Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data

Borlaf-Mena, Ignacio; García-Duro, Juan; Santoro, Maurizio; Villard, Ludovic; Badea, Ovidiu; Tanase, Mihai A.

doi:10.1016/j.rse.2023.113728

Cited by 3 publications

(2 citation statements)

References 61 publications

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“…than VV (Sugimoto et al, 2022;Borlaf-Mena et al, 2023). Partial deforestation changes the radar signal less than obvious deforestation (Lei et al, 2018;Hethcoat et al, 2021).…”

Section: Principle Of 3dcmentioning

confidence: 95%

Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Zhao,

Pan,

Zhu

et al. 2023

Front. For. Glob. Change

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Many countries and regions are currently developing new forest strategies to better address the challenges facing forest ecosystems. Timely and accurate monitoring of deforestation events is necessary to guide tropical forest management activities. Synthetic aperture radar (SAR) is less susceptible to weather conditions and plays an important role in high-frequency monitoring in cloudy regions. Currently, most SAR image-based deforestation identification uses manually supervised methods, which rely on high quality and sufficient samples. In this study, we aim to explore radar features that are sensitive to deforestation, focusing on developing a method (named 3DC) to automatically extract deforestation events using radar multidimensional features. First, we analyzed the effectiveness of radar backscatter intensity (BI), vegetation index (VI), and polarization feature (PF) in distinguishing deforestation areas from the background environment. Second, we selected the best-performing radar features to construct a multidimensional feature space model and used an unsupervised K-mean clustering method to identify deforestation areas. Finally, qualitative and quantitative methods were used to validate the performance of the proposed method. The results in Paraguay, Brazil, and Mexico showed that (1) the overall accuracy (OA) and F1 score (F1) of 3DC were 88.1–98.3% and 90.2–98.5%, respectively. (2) 3DC achieved similar accuracy to supervised methods without the need for samples. (3) 3DC matched well with Global Forest Change (GFC) maps and provided more detailed spatial information. Furthermore, we applied the 3DC to deforestation mapping in Paraguay and found that deforestation events occurred mainly in the second half of the year. To conclude, 3DC is a simple and efficient method for monitoring tropical deforestation events, which is expected to serve the restoration of forests after deforestation. This study is also valuable for the development and implementation of forest management policies in the tropics.

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“…than VV (Sugimoto et al, 2022;Borlaf-Mena et al, 2023). Partial deforestation changes the radar signal less than obvious deforestation (Lei et al, 2018;Hethcoat et al, 2021).…”

Section: Principle Of 3dcmentioning

confidence: 95%

Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Zhao,

Pan,

Zhu

et al. 2023

Front. For. Glob. Change

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“…Furthermore, due to the cost limitations of UAV data, these studies are often limited to small-plot applications. In contrast, medium-to lowresolution remote sensing data (Landsat, Sentinel, and GF-1) have improved the accuracy and efficiency of forest classification using deep learning techniques, while enabling largescale classification [32][33][34][35]. However, they still fall short of the high-precision classification achieved by UAVs.…”

Section: Introductionmentioning

confidence: 99%

Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks

Zhang,

Yin,

et al. 2024

Remote Sensing

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This study aims to establish a deep learning-based classification framework to efficiently and rapidly distinguish between coniferous and broadleaf forests across the Loess Plateau. By integrating the deep residual neural network (ResNet) architecture with transfer learning techniques and multispectral data from unmanned aerial vehicles (UAVs) and Landsat remote sensing data, the effectiveness of the framework was validated through well-designed experiments. The study began by selecting optimal spectral band combinations, using the random forest algorithm. Pre-trained models were then constructed, and model performance was optimized with different training strategies, considering factors such as image size, sample quantity, and model depth. The results indicated substantial improvements in the model’s classification accuracy and efficiency for reasonable image dimensions and sample sizes, especially for an image size of 3 × 3 pixels and 2000 samples. In addition, the application of transfer learning and model fine-tuning strategies greatly enhanced the adaptability and universality of the model in different classification scenarios. The fine-tuned model achieved remarkable performance improvements in forest-type classification tasks, increasing classification accuracy from 85% to 93% in Zhengning, from 89% to 96% in Yongshou, and from 86% to 94% in Baishui, as well as exceeding 90% in all counties. These results not only confirm the effectiveness of the proposed framework, but also emphasize the roles of image size, sample quantity, and model depth in improving the generalization ability and classification accuracy of the model. In conclusion, this research has developed a technological framework for effective forest landscape recognition, using a combination of multispectral data from UAVs and Landsat satellites. This combination proved to be more effective in identifying forest types than was using Landsat data alone, demonstrating the enhanced capability and accuracy gained by integrating UAV technology. This research provides valuable scientific guidance and tools for policymakers and practitioners in forest management and sustainable development.

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Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests

van der Woude,

Reiche,

Sterck

et al. 2024

Remote Sensing

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The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors.

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Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data

Cited by 3 publications

References 61 publications

Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks

Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests

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