Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire

Beltrán-Marcos, David; Suárez‐Seoane, Susana; Fernández‐Guisuraga, José Manuel; Fernández-García, Víctor; Marcos, Elena; Calvo, Leonor

doi:10.1016/j.geoderma.2022.116290

Cited by 12 publications

(4 citation statements)

References 88 publications

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“…The results of using the DSM image to increase classification accuracy are consistent with the results of Al-Najjar et al [121] indicating that the use of a DSM has the greatest effect on improving the accuracy of identifying trees and buildings. The present study also confirms the results of Beltrán-Marcos et al [29] arguing that the use of a DSM increases the accuracy of classification and segmentation. The results of the present study showed that the use of NDVI image spectral data along with a DSM increases the accuracy of aerial image classification, which is consistent with the results of Wu et al [17].…”

Section: Discussionsupporting

confidence: 92%

“…The results of Zhao's study [28] not only indicates the possibility of combining satellite images and UAV images for land parcel-level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit the optimal choice of spatial resolution in fusing UAV images and Sentinel-2A. Research results also showed that the GS method was most suitable for using the spectral and spatial data of UAV and Sentinel-2 [29]. The results of research by Daryaei et al [30] showed that the fusion of UAV and Sentinel-2 images increased the accuracy of oak tree identification.…”

Section: Introductionmentioning

confidence: 94%

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

et al. 2023

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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.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 94%

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

et al. 2023

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“…Beltrán-Marcos et al [105] investigated the relationship between satellite and UAV remote sensing multi-spectral image data for measuring fire severity after a forest fire. The authors found that soil organic carbon (SOC) and soil water content (SMC), which can be estimated using multi-spectral image data, were highly correlated with the severity of the fire.…”

Section: Monitoring Of Forest Firesmentioning

confidence: 99%

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

Zhang

Zhu

2023

Drones

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In recent years, UAV remote sensing has gradually attracted the attention of scientific researchers and industry, due to its broad application prospects. It has been widely used in agriculture, forestry, mining, and other industries. UAVs can be flexibly equipped with various sensors, such as optical, infrared, and LIDAR, and become an essential remote sensing observation platform. Based on UAV remote sensing, researchers can obtain many high-resolution images, with each pixel being a centimeter or millimeter. The purpose of this paper is to investigate the current applications of UAV remote sensing, as well as the aircraft platforms, data types, and elements used in each application category; the data processing methods, etc.; and to study the advantages of the current application of UAV remote sensing technology, the limitations, and promising directions that still lack applications. By reviewing the papers published in this field in recent years, we found that the current application research of UAV remote sensing research can be classified into four categories according to the application field: (1) Precision agriculture, including crop disease observation, crop yield estimation, and crop environmental observation; (2) Forestry remote sensing, including forest disease identification, forest disaster observation, etc.; (3) Remote sensing of power systems; (4) Artificial facilities and the natural environment. We found that in the papers published in recent years, image data (RGB, multi-spectral, hyper-spectral) processing mainly used neural network methods; in crop disease monitoring, multi-spectral data are the most studied type of data; for LIDAR data, current applications still lack an end-to-end neural network processing method; this review examines UAV platforms, sensors, and data processing methods, and according to the development process of certain application fields and current implementation limitations, some predictions are made about possible future development directions.

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“…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.…”

Section: Limitationsmentioning

confidence: 99%

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

Chen,

Zhang,

et al. 2023

Remote Sensing

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Spatiotemporal fusion technology effectively improves the spatial and temporal resolution of remote sensing data by fusing data from different sources. Based on the strong time-series correlation of pixels at different scales (average Pearson correlation coefficients > 0.95), a new long time-series spatiotemporal fusion model (LOTSFM) is proposed for land surface temperature data. The model is distinguished by the following attributes: it employs an extended input framework to sidestep selection biases and enhance result stability while also integrating Julian Day for estimating sensor difference term variations at each pixel location. From 2013 to 2022, 79 pairs of Landsat8/9 and MODIS images were collected as extended inputs. Multiple rounds of cross-validation were conducted in Beijing, Shanghai, and Guangzhou with an all-round performance assessment (APA), and the average root-mean-square error (RMSE) was 1.60 °C, 2.16 °C and 1.71 °C, respectively, which proved the regional versatility of LOTSFM. The validity of the sensor difference estimation based on Julian days was verified, and the RMSE accuracy significantly improved (p < 0.05). The accuracy and time consumption of five different fusion models were compared, which proved that LOTSFM has stable accuracy performance and a fast fusion process. Therefore, LOTSFM can provide higher spatiotemporal resolution (30 m) land surface temperature research data for the evolution of urban thermal environments and has great application potential in monitoring anthropogenic heat pollution and extreme thermal phenomena.

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Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire

Cited by 12 publications

References 88 publications

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

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

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

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

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