干旱区生态环境知识资源中心(Arid): Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

Arid

DOI	10.3390/rs15164053
	Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery
	Arabi Aliabad, Fahime; Ghafarian Malmiri, Hamidreza; Sarsangi, Alireza; Sekertekin, Aliihsan; Ghaderpour, Ebrahim
通讯作者	Ghaderpour, E
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:16
英文摘要	In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018-2022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018-2022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 C-? in the areas that were changed from gardens to buildings.
英文关键词	arid regions data fusion land surface temperature machine learning remote sensing vegetation monitoring urban planning
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:001056690900001
WOS关键词	SURFACE TEMPERATURE RETRIEVAL ; SPLIT-WINDOW ALGORITHM ; VEGETATION COVER ; FUSION ; CLASSIFICATION ; COEFFICIENTS ; NDVI ; TM
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398308
推荐引用方式 GB/T 7714	Arabi Aliabad, Fahime,Ghafarian Malmiri, Hamidreza,Sarsangi, Alireza,et al. Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery[J],2023,15(16).
APA	Arabi Aliabad, Fahime,Ghafarian Malmiri, Hamidreza,Sarsangi, Alireza,Sekertekin, Aliihsan,&Ghaderpour, Ebrahim.(2023).Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery.REMOTE SENSING,15(16).
MLA	Arabi Aliabad, Fahime,et al."Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery".REMOTE SENSING 15.16(2023).