scholarly journals A new tool for the remote sensing of groundwater tables: satellite images of pastoral wells

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Bernard Collignon
Keyword(s):  
Remote Sensing ◽  
Water Table ◽  
Satellite Images ◽  
Open Data ◽  
Google Earth ◽  
Livestock Farming ◽  
The Sustainable Development ◽  
Remote Sensing Technique ◽  
High Resolution Satellite Images ◽  
Sahel Region

Abstract In the Sahara and the Sahel, groundwater is a limited and indispensable resource for pastoral livestock farming. The daily life and work of the herders are organised around the location of the wells and the depth of the water table. To ensure the sustainable development of these regions, it is therefore essential to develop accurate piezometric maps, even in the areas that are most difficult to access. Thanks to high-resolution satellite images, the tracks made by cattle, goats and camels in the Sahara and Sahel could become a key indicator of the depth of the water table. In the northern Sahel, pastoralists water their livestock from deep wells. To draw water, they hitch oxen or camels to a rope whose length is an accurate measure of the depth of the piezometric surface of the water table. When pulling on this rope, the animals leave deep tracks on the ground that can be observed and measured on satellite images. We have developed a remote sensing technique that allows us to (a) identify pastoral wells, (b) isolate the tracks left by the animals used to draw water, and (c) use these animal tracks to estimate the water depth. After carefully calibrating the method, we were able to use open data (Landsat) and satellites images freely accessible data thanks to Google Earth Pro (SPOT and Worldview) to draw up, in just a few weeks, the piezometric map of a large aquifer (200,000km2) that is not easily accessible by other means due to the prevailing insecurity that has persisted in this part of the Sahel region for several years. This same method was then subsequently tested and validated on two other aquifers, one in Nigeria and one in Niger.

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 Correction of Very High-Resolution Satellite Images using Control Points Captured by Web Map Service (WMS) server: Google Earth

2020 ◽  
Vol 9 (8) ◽  
pp. 188-194
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Google Earth ◽  
Ground Control ◽  
Control Points ◽  
Web Map ◽  
Ground Control Points ◽  
High Resolution Satellite Images ◽  
Map Scale ◽  
Map Service

The recent progress for spatial resolution of remote sensing imagery led to generate many types of Very HighResolution (VHR) satellite images, consequently, general speaking, it is possible to prepare accurate base map larger than 1:10,000 scale. One of these VHR satellite image is WorldView-3 sensor that launched in August 2014. The resolution of 0.31m makes WorldView-3 the highest resolution commercial satellite in the world. In the current research, a pan-sharpen image from that type, covering an area at Giza Governorate in Egypt, used to determine the suitable large-scale map that could be produced from that image. To reach this objective, two different sources for acquiring Ground Control Points (GCPs). Firstly, very accurate field measurements using GPS and secondly, Web Map Service (WMS) server (in the current research is Google Earth) which is considered a good alternative when GCPs are not available, are used. Accordingly, three scenarios are tested, using the same set of both 16 Ground Control Points (GCPs) as well as 14 Check Points (CHKs), used for evaluation the accuracy of geometric correction of that type of images. First approach using both GCPs and CHKs coordinates acquired by GPS. Second approach using GCPs coordinates acquired by Google Earth and CHKs acquired by GPS. Third approach using GCPs and CHKs coordinates by Google Earth. Results showed that, first approach gives Root Mean Square Error (RMSE) planimeteric discrepancy for GCPs of 0.45m and RMSE planimeteric discrepancy for CHKs of 0.69m. Second approach gives RMSE for GCPs of 1.10m and RMSE for CHKs of 1.75m. Third approach gives RMSE for GCPs of 1.10m and RMSE for CHKs of 1.40m. Taking map accuracy specification of 0.5mm of map scale, the worst values for CHKs points (1.75m&1,4m) resulted from using Google Earth as a source, gives the possibility of producing 1:5000 large-scale map compared with the best value of (0.69m) (map scale 1:2500). This means, for the given parameters of the current research, large scale maps could be produced using Google Earth, in case of GCPs are not available accurately from the field surveying, which is very useful for many users.

scholarly journals Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

2019 ◽  
Vol 11 (20) ◽  
pp. 2389 ◽  
Author(s):  
Deodato Tapete ◽  
Francesca Cigna
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Google Earth ◽  
Optical Data ◽  
Mena Region ◽  
Processing Methods ◽  
Optical Imagery

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

scholarly journals Use of Satellite Imagery for Pastoral Resources Monitoring in Kossi Province (Burkina Faso)

2019 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Sieza Yssouf ◽  
Gomgnimbou P. K Alain ◽  
Belem Adama ◽  
Serme Idriss
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Burkina Faso ◽  
Satellite Imagery ◽  
Field Data ◽  
Satellite Images ◽  
Temporal Evolution ◽  
Livestock Farming ◽  
Important Place ◽  
Livestock Sector

In Burkina Faso, livestock sector has an important place in the country's economy. Essentially extensive, this livestock farming is characterized by transhumance system, which consists of leading livestock sometimes over long distances in search of good pastures and water.Satellite images from different periods can be used to monitor the evolution of pastoral resources (pasture areas and surface water points) in a given area. Field data, coupled with satellite images, provide a better understanding of livestock transhumance movements in the study area. The objective of this study was to monitor the spatial and temporal evolution of pastoral resources using remote sensing tools in Kossi province. Field data, coupled with satellite images, provide a better understanding of livestock transhumance movements in the study area.

scholarly journals THE REMOTE SENSING IMAGE GEOMETRICAL MODEL OF BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 381-386 ◽  
Author(s):  
C. Y. Yue ◽  
T. Sun ◽  
J. F. Xie
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Bp Neural Network ◽  
Satellite Images ◽  
Back Propagation ◽  
Geometrical Model ◽  
Approximation Accuracy ◽  
Sensor Model ◽  
High Resolution Satellite Images ◽  
Sensor Models

Abstract. Imagery geometry models (IGMs) of the high-resolution satellite images (HRSIs) are always of great interest in the photogrammetry and remote sensing community for the raising new kinds of sensors and imaging systems. Especially the generalized sensor models (GSMs) have been widely used for positioning of satellite images, and the accuracy are already validated. Since Back propagation (BP) neural network is a better choice for the two key reasons of the replacement of physical sensor models by generalized sensor models, numerous mathematical estimations for every specialized sensor, and secret equations of the IGMs. Experiments are carried out to test the approximation accuracy of the new generalized sensor model. And the experimental results show that, the BP neural network is of extremely high accuracy for satellite imagery photogrammetric restitution.

scholarly journals Hangar Detection from Satellite Images with Mask-RCNN and YOLOv2 Algorithms

2019 ◽  
Vol 1 (2) ◽  
pp. 5-9
Author(s):  
Emin Argun Oral
Keyword(s):  
Satellite Images ◽  
Google Earth ◽  
The Other ◽  
Detection Accuracy ◽  
Challenging Problem ◽  
Detection Problem ◽  
Bounding Box ◽  
High Resolution Satellite Images ◽  
Real Time Applications ◽  
Instance Segmentation

The contribution of this paper is twofold. It first proposes a new dataset of high resolution satellite images of hangars located at civil and military airports. It also presents a hangar detection problem results from satellite images using this new dataset obtained by Mask R-CNN and YOLOv2 algorithms. The satellite dataset contains one thousand pictures obtained from Google Earth at 8, 11, 17, 29 degrees angles from height of 500,800 and 1000 meters. Among 1000 hangar images 650 and 200 images are used for training and validation, respectively, while the remaining 150 images are used for detection test purposes. The total number of hangar object instants in the dataset images is about 3000. The detection of hangars is a challenging problem as the dataset contains camouflaged and non-camouflaged targets in different sizes. Among the two approaches used in the detection problem Mask R-CNN utilizes a regional based algorithm and enables instance segmentation with a bounding box. YOLOv2, on the other hand, is a regression based algorithm, used in real-time applications, and provides a bounding box only. The object detection accuracy in terms of Average Precisions by using Mask R-CNN and YOLOv2 algorithms to detect different sized camouflaged and non-camouflaged hangar objects was obtained as 72% and 74%, respectively.

scholarly journals Multi-Temporal Change Detection Analysis of Vertical Sprawl over Limassol City Centre and Amathus Archaeological Site in Cyprus during 2015–2020 Using the Sentinel-1 Sensor and the Google Earth Engine Platform

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1884
Author(s):  
Athos Agapiou
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Archaeological Site ◽  
Google Earth ◽  
City Centre ◽  
Time Interval ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
The City

Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the acquisition of high-resolution satellite images, the cost of which is increases according to the size of the area under study, as well as the time interval of the analysis. In this paper, we implemented a quick, automatic and low-cost exploration of large areas, for addressing this purpose, aiming to provide at a medium resolution of an overview of the landscape changes. This study focuses on using radar Sentinel-1 images to monitor and detect multi-temporal changes during the period 2015–2020 in Limassol, Cyprus. In addition, the big data cloud platform, Google Earth Engine, was used to process the data. Three different change detection methods were implemented in this platform as follow: (a) vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH) polarisations pseudo-colour composites; (b) the Rapid and Easy Change Detection in Radar Time-Series by Variation Coefficient (REACTIV) Google Earth Engine algorithm; and (c) a multi-temporal Wishart-based change detection algorithm. The overall findings are presented for the wider area of the Limassol city, with special focus on the archaeological site of “Amathus” and the city centre of Limassol. For validation purposes, satellite images from the multi-temporal archive from the Google Earth platform were used. The methods mentioned above were able to capture the urbanization process of the city that has been initiated during this period due to recent large construction projects.

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