat sensors, respectively Landsat 5 TM, Landsat 7 ETM, and Landsat 8 OLI/TIRS. The various LULC classes were derived via ArcGIS using a supervised classification system, and the accuracy assessment was done using confusion matrixes. For all the years investigated, the overall accuracies and the kappa coefficients were higher than 80%, with 2018 as the more accurate year. The analysis of LULCC revealed that forest decreased by 20.0% between the years 1994–2004, and it decreased by 11.8% in the following period 2004–2018. Such decline in areas covered by forest is correlated to an expansion of cultivated land by 16.4% and 10.81%, respectively. After having evaluated the LULCC at the basin scale, the watershed was divided into 18 sub-watersheds, which contained 176 hydrologic response units (HRUs), having a specific LULC. Accounting for such a detailed subdivision of the Fincha’a watershed, the SWAT model was firstly calibrated and validated on past data, and then applied to infer information on the hydrological response of each HRU on LULCC. The modelling results pointed out a general increase of average water flow, both during dry and wet periods, as a consequence of a shift of land coverage from forest and grass towards settlements and build-up areas. The present analysis pointed out the need of accounting for past and future LULCC in modelling the hydrological responses of rivers at the watershed scale.

scholarly journals Land Use/Land Cover Data of the Urban Atlas and the Cadastre of Real Estate: An Evaluation Study in the Prague Metropolitan Region

Land ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 153 ◽  
Author(s):  
Ondrej Micek ◽  
Jan Feranec ◽  
Premysl Stych
Keyword(s):  
Land Use ◽  
Real Estate ◽  
Land Cover ◽  
Relevant Information ◽  
Evaluation Study ◽  
Metropolitan Region ◽  
Land Use Land Cover ◽  
Land Cover Data ◽  
Scale Levels ◽  
The Difference

Landscape research involves a large number of scientific disciplines. Different disciplinary and scale approaches have led to the creation of numerous land use/land cover databases with different classification nomenclature. It is very important for end-users of databases to know the capabilities and limits of land use/land cover data to avoid potential mistakes resulting from inappropriate combinations and interpretations. In this context, the aim of this study was to evaluate the thematic content of the Urban Atlas database and data from the Czech cadastre of real estate in the Prague metropolitan region between the years 2006 and 2012 with a focus on the meaning of the nomenclature used by both datasets. The data were processed using approaches with different levels of thematic harmonisation and statistical tools to quantify the similarities and differences among the researched data. The methods of comparison used for land use/land cover data with different nomenclature were based on an aggregation approach or modified difference indices (the overall difference index and the sub-index of the difference). The areas with high degrees of dissimilarity and similarity were found and further examined and interpreted. These intentions were documented precisely on the Czech cadastre of real estate and the Urban Atlas databases at two scale levels: 1) an analysis of the whole area of the Prague metropolitan region and 2) a detailed analysis of the selected cadastral units. It was proven that the differences between both datasets are significant and they share certain characteristics. Most of the differences are distributed in the classes of the built-up areas, gardens, and other areas. Smaller differences are characteristic for waterways, agricultural lands, and forests. This study provides relevant information on the evaluated databases with the intention of raising awareness of their limits, strengths, and weaknesses. The results enhance the scientific knowledge about the Urban Atlas and Czech cadastre of real estate databases, thereby facilitating decision-making about the options of their use.

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