Vegetation - Science topic

Hi Jens, After a bit of inspection, it appears that the DJI radiometric jpeg format is a bit different from the FLIR radiometric jpeg. Both have the thermal data hidden in the exif file rather than providing a TIFF with the thermal data up front. Metashape has not be updated to account for the DJI format while it can read the FLIR format. Probably just a matter of time before it gets the update. Meanwhile, DJI provides an SDK with the tools to develop software for processing this and I found on the IRMapper tool on Github that does just this - https://github.com/s-du/IRMapper, which works great! Thanks s-du!

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.

Website: https://earthdata.nasa.gov/

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.

Website: http://www.fao.org/geonetwork/srv/en/metadata.show?id=37196

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.

Website: http://www.ntsg.umt.edu/project/gvm

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.

Website: https://www.esa-landcover-cci.org/

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.

Website: https://databank.worldbank.org/

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.

When I did a google search using these keywords, several sources appeared: agriculture hyperspectral download.

Marine habitat loss or destruction is where the marine environment or a particular ecosystem degrades to a point where it is unable to support the animal and plant life that would usually reside there. This can be due to direct transformation, such as mining, dredging, construction or aquaculture. Extinction of one means the extinction of the others in the food chain. The whole consequence of the marine habitat loss and destruction is that it leads to death and migration of animals. Some plants also die and become extinct due to the extreme ecological conditions. Hurricanes and other storms destroy wetlands and other coastal habitats through erosion and flooding, and waves can damage coral reefs. This loss leaves coastal communities more vulnerable to future storms. Droughts and heat waves alter habitat conditions and affect the migratory patterns of fish and other wildlife. Habitat loss and restoration impact the Earth system in a variety of ways, including: Species populations, ranges, biodiversity, and the interactions of organisms. Habitat loss can fragment ecosystems and can cause species extinctions, while habitat restoration can increase local biodiversity and species populations. Human exploitation of the ocean's resources and destruction of marine habitats are driving species extinction, destroying fisheries, generating pollution, and creating ecological imbalances throughout the world. The ocean, once thought to be a limitless and resilient reservoir, is showing signs of irreparable damage. Polluted water also negatively impacts the breeding power of aquatic life. It makes fish and plants deficient in their ability to regenerate and reproduce. Also, animals fall prey to a variety of diseases due to drinking polluted water. As excess debris in the ocean slowly degrades over many years, it uses oxygen to do so, resulting in less oxygen in the ocean. Low levels of oxygen in the ocean lead to the death of ocean animals such as penguins, dolphins, whales and sharks. Excess nitrogen and phosphorus in seawater also cause oxygen depletion. With the addition of pollutants, the phytoplankton and other organisms require more oxygen for their degradation, thus decreasing the amount of oxygen available in water. Due to this reduction in the dissolved oxygen in water, there are adverse effects on the aquatic organisms leading to their deaths. Air pollution negatively affects wildlife by changing plant communities. Stunted plant growth from atmospheric ozone affects the quality of habitat and food sources. Birds are threatened directly by coal power production exhaust, which damages their respiratory systems. Air pollution also indirectly threatens birds. Phytotoxicity occurs when toxic chemicals poison plants. Signs of phytotoxicity include poor growth, dying seedlings and dead spots on leaves. For example, mercury poisoning which many people associate with fish can also affect aquatic plants, as mercury compounds build up in plant roots and bodies.

#Remote Sensing of Environment (Recommended) (link: https://www.sciencedirect.com/journal/remote-sensing-of-environment)

#Environmental Remote Sensing (link: https://www.mdpi.com/journal/remotesensing/sections/environmental_remote_sensing)

#Journal of Remote Sensing (in partnership with science) (link: https://spj.science.org/journal/remotesensing)

Access the link and find out if there is any other: https://www.gisvacancy.com/remote-sensing-journals/

Dear Dr. Wen Li Zhao,

I have same question. Did you find to another solution to your question or can I use this LAI = LAI_hv*high_cover+LAI_lv*low_cover formulation.

Best regards.

Metehan Uz.

Not that you've asked... but: in a short definition, I can say that SAR (Synthetic Aperture Radar) vegetation indices are indicators of the complexity and randomly a microwave faces interacting with tridimensional plant structures (leaves, branches, culms, trunks, etc.). As the interactions are complex, the backscattering (radar reflectance) in different polarizations are combined and simplified (in such indices) to "indicate" plant properties like aboveground biomass and crop phenology. A good repository gathering both information and code for computing SAR vegetation indices for Sentinel-1 actual mission can be found here:

And here:

The current available SAR indices in the repo were proposed for the Sentinel-1 mission. They are: Dual-polarization SAR Vegetation Index (DPSVI), the modified DPSVI (or DPSVIm), the Dual-polarization Radar Vegetation Index for Ground Range Detected products (DpRVIc), the Cross-ratio (CR), the dual-pol version of the RVI.

Krishna,

Yes, I believe you can distinguish between plant and vegetation, but first you need to define exactly what you mean by ‘plant’ and also by ‘vegetation’. It is similar to the definition of what is climate and weather - weather being the day to day stuff, whereas climate is weather averaged over much longer timeframes. Vegetation is more inclusive of plants living in association with many other plants. Plants, on the other hand, refer to specific plant species. Like in any research, it is important that you specify exactly the parameters within which you are studying any phenomena.

wishing you well with your research

George

Also, the link below may prove useful.

Thanks a lot. I highly appreciate your help

These rock, soil and vegetation movements are caused by excessive rainfall on a slope. often the water seeps into the Rock underlying this material and washes it all down the slope.

Tropical evergreen forests are found in places with high temperatures and heavy rainfall. They receive more than 200 cm rainfall per year. The Montane forests can be classified into three types according to the altitude as; alpine vegetation, temperate forests and wet temperate forests. ​ Alpine vegetation is found in areas with altitudes more than 3600m. Coniferous forests are found in areas with altitudes of 1500 to 3000m. Precipitation affects the growth of natural vegetation. Areas receiving high rainfall, such as the equatorial regions, experience-rich growth of vegetation. Vegetation of India can be divided into five types Tropical evergreen forest, Tropical deciduous forest, Thorny bushes, Mountain vegetation and Mangrove forests. The natural vegetation depends upon the climate conditions existing in the place. As evergreen forests grow in the region which experience heavy rainfall. Similarly, at higher altitudes, where the climate is extremely cold lichens and mosses grow. The major types of vegetation in the world are grouped as forests, grasslands, scrubs and tundra.” In areas of heavy rain, huge trees can be found. Forests are abundant in areas of heavy rainfall. With moisture and rainfall the density of forests declines. In moderate rainfall areas, grasslands are found. Natural vegetation refers to a plant community, which has grown naturally without human aid and has been left undisturbed by humans for a long time. This is termed as a virgin vegetation. Thus, cultivated crops and fruits, orchards form part of vegetation but not natural vegetation.

Aahed Alhamamy Thank you so much for the information. I do not have good knowledge in Fortran but I understand the syntax of the language. I am wondering is there any available user guide or weblink that explains the procedures. Thanks again

Sanjana Dutt

Hi ,

Yes, it is possible to calculate vegetation indices using the same method as fragmentation indices within zonal statistics. However, the specific toolkit you are using may have limitations in terms of its ability to calculate vegetation indices.

It is possible that the zero values you are getting for the area metrics with 5000 hectare size of hexagons may be due to the size of the hexagons being too large for the resolution of the vegetation index data. It may also be due to the method of assigning the value of 1 to all NDVI polygons, which may not be properly integrating with the zonal statistics calculations.

I would recommend trying a different tool or approach for calculating vegetation indices within the same GIS software that you are using. There may be other available tools or scripts that are more suitable for calculating vegetation indices at the scale and resolution of your data. such as :

Additionally, you can try to verify that the data you are using is properly formatted and that there are no errors in the input data or parameters used in the calculation.

The lower curve does not contain any noticeable frequencies, so it results in a flat spectrum. The upper curve contains a superimposed damped oscillation that appears as an additional peak in the FFT. Both components can be separated by high-pass / low-pass or notch filters.

Different types of natural vegetation rely on different climatic conditions, which are very important for the amount of rainfall. India has a large variety of natural vegetation due to varying climatic conditions. India's vegetation can be split into five types. Natural vegetation of a region depends mainly on climate and hence there are distinctive areas of different types of vegetation. The governing factors in general are temperature, and precipitation. The reason for the rich heritage of flora and fauna in India is due to varied climatic conditions in India, presence of many rivers, presence of rich and variety of soils. India has varied physiological features like plateau, deserts, coastal areas, islands, mountains.

The Montane forests can be classified into three types according to the altitude as; alpine vegetation, temperate forests and wet temperate forests. ​ Alpine vegetation is found in areas with altitudes more than 3600m. Coniferous forests are found in areas with altitudes of 1500 to 3000m. The climate of India is sub- tropical and hence the vegetation of India is all a result of the landmass being located in a sub- tropical region. The climate of a place is affected by the location of the region, altitude of the region, and distance from the sea and relief features. The growth of vegetation depends on temperature and moisture. It also depends on factors like slope and thickness of soil. The type and thickness of natural vegetation varies from place to place because of the variation in these factors. Photoperiod also affects the vegetation of a place. It is the variation in duration of sunlight at different places due to differences in latitude, altitude, season and duration of the day. Hence, depending on the photoperiod, you get different kinds of vegetation at different places.

The tropical region generally has a hot climate because of its location around the equator. During hot summers, the temperature may cross 40°C but these regions usually get plenty of rainfall and an important feature of this region is the tropical rainforests.Tropical Evergreen Forests These forests are also called tropical rainforests. These thick forests occur in the regions near the equator and close to the tropics. These regions are hot and receive heavy rainfall throughout the year. The trees in this area have intense growth. The major trees found in this area are Sandal Wood, Rosewood, Garjan, Mahogany, and bamboo. It has copious vegetation of all kinds’ trees, shrubs, and creepers giving it a multilayered structure. Evergreen trees with broad leaves form the major vegetation of tropical rainforests. Trees are tall and form the canopy layer. Orchids, fern, rubber trees, rosewood trees, mahogany trees, palm trees, bamboo trees and cycads, along with spice trees such as clove and cinnamon are found in these forests.

The easiest way in ArcGIS would be:

1. Make fishnet grid (500 x 500 meters or the corresponding resoluton of the MODIS product - I assume you are using MODIS data)

2. Apply Zonal Stats from the NDVI and the LST to the fishnet, with the Mean pixel values

3. Now your "fishnet" regular vector grid has 3 columsn: ID, Mean-LST, Mean-NDVI

4. Run OLS or GWR https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-statistics-toolbox/ordinary-least-squares.htm

Hello Mahdi,

Have you tried xarray, dask, or tsai?

These are some libraries which can almost efficiently handle large amounts of data (usually larger than RAM capacity).

Trying to manipulate data types, reading data directly from your disk drive, and using vectorized operations are other techniques that I can think of right now.

Maybe you could find following links useful.

#This may help:

#Import the necessary libraries:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

#Load the data:

data = pd.read_csv("your_data_file.csv")

#Split the data into training and testing sets:

from sklearn.model_selection import train_test_split

X = data['vegetation_index'].values.reshape(-1, 1)

y = data['soil_moisture'].values.reshape(-1, 1)

#Train the machine learning model:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = LinearRegression()

model.fit(X_train, y_train)

#Make predictions:

y_pred = model.predict(X_test)

from sklearn.metrics import mean_squared_error, r2_score

#Evaluate the model:

print('Mean squared error: %.2f'

% mean_squared_error(y_test, y_pred))

print('Coefficient of determination: %.2f'

% r2_score(y_test, y_pred))

Thank you Patrick Möhl

This phenomenon is possible for some species but for some others it is not suitable for their growth.

If you want to simulate albedo from 400-2500nm under different chlorophyll. Maybe you can have a look at LESS model (http://lessrt.org/), it is a 3D radiative transfer model which can simulate BRDF and albedo under complex vegetation canopies. LESS has integrated a PROSPECT model, with which you can input leaf-level chlorophyll. Therefore, you can input leaf chlorophyll and obtain canopy reflectance and albedo.

Both maceration and reflux are common methods that can be used for dewaxing fibers with an organic solvent, and the appropriate duration for each method will depend on the specific circumstances.

Maceration involves soaking the fibers in an organic solvent for an extended period of time in order to dissolve the waxes. This method can be effective for dewaxing fibers, but it can also be time-consuming and may require frequent replacement of the solvent. The appropriate duration for maceration will depend on the specific characteristics of the fibers and the solvent being used, as well as the desired level of dewaxing.

Reflux involves heating the fibers and solvent together in a closed system, allowing the solvent to evaporate and then condense on the fibers, effectively washing the fibers. This method can be more efficient than maceration, as it allows for a more thorough extraction of the waxes in a shorter period of time. However, it may be more complex to set up and requires more equipment, such as a reflux condenser. The appropriate duration for reflux will also depend on the specific characteristics of the fibers and solvent being used, as well as the desired level of dewaxing.

Ultimately, the best method for dewaxing vegetal fibers with an organic solvent will depend on the specific circumstances and the desired end result. It may be necessary to experiment with different methods and durations in order to find the one that works best for your particular fibers.

Yes, it is possible to use the Temperature Condition Index (TCI) as a proxy of soil moisture. The TCI is a ratio of the observed temperature to the temperature that would be expected for the given atmospheric conditions, and can be used to identify areas of thermal stress. Soil moisture is affected by temperature, so the TCI can be used to infer soil moisture levels. The degree of accuracy of the TCI in predicting soil moisture depends on the accuracy of the temperature data used in the calculation. Additionally, the TCI may not be able to differentiate between soil moisture thermal stress and vegetation thermal stress, so other measurements may be needed to accurately differentiate between the two.

I have the works in the link you shared and they are not enough for me. But I sincerely thank you for your interest.

When studying the territory of macaques, langurs, and gibbons, there are several factors to consider. First, it is important to observe the species’ behavior in the environment. This includes looking at how they interact with each other, as well as how they interact with other species in the environment. Additionally, it is important to look at the types of vegetation that they are utilizing, such as food sources, nesting sites, and other resources. It is also important to note the availability of these resources in the environment, and how they may be impacted by human activities such as logging or deforestation. Finally, it is important to consider the landscape of the area, including topography, soil type, and the presence of water sources, as these can all impact the species’ behavior and habitat use.

Thank you so much Peter Donkor

I do have available input data for the catchment. Do you have any doco that explains which input files I need to replace with these data? I appreciate your attention in advance. Thanks

1. Heat treatment: This involves heating the bacterial suspension to temperatures that are lethal for the bacteria.

2. Filtration: This involves passing the suspension through a filter with a pore size small enough to trap the bacteria.

3. Centrifugation: This involves spinning the bacterial suspension at a high speed in a centrifuge to separate the bacteria from the other components. 4. Ultracentrifugation: This involves spinning the suspension at even higher speeds in a centrifuge to separate the bacteria from the other components. 5. Chromatography: This involves using a special column with a specific type of material that can separate the bacteria from the other components. 6. Electrophoresis: This involves using an electric current to separate the components of the bacterial suspension.

7. UV Irradiation: This involves exposing the bacterial suspension to UV light to kill the bacteria.

It depends on the accuracy of choosing the training areas and the number of points for each training area

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

Hello Milan Boulaire

Serum starvation of cells before treatment removes the knowns and the unknowns, reduces analytical interference, and provides more reproducible experimental conditions. Moreover, it reduces basal cellular activity and makes the population of proliferating cells more homogenous, since they withdraw from the cell cycle to enter the quiescent G0/G1 phase.

Serum starvation therefore helps to induce cell cycle synchronization. Please note that serum starvation for too long will also lead to reduced cell survival and increased apoptosis.

So, you could use 0.5% - 1% serum in culture medium so that the cells remain healthy but do not proliferate.

Best.

Hi Katharina Liepe ,

After installing the exe file, you have to look for something like

C:\Program Files(x86)/EuroVegMap/Maps/Vegetation_split.shp

I use Linux. Under windows the correct name of the folder could be something similar, but not the same as I figured out.

Sandor

As I told you, the selection of suitable explants not only affect to your plant , but it's also frequent for other species. If you type "in vitro culture micropropagation selection explant sources" in Google Scholar you can find several references regading your question. In fact, the election of right explants is considered among the main factors with great influence on the final result of in vitro culture. Lignification degree, ontogenic age, accumulation of some metabolites (carbohydrates, phenolics...), among other, are considered factors that might determine which explant is suitable, or not, for that are you looking for.

Vinamra Bhushan Sharma Your request indicates that perhaps you may not have already familiarized your self with the basic fundamentals of the technology. It would helpful to you if you explained your knowledge, skills and experience and what you have found out yourself as a starting point. What follows is a short list of various aspects and types of Lidar, which generate various sorts of point clouds, which then have optimal algorithm(s) for feature extraction in situation from bone fragments to asteroids - otherwise your essentially asking me to teach a class on Lidar:

Laser Source(s):

Surface Illumination

Single pulse

Pulses in the air

Multiple Pulse

Laser type

Single

Number of sources

Multiple

Push broom

Flash LiDAR

Frame

Single Wavelength

Multi Spectral

Single footprint

Small Footprint

Large Footprint

Scanning

HighSNR

Signal Strength

Number/size of beams

Scan ning mechanism

Multibeam

Oscillating Mirror

Rotating Polygon

Risley Prism

Low SNR (Photon Counting)

Si ngle Stop

Discrete Retums

Multistop

Full Waveform

Discrete and Waveform

Fiber Optic Bundle

Electro-Optica

Crystal Waveguide

Signal Processing

Kindly see the link below:

Distribution pattern of Tugai forests species diversity and their relationship to environmental factors in an arid area of China

Published: May 13, 2020

Obrigada!

Partially, you are on the right path, but you need to make it more practical as your work objectives need. Therefore, I suggest you read about "fuzzy k-means classification of topo-climatic data derived from 100 m gridded digital elevation models (DEMs)". Moreover, you must know about NDVI and for your predictions make it CA-Markov.

Integrating all of these into your work is another story which is more accessible and possible than what I have told you here.

Good Luck

Thanks a lot for your answer.

Thank you Olawuyi.

We will look at it.

Can you look at it in various seasons, so that you can identify the vegetation from the annual crops after harvesting but before shading? But it is hard to differentiate if the agricultural crops are perenial!

Hi,

See if this can help you - https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l1c/#available-bands-and-data

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?

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.

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.

or you could use a random forest regressor to estimate the canopy fraction since you have reference data from an expert, as you mentioned

Thank you for sharing

You can go for Community structure Analysis, but stress can give you better result

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.

It sent By accident: https://www.fs.fed.us/rm/pubs_other/wo_em7170_13/wo_em7170_13_vol1.pdf

Dear Mohammad Monir Hossen,

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.

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.

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

Using clustering analysis.

Litter and detritus sampling and subsequent analysis through CHN analyzer can give you an approximation

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

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.

One answer is here in this experimental work from 1999.

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

Please take a look at

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.

Hi Claire, you might already have answer to this, but I found the answer to your question in this blog:

https://chrischizinski.github.io/rstats/adonis/

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:

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!

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!

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.

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:

Thanks for the question. The answers also interest me

Now I'll find the artical about "succulents" in arid environments.

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.

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.