Remote-sensing techniques for analysing landslide kinematics: a review

2007 ◽  
Vol 178 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Christophe Delacourt ◽  
Pascal Allemand ◽  
Etienne Berthier ◽  
Daniel Raucoules ◽  
Bérangère Casson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Laser Scanner ◽  
Surface Displacement ◽  
Radar Data ◽  
Image Correlation ◽  
Aerial Images ◽  
Optical Data ◽  
Mountainous Area ◽  
Atmospheric Conditions

Abstract Surface displacement field of landslides is a key parameter to access to their geometries and mechanical properties. Surface displacements can be calculated using remote-sensing methods such as interferometry for radar data and image correlation for optical data. These methods have been elaborated this last decade and successfully applied on sensors (radar, cameras, terrestrial 3D laser scanner imaging) either attached to space or aerial platforms such as satellites, planes, and unmanned radio-controlled platforms (drones and helicopters) or settled at fixed positions emplaced in the front of landslides. This paper reviews the techniques of image analysis (interferometry and optical data correlation) to measure displacements and examines the performance of each type of platforms. Examples of applications of these techniques in French South Alps are shown. Depending on the landslide characteristics (exposure conditions, size, velocity) as well as the goal of the study (operational or scientific purpose), one or a combination of several techniques and data (characterized by several resolution, accuracy, covered surface, revisiting time) have to be used. Radar satellite data processed with differential interferometric or PS methods are mainly suitable for scientific purposes due to various application limitations in mountainous area. Optical satellite and aerial images can be used for scientific studies at fairly high resolution but are strongly dependant on atmospheric conditions. Platforms and sensors such as drone, fixed camera, fixed radar and Lidar have the advantage of high adaptability. They can be used to obtain very high resolution and precise 3D data (of centimetric accuracy) suitable for both scientific and operational purposes.

Spatial changes of forests in a coastal and a remote mountainous area of Greece over a 65-year period

2020 ◽  
Vol 11 (4) ◽  
pp. 93-109
Author(s):  
Stavros KOLIOS ◽  
◽  
George NTOGAS ◽  
Efthimios ZERVAS ◽  
◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Arable Land ◽  
Aerial Images ◽  
Mountainous Area ◽  
Land Use Land Cover ◽  
Spatial Homogeneity ◽  
Spatial Changes ◽  
Forested Areas ◽  
Of Greece

The scope of the study is to detect spatial changes in the forested areas over six decades (1945 - 2010) of two completely different landscapes in Greece (pilot areas). The first pilot area is Kastoria which is a relatively remote and mountainous area located northwestern on the Greek peninsula, while the second one is Propontida which is a coastal area in the Chalkidiki peninsula (central Macedonia, Greece). High resolution orthorectified aerial images are used to detect the general types (classes) of land use/land cover (LULC) in these pilot areas. The results reveal that during the examined period, a notable spatial growth and thickening of the forest areas was found (10,51%) in the pilot area of Kastoria. The spatial homogeneity of the forested areas in Kastoria decreased only by 2,11%. Regarding Propontida, the forested areas decreased in total about 13,02% while the agricultural and arable land has increased by 12,10%.

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

2019 ◽  
Vol 11 (20) ◽  
pp. 2389 ◽  
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.

Confronting and combining high-resolution simulations (ICON-LEM) with remote sensing observations

2020 ◽  
Author(s):  
Vera Schemann ◽  
Mario Mech
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
General Structure ◽  
Radar Data ◽  
Synoptic Variability ◽  
Lateral Boundary Conditions ◽  
Large Eddy ◽  
Land Surfaces ◽  
Selection Of

<p>The current generation of large-eddy models (e.g. the ICON-LEM) allows us to go beyond idealized simulations and to capture synoptic variability by including heterogeneous land surfaces as well as lateral boundary conditions. This would offer the possibility to compare simulations and observations of clouds on a detailed day-to-day basis. But while LEMs are able to reach resolutions that start to be comparable to state-of-the-art observations (e.g. Radar data), they are still facing the issue of different parameter spaces: either the model output has to be transfered to observable quantities, or the other way around. We will present examples from recent field campaigns (e.g. ACLOUD, EUREC4A), where we combined ICON-LEM simulations with remote sensing observations by applying the Passive and Active Microwave TRAnsfer simulator (PAMTRA). By the selection of examples, we will show the potential of this combination of high-resolution modeling, remote sensing observations and forward simulations at different places under different conditions (Arctic, European and Caribbean). While the general structure of clouds (e.g. timing, type, height) is often already captured quite well, the comparison to the remote sensing observations allows us to also get insights into the composition of clouds and to constrain microphysical parameterizations as well as the influence of the large-scale forcing on a more detailed level.</p>

scholarly journals Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Vol 21 (9) ◽  
pp. 2753-2772
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach ◽  
Landslide Early Warning

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

scholarly journals Fuzzy Similarity Measure and its Application to High Resolution Colour Remote Sensing Image Processing

2021 ◽  
Author(s):  
Yu Li
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Similarity Measure ◽  
Remote Sensing Image ◽  
Aerial Images ◽  
Fuzzy Similarity ◽  
Short Range Ordering ◽  
Remote Sensing Image Processing ◽  
The Relationship

The focus for the study in this thesis is placed on developing basic algorithms and tools for high-resolution colour remote sensing image processing tasks such as colour morphology, multivariate clustering, and multivariate filtering. First, the fuzzy similarity measure (FSM) among vectors in a vector space is introduced. This measure is based on two assumptions for the relationship among vectors: short-range ordering and fuzzification. Second, based on the FSM, the colour morphology, multivariate fuzzy clustering, and multivariate filtering are defined. The performances of all proposed methods will be evaluated numerically and subjectively. Third, this study also places more emphases on solving some applied problems related to recognizing colour edges, detecting and extracting complex road network and building rooftops, and reducing noise in high-resolution remote sensing images such as QuickBird, Ikonos, and aerial images. The results obtained in the study demonstrate the effectiveness and efficacy of the FMS and the proposed methods.

scholarly journals The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 107 ◽  
Author(s):  
Lorena Hojas Gascón ◽  
Guido Ceccherini ◽  
Francisco García Haro ◽  
Valerio Avitabile ◽  
Hugh Eva
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Relative Efficiency ◽  
National Level ◽  
Remote Sensing Data ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Field Plot ◽  
Sensing Data ◽  
Image Objects

In this paper, we review the potential of high resolution optical satellite data to reduce the significant investment in resources required for a national field survey for producing estimates of above ground biomass (AGB). We use 5 m resolution RapidEye optical data to support a country wide biomass inventory with the objective of bringing to the attention of the traditional forestry sector the advantages of integrating remote sensing data in the planning and execution of field data acquisition. We analysed the relationship between AGB estimates from a subset of the national survey field plot data collected by the Tanzania Forest Service, with a set of remote sensing biophysical parameters extracted from a sample of fine spatial (5 m) resolution RapidEye images using a regression estimator. We processed RapidEye data using image segmentation for 76 sample sites each of 20 km by 20 km (covering 2.3% of the land area of the country) to image objects of 1 ha. We extracted reflectance and texture information from those objects which overlapped with the field plot data and tested correlations between the two using four different models: Two models from inferential statistics and two models from machine learning. The best results were found using the random forests algorithm (R2 = 0.69). The most important explicative factor extracted from the remote sensing data was the shadow index, measuring the absorption of light in the visible bands. The model was then applied to all image objects on the RapidEye images to obtain AGB for each of the 76 sample sites, which were then interpolated to estimate the AGB stock at the national scale. Using the relative efficiency measure, we assessed the improvement that the introduction of remote sensing data brings to obtain an AGB estimate at the national level, with the same precision as the full survey. The improvement in the precision of the estimate (by reducing its variance) resulted in a relative efficiency of 3.2. This demonstrates that the introduction of remote sensing data at this fine resolution can substantially reduce the number of field plots required, in this case threefold.

scholarly journals Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

2021 ◽  
Vol 13 (2) ◽  
pp. 243
Author(s):  
Amal Chakhar ◽  
David Hernández-López ◽  
Rocío Ballesteros ◽  
Miguel A. Moreno
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Optical Data ◽  
Support Vector ◽  
Sensing Data ◽  
Crop Classification

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

scholarly journals C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

2021 ◽  
Author(s):  
Nadia Ouaadi ◽  
Jamal Ezzahar ◽  
Saïd Khabba ◽  
Salah Er-Raki ◽  
Adnane Chakir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Scientific Community ◽  
Surface Soil ◽  
Radar Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Semi Arid

Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

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