Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Využití družicové SAR interferometrie pro identifikaci a mapování sesuvů ve městě Sánchez, Dominikánské republice

The landscapes we see today are the result of constant changes during millions of years. Mass movement is one of the principal geomorphology process responsible for these changes and occurs in different scales around the world, causing disaster in populated areas. Sánchez is a municipality located in Samaná province, Dominican Republic, where continuous motion had created an atmosphere of uncertainness among the inhabitants, that observed day after day the deterioration of infrastructures, loss of agricultural capability and potential life-threatening situations. Slope instability has been increased by anthropogenic activity and triggers factors as: elimination of forest for coconut plantation, lack of proper wasted drainage and drinking water supply system, growth of community, change of construction material, meteorological phenomena and seismic events. This investigation aims to identify slope movement and map it, using SENTINEL-1 satellite SAR interferometry (InSAR). By applying multi-temporal techniques to a series of SENTINEL-1 scenes, it is possible to recognize a continuous surface deformation in the area. The results will help authorities to develop short and long-term risk management plans.Zeměpisy, které dnes vidíme, jsou výsledkem neustálých změn v průběhu několika miliónů let. Masový pohyb je jedním z hlavních geomorfologických procesů zodpovědných za tyto změny a probíhá v různých měřítkách po celém světě a způsobuje katastrofu v osídlených oblastech. Sánchez je obec ležící v provincii Samaná, Dominikánská republika, kde neustálý pohyb vytvářel atmosféru nejistoty mezi obyvateli, která každodenně pozorovala zhoršování infrastruktury, ztrátu zemědělské kapacity a potenciální život ohrožující situace. Nestabilita svahu byla zvýšena antropogenní aktivitou a spouští faktory jako: odstranění lesů pro kokosové plantáže, nedostatek správného odpadního kanalizace a zásobování pitnou vodou, růst obce, změna stavebního materiálu, meteorologické jevy a seismické události. Cílem tohoto šetření je identifikovat pohyb svahu a mapovat ho pomocí SARINEL-1 satelitní SAR interferometrie (InSAR). Aplikací multičasových technik na řadu scén SENTINEL-1 je možné rozpoznat kontinuální povrchovou deformaci v oblasti. Výsledky pomohou orgánům vypracovat krátkodobé a dlouhodobé plány řízení rizik.548 - Katedra geoinformatikyvýborn

Remote Sensing for Natural or Man-made Disasters and Environmental Changes

Disasters can cause drastic environmental changes. A large amount of spatial data is required for managing the disasters and to assess their environmental impacts. Earth observation data offers independent coverage of wide areas for a broad spectrum of crisis situations. It provides information over large areas in near-real-time interval and supplementary at short-time and long-time intervals. Therefore, remote sensing can support disaster management in various applications. In order to demonstrate not only the efficiency but also the limitations of remote sensing technologies for disaster management, a number of case studies are presented, including applications for flooding in Germany 2013, earthquake in Nepal 2015, forest fires in Russia 2015, and searching for the Malaysian aircraft 2014. The discussed aspects comprise data access, information extraction and analysis, management of data and its integration with other data sources, product design, and organisational aspects

Method for landslides detection with semi-automatic procedures: The case in the zone center-east of Cauca department, Colombia

Landslides are a common natural hazard that causes human casualties, but also infrastructure damage and land-use degradation. Therefore, a quantitative assessment of their presence is required by means of detecting and recognizing the potentially unstable areas. This research aims to develop a method supported on semiautomatic methods to detect potential mass movements at a regional scale. Five techniques were studied: Morphometry, SAR interferometry (InSAR), Persistent Scatterer InSAR (PS-InSAR), SAR polarimetry (PolSAR) and NDVI composites of Landsat 5, Landsat 7, and Landsat 8. The case study was chosen within the mid-eastern area of the Cauca state, which is characterised by its mountainous terrain and the presence of slope instabilities, officially registered in the CGS-SIMMA landslide inventory. This inventory revealed that the type `slide' occurred with 77.4% from the entire registries, `fall' with 16.5%, followed by `creeps' with 3%, flows with 2.6%, and `lateral spread' with 0.43%. As a result, we obtained the morphometric variables: slope, CONVI, TWI, landform, which were highly associated with landslides. The effect of a DEM in the processing flow of the InSAR method was similar for the InSAR coherence variable using the DEMs ASTER, PALSAR RTC, Topo-map, and SRTM. Then, a multiInSAR analysis gave displacement velocities in the LOS direction between -10 and 10 mm/year. With the dual-PolSAR analysis (Sentinel-1), VH and VV C-band polarised radar energy emitted median values of backscatters, for landslides, about of -14.5 dB for VH polarisation and -8.5 dB for VV polarisation. Also, L-band fully polarimetric NASA-UAVSAR data allowed to nd the mechanism of dispersion of CGS landslide inventory: 39% for surface scattering, 46.4% for volume dispersion, and 14.6% for double-bounce scattering. The optical remote sensing provided NDVI composites derived from Landsat series between 2012 and 2016, showing that NDVI values between 0.40 and 0.70 had a high correlation to landslides. In summary, we found the highest categories related to landslides by Weight of Evidence method (WofE) for each spaceborne technique applied. Finally, these results were merged to generate the landslide detection model by using the supervised machine learning method of Random Forest. By taking training and test samples, the precision of the detection model was of about 70% for the rotational and translational types.Los deslizamientos son una amenaza natural que causa pérdidas humanas, daños a la infraestructura y degradación del suelo. Una evaluación cuantitativa de su presencia se requiere mediante la detección y el reconocimiento de potenciales áreas inestables. Esta investigación tuvo como alcance desarrollar un método soportado en métodos semi-automáticos para detectar potenciales movimientos en masa a escala regional. Cinco técnicas fueron estudiadas: Morfometría, Interferometría radar, Interferometría con Persistent Scatterers, Polarimetría radar y composiciones del NDVI con los satélites Landsat 5, Landsat 7 y Landsat 8. El caso de estudio se seleccionó dentro de la región intermedia al este del departamento del Cauca, la cual se caracteriza por terreno montañoso y la presencia de inestabilidades de la pendiente oficialmente registrados en el servicio SIMMA del Servicio Geológico Colombiano. Este inventario reveló que el tipo de movimiento deslizamiento ocurrió con una frecuencia relativa de 77.4%, caidos con el 16.5% de los casos y reptaciones con 3%, flujos con 2.6% y propagación lateral con 0.43%. Como resultado, se obtuvo las variables morfométricas: pendiente, convergencia, índice topográfico de humedad y forma del terreno altamente asociados con los deslizamientos. El efecto de un DEM en el procesamiento del método InSAR fue similar para la variable coherencia usando los DEMs: ASTER, PAlSAR RTC, Topo-map y SRTM. Un análisis Multi-InSAR estimó velocidades de desplazamiento en dirección de vista del radar entre -10 y 10 mm/año. El análisis de polarimetría dual del Sentinel-1 arrojó valores de retrodispersión promedio de -14.5 dB en la banda VH y -8.5dB en la banda VV. Las cuatro polarimetrías del sensor aéreo UAVSAR permitió caracterizar el mecanismo de dispersión del Inventario de Deslizamiento así: 39% en el mecanismo de superficie, 46.4% en el mecanismo de volumen y 14.6% en el mecanismo de doble rebote. La información generada en el rango óptico permitió obtener composiciones de NDVI derivados de la plataforma Landsat entre los años 2012 y 2016, mostrando que el rango entre 0.4 y 0.7 tuvieron una alta asociación con los deslizamientos. En esta investigación se determinaron las categorías de las variables de Teledetección más altamente relacionadas con los movimientos en masa mediante el método de Pesos de Evidencias (WofE). Finalmente, estos resultados se fusionaron para generar el modelo de detección de deslizamientos usando el método supervisado de aprendizaje de máquina Random Forest. Tomando muestras aleatorias para entrenar y validar el modelo en una proporción 70:30, el modelo de detección, especialmente los movimientos de tipo rotacional y traslacional fueron clasificados con una tasa general de éxito del 70%.Ministerio de CienciasConvocatoria 647 de 2014Research line: Geotechnics and Geoenvironmental HazardDoctorad

Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

Impact of wind-induced scatterers motion on GB-SAR imaging

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Ground-based synthetic aperture radar (GB-SAR) sensors represent a cost-effective solution for change detection and ground displacement assessment of small-scale areas in real-time early warning applications. GB-SAR systems based on stepped linear frequency modulated continuous wave signals have led to several improvements such as a significant reduction of the acquisition time. Nevertheless, the acquisition time is still long enough to force a degradation of the quality of the reconstructed images because of possible short-term variable reflectivity of the scenario. This reduction of the quality may degrade the differential interferometric detection process. In scenarios where interesting targets are surrounded by vegetation, this is normally related to atmospheric conditions, in particular, the wind. The present paper characterizes the effect of the short-term variable reflectivity in the GB-SAR image reconstruction and evaluates its equivalent blurring effect, the decorrelation introduced in the SAR images, and the degradation of the extracted parameters. In order to validate the results, the study assesses different GB-SAR images obtained with the RISKSAR-X sensor, which has been developed by the Universitat Politècnica de Catalunya.Peer ReviewedPostprint (published version

An overview of monitoring methods for assessing the performance of nature-based solutions against natural hazards

To bring to fruition the capability of nature-based solutions (NBS) in mitigating hydro-meteorological risks (HMRs) and facilitate their widespread uptake require a consolidated knowledge-base related to their monitoring methods, efficiency, functioning and the ecosystem services they provide. We attempt to fill this knowledge gap by reviewing and compiling the existing scientific literature on methods, including ground-based measurements (e.g. gauging stations, wireless sensor network) and remote sensing observations (e.g. from topographic LiDAR, multispectral and radar sensors) that have been used and/or can be relevant to monitor the performance of NBS against five HMRs: floods, droughts, heatwaves, landslides, and storm surges and coastal erosion. These can allow the mapping of the risks and impacts of the specific hydro-meteorological events. We found that the selection and application of monitoring methods mostly rely on the particular NBS being monitored, resource availability (e.g. time, budget, space) and type of HMRs. No standalone method currently exists that can allow monitoring the performance of NBS in its broadest view. However, equipments, tools and technologies developed for other purposes, such as for ground-based measurements and atmospheric observations, can be applied to accurately monitor the performance of NBS to mitigate HMRs. We also focused on the capabilities of passive and active remote sensing, pointing out their associated opportunities and difficulties for NBS monitoring application. We conclude that the advancement in airborne and satellite-based remote sensing technology has signified a leap in the systematic monitoring of NBS performance, as well as provided a robust way for the spatial and temporal comparison of NBS intervention versus its absence. This improved performance measurement can support the evaluation of existing uncertainty and scepticism in selecting NBS over the artificially built concrete structures or grey approaches by addressing the questions of performance precariousness. Remote sensing technical developments, however, take time to shift toward a state of operational readiness for monitoring the progress of NBS in place (e.g. green NBS growth rate, their changes and effectiveness through time). More research is required to develop a holistic approach, which could routinely and continually monitor the performance of NBS over a large scale of intervention. This performance evaluation could increase the ecological and socio-economic benefits of NBS, and also create high levels of their acceptance and confidence by overcoming potential scepticism of NBS implementations

Dense and long-term monitoring of Earth surface processes with passive RFID -- a review

Billions of Radio-Frequency Identification (RFID) passive tags are produced yearly to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing to monitor earth surface processes. Indeed, passive tags can cost 10 to 100 times less than wireless sensors networks and require little maintenance, facilitating years-long monitoring with ten's to thousands of tags. This study reviews the existing and potential applications of RFID in geosciences. The most mature application today is the study of coarse sediment transport in rivers or coastal environments, using tags placed into pebbles. More recently, tag localization was used to monitor landslide displacement, with a centimetric accuracy. Sensing tags were used to detect a displacement threshold on unstable rocks, to monitor the soil moisture or temperature, and to monitor the snowpack temperature and snow water equivalent. RFID sensors, available today, could monitor other parameters, such as the vibration of structures, the tilt of unstable boulders, the strain of a material, or the salinity of water. Key challenges for using RFID monitoring more broadly in geosciences include the use of ground and aerial vehicles to collect data or localize tags, the increase in reading range and duration, the ability to use tags placed under ground, snow, water or vegetation, and the optimization of economical and environmental cost. As a pattern, passive RFID could fill a gap between wireless sensor networks and manual measurements, to collect data efficiently over large areas, during several years, at high spatial density and moderate cost.Comment: Invited paper for Earth Science Reviews. 50 pages without references. 31 figures. 8 table

Early Forest Fire Detection Using Radio-Acoustic Sounding System

Automated early fire detection systems have recently received a significant amount of attention due to their importance in protecting the global environment. Some emergent technologies such as ground-based, satellite-based remote sensing and distributed sensor networks systems have been used to detect forest fires in the early stages. In this study, a radio-acoustic sounding system with fine space and time resolution capabilities for continuous monitoring and early detection of forest fires is proposed. Simulations show that remote thermal mapping of a particular forest region by the proposed system could be a potential solution to the problem of early detection of forest fires