Insights into the June 2020 Aniangzhai landslide in Danba County, China: A remote sensing analysis using satellite radar and optical data and corner reflectors

AbstractRabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.

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CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture

Remote Sensing ◽

10.3390/rs10121867 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1867 ◽

Cited By ~ 20

Author(s):

Bruno Aragon ◽

Rasmus Houborg ◽

Kevin Tu ◽

Joshua B. Fisher ◽

Matthew McCabe

Keyword(s):

Remote Sensing ◽

Eddy Covariance ◽

Water Use ◽

Spatial Resolution ◽

Precision Agriculture ◽

High Spatial Resolution ◽

Assessment Tools ◽

Optical Data ◽

Crop Water ◽

Crop Water Use

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions. .

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THE SPATIAL AND TEMPORAL AVAILABILITY DIFFERENCES OF CLOUD-FREE LANDSAT IMAGES OVER THREE GORGES RESERVOIR AREA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w9-1-2019 ◽

2019 ◽

Vol XLII-3/W9 ◽

pp. 1-8

Author(s):

B. X. Bai ◽

Y. M. Tan ◽

P. Wu

Keyword(s):

Remote Sensing ◽

Three Gorges Reservoir ◽

Google Earth ◽

Optical Data ◽

Three Gorges Reservoir Area ◽

Three Gorges ◽

Landsat Images ◽

Reservoir Area ◽

Availability Analysis ◽

Sensing Applications

Abstract. Availability analysis of cloud-free optical remote sensing data is a prerequisite for remote sensing applications. In this study, spatio-temporal availability differences of cloud-free Landsat TM, ETM+, and OLI sensors images over Three Gorges Reservoir Area (TGRA) were analyzed from 1986 to 2019 based on the Google Earth Engine (GEE). The results show that: 1) in Summer, especially in August, the probabilities of obtaining Landsat images with no more than 30% cloud cover (CC) is higher. 2) the northeast of TGRA has higher probability of acquiring cloudless images than the southwest. 3) In TGRA, annual monitoring which require at least one cloud-free observation in a year largely unaffected by CC, but when considering seasonal monitoring, cloud contaminate will become a limitation, and monthly monitoring in this area is basically not feasible even if the three sensors data are combined. The results of this paper will provide important references for the research of using optical data in this area, and although the research area is relatively small, the analysis method and the program developed in this paper have no restrictions on the area.

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Forest disturbances detection in Vietnam, Cambodia and Laos using Sentinel-1 data

10.5194/egusphere-egu21-16177 ◽

2021 ◽

Author(s):

Stéphane Mermoz ◽

Alexandre Bouvet ◽

Marie Ballère ◽

Thierry Koleck ◽

Thuy Le Toan

Keyword(s):

Remote Sensing ◽

Time Series ◽

French Guiana ◽

The United States ◽

Habitat Destruction ◽

Detection Methods ◽

Optical Data ◽

Forest Loss ◽

Forest Disturbances ◽

Synthetic Aperture Radar Data

Over the last 25 years, the world&#8217;s forests have undergone substantial changes. Deforestation and forest degradation in particular contribute greatly to biodiversity loss through habitat destruction, soil erosion, terrestrial water cycle disturbances and anthropogenic CO2 emissions. In certain regions and countries, the changes have been more rapid, which is the case in the Greater Mekong sub-region recognized as deforestation hotspot (FAO, 2020). In this region, illegal and unsustainable logging and conversion of forests for agriculture, construction of dams and infrastructure are the direct causes of deforestation. Effective tools are therefore urgently needed to survey illegal logging operations which cause widespread concern in the region.Monitoring systems based on optical data, such as the UMD/GLAD Deforestation alerts implemented on the Global Forest Watch platform, are limited by the important cloud cover which causes delays in the detections. However, it has been demonstrated in the last few years that forest losses can be timely monitored using dense time series of (synthetic aperture) radar data acquired by Sentinel-1 satellites, developed in the frame of the European Union&#8217;s Earth observation Copernicus programme. Ball&#232;re et al. (2021) showed for example that 80% of the forest losses due to gold mining in French Guiana are detected first by Sentinel-1-based forest loss detection methods compared with optical-based methods, sometimes by several months. Methods based on Sentinel-1 have been successfully applied at the local scale (Bouvet et al., 2018, Reiche et al., 2018) and can be adapted and tested at the national scale (Ball&#232;re et al., 2020).We show here the main results of the SOFT project funded by ESA in the frame of the EO Science for Society open calls. The overall SOFT project goal is to provide validated forest loss maps every month over Vietnam, Cambodia and Laos with a minimum mapping unit of 0.04 ha, using Sentinel-1 data. The results confirm the analysis of the deforestation fronts published recently by the WWF (Pacheco et al., 2021), showing that Eastern Cambodia, and Southern and Northern Laos are currently forest disturbances hotspots.&#160;References:Ball&#232;re et al., (2021). SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery. Remote Sensing of Environment, 252, 112159.Bouvet et al., (2018). Use of the SAR shadowing effect for deforestation detection with Sentinel-1 time series. Remote Sensing, 10(8), 1250.FAO. Global Forest Resources Assessment; Technical Report; Food and Agriculture Association of the United-States: Rome, Italy, 2020.Pacheco et al., 2021. Deforestation fronts: Drivers and responses in a changing world. WWF, Gland, Switzerland<div>Reiche et al., (2018). Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sensing of Environment, 204, 147-161.</div>

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Integrated analysis of remote sensing and numerical oil drift simulations for improved oil spill preparedness capabilities

10.5194/egusphere-egu21-6543 ◽

2021 ◽

Author(s):

Camilla Brekke ◽

Martine Espeseth ◽

Knut-Frode Dagestad ◽

Johannes Röhrs ◽

Lars Hole ◽

...

Keyword(s):

Remote Sensing ◽

Soybean Oil ◽

Oil Spill ◽

Mineral Oil ◽

The Arctic ◽

Optical Data ◽

Integrated Analysis ◽

Geophysical Research ◽

Oil Emulsion ◽

Detection Capabilities

Integrated analysis of remote sensing and numerical oil drift simulations for improved&#160;oil spill preparedness capabilitiesCamilla Brekke1, Martine M. Espeseth1, Knut-Frode Dagestad2, Johannes R&#246;hrs2, Lars Robert Hole2, and Andreas Reigber3&#160;1UiT The Arctic University of Norway, Troms&#248;, Norway2The Norwegian Meteorological Institute, Oslo, Norway3DLR, Microwaves and Radar Institute, Oberpfaffenhofen-We&#223;ling, Germany&#160;We present results from a successfully conducted free-floating oil spill field experiment followed by an integrated analysis of remotely sensed data and drift simulations. The experiment took place in the North Sea in the summer of 2019 during Norwegian Clean Seas Association for Operating Companies&#8217; annual oil-on-water exercise. Two types of oils were applied: a mineral oil emulsion and a soybean oil emulsion. The dataset collected contains a collection of close-in-time radar (aircraft and space-borne) and optical data (aircraft, aerostat, and drone) acquisitions of the slicks. We compare oil drift simulations, applying various configurations of wind, wave, and current information, with observed slick positions and shape. We describe trajectories and dynamics of the spills, slick extent, and their evolution, and the differences in detection capabilities in optical instruments versus multifrequency quad-polarimetric synthetic aperture radar (SAR) imagery acquired by DLRs large-scale airborne SAR facility (F-SAR). When using the best available forcing from in situ data and forecast models, good agreement with the observed position and extent are found in this study. The appearance in the optical images and the SAR time series from F-SAR were found to be different between the soybean and mineral oil types. Differences in mineral oil detection capabilities are found between SAR and optical imagery of thinner sheen regions. From a drifting perspective, the biological oil emulsions could replace the viscous similar mineral oil emulsion in future oil spill preparedness campaigns. However, from a remote sensing and wildlife perspective, the two oils have different properties. Depending on the practical application, further investigation on how the soybean oil impact the seabirds must be conducted in order to recommend the soybean oil as a viable substitute for mineral oil.&#160;This study is published as open access in Journalof Geophysical Research: Oceans[1], and we encourage the audience to read this article for detailed acquaintance with the work.&#160;Reference:[1]Brekke, C., Espeseth, M. M., Dagestad, K.-F., R&#246;hrs, J., Hole, L. R., & Reigber,A. (2021). Integrated analysis of multisensor datasets and oil driftsimulations&#8212;a free-floating oil experiment in the open ocean. Journalof Geophysical Research: Oceans, 126, e2020JC016499. https://doi.org/10.1029/2020JC016499

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Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

Remote Sensing ◽

10.3390/rs11202389 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2389 ◽

Cited By ~ 7

Author(s):

Deodato Tapete ◽

Francesca Cigna

Keyword(s):

Remote Sensing ◽

Image Processing ◽

High Resolution ◽

Satellite Data ◽

Satellite Images ◽

Google Earth ◽

Optical Data ◽

Mena Region ◽

Processing Methods ◽

Optical Imagery

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

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Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

Remote Sensing ◽

10.3390/rs10101642 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1642 ◽

Cited By ~ 56

Author(s):

Kristof Van Tricht ◽

Anne Gobin ◽

Sven Gilliams ◽

Isabelle Piccard

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Satellite Remote Sensing ◽

Random Forest Classifier ◽

Optical Observations ◽

Optical Data ◽

Crop Monitoring ◽

Crop Types ◽

Optical Imagery ◽

Sentinel 2

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

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Utilizing satellite radar remote sensing for burn severity estimation

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.07.002 ◽

2018 ◽

Vol 73 ◽

pp. 292-299 ◽

Cited By ~ 8

Author(s):

Priscilla Addison ◽

Thomas Oommen

Keyword(s):

Remote Sensing ◽

Burn Severity ◽

Radar Remote Sensing ◽

Severity Estimation ◽

Satellite Radar

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Spectral reflectance of suspended solid sediments: a laboratory experiment.

10.5194/egusphere-egu2020-10760 ◽

2020 ◽

Author(s):

Martina Carlino ◽

Silvia Di Francesco

Keyword(s):

Remote Sensing ◽

Laboratory Experiment ◽

Spectral Reflectance ◽

Suspended Solids ◽

Ocean Color ◽

Total Suspended Solids ◽

Reflectance Spectra ◽

Optical Data ◽

Band Selection ◽

Solids Concentration

Ocean color remote sensing proved to be a good alternative to traditional methods for total suspended solids concentration (TSS) monitoring purposes: numerous sensors have been developed for ocean color applications and different algorithms to retrieve TSS from remotely sensed data already exist.Nevertheless, their application is generally limited by site-specific factors, and presently there is no uniform remote sensing model to estimate TSS.The present study is focused in the development, evaluation and validation of different algorithms to estimate total suspended solids concentration based on laboratory reflectance data.At this aim, a laboratory experiment was designed to collect the spectral reflectance of water containing fixed suspended particulate matter in terms of its concentration.During the experiment, a total of 10 silty clay loam sediment samples were introduced into a tank filled with clear water up to a depth of 22 cm, illuminated by two 45 W lamps focused on center of water surface. After sieving, sediments were weighed so that TSS concentration ranging from 150 up to 2000 mg/L were obtained in the tank, being soil sediments suspension guaranteed by means of a mechanical pump-driven device.Optical data were collected few minutes after each sediment introduction, using an Ocean Optics Jaz spectroradiometer mounted on a platform above the tank.In accordance with previous studies, collected reflectance spectra of water containing sediments showed that, whatever is sediment concentration in water, reflectance in the red region is larger than that in the NIR region. Furthermore, reflectance spectra generally present two peaks: one between 550 nm and 750 nm, and the other between 750 nm and 850 nm, being the second peak insignificant for samples with very small TSS (e.g., SSC=150 mg/L), due to strong absorption of water.After collection, laboratory reflectance spectra were integrated over the bandpass of different sensors&#8217; selected bands, modulated by their relative response functions (RSR).The basic principle of using a specific band, or band ratios to estimate a water parameter is to select spectral bands representative of its scattering/absorption features.Band selection was achieved testing some previously formulated ocean color algorithms for the estimation of water quality parameters.After band selection, linear regression model was applied to estimate the relationship between sensors&#8217; reflectance at these bands and suspended solids concentration.Results showed high correlation between selected sensors&#8217; spectral red band and total suspended solids concentration higher than 500 mg/L up to 1360 mg/L, while less accuracy was observed for TSS concentrations higher than 1360 mg/L. Furthermore, the ratio between spectral red and green bands better estimates TSS in waters where total suspended concentration is not higher than 500 mg/L.&#160;

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