Remote Sensing Technology - Science topic

Dear Li Yuan ,

You shared it a little late. (25..03.2027)

We will also hold a conference:

Regards,

Laszlo

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.

D.O.I: 10.4236/jbcpr.2023.111001

Check my article and u will faund answers

Thank you for your reply Mino Sportelli

Sincerely

Abdelkrim

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.

Jihane Guenoun

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.

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.

يتم قياسها ضمن مجموعة من الخطوات الرئيسة المسخدمة في تحليل المرئيات الفضائية فيما يسمى بعلم الاستشعار عن بعد

انظر الصورة اسفل

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.

Hi Vipin Kumar Oad

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.

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

You may be interested in:

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.

Dear Gamal Seedahmed,

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

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.

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.

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). :)

demography or urban extensions

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..!!

Most GIS and RS software have the standard classification methods (ArcGIS, QGIS, ENVI, Eedas etc).

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

Gomal Amin Thank you for your response.

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.

Dear Dulan,

The link below I sent may resolve your issue

Brahim Abdelkebir Dear Dr. Thanks a lot for your comment and contribution, really thanks.

You can see this link

Yes

we can use new techniques also such as

I agree with Dr. Som Pal Singh comments.

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.

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.

Thanks Moh. Dede and Bijay, let me explore and get back..

thanks Aysar Jameel Abdalkadhum

can be isolated using different spectral signature between cloud cover and ice cover

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.

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.

Dear Tomáš Brunclík

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?

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?

Cornelis Stal and thank-you so much, the links indeed are helpful.

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).

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.

Dear Ronak Jain and René Vázquez-Jiménez , Thank you all. I have got the answers.

Hi Davit

Please see this:

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.

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: