GIS Analysis - Science topic

ArcGIS Pro provides several tools for image processing and analysis that can be used to extract information about rivers from drone images. Here are a few suggestions:

To perform Least Cost Path Analysis based on Land Use/Land Cover (LULC), you will need a raster dataset representing the LULC classes and their associated cost values. Here are the general steps you can follow in ArcGIS to perform Least Cost Path Analysis based on LULC:

It's important to note that Least Cost Path Analysis based on LULC is subject to several assumptions and limitations, such as the accuracy of the LULC dataset, the assignment of cost values to each class, and the spatial resolution of the data. Therefore, it's important to carefully validate the results and understand the limitations of the analysis.

Dear Julius Riese

Thank you for your answer!

It's quite hard to distinguish in this area what's tributary and what's not, as it's a coastal area, an inner archipelago. However, the northern stream is generally considered to be a main stem. The western eutrophicated bay is a flad (as they usually call such coastal lagoons in Finland). The characteristics of a flad are usually that they are under some kind of protection because of their unique ecosystem and the "entrance" to the flad is usually very narrow and very shallow (however inside a flad it's usually 2-8m deep).

As you can see on the maps, this flad is connected to the other bigger bay (fjärden) through a small ditch that goes through wetlands. The coordinates of the central bay of interest are 59°58'00.9"N 23°40'09.7"E.

There is basically no tide in that area of the Baltic sea. The central bay is something like 9-14km from the open sea (areas of the outer archipelago) depending on what route to choose through all these islands.

Basically, we have made a GAM analysis for that area based on our other parameters, and we have a predicted "normal level" for turbidity and a map, which shows SD from that normal level for every occasion (approx. 6 occasions during the non-ice period). So, basically, we were thinking about if it's possible to understand which source has a greater influence on this high positive SD turbidity in the bay and to which spatial extent. Like the spatial influence of a "point-source pollution". We have lots of high-resolution data, so we were thinking that probably it's possible indirectly calculate the strength and direction of influence. Kind of to draw the area where this particular point of pollution has influence, and where it stops having significant influence, being too diluted. But now thinking more about it, it seems that it's not really possible to predict such things without knowing velocities.

All the best,

Maria

There are several popular Python packages that are commonly used for GIS, remote sensing, and spatial data science:

These packages are widely used by geospatial professionals and researchers, and offer a range of capabilities for data manipulation, analysis, and visualization.

Maybe of minor concern, but let me note that it is kind of misleading to call Ripley's K and Moran's I methods of 'cluster analysis'. I know that ArcGIS does so, but still. Traditionally, 'cluster analysis' means making groups of initially ungrouped observations within a sample. In contrast, Ripley's K and Moran's I analyse the spatial structure of the sample. They do not create groups, but describe/test aggregation of a point pattern or spatial autocorrelation of a variable.

Ripley's K and Moran's I serve different purposes. Ripley's K describes the expected number of neighbours of any point in a point pattern across different radii. You can use it to analyse point patterns in the space, whether they are aggregated, random, or systematic. The only information you use is the coordinates (or distances between them). Moran's I analyses how a measured variable is structured in space. It uses a spatial neighbourhood matrix between the observations and a measured variable for each observation. It has a global and a local variant; with the latter, you can step across different distance classes to reveal scale-dependency in the spatial correlation. If reasonable null-hypotheses are provided, both can be used for hypothesis testing.

Anyway, it would be interesting to outline a real clustering method based on the Ripley's K or Moran's I. Maybe such a method exists already.

Antonio Luca Conte There are various software choices available for creating a climate change risk map for an oak ecosystem. Some common choices are:

1. ArcGIS is a geographic information system (GIS) program that may be used to produce comprehensive maps and analyze data. It is capable of analyzing climatic data and creating danger maps for specific ecosystems.

2. QGIS is a free and open-source geographic information system (GIS) program that may be used to build and analyze maps. It shares many of ArcGIS's features and may be used to generate risk maps for specific environments.

3. R is a statistical computing and graphics programming language and software environment. It's commonly used for data analysis and visualization, as well as risk mapping. Many R packages, such as 'raster' and 'dismo,' may be used to construct risk maps.

4. Google Earth Engine is a cloud-based platform for accessing and processing huge volumes of geographical data, including climate data. It may be used to generate risk maps for individual environments.

It should be noted that developing a risk map for a given ecosystem would need an understanding of GIS, data analysis, and modeling. If you lack such information, it may be preferable to seek advice from professionals in the subject, such as ecologists, climatologists, or geologists.

Journal of Climate and Water resources. Springer.

Also, visit Nature Journals. All are free.

Michael John Patrick Sir, There is no particular 3D GIS software that can do this. However, some software packages such as Autodesk 3ds Max and Unreal Engine by Epic Games have capabilities to render lighting and reflections accurately. You can also try Computer Generated Architectural (CGA) Rule method for 3D GIS.

For projects within the scope of FAA review, a glare analysis is typically required. A glare analysis involves a visual simulation of the array’s impact on the visibility of nearby aircraft pilots. The analysis includes a 3D model of the array and its location, as well as a representation of the surrounding terrain. The analysis should also account for the angle of the sun, which changes throughout the day and year, and other factors such as the reflectivity of the array’s materials.

Some states and local governments are also beginning to require a glare analysis to be conducted as part of the permitting process. While the exact requirements may vary, they generally involve a 3D model of the array and its location, a measure of the maximum uplight generated by the array, and an analysis of the maximum brightness of the array at certain angles, both on an annual basis and at specific times of day.

A glare analysis is often conducted with a computer simulation program, such as RhinoGlare or Radiance, to create a visual representation of the array’s effects on the surrounding environment. However, it is important to note that the results of the analysis are only as good as the input data. Therefore, it is important to ensure that the model is as accurate as possible, including all relevant features such as topography and nearby buildings.

The results of the analysis are typically presented in the form of a report and visual representation of the array’s impact on the surrounding environment. This report can then be used to inform the decision-making process when it comes to the proposed solar project.

Relative radiometric normalization using pseudo invariant features (PIF) in Google Earth Engine can be done by following these steps:

1. Add the input imagery to Earth Engine.

2. Identify potential PIFs by exploring the image and selecting features that do not change significantly in the image over the time period of interest.

3. Label PIFs and assign each one a unique identifier.

4. Select the sample points to be used for radiometric normalization.

5. Calculate the mean and standard deviation of the radiometric values for the sample points.

6. Calculate the radiometric value of the PIFs in the input imagery.

7. Calculate the normalization factor for each PIF by dividing the radiometric value of the PIF by the mean and standard deviation of the sample points.

8. Apply the normalization factor to the input imagery in order to achieve relative radiometric normalization using the PIFs.

Script:

// Define a function to calculate the relative radiometric normalization using PIF

var relativeRadiometricNormalization = function(image){

// Calculate the normalization factor

var normalizationFactor = image.normalizedDifference(['B7','B5']).select('B7');

// Select the bands to be used for the normalization

var bands = ['B2','B3','B4','B5','B6','B7'];

// Map the normalization over the bands

var normalized = image.select(bands).multiply(normalizationFactor).divide(100);

// Return the normalized image

return normalized;

};

// Apply the function to the image

var normalizedImage = relativeRadiometricNormalization(image);

Hope this will work.

Dear Chenyuan,

Here you are a dissertation about it

and on this webpage, you can find most of the algorithms you could need

Cheers,

Ivan

Nikolaos Tziokas at the end I ended up reading the file as a simple feature (SF), and using this package (sf) for the purpose of my research. This has been a better solution for what I aimed to do.

Thank you for reminding me

Glory be to Allah... As time progresses, new developments appear that help people to complete their needs with flexibility and ease.

For this question the factors like population and consumption in urban places can be devised.

Suitable, data model is to optimize the factors within the population demands like consumption of nature resources.

Dear Malakia David Naholo ,

URBAN BIG DATA Various sources for data, including: - sensor data for all types of urban infrastructures - [real-time] transport tracking data - social network data [information about events or opinions] - public app data - user volunteered data [including geographic data] - phone data - open data provided by government [e.g., air pollution data, crime data, meteorological data, land use data]

Regards,

Shafagat

Malakia David Naholo, BigData is a technology for maintaining data of higher volumes which are on magnitude bigger than modern software used in DBMS.

Hi,

If you expect some local variation and then a non-stationary behavior of your data, probably Empirical Bayesian Kriging will be the one. This is assuming you have a lot of data non left-skewed and you can asumme Gaussian distribution.

In any case, I recommend you to carry out a Cross Validation with a study of RMSE and AMSE, as you can see in Pellicone (2019; 10.1080/17445647.2019.1673840) or in Ferreiro-Lera (2022; 10.1080/17445647.2022.2101949).

I hope I have been helpful.

All the best,

Giovanni.

① add data .nc，Band Dimension(optional) is ‘time’

② “Export data”, to format TIFF (Location is a file, not gdb)

③Define Projection to WGS1984(for example)

④Add Tiff Data(after projection) and its all bands(Band1-Band12)

⑤Raster Calculator: calculate the sum or average of all bands and output grid or tiff

Just for reference.

Cara Rosa,

There are no universal thresholds to classify urban heat islands because each city will experience its own specific climate and environmental constraints. Since you are dealing with a single city, I suggest you start by determining the lowest and the highest temperatures in your data set, and then divide that range (Tmax - Tmin) in 3 equal intervals. For instance, if Tmin = 18 and Tmax = 24, your intervals would be 18 to 20, 20 to 22 and 22 to 24 C. In that case, all areas falling in the lower interval could be labeled 'cool', the areas falling in the next class could be called 'warm', and those belonging to the higher interval could be indicative of 'hot' conditions.

You should make sure that your data set covers a region around the city large enough to include agricultural fields or forests: those areas would provide you with a baseline environmental temperature away from the urban center. An obvious extension of this approach would be to map the city areas in single degree intervals, from blues through to greens, yellows, oranges and red. That will clearly indicate which areas are hotter.

A better approach would be to start from a preliminary question: why do you need to make a map of the urban heat island effect of Naples in the first place? If you were concerned by the health effect of temperature on morbidity and mortality of the inhabitants, for instance, then your temperature thresholds should be driven by medical rather than purely statistical considerations. Similarly, if your underlying concern were energy expenditures (cooling during the summer or heating during the winter), then your thresholds should relate to the corresponding critical rates of energy expenditure. In other words, your approach should depend on your ultimate goal.

Remember also that

- the urban heat island is quite time-dependent: it varies with cloudiness and synoptic conditions such as sea-breeze on a daily time scale, it is much more noticeable during the winter than the summer, and it may evolve on longer time scales, depending on the rates of urbanization and industrialization;

- land surface temperature is quite dependent on altitude, so you might want to acquire a topographic map of your area and look at the correlation between these two parameters;

- a land use map of the region will be useful to properly interpret your results, as the hotter area may not be the city center but some industrial area, depending on their relative rates of energy consumption.

I hope these comments may help you in your work. Michel.

The density clustering with wavelength clustering algorithms and Clustering by Wavelet Analysis may help your work

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.

I had the same problem and solved it by working on preprocessing. My steps were: Digitize the shorelines (with a fixed zoom scale) and create a baseline on the mainland, you may have to create both on the mainland and on the water (depending on our region). You must use shapefiles in the same coordinate system, and must be based on meters (UTM). In the case of your shorelines had been digitalized in different shapefiles, use the "Merge" tool to join all shorelines in one shapefile. In ArcCatalog, click with the right bottom in the folder you want to put your geodatabase, so go to "New" and in "Personal Geodatabase". After that, with a double click in the archive just created, click with the right bottom in the space of it, and go to "Import" to put there two shapefiles, one with your shorelines and other with the baseline(s).

Some classes and their types are required, so you should check if your shapefiles have:

- Baseline:

--> User created: id (Long Integer)

--> Created by DSAS through Attribute Automator: SHAPE_Length (Double), DSAS_ID (Long Integer)

- Coast:

--> User created: id (Long Integer), Uncy (Double), Date_ (Text).

--> Created by DSAS via Attribute Automator: DSAS_date (text), DSAS_uncy (Double), DSAS_type (text)

I leave the uncertainty field (Uncy) empty and DSAS defaults to 10 meters in this case. After that, I used the DSAS video (https://www.usgs.gov/media/videos/introduction-dsas-v50-sample-data-workflow) as a guide.

Dear Majid,

You may use Landsat satellites to cover these years. You may also increase the spatial resolution to 15 meters using pan sharping technique. About the classes, many methods can be applied whether supervised or non. It depends in the analysis and purposes. Envi and Erdas are good choices to apply that.

Best wishes,

Jasem

You may take a look at the following publication regarding the procedure of combination of SS and GIS in respect to land use compatibility with pedestrian demand.

Dear Ahmed Younes Kindly Check..

Dear Mohammed ..

I hope these references might be helpful. Kindly have a look.

Regards

Gowhar Meraj

Dear Dewan Salman Sunny

This tutorial might be helful.

Regards

Gowhar Meraj

Hey! Abhilash plz Share protected password with me

Tectonic processes work over long time spans. The pre-Himalaya land surface would have been well vegetated and would have supplied the diverse variety of flora. As the mountains rose, much of the plant life would have needed to adapt to colder conditions. Once steeper slopes were created, rockfall and landslides were inevitable.

Plants need soil and parent material provides the base from which soils develop. Therefore the local rock type is critical to the early development of soil. Calcareous rocks, e.g. limestone, provide a rich sweet element base to support grasses. Quartzites on the other hand break down to infertile silicates and soil develops very slowly on such. Another factor related to rock type is its propensity to weather and/or slide. Marls, clays, shales and highly micaceous schists are very prone to rapid weathering both physical and chemical and also to slippage along planes of weakness. Granites, quartzite and psammites are much more durable forming blocky scree in which little soil can form or plant get rooted. Mosses and pioneer species take hold first.

i have noted that in areas affected by recent debris flows that vegetation can very rapidly recolonise even in one year, so that any recent activity can become concealed.

George Strachan

Dear Jens Kleb Antônio Carlos Pereira dos Santos Junior Omid Vakili , for precision monitoring I use an emlid reach rs2+ GNSS antenna and its app is well supported by apple so this doesn't worry me. My basic idea is to use the iPad as if it were a topographic map on which to digitize with the pen in the field or in the laboratory the various shapes obviously geo-referenced and using as a base map a topographic map in ecw format, export in shape format and load everything into my beloved QGis, but taking full advantage of the potential of the apple pencil. Is this possible? I must also admit that I am a big fan of global mapper. Regards

Check https://www.mdpi.com/2073-4433/12/9/1109/pdf

-QGIS, ArcGIS (GIS software)

-R, Python (Programming software)

-Google Earth Engine (Online platform)

Dear Raj Sunil Kandregula

Thank you very much for this interesting question. This indeed is a research question that can only be answered after thorough research. While I was searching for some answers, I came across the attached publication. I hope it provides some insights.

Thanks and regards

Gowhar Meraj

Dear Nasser,

There should be no difference in the results if you coded (0,1) or (1,2)

In both cases, the result will be as a ratio between them. The parameter B equals the ratio of males/females (as males variable is coded with the smaller value)

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

i think the correct formula is SPI_2 where:

flow accumulation must be in square meters and then not multiplied for 10;

the slope degree must not be equal to 0, so add + 0.1 degrees

In this way, SPI should be a dimensionless positive value (more or less)

Could you give more details regarding to ABAQUA in order to help you

regards

This is a good question.

maxent software

One of my advisors once wrote:

I hope this may help in identifying key sampling sites.

That is an interesting question. KeyATM and Topic modeling allows for covariates. But I am not sure if they allow for spatial variables.

Janaki Sandamali, there are several remote sensing satellites that have been used for Sea Surface Temperature (SST) measurements. MODIS onboard Terra/Aqua, VIIRS onboard Suomi NPP, AVHRR, Metop-A,B,C are commonly used in this regard. There are also Geostationary satellites such as GOES that are used for deriving SST products. You might be interested in some of the webinars that are conducted by NASA ARSET for Coastal and Ocean Applications.

Todo índice es util siempre y cuando según la tecnología disponible se puedan resolver problrmas tecnicos.

thanks again for your reply.

A movement is not uniform. The direction is constantly changing, so a summation of rows or columns does not make sense. Also the movement starts somewhere in the middle of an area.

I will attach an example.

So if you follow the arrows backwards, in the last cell there should be the sum of all values which are passed through.

Describe the geographical features and locations of the places and roads on the map, in relation to one another

The OPGD model and "GD" R software package were recommended to identify spatial determinants from a perspective of spatial heterogeneity. You can refer the guide to use the model https://cran.r-project.org/web/packages/GD/vignettes/GD.html. As a result, you can visualise contributions of determinants, and the interactive impacts of spatial variables.

Dear Mohammed, congratulations on this innovative research work. Please, see the below links you may find some good guidance:

Use principal component analysis (PCA).

Look at this paper.

https://www.mdpi.com/1660-4601/17/8/2942/pdf#:~:text=Principal%20component%20analysis%20(PCA)%20was,the%20sources%20of%20water%20pollution.&text=It%20also%20showed%20that%20agricultural,pollution%20in%20the%20study%20area.

Check Invest model. It has serviceshed sub-model.

I hope it will be helpful.

Dear Jobin Thomas

We also faced a similar problem in the Jhelum basin. In larger basins, you are very much right that natural drainage is not always available for stream burning. Further in our case, the pseudo drainage occurred in flat surfaces and over the lakes. For lakes, we had the lake polygon layer to burn. I think increasing the threshold is one of the viable options that does reduce such spurious drainage lines.

The solution lies in between the opinions provided by W. J. van Verseveld Richard Gloaguen Neelakantan Sajikumar

Thank you all for this wonderful knowledgeable discussion.

Regards

Gowhar Meraj

Hi Ananya!

I agree with some of the answers that mention that, for the software you are planning to use the vector layer with, you will need to have the information in a common geographic reference system. In order to do that with you can use the free software QGIS for the georeferencing process (look for Raster menu - Georeferencer) and then in the same software you can perform the conversion from raster to shape using (Raster- conversion-Polygonize).

The algorithm usually works well but the result will depend a lot on the complexity of the raster data.

Hope it helps! regards, Soledad

Hi!,

Given that the crop seasons are known and the availability of sentinel data, maybe instead of post-processing you can perform a pre-processing, select a date or dates when the crops are still not developed and classify the forests, then create a mask of forest areas and apply that mask to an image or images for the crops classification. Depending on the complexity and types of crops you may have to anlyse more than one date but in this way you remove the effect of forests.

Hope it helps, I have solved this way a similar problem I had with urban and soil with stubble mixed signatures.

Regards,

Soledad

Hi Swati,

Decorrelation in highly vegetated terrain is to be expected and IMHO I don't think the choice of the software will have a significant impact on the results but rather the processing method and the properties of the input data.

SNAP by Esa is a freely available software capable of interferommetric processing, Gamma is expensive (very) but has all one can ask, there are also others e. g. Erdas Imagine

First need to determine in which stream order network delineation then based on the river order determine the threshold value.

I see that there is another scientific shortcut looming, purporting the well-known “philosopher´s stone” of geomorphology and basin analysis. It is similar to the approach taken in geochemistry where a plethora of indices and ratios pretend to avoid experience and knowledge in geology and mineralogy prior to the use of endless numerical datasets.

In the current issue there will be no by-pass of geoscientific anamnesis and visual examination (mapping, sampling leading to 2- and 3-D visualization as well as follow-up laboratory analysis.

Numerical analysis encompassing hydrography such as sinuosity of channel systems, slope angle must be supported by hydrodynamic investigations (GMS tool = granulometry, morphology, situmetry) all of which is grouped under the header “geoscientific measurements, analysis, and computations”. It needs to be corroborated by measurements in structural geology and by in-door analysis of the mineralogy, petrography and chemistry as well geochronology using methods appropriate for the Quaternary and Neogene , in particular. Any stand-alone synthetic geomorphometric analysis will yield synthetic results.

To substantiate my arguments I refer to the following papers dealing with this holistic approach in basin analysis and geomorphology.

DILL, H.G., BUZATU A., BALABAN S.-I. , UFER K., GÓMEZ TAPIAS J., BÎRGĂOANU D. and CRAMER T. (2020) The “badland trilogy” of the Desierto de la Tatacoa, Upper Magdalena Valley, Colombia, a result of geodynamics and climate: With a review of badland landscapes.- Catena 194: 1-20.

DILL, H.G., BUZATU A., BALABAN S.-I. , UFER K., TECHMER A. SCHEDLINSKY, W., and FÜSSL M., (2020) The transition of very coarse-grained meandering to straight fluvial drainage systems in a tectonized foreland-basement landscape during the Holocene (SE Germany) – A joint geomorphological-geological study.- Geomorphology (in print)

Available and ready for download on the RG server.

H.G.Dill

You need to separate two questions, first there is the number and spatial pattern of the data locations used in estimating and modeling the variogram. Secondly there is the number and spatial pattern of the data locations used in applying the kriging estimator/interpolator. These are two entirely different problems. The system of equations used to determine the coefficients in the kriging estimator only requires ONE data location but the results will not be very useful or reliable. Now you must decide whether to use a "unique" search neighborhood to determine the data locations used in the kriging equations or a "moving" neighborhood. Most geostatistical software will use a "moving" neighborhood, if you use a moving neighborhood then about 25 data locations is adequate, using more may result in negative weights and larger kriging variances. Depending on the total number of data locations and the spatial pattern there may be interpolation locations where there are less than 25 data locations. Using a "unique" search neighborhood will likely result in a very large coefficient matrix to invert.

With respect to estimating and modeling the variogram you must first consider how you are going to do this. Usually this will include computing empirical/experimental variograms but for a given data set the empirical variogram is NOT unique. It will depend on various choices made by the user such as the maximum lag distance, the width of the lag classes and whether it is directional or omnidirectional. An empirical variogram does not directly determine the variogram model type, e.g. spherical, gaussian, exponential, etc. It also does not directly determine the model parameters such as sill, range.

Silva's question may seem like a reasonable one to ask but it does NOT have a simple answer. Asking it implies a lack of understanding about geostatistics and kriging.

1991, Myers,D.E., On Variogram Estimation in Proceedings of the First Inter. Conf. Stat. Comp., Cesme, Turkey, 30 Mar.-2 April 1987, Vol II, American Sciences Press, 261-281

im=imread('GIS.jpg');

%imshow(im);

BW = im2single(im);

numclust=5;

[L,Centers] = imsegkmeans(BW,numclust); % matlab inbuilt

temp_mask = L==1;

figure,imshow(temp_mask);

Can you post a sample image to see resolution, contrast, etc. and look at image quality?

Hi Ibrahim,

to get Landsat 8 TIRS band 10 and 11 you can follow the steps below:

1- to go https://earthexplorer.usgs.gov/

2- create your account

3- Enter your search criteria (place and date range)

4- Click on the Datasets tab and expend Landsat

5- Once you expend Landsat, click on the + sign to choose Landsat Collection 1

6- Expend Landsat Collection 1 and click on Landsat Collection 1-Level-1

7- Once Landsat Collection 1 Level-1 is expended, check the box of Landsat 8 OLI/TIRS C1Level-1

8-Optionally, you can click on the Additional Criteria to narrow down your search (cloud coverage percentage)

9- Click on the Results tab to see your search results

10- Download the data to your working folder and unzip it to extract band 10 and 11.

I hope that could help. Best of luck with your research.

Indeed, good start

Hi Jake,

It is a daunting task and not very easy to answer. We have been struggling with it for a while now. From our experiences it comes back to two basic challenges. The first one is how you define land degradation (see our recent publication below), but of course also to obtain the proper datasets. In our research we are focusing on creating a time series of a range of soil properties. We are currently aiming to at a time series of soil conditions for the UN statistics division in the context of their national accounting.

Cheers Jetse

Sterk, G., Stoorvogel, J.J., 2020. Desertification–Scientific Versus Political Realities. Land 2020, 9, 156; doi:10.3390/land9060156.

Stoorvogel, J.J., Bakkenes, M., ten Brink, B.J.E., and Temme, A.J.A.M., 2017. To what extent did we change our soils? A global comparison of natural and current conditions.. Land Degrad. Develop., doi: 10.1002/ldr.2721.

Stoorvogel, J. J., Bakkenes, M., Temme, A.J.A.M., Batjes, N.H., and ten Brink, B.J.E., 2017. S-World: A Global Soil Map for Environmental Modelling. Land Degrad. Develop., 28: 22–33. doi: 10.1002/ldr.2656.

Hello Emma E.M. Evers

The "tlocoh" package for R might be of interest for you. Within this package you are able to analyse the space use of animals, including the revisit rate of specific areas by individuals, based on GPS data. Further you can calculate the amount of time spent during each visit. These information could be linked in a further step with your NDVI data.

You will find all information about this package here:

I hope this helps!

Best regards

Paul

This resource might be helpful:

It is designed for shoreline change analysis, but applications to other boundary change studies are possible ("The software is also suitable for any generic application that calculates positional change over time, such as assessing change of glacier limits in sequential aerial photos...").

Most of the comments are wrong or incomplete. First of all you need to distinguish between a particular software implementation and the underlying theory. The theory of kriging, i.e. geostatistics is not based on GIS. Nor is an software package implementing kriging limited to or necessarily related to GIS so the original question implies a serious mis-understanding about kriging and GIS

One of the basic assumptions underlying kriging is either second order stationarity or intrinsic stationarity. Unfortunately with only a finite data set it is not possible to test whether either of these assumptions is really valid. There are ways to ascertain whether it is "reasonable" to make one of these assumptions or rather whether there is evidence that both assumptions are unreasonable. e.g if the empirical variogram increases at a quadratic or higher order then intrinsic stationarity is not reasonable.

If having modeled a variogram with a sill and hence at least a practical range then you would want to be cautious about using kriging at a distance greater than the range from all the data locations. Two things will happen; the kriging variance will be much larger and secondly the "kriged" value will be essentially just the arithmetic mean of the data values. A particular software implementation of kriging might then prevent generating kriged values too far away from all data locations.

Software manuals, particularly those for commercial software are likely not sufficient to address questions such as these. Instead one should consult a book on geostatistics, e.g the one by J.- P. Chiles and P. Delfiner (John Wiley & sons)

Certainly; I wrote up the code this morning. Best of luck! https://www.timothyfraser.com/tutorials-in-r/mapping-raster-data-in-the-tidyverse

Two useful methods:

- NIR as a single band

- NDWI as index (G-NIR/G+NIR)