scholarly journals Analysis and Distribution of the Rainfall Monitoring Network in a Brazilian Pantanal Region

2017 ◽  
Vol 32 (2) ◽  
pp. 199-205 ◽  
Author(s):  
Marcia Ferreira Cristaldo ◽  
Celso Correia de Souza ◽  
Leandro de Jesus ◽  
Carlos Roberto Padovani ◽  
Paulo Tarso Sanches de Oliveira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Monitoring Systems ◽  
Sensing Data ◽  
The World ◽  
Data Gap ◽  
Flood Dynamics ◽  
Brazilian Pantanal ◽  
Current Monitoring

Abstract To better understand drought and flood dynamics in the Pantanal is crucial an adequate hydrometeorological monitoring network. However, few studies have investigated whether the current monitoring systems are suitable in this region. Here, we analyzed the hydrometeorological monitoring network of the Aquidauana region, composed of pluviometric, meteorological and fluviatile gauging stations. We obtained data of all hydrometeorological gauges available in this region to compare with the World Meteorological Organization (WMO) recommendation. We found that although the number of stations in operation is satisfactory when compared with that established by the WMO, the network is not satisfactory in the operating stations because of lack of maintenance, thus creating a need for additional stations. This fact was also observed when analyzing the meteorological network. Using remote sensing data may be possible to fill these data gap. However, to improve the knowledge on hydrological processes in this region is still necessary to install additional ground-based stations.

scholarly journals Preface: Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics

2019 ◽  
Vol 11 (8) ◽  
pp. 943 ◽  
Author(s):  
Alessio Domeneghetti ◽  
Guy J.-P. Schumann ◽  
Angelica Tarpanelli
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Flood Mapping ◽  
Special Issue ◽  
Sensing Data ◽  
Wide Range ◽  
Flood Monitoring ◽  
Novel Processing ◽  
Flood Dynamics ◽  
Processing Techniques

This Special Issue is a collection of papers that focus on the use of remote sensing data and describe methods for flood monitoring and mapping. These articles span a wide range of topics; present novel processing techniques and review methods; and discuss limitations and challenges. This preface provides a brief overview of the content.

scholarly journals Assessment of the environmental situation in Krasnoyarsk using remote sensing data

2021 ◽  
Author(s):  
K.V. Krasnoshchekov ◽  
O.E. Yakubailik
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Air Monitoring ◽  
Coefficient Of Determination ◽  
Scientific Center ◽  
Sensing Data ◽  
Environmental Situation ◽  
Analytical System

The data on ground concentrations of aerosols and small gas components (particulate matter PM2.5 and sulfur dioxide NO2) were compared with remote sensing data obtained over the territory of Krasnoyarsk from June to August 2020. We use the air monitoring system of the Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences (KSC SB RAS) to determine the concentration of PM2.5. NO2 concentrations were taken according to the data of the State departmental information and analytical system of the Ministry of Ecology of the region. It is shown that the remote sensing data of the MODIS MAIAC algorithm with a spatial resolution of 1 km can be used to determine the concentration of PM2.5 as an addition to the data obtained by the ground-based air monitoring system of the KSC SB RAS. The MAIAC data were calculated using two different models and are given to the measurement system used in the KSC SB RAS monitoring network. A high coefficient of determination between satellite and ground monitoring data was obtained. Determination coefficients were also obtained for NO2, showing how applicable the remote sensing data are for assessing the environmental situation in Krasnoyarsk.

scholarly journals PROMISING ADVANCES OF AMAZONIAN MONITORING SYSTEMS THROUGHOUT VANGUARD TECHNOLOGY AND SCIENTIFIC KNOWLEDGE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 843-849
Author(s):  
L. S. Soler ◽  
D. E. Silva ◽  
C. Messias ◽  
T. C. Lima ◽  
B. M. P. Bento ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Remote Sensing Data ◽  
Data Access ◽  
Data Cube ◽  
Monitoring Systems ◽  
Data Repositories ◽  
Sensing Data ◽  
Learning Techniques ◽  
Historical Series

Abstract. PRODES and DETER project together turned 33 years-old with an undeniably contribution to the state-of-art in mapping and monitoring tropical deforestation in Brazil. Monitoring systems all over the world have taken advantage of big data repositories of remote sensing data as they are becoming freely available together with artificial intelligence. Thus, considering the advent of new generation remote sensing data hubs, online platforms of big data that can fill in spatial and temporal resolutions gaps in current deforestation mapping, this work aims to present recent innovations at INPE´s deforestation monitoring systems in Brazil and how they are gauging new realms of technological levels. Recent innovations at INPE´s monitoring systems are: 1) the development of TerraBrasilis platform of data access and analysis; 2) the adoption of new sensors and cloud detection strategies; 3) the complementary use of multi-sensor images; 4) the complementary adoption of SAR C-band images using cloud data to sample and process Sentinel-1. Future innovations are: 1) development of a Brazilian data cube to be used in deep learning techniques of image classification; 2) Routine uncertainty analysis of PRODES data. Automatization might fasten mapping process, but the real challenge is to succeed in automatization maintaining data quality and historical series. The hyper-availability of remote sensing data, the initiative of a Brazilian Data Cube and promising machine learning techniques applied to land cover change detection, allowed INPE to reinforce its central role in tropical forest monitoring.

Remote sensing data and field survey activities for monitoring the evolution of forest systems after coppicing and soil erosion: A case study in Sardinia (Italy)

2020 ◽  
Author(s):  
Filippo Giadrossich ◽  
Antonio Ganga ◽  
Sergio Campus ◽  
Ilenia Murgia ◽  
Irene Piredda ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Vegetation Indices ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Forest Stands ◽  
Sensing Data ◽  
Long Term Monitoring ◽  
Term Monitoring

<p>The practice of coppicing is debated in the literature for the risk factors associated with soil erosion. Although erosion experiments provide useful data for estimating the susceptibility to soil erosion, there are many open questions that cannot be solved in isolated experiments, but which can be assessed by activating a long-term monitoring process. In this way, it is possible to correctly frame the spatial and temporal scale of soil erosion in coppice forests. </p><p>The aim of the work is to evaluate the effectiveness of the use of remote sensing data in combination with field data, for monitoring the evolution of forest stands interested by coppicing in relation to soil erosion. </p><p>We have installed a long-term monitoring network for erosion estimation, while Sentinel-2C satellite data were used for the period 2016-2018. Starting from this dataset, a selection of vegetation indices was calculated and compared to the morphological and topographical parameters of the study area, as well as the above-ground data collected during field activities. Using the Canonical Correspondences Analysis (CCA) the relationships between the matrix of vegetation indices, topographic and vegetational parameters and the respective performances of this protocol have been explored in order to describe the evolution of the forest stands in the study area associated to soil losses.</p>

scholarly journals Geoinformatics – An Integrated Spatial Research Tool

2004 ◽  
Vol 11 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Jan Romuald Olędzki
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Research Tool ◽  
Sensing Data ◽  
Precise Analysis ◽  
The University ◽  
Current Monitoring

Abstract The structure of geoinformatics can be understood in many ways, what can be seen from the more or the less complex schemas published in various articles. Geoinformatics creates new possibilities for the precise analysis of spatial phenomena, such as for following their dynamics or defining the associations existing between their components. The use of remote sensing data in such research, takes to another level those areas of knowledge, in which there nevertheless still is a scarcity of reliable materials. It also enables the current monitoring of those phenomena which can’t be investigated and estimated in any other way, as well as the modeling of spatial (geographical) phenomena. Since 1996, many studies have been performed at the Laboratory of Remote Sensing of the Environment at the University of Warsaw, in which remote sensing data were integrated with data obtained by other means.

scholarly journals Snow-cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data

2015 ◽  
Vol 9 (2) ◽  
pp. 451-463 ◽  
Author(s):  
A. Gafurov ◽  
S. Vorogushyn ◽  
D. Farinotti ◽  
D. Duethmann ◽  
A. Merkushkin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Snow Cover Area ◽  
Sensing Data ◽  
Mountainous Regions ◽  
Snow Cover Extent ◽  
Spatially Distributed ◽  
Data Gap ◽  
Availability Assessment

Abstract. Spatially distributed snow-cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow-cover area are usually required, e.g., for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow-cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used in mountainous regions to overcome the data gap for earlier decades when the availability of remote sensing snow-cover data was strongly limited.

scholarly journals Monitoring Grassland Tourist Season of Inner Mongolia, China Using Remote Sensing Data

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Quansheng Ge ◽  
Xi Yang ◽  
Zhi Qiao ◽  
Haolong Liu ◽  
Jun Liu
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Inner Mongolia ◽  
Remote Sensing Data ◽  
Research Interest ◽  
Spatial Trend ◽  
Sensing Data ◽  
The World

Phenology-driven events, such as spring wildflower displays or fall tree colour, are generally appreciated by tourists for centuries around the world. Monitoring when tourist seasons occur using satellite data has been an area of growing research interest in recent decades. In this paper, a valid methodology for detecting the grassland tourist season using remote sensing data was presented. On average, the beginning, the best, and the end of grassland tourist season of Inner Mongolia, China, occur in late June (±30 days), early July (±30 days), and late July (±50 days), respectively. In south region, the grassland tourist season appeared relatively late. The length of the grassland tourist season is about 90 days with strong spatial trend. South areas exhibit longer tourist season.

scholarly journals Snow cover reconstruction methodology based on historic in situ observations and recent remote sensing data

2014 ◽  
Vol 8 (5) ◽  
pp. 4645-4680
Author(s):  
A. Gafurov ◽  
S. Vorogushyn ◽  
A. Merkushkin ◽  
D. Duethmann ◽  
D. Farinotti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Snow Cover Area ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Spatially Distributed ◽  
Temporal Persistence ◽  
Data Gap ◽  
Availability Assessment

Abstract. Spatially distributed snow cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow cover area (SCA) are usually required e.g. for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross-validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used to overcome the data gap for earlier decades when the availability of remote sensing snow cover data was strongly limited.

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