scholarly journals Impact of Land Use Land Cover Change on Coastal Tourism in Kundapura, Karnataka, Using Multi-temporal Remotely Sensed Data and GIS Techniques

2018 ◽  
Vol 13 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Shekar Naik ◽  
H Gangadhara Bhat ◽  
T N Sreedhara
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Agricultural Land ◽  
Coastal Development ◽  
Land Use Land Cover ◽  
Coastal Tourism ◽  
Remotely Sensed Data ◽  
Erdas Imagine ◽  
Multi Temporal

The present study is an attempt to examine the Land Use Land Cover changes in parts of Kundapura Taluk in Karnataka for the period 2006 and 2016 and its impact on coastal tourism. IRS satellite images of 2006 and 2016 have been used and processed using ERDAS Imagine and ArcGIS. The result indicated tremendous changes, particularly in mixed urban and agricultural land and proved that RS/GIS has advantages over conventional techniques. The result obtained, based on the multi-dated satellite data study, will assist in decision making and help to take appropriate measures to monitor and regulate coastal development in order to achieve sustainable and integrated coastal development.

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 Observing land-use/land-cover dynamics in semi-arid environment: Evidence from Damaturu Town and its surrounding lands, Yobe State, Nigeria

2020 ◽  
Vol 4 (2) ◽  
pp. 55-78
Author(s):  
Modibbo Babagana-Kyari ◽  
Babagana Boso
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Arid Environment ◽  
Land Use Land Cover ◽  
Temporal Aspects ◽  
Erdas Imagine ◽  
Annual Change Rate ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Supervised Image Classification

The fragile Sudano-Sahelian ecological zone of Nigeria has been classified as a hotspot of land cover change (LCC) that has been suffering from serious anthropogenic and biophysical stresses. Damaturu, being the fastest growing town situated in the region happened to be a victim of this negative development. The purpose of this study is to remotely observe and assess the prevailing land-use/land-cover (LULC) dynamics of Damaturu town and its delicate surrounding lands from the year 1987-2017 study periods. To achieve this, a supervised image classification technique with Maximum Likelihood Classifier (MLC) algorithm was used in ERDAS Imagine version 15 software to classify the three epochs multi-temporal and multi-spectral Landsat imageries (TM 1987, ETM+7 2000 and OLI 2017). The classified LULC maps and their resulting statistics were then used to assess the spatio-temporal aspects of the observed changes by placing the results within the wider context of previous related literature and evidences. Findings revealed that the built-up area has been expanding since 1987 with an annual change rate of 4.5% between 1987-2000, and 5.3% during 2000-2017 respectively. The growth of the town is being accompanied by massive farmlands expansion and vegetal cover (trees and shrubs) lost making the surrounding arable lands seriously disturbed. Thus, if the observed trends continue, the entire studied region will be subjected to severe environmental hazard such as desertification. Overall, the study provides valuable information required for sustainable  environmental management.

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 Critical Assessment of Land Use Land Cover Dynamics Using Multi-Temporal Satellite Images

Environments ◽  
2015 ◽  
Vol 2 (4) ◽  
pp. 61-90 ◽  
Author(s):  
Md. Mondal ◽  
Nayan Sharma ◽  
Martin Kappas ◽  
P. Garg
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Critical Assessment ◽  
Land Use Land Cover ◽  
Land Cover Dynamics ◽  
Multi Temporal

scholarly journals Application of GIS and Remote Sensing to Analyse Land Use Land Cover Change Detection and Vegetation Dynamics Using Multi-temporal Satellite Images; The Case of Mariamdehan Kebele, Tigray Region, Northern Ethiopia

2019 ◽  
pp. 1-11
Author(s):  
Esayas Meresa ◽  
Yikunoamlak Gebrewhid
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Natural Resources ◽  
Change Detection ◽  
Land Cover Change ◽  
Vegetation Dynamics ◽  
Satellite Images ◽  
Land Use Land Cover ◽  
Multi Temporal ◽  
Land Cover Change Detection

Detecting Land use and land cover change and vegetation condition has become a central component in current strategies for managing and monitoring of environmental changes caused by anthropogenic activities. To come up with such decisions, geoinformatics technology is providing new tools to conduct vegetation and land use land cover change detection analysis for managing and wise utilisation of natural resources as well as to provide information for policymakers in a given study area. This study examines the use of geoinformatics technology to analyse land use land cover (LULC) change and vegetation dynamics using multi-temporal satellite images for the maryamdehan kebele in the years 1984, 2005 and 2015. Both primary and secondary data were used from different sources. Satellite images of the year 1984, 2005 and 2015 were downloaded from the govis.usgs.gov website and ground control points (GCP) data were collected by handheld GPS for supervised image classification in Erdas imagine and ArcGIS environment. The findings show that six main land use land cover classes were detected and vegetation values were also computed in each period.  As a result, the total area of the kebele was 3646.49 hectare, from which in 1984 forest area (40.691%), grassland (26.15%) and farmland (10.81%) were dominant classes and in 2005 settlement (52.41%), forest area (25.04%) & farmland (11.71%) and in 2015, 35.14% was covered by forest land, 30.04% by Settlement, and 14.74% by farmland. Water resource decreases from 9.3% to 0.64% in 2015 and the bare land also changes from 3.18% to 0.903% because of urban expansion and agricultural activities in the kebele. In addition, the vegetation condition looks like a sinusoidal trend from the year 1984 up to 2015 because of climate change and human interventions in the kebele. To conclude that detecting LULC change and analysis of vegetation dynamics plays a great role in land use management and wise utilisation of natural resources by applying Geoinformatics tools in the kebele and it provides information for the policymakers to prepared future plan and for sustainable development.

scholarly journals Change Detection in Landuse/ Landcover of Abeokuta Metropolitan Area, Nigeria Using Multi-Temporal Landsat Remote Sensing

2019 ◽  
Vol 51 (2) ◽  
pp. 217
Author(s):  
Adebayo Oluwasegun Hezekiah ◽  
Otun. W. O ◽  
Daniel, I. Samuel
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Planning ◽  
Water Body ◽  
Land Use Land Cover ◽  
Land Utilization ◽  
Erdas Imagine ◽  
Multi Temporal ◽  
Landsat Satellite

This research paper examined the changes in land use/ land cover of Abeokuta, Nigeria between 1984 and 2015 using Multi-Temporal Landsat Remote Sensing paired with Geographic Information System (GIS) techniques. The evaluation of the trend, rate and magnitude changes was the objectives of this study.  Five Landsat satellite images of different dates,  i.e., Landsat Thematic Mapper (TM) of 1984, 2001, 2006, 2011 and 2015 with spatial resolution ranging from 15, 30 and 60metres were obtained from National Aeronautics Space Administration(NASA),United State Geological Survey Website and  GIS facility of Sioux Falls Website  and quantify the changes  over a period of thirty-one (31) years. Supervised classification methodology was applied to the acquired multi-band raster imageries using maximum livelihood technique in ERDAS Imagine 9.3. The images of the study area were classified into three (3) classes namely; vegetation, water body and built-up area and were overlay with vector maps of the study area generated in ArcGIS 10. The results show that for the period of 31years (1984-2015), vegetation which covered 76.20% of the total area has decreased to 39.29%, water body decreases from 6.63% to 1.89% while the built –up area which initially was 17.14% as at 1984 increased to 58.82%. The study, however, recommended that there is a need for a timely Land use/ Land cover mapping of the entire Abeokuta and its environs in order to reduce the effects of undiscrimate land utilization in the area. This will also facilitate necessary Land use planning and forestall the rising sprawl not only in Abeokuta but also in other urban centres.

scholarly journals Assessment of spatial changes of land use/land cover dynamics, using multi-temporal Landsat data in Dadri Block, Gautam Buddh Nagar, India

2020 ◽  
Vol XIX (1) ◽  
pp. 72-77
Author(s):  
Sushma Shastri ◽  
Prafull Singh ◽  
Pradipika Verma ◽  
Praveen Kumar Rai ◽  
A. P. Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landsat Data ◽  
Land Use Land Cover ◽  
Spatial Changes ◽  
Land Cover Dynamics ◽  
Multi Temporal

scholarly journals Land Cover Change Assessment of Vaal Harts Irrigation Scheme using Multi-temporal Satellite Sata

2013 ◽  
Vol 39 (4) ◽  
pp. 59-70 ◽  
Author(s):  
Fredrick Ao Otieno ◽  
Olumuyiwa I Ojo ◽  
George M. Ochieng
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Program ◽  
Land Use Land Cover ◽  
Irrigation Scheme ◽  
Multi Temporal

Abstract Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

scholarly journals Land Use/Land Cover Changes in a Disturbed River Watershed Kenya

2019 ◽  
Vol 3 (2) ◽  
pp. 29
Author(s):  
Zachary Gichuru Mainuri ◽  
John M. Mironga ◽  
Samuel M. Mwonga
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Remote Sensing Image ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Human Needs ◽  
Observation Systems ◽  
Erdas Imagine ◽  
Remote Sensing Image Processing

Drivers of land use change were captured by the use of DPSIR model where Drivers (D) represented human needs, Pressures (P), human activities, State (S), the ecosystem, Impact (I) services from the ecosystem and Response (R), the decisions taken by land users. Land sat MSS and Land sat ETM+ (path 185, row 31) were used in this study. The Land sat ETM+ image (June 1987, May, 2000 and July, 2014) was downloaded from USGS Earth Resources Observation Systems data website. Remote sensing image processing was performed by using ERDAS Imagine 9.1. Two land use/land cover (LULC) classes were established as forest and shrub land. Severe land cover changes was found to have occurred from 1987-2000, where shrub land reduced by -19%, and forestry reduced by -72%. In 2000 – 2014 shrub land reduced by-45%, and forestry reduced by -64%. Forestry and shrub land were found to be consistently reducing.

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