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- Aliabad, Fahime Arabi, et al.
(författare)
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Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2
- 2022
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 14:13
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Tidskriftsartikel (refereegranskat)abstract
- One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method.
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| 2. |
- Aliabad, Fahime Arabi, et al.
(författare)
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Use of Landsat 8 and UAV Images to Assess Changes in Temperature and Evapotranspiration by Economic Trees following Foliar Spraying with Light-Reflecting Compounds
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:23
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Tidskriftsartikel (refereegranskat)abstract
- Pistachio is an important economic crop in arid and semi-arid regions of Iran. A major problem leading to a reduction in crop quality and reduced marketability is extreme air temperature in summer, which causes sunburn of pistachio leaves and fruit. A solution proposed to deal with the negative effects of high temperatures and increase water consumption efficiency in pistachio orchards is use of light-reflecting compounds. This study investigated the effect of foliar application of gypsum, sulfur, and NAX-95 (calcium-based suspension coating) to trees in a pistachio orchard (150 ha) in central Iran. The effect of these foliar products is assessed at plot scale, using control plots sprayed with calcium sulfate, based on temperature and evapotranspiration changes analyzed through remote sensing. Landsat 8 sensor images and RGB images collected by UAVs (spatial resolution of 30 m and 20 cm, respectively), on the same dates, before and after foliar spray application, were merged using the PCA method and bilinear interpolation re-sampling. Land surface temperature (LST) was then estimated using the split-window algorithm, and daily evapotranspiration using the surface energy balance algorithm for land (SEBAL) algorithm. A land use map was prepared and used to isolate pistachio trees in the field and assess weed cover, whose effect was not accounted. The results showed that temperature remained constant in the control plot between the spraying dates, indicating no environmental changes. In the main plots, gypsum had the greatest effect in reducing the temperature of pistachio trees. The plots with foliar spraying with gypsum displayed a mean tree temperature (47–48 °C) decrease of 3.3 °C in comparison with the control plots (>49 °C), leading to an average decline in evapotranspiration of 0.18 mm/day. NAX-95 and sulfur reduced tree temperature by on average 1.3 °C and 0.6 °C, respectively. Thus, gypsum is the most suitable foliar-spraying compound to lower the temperature of pistachio trees, reduce the water requirement, and increase crop productivity.
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