Carbon Emissions from Land-Use Change: Model Estimates Using Three Different Data Sets

Meiyappan, Prasanth; Richardson, T. K.

doi:10.1017/cbo9780511894824.013

Cited by 3 publications

(5 citation statements)

References 28 publications

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“…The third approach disaggregates global CE data to a finer resolution relating to the indicators describing the built environment and industrial activities. This is because there was a strong alignment between the surface fluxes of atmospheric CO 2 and bottom-up inventories [92,93] or urban activity indicators like land use [94,95] and road length [96]. On the one hand, nighttime light (NTL) images are found to reflect human activities correlated with energy consumption.…”

Section: Conventional Urban Energy Modelsmentioning

confidence: 99%

“…Meanwhile, SVI data are publicly available and frequently updated to capture groundlevel panorama street scenes [114]. SVIs are an ideal dataset to comprehensively describe the urban environmental variability [115] and citizen behaviors, including building height [116], streetscape features [117], green and water systems [118], land-use classification [94,119,120], openness [121], road networks [122], mobile monitoring [98], mobility patterns [123,124], sun-glare-related traffic crashes [125], land use [79,[126][127][128], and residential behavior [129].…”

Section: Svis For Urban Form Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Remote Sensing

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Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an effective machine learning (ML) framework to predict neighborhood-level residential CEs based on a single data source, street view images (SVIs), which are publicly available worldwide. Specifically, more than 30 streetscape elements were classified from SVIs using semantic segmentation to describe the micro-level street environment, whose visual features can indicate major socioeconomic activities that significantly affect residential CEs. A ten-fold cross-validation was deployed to train ML models to predict the residential CEs at the 1 km grid level. We found, first, that random forest (R2 = 0.8) outperforms many traditional models, confirming that visual features are non-negligible in explaining CEs. Second, more building, wall, and fence views indicate higher CEs. Third, the presence of trees and grass is inversely related to CEs. Our findings justify the feasibility of using SVIs as a single data source to effectively predict neighborhood-level residential CEs. The framework is applicable to large regions across diverse urban forms, informing urban planners of sustainable urban form strategies to achieve carbon-neutral goals, especially for the development of new towns.

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Section: Conventional Urban Energy Modelsmentioning

confidence: 99%

Section: Svis For Urban Form Modelingmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Remote Sensing

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“…The third approach disaggregate global CE data to a finer resolution relating to the indicators describing the built environment and industrial activities. It's because there was a strong alignment between surface fluxes of atmospheric CO2 and bottom-up inventories (Schuh et al, 2013;Ogle et al, 2015) or urban activities indicators like land use (Jain, Meiyappan and Richardson, 2013;Chuai and Feng, 2019) and road length (Song et al, 2021). On the one hand, nighttime light (NTL) image is found to reflect human activities correlated with energy consumption.…”

Section: Conventional Urban Energy Modelsmentioning

confidence: 99%

“…SVI is an ideal dataset to comprehensively describe the urban environmental variability. For example, it has been used to model buildings (Gurney et al, 2012) including building height (Yan and Huang, 2022), streetscape features (Wang, Liu and Gou, 2022), green and water systems (Jiang, Jiang and Shi, 2020), land use classification (Jain, Meiyappan and Richardson, 2013;Tian, Han and Xu, 2021;Fang et al, 2022), the openness (Xia, Yabuki and Fukuda, 2021), road network (Zhang et al, 2023), mobile monitoring (Sun et al, 2017) and POI (Gao, Janowicz and Couclelis, 2017;Huang et al, 2022;Song et al, 2022;X. Xu, Qiu, Li, Liu, et al, 2022).…”

Section: Street View Image and Ai To Model Urban Formsmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

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“…Land use and land cover changes (LULCC) is the key topic in global change studies since they can alter regional and global climate through changing biophysical, biogeochemical, and biogeographical characteristics of the Earth system (Jain et al 2013;Robinson et al 2013;Xu et al 2016). Understanding LULCC dynamics and drivers can help to better understand the LULCC processes and mechanisms to develop models and land policies for a country.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and drivers of land use and land cover changes in Bangladesh

Xu¹,

Shrestha²,

Gilani³

et al. 2020

Reg Environ Change

Self Cite

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Bangladesh has undergone dramatic land use and land cover changes (LULCC) in recent years, but no quantitative analysis of LULCC drivers at the national scale exists so far. Here, we quantified the drivers of major LULCC in combination with biophysical and socioeconomic observations at the sub-district level. We used Landsat satellite data to interpret LULCC from 2000 to 2010 and employed a Global Surface Water Dataset to account for the influences of water seasonality. The results suggest that major LULCC in Bangladesh occur between agricultural land and waterbodies and between forest and shrubland. Exclusion of seasonal waterbodies can improve the accuracy of our LULCC results and driver analysis. Although the gross gain and loss of agricultural land are large on the local scale, the net change (gross gain minus gross loss) at a country scale is almost negligible. Climate dynamics and extreme events and changes in urban and rural households were driving the changes from forest to shrubland in the southeast region. The conversion from agricultural land to standing waterbodies in the southwest region was mainly driven by urban household dynamics, population growth, distance to cities and major roads, and precipitation dynamics. This study, which is the first effort accounting for water seasonality and quantifying biophysical and socioeconomic drivers of LULCC at the national scale, provides a perspective on overall LULCC and underlying drivers over a decadal time scale and national spatial scale and can serve as a scientific basis for developing land policies in Bangladesh.

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Carbon Emissions from Land-Use Change: Model Estimates Using Three Different Data Sets

Cited by 3 publications

References 28 publications

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Dynamics and drivers of land use and land cover changes in Bangladesh

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