scholarly journals Organohalogen emissions from saline environments – spatial extrapolation using remote sensing as most promising tool

2012 ◽  
Vol 9 (3) ◽  
pp. 1225-1235 ◽  
Author(s):  
K. Kotte ◽  
F. Löw ◽  
S. G. Huber ◽  
T. Krause ◽  
I. Mulder ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rapid Change ◽  
Research Area ◽  
Climatic Conditions ◽  
Aral Sea ◽  
Daily Production ◽  
Saline Environments ◽  
Laboratory Setup ◽  
Promising Tool ◽  
Supervised Image Classification

Abstract. Due to their negative water budget most recent semi-/arid regions are characterized by vast evaporates (salt lakes and salty soils). We recently identified those hyper-saline environments as additional sources for a multitude of volatile halogenated organohalogens (VOX). These compounds can affect the ozone layer of the stratosphere and play a key role in the production of aerosols. A remote sensing based analysis was performed in the Southern Aral Sea basin, providing information of major soil types as well as their extent and spatial and temporal evolution. VOX production has been determined in dry and moist soil samples after 24 h. Several C1- and C2 organohalogens have been found in hyper-saline topsoil profiles, including CH3Cl, CH3Br, CHBr3 and CHCl3. The range of organohalogens also includes trans-1,2-dichloroethene (DCE), which is reported here to be produced naturally for the first time. Using MODIS time series and supervised image classification a daily production rate for DCE has been calculated for the 15 000 km2 ranging research area in the southern Aralkum. The applied laboratory setup simulates a short-term change in climatic conditions, starting from dried-out saline soil that is instantly humidified during rain events or flooding. It describes the general VOX production potential, but allows only for a rough estimation of resulting emission loads. VOX emissions are expected to increase in the future since the area of salt affected soils is expanding due to the regressing Aral Sea. Opportunities, limits and requirements of satellite based rapid change detection and salt classification are discussed.

scholarly journals Organohalogen emission from saline environments – spatial extrapolation using remote sensing as most promising tool

2011 ◽  
Vol 8 (4) ◽  
pp. 7525-7550 ◽  
Author(s):  
K. Kotte ◽  
F. Löw ◽  
S. G. Huber ◽  
I. Mulder ◽  
H. F. Schöler
Keyword(s):  
Remote Sensing ◽  
Rapid Change ◽  
Research Area ◽  
Climatic Conditions ◽  
Aral Sea ◽  
Daily Production ◽  
Saline Environments ◽  
Promising Tool ◽  
Salt Affected Soils ◽  
Supervised Image Classification

Abstract. Due to their negative water budget most recent semi-/arid regions are characterized by vast evaporates (salt lakes and salty soils). We recently identified those hyper-saline environments as additional sources for a multitude of volatile halogenated organohalogens (VOX). These compounds affect the ozone layer of the stratosphere and play a key role in the production of aerosols. A remote sensing based analysis was performed in the southern Aral Sea basin, providing information of main soil types as well as their extent and spatial and temporal evolution. VOX production has determined in dry and moist soil samples for 24 h. Several C1- and C2 organohalogens, including chloromethane and bromomethane, have been found in hyper-saline topsoil profiles. The range of naturally produced organohalogens includes dichloroethene. For the 15 000 km2 ranging research area in the southern Aralkum desert a daily production of up to 23 t dichloroethene has been calculated using MODIS time series and supervised image classification. The applied setup reproduces a short-term change in climatic conditions starting from dried-out saline soil, instantly humidified during rain events or flooding. VOX emission from dry fallen Aral Sea sediments will further increase since the area of salt affected soils is expected to increase in future. Opportunities, limits and requirements of satellite based rapid change detection and salt classification are discussed.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Landscape Change in the Steppe of Algeria South-West Using Remote Sensing

2018 ◽  
Vol 18 (1) ◽  
pp. 41-52
Author(s):  
Tayeb Sitayeb ◽  
Ishak Belabbes
Keyword(s):  
Remote Sensing ◽  
Social Systems ◽  
Bare Soil ◽  
Climatic Conditions ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Natural Ecosystems ◽  
Stipa Tenacissima ◽  
Steppe Species ◽  
Sparse Vegetation

Abstract Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil.

scholarly journals Observation of Oceanic Eddy in the Northeastern Arabian Sea Using Multisensor Remote Sensing Data

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
R. K. Sarangi
Keyword(s):  
Remote Sensing ◽  
Arabian Sea ◽  
Chlorophyll Concentration ◽  
Climatic Conditions ◽  
Late Winter ◽  
Surrounding Water ◽  
Noaa Avhrr ◽  
Oceanic Eddy ◽  
Remote Sensing Satellite ◽  
Cold Core

An oceanic eddy of size about 150 kilometer diameter observed in the northeastern Arabian Sea using remote sensing satellite sensors; IRS-P4 OCM, NOAA-AVHRR and NASA Quickscat Scatterometer data. The eddy was detected in the 2nd week of February in Indian Remote Sensing satellite (IRS-P4) Ocean Color Monitor (OCM) sensor retrieved chlorophyll image on 10th February 2002, between latitude 16°90′–18°50′N and longitude 66°05′–67°60′E. The chlorophyll concentration was higher in the central part of eddy (~1.5 mg/m3) than the peripheral water (~0.8 mg/m3). The eddy lasted till 10th March 2002. NOAA-AVHRR sea surface temperature (SST) images generated during 15th February-15th March 2002. The SST in the eddy’s center (~23°C) was lesser than the surrounding water (~24.5°C). The eddy was of cold core type with the warmer water in periphery. Quickscat Scatterometer retrieved wind speed was 8–10 m/sec. The eddy movement observed southeast to southwest direction and might helped in churning. The eddy seemed evident due to convective processes in water column. The processes like detrainment and entrainment play role in bringing up the cooler water and the bottom nutrient to surface and hence the algal blooming. This type of cold core/anti-cyclonic eddy is likely to occur during late winter/spring as a result of the prevailing climatic conditions.

scholarly journals Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

2020 ◽  
Vol 12 (12) ◽  
pp. 2013
Author(s):  
Konstantinos Topouzelis ◽  
Dimitris Papageorgiou ◽  
Alexandros Karagaitanakis ◽  
Apostolos Papakonstantinou ◽  
Manuel Arias Ballesteros
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Field Experiments ◽  
Spectral Unmixing ◽  
Sea Surface ◽  
Spectral Signature ◽  
Unmanned Aerial Systems ◽  
Real Environment ◽  
Promising Tool ◽  
Sentinel 2

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.

scholarly journals Agroecological optimization of the technology for growing spring barley in the Penza region using the DSS

2021 ◽  
Vol 4 (46) ◽  
pp. 22-22
Author(s):  
Alexander Saakian ◽  
◽  
Keyword(s):  
Spring Barley ◽  
Calculated Data ◽  
Farming Systems ◽  
Research Area ◽  
Climatic Conditions ◽  
Mineral Fertilizers ◽  
Soil Conditions ◽  
Complete Set ◽  
Actual Yield ◽  
Program Modules

Abstract The article presents the results of modeling the cultivation of barley on leached chernozems of the Penza region. In order to conduct modeling, the Decision Support System (DSS) for agroecological optimization of adaptive farming systems was modernized. The adaptation of the program modules to the climatic and soil conditions of a particular research area allowed us to reach 7% of the error when modeling the cultivation of agricultural crops in the presence of a complete set of indicators necessary for building the model. Technological calculations of the model made it possible to reduce the number of minimum necessary technological operations, as well as rationally distribute the application of mineral fertilizers for the planned yield. The economic calculations of the model allowed us to achieve a high profitability of production of 66±7%. The constructed model was tested at the experimental field in 2020. Practical verification showed the possibility of using the model in agricultural production under normal climatic conditions and its high correlation with the actual results obtained. Statistical analysis of the calculated data of the model and the actual yield with the achieved economic indicators in the conditions of the model field showed the level of reliability of calculations of 95%. Keywords: AGROECOLOGICAL OPTIMIZATION, AGROECOLOGICAL ASSESSMENT, AGROECOLOGICAL MODELING

scholarly journals Estimation of ecosystem services value based on land use and cover change information derived from remote sensing data

2020 ◽  
Vol 198 ◽  
pp. 04026
Author(s):  
Liyan Wang ◽  
Chao Chen ◽  
Kai Wang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Ecosystem Services ◽  
Quantitative Estimation ◽  
Research Area ◽  
Cultivated Land ◽  
Land Area ◽  
Construction Land ◽  
Ecosystem Services Value ◽  
Land Use And Cover

It is an effective method to study the value change of ecological services based on land use and cover change information. This paper analyzed the land use and cover change information in the research area, which is based on the remote sensing images and social statistics data of 2005, 2010, and 2015, and then, quantitative estimation of the ecosystem service value was performed. Yangtze-Huaihe river basin, China is a fragile ecological area, which is selected as the research area. During 2005-2015, the area of cultivated land and construction land was the main land use types in the study area, the land use and cover change in the study area were obvious, which was characterized by the increasing of construction land area and the decreasing of cultivated land area, and the total ecosystem services value in the research area has been decreasing continuously, the value from 34.376 billion yuan in 2005 to 26.161 billion yuan in 2015.

Dynamics of Dust Transfer from the Desiccated Aral Sea Bottom Analysed by Remote Sensing

2011 ◽  
pp. 97-106 ◽  
Author(s):  
L. Spivak ◽  
A. Terechov ◽  
I. Vitkovskaya ◽  
M. Batyrbayeva ◽  
L. Orlovsky
Keyword(s):  
Remote Sensing ◽  
Aral Sea ◽  
Sea Bottom

scholarly journals Research on Cultivated Land Change Based on GIS and High Resolution Remote Sensing Images

2018 ◽  
Vol 228 ◽  
pp. 02013
Author(s):  
Haibo Yu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Automatic Monitoring ◽  
Research Area ◽  
Decision Fusion ◽  
Land Change ◽  
Fuzzy Decision ◽  
Remote Sensing Images ◽  
Monitoring Method ◽  
Gis Data

This paper study an automatic monitoring method for land change based on high resolution remote sensing images and GIS data, and we use three classification methods to classify and fuse the research area. Secondly, the paper calculates the corresponding map class components and compares them with their historical attributes; it can automatically monitor land use change. The experimental results show that the fuzzy decision fusion classification can significantly improve the classification effect, and it can accurately determine the change area accurately and automatically. However, there are some partial errors in the region.

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