scholarly journals Land Cover Classification Using Remote Sensing in Amadiyah Province

2021 ◽  
Vol 910 (1) ◽  
pp. 012125
Author(s):  
Muzahim Saeed Younis ◽  
Saifaldeen Maadh Mustafa
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Water Medium ◽  
Low Density ◽  
Medium Density ◽  
Residential Areas ◽  
Error Matrix ◽  
Northern Iraq ◽  
Kappa Test ◽  
Sloping Lands

Abstract This study was conducted on the vegetative and non-vegetative land cover spread in the Amadiya District of Dohuk Governorate, northern Iraq, located between longitudes (43 ° 25'24.309 "- 43 ° 11'6.839") to the east and latitudes (37 ° 12'36.359 "- 37 7'25.484") north. They rely on a spatial indication of accuracy (10 m) and are reduced to (5 m) from Sentinel -2. Using unsupervised classifications, to form a general perception of the items in the studied area. As the number of varieties and the number of spectral bands used were determined, then the Supervised Classification to classify the spatial indication at the site to determine the plant and non-plant ground targets. These two classifications resulted, using the (Arc GIS) program, we obtained 12 types when classifying the space declaration for the Amadiyah district. We noticed that the area occupied by the terrestrial targets of the site are (water, medium-density forests (sloping lands), medium-density forests (flatlands), low-density forests (sloping lands), low-density forests (flatlands), limestone rocky areas, dense forests. (Sloping lands), limestone and paved roads, barren lands, residential areas, pastures, dense forests (flatlands) and their areas respectively are (283.9 - 408.6 - 556.2 - 829.2 - 983.6 - 1022.8 - 1066.4 - 1138.8 - 1148.5 - 1172.2 - 1218.4. - 1272.4) km2. The classification accuracy of the spatial indication was estimated based on the error matrix and the Kappa test. From there we found that the accuracy was (84.6%) for the error matrix and (83.34%) for the Kappa test, and this indicates that the classification accuracy is very good It is acceptable and can be relied upon and recommended for classification.

scholarly journals Time Series Analysis Using Digital Classification of Data on Land Use and Land Cover in Dohuk Governorate

2021 ◽  
Vol 910 (1) ◽  
pp. 012124
Author(s):  
Mohammed Younis Salim ◽  
Narmin Abduljaleel Ibrahim
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Urban Areas ◽  
Bare Soil ◽  
Error Matrix ◽  
Ndvi Index ◽  
Open Forests ◽  
Soil And Water

Abstract This study deals with the analysis and detection of changes in land cover patterns and land uses, especially forests in Amadiya district in Dohuk Governorate. It carred out in northern of Iraq by area is (2775.21) km2 and the district is located astronomically between longitudes (01/04 ° 43), (17/08 ° 44), it extends between two circles of latitude, which are (16/50 ° 36) and ('30.'21 ° 37) north, during the periods (1999-2006-2013-2019). Application of the Supervised Classification and the detection of change over time in a comparative manner and by relying on the satellite images of the Land sat ETM satellite were used. The Landsat OLI satellite with a distinctive capacity of 30 meters in the Arc map 10.6.1 program, and one of the indicators of environmental degradation in the land cover patterns, which is the NDVI index for all study periods, was used to reveal the role of natural and human factors that lead to changes in the land cover patterns in the study area. The classification revealed the existence of five types of common land cover, which included dense forests, open forests, urban areas, bare soil and water, which showed clear changes in these land coverings during the period from 1999 to 2019, which were represented by a decrease in forests, bare soil and water by a percentage of (54.76601%), (5.212329%), (2.149469%) respectively, while the Dense and urban areas by (16.35919%) and (21.51301%) in 2019, respectively. The classification accuracy of the Spatial indication was estimated based on the error matrix from there we found that the accuracy was (93.29%) this indicates that the classification accuracy is very good It is acceptable and can relied upon and recommended for classification.

Spatial analysis of the factors influencing housing quality for renters in a traditional Nigerian city

2019 ◽  
Vol 12 (2) ◽  
pp. 181-209
Author(s):  
Falilat Yetunde Olowu ◽  
Emmanuel Babatunde Jaiyeoba ◽  
Hafeez Idowu Agbabiaka ◽  
Olawunmi Johnson Daramola
Keyword(s):  
Rental Housing ◽  
High Density ◽  
Housing Quality ◽  
Systematic Sampling ◽  
Low Density ◽  
Medium Density ◽  
Residential Areas ◽  
Content Type ◽  
Factors Influencing ◽  
Housing Characteristics

Purpose Rental housing is an important form of accommodation; evaluating its quality will improve the quality of designs, standard living of renters, new dimension to policy guiding rental housing and enhance the values of rental houses. This study aims to examine the factors influencing rental housing quality in Ile-Ife, Nigeria. Design/methodology/approach Multi-stage sampling procedure was used to select tenants for the study. Residential areas were stratified into three densities: high, medium and low. Out of the 18 residential areas identified, six, eight and four were in the high, medium and low densities, respectively. Five residential areas were selected based on high concentrations of rental housing. The selected areas are Mokuro and Iloro (high density), Ife City and Eleyele (medium density) and Aladanla (low density). Systematic sampling technique was used to select 550 buildings where an adult tenant was selected per building for questionnaire administration. Findings The results of the principal component analysis established that four factors were generated for the high-density, nine factors for the medium-density and five factors for low-density areas as the major factors influencing rental housing quality. The variation in the number of factors generated and the percentage variance explained by the factors could be associated to the peculiarities across the densities in terms of the socioeconomic characteristics and housing characteristics of the renters. Originality/value This study examined the factors influencing housing quality for renters in Ile-Ife, Nigeria. It provides information on the three residential densities in terms of the variation in their housing morphology. The study went further to establish the relationship among the three musketeers such as socioeconomic characteristic of renters, housing characteristics and housing quality, under three dimensions environmental, internal building and external Building. Therefore, the contribution of this study strengthens the position that a minimum standard and schedule of upgrade and maintenance should be meted out for landlords to carry out repairs at interval, so as to make the housing unit and environment habitable for tenants.

Spatial organization of rural territories

2021 ◽  
pp. 751-756
Author(s):  
Sevostyanov A.V. Sevostyanov A.V. ◽  
V.A. Sevostyanov ◽  
A.P. Spiridonova
Keyword(s):  
Scale Development ◽  
Rural Population ◽  
Large Scale ◽  
Spatial Organization ◽  
Low Density ◽  
Rural Settlements ◽  
Residential Areas ◽  
Complex Development

This article covers the issues raised by the objectives of the "The Program for complex development of rural territories" and its subprogram "Providing rural population with affordable and comfortable housing". The authors substantiate the concept "rural agglomeration" and make the suggestions on how to choose rural settlements and land plots suitable for large-scale development of low-density residential areas.

scholarly journals Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

2021 ◽  
Vol 14 (1) ◽  
pp. 160
Author(s):  
Najmeh Mozaffaree Pour ◽  
Tõnu Oja
Keyword(s):  
Land Cover ◽  
Driving Forces ◽  
Urban Expansion ◽  
Network Models ◽  
Influential Factors ◽  
Residential Areas ◽  
Neural Network Models ◽  
Independent Variables ◽  
The Core ◽  
Mlp Model

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

scholarly journals A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan

2021 ◽  
Vol 64 (3) ◽  
pp. 265-274
Author(s):  
Fatima Mushtaq ◽  
Khalid Mahmood ◽  
Mohammad Chaudhry Hamid ◽  
Rahat Tufail
Keyword(s):  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Support Vector ◽  
Kappa Statistics ◽  
Error Matrix ◽  
Classification Techniques ◽  
Machine Learning Classification ◽  
Hyper Plane ◽  
Better Than

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.  

scholarly journals Fourier Landscape Pattern Indices for Predicting South Carolina Watershed Fecal Coliform

2017 ◽  
Vol 10 (1) ◽  
pp. 20-34
Author(s):  
Alvin Spivey ◽  
Anthony Vodacek
Keyword(s):  
Land Use ◽  
South Carolina ◽  
Land Cover ◽  
Landscape Pattern ◽  
Fecal Coliform ◽  
Model Parameters ◽  
Residential Areas ◽  
Ecological Processes ◽  
Bare Rock ◽  
Decadal Scale

AbstractExtending the Landscape Pattern Metric (LPM) model analysis in Smith et al. (2001) into a LPM decision model, decadal scale prediction of fecal coliform compromised South Carolina watersheds is developed. The model’s parameter variability identifies the greatest contributors to a compromised watershed’s prediction. The complete set of model parameters include Land Cover Land Use (LCLU) & slope,along stream proportion, Fourier Metric of Fragmentation (FMF), Fourier Metric of Proportion (FMP), and Least Squares Fourier Transform Fractal Dimension (LsFT). The 1992 National Land Cover Data (NLCD) Land Cover Land Use (LCLU) within fecal coliform compromised watersheds is used to train the model parameters, and the 2001 NLCD LCLU is used to test the LPM model. The most significant model parameters arealong stream bare rock LsFT,FMF between urban/recreational grasses and evergreen forests, andFMF between deciduous forests and high density residential areas. These metrics contribute significantly more than the bestproportiondescriptor:proportion of urban/recreational grasses. In training, the proposed model correctly identified 92 % of the compromised watersheds; while the Smith et al. (2001) model 94 % of the compromised watersheds were correctly identified. This study reveals the ability of Fourier metrics to interpret ecological processes, and the need for more appropriate landscape level models.

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