Land Use and Land Cover Classification for Bangladesh 2005 on Google Earth Engine

Yu, Zhiqi; Di, Liping; Tang, Jinshan; Zhang, Chen; Lin, Li; Yu, Genong; Rahman, Md. Shahinoor; Gaigalas, Juozas; Sun, Ziheng

doi:10.1109/agro-geoinformatics.2018.8475976

Cited by 19 publications

(14 citation statements)

References 12 publications

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“…The Surface Reflectance (annual median pixel) of the multispectral bands (Blue, Green, Red, near-infrared (NIR), Short-wave infrared (SWIR) 1 and Short-wave infrared (SWIR) 2), was extracted based on the GEE algorithms. Our assumption here was that the annual median pixel provides precise information regarding the land cover in the study area [58]. In this section, we used less than 10% cloud cover images.…”

Section: Datamentioning

confidence: 99%

Spatiotemporal Variation of Urban Heat Islands for Implementing Nature-Based Solutions: A Case Study of Kurunegala, Sri Lanka

Ranagalage

Ratnayake

Dissanayake

et al. 2020

IJGI

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Changes in the urban landscape resulting from rapid urbanisation and climate change have the potential to increase land surface temperature (LST) and the incidence of the urban heat island (UHI). An increase in urban heat directly affects urban livelihoods and systems. This study investigated the spatiotemporal variation of the UHI in the Kurunegala urban area (KUA) of North-Western Province, Sri Lanka. The KUA is one of the most intensively developing economic and administrative capitals in Sri Lanka with an urban system that is facing climate vulnerabilities and challenges of extreme heat conditions. We examined the UHI formation for the period 1996–2019 and its impact on the urban-systems by exploring nature-based solutions (NBS). This study used annual median temperatures based on Landsat data from 1996 to 2019 using the Google Earth Engine (GEE). Various geospatial approaches, including spectral index-based land use/cover mapping (1996, 2009 and 2019), urban-rural gradient zones, UHI profile, statistics and grid-based analysis, were used to analyse the data. The results revealed that the mean LST increased by 5.5 °C between 1996 and 2019 mainly associated with the expansion pattern of impervious surfaces. The mean LST had a positive correlation with impervious surfaces and a negative correlation with the green spaces in all the three time-points. Impacts due to climate change, including positive temperature and negative rainfall anomalies, contributed to the increase in LST. The study recommends interactively applying NBS to addressing the UHI impacts with effective mitigation and adaptation measures for urban sustainability.

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Section: Datamentioning

confidence: 99%

Spatiotemporal Variation of Urban Heat Islands for Implementing Nature-Based Solutions: A Case Study of Kurunegala, Sri Lanka

Ranagalage

Ratnayake

Dissanayake

et al. 2020

IJGI

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“…Remote sensing imagery classification is one of the most popular techniques in land use/land cover studies [102,[120][121][122]. Studies have combined remote sensing classification with ontology from local experts to improve the accuracy of the identification of poor communities using remote sensing images [88].…”

Section: Mapping and Monitoring Poverty Using Satellite Data And Gismentioning

confidence: 99%

“…Many researchers use NTL data to represent socioeconomic status [139,141,142]. Knowledge learned by CNN could be transferred to related problems through transfer learning [120,125,133]. NTL has been proven to be correlated with poverty and has been adopted in poverty research in developing countries [134,143,144].…”

Section: Poverty Identification With Machine Learningmentioning

confidence: 99%

Remote Sensing of Urban Poverty and Gentrification

Lin

Zhang

et al. 2021

Remote Sensing

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In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.

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“…However, the growing aquaculture puts pressure on already limited croplands. In recent years, a great portion of croplands in Bangladesh has been gradually transforming to other land use types, such as fishponds, brickyards, and residential area [1,4]. While Bangladesh Statistical Bureau (BSB) publishes statistical yearbooks every year, it lacks information of the spatial distribution of the land use changes.…”

Section: Introductionmentioning

confidence: 99%

Fishpond Mapping by Spectral and Spatial-Based Filtering on Google Earth Engine: A Case Study in Singra Upazila of Bangladesh

Yu¹,

Di²,

Rahman³

et al. 2020

Remote Sensing

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Inland aquaculture in Bangladesh has been growing fast in the last decade. The underlying land use/land cover (LULC) change is an important indicator of socioeconomic and food structure change in Bangladesh, and fishpond mapping is essential to understand such LULC change. Previous research often used water indexes (WI), such as Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), to enhance water bodies and use shape-based metrics to assist classification of individual water features, such as coastal aquaculture ponds. However, inland fishponds in Bangladesh are generally extremely small, and little research has investigated mapping of such small water objects without high-resolution images. Thus, this research aimed to bridge the knowledge gap by developing and evaluating an automatic fishpond mapping workflow with Sentinel-2 images that is implemented on Google Earth Engine (GEE) platform. The workflow mainly includes two steps: (1) the spectral filtering phase that uses a pixel selection technique and an image segmentation method to automatically identify all-year-inundated water bodies and (2) spatial filtering phase to further classify all-year-inundated water bodies into fishponds and non-fishponds using object-based features (OBF). To evaluate the performance of the workflow, we conducted a case study in the Singra Upazila of Bangladesh, and our method can efficiently map inland fishponds with a precision score of 0.788. Our results also show that the pixel selection technique is essential in identifying inland fishponds that are generally small. As the workflow is implemented on GEE, it can be conveniently applied to other regions.

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Land Use and Land Cover Classification for Bangladesh 2005 on Google Earth Engine

Cited by 19 publications

References 12 publications

Spatiotemporal Variation of Urban Heat Islands for Implementing Nature-Based Solutions: A Case Study of Kurunegala, Sri Lanka

Spatiotemporal Variation of Urban Heat Islands for Implementing Nature-Based Solutions: A Case Study of Kurunegala, Sri Lanka

Remote Sensing of Urban Poverty and Gentrification

Fishpond Mapping by Spectral and Spatial-Based Filtering on Google Earth Engine: A Case Study in Singra Upazila of Bangladesh

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