Land Use/Land Cover Factor Values and Accuracy Assessment Using a GIS and Remote Sensing in the Case of the Quashay Watershed in Northwestern Ethiopia

Tadele, Habtamu; Mekuriaw, Asnake; Selassie, Yihenew G.; Tsegaye, Lewoye

doi:10.5027/jnrd.v7i0.05

Cited by 14 publications

(7 citation statements)

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“…Hence, Google Earth images for these two periods were adopted for references. Tilahun and Teferie 2015;and Tadele et al 2017 adopted similar approach in their studies with reasonable results. The randomly generated points in the classified 1984 and 2000 images were imported into Google Earth images of 31/12/ 1984 and 31/12/2000, respectively to verify the accuracy of the classifications.…”

Section: Methodsmentioning

confidence: 94%

“…The randomly generated points in the classified 1984 and 2000 images were imported into Google Earth images of 31/12/ 1984 and 31/12/2000, respectively to verify the accuracy of the classifications. Also, the randomly generated points in the classified 2015 image was imported into Google Earth image of 14/12/ 2015 to verify the accuracy of the classification (Tilahun and Teferie, 2015;Tadele et al, 2017) and the generated confusion matrix derived from the image and the points collected during ground truthing was also used for the accuracy assessment (Ismail and Jusoff, 2008).Thereafter, the Kappa coefficient and overall accuracy were calculated to assess the mapping accuracy of the LULC classification.…”

Section: Methodsmentioning

confidence: 99%

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Assessing the Spatio-Temporal Pattern of Land Use and Land Cover Changes in Osun Drainage Basin, Nigeria

Ashaolu

Olorunfemi

Ifabiyi

2019

Journal of Environmental Geography

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Over the years, Osun drainage basin has witnessed tremendous increase in population, and urbanization that have changed the landscape of the area. This study evaluated the spatio-temporal pattern of land use/land cover change (LULC) in the study area, and made hydrological inferences. Landsat imageries were acquired from USGS-EROS satellite image database for the period 1984, 2000 and 2015, while the Digital Elevation Model (DEM) was obtained from Shuttle Radar Topography Mission (SRTM) of the National Aeronautics and Space Agency (NASA). Supervised image classification using the Maximum Likelihood Algorithm in Erdas Imagine was adopted to classified the land use/land cover of the study area into seven classes. Elevation, aspect and slope of the study area were processed from DEM using ArcGIS. Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS was used to simulate the basin future LULC change, using change driving factors of population, elevation, aspect and slope of the study area. There was about 234% increase in built up areas and 89.22% in crop/shrubs between 1984 and 2015. The most significant decrease in LULC occurred in forest (58.75%) and wetland (84.69%) during this period. The predicted future LULC change suggests that only about 12% of the basin will remain under forest cover by the year 2046. The results underscored the increasing anthropogenic activities in the basin that influenced recharge rate, surface runoff, incidences of soil erosion, etc., in Osun drainage basin. The planting of the lost native trees was recommended for the sustainability of the basin’s ecosystem.

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Section: Methodsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Assessing the Spatio-Temporal Pattern of Land Use and Land Cover Changes in Osun Drainage Basin, Nigeria

Ashaolu

Olorunfemi

Ifabiyi

2019

Journal of Environmental Geography

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“…Since the resolution of the historical maps for 1987 obtained from the Ethiopian Mapping Agency was poor, a high resolution of Google Earth was used to better identify the land-use classes. A similar approach was used by different studies where the historical maps were poor in Ethiopia [21,22,[40][41][42][43] and Italy [44].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Drivers and Implications of Land Use/Land Cover Dynamics in Finchaa Catchment, Northwestern Ethiopia

Dibaba

Demissie

Miegel

2020

Land

121

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Understanding the trajectories and extents of land use/land cover change (LULCC) is important to generate and provide helpful information to policymakers and development practitioners about the magnitude and trends of LULCC. This study presents the contributing factors of LULCC, the extent and implications of these changes for sustainable land use in the Finchaa catchment. Data from Landsat images 1987, 2002, and 2017 were used to develop the land use maps and quantify the changes. A supervised classification with the maximum likelihood classifier was used to classify the images. Key informant interviews and focused group discussions with transect walks were used for the socio-economic survey. Over the past three decades, agricultural land, commercial farm, built-up, and water bodies have increased while forestland, rangeland, grazing land, and swampy areas have decreased. Intensive agriculture without proper management practice has been a common problem of the catchment. Increased cultivation of steep slopes has increased the risk of erosion and sedimentation of nearby water bodies. Multiple factors, such as biophysical, socio-economic, institutional, technological, and demographic, contributed to the observed LULCC in the study area. A decline in agricultural yield, loss of biodiversity, extended aridity and drought, land and soil degradation, and decline of water resources are the major consequences of LULCC in the Finchaa catchment. The socio-economic developments and population growth have amplified the prolonged discrepancy between supply and demand for land and water in the catchment. More comprehensive and integrated watershed management policies will be indispensable to manage the risks.

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“…contribution and ground truth (Lillesand et al, 2008). The Kappa coefficient is computed using the following equation (Tadele et al, 2017;Congalton and Green, 2019):…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Multi-Layer Perceptron Neural Network and Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change

Shen¹,

Li²,

Wheate³

et al. 2020

J ENVIRON INFORM LET

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We combined multi-layer perceptron (MLP) neural network and Markov Chain (MC) modeling with object-based image analysis (OBIA) to map and predict land use and land cover (LULC) changes in Stoney Creek Watershed (SCW), British Columbia, Canada. Unsupervised classification was performed using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to produce LULC maps of years 1986, 1999 and 2016. The classification resulted in an overall accuracy of 91.50%. The results show that coniferous forest in SCW experienced a sharp loss while agriculture area increased (4.77% land gain) from 1986 to 2016. LULC scenarios were predicted through MLP neural network and MC modeling based on LULC change analysis data and transition potential. The results indicated that 'Coniferous Forest' LULC type had the highest (3.38% land loss) transition potential and 'Water' and 'Urban Area' LULC types had the lowest transition potential. Application of the proposed method provided valuable information of LULC patterns and dynamics for planners and researchers. The method also has the potential for improved management in other watersheds with similar LULC types.

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Land Use/Land Cover Factor Values and Accuracy Assessment Using a GIS and Remote Sensing in the Case of the Quashay Watershed in Northwestern Ethiopia

Cited by 14 publications

References 0 publications

Assessing the Spatio-Temporal Pattern of Land Use and Land Cover Changes in Osun Drainage Basin, Nigeria

Assessing the Spatio-Temporal Pattern of Land Use and Land Cover Changes in Osun Drainage Basin, Nigeria

Drivers and Implications of Land Use/Land Cover Dynamics in Finchaa Catchment, Northwestern Ethiopia

Multi-Layer Perceptron Neural Network and Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change

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