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Vegetation - Science topic

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Questions related to Vegetation
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Can anyone recommend papers or any kind of publication dealing with updated perspectives on ecological-vegetation succession? Any researchers working on this subject?
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Thanks a lot for your answer.
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Hello everyone.
My team is seeking for a researcher who is conversant with the use of Remote Sensing tools to model vegetation cover/height. We have modelled high-quality daily wind speed data and need to incorporate vegetation height information into it before a highly rated journal accepts the manuscript for publication.
Kindly contact me if you have the required skill.
Thank you.
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Thank you Olawuyi.
We will look at it.
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Hello Everyone,
I am working on a study area dominated by the agricultural landscape, and I have sentinel 2 image for October. I just wanted to understand if there is any way to distinguish / separate crops from natural vegetation in satellite image.
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Prem Baboo Natural vegetation is the plant that grows freely without much influence from man, most of which are not domesticated yet, while crops are plants whose value and benefits have been known and are mostly cultivated by man for food, fiber, or industrial use................................I hope this help. Thank you
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Hi,
I am a newbie, I have a sentinel2 .tiff image, I would like to calculate some vegetation indices, my question is how can I know names of bands composing the .tiff image in-order to select the right bands for the right VI calculation?
I use QGIS SOFTWARE.
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We know that the threshold value varies by geography. For the reason identifying this value is challenging and there is a considerable risk of identifying the incorrect value. That is why my primary goal is to precisely determine threshold value so that I can separate vegetations for a specific area from the rest of the landscape.
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NDVI values vary depending on the volume and condition of the vegetation cover. In the same region there may be crops with soil gaps, or meadows (with high/medium/low green (fresh) or dry yellowed grass, and forests (coniferous or deciduous)/ Each of these sites/vegetation states may correspond to different NDVI values. For this reason, each of these landscapes will have a different range of NDVI values. That's why your question misses the main point - from which landscape do you want to separate your particular site? What index values will be found in your study area?
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I want to compare percent vegetation cover on a reclaimed site vs. percent vegetation cover in a reference area (i.e., an adjacent, not disturbed area). Can I do this with Sentinel or Landsat imagery? If so, would it be best to just create a ratio between something like NDVI in the reclaimed area vs. that in the reference?
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Below two articles, decades apart, that may assist you in approaching your question from first principle. NDVI is a helpful proxy for green biomass up to certain amount. Whether green biomass is correlated with plant cover depends on the season, if any, and the type of rangeland (annuals, perennials, low evergreen shrub....). Please refer to my papers on typologies of rangelands. Finally, I am interested in your reason for the estimation of cover. The only reason that comes to my mind is cover as predictive variable for erosion and infiltration.
Of course, you may approach your estimation purely empirically. Measure cover on the ground and correlate cover values with various temporal NDVI variables.
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The plants were healthy at vegetative stage but at flowering stage in some plant leaf structure changed and gradually this symptom spread in many plants in field. We uprooted more than 20 plants and still this disease in field has potential to reduce yield dramatically.
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Yes, it is a viral disease of Cucurbita pepo caused by Zucchini yellow mosaic virus (ZYMV), which belongs to the genus potyvirus and is one of the most destructive and widespread viral pathogens on Cucurbits worldwide.
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Hello,
I have a 1x1m image of a winter wheat and a reference value from an expert regarding canopy cover. Now I am supposed to derive the canopy cover using vegetation indices (NDVI, SAVI, VARI and GRDI) to find out which index works best.
My question is: So how do I get the percent canopy cover from the indices?
Here's what I've been thinking about: First, I classify the images using the thresholds in the literature (example NDVI: >0.2 = soil; <0.2 = vegetation). Then I reclassify them to access the attribute table and pixel count. Now I would actually only have to count these. So: canopy cover (%) = number of vegetation pixels / number of total pixels.
Is this variant a possibility? Or should I rather go via training areas and maximum likelihood methods?
Thanks for advice.
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or you could use a random forest regressor to estimate the canopy fraction since you have reference data from an expert, as you mentioned
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Kindly tell me the wattage and time for cell lysis of Bacillus endospore and vegetative cell.
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Thank you for sharing
Stefan Zimmermann
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What quantitative index/indices should be used when measuring biodiversity of Urban Spontaneous Vegetation (USV)?
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You can go for Community structure Analysis, but stress can give you better result
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I was producing vegetation indices in a study area in the tropics with MOD09 product (Land Surface Reflectance, 8-days, 500m) and also with MOD13 (Vegetation index, 16-days, 500m). I got more or less the same result with some small differences due to the fact that the former product is an 8-days composite and the latter is 16-days composite. But I noticed that the quality flag are completely different, the one from MOD09 is very 'permissive', the could masking is almost unnecessary but with MOD13 is the opposite, because I lose a lot of data due to clouds. I think that the QA flag of MOD13 should be the correct one because my study area is in the tropics with high presences of clouds, but I wonder why the QA flag of MOD09 is so different and seems to be unreliable.
Can someone give me a clue?
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Thanks for your reply Prof. Hack. And yes, I have used the quality flag as it says in the guidelines. I will take a look to the paper you just recommended me.
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At present, the research direction of vegetation on slope stability has changed from mechanical mechanism to what direction? We know that the effect of vegetation roots to reinforcement makes the slope anti-slide stability enhanced, but under the condition of heavy rainfall, the roots make the potential flow increased. How can we further study this mechanism?
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Dear researchers,
I need satellite images with a resolution of 0.5 metres to create a land cover classification map.
Can you tell me from which sources or which satellite images I can use?
Many thanks in advance!
I need to classify these:
1. Hard Surfaces
2. Soft Surfaces
3. Sparse Vegetation
4. Dense Vegetation
5. Buildings
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For this spatial resolution (0.5m), you might find high-resolution commercial satellites such as WorldView-1, WorldView-2, Pleiades-1A, Pleiades-1B, and GeoEye-1.
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I am looking for a natural (potential?) vegetation map of Europe that I could use in a GIS framework. I found
Udo B. et al. 2004 Karte der natürlichen Vegetation Europas. Map of the Natural Vegetation of Europe. Maßstab / Scale 1 : 2 500 000
Is there he digital GIS version of it? Is it available on the Internet? Or is there something similar I could use. I would like to use it as a background to illustrate my species distribution maps, showing the main vegetation types in different regions.
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Samuel Akande Thank you for trying to help me with the the links. Unfortunately the Inspire website doesn't point to any GIS layers. It lists only a dead link to an xls file. I have also found the article you shared, but there was no trace of the original GIS version of the vegetation map. Douglas Evans shared a website hosting the software (includes the GIS layer) for viewing the vegetation map. Its interesting that Google search did not list it. Thank you all for helping me, it's great to be part of the Researchgate community!
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I am working with MOD17A3 time series for trend analysis. Not sure I suppose to exclude Fillvalue since I am focusing on vegetation. For example 32762 = land cover assigned as urban/built-up
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Dear Hamad,
As a matter of principle, if you wish to characterize the presence of a trend in a time series of vegetation Net Primary Productivity, you should exclude any and all data points that are not explicitly representative of vegetation from that time series, such as data characterizing water bodies, buildings or clouds, for instance.
This being said, the way you cast your question is somewhat ambiguous and potentially problematic, because a time series is presumably constructed for a particular location: in your case for a vegetated patch. If indeed your target is vegetated, it should not include data points identified as 'urban/built-up'. Conversely, if your time series does include such data points, then it certainly does not represent the evolution of a vegetated area (in the sense of agriculture, or forest, or a protected area).
Are you looking at an area that used to be vegetated and was subsequently built-up? In that case, limit your analysis to the period where the area is vegetated. Or are you looking at a large area that includes multiple land cover types? Then simplify your problem by isolating a smaller area that only contains vegetation.
You should perhaps sharpen your request or explain in more detail how you end-up having urban data in a vegetation time series.
Good luck with your investigations. Michel.
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The Geology of the Himalaya is a record of the most dramatic and visible creations of modern plate tectonic forces. I wonder how vegetation flourished in such a mountain system.
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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
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prospection minière dans le site de khouribga
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Using clustering analysis.
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Non-vegetation is widespread in arid/semi-arid areas, and non-vegetation plays a pivotal role in soil and water conservation and carbon sequestration. However, the current assessment of non-vegetation carbon sequestration has always been a bottleneck. Commonly used ecological models are mostly based on green vegetation, and the amount of carbon sequestration is further obtained through the utilization of light energy. However, how can non-vegetation use ecological models to assess their carbon sequestration?
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Litter and detritus sampling and subsequent analysis through CHN analyzer can give you an approximation
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If we apply atmospheric correction to the data to enhance the coherence, will it provide accurate deformation result in a densely vegetated mountainous region like the Himalayas.
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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
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Hi!
I realize a time series with MOD09GQ to finally extract index vegetation but there are many many many solutions to create mask cloud and each has his complex methods with personel algorithm. So the first question is :
- Can I used layer QC_250m from MOD09GQ to make mask ? Should it necessary to create shapefile from layer and applicate in surf_b01 or b02 layer ?
Or
- I must used MOD35 but.... BUT... the time including date and hour and they are not the same between both
T_T
Thanks to your rely
best regards :)
Charlène
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try to extract Band 4 (QC_250m_1) as a subdataset.
from the documentation:
The QC_250m_1 layer is stored in an efficient bit-encoded manner. The unpack_sds_bits executable from the LDOPE Tools is available to the user community to help parse and interpret this layer.
In addition to data access and transformation processes, AppEEARS also has the capability to unpack and interpret the quality layer.
The QA bit flags for the QC_250m_1 layer is provided in Table 8 of the User Guide on page 19.
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Hello, another Q to experts out there. Can someone point out reference to measurements of global and diffuse NIR (700-1500 nm) down-welling measurements in clear sky conditions. We are having a hard time explaining as to why at-sensor NIR readings (1.5 km above ground) increase with increasing solar elevation in low vegetation, asphalt, sand etc, but not to the same extent in trees, and especially some deciduous tree species behave really oddly as we see no increase in signals at all! (at view-zenith angles of 0-20 degrees, while solar elevation goes up from 30 to 40 degrees and irradiance should increase 25%). Visible range signals go up with solar illumination as can be expected. If this is not an artifact it has certainly been noted by someone. Anyone familiar with reference or textbook? Our vegetation is very dark at 400-700 nm (rho 0.02-0.04), while rho are 0.2-0.5 in green vegetation, incl. trees).
ilkka
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One answer is here in this experimental work from 1999.
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Hi all
I'm asking if we could be helped by someone so that we don't miss relevant research!
We are looking at the radiometry and geometry of repeated waveform LiDAR scans in vegetation such that non-scene-related aspects (sensor etc.) that influence the signals are minimized (by using same trajectories, same sensor, same sensor settings) so that signal changes would reflect the structural changes in the vegetation/trees over time.
Is anyone aware of relevant papers that deal with multi-temporal LiDAR (waveform or discrete), impact of vegetation phenology on LiDAR (radiometric) features?
I'm also willing to discuss the topic, if someone shares an interest.
Thanks, ilkka korpela
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Adedeji Oluwatola Hello, GEDI data must be fascinating! Never set my hands on that. Have done some peer-reviewing of papers in GEDI and other satellite LiDAR. I never stepped outside a LiDAR footprint of wider than 60 cm :)
And then you go for the other extreme, UAS. I'm envious at the latter opportunity. There are some commercial UAS operators in Finland (my country) and hope to be able play with UAS-borne LiDAR some day.
Monitoring applications are interesting and therefore time-series and multi-temporal data analyses interest me and I'm interested to know if radiometrically accurate repeated datasets have been analysed and reported somewhere.
ilkka
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I've been collecting & planting seeds of butterfly host plants for the restoration program. And I need research references especially for tropical Southeast Asia native species (include all types of herbs, bush, or shrubs). I looking forward to having some recommendations from botanical experts.
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The idea of a tree belt in Africa may be fine and helpy, but it is also realistic when then land is missing for nourishing the people? In the Kagera district I saw in reality daily GREEN (!) trees cutting people carrying with bicycles from thinning forests only to make for cooking with open fire with green tree stems extremely energy lossy breakfast, lunch or dinner for their families.
I saw the full tree covered Rubondo National Park in Tanzanias Victoria sea, but here where nature is tree covered, no people would find enough human food. But reforestation of cleared areas I find always a good and sustainable practice. If you fly over Tanzania most land is deforested for crops for food production.
Are not models saying that tropical ecosystems will store less carbon in a warmer climate while at high latitudes warming will increase storage of carbon in trees (10.1016/B978-0-12-382225-3.00095-5) ?
So what helps more to help people?
Or people should not be helped and more delivered to their destiny as nature itself does regulation overpopulation and undergoing in a Social Darwinism kind?
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Planting trees doesn’t always help with climate change. Reforestation is seen as a way to help cool the climate, sucking excess warming carbon out of the atmosphere. But it’s not always that simple.
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I am comparing the differences in microbiome community composition between animals according to three different dietary regimes: hay, green vegetation, and restricted feed. Looking at a PCoA plot, the centroid and dispersion of the restricted feed group is obviously different from the two others, and comparing bray-curtis distances with the vegan::adonis() function yields a high R squared and a significant p-value. The green vegetation and hay groups appear the same, but running an adonis test comparing these two groups yields a significant p-value (albeit a very low r squared value of 0.02). Must I reject the null hypothesis that the hay fed group and the green vegetation groups are homogeneous, despite the low r-squared value and the disagreement with the PCoA plot results?
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Hi Claire, you might already have answer to this, but I found the answer to your question in this blog:
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I am interning with the Forestry Division and we had a pipeline leak on thousands of acres and it discolored the trees to brown and some to black. I was wanting to find out if this gas has any fetal effect on pine of hardwood. If you all know of any articles, journals, etc that can tell me what happens to vegetation would be very helpful on what we are dealing with.
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Dear Jessica,
thanks for sharing this interesting technical question with the RG community. In addition to the relevant article suggested by Andrew Paul McKenzie Pegman please also have a look at the following potentially useful link:
Will Ammonia Kill Plants Outside?
It is mentioned here that (citation) "plants may exhibit ammonia toxicity in the form of burnt leaves, blackened roots or even death."
As a minor aspect please note that there is no such thing as "anhydrous ammonia" in the forest. As soon as ammonia escapes into the environment it will get in contact with the moisture of the ambient air and is not anhydrous any more. 😎
Good luck with your work!
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We reassessed the vegetation of Mormon Island, the largest contiguous tract of wet meadow and lowland tallgrass prairie remaining the Central Platte River Valley of Nebraska 40 years after its initial inventory and examined species invasion, climate change, and restoration as drivers of community change. It is on the long side at just over 8,000 words of text, not including the citations. I have been considering a number of journals, but feel like most journals oriented toward a priori research questions don’t publish inventories, and we would love to include the plant list at least as an in-print appendix. However, it goes beyond the traditional scope of a straightforward inventory because of the long-term nature of the data and the theoretically grounded questions we ask. Any suggestions would be greatly appreciated!
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I second Tom's suggestion: Vegetation Classification and Survey. It's a new journal, and one of the official ones of the International Association of Vegetation Science. Here is its website: https://vcs.pensoft.net/
You could give it a try!
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I have some forest polygons and would want to assess the effects the direction (south, north etc) have on vegetation. How do i then determine which forest edge faces which direction either in QGIS or R?
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1. Generate the aspect from dem
2. convert aspect raster to vector
3. Take intersection of Forest Edge vector data with aspect vector data. Now forest edge has the direction value measured from north.
Later, you may convert these values into direction text. E.g. 0degree value represents North.
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We are launching our project CoKLIMAx, which is the application of COPERNICUS data for climate-relevant urban planning using the example of water, heat, and vegetation.
Are you aware of any digital toolboxes that are already being used in the urban planning context with the objective to increase urban resilience?
We are looking forward to your reply.
Best regards,
Michael.
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Dear Dr. MM Bühler,
It would be interesting to take into account the HerCity platform or digital toolbox for sustainable, equitable and inclusive cities. HerCity is a platform that involves women in Urban Development, in order to improve the cities in which we live. By changing the team and putting girls in the position of experts, this digital toolbox aims to "create more inclusive, equitable and sustainable Cities and Communities." This initiative offers methods and tools available to urban users around the world and its purpose is to help cities to "integrate girls in a participatory way" in their long-term strategies. It is a platform that was launched on "Women's Day" in 2021 and its guide to urban planning and design represents a collaborative effort between UN-Habitat and Global Utmaning. Although cities are supposed to be built for everyone, most of the time they are thought, planned and designed by adults. Given the lack of knowledge in planning and "participatory" urban design, at the decision-making level, it requires that girls and / or women conceive of the public spaces of a city in the same different measure compared to boys or men, THERE IS WHAT TO INTEGRATE THEM, not exclude or consider them separately.
You can take a look at the following link:
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Hello! I am looking for vegetation data and aerial images for a region of Costa Rica, possibly going as far back as 1998. I have gone through Landview and EarthExplorer but I'm so overwhelmed - half the time I get spotty data or partial images. I'm looking for a resolution of 15m or less. I understand older stuff won't have this resolution but to be honest, even though data from some satellites are supposed to have this resolution, the images show otherwise. I keep getting general advice from colleagues, but when it comes down to it, I can't find what I'm looking for, and I'm hesitant to pay for maps when I am not even sure they will be what I'm looking for. Thanks in advance.
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Thanks for the question. The answers also interest me
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I want to know how the desert vegetation storage water, such as "Bottle Trees"(Cavanillesia-arborela), so scientists can use genetic technology to modify vegetation so that it can store more water in arid regions.
Now trying to find useful literature about it - any literature tips are greatly appreciated. Thanks a lot
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Now I'll find the artical about "succulents" in arid environments.
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We are using Specim IQ hyperspectral camera for image-based phenotyping in soybean plants at the vegetative stage. On processing the image on ENVI software, we got negative values for ARI1 and ARI2 in both drought stress and control. When I checked some published articles for reference, I found that the range for ARI is 0-0.2.
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Dear @Pooja Tripathi
Hope, the below reference can throw some light towards solution to your question:
Gitelson, A., et al. "Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy." Photochemistry and Photobiology 75 (2002): 272-281.
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I have made a listing of the flora of some region, obviously I made the sampling using polygons covering a wide range of the studied area.
Now I want to classify the polygons I have made by their similarity/dissimilarity on the diversity and abundance of the flora.
The questions comes next, how can I do it? Which index or coefficients should I use? (I have find bray-curtis, jaccard and other)
And which algorithm/method should I use for the clustering diagram?
Any help or literature that helps me to clarify this will be much appreciated.
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Hi David Becerril-Gonzalez, You can classify the vegetation of a plant according to Plant species and genus or by name of the number plant.
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I need a raster layer of recent global vegetation data including different classes of vegetation at spatial resolution of 1 Km square.
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Hello Mohammed,
although it is not observed but potential (predicted) vegetation, you might find this 1-km-resolution dataset useful for your research:
Have a nice weekend,
Ákos
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Hello, I search how to calculate habitat/vegetation closure using orthophoto from 1950 to 2020 in France? I've read a lot and find different options like: difference of forest cover, difference of NDVI or digitalisation. The use of NDVI on Landsat + CLC comes back often. What do you recommend to be precise as possible? I would be grateful for any suggestions.
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These links may help;
Kobayashi, N., Tani, H., Wang, X., & Sonobe, R. (2019). Crop classification using spectral indices derived from Sentinel-2A imagery. Journal of Information and Telecommunication, 1–24. doi:10.1080/24751839.2019.1694765
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How do we calculate vegetation index of RGB images (acquired from drone) in MATLAB? The code below is for multispectral images taken by satellite. How to set these parameters for aerial RGB images?
% Import Color-Infrared Channels from a Multispectral Image File
CIR = multibandread('paris.lan',[512, 512, 7],'uint8=>uint8',128,'bil','ieee-le',{'Band','Direct',[4 3 2]});
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Please use the attached MATLAB file. If any thing need to be corrected please let me know. First read the image file in to a variable and then call the function in this file including the variable to run the function. Here in this function it has 11 different RGB vegetation indexes.
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Hello researchers, we all know that NDWI has 3 different formulas for different context. Here we consider the definition of vegetative moisture content as defined by NDWI.
Considering the practical applicability of NDWI in this context, vegetative moisture does not correlate with soil moisture condition. Vegetative moisture content is affected by air temperature, evapotranspiration rate, leaf canopy etc. whereas soil moisture is affected by soil type & porosity apart from evapotranspiration. The automated drip irrigation systems sense soil moisture & apply irrigation. Whereas the remote sensing researchers or decision makers consider vegetation moisture content to identify water stress zones in the field. Can there be any correlation between decision making based on these two approaches?
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Hi Adrij,
According to the literature the NDWI is defined by only one mathematical formulation. The NDWI is a remote sensing based indicator sensitive to the change in the water content of leaves (Gao, 1996). NDWI is computed using the near infrared (MODIS band 2 (NIR)) and the short wave infrared (MODIS band 6 (SWIR)) reflectance according to:
NDWIt=(NIRt-SWIRt)/(NIRt+SWIRt) where t is time
or for MODIS:
(ModB2-ModB6)/((ModB2+ModB6)
The EFFIS Delivery Data Products delivered by the German Aerospace Centre( DLR) are used to compute the NDWI. MODIS data are delivered per tile in the ETRS_LAEA coordinate reference system. MODIS spectral bands 2 and 6 are provided by the German Aerospace Centre (DLR) and pre-processed by the FOREST EU Action (IES, JRC). Frequency of data collection: every day.
The NDWI is a remote sensing based indicator sensitive to the changes in water content of leaves (Gao, 1996). NDWI is computed using the near infrared (NIR–MODIS band 2) and the short wave infrared (SWIR–MODIS band 6) reflectance’s. The EFFIS Delivery Data Products delivered by the German Aerospace Centre(DLR) are used to compute the NDWI. MODIS data are delivered per tile in ETRS_LAEA coordinate reference system. For each pixel, a maximum value time composite is produced every 10-days using the daily MODIS composite files that combine all TERRA or AQUA passes of the day.
Each month is split in 3 periods: from the 1st to the 10th, from the 11th to the 20th, and from the 21st to the end of the month. The 10-day composite is then resampled from 250 m to 1 km and the ratio between band 2 and band 6 is then calculated following the equation above. The different tiles are then mosaicked to produce a unique map for Europe. Finally, a mask derived from GLC 2000 is applied to recode water bodies, cities, permanent snow and desert as 'no data'.
These maps, Adrij, are used to monitor drought periods in space and time for the European continent.
Concomitantly you can use Soil Moisture content (SMC) as well in combination with NDWI data. SMC can be determined with a method based on thermal inertia See:
Cheers,
Frank
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Hello,
We are looking for rating ou scoring scales (from 0 to 9) to evaluate blooming intensity and vegetative development of plants (tomatos and ornamental plants) in researches.
Thanks !
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Interesting technical question.
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I am currently working on a project and I need to know the pros and cons, the applications, and even the limitations of vegetation indices particularly those that are useful in the RGB spectrum in the assessment of crop health. The crop in question is maize.
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This is not a direct answer to your question, but rather a caution not to over-interpret what NDVI can tell you. It's a metric of relative vegetative growth compared to a long-term 'norm', not the health of a crop. A clear example was from Ethiopia in the 1980s when locations of high enset (false banana) cultivation showed bright green on NDVI due to high precipitation and above-ground growth, but below ground blight had begun to destroy the enset harvest. In other words, the enset regions were green but the crop was not healthy. Hope that helps.
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I am trying to generate map to indicate species distribution of a wildlife sanctuary. The sharp boundary of this area is not visible on Google map but vegetation cover and longitude and latitude is visible. From where can I get or create map of this area to locate my collection site for my manuscript? Few representative maps of other locations are attached with this question. From where can I get or create such images?
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Dear Rahul
You can use QGIS which is free, use xyz tiles, active the srtm and google satellite images. Then use layout option to make a publish-quality layouts.
Good luck,
Hormoz
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for example, after calculation of EVI in GEE, I want to use this index as an input band in classification but I get the following message:
Image.select: Pattern 'EVI' did not match any bands.
What can be done to solve this problem?
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Please see an example for Sentinel2
dataset=your image collection
// take the median
var st2median = dataset.median();
// the normalized difference vegetation index
var ndvi = st2median.normalizedDifference(['B8', 'B4']);
var bands=['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12'];
var img=st2median.select(bands).addBands((ndvi).rename('NDVI'));
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There are many tipps and tricks and stretegies how to protect wild insects during mowing. It's clear: every mowing kills many insects. One is clear and quasi-standard in nature conservation resp. extensive farming on grassland: mowing later in the year (e.g. june) and mowing only once or two times a year.
More difficult is the question about the time of day to save as many insects as possible.
The mostly published tip is to mow in the morning when it's still cold and moist. The reason mostly mentioned for this strategy is: in the morning, no insects are flying in/over the meadows so the mortality risk is significant lowered. You can read this tip quite often, in nature conservation papers as well as in agricultural papers and media.
I assume this tip to be more or less completely nonsens. Only honey bees and probably some bumble bees are still in their hive or nest in the early morning. The vast majority of the grassland insects is staying inside the vegetation, both during the night, and during cool and moist wheather conditions. I guess this tip results from the observation that no (honey) bees are flying in the early morning.
To save as many insects as possible during mowing, I believe (like many other experirenced colleagues do) that mowing at noon or in the afternoon during hot and sunny wheather conditions can save many more flying insects (esp. pollinators) because:
1) they are flying instead of sitting inside the vegetation
2) they are more mobile overall
3) they react faster
4) they can escape/fly away better and faster
My suggestion is to spread this tip as far as possible inside both the nature conservation community, and the agricultural community and to explain whenever it's possible why the old tip is rather harmful than helpful in terms of insect conservation.
What do you think? Do publications exist about this topic?
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I support the opinion of Janet Lane and Len Leonid Mizrakh.
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Fellow Ecologists,
In order to study plant species evenness and diversity for a plant community we count either individuals to calculate the appropriate indices or each species percentage cover/specific ground area (quadrat). The second is my case as we have 8x8 m2 which is quite large area and time consuming to estimate individuals.
Of course the second option is a tradeoff between quality and quantity as some plants species will have many individuals with low ground cover while others a small number of individuals with large cover area.
What would be the case if we count species percentage cover/ specific ground area (quadrat) and a metric of percentage cover of each species/ total vegetation percentage cover (technically relative abundance/100). Could we combine those to metrics to have a more representative value of flora status without counting the populations' individuals ?
Thanks in advance
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These two metrics represents two different parameter. Species percentage cover represents overall ground cover occupied by all species, whereas percentage cover of each species represents its abundance. However, I think there is no short cut as such, we have to count the number of individuals in both parameters. So representative value of flora status without counting the populations' individuals will be of little help.
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The area of my university is more than 150 Acre. It consisting of variety of vegetation patches. Among them dominating vegetation types are deciduous forest patches, plantation area, urban area etc. I want to now what will be best survey techniques to study & stratify vegetation of my university campus.
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With a focus on reducing the linear flow of nutriencts, what magnitude of nutrient circularity could be presented by forest establishment.
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Taruvinga Badza interesting question
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How many vegetation indices are there using radar images not multispectral bands?
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I am using landsat 7 & 8 imagery to detect the forest vegetation and land cover change in a tropical swamp forest over 20 years time period. I have choosed two vegetation indices -NDVI & EVI. I have the multitemporal mean values of NDVI & EVI for the same study area & period. In other hand, i am using maximum likelihood supervised classification to classify the NDVI and EVI derived map. I am using confusion or error matrix to assess the accuracy of the classification with ground truth data. I want to know that, can i employ any further statistical test to compare the mean NDVI and EVI value? Is there any suitable test to compare NDVI and EVI mean value ?
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you can you t test to compare means if the data is normally distributed, otherwise use Mann Whitney test :)
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I am working on species foraging and roosting data over two different regions. I have worked out for each region what percentage of the data within their home range occurs in native vegetation. However, how do I statistically compare the use of native vegetation as their home ranges differ per region? My primary concern, however, is that the home range sizes are different which means I cannot compare percentages of native vegetation used between these areas. My main hypothesis is: is there a significant difference in native vegetation used between regions (HR1 and HR2)?
Example: HR1: 490km2 native vegetation within HR1: 212km2 foraging in native vegetation: 20% HR2: 41km2 native vegetation within HR2: 9.5km2 foraging in native vegetation: 18%
As usual, it is difficult for me to pin down the best statistical method to compare the use of native vegetation between regions: possibly lme? Any help is greatly appreciated!!
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You use percentage, which in theory does not depend on the difference in tge area between the regions. The values are close, I would not expect any signigicant difference between 20% and 18%. But you need to statistically prove this. Could your regions be divided into smaller units? Eg in 1 sq km quadrats?. Than assess the area of natural vegetation in each unit and create a matrix (Columns for regions and rows for subunits). Then jackknifing, bootstraffing or any other randomisation techique can be used to calculate p-values. GLM can also do.
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In microwave radiometry of soil moisture, what is the typical magnitude or range of errors introduced by variations in soil roughness and vegetation cover? Soil texture can also affect accuracy, but soil roughness and vegetation cover seem to be most important.
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I strongly suggest you reading our new article, which presented a reliable way to estimate soil moisture “Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model”
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Modern Grand Solar Minimum (2020 – 2053):
What are the possible consequences of a reduction in solar magnetic field (mitigation of total solar irradiance) resulting from modern GSM?
Whether the reduction in terrestrial temperature of around just 1 degree Celcius – during this modern GSM - deserve special attention in terms of vegetation, agriculture and food supplies?
Do we have reliable data either from Maunder minimum (1645-1710) or from Wolf minimum (1270-1350)?
Will Modern GSM counterpoise the signs of global warming for the next three decades?
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Dear colleagues,
regarding the Grand Minimum of solar activity in the coming decades, then perhaps its prospects will become clear in the next 4-5 years. Namely, for this it may be sufficient to observe the maximum solar activity in the 25th cycle, which is predicted for 2025. According to Prof. V. Zharkova, the 25th cycle will be about 10% weaker than the last 24th cycle, see the link:
But if the Grand Minumum of solar activity will be weaker or will have a narrower time scale, then the forthcoming 25th cycle will be 30-40% more powerful than the 24th cycle. So, we have to wait 4-5 years. I sincerely wish you, dear colleagues, that you not only live these 4-5 years, but also that these years be happy for you! Sincerely, V. Lozitsky.
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For my Masters I am looking into elephant space-use and its effect on vegetation change. I have elephant GPS collar data and was going to use Google Earth Engine to obtain NDVI values. I would like to establish elephant space-use across a fenced area in the form of either areas most frequently occupied (at the pixel level) or pixels the most time was spent in. I would then like to use that data to determine whether any vegetation changes (recent NDVI as a percentage of long term average NDVI) are linked to the elephant space-use (i.e. to determine whether vegetation change is elephant-mediated). I have looked at methods such as UD and BBMM but was wondering if there were more suggestions out there? Ultimately the method I use for space-use needs to have an output I can use to compare it to the NDVI values. Thanks in advance!
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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
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Hi, I am working on the effect of heavy metals on a medicinal plant by pot experimentation. Analyzing the effect of heavy metals on secondary metabolites is one of the parameters. I will be harvesting the plant at the vegetative stage and not at the reproductive stage. However studies reveal that the secondary metabolite is highest in the seed. Would it make sense if I am checking for secondary metabolite production in other plants of the plant and not the seed? (Please note: The reproductive stage of the plant takes quite a lot of time and the aim of m work is just to study the effect of secondary metabolite production and not its enhancement). Kindly someone answer
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Hallo,
I have to model a quentitative vegetation featur using vegetation indices based on Landsat OLI reflectances. Model is based on supervised procedur as multiple regression method. I clipped all OLI bands around my study area.
Questions:
In my case, is it suitble to strech spectral data ?
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yes
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Hello all,
I have two raster layers of the same area. one with land-use values (3 types) and the other with vegetation index (also 3 types). both rasters have the same resolution and same geo reference. now, I wish to replace the pixels with a certain value of land use (only one out of the three) with the vegetation index values. can anyone suggest a way to do that?
if necessary, I also have the land-use layer as a shapefile of polygons.
thanks!
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Namaa,
You may find the correlation between the 2 layers and then simply use the raster calculator.
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I'm aware that in order to perform any vegetation index like NDVI, SAVI or OSAVI you need to do some atmospheric, radiometric and terrain corrections. I'm pretty sure that Level 2 images from EarthExplorer are already atmospheric and radiometric corrected but I'm not sure about the terrain or the topography... If so, do I just oreder them, download them and perform the raster calculation? I'm using QGIS 3.16.1 btw.
Thanks everyone for your answers!
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Nasir Hameed Thank you so much!
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Many relict species can reproduce very poorly from seed. In this case, vegetative propagation of these plants and their collection in situ can give good results. What are the useful methods of vegetative propagation of shrubs and semi-shrubs?
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What method do you use to root the branches?
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The vegetation cover is very large in the world, and there are many plant families, so what is the largest of these families?
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I think Asteraceae (Compositae) is the largest plant family in the world.
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I was trying to detect a massive Landslide using the DInSAR technique on Sentinel 1 SLC data but after running interferometry on different pair of Sentinel 1 images, I am unable to detect any deformation in the interested area.
Maybe I have lost coherence values due to Aspect, slope, vegetation, and soil moisture.
Now I am curious to know how to overcome these challenges, and what suitable technique should I adopt to get my objectives using Sentinel 1 data?
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Dear Syed Ahatsham Haider The best technique to work on the landslides or land deformation with vegetation cover is to employ Small Baseline Subset (SBAS) approach; where SBAS approaches account for signal decorrelations by partitioning the data set into several subsets with short spatial and temporal baseline possible (i.e., excluded large baseline interferograms). Within the subsets, the interferograms are spatially multilooked and unwrapped.
he SBAS analysis can be considered as a linear model inversion problem. For each pixel, based on the equation given below:
φ=Ax+Δw
Now coming to coherence, the area as you mentioned is covered with vegetation so there is high probability of decorrelation (within 30 days or less). To overcome this issues you have to use SAR stacks with minimum temporal baseline (incerease the number of SAR images)
Secondly, if your target area is within mountainous region then you have to account for errors due to topographic phase.
The residual topographic phase consists of the height-to-phase factor, elevation error; normal baseline relative to the n-th image with respect to the reference (master) image; local incidence angle; carrier wavelength; zero-Doppler distance between the target and the n-th orbit acquisition.
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I'm searching a (free) software that simulates the effect of strong wind on the vulnerability a tree.
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Just a small correction to Osama's answer, there are some free CFD packages and OpenFOAM is one of them.
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It has been alarming that human activities had increased many proportions and had led to many anthropogenic activities which had been directly and indirectly affecting the natural ecosystems. These activities have replaced many of the endemic natural vegetation with non-native species of vegetation with choices to few species by mostly raising monoculture plantations so as to have increased returns meet the increasing needs of the society. In the process, there has been the dominance of non-native invasive species. Even we see hunting and trade in many of the wild animals. Even many of the countries have even made wildlife rearing legal just to meet their culinary needs. Under the above background, it has been obvious that this is leading to increased incidence and virulence of the diseases.
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The removal of trees without sufficient reforestation has resulted in habitat damage, biodiversity loss, and aridity. Deforestation causes extinction, changes to climatic conditions, desertification, and displacement of populations, as observed by current conditions and in the past through the fossil record.[12] Deforestation also has adverse impacts on biosequestration of atmospheric carbon dioxide, increasing negative feedback cycles contributing to global warming. Global warming also puts increased pressure on communities who seek food security by clearing forests for agricultural use and reducing arable land more generally. Deforested regions typically incur significant other environmental effects such as adverse soil erosion and degradation into wasteland.
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My project area is 55 sq. km. in which 14 sq. km.is forest. I am going to take sample for vegetation enumeration in the area. I have spread quadrats of 25X20 square m. throughout forest area. Now I would like to take just samples of it. Each quadrat measures 500 sq. m. and hence total there would be 28000 quadrats. How many quadrats (%) can be acceptable for sample?
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I think this video will help you. https://www.youtube.com/watch?v=enrWhgIOqWQ
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Welcome, all Suggestions for mitigation of the impact of Spatio-temporal change on Snow, Vegetation, and Timberline in the Indian Himalaya.
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D. S. Parihar , good day! For many people, the Himalayas are a mountainous region where all natural and anthropogenic processes and factors are interrelated. The Himalayas! There are few places in the world that can match the beauty of the Himalayas! As a geographer, it seems necessary for me to address the following issues: 1) further study of high-altitude terrain; 2) organization of monitoring of dangerous natural processes; 3) development of integrated geographical zoning; 4) solution of environmental problems. Perhaps there is still a group of unresolved issues and problems to be resolved. Good luck!
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I am using the LAI as an objective would be to reach a deforestation value applying the vegetation indices within a time scale ... of 20 years, within this scale, NDVI, SAVI presented similar values, and negative values ​​at the end of the twenties years, that is, showed a reduction in biomass, but the LAI, at the end of 20 years, showed different values, and conversely, which in my interpretation would be an increase in biomass .... what to do? What does that mean ?
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Do you have field data to accuracy assessment?
Pre-processing and methods done correctly?
In my opinion, it is not necessary that the LAI has a positive correlation with biomass.
This can be influenced by PFTs and plant types.
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Hello RG community,
Could someone please point me towards Remote Sensing/GIS approaches that are used to detect/map floodwater under vegetation? I am using optical imagery.
I am aware of radar technologies that can circumnavigate the water-under-canopy problem, but my objective is somewhat different, and am explicitly looking for approaches that can be used with optical imagery.
Any help with this is greatly appreciated!
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Not sure what type of optical imagery you intend to use, but if you're talking about multi-spectral satellite imagery such as Sentinel-2 or Landsat-8, you could have a look at the approach presented here:
Using optical satellite imagery obviously has its limitations when it comes to 'detecting something under closed canopy' and the quality of the results will largely depend on the type of vegetation, the duration of flooding and the persistence of clouds...
Best,
Sebastian
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How can i extract a summary about vegetation (number and types of 3d plants) in ENVI-met?
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There are standard methods for determining the density of vegetation and evaluating dominant, co-dominant and rare species in a natural forest. It depends as to the objective of evaluation. Quadrat, bisect and transects are for different types - herbs, shrubs and trees. This will be different in non forest areas.
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Turmeric is a polyploid and vegetatively propagated crop. Usually, F1 populations are used to identify QTLs. As this crop generally has no viable seed, what is the best approach to genarate QTLs.
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An F1 clonal propagated population should work fine for qtl mapping.
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Rangelands invaded by Woody vegetation at the expense of grass for ruminant grazing. What are recent safe weed killer s?
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While I agree that biological control is preferable, it isn't always possible to achieve the results required due to a range of factors many authors have explored. Sometimes a suitable agent cannot be found even after extensive searches and testing.
There are a number of woody weed killing herbicides being used on Australian rangelands for plants such as Vachellia nilotica, Prosopis spp., Parkinsonia aculeata, etc. Products include Access herbicide and Starane Advanced Herbicide. These are considered safe if used according to their registered label directions. Google search to see active ingredients.
Residual herbicides such as tebuthiuron, while they may incur off-target damage if used incorrectly, are also very useful (though care needed).
In one of my recent projects, I and my colleagues examined new approaches to management of Vachellia nilotica in Australia. You can access a Story Map overview here https://qgsp.maps.arcgis.com/apps/MapSeries/index.html?appid=2ede10d341c8438c98ebc3305499055d
Bioherbicides Australia have been investigating naturally occurring fungal pathogens as woody weed control tools and have now registered Di-Bak Parkinsonia. See https://www.bioherbicides.com.au/bha-weed-control/parkinsonia/
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hello all.
I created an NDVI raster based on images I downloaded from the Planet satellite. then, I created the raster using the Raster Calculator tool on ArcMap, and then reclassified in order to create a vegetation/non vegetation raster. I used the known values of NDVI>0.1/0.15/0.2 as vegetation and everything below as non-vegetation, but non of them included areas where I know for fact that there is vegetation. only when I reclassified vegetation as NDVI>(-0.05) those areas were marked as vegetation.
any suggestions to why that happens and how to fix it so that I could use this analysis in my research (with proper references for the NDVI values)?
thanks.
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thank you all.
I think the problem was with the specific image I was using. I tried using an image from a different date and it worked perfectly.
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what kind of statistics are usually applied in the pollen analyses to reconstruct the past vegetation and environmental evolution?
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Hader Sheisha yes, whatever you like, there are no rules :)
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In connection with the reform of agriculture in Ukraine, the problem of using postagrogenic soils (fallows) arose. As a rule, spontaneous vegetation growth (reforestation) or artificial planting of forests occurs on these soils.
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You can compare biodiversity in plots using the two different types of growth
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In my last paper, I have used the TCI index to assess the stress caused by to the anomaly temperature condition, but I want to know if there are some other indexes to detect the same stress condition.
Thank you very much.
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Thank you very much Nasir Hameed , very interesting.
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I'm thinking about doing a research in satellite image processing field. Can I calculate the area covered of cloud/forest/vegetation/rural/water using deep learning/AI? If so, can someone provide links of related literatures. It would be very helpful, thank you!
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It is an interesting hot field of research with many applications, see e.g., https://www.satimagingcorp.com/applications/ and .
A s for as computational algorithm for experimental process is concern, you will understand a suitable model after literature reviews (Deep learning is an application for training, it is not suitable for every data, neither it is yet really understood).
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hello everyone,
i want to examine the mineral distribution for exploration purposes using sattelite images (ETM, Aster, hyperion etc.)
but how can i reduce the vegetation effect on my pocessing steps?
as well which mounths of year the vegetaion coverage in a region is at its min level?
berifly i should take ETM or Aster images of whitch mounts to get the best results?
or is there any way to eliminate vegetation effect to get the indicatior spectral signatures of minerals in the region?
does NDVI index a solution for this and how is it utilized ?
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thanks Tohid Nouri but i need more accurate and efficient apporoach than NDVI
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I'm finding papers about plant spacing in phytoremediation, I only found vegetation pattern like, what types of plants must be planted together. As for plant spacing, I still found none. Are there existing papers for that?
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Dear Jane Eilyza Aballa ,
Thanks for your great question. Apropos to your question, Phytoremediation technologies use living plants to clean up soil, air, and water contaminated with hazardous contaminants.
But how much space should be allowed between plants for the best results? The rule of thumb is a minimum of 18 inches apart and a maximum of 30 inches, for plants that are less than three feet tall. For anything more than this amount of space, you will experience smaller yields.
***Please specify your question what you are exactly wish to know.
Best Regards,
Md Osim,CSIR-NEERI
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I am not a botanist but need to estimate the average percent vegetation cover of my study area. I have researched this and found articles that use the "releve" method for determining plot size and sample size. It seems very subjective but reasonable if you are very familiar with your study site. Is this still standard protocol among botanists?
Thanks
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Thank you all for the comments. Maybe I can be more specific with my question, since I can't seem to decide the best strategy for my project. I have 3 large areas (14 km^2, 21 km^2, and 91 km^2). I'd like to estimate % cover of vascular plants in each. There is a paper that I am modeling my design after, because I'd like to be able to compare my results with theirs (they are on one Hawaiian island, I'm on another). Their study area is an entire national park on Maui (134 km^2), and study design is the following: 4 transects each at a different elevation range. Each transect has 4 ,10m x 10m plots that are spaced equally on the linear transect. They fail to mention the length, orientation, or placement criteria for the transects. I would not consider my sampling areas homogenous, so I hesitate to only use 1 linear transect with 4 relatively small plots for each area. It seems like randomly placing the plots would provide better representation of the area. And it seems like many small plots (1m^2 plots) would represent the areas better than few, larger plots (100m^2 plots). Person-hours are also a limiting factor, so that must be considered. Any ideas appreciated!
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How can I process the atmospheric correction in sentinel-2 images using SNAP for under water vegetation (marine and coastal ecosystem)? (from TOA to the water colomne).
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