scholarly journals Change Detection Analysis Using Surveying and Geoinformatics Techniques

2018 ◽  
Vol 2 (1) ◽  
pp. 28-38
Author(s):  
I. C. Onuigbo ◽  
J. Y. Jwat
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Research Work ◽  
Temporal Data ◽  
Landsat Images ◽  
Detection Analysis ◽  
Ground Truthing ◽  
Landsat Satellite ◽  
Geoinformatics Techniques

The study was on change detection using Surveying and Geoinformatics techniques. For effective research study, Landsat satellite images and Quickbird imagery of Minna were acquired for three periods, 2000, 2005 and 2012. The research work demonstrated the possibility of using Surveying and Geoinformatics in capturing spatial-temporal data. The result of the research work shows a rapid growth in built-up land between 2000 and 2005, while the periods between 2005 and 2012 witnessed a reduction in this class. It was also observed that change by 2020 may likely follow the trend in 2005 – 2012 all things being equal. Built up area may increase to 11026.456 hectares, which represent 11% change. The study has shown clearly the extent to which MSS imagery and Landsat images together with extensive ground- truthing can provide information necessary for land use and land cover mapping. Attempt was made to capture as accurate as possible four land use and land cover classes as they change through time.

scholarly journals Application of remote sensing and GIS in land use/land cover mapping and change detection in Shasha forest reserve, Nigeria

2014 ◽  
Vol XL-8 ◽  
pp. 613-616 ◽  
Author(s):  
O. S. Olokeogun ◽  
K. Iyiola ◽  
O. F. Iyiola
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Sustainable Forest Management ◽  
Cost Effective ◽  
Remote Sensing And Gis ◽  
Clear Understanding ◽  
Forest Reserve ◽  
Landsat Satellite

Mapping of LULC and change detection using remote sensing and GIS techniques is a cost effective method of obtaining a clear understanding of the land cover alteration processes due to land use change and their consequences. This research focused on assessing landscape transformation in Shasha Forest Reserve, over an 18 year period. LANDSAT Satellite imageries (of 30 m resolution) covering the area at two epochs were characterized into five classes (Water Body, Forest Reserve, Built up Area, Vegetation, and Farmland) and classification performs with maximum likelihood algorithm, which resulted in the classes of each land use. <br><br> The result of the comparison of the two classified images showed that vegetation (degraded forest) has increased by 30.96 %, farmland cover increased by 22.82 % and built up area by 3.09 %. Forest reserve however, has decreased significantly by 46.12 % during the period. <br><br> This research highlights the increasing rate of modification of forest ecosystem by anthropogebic activities and the need to apprehend the situation to ensure sustainable forest management.

scholarly journals The analysis of spatial and temporal changes of land cover and land use in the reclaimed areas with the application of airborne orthophotomaps and LANDSAT images

2015 ◽  
Vol 64 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Marta Szostak ◽  
Piotr Wężyk ◽  
Paweł Hawryło ◽  
Marcin Pietrzykowski
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Modern Technology ◽  
General Reference ◽  
Landsat Images ◽  
Corine Land Cover ◽  
Spoil Heap ◽  
Object Based ◽  
Mine Sites ◽  
Landsat Satellite

Abstract The aim of this study was to investigate the possible use of geoinformatics tools and generally available geodata for mapping land cover/use on the reclaimed areas. The choice of subject was dictated by the growing number of such areas and the related problem of their restoration. Modern technology, including GIS, photogrammetry and remote sensing are relevant in assessing the reclamation effects and monitoring of changes taking place on such sites. The LULC classes mapping, supported with thorough knowledge of the operator, is useful tool for the proper reclamation process evaluation. The study was performed for two post-mine sites: reclaimed external spoil heap of the sulfur mine Machów and areas after exploitation of sulfur mine Jeziórko, which are located in the Tarnobrzeski district. The research materials consisted of aerial orthophotos, which were the basis of on-screen vectorization; LANDSAT satellite images, which were used in the pixel and object based classification; and the CORINE Land Cover database as a general reference to the global maps of land cover and land use.

scholarly journals SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 31-35 ◽  
Author(s):  
A. E. Akay ◽  
B. Gencal ◽  
İ. Taş
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Short Paper ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Spatiotemporal Change ◽  
Change Detection Analysis ◽  
Flooded Forest ◽  
Detection Analysis

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

A study of decadal Land use-land cover change in Dehradun city.

2021 ◽  
Author(s):  
Gayatri Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Water Levels ◽  
Physical Factors ◽  
Infrastructure Development ◽  
Land Use Land Cover ◽  
Detection Analysis ◽  
The Impact

&lt;p&gt;The present study is to quantify the spatial-temporal pattern of the Land Use/ Land Cover Change (LULCC) during a decade (i.e., 2010 to 2020) in the Dehradun city which is situated in the foothills of the Himalaya, using Landsat data. The study helps in identifying the major bio-physical factors governing LULCC through modern geospatial techniques. Change detection shows that the study area experienced an increase in its urban area from 2010 to 2020 and a comparatively decrease in cropland and forest area. This was due to an increase in its urban population, rapid increase in industrialization and tourism during the same period. The change detection analysis further shows that 2010-2020, associated with change in croplands, change in built-up, forest lands, change in water-bodies, water levels, and rainfall. With comparison of above results and collected socio-economic data in this region, the impact of changing land use &amp; bio-physical/ economic factors on agricultural profitability were analyzed. The result of this study could thus lead to a detailed and lucid spatiotemporal assessment of the major bio-physical factors. It is expected that the study will help in facilitating better policy making and infrastructure development for industries and urbanization.&lt;br&gt;&lt;br&gt;&lt;/p&gt;

scholarly journals A GEOBIA Approach for Multitemporal Land-Cover and Land-Use Change Analysis in a Tropical Watershed in the Southeastern Amazon

2018 ◽  
Vol 10 (11) ◽  
pp. 1683 ◽  
Author(s):  
Pedro Souza-Filho ◽  
Wilson Nascimento ◽  
Diogo Santos ◽  
Eliseu Weber ◽  
Renato Silva ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Landsat Images ◽  
Change Analysis ◽  
Human Settlements ◽  
Object Based ◽  
Detection Approach ◽  
Tropical Watershed

The southeastern Amazon region has been intensively occupied by human settlements over the past three decades. To evaluate the effects of human settlements on land-cover and land-use (LCLU) changes over time in the study site, we evaluated multitemporal Landsat images from the years 1984, 1994, 2004, 2013 and Sentinel to the year 2017. Then, we defined the LCLU classes, and a detailed “from-to” change detection approach based on a geographic object-based image analysis (GEOBIA) was employed to determine the trajectories of the LCLU changes. Three land-cover (forest, montane savanna and water bodies) and three land-use types (pasturelands, mining and urban areas) were mapped. The overall accuracies and kappa values of the classification were higher than 0.91 for each of the classified images. Throughout the change detection period, ~47% (19,320 km2) of the forest was preserved mainly within protected areas, while almost 42% (17,398 km2) of the area was converted from forests to pasturelands. An intrinsic connection between the increase in mining activity and the expansion of urban areas also exists. The direct impacts of mining activities were more significant throughout the montane savanna areas. We concluded that the GEOBIA approach adopted in this study combines the advantages of quality human interpretation and the capacities of quantitative computing.

scholarly journals LAND COVER CHANGE DETECTION FOR AMALGAMATED TERRITORIAL COMMUNITIES: EXAMPLE OF USING REMOTE SENSING FOR FOREST CLASSIFICATION AND DEFORESTATION DISCLOSURE

2020 ◽  
pp. 101-107
Author(s):  
V. Panchenko
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Local Communities ◽  
Data Availability ◽  
Landsat Images ◽  
Forest Classification ◽  
Land Cover Change Detection

The study is aimed to apply remote sensing for purposes of land cover detection in researches of new territorial units in Ukraine. The example of forest detection using Landsat images is particularly presented in the study. While the study area presented by Korovyntsi amalgamated territorial community in the Sumy region. The forest classification and deforestation detection have been processed every 5 years from 1990 through 2020. The Landsat 5, 7, and 8 data from the United States Geological Survey (USGS) have been used for the research. The image choice depended on the date of data availability and reliability, but in time between mid-May to early July. The dataset of 11 total images was processed in the Harris Geospatial Solutions’ Environment for Visualizing Images (ENVI). The data were calibrated by using the ENVI Landsat calibration tool, the atmospheric correction applied by using the ENVI FLAASH tool, and seamless mosaicking was used for some periods with more than one image needed. Normalized Difference Vegetation Index (NDVI) is the basis for forest classification applied. Comparing remote sensing data from different years and different Landsat satellites allowed not just to identify vegetation type of forest, but also to detect land cover changes. The change detection has been analyzed in two ways. The first method was based on changes in classification status. The second method was based on a difference in NDVI values, while forest classification was held for masking out non-forest areas. The applied study observed ways of cost-efficient land use research for local communities. Those methods could be used by NGO’s, local activists, citizen scientists, local authorities for improving land use management with the most updated data, and identifying problems of deforestation, in the case of the study presented. Nonetheless, land cover change detection is not limited to forest cover presented in the study. Anyway, in the case of forest detection, Landsat images from different satellites could be compared and present historical data for the rural areas, which had a low research interest in the past, but it changed due to administrative reform in Ukraine and switching governance power to the local communities.

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