scholarly journals Remote sensing techniques in mapping spatial variability of salinity in Kano River Irrigation Project (KRIP), Nigeria

2021 ◽  
Vol 40 (4) ◽  
pp. 732-739
Author(s):  
D. Mohammed ◽  
M.M. Maina ◽  
I. Audu ◽  
I.Y. Tudun Wada ◽  
N.K. Nasir
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Close Monitoring ◽  
Output Analysis ◽  
Irrigation Project ◽  
Spectral Bands ◽  
Building Models ◽  
Erdas Imagine ◽  
Inflation Factor ◽  
Remote Sensing Techniques

Salinity has become a major issue in most large scale irrigation schemes, assessing the extent of the spread has become daunting and laborious. Remote sensing techniques were used to map salinity and develop models for extracting and identifying salinity in soils. Sentinel-2B optical imaging satellite with 13 spectral bands and 10 m spatial resolution was used. SNAP Desktop, ERDAS Imagine, and ArcGIS 10.6 software were used as the main GIS packages for building models and running functions such as input, output, analysis, and processing. Stepwise Multiple Linear Regression (MLR) techniques were carried out for the assessment of the spatial distribution of ECe and to predict salinity level at different locations of the Kano River Irrigation Project (KRIP). Four models were developed, however, due to the lower Variance Inflation Factor (VIF), model 2 which is a combination of salinity Index and band 3 (Green band) was used in delineating the spatial extent of the salinity. Close monitoring of the salt development and application of reversal measures were recommended.

scholarly journals How can remote sensing techniques help monitoring the vine and maximize the terroir potential?

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sampling Strategy ◽  
Wine Quality ◽  
Wine Production ◽  
Spectral Bands ◽  
High Resolution Satellite Images ◽  
Remote Sensing Techniques ◽  
Set Up

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.

scholarly journals Classifying vineyards from satellite images: a case study on Burgundy’s Côte d’Or

OENO One ◽  
2014 ◽  
Vol 48 (4) ◽  
pp. 247 ◽  
Author(s):  
Jorge R. Ducati ◽  
Magno G. Bombassaro ◽  
Jandyra M. G. Fachel
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Satellite Images ◽  
Vegetation Index ◽  
Pinot Noir ◽  
Grape Variety ◽  
Spectral Bands ◽  
Remote Sensing Techniques ◽  
Grape Varieties

<p style="text-align: justify;"><strong>Aim</strong>: To use Remote Sensing imagery and techniques to differentiate categories of Burgundian vineyards.</p><p style="text-align: justify;"><strong>Methods and results</strong>: A sample of 201 vine plots or “climats” from the Côte d’Or region in Burgundy was selected, consisting of three vineyard categories (28 Grand Cru, 74 Premier Cru, and 99 Communale) and two grape varieties (Pinot Noir and Chardonnay). A mask formed by the polygons of these vine plots was made and projected on four satellite images acquired by the ASTER sensor, covering the Côte d’Or region in years 2002, 2003 (winter image), 2004 and 2006. Mean reflectances were extracted from pixels within each polygon for each of the nine spectral bands (visible and infrared) covered by ASTER. The database had a total of 797 reflectance spectra assembled over the four images. Statistical discriminant analysis of percentage classification accuracy was made separately for Côte de Nuits and Côte de Beaune, and for each year. Results showed that for individual years and Côtes, classification accuracy for vineyard category was as high as 73.7% (Beaune 2002) and as low as 66.7% (Beaune 2003). There were no significant differences in accuracy between spring, summer and winter images. Classification accuracy for grape variety in Côte de Beaune over the four study years was between 73.5% for Pinot Noir climats in 2004 and 91.9% for Chardonnay climats in 2006, including the winter image. Concerning the vegetation index NDVI, there were no significant differences between vineyard categories.</p><p style="text-align: justify;"><strong>Conclusions</strong>: Satellite data is shown to be functional to reveal vineyard quality. Spectral differences between categories of Burgundian vineyards are at least partially due to terroir characteristics, which are transmitted to vine and vine canopy.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: This work indicates that Remote Sensing techniques can be used as an auxiliary tool for the monitoring of vineyard quality in established viticultural regions and for the study of quality potential in new regions.</p>

scholarly journals Very-high-resolution mapping of river-immersed topography by remote sensing

2008 ◽  
Vol 32 (4) ◽  
pp. 403-419 ◽  
Author(s):  
Denis Feurer ◽  
Jean-Stéphane Bailly ◽  
Christian Puech ◽  
Yann Le Coarer ◽  
Alain A. Viau
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Aerial Image ◽  
Depth Measurement ◽  
Very High Spatial Resolution ◽  
Remote Sensing Techniques ◽  
Logarithmic Relationship ◽  
Ground Penetrating ◽  
Very High

Remote sensing has been used to map river bathymetry for several decades. Non-contact methods are necessary in several cases: inaccessible rivers, large-scale depth mapping, very shallow rivers. The remote sensing techniques used for river bathymetry are reviewed. Frequently, these techniques have been developed for marine environment and have then been transposed to riverine environments. These techniques can be divided into two types: active remote sensing, such as ground penetrating radar and bathymetric lidar; or passive remote sensing, such as through-water photogrammetry and radiometric models. This last technique — which consists of finding a logarithmic relationship between river depth and image values — appears to be the most used. Fewer references exist for the other techniques, but lidar is an emerging technique. For each depth measurement method, we detail the physical principles and then a review of the results obtained in the field. This review shows a lack of data for very shallow rivers, where a very high spatial resolution is needed. Moreover, the cost related to aerial image acquisition is often huge. Hence we propose an application of two techniques, radiometric models and through-water photogrammetry, with very- high-resolution passive optical imagery, light platforms, and off-the-shelf cameras. We show that, in the case of the radiometric models, measurement is possible with a spatial filtering of about 1 m and a homogeneous river bottom. In contrast, with through-water photogrammetry, fine ground resolution and bottom textures are necessary.

scholarly journals LAND COVER CHANGE DETECTION ANALYSIS ON URBAN GREEN AREA LOSS USING GIS AND REMOTE SENSING TECHNIQUES

2013 ◽  
Vol 11 ◽  
Author(s):  
Noorzailawati Mohd Noor ◽  
Alias Abdullah ◽  
Mohd Nasrul Hanis Manzahani
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Kuala Lumpur ◽  
Gis And Remote Sensing ◽  
Detection Analysis ◽  
Green Area ◽  
Urban Green Area ◽  
Green Areas ◽  
Erdas Imagine ◽  
Remote Sensing Techniques

The loss of green area has been rising all over the world particularly in big cities. For a number of decades, urban sprawl and developments have changed the natural landscapes of urban areas where areas with green areas have been converted into built up developments and other land uses. Thus this research intends to study the changes of green areas in Kuala Lumpur based on land use detection analysis approach where 3 series of remote sensing images namely SPOT2, SPOT4 and IKONOS for year 1990, 2001 and 2010 have been used to acquire the data on the green area changes aided by ERDAS IMAGINE 2011 and ARGIS 9.2. The finding of the study shows that there is a decrease in the size of green area in Kuala Lumpur from year 1990-2010 due to pressure of urban developments. Two significant factors which contribute to the changes of green area in Kuala Lumpur have been identified in the study, which are the increase in built up areas and sprawl development pattern.

LAND COVER CHANGE DETECTION ANALYSIS ON URBAN GREEN AREA LOSS USING GIS AND REMOTE SENSING TECHNIQUES

2013 ◽  
Vol 11 (3) ◽  
Author(s):  
Norzailawati Mohd Noor ◽  
Alias Abdullah ◽  
Mohd Nasrul Hanis Manzahani
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Kuala Lumpur ◽  
Gis And Remote Sensing ◽  
Detection Analysis ◽  
Green Area ◽  
Urban Green Area ◽  
Green Areas ◽  
Erdas Imagine ◽  
Remote Sensing Techniques

The loss of green area has been rising all over the world particularly in big cities. For a number of decades, urban sprawl and developments have changed the natural landscapes of urban areas where areas with green areas have been converted into built up developments and other land uses. Thus this research intends to study the changes of green areas in Kuala Lumpur based on land use detection analysis approach where 3 series of remote sensing images namely SPOT2, SPOT4 and IKONOS for year 1990, 2001 and 2010 have been used to acquire the data on the green area changes aided by ERDAS IMAGINE 2011 and ARGIS 9.2. The finding of the study shows that there is a decrease in the size of green area in Kuala Lumpur from year 1990-2010 due to pressure of urban developments. Two significant factors which contribute to the changes of green area in Kuala Lumpur have been identified in the study, which are the increase in built up areas and sprawl development pattern.

scholarly journals Investigating Remote-Sensing Techniques to Reveal Stealth Coronal Mass Ejections

2021 ◽  
Vol 8 ◽  
Author(s):  
Erika Palmerio ◽  
Nariaki V. Nitta ◽  
Tamitha Mulligan ◽  
Marilena Mierla ◽  
Jennifer O’Kane ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coronal Mass Ejections ◽  
Large Scale ◽  
Geomagnetic Storms ◽  
A Priori ◽  
Source Region ◽  
Remote Sensing Data ◽  
Additional Information ◽  
Remote Sensing Techniques ◽  
Geometric Techniques

Eruptions of coronal mass ejections (CMEs) from the Sun are usually associated with a number of signatures that can be identified in solar disc imagery. However, there are cases in which a CME that is well observed in coronagraph data is missing a clear low-coronal counterpart. These events have received attention during recent years, mainly as a result of the increased availability of multi-point observations, and are now known as “stealth CMEs.” In this work, we analyze examples of stealth CMEs featuring various levels of ambiguity. All the selected case studies produced a large-scale CME detected by coronagraphs and were observed from at least one secondary viewpoint, enabling a priori knowledge of their approximate source region. To each event, we apply several image processing and geometric techniques with the aim to evaluate whether such methods can provide additional information compared to the study of “normal” intensity images. We are able to identify at least weak eruptive signatures for all events upon careful investigation of remote-sensing data, noting that differently processed images may be needed to properly interpret and analyze elusive observations. We also find that the effectiveness of geometric techniques strongly depends on the CME propagation direction with respect to the observers and the relative spacecraft separation. Being able to observe and therefore forecast stealth CMEs is of great importance in the context of space weather, since such events are occasionally the solar counterparts of so-called “problem geomagnetic storms.”

scholarly journals SPACE-AERIAL-GROUND INTEGRATED CITY DYNAMIC MONITORING BASED ON REMOTE SENSING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 29-34
Author(s):  
L. Chen ◽  
J. Hu ◽  
G. Lv ◽  
Y. Liu
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Full Range ◽  
Dynamic Monitoring ◽  
Scientific Development ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Information Platform ◽  
Periodic Monitoring ◽  
Remote Sensing Techniques

Abstract. With the further acceleration of the new urbanization process, China's urbanization construction has entered a new stage of more standardized and scientific development, which puts forward higher requirements for national land and space monitoring. In view of the rapid changes in looks of our cities, the periodic monitoring methods such as personnel patrolling inspection, public reporting or satellite remote sensing are difficult to meet the needs of full-range, dynamic and precise monitoring. Therefore, it is urgent to establish a new spatial monitoring system to make up for the deficiencies of current urban spatial monitoring. In the article, the authors provide an implementation idea of a space-aerial-ground integrated remote sensing monitoring method system, which integrates various remote sensing techniques and information platform to meet the needs of large-scale urban change cycle monitoring and continuous monitoring of key construction areas Space monitoring provides modern technical support. Some of the techniques in the method system described here have been applied in some regions, which provides a basic platform for regional city monitoring, and have further research and application prospects.

scholarly journals Forest Structural Complexity Tool—An Open Source, Fully-Automated Tool for Measuring Forest Point Clouds

2021 ◽  
Vol 13 (22) ◽  
pp. 4677
Author(s):  
Sean Krisanski ◽  
Mohammad Sadegh Taskhiri ◽  
Susana Gonzalez Aracil ◽  
David Herries ◽  
Allie Muneri ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Open Source ◽  
Point Cloud ◽  
Large Scale ◽  
Structural Complexity ◽  
Point Clouds ◽  
Great Promise ◽  
Measurement Tool ◽  
Forest Mensuration ◽  
Remote Sensing Techniques

Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.

scholarly journals Application of remote sensing techniques to discriminate between conventional and organic vineyards in the Loire Valley, France

OENO One ◽  
2014 ◽  
Vol 48 (3) ◽  
pp. 135 ◽  
Author(s):  
Jorge R. Ducati ◽  
Rafael E. Sarate ◽  
Jandyra M. G. Fachel
Keyword(s):  
Remote Sensing ◽  
Success Rate ◽  
Vegetation Index ◽  
Cell Structure ◽  
Relevant Information ◽  
Short Wave ◽  
Spectral Difference ◽  
Spectral Bands ◽  
Remote Sensing Techniques ◽  
Loire Valley

<p style="text-align: justify;"><strong>Aim</strong>: To test the use of Remote Sensing imagery and techniques to differentiate between conventional and organic vineyards.</p><p style="text-align: justify;"><strong>Methods and results</strong>: Conventional and organic vineyards were identified on three satellite images acquired by the ASTER sensor of the Loire Valley. A sample of 46 conventional and 12 organic plots was used; grape varieties were Chenin Blanc (33 plots) and Cabernet Franc (25 plots). Mean reflectances were extracted from pixels inside each plot for the nine spectral bands (visible and infrared) of ASTER. A statistical discriminant analysis was performed. The vegetation index NDVI was also analysed. Results showed that all 12 organic plots, and 41 out of 46 conventional plots were correctly separated, a 91.4% success rate. Also, 23 out of 25 Cabernet, and 30 out of 33 Chenin plots were also correctly identified, also a 91.4% success rate. Regarding NDVI, there are no differences between conventional and organic vineyards within a 5% significant level. Analyses focused on the influences of chemical treatments on vineyard colors and on the effects of light reflected by inter-row spaces, suggested that both processes introduce spectral changes in conventional vineyards, mainly in short-wave infrared. Results also indicate that infrared information is essential to spectral discrimination.</p><p style="text-align: justify;"><strong>Conclusion</strong>: The use of chemicals, typical to conventional viticulture, has an impact on leaf composition and cell structure, being an important factor to imprint a characteristic reflectance pattern to these vineyards; the contribution to the integrated reflectance from inter-row vegetation is probably also a differentiating factor. Both causes act synergistically to build a significant spectral difference between conventional and organic vineyards.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: Remote Sensing techniques can be used as a first approach to vineyard monitoring, producing relevant information on viticultural methods, which can be used as early indicators of the need for field inspection or conventional laboratory analysis.</p>

scholarly journals Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest

2021 ◽  
Vol 13 (11) ◽  
pp. 2063
Author(s):  
Luka Jurjević ◽  
Mateo Gašparović ◽  
Xinlian Liang ◽  
Ivan Balenović
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Large Scale ◽  
Deciduous Forest ◽  
Information Source ◽  
Data Sources ◽  
Pedunculate Oak ◽  
Absolute Deviation ◽  
Close Range ◽  
Remote Sensing Techniques

Digital terrain models (DTMs) are important for a variety of applications in geosciences as a valuable information source in forest management planning, forest inventory, hydrology, etc. Despite their value, a DTM in a forest area is typically lower quality due to inaccessibility and limited data sources that can be used in the forest environment. In this paper, we assessed the accuracy of close-range remote sensing techniques for DTM data collection. In total, four data sources were examined, i.e., handheld personal laser scanning (PLShh, GeoSLAM Horizon), terrestrial laser scanning (TLS, FARO S70), unmanned aerial vehicle (UAV) photogrammetry (UAVimage), and UAV laser scanning (ULS, LS Nano M8). Data were collected within six sample plots located in a lowland pedunculate oak forest. The reference data were of the highest quality available, i.e., total station measurements. After normality and outliers testing, both robust and non-robust statistics were calculated for all close-range remote sensing data sources. The results indicate that close-range remote sensing techniques are capable of achieving higher accuracy (root mean square error < 15 cm; normalized median absolute deviation < 10 cm) than airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data that are generally understood to be the best data sources for DTM on a large scale.

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