Mapping and Quantification of Land Area and Cover Types with LandsatTM in Carey Island, Selangor, Malaysia

Kamaruzaman, J.; Hasmadi, I. Mohd

doi:10.5539/mas.v3n1p42

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Klasifikavimo tikslumas įvertintas pagal kappa koeficientą (Bektas 2003;Kamaruzaman, Mohd Hasmadi 2009), kuris taikomas tada, kai abiejų kintamųjų kategorijų reikšmės tos pačios, ir vienodas kategorijų skaičius. Kappa koeficientas yra statistinis matas, šiuo atveju taikomas dviem klasifikavimo metodais gautiems rezultatams palyginti (Senseman et al 1995).…”

Section: Tikslumo Vertinimasunclassified

Comparison of Methods for Landsat Tm Satellite Image Segmentation in Land Cover Classification / Landsat Tm Kosminių Nuotraukų Segmentavimo Metodų Palyginimas Atliekant Žemės Dangos Klasifikavimą / Сравнение Методoв Сегментации В Космических Изображениях Landsat Tm При Классификации Земельного Полога

Milieškaitė

Vaitkus

2011

Geodesy and Cartography

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Santrauka.Kosminiai vaizdai yra vienas iš informacijos šaltinių apie žemės paviršiuje esančius objektus, tačiau praktiškai kosminiai vaizdai Lietuvoje naudojami dar neseniai. Pateiktas kosminio vaizdo segmentavimas taikant vieną iš skaitmeninių vaizdų klasifikavimo metodų -kontroliuojamąjį klasifikavimą (angl. supervised classification). Landsat TM skaitmeninio vaizdo klasifikavimas (nusistatytų klasių išskyrimas) atliktas taikant daugiaspektrius SMAP (sequential maximum a posteriori) ir MCL ("maximum likelihood" classifier) segmentavimo algoritmus geografinės informacinės sistemos GRASS (geographic resources analysis support system -geografinių duomenų analizės palaikymo sistema) aplinkoje. Klasifikavimo tikslumui įvertinti skaičiuota klaidų matrica (confusion ar error matrix) ir kappa koeficientas. Tyrimų duomenys rodė, kad nuotolinio stebėjimo ir GIS technika yra puiki priemonė Žemės dangos tipams identifikuoti ir plotams apskaičiuoti, tačiau aprašytaisiais metodais gautiems rezultatams įvertinti tiksliau būtini lauko tyrimai vietovėje.Reikšminiai žodžiai: kontroliuojamasis klasifikavimas, Landsat TM skaitmeninis vaizdas, segmentavimas, žemės dangos klasifikavimas, SMAP (a posteriori nuoseklusis maksimumas), MLC ("didžiausiojo panašumo" klasifikatorius).

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Section: Tikslumo Vertinimasunclassified

Comparison of Methods for Landsat Tm Satellite Image Segmentation in Land Cover Classification / Landsat Tm Kosminių Nuotraukų Segmentavimo Metodų Palyginimas Atliekant Žemės Dangos Klasifikavimą / Сравнение Методoв Сегментации В Космических Изображениях Landsat Tm При Классификации Земельного Полога

Milieškaitė

Vaitkus

2011

Geodesy and Cartography

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“…Remote sensing can also offer an exceptional opportunity to complete and update existing mapped area in a given location. This has been applied in generating data on LULC changes in the many coastal areas of the world (Kamaruzaman & Hasmadi, ; Alphan, ), whereas such spatio‐temporal data are not available for the coastal area of Bangladesh. Therefore, the objectives of this study were to as follows: (i) quantify the long‐term changes in LULC using satellite images and (ii) discuss the impact of land use changes in the specific coastal zones of Bangladesh.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Land use and Land Cover Changes in the Coastal Area of Bangladesh using Landsat Imagery

2015

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Coastal land use across the globe has experienced remarkable rapid change over the recent decades because of extraordinary anthropogenic pressure and climate variability and change. Therefore, quantitative information about coastal land use change is imperative for effective management and planning resources for sustainable development. We analysed the quantitative land use and land cover changes during 1989-2000-2010 periods in three important agroecological zones of the most vulnerable coastal region of Bangladesh using Landsat images (Thematic Mapper/Enhanced Thematic Mapper Plus). In the Ganges Tidal Floodplain, the area under shrimp cultivation greatly increased at the rate of 2·05% per annum. The majority of the shrimp area gained from conversion of single cropland. In the Meghna Estuarine Floodplain, decreased mudflat and water bodies were observed, which was predominantly converted into cropland. In Chittagong Coastal Plain, salt pan-shrimp area increased with the expense of single and/or double cropland. In all the study areas, settlement area considerably increased over time. The dynamics of land use change have been attributed to low and unstable food production in the coastal region. The approach adopted in study and the results obtained from the study would likely be useful for policy making and identifying direction for future studies on the coastal land use in Bangladesh.

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“…Elsewhere in the world, more complicated surface features may be required to incorporate in the model (e.g. Hj Kamaruzaman & Mohd Hasmadi, 2009). The under-resolved surface structures can be incorporated in the model boundary conditions in three ways: traditional parameterization methodologies (e.g.…”

Section: Introductionmentioning

confidence: 99%

On Application of Artificial Neural Network Methods in Large-eddy Simulations with Unresolved Urban Surfaces

Esau

2010

MAS

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Micro-meteorological aspects of city comfort, land use management and air quality monitoring are rapidly growing areas of applications where environmental turbulence-resolving or large-eddy simulation (LES) models play the central role. Complex details of the urban surface morphology however remain unresolved or poorly resolved in the state-of-the-art LES due to severe limitations from computer facilities. The LES code LESNIC is applied in this study to simulate turbulent flow interaction with elements of the urban surface morphology. The study investigates a possibility to utilize a trained three layers’ artificial neural network (ANN) to parameterize the flow-to-surface interactions in the coarse LES where surface features are unresolved. It is concluded that the ANN can be a robust predictor for scalar concentrations and components of the surface stress tensor in the urban sub-layer with unresolved scalar sources and surface morphology. It has been noted however that dynamic training of the ANN may require more computational resources than adequate refinement of the LES resolution.

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Mapping and Quantification of Land Area and Cover Types with LandsatTM in Carey Island, Selangor, Malaysia

Cited by 3 publications

References 3 publications

Analysis of Land use and Land Cover Changes in the Coastal Area of Bangladesh using Landsat Imagery

On Application of Artificial Neural Network Methods in Large-eddy Simulations with Unresolved Urban Surfaces

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