• Journal of Forest and Environmental Science
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• v.37 no.1
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• pp.44-51
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• 2021

Rapid urbanization and population growth have caused substantial land use land cover (LULC) change in the Kathmandu valley. The lack of temporal and geographical data regarding LULC in the middle mountain region like Kathmandu has been challenging to assess the changes that have occurred. The purpose of this study is to investigate the changes in LULC in Chandragiri Municipality between 1996 and 2017 using geographical information system (GIS) and remote sensing. Using Landsat imageries of 1996 and 2017, this study analyzed the LULC change over 21 years. The images were classified using the Maximum Likelihood classification method and post classified using the change detection technique in GIS. The result shows that severe land cover changes have occurred in the Forest (11.63%), Built-up areas (3.68%), Agriculture (-11.26%), Shrubland (-0.15%), and Bareland (-3.91%) in the region from 1996 to 2017. This paper highlights the use of GIS and remote sensing in understanding the changes in LULC in the south-west part of Kathmandu valley.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.1
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• pp.69-88
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• 2023

Efficient spatial planning is one of the necessary factors to successfully respond to climate change. And researchers often use LULC(Land-Use/Cover) data to conduct land use and spatial planning research. However, LULC data has a limited number of grades related to urban surface, so each different urban structure appearing in several cities is not easily analyzed with existing land cover products. This limitation of land cover data seems to be overcome through LCZ(Local Climate Zone) data used in the urban heat island field. Therefore, this study aims to first discuss whether LCZ data can be applied not only to urban heat island fields but also to other fields, and secondly, whether LCZ data still have problems with existing LULC data. Research methodology is largely divided into two categories. First, through literature review, studies in the fields of climate, land use, and urban spatial structure related to LCZ are synthesized to analyze what research LCZ data is currently being used, and how it can be applied and utilized in the fields of land use and urban spatial structure. Next, the GIS spatial analysis methodology is used to analyze whether LCZ still has several errors that are found in the LULC.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.353-364
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• 2016

Yangon city is experienced a rapid urban expansion over the last two decades due to accelerate with the socioeconomic development. This research work studied an investigation into the application of the integration of the Remote Sensing (RS) and Geographic Information System (GIS) for observing Land Use and Land Cover (LULC) patterns and evaluate its impact on Land Surface Temperature (LST) of the downtown, suburban 1 and suburban 2 of Yangon city. The main purpose of this paper was to examine and analyze the variation of the spatial distribution property of the LULC of urban spatial information related with the LST and Normalized Difference Vegetation Index (NDVI) using RS and GIS. This paper was observed on image processing of LULC classification, LST and NDVI were extracted from Landsat 8 Operational Land Imager (OLI) image data. Then, LULC pattern was linked with the variation of LST data of the Yangon area for the further connection of the correlation between surface temperature and urban structure. As a result, NDVI values were used to examine the relation between thermal behavior and condition of land cover categories. The spatial distribution of LST has been found mixed pattern and higher LST was located with the scatter pattern, which was related to certain LULC types within downtown, suburban 1 and 2. The result of this paper, LST and NDVI analysis exhibited a strong negative correlation without water bodies for all three portions of Yangon area. The strongest coefficient correlation was found downtown area (-0.8707) and followed suburban 1 (-0.7526) and suburban 2(-0.6923).

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.39-48
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• 2016

Yangon Mega City is densely populated and most urbanization area of Myanmar. Rapid urbanization is the main causes of Land Use and Land Cover (LULC) change and they impact on Land Surface Temperature (LST). The objectives of this study were to investigate on the LST with respect to LULC of Yangon Mega City. For this research, Landsat satellite images of 1996, 2006 and 2014 of Yangon Area were used. Supervised classification with the region of interest and calculated change detection. Ground check points used 348 points for accuracy assessment. The overall accuracy indicated 89.94 percent. The result of this paper, the vegetation area decreased from $1061.08sq\;km^2$ (24.5%) in 1996 to $483.53sq\;km^2$ (11.2%) in 2014 and built up area clearly increased from $485.33sq\;km^2$ (11.2%) in 1996 to $1435.72sq\;km^2$ (33.1%) in 2014. Although the land surface temperature was higher in built up area and bare land, lower value in cultivated land, vegetation and water area. The results of the image processing pointed out that land surface temperature increased from $23^{\circ}C$, $26^{\circ}C$ and $27^{\circ}C$ to $36^{\circ}C$, $42^{\circ}C$ and $43.3^{\circ}C$ for three periods. The findings of this paper revealed a notable changes of land use and land cover and land surface temperature for the future heat management of sustainable urban planning for Yangon Mega city. The relationship of regression experienced between LULC and LST can be found gradually stronger from 0.8323 in 1996, 0.8929 in 2006 and 0.9424 in 2014 respectively.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.516-518
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• 2003

This study discusses an effort to build a model to link normalized difference vegetation indices (NDVI) and socio-economic indicators derived from village survey (1990, 1993, 1996, and 2000) statistical data in Bekasi, West Java, Indonesia. Socio-economics indicators of sub-district level, in this study the number of agricultural households (AH), are aggregated from village level data. NDVI from Landsat-TM resolution data (1989 and 1997) are computed to detect land use/land cover (LULC) dynamics in the sub-district areas. Attention is mainly paid on the examination of agricultural land cover changing in the sub-district level. NDVI measurements might be used to predict AH dynamics as showed by computed linear regression models.

• Journal of Forest and Environmental Science
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• v.39 no.3
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• pp.167-179
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• 2023

The extent of change in the Land use/Land cover (LULC) of Okomu National Park (ONP) and fringe communities was evaluated. High resolution Landsat imagery was used to identify the major vegetation cover/land use systems and changes around the national park and fringe communities while field visits/ground truthing, involving the collection of coordinates of the locations was carried out to ascertain the various land cover/land use types identified on the images, and the extent of change over three-time series (2000, 2010 and 2020). The change detection was analyzed using area calculation, change detection by nature and normalized difference vegetation index (NDVI). The result of the classification and analysis of the LULC Change of ONP and fringe communities revealed an alarming rate of encroachment into the protected area. All the classification features analyzed had notable changes from 2000-2020. The forest, which was the dominant LULC feature in 2000, covering about 66.19% of the area reduced drastically to 36.12% in 2020. Agricultural land increased from 6.14% in 2000 to 34.06% in 2020 while vegetation (degraded land) increased from 27.18% in 2000 to 38.89% in 2020. The magnitude of the change in ONP and surroundings showed the forest lost -247.136 km² (50.01%) to other land cover classes with annual rate change of 10%, implying that 10% of forest land was lost annually in the area for 20 years. The NDVI classification values of 2020 indicate that the increase in medium (399.62 km² ) and secondary high (210.17 km² ) vegetation classes which drastically reduced the size of the high (38.07 km² ) vegetation class. Consequent disappearance of the high forests of Okomu is inevitable if this trend of exploitation is not checked. It is pertinent to explore other forest management strategies involving community participation.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.111-126
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• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.141-153
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• 2016

Reliable assessment of Land Use and Land Cover (LULC) changes greatly improves many practical issues in hydrography, socio-geographical research such as the observation of erosion and accretion, coastal monitoring, ecological effects evaluation. Remote sensing imageries can offer the outstanding capability to monitor nature and extent of land and associated changes over time. Nowadays accurate analysis using remote sensing imageries with high spatio-temporal resolution is required for environmental monitoring. This study develops a methodology of mapping and change detection in LULC by using classified Korea Multi-Purpose Satellite-2 (KOMPSAT-2) multispectral imageries at Jeonbuk and Jeonnam provinces including protected tidal flats located in the west coasts of Korean peninsula from 2008 to 2015. The LULC maps generated from unsupervised classification were analyzed and evaluated by post-classification change detection methods. The LULC assessment in Jeonbuk and Jeonnam areas had not showed significant changes over time although developed area was gradually increased only by 1.97% and 4.34% at both areas respectively. Overall, the results of this study quantify the land cover change patterns through pixel based analysis which demonstrate the potential of multispectral KOMPSAT-2 images to provide effective and economical LULC maps in the coastal zone over time. This LULC information would be of great interest to the environmental and policy mangers for the better coastal management and political decisions.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.3
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• pp.196-210
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• 2009

The land surface precesses is very important to predict urban meteorological conditions. Thus, the latest land use data set to reflect the rapid progress in urbanization was applied to simulate urban thermal environment in Daegu. Because use of the U.S geological Survey (USGS) 25-category data, currently in the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5), does not accurately described the heterogeneity of urban surface, we replaced the land use data in USGS with the latest land-use data of the Korea Ministry of Environment over Daegu. The single urban category in existing 24-category U.S. Geological survey land cover classification used in MM5 was divided into 5 classes to account for heterogeneity of urban land cover. The new land cover classification (MC-LULC) improved the capability of MM5 to simulate the daytime part of the diurnal temperature cycle in the urban area. The 'MC-LULC' simulation produced the observed temperature field reasonably well, including spatial characteristics. The warm cores in western Daegu is characterized by an industrial area.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.