Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests

Tanase, Mihai A.; Villard, Ludovic; Apostol, Bogdan; Petrila, Marius; Chivulescu, Șerban; Leca, Ştefan; Borlaf-Mena, Ignacio; Pascu, Ionuț-Silviu; Dobre, Alexandru-Claudiu; Pitar, Diana; Guiman, Gheorghe; Lorenţ, Adrian; Anghelus, Cristian; Ciceu, Albert; Nedea, Gabriel; Stanculeanu, Raducu; Popescu, Flaviu; Aponte, Cristina; Badea, Ovidiu

doi:10.1016/j.scitotenv.2019.06.494

Cited by 30 publications

(22 citation statements)

References 66 publications

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“…However, most studies have focused on the effects caused by single-band microwaves, and only a few researchers have been concerned with the hazards from multi-frequency microwave radiation 6,11,12 . L-and C-band microwaves are commonly used for communication in daily life, such as weather satellites 13 , and some synthetic aperture radars 14,15 , etc. And these devices can emit microwaves of these two frequencies at the same time.…”

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confidence: 99%

Effects of 1.5 and 4.3 GHz microwave radiation on cognitive function and hippocampal tissue structure in Wistar rats

Zhu

Wang

et al. 2021

Sci Rep

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Previous studies have shown that single-frequency microwave radiation can lead to cognitive decline in rats. However, few studies have focused on the combined effects of irradiation with different frequencies of microwaves. Our research aimed to investigate the effects of 1.5 GHz and 4.3 GHz microwave radiation, singly and in combination, on cognitive function and hippocampal tissue structure in rats. A total of 140 male Wistar rats were randomly divided into 4 groups: the S group (sham radiation group), L10 group (10 mW/cm2 1.5 GHz group), C10 group (10 mW/cm2 4.3 GHz band group) and LC10 group (10 mW/cm2 1.5 and 4.3 GHz multi-frequency radiation group). For 1–28 days after microwave radiation, we analyzed the average escape latency for the Morris water maze task, electroencephalograms, change in hippocampal tissue structure and ultrastructure, content of the Nissl body in the hippocampus, and activities of lactate dehydrogenase and succinate dehydrogenase. Compared to the S group, all exposure groups showed varying degrees of learning and memory decline and hippocampal structural damage. The results showed that 1.5 GHz and 4.3 GHz microwave radiation was able to induce cognitive impairment and hippocampal tissue damage in rats and combined radiation with both frequencies caused more serious injuries, but none of these damaging effects varied with microwave frequency.

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confidence: 99%

Effects of 1.5 and 4.3 GHz microwave radiation on cognitive function and hippocampal tissue structure in Wistar rats

Zhu

Wang

et al. 2021

Sci Rep

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“…The C-band RADARSAT Constellation Mission (RCM), operational from November 2019 and with a lifespan of 7 years, will provide additional C-band data with a revisit time of 4-days [61,62], a fact which will contribute to shorten the times between acquisitions [16]. Moreover, the recent announcement of the future free-data policy of ALOS-2/(ScanSAR), the upcoming ALOS-4 L-band (launch date: 2021), and NASA-ISRO (NISAR) L,S-band (launch date: 2022) SAR missions might serve to enhance the performance of the CUSUMs, due to the higher sensitivity that longer wavelengths have to structural forest disturbances [17].…”

Section: Adaptability To Other Sensors and Applications (Synergies)mentioning

confidence: 99%

“…The work presented here utilised synthetic aperture radar (SAR) to address the inadequacies of forest inventories. Historically, the use of RS for forestry purposed has focused on optical systems, however this approach is hindered in areas with recurrent cloud coverage (e.g., Tropical areas) or limited solar illumination (e.g., Polar latitudes) [16][17][18]. Another disadvantage of optical systems derives from the fact that they can only extract information from the very top canopy layer.…”

Section: Introductionmentioning

confidence: 99%

“…The launch of the European Space Agency's (ESA) Sentinel-1 satellite constellation in 2014, opened the door to new opportunities for environmental monitoring. With freely available data and improved sensor characteristics (e.g., dual polarization 6-or 12-day revisit period), Sentinel-1 rapidly stood out as a valuable resource for effective monitoring of highly dynamic forest ecosystems [17,26].…”

Section: Introductionmentioning

confidence: 99%

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Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

et al. 2020

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Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum–spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user’s accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbor filter increased the detection performance to a user’s accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4 ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories.

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“…SAR wavelengths also penetrate further through the canopy than optical wavelengths, potentially obtaining information on leaf area and leaf density beyond the saturation point for optical data. Calibrating an LAI relationship with SAR data can be difficult as other aspects of forest structure variation may have a large impact on the resulting signal, but there have been successes in monitoring seasonal leaf area changes using C-band SAR backscatter [34,35].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Mega-Crown Leaf Turnover from Space

et al. 2020

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Spatial and temporal patterns of tropical leaf renewal are poorly understood and poorly parameterized in modern Earth System Models due to lack of data. Remote sensing has great potential for sampling leaf phenology across tropical landscapes but until now has been impeded by lack of ground-truthing, cloudiness, poor spatial resolution, and the cryptic nature of incremental leaf turnover in many tropical plants. To our knowledge, satellite data have never been used to monitor individual crown leaf phenology in the tropics, an innovation that would be a major breakthrough for individual and species-level ecology and improve climate change predictions for the tropics. In this paper, we assessed whether satellite data can detect leaf turnover for individual trees using ground observations of a candidate tropical tree species, Moabi (Baillonella toxisperma), which has a mega-crown visible from space. We identified and delineated Moabi crowns at Lopé NP, Gabon from satellite imagery using ground coordinates and extracted high spatial and temporal resolution, optical, and synthetic-aperture radar (SAR) timeseries data for each tree. We normalized these data relative to the surrounding forest canopy and combined them with concurrent monthly crown observations of new, mature, and senescent leaves recorded from the ground. We analyzed the relationship between satellite and ground observations using generalized linear mixed models (GLMMs). Ground observations of leaf turnover were significantly correlated with optical indices derived from Sentinel-2 optical data (the normalized difference vegetation index and the green leaf index), but not with SAR data derived from Sentinel-1. We demonstrate, perhaps for the first time, how the leaf phenology of individual large-canopied tropical trees can directly influence the spectral signature of satellite pixels through time. Additionally, while the level of uncertainty in our model predictions is still very high, we believe this study shows that we are near the threshold for orbital monitoring of individual crowns within tropical forests, even in challenging locations, such as cloudy Gabon. Further technical advances in remote sensing instruments into the spatial and temporal scales relevant to organismal biological processes will unlock great potential to improve our understanding of the Earth system.

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Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests

Cited by 30 publications

References 66 publications

Effects of 1.5 and 4.3 GHz microwave radiation on cognitive function and hippocampal tissue structure in Wistar rats

Effects of 1.5 and 4.3 GHz microwave radiation on cognitive function and hippocampal tissue structure in Wistar rats

Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

Monitoring Mega-Crown Leaf Turnover from Space

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