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IEEE 2025 6th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2025) is hosted by School of Remote Sensing and Information Engineering, Wuhan University and organized by Computer Vision & Remote Sensing (CVRS) Lab, Wuhan University, supported by Suzhou University of Science and Technology. ICGMRS 2025 will be held in Wuhan, Hubei, on April 25-27, 2025.
Conference Website: https://ais.cn/u/EzqA3u
---Call for papers---
The topics of interest for submission include, but are not limited to:
◕ Surveying & Mapping
· Photogrammetry
· Aerial Photogrammetry
· Cartography
· City Brains, Smart Oceans and Digital Earth
· Computer Graphics
......
◕ Remote Sensing
· Optical Remote Sensing
· Microwave Remote Sensing
· Remote Sensing of Atmospheric Environment
· Planetary Remote Sensing and Mapping
· Remote Sensing Information Engineering
......
◕ Application of Remote Sensing Technology
· Remote Sensing Monitoring of Carbon Neutrality and Emissions
· Remote Sensing Monitoring of Key Climate Factors of Global Change
· Remote Sensing, GIS and Navigation and Positioning System (3S) Integration and Application
· Meteorological Application
· Marine Applications
......
◕ Maritime Communications
· 6G Maritime Remote Sensing Information Transmission Technology and Related Applications
· Intelligent Maritime Communications
· 5G for Marine Applications
· Maritime Satellite Communications
· Underwater Acoustic Communications
......
---Publication---
The accepted papers will be published by IEEE (ISBN: 979-8-3315-3731-9), which will be submitted for IEEE Xplore, EI Compendex, Inspec and Scopus.
---Important Dates---
Full Paper Submission Date: March 31, 2025
Registration Deadline: April 20, 2025
Final Paper Submission Date: April 20, 2025
Conference Dates: April 25-27, 2025
--- Paper Submission---
Please send the Full paper(word+pdf) to Submission System:
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Dear Li Yuan ,
You shared it a little late. (25..03.2027)
We will also hold a conference:
Regards,
Laszlo
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Hi everyone,
I’m currently researching the fusion of social media data with remote sensing technologies (such as satellite or aerial imagery) for more effective disaster detection and analysis. While social media offers real-time, on-the-ground information during crises, remote sensing provides a macro-level, systematic overview. The challenge lies in combining these two data sources effectively to improve situational awareness and response during disasters.
I’m particularly interested in hearing about technical approaches or models you have come across or used for integrating these data streams. Some challenges I’m exploring include:
  • Data fusion techniques: What methods have worked best for merging unstructured, real-time social media data with structured remote sensing imagery?
  • Geospatial accuracy: How can we overcome the issue of inaccurate or missing geolocation in social media data when aligning it with high-precision satellite imagery?
  • Temporal alignment: What are some best practices for syncing real-time social media data with remote sensing data that may have acquisition delays?
If you’ve worked on or seen examples of solutions to these challenges, I’d love to hear about them. Any algorithms, frameworks, or case studies would be incredibly valuable.
Looking forward to your technical insights and suggestions for overcoming these barriers!
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Google Scholar is your friend. Cal Fire ( https://www.fire.ca.gov/ ) is the research epicenter ( not surprising considering where Silicone Valley is located and the state's susceptibility to every form of human and natural disaster ). I participated in a hackathon sponsored by them and the US DoD on this theme. But one recurrent theme was that all the typical pathologies present in social media become magnified in an emergency situation resulting in a low signal to noise ratio.
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I want to assess the trees in an rainforest habitat and collect every necessary detailed data by utilizing GIS and Remote sensing technology techniques. And build a database management system for all people of different expertise to have accessed.
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D.O.I: 10.4236/jbcpr.2023.111001
Check my article and u will faund answers
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Dear Colleagues,
Ready to showcase your research on cutting-edge crop yield predictions?
We are thrilled to announce a special issue dedicated to the intersection of artificial intelligence and remote sensing in predicting crop yields.
This special issue focuses on AI and remote sensing technologies to provide early and precise yield estimations, thereby revolutionizing farming practices.
Researchers are invited to submit their innovative solutions and research findings on a wide array of topics, including:
🚜 IAI and LiDAR precision agriculture.
📡 satellite imagery for crop monitoring and yield estimation.
🌿 multispectral and hyperspectral imaging in horticulture.
🌦️ Machine learning models for weather impact on crop yields.
🦠 AI-driven pest and disease detection .
💧 Optimization of irrigation systems using remote sensing.
🤖 Deep learning for crop classification.
🌱 Predictive analytics for soil health impact on crop yields.
🔢 Automated crop counting and size estimation.
🌍 Impact of climate change on crop yields.
Don't miss the chance to contribute to this exciting field! Submit your research now: mdpi.com/si/199287
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Thank you for your reply Mino Sportelli
Sincerely
Abdelkrim
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Remote sensing technologies contribute to accurate forest carbon stock estimation and monitoring by providing detailed and continuous data, enabling global coverage, and facilitating the integration of various data sources for a more holistic understanding of forest ecosystems. However, its contribute to accurately estimating and monitoring forest carbon stock at both regional and global scales is lacking.
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Accuracy of Forest carbon stock estimation and monitoring using Remote sensing technologies depends upon the accurate forest biomass estimation at regional and global level. Various remote sensing data Optical, Hyperspectral, SAR & Lidar are used for Forest biomass estimation based on their spatial, spectral and temporal resolution. At regional level Hyperspectral imageries are used for vegetation structure identification and various SAR & LiDAR data are used for forest tree height mapping to estimate the regional level forest biomass and respective carbon stock. However for global level a more holistic methodology is adopted for the forest biomass estimation by integrating the various data sources. Also ground measurement is must required activity for forest biomass and carbon stock estimation and monitoring.
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Hi,
I am trying to understand the limitations of the Simplified Surface Energy Balance (SSEB) approach and Landsat Collection 2 (C2) Provisional ETa Science Products to estimate actual evapotranspiration of different crops in various locations.
These would be used by an agribusiness to monitoring water consumption and water availability for crops (wheat, rice and corn) grown in 14 different countries
I am struggling to understand if and how these can be applied to different crop / locations couples as Landsat Collection 2 (C2) Provisional ETa Science Products are yet to be validated.
Thanks for your help,
Best regards.
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while satellite remote-sensing techniques can provide valuable insights into crop water use and water availability, there are several limitations to their accuracy and reliability. These limitations must be carefully considered when using satellite-derived ETa estimates for monitoring and managing crop water use in different locations and for different crops.
There are several limitations to estimating actual crop evapotranspiration (ETa) using satellite remote-sensing techniques such as the Simplified Surface Energy Balance (SSEB) approach and Landsat Collection 2 (C2) Provisional ETa Science Products.
Spatial resolution: The spatial resolution of satellite imagery may not be fine enough to capture small-scale variations in ETa, particularly in heterogeneous landscapes with multiple crop types, varying topography, and soil characteristics.
Atmospheric interference: Atmospheric conditions such as cloud cover, haze, and aerosols can interfere with satellite measurements of ETa, particularly in regions with high levels of atmospheric pollution.
Sensor limitations: The accuracy of ETa estimates can be affected by sensor limitations, such as saturation of sensor values, band-to-band misregistration, and sensor noise.
Surface characteristics: The accuracy of ETa estimates can also be affected by surface characteristics, such as the presence of vegetation canopies, soil moisture, and surface temperature variations.
Crop variability: Crop variability, including differences in planting dates, crop management practices, and genetic traits, can result in variations in crop growth and water use that are difficult to capture using satellite remote-sensing techniques.
Calibration and validation: Accurate calibration and validation of satellite-derived ETa estimates is critical to ensure the accuracy and reliability of the data. This requires ground-based measurements of ETa, which can be difficult and expensive to obtain, particularly in remote or inaccessible areas.
Cost and accessibility: The cost of acquiring, processing, and analyzing satellite imagery can be prohibitive, particularly for small-scale farmers or resource-limited agribusinesses. Additionally, satellite imagery may not be readily accessible in some regions, particularly in areas with limited internet connectivity or data infrastructure.
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I'm doing a study based on compare two orbital sensors data, and on the study i'm basing on there is this normalization formula for the rasters: ((Bi<= 0) * 0) + ((Bi >= 10000) *1) + ((Bi >= 0) & (Bi < 10000)) * Float((Bi)/10000), Where "Bi" means "band". Is there someone who understad e could explain this formula? Thanks very much.
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It just makes values less than 0 to 0, and greater than 10000 to 1, while anything in between will be from 0 to 1. as the value is divided by 10000.
The way of writing formula is similar to conditional but without using if statement.
For example: (x>=0)*0+(x<0)*1 will evaluate to 0 for positive number and 1 for negatives. the expression (x>0) will either be 1 or 0, based on the value of x.
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In a GIS environment
in remote sensing technology
Using satellite visuals to measure change over different time periods and detect spatial changes
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يتم قياسها ضمن مجموعة من الخطوات الرئيسة المسخدمة في تحليل المرئيات الفضائية فيما يسمى بعلم الاستشعار عن بعد
انظر الصورة اسفل
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Hello everyone! I'm doing a study based on comparing spectral indices from Sentinel-2/MSI and Landsat-8/OLI data. The Sentinel-2/MSI - Level-1C are provided on TOA reflectance data, and Level-2A on BOA reflectance data. However, Landsat-8/OLI data are provided only on BOA reflectance through Collection 2, Level 2. Considering that I have to convert the Landsat-8/OLI Collection 2, Level 1 data to TOA reflectance, do I need to do another type of pre-processing in all other data?
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You can use surface reflectance datasets for both, Sentinel and Landsat-8, which area already atmospherically corrected.
Google Earth Engine Snippet:
Sentinel image Snippet: ee.ImageCollection("COPERNICUS/S2_SR")
Lt 8 image Snippet:ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
In order to compare these two datasets using spectral indices, as a preprocessing step, you have to harmonize them. You can read more about this process in these articles.
A GEE tutorial that might help.
Good luck.
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Dear colleagues I'm trying to calculate the land surface temperature for the Bakun Catchment, Sarawak, Malaysia region using different landsat satellite images of different years with the help of Google Earth Engine Code Editor. I'm not sure why I'm getting incomplete results for just this region and even the pixel values are too high in some of the years (please check the attached screenshots). Yes, for the NDVI and NDWI the results are covering the whole region.
I tried the same code for other regions of Pakistan and the results are very well.
So, my question is can anyone help me to get to know that this region the Bakun Catchment, Sarawak, Malaysia comes under special consideration or are there any other satellite data issues or maybe something wrong with the code? Also, does this region's LST goes down to -3 to -10?
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Just curious, are you using the ready to use LST product from USGS or are using a separate algorithm to derive Landsat based LST? In the former0 case, most probably, the negative values in the Landsat Collection 2 ST products from USGS are due to the presence of 'clouds' in the satellite images. Cross check with the cloud mask/Quality Assessment band to make sure there are no clouds in your study area in Sarawak. If you are using the USGS Surface Temperature product, I assume, the reason for incomplete results could be because these LST products are based on ASTER Global Emissivity Database (GED). Wherever, GED has missing data, there will be missing data in the Landsat ST product as well.
I might be wrong, I will have to look at the ST products for further scrutiny.
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Hi,
Can I prove or model a reclamation in the past using GIS/RS? Can you provide me some discussion, readings, or techniques?
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Yes, you can via the satellite images of the same area using the 4D analysis, the time period between these images have to be equal, this can help you to predict the future effects. I hope the link below can help you a little https://doi.org/10.3390/app10072266
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I am performing a time-series analysis and using Sentinel-2 for the latest data and Landsat-5 for the older data. Now in the time-series graph, I see a significant rise in the output values for the last two years' data (sentinel imagery). The first step of my algorithm is to derive NDVI so does the difference between the two sensors still matter? If so then how to correct the process?
P.S- I am using top-tier products for all the imagery so is there a question of DN to TOA conversion?
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You may be interested in:
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I face a problem in LULC classification, like as an industrial area showing as a water body.
Please help me.
Thank you.
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band 4, 5, 6 is the best three-band combination and band 1, 2, 5, 7 is the best four-band combination which achieved almost identical performance as using all bands for LULC classification.
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As is well, the remote sensing imaging technoloy reach an acceptable level of maturity and as shown in so many applications remote sensing can serve in so many areas. It came to my mind and the through the professional practice with different clients, some basic understanding of management from system theory perspective is required to well position the remote sensing technology within the business context. In other words, a business understanding is required to maximize the value proposition of remote sensing within the solutions chain. Your comments and opinions are highly appreciated.
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You have put across a very valid point on linking remote sensing for the value proposition of business and enterprises. In India, there are numerous social enterprise who have come forward and using remote sensing in the agricultural domain to improve the agricultural production. We are improving financial inclusion and productivity by enhancing platform of choice for all stakeholders in the agricultural value chain by delivering farm and farmer centric analytics and actionable insights, harnessing the transformative power of artificial intelligence, remote sensing. The remote sensing helps in getting land parcel data and other granular analytics. This has even helped the credit institutions to disperse loan and other credits to farmers through milestone-based lending (a new credit innovation). Remote Sensing Imaging alone in agriculture in India has helped in creating a new line of business for credit institutions for an untapped market and improvement of other stakeholders in agricultural value chain.
Thanks and Regards
Kisslay Anand
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We work in groundwater dependent region. Agriculture has been the largest consumer of the groundwater. In order to manage the groundwater, we have to manage agricultural groundwater use. In order to manage agricultural groundwater use, we have to monitor the cropping pattern in the village. What is the most accurate and cost effective method to find out the village level cropping pattern (season wise acreage of each crop grown)?
Does remote sensing provide any solution?
Is physical survey the feasible solution? Or is there any other solution?
If you have conducted any such study, please share the publication.
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Land use Land cover mapping using ARC GIS or ERDAS software is the best method for monitoring of agricultural crops. As well as google earth is the best option for monitoring the crops on spatial manner.
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Belongs to Geology and Remote Sensing?
What are the main references available for free (pdf) in this field of knowledge?
Is it still a usual field within geology?
Best Regards.
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It is a branch of geology to study the geological features through aerial photography. There are three types of aerial photography like vertical, low oblique and high oblique. Yes, it is a usual field withing geology.
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How do I measure the physical properties of marine waters using remote sensing technology and image processing?
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Hi everyone.
I am trying to create an NDVI with a one-kilometer resolution. I have downloaded MODIS images and then I realized bands 1 and 2 of the images, which are used to create NDVI, come at a 250 m resolution. However, when I opened the 1km-resolution image I saw that a field is there named: "1km monthly NDVI", as in the picture below. What's more, I don't understand the "monthly" word in the name as the MODIS image I picked belonged to only one month! Furthermore, when I convert it to tiff and saw its value in ArcGIS, it ranged from -300 to 9000, instead of -1 to +1.
I have moved on to AVHRR to do what I need to do, but I really wanna know what it is.
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Hi Shabnam,
Yurii has already given a perfect answer.
Just in case you are looking for MODIS NDVI data for an entire month, you can download the daily data product instead (https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD09GA_006_NDVI). The monthly product is the monthly average of daily NDVI images (the same goes for the 16-day average). :)
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The major applications, uses and purposes of civilian drones. How the world is transforming by the advancement of technology in mapping sector? How drone is aiding in mapping?
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demography or urban extensions
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Do you know any example/case study using state of the art technology for waste management? (e.g., GIS, BIM, Computational tools, Computation Design)
We are working on Construction and demolition waste management in the built environment context, which one can be more useful for waste management in your area (e.g., construction, food, electronic waste etc.)?
We are working on the visualisation techniques and welcome to your participation.
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Hi Samad Sepasgozar your profile indicates that you are from UNSW, Sydney. I would like to suggest you that read a research articles from Prof. Veena Sahajwalla. Her work in the field of E-waste is interesting, additionally you both are in the same university. This could be better for you..!!
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Respected sir/mam,
I have using satellite image of Landsat 8 and 5 for LULC classification. I am confused that which software and method are the best for LULC classification?
Please help me.
Thanks and Regards
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Most GIS and RS software have the standard classification methods (ArcGIS, QGIS, ENVI, Eedas etc).
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Models that evaluate parameters to water quality. For example clorofila a, turbidity, DO, etc.
Models for lakes in the tropical zone.
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Six main public domain water quality models which are presently available for Rivers and streams are being captured in this article. These main models could produce important results if they are used in the correct manner, because they are different in terms of assumptions, strength and weaknesses, processes they represent, modeling capability and data input requirements. The Model review discussed includes, water quality analysis simulation program (WASP7), simulation catchment (SIMCAT), quality simulation along Rivers (QUASAR), and the temporal overall model for catchment (TOMCAT), QUAL2KW, QUAL2EU. The models are described individually according to a consistent set of criteria-conceptualization, model capability, model strengths, limitations, input data and how it utilized. The outcome showed that TOMCAT and SIMCAT are important in ASSESSING effect of point sources in a very simple way. The QUAL2KW, unlike the QUAL2EU where macrophytes play a major interaction, it can convert algal death to carbonaceous Biochemical Oxygen Demand (CBOD), thereby making it more suitable. In addition to the extensive requirement of data, it is expensive and time consuming to set up these complex models such as QUASAR and WASP7. Therefore, one model cannot be used for all the required functionalities. Choosing a model would depend on a specific application, financial cost and time availability. This article may be of help in choosing a suitable model for a specific water quality problem.
Article Water Quality Model for Streams: A Review
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I want to extract RCS values or backscattering coefficients from available Radar imagery and create a Geospatial layer out of it.
Thank you.
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Gomal Amin Thank you for your response.
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I have been working in the field of forest research for a decade and a half so far.
I have experienced different events during submitting manuscripts and revisions and I think the process of reviewing manuscripts isn't fair and is only in the favor of the editors.
Let me explain a real situation, I have submitted a manuscript in "Arabian Journal of Geoscience" ( https://www.springer.com/earth+sciences+and+geography/journal/12517) on 12 Mar 2019 and today it has been 196 days since I made the mistake and the status of the MS is still "pending for editor". I have sent several emails to the editor and the manager, but mostly I receive no response or the same email which I think is only copy and past of the same text.
Now, my student so far has lost half a year and I can't even withdraw the ms.
I even sent springer "contact us' ID explaining the problem, but still, nothing happened.
I am really getting more and more disappointed to see that the whole process is mostly a marketing job rather than a scientific responsibility or an ethical act.
Yours
HORMOZ
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Due to COVID-19, maybe there is some suspension in the review process. But if you feel a wastage if time simply, withdraws that paper and submit to another journal of the same field for timely publication.
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In these days, i want figure out what is suitable threshold value for extraction land from water bodies Using Modified normalize differential water index. I want determine boundary of shoreline precisely. So i need reflectance value of specially green band and SWIR2 band of thematic mapper for Sea water.
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Dear Dulan,
The link below I sent may resolve your issue
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I am looking anyone specialist in monitoring water quality by RS & GIS can help me in discuss this question
All comments are welcome
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Brahim Abdelkebir Dear Dr. Thanks a lot for your comment and contribution, really thanks.
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Hello, I am currently doing research on shoreline mapping using remote sensing technology with tidal variation consideration. After mapping the shoreline changes using DSAS tools and determine the shoreline change rate with End Point Rate and Linear Regression output, I have problem in correcting the tidal data. I have searched on the one-line shift method and right triangle method but I have no idea how to do it. Can anyone shed some light to me?
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Use of precision technologies for identification of COVID-19 patients
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Yes
we can use new techniques also such as
  1. Geo-fencing to patients to track the movement
  2. demarcating containment zone
  3. spatial distribution of patients
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Agricultural scientists especially soil scientist are measuring nitrogen use efficiencies in various crop varieties under different nutrient managements. Though this method has got more precision in measuring NUE, it is limited to small / specific area. In order to study N use efficiency in larger area with multiple crop varieties, remote sensing drone technology will be highly useful. Any study relating N uptake, N utilization or N use efficiency of the crop with remote sensing technology is invited for discussion and fine tuning the research proposals.
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I agree with Dr. Som Pal Singh comments.
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I have been trying to create raster images, but have been having issues in get to subset (extract by mask) to the same extent that I need. Initially I tried sub-setting them using a vector file but the results were not desirable. After some exploration on YouTube, I realized the need to use a subset raster image as the file for sub-setting other images. Still I can't get the favorable outcomes. Please suggest methods that can be used to create images with same extent.
Thank You.
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The issue right now is that I am using data with different extent and coordinate systems (both projected and geographic), which is probably the reason for not being able to clip the same extent.
Ákos Bede-Fazekas I will try using R.
Subhash Chavare , Sumedh Rajkumar Kashiwar thank you for the suggestions.
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Good day, senior colleagues, I need your help/ suggestions has to which spatial resolution satellite imageries/sensors can be your used to capture Lake ecological states. Also, if you could suggest a strong research methodology/ reading material regarding this, would go a long way.
Thank you everyone, in anticipation for your suggestions.
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The medium spatial resolution images could be used to capture the Ecological state of the Lake.
Examples of these are , the Landsat (TM, ETM+, OLI), ASTER (LIB, LIT), Sentinel-2, etc.
I have attached the (Spatial and Spectral band ) information of the above listed satellite images. Please see attached. You would find it helpful.
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Hi,
I have been trying to determine LST of a area of interest using MODIS level 2 NRT product MYD11_L2 . Previously I was using the standard 1km resolution level 3 product MYD11A1 for computing LST at a place. However since I need the data at a more real time basis I am exploring MODIS NRT products.
If somebody could help me or point me in the right direction as to how to determine LST of any place from the level 2 NRT 5 mins swath product MYD11_L2 it would be very helpful.
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Thanks Moh. Dede and Bijay, let me explore and get back..
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hello
What do you think about how ortho- photos are made from historical analogue aerial imagery?
Suppose you have a set of analogue aerial images of several Flight Lines.
What software and methods will you use for ortho- photos production?
How to choose photo control points?
How do you measure the precision of the final ortho-photo?
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thanks Aysar Jameel Abdalkadhum
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Hello,
I am interested in using Landsat 5-8 images to map snow and ice cover. I am trying to construct a time series showing how late into the year snow and ice cover lasts. I noticed that for Landsat ARD tiles obtained from USGS Earth Explorer there is a Pixel Quality Assessment band that accompanies surface reflectance products and that this PQA raster includes bit designations for pixels where snow or ice are present (bits 80 and 144 for Landsat 4/5). After reading more I have gathered that this PQA product is generated using the Fmask algorithm which was developed primarily for generating cloud masks. However, I decided to employ these products to see how they perform when generating fractional snow cover rasters.
I noticed that for some images very late into the year (May and June) the Fmask algorithm did classify many pixels as snow or ice, although after generating RGB composites and using the thermal infrared band to look at temperature, I determined that there was no snow or ice cover present in the image although it did look like some clouds were present. After reading more of the literature I found out that the Fmask algorithm has a tendency to sometimes classify cloud pixels as snow or ice, but I could not find an explanation as to why this happens. Is there a particular cloud type that the algorithm classifies as snow or ice, or is it unpredictable? Is there a better algorithm that is designed specifically for generating maps of fractional snow cover?
Thanks for you help,
Best,
Ryan Lennon
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can be isolated using different spectral signature between cloud cover and ice cover
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I am trying to orthorectify an IRS 1C and an IRS 1D multi - spectral image using a DTM/ DEM. I have Cartosat 1 DEM of 1 Arc - Sec (30m). There are no RPCs provided by the vendor and I was told that the product isn't orthorectified nor geometrically corrected.
I use Erdas Imagine 2015 for working with Satellite Data. Any help on this will be highly appreciated.
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Hi When you do not have any of the RPC coefficients, you need to consider your work in several ways. One solution is to change the software used. I think you can use pci geomatica software and do it with DEM and polynomial rational function. The next solution is to use topographic maps either DTM or DEM to extract photographic control points. In this case you have to rectify your image with software like PCI or ERDAS.
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There are many electronic triage algorithms founded in the literature for detect the emergency level of the patients. But which one are really used in the hospitals and clinics nowdays?
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In our Hospital we have implemented the Manchester triage system with very good results. From 51 reasons of consultation and through some questions addressed in a diagram. That is, according to the answer yes / no, classification occurs, with 5 severity levels. It also has a digital version.
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Dear Respected Researchers,
I would like to know that is it theoretically possible that satellite-derived surface reflectance at visible-to-shortwave wavelengths using the equation mentioned in the attached file can be greater than the top of atmosphere (TOA) reflectance, i.e. TOA reflectance - SR reflectance = Negative (mean difference)? If yes, then what are the possible reasons/conditions? Your answer supported by reference will be appreciated.
Kind regards.
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Thanks for your valuable inputs. As you mentioned that BOA > TOA is possible for spectral regions with relatively strong absorption of atmospheric components , i.e. red to infrared channels. Actually, I also found similar results for Landsat but I am looking for a reference to support these results. Do you have any reference?
I have further two questions:
1. How about if BOA > TOA at green wavelength as it does not have strong absorption of atmospheric components?
2. In comparison of two BOA methods, one has BOA > TOA for red to infrared channels, whereas, the other has BOA < TOA, so which one can be considered more accurate?
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Do you use satellite imagery that needs to be corrected (radiometric, atmospheric & geo) or download the reflectance level "ready to go"?
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Offer Rozenstein, Can we get 'ready to go' Level 2 Sentinel 2 which is atmospherically corrected from any source? I would be interested to know.
As you know, USGS provides both Level 2 Landsat data in addition to Analysis Ready Data (ARD only for CONUS). However, as you rightly pointed out, while doing time series analysis employing multiple sensors such as Sentinel-2 and Landsat 8 or even Landsat 8 and Landsat 5 TM/7 ETM+, the differences in spectral bands are not accounted for in Level 2 data. Therefore, there is a need for additional processing such as spectral statistical transformations, radiometric normalizations etc.. In this regard, I would be very interested in following what your team is developing.
Ittai Herrmann, as far as the original question is concerned, I also think Level 2 'ready to go' data works just fine as long as it does not involve multi-sensor temporal continuity applications. I would say, for Applied Earth Science students like me, getting bothered with image processing and calibration takes a lot of valuable time which could otherwise be utilized for making discussions/conclusions/decisions on the final products. That is the reason why USGS came up with ARD data in the first place. However, it is perfectly normal for young researchers and students, in addition to algorithm developing science teams to learn the science of image processing, calibration and analysis by manually performing the pre-processing steps from DN to Radiance and TOA Reflectance to Surface Relectance (BOA) etc. going from Level 0 to Level 1 to Level 2 and so on. I also agree with Sara Salehi 's point that the best way to learn is to do the pre-processing of raw data manually so that one has full control of the process instead of a black box output.
I would, in addition, like to point to some other issues with regard to Cloud Masking and correction. Cirrus Cloud band (1.38µm) were only introduced in Landsat 8 and Sentinel 2 in addition to MODIS and VIIRS. Previous Landsat sensors such as TM, ETM+, MSS and other multi-spectral sensors did not have this Cirrus band, which makes it rather difficult to pin point their presence in historical satellite imagery without any coincident and contemporary spaceborne sensor with a cirrus band. So the question that is still puzzling me is how do you deal with correcting Cirrus clouds and their effects that are present (or not?), for example, in Landsat TM, ETM+, MSS but that are not seen in the visible range of the spectrum?
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In order to estimate the extent of rooftop area available for solar PV for domestic electricity demands, I am calculating the rooftop area of a district by extrapolating the rooftop to ground surface area ratio as calculated by randomly generated samples of clusters in Google Earth. So, I was interested if there are any pre-defined algorithm which might be more efficient for the same.
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Cornelis Stal and
Harish Puppala
thank-you so much, the links indeed are helpful.
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Dear all,
I'm looking for an environmental journal which has Impact Factor and NO paper processing fee. Currently I'm preparing an air pollution related paper for my academic publication.
Pls help provide a few names in which I can submit.
Thanks in advance, Shareful
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Dear Devraj Chalise , are you sure for Elsevier? I've seen the list of fees for their journals, and they are pretty high (2000 + euros).
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I have calculated NDVIre3 from Sentinel 2A MS data in QGIS. As a pre-processing step, the Sentinel 2 bands (L1C product) were corrected using DOS1 algorithm available in QGIS 3.4.1 SCP plugin.
The NDVIre3 involves band 8 (NIR) and 7 (Red-edge 3) of Sentinel 2 MS sensor.
The Equation i.e. (B8-B7)/(B8+B7) was applied on scaled reflectance values derived from SCP Sentinel 2 Pre-processing module. The rasters were exported to ArcMap for visual appreciation.
However, the resultant raster contains much noise as seen in Fig_1 (attached);
While, the NDVI raster calculated using equation (B8-B4)/(B8+B4) is realistic to ground realities (Please see Fig_2 attached).
I am not sure what might be the cause of this problem? Looking for solution.
for NDVIre3 reference;
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And in case the image noise is the reason why it happens, an image smoothing technique would help, although sacrifying some of the detail level.
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I wish to utilize cartosat DEM, publicly available form bhuvan site. However, I have analyzed and seen that the elevation values from cartosat DEM are very similar to ellipsoidal height of GLAS/ICEsat altimetry data. Before I use the DEM data, I wish to know whether the elevation values show actual orthometric height or ellipsoidal height?
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Dear Ronak Jain and René Vázquez-Jiménez , Thank you all. I have got the answers.
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Dear Colleagues,
I am looking for a possibilities to download ASTERs new (2018 year) images with a good SWIR bands, because the earthdatas ASTER new images did not have Swir bands since 2008, i am interested it is possible to download 2018 year asters good swir bands?
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Hi Davit
Please see this:
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In the name of God
Today, the development of remote sensing technology and geographic information system is so fast that quickly improves its applications in various sciences.
Since people, or , in other words, users are one of the important parts of the GIS system, the introduction of remote sensing and GIS to the public can increase the application and introduction of this branch of technology to various sciences with a twofold speed.
With this view, we have formed a group of social networks called GisAndRs that, beside all the talk and contents, introduces remote sensing and GIS to the public, more than the past.
So, we invite you to join this group, and while taking advantage of the posts, help us to enrich the content.
Here is the group link:
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I thinks you mean some application such as google maps, any application related to locations and GPS, but if you mean others specialized remote sensing and GIS softwares it will be so difficult to do this and there no need to do it. This is because the users in such applications should be a person who has trained and specialized in specific field and can use these applications in solving any related problem. So the public is not included in this matter.
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* i have collected soil samples during hyperion pass over the study area and chemical analysis was done.
* Lab spectral signatures has not taken.
* How can i correlate the chemical analysis results to hyperion data after preprocessing?
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You need to model multi-variate regression (PLSR/PCR) between each laboratory obtained soil nutrient data of the sample points, keeping as dependent variable (Y) and spectral reflectance of the sampling points obtained from HYPERION data as independent variable (X). One usually predicts the nutrient value from the relationship between X and Y and cross-validates the predicted value through examining the precision of fit in linear regression between the actual and predicted values of nutrient.
You can learn about the MATLAB implementation of PLSR/PCR from the following link: