scholarly journals Spatial Allocation Based on Physiological Needs and Land Suitability Using the Combination of Ecological Footprint and SVM (Case Study: Java Island, Indonesia)

2021 ◽  
Vol 10 (4) ◽  
pp. 259
Author(s):  
Sitarani Safitri ◽  
Ketut Wikantika ◽  
Akhmad Riqqi ◽  
Albertus Deliar ◽  
Irawan Sumarto
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Ecological Footprint ◽  
Agricultural Land ◽  
Land Suitability ◽  
Sustainable Land Use ◽  
Public Works ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Spatial Allocation

Indonesia currently has 269 million people or 3.49% of the world’s total population and is ranked as the fourth most populous country in the world. Analysis by the Ministry of Public Works and Public Housing of Indonesia in 2010 shows that Java’s biocapacity is already experiencing a deficit. Therefore, optimization needs to be done to reduce deficits. This study aims to optimize and assess spatial allocation accuracy based on land-use/land cover suitability. In this study, the ecological footprint (EF) is utilized as a spatial allocation assessment based on physiological needs. The concept of land suitability aims for optimal and sustainable land use. Moreover, the land suitability model was conducted using the support vector machine (SVM). SVM is used to find the best hyperplane by maximizing the distance between classes. A hyperplane is a function that can be used to separate land-use/land cover types. The land suitability model’s overall-accuracy model was 86.46%, with a kappa coefficient value of 0.812. The final results show that agricultural land, plantations, and pastureland are still experiencing deficits, but there is some reduction. The deficit reduction for agricultural land reached 510,588.49 ha, 18,986.14 ha for plantations, and 1015.94 ha for pastures. The results indicate that the SVM algorithm is efficient in mapping the land-use suitability and optimizing spatial allocation.

scholarly journals Classification of various land features using RISAT-1 dual polarimetric data

2014 ◽  
Vol XL-8 ◽  
pp. 833-837 ◽  
Author(s):  
V. N. Mishra ◽  
P. Kumar ◽  
D. K. Gupta ◽  
R. Prasad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Uttar Pradesh ◽  
Land Cover Classification ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Remotely Sensed Data ◽  
Sar Data ◽  
Land Cover Maps

Land use land cover classification is one of the widely used applications in the field of remote sensing. Accurate land use land cover maps derived from remotely sensed data is a requirement for analyzing many socio-ecological concerns. The present study investigates the capabilities of dual polarimetric C-band SAR data for land use land cover classification. The MRS mode level 1 product of RISAT-1 with dual polarization (HH & HV) covering a part of Varanasi district, Uttar Pradesh, India is analyzed for classifying various land features. In order to increase the amount of information in dual-polarized SAR data, a band HH + HV is introduced to make use of the original two polarizations. Transformed Divergence (TD) procedure for class separability analysis is performed to evaluate the quality of the statistics prior to image classification. For most of the class pairs the TD values are greater than 1.9 which indicates that the classes have good separability. Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes like urban land, agricultural land, fallow land, vegetation and water bodies. The overall classification accuracy achieved by SVM is 95.23 % with Kappa coefficient 0.9350.

scholarly journals Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework

2021 ◽  
Vol 13 (16) ◽  
pp. 3337
Author(s):  
Shaker Ul Din ◽  
Hugo Wai Leung Mak
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Land Cover ◽  
Land Cover Change ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Barren Land

Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies.

scholarly journals A study of spatial and temporal distribution of land utilization pattern in Mopka village using RS & GIS

2015 ◽  
Vol 5 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Soni Prasoon ◽  
Singh Pushpraj
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Anthropogenic Activities ◽  
Natural Habitat ◽  
Sustainable Land Use ◽  
Land Use Land Cover ◽  
Land Utilization ◽  
Land Use Pattern ◽  
Barren Land

Remote Sensing and GIS is a very good modality for retrospection and the strategy for better exploitation of sustainable land use system. The present study was conducted in the Bilaspur district for analyzing the spatial distribution of Land Use Change. During last decades the increasing population of Bilaspur city, affect the land use pattern of Mopka Village. The anthropogenic activities were affecting the agricultural land along with barren land. For the development of civic amenities the land of the above village was used. The main objective of the present study is to analyses the land use/land cover distribution in Mopka village, Bilaspur district in between last 12 years and to identify the main forces behind the changes. The objectives of present studies are, to create a land use land cover maps of Mopka village using satellite imagery. To analysis the temporal changes of village area in between the year 2000 and 2012, the primary, secondary and satellite data were used. The results of the present study show that the decadeial changes due to population growth and increasing demand of infrastructure were destroying the natural resources, natural habitat and soil structure of area.Int. J. Agril. Res. Innov. & Tech. 5 (1): 1-9, June, 2015

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

Land ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 113 ◽  
Author(s):  
Wakjira Takala Dibaba ◽  
Tamene Adugna Demissie ◽  
Konrad Miegel
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Management Practice ◽  
Supply And Demand ◽  
Sustainable Land Use ◽  
Water Bodies ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Grazing Land

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.

scholarly journals Spatiotemporal Influence of Land Use/Land Cover Change Dynamics on Surface Urban Heat Island: A Case Study of Abuja Metropolis, Nigeria

2021 ◽  
Vol 10 (5) ◽  
pp. 272
Author(s):  
Auwalu Faisal Koko ◽  
Wu Yue ◽  
Ghali Abdullahi Abubakar ◽  
Akram Ahmed Noman Alabsi ◽  
Roknisadeh Hamed
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Urban Expansion ◽  
Urban Heat Islands ◽  
Sustainable Land Use ◽  
Land Use Land Cover ◽  
Urban Centers ◽  
Rapid Urbanization ◽  
Urban Heat

Rapid urbanization in cities and urban centers has recently contributed to notable land use/land cover (LULC) changes, affecting both the climate and environment. Therefore, this study seeks to analyze changes in LULC and its spatiotemporal influence on the surface urban heat islands (UHI) in Abuja metropolis, Nigeria. To achieve this, we employed Multi-temporal Landsat data to monitor the study area’s LULC pattern and land surface temperature (LST) over the last 29 years. The study then analyzed the relationship between LULC, LST, and other vital spectral indices comprising NDVI and NDBI using correlation analysis. The results revealed a significant urban expansion with the transformation of 358.3 sq. km of natural surface into built-up areas. It further showed a considerable increase in the mean LST of Abuja metropolis from 30.65 °C in 1990 to 32.69 °C in 2019, with a notable increase of 2.53 °C between 2009 and 2019. The results also indicated an inverse relationship between LST and NDVI and a positive connection between LST and NDBI. This implies that urban expansion and vegetation decrease influences the development of surface UHI through increased LST. Therefore, the study’s findings will significantly help urban-planners and decision-makers implement sustainable land-use strategies and management for the city.

scholarly journals An Analysis of Urban Land Use/Land Cover Changes in Blantyre City, Southern Malawi (1994–2018)

2020 ◽  
Vol 12 (6) ◽  
pp. 2377 ◽  
Author(s):  
John Mawenda ◽  
Teiji Watanabe ◽  
Ram Avtar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Land ◽  
Urban Land Use ◽  
Sustainable Growth ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Lulc Changes ◽  
Bare Land

Rapid and unplanned urban growth has adverse environmental and social consequences. This is prominent in sub-Saharan Africa where the urbanisation rate is high and characterised by the proliferation of informal settlements. It is, therefore, crucial that urban land use/land cover (LULC) changes be investigated in order to enhance effective planning and sustainable growth. In this paper, the spatial and temporal LULC changes in Blantyre city were studied using the integration of remotely sensed Landsat imageries of 1994, 2007 and 2018, and a geographic information system (GIS). The supervised classification method using the support vector machine algorithm was applied to generate the LULC maps. The study also analysed the transition matrices derived from the classified map to identify prominent processes of changes for planning prioritisation. The results showed that the built-up class, which included urban structures such as residential, industrial, commercial and public installations, increased in the 24-year study period. On the contrary, bare land, which included vacant lands, open spaces with little or no vegetation, hilly clear-cut areas and other fallow land, declined over the study period. This was also the case with the vegetation class (i.e., forests, parks, permanent tree-covered areas and shrubs). The post-classification results revealed that the LULC changes during the second period (2007–2018) were faster compared to the first period (1994–2007). Furthermore, the results revealed that the increase in built-up areas systematically targeted the bare land and avoided the vegetated areas, and that the vegetated areas were systematically cleared to bare land during the study period (1994–2018). The findings of this study have revealed the pressure of human activities on the land and natural environment in Blantyre and provided the basis for sustainable urban planning and development in Blantyre city.

Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine

2013 ◽  
Vol 71 (5) ◽  
pp. 2245-2255 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Prashant K. Srivastava ◽  
Manika Gupta ◽  
Jay Krishna Thakur ◽  
Saumitra Mukherjee
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Land Cover ◽  
Forest Ecosystem ◽  
Mangrove Forest ◽  
Support Vector ◽  
Land Use Land Cover

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

scholarly journals Assessment of Change in the Built-Up Index of Uyo Metropolis and Its Environs Using Remote Sensing

2019 ◽  
Vol 4 (6) ◽  
pp. 84-89 ◽  
Author(s):  
Aniekan Effiong Eyoh ◽  
Akwaowo Ekpa
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Quantitative Analysis ◽  
Land Cover ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Positive Growth ◽  
Bare Land ◽  
Erdas Imagine

The research was aim at assessing the change in the Built-up Index of Uyo metropolis and its environs from 1986 to 2018, using remote sensing data. To achieve this, a quantitative analysis of changes in land use/land cover within the study area was undertaken using remote sensing dataset of Landsat TM, ETM+ and OLI sensor images of 1986, 2000 and 2018 respectively. Supervised classification, using the maximum likelihood algorithm, was used to classify the study area into four major land use/land cover types; built-up land, bare land/agricultural land, primary swamp vegetation and secondary vegetation. Image processing was carried out using ERDAS IMAGINE and ArcGIS software. The Normalised Difference Built-up Index (NDBI) was calculated to obtain the built-up index for the study area in 1986, 2000 and 2018 as -0.20 to +0.45, -0.13 to +0.55 and -0.19 to +0.63 respectively. The result of the quantitative analysis of changes in land use/land cover indicated that Built-up Land had been on a constant and steady positive growth from 6.76% in 1986 to 11.29% in 2000 and 44.04% in 2018.

scholarly journals SPATIO-TEMPORAL ANALYSIS AND MODELING OF URBAN GROWTH OF BIRATNAGAR CITY, NEPAL

2019 ◽  
Vol IV-5/W2 ◽  
pp. 97-102
Author(s):  
S. Shrestha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Urban Expansion ◽  
Urban Change ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Spatial Metrics ◽  
Land Cover Map ◽  
Spatio Temporal

Abstract. Increasing land use land cover changes, especially urban growth has put a negative impact on biodiversity and ecological process. As a consequences, they are creating a major impact on the global climate change. There is a recent concern on the necessity of exploring the cause of urban growth with its prediction in future and consequences caused by this for sustainable development. This can be achieved by using multitemporal remote sensing imagery analysis, spatial metrics, and modeling. In this study, spatio-temporal urban change analysis and modeling were performed for Biratnagar City and its surrounding area in Nepal. Land use land cover map of 2004, 2010, and 2016 were prepared using Landsat TM imagery using supervised classification based on support vector machine classifier. Urban change dynamics, in term of quantity, and pattern was measured and analyzed using selected spatial metrics and using Shannon’s entropy index. The result showed that there is increasing trend of urban sprawl and showed infill characteristics of urban expansion. Projected land use land cover map of 2020 was modeled using cellular automata-based approach. The predictive power of the model was validated using kappa statistics. Spatial distribution of urban expansion in projected land use land cover map showed that there is increasing threat of urban expansion on agricultural land.

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