scholarly journals Modelling soil salinity in Oued El Abid watershed, Morocco

2018 ◽  
Vol 37 ◽  
pp. 04002 ◽  
Author(s):  
El Mouatassime Sabri ◽  
Ahmed Boukdir ◽  
Ismail Karaoui ◽  
Abdelkrim Arioua ◽  
Rachid Messlouhi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Soil Salinity ◽  
Early Stage ◽  
Landsat Imagery ◽  
Low Cost ◽  
Drainage System ◽  
High Accuracy ◽  
Anthropogenic Factors ◽  
Moderate Amount

Soil salinisation is a phenomenon considered to be a real threat to natural resources in semi-arid climates. The phenomenon is controlled by soil (texture, depth, slope etc.), anthropogenic factors (drainage system, irrigation, crops types, etc.), and climate factors. This study was conducted in the watershed of Oued El Abid in the region of Beni Mellal-Khenifra, aimed at localising saline soil using remote sensing and a regression model. The spectral indices were extracted from Landsat imagery (30 m resolution). A linear correlation of electrical conductivity, which was calculated based on soil samples (ECs), and the values extracted based on spectral bands showed a high accuracy with an R2 (Root square) of 0.80. This study proposes a new spectral salinity index using Landsat bands B1 and B4. This hydro-chemical and statistical study, based on a yearlong survey, showed a moderate amount of salinity, which threatens dam water quality. The results present an improved ability to use remote sensing and regression model integration to detect soil salinity with high accuracy and low cost, and permit intervention at an early stage of salinisation.

scholarly journals Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259695
Author(s):  
Elif Günal ◽  
Xiukang Wang ◽  
Orhan Mete Kılıc ◽  
Mesut Budak ◽  
Sami Al Obaid ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Soil Salinity ◽  
Multivariate Regression ◽  
Regression Models ◽  
Satellite Images ◽  
Remotely Sensed ◽  
Small Scale ◽  
Landsat 8 ◽  
Landsat 8 Oli

Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.

scholarly journals INVESTIGATION OF CRYOSPHERE DYNAMICS VARIATIONS IN THE UPPER INDUS BASIN USING REMOTE SENSING AND GIS

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 59-63
Author(s):  
J. Iqbal ◽  
M. Ali ◽  
A. Ali ◽  
D. Raza ◽  
F. Bashir ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Mass Balance ◽  
Snow Cover ◽  
Landsat Imagery ◽  
Low Cost ◽  
Melting Rate ◽  
Indus Basin ◽  
Upper Indus Basin ◽  
Snow Mass

Abstract. Glaciers are storehouses for freshwater. Glaciers Monitoring is one of the most important research areas especially when climate change has been accelerated snowmelt process. The major goal of research was to find snow cover trend for glaciated regions of Pakistan followed by estimation of snow mass balance. The area chosen for it was Upper Indus basin, which includes ranges of Hindukush, Karakoram and Himalayas extended in Pakistan, India and China. This region exhibits high topographic relief and climate change variability. Snow cover trend analysis was performed for eleven years ranging from 2004 to 2014 using Moderate Resolution Imaging Spectroradiometer (MODIS) data imagery product with daily temporal resolution. These results were combined with respective year’s average monthly temperature. Further quantitative analysis was performed to relate presence of greater vegetation as an indication of greater snowmelt using Landsat Imagery for these years. Snow mass balance curves reveal that glaciers are regaining their mass balance after losing mass balance in middle of last decade. In addition to that, only freely available data is used for this study. This purpose behind this approach is to prove RS and GIS has an effective and low-cost tool for snow cover monitoring, also mass balance calculations. Continuous monitoring of snow cover dynamics is effective for prediction and mitigation of hazards associated with areas in proximity of glaciated regions. One common hazard is glacial lake outburst phenomenon, which cause severe flash flooding in downstream areas. Year 2004 has the lowest mass snow balance and 2014 has the highest snow mass balance. These different parameters were analysed and results show that snow start melting in months of May and June and faster melting rate observed in months of July and August. With the advancement in computing technologies, it has been easier for computers to handle and manipulate massive datasets. Remote sensing has proved to be an excellent tool for extraction of data from glaciers, snow and oceans for remote areas. In particular, snow cover/snowmelt can tell us continuously changing melting patterns, which helps concerned authorities to take necessary measures for preserving these storehouses of water and to mitigate effect of global warming.

Remote sensing can locate and assess the changing abundance of hollow-bearing trees for wildlife in Australian native forests

2014 ◽  
Vol 41 (8) ◽  
pp. 703 ◽  
Author(s):  
Christopher J. Owers ◽  
Rodney P. Kavanagh ◽  
Eleanor Bruce
Keyword(s):  
Remote Sensing ◽  
Early Stage ◽  
Structural Parameters ◽  
Low Cost ◽  
Species Abundance ◽  
Aerial Imagery ◽  
Individual Tree ◽  
Persistent Problem ◽  
Native Forests ◽  
Remote Sensing Techniques

Context Hollow-bearing trees are an important breeding and shelter resource for wildlife in Australian native forests and hollow availability can influence species abundance and diversity in forest ecosystems. A persistent problem for forest managers is the ability to locate and survey hollow-bearing trees with a high level of accuracy at low cost over large areas of forest. Aims The aim of this study was to determine whether remote-sensing techniques could identify key variables useful in classifying the likelihood of a tree to contain hollows suitable for wildlife. Methods The data were high-resolution, multispectral aerial imagery and light detection and ranging (Lidar). A ground-based survey of 194 trees, 96 Eucalyptus crebra and 98 E. chloroclada and E. blakelyi, were used to train and validate tree-senescence classification models. Key results We found that trees in the youngest stage of tree senescence, which had a very low probability of hollow occurrence, could be distinguished using multispectral aerial imagery from trees in the later stages of tree senescence, which had a high probability of hollow occurrence. Independently, the canopy-height model used to estimate crown foliage density demonstrated the potential of Lidar-derived structural parameters as predictors of senescence and the hollow-bearing status of individual trees. Conclusions This study demonstrated a ‘proof of concept’ that remotely sensed tree parameters are suitable predictor variables for the hollow-bearing status of an individual tree. Implications Distinguishing early stage senescence trees from later-stage senescence trees using remote sensing offers potential as an efficient, repeatable and cost-effective way to map the distribution and abundance of hollow-bearing trees across the landscape. Further development is required to automate this process across the landscape, particularly the delineation of tree crowns. Further improvements may be obtained using a combination of these remote-sensing techniques. This information has important applications in commercial forest inventory and in biodiversity monitoring programs.

scholarly journals Remote Sensing for Agricultural Crops Based on a Low Cost Quadcopter

2015 ◽  
Vol 13 (34) ◽  
pp. 49-63 ◽  
Author(s):  
Liseth Viviana Campo Arcos ◽  
Juan Carlos Corrales Muñoz ◽  
Agapito Ledezma Espino
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Early Stage ◽  
Low Cost ◽  
Aerial Photographs ◽  
Second Stage ◽  
Stage Of Development ◽  
Shortest Route ◽  
Shortest Path Algorithms ◽  
Ar Drone

This paper presents a proposal for information gathering from crops by means of a low-cost quadcopter known as the AR Drone 2.0. To achieve this, we designed a system for remote sensing that addresses challenges identified in the present research, such as acquisition of aerial photographs of an entire crop and AR Drone navigation on non-planar areas arises. The project is currently at an early stage of development. The first stage describes platform and hardware/software tools used to build the proposed prototype. Second stage characterizes performance experiments of sensors stability and altitude in AR Drone, in order to design an altitude strategy control over non-flat crops. In addition, path planning algorithms based on shortest route by graphs (Dijkstra, A* and wavefront propagation) are evaluated with simulated quadcopter. The implementation of the shortest path algorithms is the beginning to full coverage of a crop. Observations of quadcopter behavior in Gazebo simulator and real tests demonstrate viability to execute the project by using AR Drone like platform of a remote sensing system to precision agriculture.

scholarly journals Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model

2019 ◽  
Vol 08 (03) ◽  
pp. 77-88
Author(s):  
Sarra Hihi ◽  
Zouhair Ben Rabah ◽  
Moncef Bouaziz ◽  
Mahmoud Yassine Chtourou ◽  
Samir Bouaziz
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Regression Model ◽  
Soil Salinity ◽  
Linear Regression Model

scholarly journals Analysis of Shoreline Shift using Satellite Imagery near Makassar City

2020 ◽  
Vol 5 (3) ◽  
pp. 133-138
Author(s):  
Rian Amukti ◽  
Arif Seno Adji ◽  
Syamsuri Ruslan
Keyword(s):  
Remote Sensing ◽  
Central Government ◽  
Landsat Imagery ◽  
Coastal Region ◽  
Anthropogenic Factors ◽  
Temporal Data ◽  
City Region ◽  
Sensing Technology ◽  
Multi Temporal ◽  
Remote Sensing Techniques

Shoreline shift have occurred in the Coastal region of Makassar City in recent years due to abrasion and accretion. Spatial temporal feature extraction of the Makassar City Region has been carried out using remote sensing techniques  withRadiometri,  Geometric Corrections and Composite Imagein the Landsat image dataset in 2009 and 2019. This study aims to analyze shoreline shift near Makassar City with remote sensing technology using Landsat imagery data, based on multi-temporal data with visual and digital analysis techniques between 2009 and 2019. This research contributes to local and central government as baseline data (data base) in making decisions for handling coastal areas. The results showed that the length of the Makassar City coastline without including the coastline length of the islands separated from land in a row that is equal to 37.79 km in 2009. While in 2019 there was a significant change that is 49.82 km. This shows the addition of a coastline of 12.03 km in the span of 10 years. These changes are mainly caused by anthropogenic factors, namely the construction of the pier / port and the reclamation and hydro-oceanographic factors, namely waves, currents and tides.

scholarly journals Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5460
Author(s):  
Lei Lang ◽  
Ke Xu ◽  
Qian Zhang ◽  
Dong Wang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
High Speed ◽  
Complex Geometry ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Remote Sensing Images ◽  
Channel Information ◽  
Sensing Application

Deep learning-based object detection in remote sensing images is an important yet challenging task due to a series of difficulties, such as complex geometry scene, dense target quantity, and large variant in object distributions and scales. Moreover, algorithm designers also have to make a trade-off between model’s complexity and accuracy to meet the real-world deployment requirements. To deal with these challenges, we proposed a lightweight YOLO-like object detector with the ability to detect objects in remote sensing images with high speed and high accuracy. The detector is constructed with efficient channel attention layers to improve the channel information sensitivity. Differential evolution was also developed to automatically find the optimal anchor configurations to address issue of large variant in object scales. Comprehensive experiment results show that the proposed network outperforms state-of-the-art lightweight models by 5.13% and 3.58% in accuracy on the RSOD and DIOR dataset, respectively. The deployed model on an NVIDIA Jetson Xavier NX embedded board can achieve a detection speed of 58 FPS with less than 10W power consumption, which makes the proposed detector very suitable for low-cost low-power remote sensing application scenarios.

scholarly journals Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Germano Heinzelmann ◽  
Michael K. Gilson
Keyword(s):  
Free Energy ◽  
Early Stage ◽  
Binding Free Energy ◽  
Low Cost ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Drug Candidates ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

AbstractAbsolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

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