Classification of Landsat 8 Satellite Data Using Unsupervised Methods

Taufik, Afirah; Ahmad, Sharifah Sakinah Syed; Azmi, Ezzatul Farhain

doi:10.1007/978-981-13-6031-2_46

Cited by 29 publications

(41 citation statements)

References 6 publications

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“…The different thresholds value is used for extracting the respective water bodies. The threshold value is 0.2 in NDVI for distinguishing vegetation area from non-vegetation area (Taufik, Syed Ahmad, & Azmi, 2019). NDVI helps to determine the condition of the plant of different areas whether it is deteriorated, unchanged or improved (Wijitdechakul, Sasaki, Kiyoki, & Koopipat, 2017).…”

Section: Methodsmentioning

confidence: 99%

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Raut¹,

Chaudhary²,

Thapa³

2020

GEP

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Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.

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Section: Methodsmentioning

confidence: 99%

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Raut¹,

Chaudhary²,

Thapa³

2020

GEP

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“…To strengthen the management of land use, the identification area and purpose of land use are significant, including maps of land use and land cover status. Many methods of mapping land use and land cover, including using the threshold values of Normalised Difference Vegetation Index (NDVI) (Bhandari, et al, 2012;Ibrahim, et al, 2013;Taufik et al, 2016;Dalezios, et al, 2017;Hashim et al, 2019) have used GIS technology due to its high efficiency in information processing, easy change detection and continuous update information; and its detailed evaluation of coastal land use changes (Areendran et al, 2013;Mohammad et al, 2017). In Hai Phong City, coastal land use planning is a key step of the assessment in land use status (The Government of Vietnam, 1998, 2001, 2006.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Changes in Coastal Mangrove Extents Using Multi-Temporal Satellite Data in Selected Communes, Hai Phong City, Vietnam

Nguyen¹,

Tran²,

Le³

et al. 2020

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Mangrove forests are important and known as one of the most productive ecosystems in the tropics. They reduce the impacts of extreme events, provide important breeding grounds for aquatic species and build the resilience of ecosystem-dependent coastal communities. On the contrary, they are also known as one of the most threatened and vulnerable ecosystems worldwide, which have experienced a dramatic decline due to extensive coastal development during the last half-century. Remote sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and its changes, which is reflected by a large number of scientific papers published on this topic. The aim of this study was to investigate the multi-decadal changes of mangrove forests selected communes in Hai Phong city, North Vietnam, based on using Landsat and Sentinel 2 data from 2000 to 2018. The study used these continuous steps: 1) data pre-processing; 2) image classification using Normalized Difference Vegetation Index; 3) accuracy assessments; and 4) multi-temporal change detection and spatial analysis of mangrove forests. The classification maps in comparison with the ground reference data showed the satisfactory agreement with the overall accuracy was higher than 80.0%. From 2000 to 2018, the areas of mangrove forests in the study regions increased by 584.2 ha in Dai Hop and Bang La communes (Region 1) and by 124.2 ha in Tan Thanh, Ngoc Xuyen and Ngoc Hai communes (Region 2), mainly due to the boom of mangrove planting projects and good mangrove management at the local community level.

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“…As a result, the cover of the green spaces was sparse, especially around the centroid point. From determining the boundary for consideration around the centroid point covering an area of 7 km 2 , it indicated NDVI of 0.28, which represented the area at the level of low to medium vegetation cover (Taufik et al, 2016;Hashim et al, 2019).…”

Section: Results Of Ndvi Analysis and Buffer Zonesmentioning

confidence: 99%

The Relationship Between Normalized Difference Vegetation Index and Canopy Temperature That Affects the Urban Heat Island Phenomenon

Prohmdirek¹,

Chunpang²,

Laosuwan³

2020

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The objective of this research is to analyze the relationship between Normalized Difference Vegetation Index (NDVI) and canopy temperature that affects the Urban Heat Island (UHI) phenomenon in Mueang Maha Sarakham Municipality. The operational methods were divided into three main steps: 1) Analyzing NDVI by using Landsat 8 OLI satellite data 2) Monitoring canopy temperature with mobile traverses installed with digital thermometer and auto-record GPS receiver 3) Analyzing the relationship between NDVI and canopy temperature that passed through the spatial interpolation process using the kriging method. The results of the operational methods indicated that the trend of average atmospheric temperature in the urban area was higher than the outer area. Mueang Maha Sarakham Municipality had the highest UHI in the atmosphere at 4.65 o C. And from the buffer zone with a distance of 800 m around the centroid point, the highest UHI in the atmosphere was 5.08 o C. From the regression analysis between NDVI and the atmospheric temperature, it could be found a negative relationship between the data with a significance level of 0.01 and the decision coefficient (R 2) of 0.9392. This can be explained that if the area from the buffer zone decreased towards the centroid point, the value of NDVI decreased as well. In addition, when the equation set from the regression analysis was brought to predict the difference in atmospheric temperature in the area of Mueang Maha Sarakham Municipality and the surrounding area, the UHI sizes at all periods (16.00-20.00 hrs.) increased in accordance with the decreases of NDVI values. Meanwhile, considering the tendency between the changes in UHI size in the atmosphere in each period and NDVI values with the polynomial regression analysis (degree 4), it was found that the maximum formation of UHI was during 18.00-19.00 hrs.'. The decreases in NDVI values resulted in a higher UHI size in each period. In addition, when the NDVI values in Mueang Maha Sarakham Municipality were less than 0.302, the largest size of UHI in the atmosphere will change at the time approaching 19.00 hrs.

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Classification of Landsat 8 Satellite Data Using Unsupervised Methods

Cited by 29 publications

References 6 publications

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Monitoring Changes in Coastal Mangrove Extents Using Multi-Temporal Satellite Data in Selected Communes, Hai Phong City, Vietnam

The Relationship Between Normalized Difference Vegetation Index and Canopy Temperature That Affects the Urban Heat Island Phenomenon

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