Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-08, Vol.15 (16), p.4053
• Main Authors: Arabi Aliabad, Fahime, Ghafarian Malmiri, Hamidreza, Sarsangi, Alireza, Sekertekin, Aliihsan, Ghaderpour, Ebrahim
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; arid regions; Buildings; Classification; Computer vision; data fusion; Digital imaging; Drones; Drying; Earth temperature; Environmental aspects; Environmental impact; Environmental monitoring; Environmental stress; Gardens; Gardens & gardening; Geospatial data; Land surface temperature; Land use; Landsat satellites; machine learning; Methods; Metropolitan areas; Monitoring; Normalized difference vegetative index; Remote sensing; Root-mean-square errors; Satellite imagery; Spatial data; Spatial discrimination; Spatial resolution; Support vector machines; Time series; Unmanned aerial vehicles; Urban areas; Vegetation; vegetation monitoring; Weather
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15164053

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.