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.

scholarly journals An Assessment of Changes the Land Use Land Cover and Impact of the It Park at Talwade Pune On the Nearby Villages

2020 ◽  
pp. 172-179
Author(s):  
Ujjwala Khare ◽  
Prajakta Thakur
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Rapid Growth ◽  
Urban Areas ◽  
Satellite Images ◽  
Ground Truth ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Tertiary Sector ◽  
Land Cover Maps

<p>The expansion of urban areas is common in metropolitan cities in India. Pune also has experienced rapid growth in the fringe areas of the city. This is mainly on account of the development of the Information Technology (IT) Parks. These IT Parks have been established in different parts of Pune city. They include Hinjewadi, Kharadi, Talwade and others like the IT parks in Magarpatta area. The IT part at Talwade is located to close to Pune Nashik Highway has had an impact on the villages located around it. The surrounding area includes the villages of Talwade, Chikhli, Nighoje, Mahalunge, Khalumbre and Sudumbre.</p> <p>The changes in the land use that have occurred in areas surrounding Talwade IT parks during the last three decades have been studied by analyzing the LANDSAT images of different time periods. The satellite images of the 1992, 2001 and 2011 were analyzed to detect the temporal changes in the land use and land cover.</p> <p>This paper attempts to study the changes in land use / land cover which has taken place in these villages in the last two decades. Such a study can be done effectively with the help of remote sensing and GIS techniques. The tertiary sector has experienced a rapid growth especially during the last decade near the IT Park. The occupation structure of these villages is also related to the changes due to the development of the IT Park.</p> <p>The land use of study area has been analysed using the ground truth applied to the satellite images at decadal interval. Using the digital image processing techniques, the satellite images were then classified and land use / land cover maps were derived. The results show that the area under built-up land has increased by around 14 per cent in the last 20 years. On the contrary, the land under agriculture, barren, pasture has decreased significantly.</p>

scholarly journals POI Mining for Land Use Classification: A Case Study

2020 ◽  
Vol 9 (9) ◽  
pp. 493 ◽  
Author(s):  
Renato Andrade ◽  
Ana Alves ◽  
Carlos Bento
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Smart Cities ◽  
Ground Truth ◽  
Land Use Classification ◽  
Data Types ◽  
Ground Truth Data ◽  
Functional Roles ◽  
Artificial Neural Network Ann

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types.

Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

2020 ◽  
Vol 11 (5) ◽  
pp. 529-535
Author(s):  
Dan Abudu ◽  
Nigar Sultana Parvin ◽  
Geoffrey Andogah
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Land Use Changes ◽  
Land Cover Classification ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Land Cover ◽  
Sensing Technology

Conventional approaches for urban land use land cover classification and quantification of land use changes have often relied on the ground surveys and urban censuses of urban surface properties. Advent of Remote Sensing technology supporting metric to centimetric spatial resolutions with simultaneous wide coverage, significantly reduced huge operational costs previously encountered using ground surveys. Weather, sensor’s spatial resolution and the complex compositions of urban areas comprising concrete, metallic, water, bare- and vegetation-covers, limits Remote Sensing ability to accurately discriminate urban features. The launch of Sentinel-1 Synthetic Aperture Radar, which operates at metric resolution and microwave frequencies evades the weather limitations and has been reported to accurately quantify urban compositions. This paper assessed the feasibility of Sentinel-1 SAR data for urban land use land cover classification by reviewing research papers that utilised these data. The review found that since 2014, 11 studies have specifically utilised the datasets.

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.

scholarly journals Application of GIS and Remote Sensing Technique to Change Detection in Land Use/Land Cover Mapping of Igbokoda, Ondo State, Nigeria

1970 ◽  
Vol 3 (1) ◽  
Author(s):  
James R. Adewumi ◽  
James K. Akomolafe ◽  
Fidelis O. Ajibade ◽  
Blessing B. Fabeku
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Rapid Change ◽  
Land Use Land Cover ◽  
Remotely Sensed Data ◽  
Landsat Images ◽  
Annual Increment ◽  
Rapid Urbanization

This paper aims at establishing changes in land use and land cover in Igbokoda municipality using Geographic Information System and remote sensing techniques. Three satellite images for three different epochs 1986, 1999 and 2013 were used to produce a land use/land cover map classification for Igbokoda. In determining the extent of land use/land cover changes in the township from 1986 through 1999 to 2013, Landsat images of the town were downloaded from the United State Geological Survey online archive. The images were analyzed using change detection technique (NDVI differencing) along with SRTM 90m DEM of the study area to generate the extent of the changes that have occurred. Ground trotting was carried out to ascertain the accuracy of data and the major changes in the land use/land cover. Results show that vegetation has decreased from 75.04% in 1986 to 46.81% in 2013 which was due to increase in population and rapid urbanization. In 1996 the Built-up area covers 19.6321 km2 of the study area but has increased rapidly to 39.1505 km2 in the year 1999 with an average annual increment of 2.025Km2/year. By the year 2013, the built-up area has increased to 64.1520Km2. Also in the same vein, the bare surface area which was 13.28029km2 in 1986 was increased to 39.6053 and 50.240Km2 in 1999 and 2013 respectively. On the contrary, the vegetated area of Igbokoda reduced from 196.3046Km2 in 1999 to 122.4680Km2 in 2013. This study has demonstrated that remotely sensed data and GIS based approach is timely and cost effective than the conventional method of analysis, classification of land use pattern effective for planning and management. It further shows that If the rapid change in land use is not properly manage, the situation poses a serious threat to Igbokoda town by increasing surface runoff and susceptibility to flooding.

scholarly journals Vegetation cover change detection and assessment in arid environment using multi-temporal remote sensing images and ecosystem management approach

Solid Earth ◽  
2016 ◽  
Vol 7 (2) ◽  
pp. 713-725 ◽  
Author(s):  
Anwar Abdelrahman Aly ◽  
Abdulrasoul Mosa Al-Omran ◽  
Abdulazeam Shahwan Sallam ◽  
Mohammad Ibrahim Al-Wabel ◽  
Mohammad Shayaa Al-Shayaa
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Ecosystem Management ◽  
Vegetation Cover ◽  
Secondary Data ◽  
Ground Truth ◽  
Management Approach ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Multi Temporal

Abstract. Vegetation cover (VC) change detection is essential for a better understanding of the interactions and interrelationships between humans and their ecosystem. Remote sensing (RS) technology is one of the most beneficial tools to study spatial and temporal changes of VC. A case study has been conducted in the agro-ecosystem (AE) of Al-Kharj, in the center of Saudi Arabia. Characteristics and dynamics of total VC changes during a period of 26 years (1987–2013) were investigated. A multi-temporal set of images was processed using Landsat images from Landsat4 TM 1987, Landsat7 ETM+2000, and Landsat8 to investigate the drivers responsible for the total VC pattern and changes, which are linked to both natural and social processes. The analyses of the three satellite images concluded that the surface area of the total VC increased by 107.4 % between 1987 and 2000 and decreased by 27.5 % between years 2000 and 2013. The field study, review of secondary data, and community problem diagnosis using the participatory rural appraisal (PRA) method suggested that the drivers for this change are the deterioration and salinization of both soil and water resources. Ground truth data indicated that the deteriorated soils in the eastern part of the Al-Kharj AE are frequently subjected to sand dune encroachment, while the southwestern part is frequently subjected to soil and groundwater salinization. The groundwater in the western part of the ecosystem is highly saline, with a salinity  ≥ 6 dS m−1. The ecosystem management approach applied in this study can be used to alike AE worldwide.

scholarly journals Image Segmentation Techniques Using K-Means Clustering to Identify the Land Use Change Detection

2021 ◽  
Vol 10 (2) ◽  
pp. 1200-1206
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Segmentation ◽  
Land Use Change ◽  
Change Detection ◽  
Ground Truth ◽  
Important Application ◽  
Major Objective ◽  
Ground Truth Data ◽  
Point Of Interest

The change detection of the agriculture land and other land useis one of the important application of remote sensing imagery.The major objective of this paper tomeasure the different boundary regionsof the land classes using an image segmentation techniques. The initial categorizing of different land use classes is experimented by using k-means clustering, which basically clusters the point of interest with the pixel similarity. The measurement of the different pixel region represent the different classes of agriculture area is a challenging task with the real and synthetic images. The important characteristics of the algorithm preserves the cluster pixel details at most of the iterations, however for the similar canopy values the cluster effeminacy varies and the identification of the land clusters also deviates as compared with the ground truth data.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Dereje Gebrie Habte ◽  
Satishkumar Belliethathan ◽  
Tenalem Ayenew
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Catchment Area ◽  
Environmental Conservation ◽  
Land Use Land Cover ◽  
Cultivated Land ◽  
Plantation Forest ◽  
Bare Land

AbstractEvaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

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