المؤلفون: Chuai, Xiaowei, Xu, Hongbo, Liu, Zemiao, Xiang, Ai, Luo, Yuting, Mao, Wanliu, Wang, Tong, Ye, Xin, Miao, Lijuan, Zhao, Rongqin, Zhang, Fengtai

المصدر: Humanities & Social Sciences Communications; 6/4/2024, Vol. 11 Issue 1, p1-14, 14p

مصطلحات موضوعية: LAND use, CARBON cycle, CARBON emissions, LAND use planning, URBAN planning

مستخلص: Cities are main carbon emissions generators. Land use changes can not only affect terrestrial ecosystems carbon, but also anthropogenic carbon emissions. However, carbon monitoring at a spatial level is still coarse, and low-carbon land use encounters the challenge of being unable to adjust at the patch scale. This study addresses these limitations by using land-use data and various auxiliary data to explore new methods. The approach involves developing a high-resolution carbon monitoring model and investigating a patch-scale low-carbon land use model by integrating high carbon sink/source images with the Future Land Use Simulation model. Between 2000 and 2020, the results reveal an increasing trend in both carbon emissions and carbon sinks in the Shangyu district. Carbon sinks can only offset approximately 3% of the total carbon emissions. Spatially, the north exhibits net carbon emissions, while the southern region functions more as a carbon sink. A total of 14.5% of the total land area witnessed a change in land-use type, with the transfer-out of cropland constituting the largest area at 96.44 km², accounting for 50% of the total transferred area. Land-use transfer resulted in an annual increase of 77.72 × 10⁴ t in carbon emissions between 2000 and 2020. Through land-use structure optimisation, carbon emissions are projected to increase by only 7154 t C/year from 2000 to 2030, significantly lower than the amount between 2000 and 2020. Further low-carbon land optimisation at the patch scale can enhance the carbon sink by 129.59 t C/year. The conclusion drawn is that there is considerable potential to reduce carbon emissions through land use control. The new methods developed in our study can effectively contribute to high-resolution carbon monitoring in spatial contexts and support low-carbon land use, promoting the application of low-carbon land use from theory to practice. This will provide technological guidance for land use planning, city planning, and so forth. [ABSTRACT FROM AUTHOR]

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المؤلفون: Lu, Debin, Xu, Jianhua, Yue, Wenze, Mao, Wanliu, Yang, Dongyang, Wang, Jinzhu

المساهمون: Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road, National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

المصدر: Journal of Cleaner Production ; volume 244, page 118741 ; ISSN 0959-6526

الإتاحة: https://doi.org/10.1016/j.jclepro.2019.118741Test
https://api.elsevier.com/content/article/PII:S095965261933611X?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S095965261933611X?httpAccept=text/plainTest

المؤلفون: Gong, Peng, Chen, Bin, Li, Xuecao, Liu, Han, Wang, Jie, Bai, Yuqi, Chen, Jingming, Chen, Xi, Fang, Lei, Feng, Shuailong, Feng, Yongjiu, Gong, Yali, Gu, Hao, Huang, Huabing, Huang, Xiaochun, Jiao, Hongzan, Kang, Yingdong, Lei, Guangbin, Li, Ainong, Li, Xiaoting, Li, Xun, Li, Yuechen, Li, Zhilin, Li, Zhongde, Liu, Chong, Liu, Chunxia, Liu, Maochou, Liu, Shuguang, Mao, Wanliu, Miao, Changhong, Ni, Hao, Pan, Qisheng, Qi, Shuhua, Ren, Zhehao, Shan, Zhuoran, Shen, Shaoqing, Shi, Minjun, Song, Yimeng, Su, Mo, Ping Suen, Hoi, Sun, Bo, Sun, Fangdi, Sun, Jian, Sun, Lin, Sun, Wenyao, Tian, Tian, Tong, Xiaohua, Tseng, Yihsing, Tu, Ying, Wang, Hong

المساهمون: Tsinghua University, National Key Research and Development Program of China, National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

المصدر: Science Bulletin ; volume 65, issue 3, page 182-187 ; ISSN 2095-9273

الإتاحة: https://doi.org/10.1016/j.scib.2019.12.007Test
https://api.elsevier.com/content/article/PII:S2095927319307054?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S2095927319307054?httpAccept=text/plainTest

المؤلفون: Gong Peng, Chen Bin, Li Xuecao, Liu Han, Wang Jie, Bai Yuqi, Chen Jingming, Chen Xi, Fang Lei, Feng Shuailong, Feng Yongjiu, Gong Yali, Gu Hao, Huang Huabing, Huang Xiaochun, Jiao Hongzan, Kang Yingdong, Lei Guangbin, Li Ainong, Li Xiaoting, Li Xun, Li Yuechen, Li Zhilin, Li Zhongde, Liu Chong, Liu Chunxia, Liu Maochou, Liu Shuguang, Mao Wanliu, Miao Changhong, Ni Hao, Pan Qisheng, Qi Shuhua, Ren Zhehao, Shan Zhuoran, Shen Shaoqing, Shi Minjun, Song Yimeng, Su Mo, Suen Hoi Ping, Sun Bo, Sun Fangdi, Sun Jian, Sun Lin, Sun Wenyao, Tian Tian, Tong Xiaohua, Tseng Yihsing, Tu Ying, Wang Hong

مصطلحات موضوعية: geo, envir

الوصف: Land use reflects human activities on land. Urban land use is the highest level human alteration on Earth, and it is rapidly changing due to population increase and urbanization. Urban areas have widespread effects on local hydrology, climate, biodiversity, and food production 1,2. However, maps, that contain knowledge on the distribution, pattern and composition of various land use types in urban areas, are limited to city level. The mapping standard on data sources, methods, land use classification schemes varies from city to city, due to differences in financial input and skills of mapping personnel. To address various national and global environmental challenges caused by urbanization, it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods. This is because, only with urban land use maps produced with similar criteria, consistent environmental policies can be made, and action efforts can be compared and assessed for large scale environmental administration. However, despite of the fact that a number of urban-extent maps exist at global scales 3,4

العلاقة: http://ir.imde.ac.cn/handle/131551/51742Test

الإتاحة: http://ir.imde.ac.cn/handle/131551/51742Test

المؤلفون: Lu, Debin, Mao, Wanliu, Yang, Dongyang, Zhao, Jianan, Xu, Jianhua

المصدر: Atmospheric Pollution Research ; volume 9, issue 4, page 705-713 ; ISSN 1309-1042

الإتاحة: https://doi.org/10.1016/j.apr.2018.01.012Test
https://api.elsevier.com/content/article/PII:S1309104217304816?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1309104217304816?httpAccept=text/plainTest

المؤلفون: Lu, Debin¹ (AUTHOR) ludebin@zju.edu.cn, Mao, Wanliu^1,2 (AUTHOR) 11922077@zju.edu.cn, Xiao, Wu¹ (AUTHOR), Zhang, Liang³ (AUTHOR) zhangliang0930@zju.edu.cn, Knibbs, Luke (AUTHOR)

المصدر: Remote Sensing. May2021, Vol. 13 Issue 9, p1612-1612. 1p.

مصطلحات موضوعية: *LAND use, *FORESTS & forestry, *POLLUTION management, *POLLUTION, *REMOTE sensing, *URBAN forestry

مصطلحات جغرافية: CHINA

مستخلص: Land use change has an important influence on the spatial and temporal distribution of PM2.5 concentration. Therefore, based on the particulate matter (PM2.5) data from remote sensing instruments and land use change data in long time series, the Getis-Ord Gi* statistic and SP-SDM are employed to analyze the spatial distribution pattern of PM2.5 and its response to land use change in China. It is found that the average PM2.5 increased from 25.49 μg/m3 to 31.23 μg/m3 during 2000-2016, showing an annual average growth rate of 0.97%. It is still greater than 35 μg/m3 in nearly half of all cities. The spatial distribution pattern of PM2.5 presents the characteristics of concentrated regional convergence. PM2.5 is positively correlated with urban land and farmland, negatively correlated with forest land, grassland, and unused land. Furthermore, the average PM2.5 concentrations show the highest values for urban land and decrease in the order of farmland > unused land > water body > forest > grassland. The impact of land use change on PM2.5 is a non-linear process, and there are obvious differences and spillover effects for different land types. Thus, reasonably controlling the scale of urban land and farmland, optimizing the spatial distribution pattern and development intensity, and expanding forest land and grassland are conducive to curbing PM2.5 pollution. The research conclusions provide a theoretical basis for the management of PM2.5 pollution from the perspective of optimizing land use. [ABSTRACT FROM AUTHOR]

المؤلفون: Lu, Debin¹ (AUTHOR) ludebin@zju.edu.cn, Mao, Wanliu^1,2 (AUTHOR) 11922077@zju.edu.cn, Zheng, Lilin³ (AUTHOR) zhenglilin17@mails.ucas.ac.cn, Xiao, Wu¹ (AUTHOR) xiaowu@zju.edu.cn, Zhang, Liang⁴ (AUTHOR) zhangliang0930@zju.edu.cn, Wei, Jing⁵ (AUTHOR) weijing_rs@163.com, Knibbs, Luke (AUTHOR), Wang, Qi (AUTHOR)

المصدر: Remote Sensing. Apr2021, Vol. 13 Issue 8, p1423. 1p.

مصطلحات موضوعية: *COVID-19 pandemic, *MACHINE learning, *AIR pollution potential, *AIR pollution control, *STANDARD deviations, *PANDEMICS

مستخلص: The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies. [ABSTRACT FROM AUTHOR]

المؤلفون: Mao, Wanliu^1,2 (AUTHOR) 11922077@zju.edu.cn, Lu, Debin¹ (AUTHOR) ludebin@zju.edu.cn, Hou, Li¹ (AUTHOR) houli@cau.edu.cn, Liu, Xue¹ (AUTHOR) xueliu@zju.edu.cn, Yue, Wenze¹ (AUTHOR) wzyue@zju.edu.cn

المصدر: Remote Sensing. sep2020, Vol. 12 Issue 17, p2817. 1p.

مصطلحات موضوعية: *URBAN land use, *LAND surface temperature, *URBAN planning, *SUPPORT vector machines

مصطلحات جغرافية: HANGZHOU (China)

مستخلص: Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition. [ABSTRACT FROM AUTHOR]