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

Abstract

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.

A Spatiotemporal Fusion Model of Land Surface Temperature Based on Pixel Long Time-Series Regression: Expanding Inputs for Efficient Generation of Robust Fused Results

“…Aerial data holds significant potential for integration into spatiotemporal fusion methods, especially given the rapid development of aerial unmanned drone technology. The fusion of aerial and satellite data can lead to the generation of higher-resolution and more accurate data, which has already been used for monitoring soil, crops, and forests [92][93][94]. However, while the integration of aerial data into surface temperature fusion is not yet widespread, it does not detract from recognizing it as one of the promising research directions.…”

Improving the Accuracy of Landsat 8 Land Surface Temperature in Arid Regions by MODIS Water Vapor Imagery

“…Therefore, in areas where NDVI is more than 0.2, it indicates the presence of vegetation, so it was considered as NDVI v and lower values were placed in NDVI s category. The fractional vegetation cover (FVC) index was estimated, and then LSE was computed to estimate LST, see [59][60][61] for the mathematical formulas of spectral radiation, BT, NDVI, FVC, LSE, and LST, e.g., see Equations ( 1)-( 6) in [61]. In this research, the amount of water vapor has been estimated using the MODIS images.…”

A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data

“…Many crop type mapping approaches primarily relied on MS data due to its strong ability to capture the spectral properties of crops and track vegetation phenology. Widely used MS RS data sources include the Moderate Resolution Imaging Spectroradiometer (MODIS) [5,6], Landsat (L) series (particularly L4, 5, 8, and 9) [7,8], and Sentinel-2 (S2) [9][10][11][12][13]. On the other hand, some studies focused solely on SAR data for crop type mapping, with Sentinel-1 (S1) being the most commonly utilized one due to its public availability [14].…”