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Hello forum members,
I'm conducting research on vegetable oil fingerprinting. I need annual precipitation (rainfall) data for provinces in Thailand, Malaysia, and Indonesia. Specifically, I don't know what it is called but, I need the **single annual precipitation value** (in millimeters) for each province for the years 2018 or 2019 or later, just any year after 2016, or maybe average monthly rainfall data. A data that can differentiate and groups between provinces.
Could anyone kindly provide this data or guide me to reliable sources where I can obtain it? Your assistance would be greatly appreciated!
Thank you in advance.
Best regards,
Note: I actually also need temperature and humidity data, but it's a bonus.
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source. Using global climate datasets is the best approach to obtain annual precipitation data for Thai, Malaysian, and Indonesian provinces using the same standard and source for your research. These datasets offer global rainfall, temperature, and humidity data in a consistent format. Among the reliable sources are: WorldClim (www.worldclim.org) is one source. It provides free climate data, including humidity, temperature, and rainfall. Average monthly or annual rainfall data at the province or grid level, spanning from 1970 to the present, is available for download. Province-by-province data can be extracted using GIS software. CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) is an additional source. It offers reasonably accurate rainfall data for tropical nations like Thailand, Malaysia, and Indonesia from 1981 to the present. Data from the Copernicus Climate Data Store's ERA5 Climate Reanalysis can also be used. It offers daily, monthly, or annual rainfall, temperature, and humidity data going back to 1950. Although the data is free, you must download and register. Check out ClimateData.org or the World Bank Climate Knowledge Portal for pre-made province-by-province data that doesn't require GIS work. They provide summaries of climate data at the national and provincial levels, though occasionally they only provide long-term averages. Since WorldClim, CHIRPS, or ERA5 data covers all three nations in a single, standardised format, this is the best method. Any year after 2016 can have its annual rainfall easily calculated, and the same source can also provide the temperature and humidity.
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I participate in an international research project focusing on the relationship of happiness feelings to environmental conditions. Our hypothesis is that happiness may be valued more in regions with favorable environmental conditions. Among other variables we would like to examine the length of the vegetation period in several world regions. Therefore we are looking for data on the different vegetation period arount the globe that can be processed numerically (all our other variables are processed in SPSS). I found some databases but they are either local or employ a formats that are not accessible to most psychologists. I would appreciate any suggestions about how to find global vegetation data.
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Here are some suggestions on how to find accessible and numerically processed global vegetation data:
1. **NASA Earthdata:** NASA provides a wealth of environmental data, including vegetation-related information. The MODIS (Moderate Resolution Imaging Spectroradiometer) dataset, available through NASA's Earthdata platform, offers global vegetation indices such as NDVI (Normalized Difference Vegetation Index) that can be processed numerically. You can access this data in various formats, including HDF, which can be converted for use in SPSS.
2. **UN Food and Agriculture Organization (FAO):** The FAO maintains datasets related to vegetation and land cover. The Global Land Cover SHARE dataset (GLC-SHARE) is a global land cover dataset that includes information on vegetation. While it may require some data processing, it is a valuable resource for your research.
3. **Global Vegetation Monitoring (GVM) Project:** The GVM project provides access to global vegetation datasets, including the GIMMS NDVI dataset, which covers several decades. These datasets are commonly used in ecological and environmental research and can be processed for use in SPSS.
4. **European Space Agency (ESA):** The ESA offers various Earth observation datasets, including vegetation-related data. The Climate Change Initiative (CCI) Land Cover project provides global land cover information, including vegetation, in formats suitable for scientific analysis.
5. **Data Repositories:** Consider exploring data repositories and archives such as the World Bank's DataBank, which provides access to environmental and climate-related datasets. While the focus may not be solely on vegetation, you can find relevant data.
6. **Collaboration with Remote Sensing Experts:** Collaborating with experts in remote sensing and geospatial analysis can be beneficial. They can help you access and process vegetation data for your specific research needs.
7. **Data Conversion Tools:** If you find data in formats not directly compatible with SPSS, consider using data conversion tools or software to transform the data into a usable format. Tools like R or Python can be helpful for this purpose.
8. **Consult Local Universities or Research Institutions:** Local universities or research institutions in your region or in regions of interest may have access to relevant vegetation data or can guide you on where to find it.
When working with global environmental data, it's essential to ensure that the datasets are up-to-date and cover the regions of interest for your research.
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How do the pools, ponds form in the middle of the forest and are they highly dependent on rainfall?
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Tropical forests have the capacity to absorb water from the ground and hold water in their bodies and release it through the root hairs, that is why even in summer months drought time water pools are still continuously supplied with water. But water pools are enhanced with water through the hydrologic cycle and I would like to catch this caption I read " No Trees, No Water, No Water, No Life."
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I have some environmental covariates derived from the digital elevation model (slope, gradient, channel network distance, etc.) in raster format.
I want to identify areas of similarity between the covariates and somehow identify the smallest possible size area (or areas) to serve as a reference area.
  1. Soil data points will be collected to create predictive models in this reference area.
  2. The predictive models developed in the reference areas should fit when extrapolated to regions outside the reference area.
  3. Therefore, the covariates will cover this external area.
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Another possible way is to use Spatial Feature Coverage Sampling, but with high number of sample points is not relevant. We are discussing its weakness in our ongoing paper.
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I have attached the OSM map of Pannipitiya, Sri Lanka. So looking at the map, what kind of geographical questions you can ask?
To me, the following came to my mind
1. What are the places where house dwellers can walk and reach within 1 minute (600 m ?) ?
2. What is the calmest and quiet place to meditation?
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Describe the geographical features and locations of the places and roads on the map, in relation to one another
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Working with climate/Geographical data, there is some moment you definitely need to analyze data with programming language or software tools. In that case which one you will prefer more to do that task?
  • Python
  • R
  • Matlab
  • QGIS/ArcGIS
  • Other( Please mention the name with details)
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ARCGIS AND ERDAS
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Everyone knows, the fast changing climatic condition with altered physical factors due to various anthropologic activities. Humans are unable to handle the issue of climate change due to growing needs and population. So, how nature will resolve the issue of climate change without human interference?
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If greenhouse gas emissions continue to grow in the coming years, the global warming process will accelerate and become an irreversible process in the next several dozen years. Therefore, climate change can be so great that nature will not solve the problem either. Forest fires, droughts, and weather anomalies will intensify. The biodiversity of natural ecosystems will quickly decline, it will be impoverished in terms of the composition of flora, fauna, fungi and microorganisms, and in terms of the genetic composition of the planet Earth's biosphere. Nature will not stop these unfavorable processes of climate change, but will be subject to these processes. So nature, preserving the biodiversity of natural ecosystems, can be helped primarily by people who, by implementing the principles of sustainable development, social environmental (ecological) responsibility, pro-ecological reforms of implementing eco-innovations in economic processes, etc., and other pro-ecological transformation of the traditional brown economy into a sustainable green economy / circular economy can save the biosphere of planet Earth from the climate crisis. If it's not too late, of course. But no matter how much time is left for the implementation of this pro-ecological action plan, it must be implemented urgently.
Best regards,
Dariusz Prokopowicz
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I am particularly interested in conceptual literature and pieces of research that aim at advancing the human geographic debate on (local) climate governance. Thank you very much!
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How we can monitoring siltation in harbours using remote sensing?
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Simplified Method for the Assessment of Siltation in Semiarid ...
www.mdpi.com › pdf
PDFby JWB Lopes - ‎2019
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Dear GIS experts,
Please, I need your assistance to draw the map of my study sites located at Arusha and Kilimanjaro regions of Tanzania (Eastern Africa). Please, I simply need the map of the two regions (Arusha & Kilimanjaro) showing my study sites with the following coordinates
First study site: Latitude 3º21’50.08”N and Longitude 36º38’06.29”E in Arusha region (DMS latitude longitude coordinates: 3°22'0.01"S, 36°40'59.99"E)
- Second study site: Latitude 3°13’59.59”S Longitude 37°14’54”E in Kilimanjaro region (DMS latitude longitude coordinates: 3°21'0"S, 37°19'59.99"E)
Please, I need your kind assistance on this; be it by advice or draw the map and sent to me. I highly appreciate your kind gesture in advance. Attached is a copy of the raster map of the two region but I need to locate my study sites. Thank you
Thank you
Best regards!
Difo
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Infectious ecology is a new line of research. In a clear way, it was formulated in 2017. In 2018, some works were carried out that showed the significance and real novelty of this scientific approach. On its basis, new theoretical interpretations of a whole range of infectious diseases can be given.
The case of KDD is not unique. There are a considerable number of infectious novelties that are difficult to understand in the dominant scientific terms. An example would be ebola. In the infectious ecology, an attempt is made to re-state the problem and to find a new solution for the KDD.
The basic idea is that we are dealing with another manifestation of the discrete activation of the pathogenic properties of microorganisms. As in many other cases, the cause is a change in soil geochemistry. That it is a pretext for the manifestation of the pathogenic properties of Phytophthora agathidicida. This is usually associated with agriculture and anthropogenic changes.
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Dear Dimitry,
You may be interested in Chaboussou, Sante des Plantes, une revolutions agronomique, (1985) translated as Healthy Crops, A New agricultural Revolution. (2004). It describes how agrochemicals cause the proliferation of pests and diseases. We are starting a new project to bring this up to date and confirm/or deny the ideas. I would be happy to cooperate.
Best wishes, Ulrich
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Christian Nold (2009) in his book of essay Emotional Cartography aims to 
explore the political, social and cultural implications of visualising intimate biometric data and emotional experiences using technology.
In my research (http://www.pocitovemapy.cz/index-en.html) I work with term emotional mapping, but I have been told by some other researchers, readers and reviewers, that emotional mapping is not the correct term, as emotions is not what is being mapped.
I usually ask people to identify places on the map, where they feel afraid (mapping of safety), where they "like it" (spatial preferences), but also places where they spend free time, or places where it is dirty (environment pollution, etc.).
Which term, would you suggest for this kind of subjective mapping - would it be emotional maps or maps of perceptions? And what do you think is the main difference?
Thank you!
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Kind Jiri,
may be "Sentimapping" a more suitable neologism for Your theoretical and practical purposes ? At least, just to respect the difference between "emotion" and "sentiment", given that in a map, usually, we try to represent something that is closer to a sentiment towards a place, an object, ecc., much more than to a unique emotion...
Just an idea.
Best Regards
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I have list of ~3000 geographical locations from various parts of India. I want to find out their Latitude and Longitude co-ordinates. I want to know if there is any tool / software that will together find out the co-ordinate and put the location on the map.
Thanks
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Google Maps has the most reliable and accurate database for geocoding (assigning lat lon coordinates to addresses), here are instructions for their API: https://developers.google.com/maps/documentation/geocoding/intro
If you're familiar with QGIS (https://www.qgis.org/en/site/forusers/download.html), you can install plugin called MMQGIS and easily geocode list of locations in CSV format, here are instructions how to download and use the plugin: https://www.lib.uwo.ca/madgic/projects/gis/Geocoding%20Using%20QGIS.pdf
You should prefer using Google as a source rather than OpenStreetMap (OSM).
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I have several layers in my overlay, but when I run it they show for the whole area they have data for (global), whereas I would like them to display their results only within the area of a polygon I have created. Does anyone have any ideas as to the code required for this?
Many thanks
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Besides filtering the image collection itself, you can also clip the output you are visualizing using the following (.clip(roi)), where the roi is the region of interest you have set:
Map.addLayer(your_data.clip(roi), {}, "Your_data_name", true);
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From January 2014 to December 2016, I conducted monthly field trips in three different areas of Dhaka city, Bangladesh to investigate how butterflies survive in an inner-city habitat. I have found that the species richness (I did not count the species abundance) is declining with time.
Presently, I have:
* monthly species-specific data of three continuous years (36 continuous months) of the three different areas
* Four environmental variables (temperature, rainfall, humidity and sunshine hours)
* Pollution level
* IUCN Red List status
* Land-use analysis of these three different areas
Now, I am really confused considering the proper way to link these pieces of information! Could anyone of you please share some ideas?
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Look at the Material and Methods section. Perhaps it could help?
Best,
Guy
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Urgent - trying to find a program that will convert a scanned pollen diagram into either graphs, a digital, clearer pollen diagram or data tables.
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You can use the Getdata software, it could precisely get the point (x,y) from the pollen diagram, then use the data to draw another digital diagram.
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What were the general inferences concluded after the phylogeny reconstruction?
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Check the attached articles.
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Can it help to understand adaptive strategies of them?
Any idea?
Thanks for your answers 
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The conditions in the different geographic areas are clearly different, this causes that the same taxa undergo transformations that can be both morphological and physiological, this is used in studies related to the adaptability that is able to develop a certain organism before different conditions, which Tells us its ability to survive under unequal conditions, and in case of possessing any component that can be exploited, such as pigments, it is possible to extrapolate the conditions of the environment where it produces to be exploited later.
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Use of MCA analysis for smallholder communities
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Thank you so much Eefe. It has helped me a lot. Please send me your email address.
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Please provide data on the history of the Ziban region and its palm groves ?
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Mohammed:
This link should prove useful:
Best
Syed
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From January 2014 to June 2016, through the continuous field trips, I have listed all those butterflies which were spotted. Now, I have analysed the land area management of Dhaka city which contains over 17 million inhabitants. The total species number is 136 and a good number of them are Endangered and Vulnerable according to regional IUCN Red List 2016. How can I synchronize these data? Is it possible to make a conservation management plan with the help of this research? Please help..
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Field data are always useful to draw CM plans. In fact, it often happens that CM plans are incomplete because information that actually existed could was not incorporated to them. The papers below show three such cases (for various taxa) where Management Plans, field data, and literature or IUCN data all had occurrence data that were absent from the other sources, resulting in partial knowledge and therefore less-than-optimal plans. Data synchronization was accomplished through database mining and GIS analytics. Methods are explained in the papers.
The difference between the amount of knowledge in a plan, and the amount of existing knowledge that could have been put in a plan, should become a priority especially when endangered species are present. The fourth reference (link) below is a draft for a document dealing with this type of gap analysis.
Regards
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I know this is question highly depended on geographic location, but is there an average deposition of NH4 from the atmosphere into a body of freshwater? I'm specifically looking for a reference that gives the amount of NH4 deposited into a litre (or m2- surface area) of water per year. I'm looking for references ideally from the UK. 
I'm specifically looking for references that report atmospheric deposition, independent of dust/rain/bird droppings etc. 
Any help or advice pointing me in the right direction will be very much appreciated.
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Ammonia is NH3, which is a very light gas,whereas ammonium (NH4) is a basic radical. The gas being far lighter than air, & very sparingly soluble under high pressure, I find it surprising as to how the gas can dissolve in fresh waters from atmosphere under natural conditions. 
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Hello
I would like to know if any simple bioclimatic index has been proposed (as Humidex or Heat Index for mixing the effect of temperature and humidity) to include the deleterious effect of strong winds when T > 37°C or so. I know that some indices already include the cooling effect of strong winds (as net effective temperature) when T is low but I am rather interested by the warming effect of winds. I add that I am looking for a synthetic index (not as complex as UTCI for example).
Thank you in advance
Vincent
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Dear Vincent Moron
The most simple and enough  accurate Index you need was developped by Baruch Givoni "The Index of Thermal Stress" ITS  # 5.6 and #16.1,c (The building Bioclimatic Chart).
.See in his book "Man Climate and Architecture" Applied Science Publishers - London 1976 Reprinted 1901
Take into account that certainly you must consider that no "acceptable discomfort" will you found under the sun, but in the shade properly dressed. Then the formulation is much simple if direct solar radiation is not considered.
Then you need to evaluate which is the acceptable E/Emax value you need according to the sweat rate Inex of humidity. Certainly no Comfort will be found at 37 degC, But you can define the (also) acceptable low metabolic rate...
If you are interested in the way I use it, please write to my email  
Milo E.  Hoffman <mhoffman@tx.technion.ac.il> you will be welcomed 
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Relating location of the Redom River in NW Africa. I know only the former name of this river and I need for any information about its current name and geographic position (coordinates)
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Dear Dominique, thank you very much for your useful tooltip! Yes, it is likely the Oued Rdom in the Sebou Basin! Sincerely yours, Ivan
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I need its exact meaning
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Dear Subir,
Oro- is an element, from the greek, that makes up names with meaning of 'mountain'.
Orotemperate is a thermotype from temperate climates.
Thermotypes clasification are: infra-, thermo-, meso-, supra-, oro-, cryo-,
Have a happy New Year.
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The great alluvial Plain of Catania (Piana di Catania) is situated in the East of the mediterranean island of Sicily. Basically two rivers flow there along the sides of the plain into the sea: Simeto and Gornalunga. The region is absolutely ideal for bronze age water engineers to build the usual bronze age water engineering. Yet, I cannot find any academic work on this. Sicilian archaeology is concerned only with the Greek era or with single settlements, not with landscape archaeology, as it seems. Any helpful hint is welcome!
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You can find something helpful here:
Matthew Fitzjohn (ed.)
Uplands of Ancient Sicily and Calabria. The archaeology of landscape revisited
Specialist Studies on Italy 13
2007
And you can even contact me in private at tanasid@arcadia.edu as I teach even a course about Landscape archaeology in Sicily at the Arcadia University, Sicily Center.
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What is the mathematical function that determines the solar radiation received by the area defined by two parallels and two meridians depending on latitude? How can the tilt of the axis of rotation affect the received radiation?
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This formula is very basic, and provides only the horizontal EXTRATERRESTRIAL irradiance, thus not taking any atmospheric effect into account.
Not of much practical application, considering the original question.
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I know of RSI, THI, ET, PMV, AT(apparent temperature) and PET. I will appreciate indicies that is applicable in the Tropics.
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For Nigeria: stiudies by Akinyemi Gabriel Omonijo