干旱区生态环境知识资源中心(Arid): Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India

Arid

DOI	10.1016/j.ufug.2021.127445
	Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India
	Uniyal, Swati; Purohit, Saurabh; Chaurasia, Kuldeep; Amminedu, Eadara; Rao, Sitiraju Srinivas
通讯作者	Uniyal, S
来源期刊	URBAN FORESTRY & URBAN GREENING
ISSN	1618-8667
EISSN	1610-8167
出版年	2022
卷号	67
英文摘要	Urban forests play a significant role in carbon cycling. Quantification of Aboveground Biomass (AGB) is critical to understand the role of urban forests in carbon sequestration. In the present study, Machine learning (ML) based regression algorithms (SVM, RF, kNN and XGBoost) have been taken into account for spatial mapping of AGB and carbon for the urban forests of Jodhpur city, Rajasthan, India, with the aid of field-based data and their correlations with spectra and textural variables derived from Landsat 8 OLI data. A total of 198 variables were retrieved from the satellite image, including bands, Vegetation Indices (VIs), linearly transformed variables, and Grey Level Co-occurrence textures (GLCM) taken as independent input variables further reduced to 29 variables using Boruta feature selection method. All the models have been compared where with RF algorithm, R-2 = 0.83, RMSE = 16.22 t/ha and MAE = 11.86 t/ha. For kNN algorithm R-2 = 0.77, RMSE = 28.04 t/ha and MAE = 24.24 t/ha and SVM where R-2 = 0.73, RMSE = 89.21 t/ha and MAE = 74.22 t/ha and the best prediction accuracy has been noted with XGBoost algorithm (R-2 = 0.89, RMSE = 14.08 t/ha and MAE = 13.66 t/ha) with predicted AGB as 0.51-153.76 t/ha. The study indicates that ML-based regression algorithms have great potential over other linear and multiple regression techniques for spatial mapping of AGB and carbon of urban forests for arid regions.
英文关键词	Aboveground biomass Carbon Landsat 8 OLI Machine learning Urban forests
类型	Article
语种	英语
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000789613300005
WOS关键词	ABOVEGROUND BIOMASS ; ALLOMETRIC EQUATIONS ; TREE ALLOMETRY ; STORAGE ; AFRICA ; MODELS
WOS类目	Plant Sciences ; Environmental Studies ; Forestry ; Urban Studies
WOS研究方向	Plant Sciences ; Environmental Sciences & Ecology ; Forestry ; Urban Studies
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394755
推荐引用方式 GB/T 7714	Uniyal, Swati,Purohit, Saurabh,Chaurasia, Kuldeep,et al. Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India[J],2022,67.
APA	Uniyal, Swati,Purohit, Saurabh,Chaurasia, Kuldeep,Amminedu, Eadara,&Rao, Sitiraju Srinivas.(2022).Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India.URBAN FORESTRY & URBAN GREENING,67.
MLA	Uniyal, Swati,et al."Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India".URBAN FORESTRY & URBAN GREENING 67(2022).