scholarly journals Land use classification and land use change analysis using satellite images in Lojing, Kelantan.

2019 ◽  
Vol 7 (2) ◽  
pp. 53-60
Author(s):  
Jacqueline John Hiew ◽  
Amal Najihah M. Nor ◽  
Nur Hairunnisa Rafaai ◽  
Nur Hanisah Abdul Malek ◽  
Hasifah Abdul Aziz ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Land Use Changes ◽  
Unsupervised Classification ◽  
United States Geological Survey ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Change Analysis

Remote sensing is widely used to capture the images of land use/land cover on earth. This paper studies on the land use changes in Lojing, Kelantan in 1989 dan 2006. The land use is then classified, and the classification scheme was adopted from United States Geological Survey (USGS) Land Use/ Land Cover Classification System. Supervised classification method has been used since it was proved by other research to be more accurate compared to unsupervised classification. Accuracy assessment was conducted to calculate the accuracy of the land use map produced so that at the end, a good quality of land use map is produced. The findings of this study is that, there had been an insignificant land use changes between the year 1989 and 2006. The conclusion is, Lojing had been experiencing changes in term of land use due to the increased socioeconomic activities especially agriculture and logging at the highlands of Lojing.

scholarly journals Remote Sensing and GIS Application for Land use/ Land cover Change Analysis: Case Study of Jarmet Wet-Land and its Surrounding Environments in Western Ethiopia

2019 ◽  
Author(s):  
Amanuel Kumsa ◽  
Professor Sileshi Nemomissa ◽  
Asmamaw (PhD) Legas ◽  
Dessalegn Gurmessa
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Environmental Change ◽  
Land Cover Change ◽  
Natural Resource ◽  
Accuracy Assessment ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Lulc Change

Wetlands are one of the crucial natural resources. They provide invaluable biodiversity resources, aid in water quality improvement, support ground water recharge, help in moderating climate change and support flood control. Environment is in the other hand, where we live and something, we are very familiar with our day to day life. Geographic Information Systems (GIS), Remote Sensing and Global Positioning System (GPS) were a useful tool for wetland and environmental change analysis and to improve on the classification accuracy. This study investigates population and environmental change of Jarmet wetland and its surrounding area change analysis over the period of 1972 to 2015. The purpose of this study was to show land use/ land cover change of Jarmet wetland and its surrounding environment over years as a response to population growth. For this purpose, multi-temporal satellite imageries (Landsat MSS 1972, TM1986, ETM+ 2000, 2005 and 2015 and SRTM 2000) were obtained and used for LULC change analysis, elevation analysis and change detection analysis. ERDAS Imagine 2015, ARC GIS 10.5.1, Global Mapper11, ENVI 5.0 and DNR Garmin softwares were used to process the image data and accuracy assessment analysis. The result of LULC showed that there is spatial reduction in wetland, forest, Shrubland and grassland in the period of 43 years (1972-2015) by -1,722.8 ha, -296.2 ha, -1,718.7 ha and -661.9 ha respectively, due to increase in the farmland and plantation area as a response to overpopulation, lack of environmental policy implementation and irresponsible for natural resource degradation. The accuracy assessment of LULC change are done for recent satellite image showed the overall accuracy of 84.06% with Kappa index 75.19% this means this classification is accurately classified and handle greater than 75% of error. Finally, this study suggests that create strictly natural resource conservation law, stopping illegal expansion of farmland, educating society about the value of natural resource especially wetland and create a source of income for society rather than farming.

scholarly journals Land Use/Land Cover Change of Rabi Season of Rahuri Taluka of Ahmednagar District Maharashtra

2020 ◽  
pp. 20-27
Author(s):  
Payal A. Mahadule ◽  
A. A. Atre ◽  
Ankita P. Kamble ◽  
C. Pande ◽  
S. D. Gorantiwar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
United States Geological Survey ◽  
Fallow Land ◽  
Land Use Land Cover ◽  
Rabi Season ◽  
Agriculture Land ◽  
Spatio Temporal ◽  
Sentinel 2A

Advanced change location procedures by utilizing multi-temporal satellite symbolism helps in understanding landscape dynamics. The present examination shows the spatio- temporal elements of land use of Rahuri Taluka, Ahmednagar District, Maharashtra, India.  Sentinel 2A satellite imageries of four different months of Rabi season (2019-2020) were acquired by United States Geological Survey (USGS) earth explorer site and quantify the changes in the Rahuri Taluka from October 2019 to January 2020 over a period of 3 months.This study applied supervised classification-maximum likelihood algorithm by using Arc GIS 10.1 Map envision to distinguish land use changes of Rahuri. Land Use/Land Cover (LULC) in the Rahuri has experienced a progression of changes in the course of the last three months. Four significant LULC classes viz; Water body, Built-up Land, Waste/Fallow land, Agriculture land have been distinguished and demonstrate that significant land use in the Rahuri Taluka. Results appears, water bodies was highest in month of October 15.68% (166.48 km2), Agriculture land was highest in month of November 59.77% (634.56 km2) and Waste/Fallow land was significantly higher in month of October 41.1% (437.47 km2) and December 41.7% (442.77 km2) than November 30.54% (324.28 km2). The examination and discoveries of the investigation features significant approach suggestions for the maintainable Land Use/Land Cover the board in the Rahuri.

scholarly journals DISTRICT-WISE CHANGE ANALYSIS OF LAND USE-LAND COVER IN DELHI TERRITORY USING REMOTE SENSING & GIS

2017 ◽  
Vol 10 (2) ◽  
pp. 201-213
Author(s):  
Surya Prakash Pattanayak ◽  
Sumant Kumar Diwakar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
High Growth ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Multi Temporal

Digital change detection is the process that helps in determining the changes associated with Land use and Land cover properties with reference to geo-referenced multi-temporal remote sensing data. It helps in identifying change between two or more dates that is uncharacterized of normal variation. This work is an attempt to assess the district-wise changes in land use/land cover in Delhi, India. The study made use of LISS -III imageries of 2008 and 2012 year. The images were classified using Maximum Likelihood classification method. The output can be useful in many applications such as Land use changes, habitat fragmentation, rate of deforestation, urban sprawl and other cumulative changes through spatial and temporal analysis. The study shows that Delhi land cover from 2008 to 2012 a major rapid changes in the landscape as there is high growth in the fallow and built up area. Agriculture land and forest area has reduced marginally and water body is showing almost stagnant condition over time.

scholarly journals A COMPARATIVE STUDY OF ADVANCED LAND USE/LAND COVER CLASSIFICATION ALGORITHMS USING SENTINEL-2 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 665-670 ◽  
Author(s):  
K. Nivedita Priyadarshini ◽  
M. Kumar ◽  
S. A. Rahaman ◽  
S. Nitheshnirmal
Keyword(s):  
Land Use ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Unsupervised Classification ◽  
Sensor Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Land Use Land Cover ◽  
Sentinel 2A

<p><strong>Abstract.</strong> Land Use/ Land Cover (LU/LC) is a major driving phenomenon of distributed ecosystems and its functioning. Interpretation of remote sensor data acquired from satellites requires enhancement through classification in order to attain better results. Classification of satellite products provides detailed information about the existing landscape that can also be analyzed on temporal basis. Image processing techniques acts as a platform for analysis of raw data using supervised and unsupervised classification algorithms. Classification comprises two broad ranges in which, the analyst specifies the classes by defining the training sites called supervised classification where as automatically clustering of pixels to the defined number of classes namely the unsupervised classification. This study attempts to perform the LU/LC classification for Paonta Sahib region of Himachal Pradesh which is a major industrial belt. The data obtained from Sentinel 2A, from which the stacked bands of 10<span class="thinspace"></span>m resolution are only used. Various classification algorithms such as Minimum Distance, Maximum Likelihood, Parallelepiped and Support Vector Machine (SVM) of supervised classifiers and ISO Data, K-Means of unsupervised classifiers are applied. Using the applied classification results, accuracy assessment is estimated and compared. Of these applied methods, the classification method, maximum likelihood provides highest accuracy and is considered to be the best for LU/LC classification using Sentinel-2A data.</p>

scholarly journals TEXTURE ANALYSIS FOR LAND USE LAND COVER (LULC) CLASSIFICATION IN PARTS OF AHMEDABAD, GUJARAT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 275-279
Author(s):  
V. Nizalapur ◽  
A. Vyas
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Land Use Land Cover ◽  
Good Discrimination ◽  
Colour Composite ◽  
Single Polarization ◽  
Occurrence Matrix

Abstract. The present study addresses the potential of RADARSAT-2 data for Land Use Land Cover (LULC) Classification in parts of Ahmedabad, Gujarat, India. Texture measures of the original SAR data were obtained by the Gray Level Co-occurrence Matrix (GLCM). Results suggested False Colour Composite (FCC) of Mean, Homogeneity and Entropy showed a good discrimination of different land cover classes. Further, Principal Component Analysis (PCA) was also applied to the eight texture measures and FCC of Principal components is generated. Unsupervised classification is carried out for the above generated FCCs and accuracy assessment is carried out. The result of classification shows that the PCA generated from GLCM texture measures could obtain higher accuracy than using only the classification carried out by texture measures. Overall results of the study suggested possible use of single polarization and single date Radarsat-2 data for LULC classification with better accuracy using PCA generated image.

scholarly journals DISTRICT-WISE CHANGE ANALYSIS OF LAND USE-LAND COVER IN DELHI TERRITORY USING REMOTE SENSING & GIS

2017 ◽  
Vol 10 (2) ◽  
pp. 201-213
Author(s):  
Surya Prakash Pattanayak ◽  
Sumant Kumar Diwakar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
High Growth ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Multi Temporal

Digital change detection is the process that helps in determining the changes associated with Land use and Land cover properties with reference to geo-referenced multi-temporal remote sensing data. It helps in identifying change between two or more dates that is uncharacterized of normal variation. This work is an attempt to assess the district-wise changes in land use/land cover in Delhi, India. The study made use of LISS -III imageries of 2008 and 2012 year. The images were classified using Maximum Likelihood classification method. The output can be useful in many applications such as Land use changes, habitat fragmentation, rate of deforestation, urban sprawl and other cumulative changes through spatial and temporal analysis. The study shows that Delhi land cover from 2008 to 2012 a major rapid changes in the landscape as there is high growth in the fallow and built up area. Agriculture land and forest area has reduced marginally and water body is showing almost stagnant condition over time.

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

scholarly journals Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018 ◽  
Vol 10 (10) ◽  
pp. 3421 ◽  
Author(s):  
Rahel Hamad ◽  
Heiko Balzter ◽  
Kamal Kolo
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Cover ◽  
Land Use Changes ◽  
Landsat 8 ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Kurdistan Region ◽  
The Past ◽  
Past Trend

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

scholarly journals Land use changes in the hinterland of major Slovak cities

Geografie ◽  
2010 ◽  
Vol 115 (4) ◽  
pp. 413-439 ◽  
Author(s):  
Martin Šveda ◽  
Daniela Vigašová
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Structure ◽  
Land Use Changes ◽  
Detailed Comparison ◽  
Land Use Land Cover ◽  
Residential Areas ◽  
Shopping Centres ◽  
Agricultural Use ◽  
Land Types

The countryside around major Slovak cities is undergoing significant transformation. The construction of shopping centres, administrative buildings, logistical sites, residential areas and changes in the agricultural use of land are causing vast changes in land use (land cover). The objective of this paper is to examine changes in the spatial structure of land use in the hinterland of 11 Slovak cities, with more than 50 thousand inhabitants, during the period from 2000 to 2008. On the basis of a detailed comparison of data obtained from the Aggregated Areas of Land Types database (Úhrnné hodnoty druhov pozemkov) we analyzed changes in land use in 847 municipalities within the Functional Urban Regions of Bratislava, Košice, Prešov, Nitra, Žilina, Banská Bystrica, Trnava, Trenčín, Poprad and Prievidza. The results of the research confirmed significantly differentiated changes in land use. Whereas in the capital of Bratislava changes in land use are primarily caused by suburbanization, creating a relatively compact suburban zone, changes in land use were recorded only in selected sites in the rest of these major Slovak cities.

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