Large and Small Scale Multi-Sensors Remote Sensing for Landslide Characterisation and Monitoring

2020 ◽  
pp. 349-359
Author(s):  
Carlo Tacconi Stefanelli ◽  
Teresa Gracchi ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Small Scale

Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Human Life ◽  
Remote Sensing Data ◽  
Modern World ◽  
Small Scale ◽  
Vegetation Map ◽  
Gis Technologies ◽  
Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

scholarly journals Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3000
Author(s):  
Georg Zitzlsberger ◽  
Michal Podhorányi ◽  
Václav Svatoň ◽  
Milan Lazecký ◽  
Jan Martinovič
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Urban Change ◽  
Small Scale ◽  
Atmospheric Effects ◽  
Wide Field ◽  
Ground Truth Data ◽  
Automated Method ◽  
Urban Monitoring ◽  
Sar Data

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

scholarly journals Characterization of Arctic mixed-phase cloud properties at small scale and coupling with satellite remote sensing

2017 ◽  
Author(s):  
Guillaume Mioche ◽  
Olivier Jourdan ◽  
Julien Delanoë ◽  
Christophe Gourbeyre ◽  
Guy Febvre ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Crystal Size ◽  
Mixed Phase ◽  
Ice Crystals ◽  
Small Scale ◽  
Vertical Profiles ◽  
Liquid Droplets ◽  
Ice Crystal ◽  
High Concentration ◽  
Ice Crystal Size

Abstract. This study aims to characterize the microphysical and optical properties of ice crystals and supercooled liquid droplets within low-level Arctic mixed-phase clouds (MPC). We compiled and analyzed cloud in situ measurements from 4 airborne campaigns (18 flights, 71 vertical profiles in MPC) over the Greenland Sea and the Svalbard region. Cloud phase discrimination and representative vertical profiles of number, size, mass and shapes of ice crystals and liquid droplets are assessed. The results show that the liquid phase dominates the upper part of the MPC with high concentration of small droplets (120 cm−3, 15&amp;tinsp;μm), and averaged LWC around 0.2 g m−3. The ice phase is found everywhere within the MPC layers, but dominates the properties in the lower part of the cloud and below where ice crystals precipitate down to the surface. The analysis of the ice crystal morphology highlights that irregulars and rimed are the main particle habit followed by stellars and plates. We hypothesize that riming and condensational growth processes (including the Wegener-Bergeron-Findeisein mechanism) are the main growth mechanisms involved in MPC. The differences observed in the vertical profiles of MPC properties from one campaign to another highlight that large values of LWC and high concentration of smaller droplets are possibly linked to polluted situations which lead to very low values of ice crystal size and IWC. On the contrary, clean situations with low temperatures exhibit larger values of ice crystal size and IWC. Several parameterizations relevant for remote sensing or modeling are also determined, such as IWC (and LWC) – extinction relationship, ice and liquid integrated water paths, ice concentration and liquid water fraction according to temperature. Finally, 4 flights collocated with active remote sensing observations from CALIPSO and CloudSat satellites are specifically analyzed to evaluate the cloud detection and cloud thermodynamical phase DARDAR retrievals. This comparison is valuable to assess the sub-pixel variability of the satellite measurements as well as their shortcomings/performance near the ground.

Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

2011 ◽  
Vol 90-93 ◽  
pp. 2836-2839 ◽  
Author(s):  
Jian Cui ◽  
Dong Ling Ma ◽  
Ming Yang Yu ◽  
Ying Zhou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Mean Shift ◽  
Image Features ◽  
Small Scale ◽  
Region Merging ◽  
Segmentation Method ◽  
Shift Method ◽  
Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

Global carbon and water exchange during drought reflects adaptation of rooting depth to climate and topography

2021 ◽  
Author(s):  
Benjamin Stocker ◽  
Shersingh Tumber-Davila ◽  
Alexandra Konings ◽  
Rob Jackson
Keyword(s):  
Remote Sensing ◽  
Snow Accumulation ◽  
Rooting Depth ◽  
Small Scale ◽  
Observation Data ◽  
Systematic Bias ◽  
Thermal Infrared Remote Sensing ◽  
Near Surface ◽  
Rooting Zone ◽  
Zone Water

&lt;p&gt;The rooting zone water storage capacity (S) defines the total amount of water available to plants for transpiration during rain-free periods. Thereby, S determines the sensitivity of carbon and water exchanges between the land surface and the atmosphere, controls the sensitivity of ecosystem functioning to progressive drought conditions, and mediates feedbacks between soil moisture and near-surface air temperatures. While being a central quantity for water-carbon-climate coupling, S is inherently difficult to observe. Notwithstanding scarcity of observations, terrestrial biosphere and Earth system models rely on the specification of S either directly or indirectly through assuming plant rooting depth.&lt;/p&gt;&lt;p&gt;Here, we model S based on the assumption that plants size their rooting depth to maintain function under the expected maximum cumulative water deficit (CWD), occurring with a return period of 40 years (CWD&lt;sub&gt;X40&lt;/sub&gt;), following Gao et al. (2014). CWD&lt;sub&gt;X40&lt;/sub&gt; is &amp;#8220;translated&amp;#8221; into a rooting depth by accounting for the soil texture. CWD is defined as the cumulative evapotranspiration (ET) minus precipitation, where ET is estimated based on thermal infrared remote sensing (ALEXI-ET), and precipitation is from WATCH-WFDEI, modified by accounting for snow accumulation and melt. In contrast to other satellite remote sensing-based ET products, ALEXI-ET makes no a priori assumption about S and, as our evaluation shows, exhibits no systematic bias with increasing CWD. It thus provides a robust observation of surface water loss and enables estimation of S with global coverage at 0.05&amp;#176; (~5 km) resolution.&lt;/p&gt;&lt;p&gt;Modelled S and its variations across biomes is largely consistent with observed rooting depth, provided as ecosystem-level maximum estimates by Schenk et al. (2002), and a recently compiled comprehensive plant-level dataset. In spite of the general agreement of modelled and observed rooting depth across large climatic gradients, comparisons between local observations and global model predictions are mired by a scale mismatch that is particularly relevant for plant rooting depth, for which the small-scale topographical setting and hydrological conditions, in particular the water table depth, pose strong controls.&lt;/p&gt;&lt;p&gt;To resolve this limitation, we investigate the sensitivity of photosynthesis (estimated by sun-induced fluorescence, SIF), and of the evaporative fraction (EF, defined as ET over net radiation) to CWD. By employing first principles for the constraint of rooting zone water availability on ET and photosynthesis, it can be derived how their sensitivity to the increasing CWD relates to S. We make use of this relationship to provide an alternative and independent estimate of S (S&lt;sub&gt;dSIF&lt;/sub&gt; and S&lt;sub&gt;dEF&lt;/sub&gt;), informed by Earth observation data, to which S, modelled using CWD&lt;sub&gt;X40&lt;/sub&gt;, can be compared. Our comparison reveals a strong correlation (R&lt;sup&gt;2&lt;/sup&gt;=0.54) and tight consistency in magnitude between the two approaches for estimating S.&amp;#160;&lt;/p&gt;&lt;p&gt;Our analysis suggests adaptation of plant structure to prevailing climatic conditions and drought regimes across the globe and at catchment scale and demonstrates its implications for land-atmosphere exchange. Our global high-resolution mapping of S reveals contrasts between plant growth forms (grasslands vs. forests) and a discrepant importance across the landscape of plants&amp;#8217; access to water stored at depth, and enables an observation-informed specification of S in global models.&lt;/p&gt;

scholarly journals A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1767
Author(s):  
Davide Cammarano ◽  
Hainie Zha ◽  
Lucy Wilson ◽  
Yue Li ◽  
William D. Batchelor ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Farming Systems ◽  
Small Scale ◽  
Sustainable Food ◽  
Environmentally Sustainable ◽  
Management Zone ◽  
The North ◽  
Landsat Satellite ◽  
The Village

Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N; 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

scholarly journals Remote sensing of cloud droplet radius profiles using solar reflectance from cloud sides – Part 1: Retrieval development and characterization

2019 ◽  
Vol 12 (2) ◽  
pp. 1183-1206 ◽  
Author(s):  
Florian Ewald ◽  
Tobias Zinner ◽  
Tobias Kölling ◽  
Bernhard Mayer
Keyword(s):  
Remote Sensing ◽  
Solar Radiation ◽  
Droplet Size ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Properties ◽  
Trade Wind ◽  
Small Scale ◽  
Passive Remote Sensing ◽  
Remote Sensing Techniques

Abstract. Convective clouds play an essential role for Earth's climate as well as for regional weather events since they have a large influence on the radiation budget and the water cycle. In particular, cloud albedo and the formation of precipitation are influenced by aerosol particles within clouds. In order to improve the understanding of processes from aerosol activation, from cloud droplet growth to changes in cloud radiative properties, remote sensing techniques become more and more important. While passive retrievals for spaceborne observations have become sophisticated and commonplace for inferring cloud optical thickness and droplet size from cloud tops, profiles of droplet size have remained largely uncharted territory for passive remote sensing. In principle they could be derived from observations of cloud sides, but faced with the small-scale heterogeneity of cloud sides, “classical” passive remote sensing techniques are rendered inappropriate. In this work the feasibility is demonstrated to gain new insights into the vertical evolution of cloud droplet effective radius by using reflected solar radiation from cloud sides. Central aspect of this work on its path to a working cloud side retrieval is the analysis of the impact unknown cloud surface geometry has on effective radius retrievals. This study examines the sensitivity of reflected solar radiation to cloud droplet size, using extensive 3-D radiative transfer calculations on the basis of realistic droplet size resolving cloud simulations. Furthermore, it explores a further technique to resolve ambiguities caused by illumination and cloud geometry by considering the surroundings of each pixel. Based on these findings, a statistical approach is used to provide an effective radius retrieval. This statistical effective radius retrieval is focused on the liquid part of convective water clouds, e.g., cumulus mediocris, cumulus congestus, and trade-wind cumulus, which exhibit well-developed cloud sides. Finally, the developed retrieval is tested using known and unknown cloud side scenes to analyze its performance.

scholarly journals Large-Eddy Simulations of Real-World Episodes in Complex Terrain Based on ERA-Reanalysis and Validated by Ground-Based Remote Sensing Data

2019 ◽  
Vol 147 (12) ◽  
pp. 4325-4343 ◽  
Author(s):  
Cornelius Hald ◽  
Matthias Zeeman ◽  
Patrick Laux ◽  
Matthias Mauder ◽  
Harald Kunstmann
Keyword(s):  
Remote Sensing ◽  
Boundary Conditions ◽  
Complex Terrain ◽  
Computing Time ◽  
Remote Sensing Data ◽  
Large Eddy Simulations ◽  
Small Scale ◽  
Sensing Data ◽  
Large Eddy ◽  
Local Weather

Abstract A computationally efficient and inexpensive approach for using the capabilities of large-eddy simulations (LES) to model small-scale local weather phenomena is presented. The setup uses the LES capabilities of the Weather Research and Forecasting Model (WRF-LES) on a single domain that is directly driven by reanalysis data as boundary conditions. The simulated area is an example for complex terrain, and the employed parameterizations are chosen in a way to represent realistic conditions during two 48-h periods while still keeping the required computing time around 105 CPU hours. We show by evaluating turbulence characteristics that the model results conform to results from typical LES. A comparison with ground-based remote sensing data from a triple Doppler-lidar setup, employed during the “ScaleX” campaigns, shows the grade of adherence of the results to the measured local weather conditions. The representation of mesoscale phenomena, including nocturnal low-level jets, strongly depends on the temporal and spatial resolution of the meteorological boundary conditions used to drive the model. Small-scale meteorological features that are induced by the terrain, such as katabatic flows, are present in the simulated output as well as in the measured data. This result shows that the four-dimensional output of WRF-LES simulations for a real area and real episode can be technically realized, allowing a more comprehensive and detailed view of the micrometeorological conditions than can be achieved with measurements alone.

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