scholarly journals Landscape Signature as an Integrative View of Landscape Metrics: A Case Study in Brazil-French Guiana Border

2020 ◽  
Vol 85 ◽  
pp. 1-18
Author(s):  
Romero Gomes Pereira Silva ◽  
Sofia Araújo Zagallo ◽  
Anne-Elisabeth Laques ◽  
Carlos Hiroo Saito
Keyword(s):  
French Guiana ◽  
Remote Sensing Data ◽  
Border Region ◽  
Landscape Analysis ◽  
Landscape Classification ◽  
Landscape Type ◽  
Quantitative Metrics ◽  
Integrative View ◽  
Landscape Geography

The present work presents an integrated view of landscape analysis through the construction of a signature system for the analysis of landscape types. These signatures were based on metrics that informed different patterns for each landscape type, which allowed the behavior of the landscape to be visually analyzed. The signature system was applied through a landscape classification developed through fieldwork to gather data on socioenvironmental categories combined with remote sensing data. The study site was the border region between Brazil and French Guiana. The results of this work showed that in situ landscape classification techniques can be supported by the analysis of quantitative metrics of landscape analysis, reinforcing the need for integrative and systemic studies in landscape geography.

scholarly journals Development of the Statistical Model for Monitoring Salinization in the Mekong Delta of Vietnam Using Remote Sensing Data and In-Situ Measurements

Proceedings ◽  
2018 ◽  
Vol 2 (10) ◽  
pp. 565
Author(s):  
Nguyen Nguyen Vu ◽  
Le Van Trung ◽  
Tran Thi Van
Keyword(s):  
Statistical Model ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Measured Data ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Component Band

This article presents the methodology for developing a statistical model for monitoring salinity intrusion in the Mekong Delta based on the integration of satellite imagery and in-situ measurements. We used Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat- 8 OLI and TIRS) satellite data to establish the relationship between the planetary reflectance and the ground measured data in the dry season during 2014. The three spectral bands (blue, green, red) and the principal component band were used to obtain the most suitable models. The selected model showed a good correlation with the exponential function of the principal component band and the ground measured data (R2 > 0.8). Simulation of the salinity distribution along the river shows the intrusion of a 4 g/L salt boundary from the estuary to the inner field of more than 50 km. The developed model will be an active contribution, providing managers with adaptation and response solutions suitable for intrusion in the estuary as well as the inner field of the Mekong Delta.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

scholarly journals Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

2015 ◽  
Vol 19 (9) ◽  
pp. 3845-3856 ◽  
Author(s):  
F. Todisco ◽  
L. Brocca ◽  
L. F. Termite ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Loss ◽  
Large Scale ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Loss Estimation ◽  
Balance Model ◽  
Estimation Accuracy ◽  
Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

Brazilian aviation nowcasting system: current stage and some results

2021 ◽  
Author(s):  
Vinícius Almeida ◽  
Gutemberg França ◽  
Francisco Albuquerque Neto ◽  
Haroldo Campos Velho ◽  
Manoel Almeida ◽  
...  
Keyword(s):  
Rio De Janeiro ◽  
Remote Sensing Data ◽  
Weather Forecast ◽  
Weather Data ◽  
Vertical Acceleration ◽  
In Situ Data ◽  
Forecasting System ◽  
Under Construction ◽  
Convective Weather

<p>Emphasizes some aspects of the aviation forecasting system under construction for use by the integrated meteorological center (CIMAER) in Brazil. It consists of a set of hybrid models based on determinism and machine learning that use remote sensing data (such as lighting sensor, SODAR, satellite and soon RADAR), in situ data (from the surface weather station and radiosonde) and aircraft data (such as retransmission of aircraft weather data and vertical acceleration). The idea is to gradually operationalize the system to assist CIMAER´s meteorologists in generating their nowcasting, for example, of visibility, ceiling, turbulence, convective weather, ice, etc. with objectivity and precision. Some test results of the developed nowcasting models are highlighted as examples of nowcasting namely: a) visibility and ceiling up to 1h for Santos Dumont airport; b) 6-8h convective weather forecast for the Rio de Janeiro area and the São Paulo-Rio de Janeiro route. Finally, the steps in development and the futures are superficially covered.</p>

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

scholarly journals Use of Global Reanalysis Data in the Study of the Aridity Index in the Magdalena-Cauca Macro-Basin, Colombia

10.29007/92l9 ◽  
2018 ◽  
Author(s):  
Carolina Vega-Viviescas ◽  
David A. Zamora ◽  
Erasmo A. Rodríguez
Keyword(s):  
Southern Oscillation ◽  
Pearson Correlation ◽  
Regional Scale ◽  
Correlation Coefficients ◽  
Aridity Index ◽  
Remote Sensing Data ◽  
Reanalysis Data ◽  
In Situ Data ◽  
Global Reanalysis

The Magdalena-Cauca macro-basin (MCMB) in Colombia, by its tropical location, annually experiences the effects of movement of the Intertropical Convergence Zone, and it is highly affected by interannual macro-climatic phenomena, such as El Niño– Southern Oscillation (ENSO). With the aim of increasing the use of global reanalysis and remote sensing data for supporting water management decisions at the watershed scale and within the framework of the eartH2Observe research project, the aridity index (AI) was calculated with three different data sources. Precipitation products and AI results were compared with their corresponding in-situ national official data. The comparison shows high correlations between the AI derived from observed data and AI obtained from the reanalysis, with Pearson correlation coefficients above 0.8 for two of the products investigated. This shows the importance of using global reanalysis data in water availability studies on a regional scale for the MCMB and the potential of this information in others macrobasins in Colombia including the Orinoquia and Amazon regions, where in-situ data is scarce.

scholarly journals Estudo do ciclo diário da Camada Limite Planetária durante a estação cChuvosa da Amazônia (GOAmazon 2014/15)

2018 ◽  
Vol 40 ◽  
pp. 63 ◽  
Author(s):  
Rayonil Gomes Carneiro ◽  
Alice Henkes ◽  
Gilberto Fisch ◽  
Camilla Kassar Borges
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Remote Sensing Data ◽  
Great Depth ◽  
Wet Season ◽  
Daily Cycle ◽  
Convective Boundary ◽  
In Situ Observations

In the present study, the evolution the diurnal cycle of planetary boundary layer in the wet season at Amazon region during a period of intense observations carried out in the GOAmazon Project 2014/2015 (Green Ocean Amazon).The analysis includes radiosonde and remote sensing data. In general case, the results of the daily cycle in the wet season indicate a Nocturnal boundary layer with a small oscillation in its depth and with a tardy erosion. The convective boundary layer did not present great depth, responding to the low values of sensible heat of the wet season. A comparison between the different techniques(in situ observations and remote sensing)  for estimating the planetary boundary layer is also presented.

scholarly journals A snow cover climatology for the Pyrenees from MODIS snow products

2014 ◽  
Vol 11 (11) ◽  
pp. 12531-12571 ◽  
Author(s):  
S. Gascoin ◽  
O. Hagolle ◽  
M. Huc ◽  
L. Jarlan ◽  
J.-F. Dejoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Forested Areas ◽  
Land Cover Map ◽  
Hydropower Production ◽  
Snow Cover Duration

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (we) and 105 mm respectively, for both MOD10A1 and MYD10A1. Kappa coefficients are within 0.74 and 0.92 depending on the product and the variable. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both datasets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decreases over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gapfilling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

Deriving CME volume and density from remote sensing data

2021 ◽  
Author(s):  
Manuela Temmer ◽  
Lukas Holzknecht ◽  
Mateja Dumbovic ◽  
Bojan Vrsnak ◽  
Nishtha Sachdeva ◽  
...  
Keyword(s):  
Solar Wind ◽  
Solar Wind Speed ◽  
Remote Sensing Data ◽  
Propagation Speed ◽  
Solar Wind Density ◽  
Sheath Region ◽  
Pile Up ◽  
Mass Ejection ◽  
Interplanetary Propagation

&lt;p&gt;Using combined STEREO-SOHO white-light data, we present a method to determine the&amp;#160;volume and density of a coronal mass ejection (CME) by applying the graduated cylindrical&amp;#160;shell model (GCS) and deprojected mass derivation. Under the assumption that the CME &amp;#160;mass is roughly equally distributed within a specific volume, we expand the CME self-similarly and calculate the CME density for distances close to the Sun (15&amp;#8211;30 Rs) and at 1 AU.&amp;#160;The procedure is applied on a sample of 29 well-observed CMEs and compared to their&amp;#160;interplanetary counterparts (ICMEs). Specific trends are derived comparing calculated and&amp;#160;in-situ measured proton densities at 1 AU, though large uncertainties are revealed due to&amp;#160;the unknown mass and geometry evolution: i) a moderate correlation for the magnetic&amp;#160;structure having a mass that stays rather constant and ii) a weak&amp;#160;correlation for the sheath density by assuming the sheath region is an extra mass&amp;#160;- as expected for a mass pile-up process - that is in its amount comparable to the initial CME&amp;#160;deprojected mass. High correlations are derived between in-situ measured sheath density&amp;#160;and the solar wind density and solar wind speed as measured 24&amp;#160;hours ahead of the arrival of the disturbance. This gives additional confirmation that the&amp;#160;sheath-plasma indeed stems from piled-up solar wind material. While the CME&amp;#160;interplanetary propagation speed is not related to the sheath density, the size of the CME&amp;#160;may play some role in how much material is piled up.&lt;/p&gt;

Cloud-based Research Data Infrastructures Integrating In-Situ and Remote Sensing Data

2021 ◽  
Author(s):  
Simon Jirka ◽  
Benedikt Gräler ◽  
Matthes Rieke ◽  
Christian Autermann
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Research Data ◽  
Monitoring Networks ◽  
Sensing Data ◽  
Data Infrastructures ◽  
Practical Experiences ◽  
The One ◽  
Cloud Infrastructures

&lt;p&gt;For many scientific domains such as hydrology, ocean sciences, geophysics and social sciences, geospatial observations are an important source of information. Scientists conduct extensive measurement campaigns or operate comprehensive monitoring networks to collect data that helps to understand and to model current and past states of complex environment. The variety of data underpinning research stretches from in-situ observations to remote sensing data (e.g., from the European Copernicus programme) and contributes to rapidly increasing large volumes of geospatial data.&lt;/p&gt;&lt;p&gt;However, with the growing amount of available data, new challenges arise. Within our contribution, we will focus on two specific aspects: On the one hand, we will discuss the specific challenges which result from the large volumes of remote sensing data that have become available for answering scientific questions. For this purpose, we will share practical experiences with the use of cloud infrastructures such as the German platform CODE-DE and will discuss concepts that enable data processing close to the data stores. On the other hand, we will look into the question of interoperability in order to facilitate the integration and collaborative use of data from different sources. For this aspect, we will give special consideration to the currently emerging new generation of standards of the Open Geospatial Consortium (OGC) and will discuss how specifications such as the OGC API for Processes can help to provide flexible processing capabilities directly within Cloud-based research data infrastructures.&lt;/p&gt;

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