Vegetation Ecology - Science topic

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.

Dear Antonio Luca Conte ,

if your question is still relevant, see our article, where various vegetation indices were compared (NDVI, NDMI, FMI, SR, TVI, wNDII) for long-term forest change detection in Slovakia and Czechia using Landsat data: (NDMI appeared to the best for evaluating the individual stages of disturbance - especially the bark beetle calamity)

and the paper dealing with comparison of vegetation indexes (NDVI and NDMI) and orthogonal indexes (TCG, TCW) to distinguish between healhty forests, forests after disturbane and in recovery phase using Sentinel-2 data: (using the NDMI we achieved the best results again)

All the best,

Daniel

1. Keep in mind that 'DN' stands for 'Digital Number' and has nothing to do with an image being calibrated or not calibrated or raw spectral band or ratio or PCA results or anything else.  EVERY image assigns a DN value to the pixels.  What is important is what the DN values represent !  If it is raw Landsat TM or drone image data it is the radiance received at the sensor and adjusted/converted to a digital number range based on the specifications of the sensor typically using gains and offset (or bais) values; if an image has radiometric calibration procedures applied then it can represent TOA radiance, TOA reflectance, surface reflectance, or whatever (even for DEM images the pixel values are DNS that in this case represent elevation).  Again, every image (no matter what they represent) has DN values and I would suggest that 'DN' not be used to imply raw image data only as it is being used in this conversion.

2. Since the early days of Landsat MSS 'broad' spectral bands have been used for digital remote sensing analyses and mapping, so I do not agree that your drone aerial images can only be used for photo interpretation.  I worked with some of the very first Landsat MSS images in early 1972 and have since then used many 'broad' band spectral image data sets for various applications.

Dear Maciek,

If you can read at least a little German you can find this info for many species via http://floraweb.de/. On the page for the species there is a direct link to the full BiolFlor data sheet.

Kind regards,

Uwe

The common properties of the systems described in the question are the complexity, the multi-scale nature and the self-similarity of the governing laws with respect to scaling. The concept of complexity used here is very general and indicates that the system is composed of a large number of interacting parts and has a marked tendency to self-organization (emergence of macroscopic structures).

Multiscale complex systems are described by a non-Gaussian statistical distribution. They also exhibit the self-organized criticality observed in phenomena ranging from earthquakes and landslides to undesirable stock market behavior.

In examining the problem from the historical point of view, it is worth mentioning that the non-Gaussian statistical distribution was identified in 1892 by Wilfredo Pareto after a survey of the income distribution. Asymmetrical distributions, similar to those studied by Pareto, have also been observed in the natural sciences.

This fact led to the creation of a physical economy based on the methods developed in statistical physics. This new disciplines shows significant predictive skill with respect to the forecasting of general trends.

It depends on the sampling methodology and total area of the location.

Regards

Thanks for the answers.

Have you thought of using Hill numbers instead? Hill 1 and 2 are equivalent to Shannon and Simpson, respectively. But have much better properties thann the traditional use. Firstly, they have units, that is the value provided is in number of species (SW &S are unitless) thus are comparable and more meaningful. Secondly, they comply with the doubling principle. That is, if true diversity doubles, their value does so (SW &S don't). You can read Jost or Chao for more information. Lastly, the use of Shannon or Simpson (or their equivalents) depends on how much weight you want to give to common species. Hope this helps.

Рекомендую обратиться к геоботанику-болотоведу Ивченко Татьяне Георгиевне Tatiana Ivchenko

In Greenland the Inuit home rule government has a policy to install hydropower in every town tapping the energy from the meltwater. So far they have build six. Each city supplied has ceased to use their diesel generator power plant.

The second photo is of the Sisimut plant close to my universities Sisimut campus.

Hi Alena, in reality there is no good or bad way to measure diversity, it is all relative to your research question, what you really want to give more weight to (i.e., common species, uncommon species, etc). However, most measures, but species richness, have serious problems in reflecting the 'true' diversity and are difficult to compare. One very clever alternative is to use instead Hill numbers, which are related to these traditional ways of measuring diversity (Shannon, Simpson, etc) and deal with the comon problems they have. You can read a number of recent papers that describe in more detail what the Hill numbers are, but I would start with Jost 2006 Oikos 113:363 and Jost 2007 Ecology 88:2427 (and check Jost's web page www.loujost.com). Hope this helps.

please fallow link

thanks for great answers @janice @andrew

Hi Miguel,

We have tested a model for forest distirbance and resilience in the linked papers.

The datas you'd need are diameter distribution of the forests you want to study, tree heights and crown volume.

You need to do an height vs crown volume power regression. This tells you the optimal size distribution of the diameters in the stand. Then you compare this with the real diameter distribution. The farther the two distributions are, the most disturbed is the forest. Different forests will then have different disturbance degrees, and if the time at which disturbance happened (es. Fire) is known, then you can get resilience.

Best whishes

Giacomo

THANK YOU Dr Singh

Third fully expanded healthy leaf from the top is used for the measurement. Clip the lead in LICOR leaf area meter. Nowadays digitalised instruments are available that give the readings instantly. Take at least 5 readings to minimise error.

C3, C4 and CAM photosynthesis occurs in small plants but only C3 in trees :)

I was using the concept that species accumulation curve uses sampling effort. For bats, each night sampling is also used instead of area or net, so I would like to try an alternatiive with herbaria specimens.

Thank you all for your ideas, I'll consider them all.

Thank you very much, Dr. Ali Hussein and Dr.Jo-Anne Joyce. I get the point and match with the pH data.

First, decide on the gradient along which the regeneration is to be measured, Second, decide on plot size and whether placement will be random or not. 1-ha plot size within which complete enumeration of all erect woody species is done is sufficient. Number of the plots will depend on sampling intensity, desired level of accuracy and cost and time implications. Then have a technical definitions of seedling, sapling, medium-sized trees and large trees. Collect the data and segregate it according to size classes and habitat gradient (altitude?), You can the compare composition similarity by Spearman's rank correlation coefficient or any other appropriate statistic. You will document all species in each plot, and determine the proportional composition of the species of interest therein. Good luck.

According to my knowledge, any tree species consumes water depending on its vegetative mass and effective or active depth penetration of the root system. Though there is a general belief that Eucalyptus trees consume more water, there are reports contradicting the same, stating that there are more than 20 species of Eucalyptus & not all of them consume same amount of water. The only argument against growing Eucalyptus is that it should not be done in water scarce region, as these trees can be grown in any dry areas or saline pockets or water logged areas. Other reasons against growing Eucalyptus trees are the ecological & soil fertility problems. As far as Kolar region is concerned, the area is water scarce and experiences recurring drought and hence definitely not advisable to grow any water intensive crops/trees.

SILAM (Finland) - data for all Europe including Germany аrom various stations and data of satellite remote sensing of the Earth (grass cover)

for example: http://silam.fmi.fi/pollen.html?parameter=birch&region=Europe&height=0

Contact: http://pollen.fmi.fi/contact.htm

I cooperate with them

Dr Ronald we have four crop rotations in the main plots i.e. Maize+Cowpea-Oats-Cowpea, Maize-Oats-Mungbean, Maize+cowpea(2:1)-oats-mungbea and Cowpea-oats-cowpea and 5 nutrient management treatments in sub plots  I.e. Control, 100% NPK (inorganic, 125% NPK inorganic, 75%NPK+FYM+PGPR, 100%NPK+FYM. The experiment is ongoing under split plot design.

Hi.. please check: http://ac.els-cdn.com/S2212094715300116/1-s2.0-S2212094715300116-main.pdf?_tid=390c1908-fd6d-11e6-b67f-00000aab0f6c&acdnat=1488255803_277dce6540d7a59c1a7d5933b9cc84f4 or attached file

We use UAV multispectral data with 5cm GSD for invasive species classification. More details here:

Thank you all for your suggestions and idea. 

I think this is going to be a two-step process.  First search through the Research Gate PUBLICATIONS for the five kinds of native UK grasslands like at https://www.researchgate.net/search.Search.html?query=england%20chalk%20grassland&type=publication

Dear Colleagues,

Tahnk you very much for your suggestions. That will be very helpfull for my studies.

Cordially

is connectivity is reverse of fragmentation?

Weber et al. 2016. pp. 479-498. In Biological soil crusts: an organizing principle in drylands. Springer-Verlag. - summarizes a wide variety of recovery rates from disturbances including foot traffic and vehicle tracks. No data on mountain bikes that I know of. 

Cole.  1990. Great Basin Naturalist 50:321-325

The tree is well known to me from around the Med and as invasive alien in Ireland. If I would have to answer this question in my Majella research area, I would consider the following. Preparation of a SDM with a comprehensive set of environmental variables. Secondly, from my observations the species is more narrowly eu-mediterranean (oceanic) than say Quercus ilex and other broad-leaf evergreens. To obtain fine resolution data or proxies (terrain/cold air sinks) for minimum winter T would seem worthwhile. Cloudiness could also be relevant as this would decrease night frost.

It might be useful for you to have a look at sPlot which is a new initiative aimed at forming a database of plant plots across the globe. It includes the EVA data, as well as many other local initiatives but is of course not 100% complete (e.g. missing some plots in Sub-Saharan Africa).

Its depends on location of vegetation and seasons as per your study location it will be November to January. please find the link for details methodology:

https://www.researchgate.net/publication/309589918_Driving_force_for_forest_fragmentation_explored_by_land_use_change_in_Song_watershed_India  

Hi,

you could try the Semi-Automatic Classification Plugin in QGIS:

It is free, and there are plenty tutorials on how to conduct a supervised classification.

Hope that helps!

Best,

André

Thank you Mehebub ... interesting paper

Dear Rodrigo,

that is pretty helpful, and an interesting topic, too (biodiversity ~ restoration). Most interesting is the fact that Tropek et al. used relative cover for the vascular plants. This is something I always was afraid of, as the methods clearly (?) depend on abundance "counts". Anyway, this is another problem :) Actually, I already asked Chao and Jost etc. but never got any answer. What a pity. Thanks for your thoughts! I will now pool the dataset and refer to Gotelli & Colwell 2001 and Tropek et al. 2010.

best regards from Hamburg

I looked at table, the data is column separated, and the daily times were so unusual.  Then as suggested in your question, there were 31 codes of columns with data in it, probably some flow or other indicator.  Since I do not know Spanish, I could not decipher details, but the formating of the daily data in 31 columns verified the daily max, min and average, I could not figure out the 31 daily codes, what they might be (possibly discharge readings at different times), and why the three readings per day, one with no time, one with 0700 hours and one with 1700 hours.  The 31 codes (coda) were possibly a measure of discharge during the day. 

I would suggest contacting the agency collecting the data for an explanation.  The format may be clear to them, but they might not be aware that its formatting is creating issues for others in its reading and interpretation.  

In downloading data from USGS website, it is not uncommon to have to adjust the date time indicator into separate columns, and select out specific data if you want only date, time, and stream discharge, there are several columns such as stream stage removed.  I usually have a spreadsheet with original data, and then I copy and paste into another spreadsheet for alterations, adjustments for analysis, plots, etc. 

just go through the attachment in these paper they have calculated threshold value empirically 

Hello Yadav,

You may find the following articles useful.  They use/highlight population viability methods in varying ways.

1) https://scholar.google.com/citations?view_op=view_citation&hl=en&user=OPcgE2IAAAAJ&citation_for_view=OPcgE2IAAAAJ:9yKSN-GCB0IC

2) https://scholar.google.com/citations?view_op=view_citation&hl=en&user=OPcgE2IAAAAJ&citation_for_view=OPcgE2IAAAAJ:0EnyYjriUFMC

3) http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.566.5181&rep=rep1&type=pdf

4) https://cran.r-project.org/web/packages/popbio/popbio.pdf

I hope they help.  Feel free to email me with any questions.  Also, if you are trying to do some basic analyses, I also suggest looking at the "Pop-tools" extension in Excel.  It is very useful for estimating the basic parameters that are included in most PVA's

Cordially,

Benjamin

Thank you Matheus. I will look.

Hi Charuta,

currently, there are two "slightly" different implementation of REVEALS in R.

One is available in DISQOVER package (http://disqover.botanik.uni-greifswald.de/revealsinr/) and described in a recent paper: http://link.springer.com/article/10.1007%2Fs00334-016-0572-0

Second is available from Petr Kuneš GitHub site: https://github.com/petrkunes/LRA

HTH,

martin

 I will advise you to same way: Erdas imagine Accuracy assessment tools to evaluate your LU/LC also you can use kappa index but it is not usefull at latest studies. You will use maximum likelihood algorithm and accuracy measures like 0.82, 0.90  ....

Dear Mr. Nagar,

        Based on my experience in assessing e-flow in respect of hydro-electricity project clearance from MoEF, I attach the required write-up for your use. Best method of e-flow assessment at present is to follow MIKE 11 tool.

 Environmental Flows

Riverine ecosystems are among the most impacted ecosystems worldwide. Human activities, such as the construction of dikes, dams, barrages and weirs, the straightening and deepening of river channels, the conversion of floodplains to agricultural land, water abstraction, water transfer and pollution; etc., have heavily modified most large lowland rivers. As a result, a large number of fish species became threatened or endangered and the fish productivity of most riverine ecosystems has declined. This is all the more troubling since, large lowland rivers support a significant proportion of the world’s fish diversity and their fisheries provide a major source of food, employment and income to society. Until now, the flow regimes of most large lowland rivers have been heavily manipulated to serve the needs of society. To balance the interests of different stakeholder groups, many countries have implemented water resources management plans. Water policy decisions are typically made with little or no consideration of fish conservation and fisheries, despite a high public perception of fish. The reason is not only the relative lower socioeconomic importance of fisheries compared to ecosystem services such as flood protection and navigation. Whereas the optimal flow regime for navigation, agriculture and flood protection (in terms of magnitude, timing and duration of flow events) is comparably easy to determine, the consequences for fish diversity and fisheries are much more difficult to quantify.

Environmental flow refers to the water considered sufficient for protecting the structure and function of an ecosystem and its dependent species. Environmental flows are required to be maintained through a river reach for sustaining its ecosystem and dependent species. It means enough water is to be released in the downstream of the river system after utilizing the water for the development projects in order to ensure downstream environmental, social and economic benefits. Realizing its importance, several countries have made ensuring environmental flows mandatory. For example, The Mekong River Agreement, 1995; South Africa’s National Water Act, 1998 and the Swiss Water Protection Act, 108. These legislations attempt to ensure required minimum flow in the river system to sustain ecosystem services. A wide range of environmental flow methodologies (EFMs) have been developed to determine flow thresholds for various objectives such as the preservation of natural conditions, the maintenance or restoration of ecological integrity and cultural and recreational values. Most of these methods were developed primarily to protect endangered fish species and to maintain fisheries resources in human-modified rivers. Until now, these methodologies have been mostly applied to small upland rivers and headwater streams. Although a growing body of literature summarizes the status of available EFMs the question remains whether or not these EFMs are suitable to protect fish diversity and fisheries resources in regulated large lowland rivers.

 Methodologies for estimation of environmental flows worldwide

Most currently available EFMs can be grouped in four main categories:

1. Hydrological methods

2. Hydraulic rating

3. Habitat simulation methods

4. Holistic methodologies.

In European countries, hydrological and habitat simulation are the prevailing methods, while some developing countries and countries with newer environmental legislation have focused on holistic methods. Hydrological methods rely primarily on flow measures and indices, which are drawn from historical time series data on annual or daily mean flow. Still widely used is the Tennant method (also known as Montana method), which relates the ratio between river discharge and fish habitat availability to certain percentages of annual flows to meet predefined requirements (Tennant, 1976). The Tennant method assumes similarity of aquatic habitats when carrying the same proportion of average flow but rarely considered the effective habitat quality at varying flows. The method lists eight categories of in-stream flow that range from maximum to severe degradation. Below the threshold of 10% mean flow, the environmental conditions for fish are judged to be degraded, whereas 50% provides for excellent conditions in terms of stream width, water depth and velocity. While the Tennant method does not explicitly consider duration or the timing of flow events, some extensions integrate flow duration and frequencies. Other hydrological methods include duration percentiles or single flow indices that are usually generated from historical stream flow databases. One example is the widely used 7Q10, which is defined as the ‘seven-day, consecutive low flow with a ten-year return frequency; the lowest stream flow for seven consecutive days that would be expected to occur once in ten years’ (United States Environmental Protection Agency) or similar discharge indices. 

 Hydraulic rating considers the channel morphology of a given river and calculates acceptable flows by relating river discharge with a variety of hydraulic characteristics such as water depth, velocity or wetted perimeter. These methods rely on transects measured across a river section comprising habitat factors that are assumed to be limiting factors for target biota. The wetted perimeter method considers the variation in wetted perimeter or river width with water discharge. Plotting the wetted perimeter against discharge shows a breakpoint where a comparable small decrease in discharge results in a comparably larger decrease in wetted perimeter. This breakpoint is used as a minimum in-stream flow recommendation. The widely used habitat simulation methods are sophisticated extensions of hydraulic rating methods within a framework addressing many ecological components of riverine ecosystems (IFIM, In-stream Flow Incremental Methodology, Bovee, 1982). Within the context of IFIM, a broad range of modelling tools such as PHABSIM aim to predict how physical habitat conditions (i.e. water depth, velocity, cover, substrata) change with discharge. Typically, detailed hydrological and hydraulic data for a grid of cells in a river stretch are compared with the habitat suitability of a target species. The habitat suitability is expressed as a habitat suitability curve (e.g. suitability index curve, probability of use or preference curve), often seasonally defined, which specifies the assumed seasonal requirements of different species, life stages or habitat guilds. The curves depict the relationship of target organism’s response to a gradual changing habitat variable scaling from unsuitable to suitable, which are usually obtained from existing data or field measurements. By comparing the curves with the predicted habitat area at various flows, the minimum flow thresholds can be defined in a way so that the discharge provides optimal habitat conditions, retains a percentage of habitats at average flow or provides a minimum amount of habitat area. Most commonly, the flow threshold is set at the breakpoint in the habitat/flow curve where proportionally more habitat is lost with decreasing flow than is gained with increasing flow. The commonly used output of, for example PHABSIM quantifies the suitability of a location for a target species in terms of a weighted usable area (WUA; expressed as, e.g. m2 1000m stream length1). More advanced software tools such as 2D and 3D models achieve greater hydraulic representation. Other models allow for inclusion of water quality, temperature and other biological factors such as prey densities, energy allocation and behavioral components. The complexity of current models is growing and there are many approaches to establish statistical techniques that improve the predictability of species abundance on the basis of biotic and abiotic variables. The growing recognition that rivers are closely connected to their watersheds has led to the realization that protecting and rehabilitating riverine ecosystems requires sensitivity, not only to the key hydrological, biological and ecological, but also to the economic and social aspects of a riverine ecosystem. Assuming that a natural flow system will maintain the ecological function of a riverine ecosystem, so-called holistic methodologies will define the critical environmental flows of an entire riverine ecosystem rather than focusing on the needs of a single species.

Holistic methods rely less on modelling and more on multidisciplinary panels covering biophysical disciplines such as hydrology, geomorphology, sedimentology, water chemistry, botany and zoology. Advanced methodologies such as DRIFT (Downstream Response to Intended Flow Transformations Methodology, King et al., 2003) consist of different modules that integrate biophysical, and economic and social factors and aim at participating stakeholder groups. Within the biophysical module, various EFMs such as habitat-modelling tools can also be implemented. The growing recognition that rivers are closely connected to their watersheds has led to the realization that protecting and rehabilitating riverine ecosystems requires sensitivity not only to the key hydrological, biological and ecological, but also to the economic and social aspects of a riverine ecosystem. Assuming that a natural flow system will maintain the ecological function of a riverine ecosystem so-called holistic methodologies will define the critical environmental flows of an entire riverine ecosystem rather than focusing on the needs of a single species. Holistic methods rely less on modelling and more on multidisciplinary panels covering biophysical disciplines such as hydrology, geomorphology, sedimentology, water chemistry, botany and zoology. Advanced methodologies such as DRIFT consist of different modules that integrate biophysical, and economic and social factors and aim at participating stakeholder groups. Within the biophysical module, various EFMs such as habitat-modelling tools can also be implemented.

 Analysis of Environmental flows

Mostly, to maintain specific river classes, the E-flows are estimated world-wide at monthly time-scale using the Flow-Duration curve analysis. For this, generally, a minimum of 25 years river discharge time series data is used. It is the management and planning authority, who decides the quantity of E-flow to be released to maintain a particular environmental management class (EMC) of the river. A river may be classified into six environmental management classes of A, B, C, D, E, and F depending on the following flow.

modification category (Kleynhans, 1996). The more pristine is the desired management class, the higher is the E-flow requirement.

 Class A (Natural): Pristine river condition or minor modification of in-stream and riparian habitat.

 Class B (Slightly modified): Largely intact biodiversity and habitats in the river despite water resources development and/or basin modifications

 Class C (Moderately modified): The river habitats and dynamics of the biota have been disturbed, but the basic ecosystem functions are still intact. Some sensitive species are lost/or reduced in extent; however, the alien species are present.

 Class D (Largely modified): There is large changes in the river natural habitats, biota and basic ecosystem functions with a clearly lower than expected species richness. A clearly lower than expected species richness.

 Class E (Seriously modified): Habitat diversity and availability have declined. A strikingly lower than expected species richness. Alien species invaded the ecosystem.

 Class F (Critically modified): Modifications have reached a critical level and ecosystem has been completely modified with almost total loss of natural habitat and biota. This river status is not acceptable at all.

Hence, the E-flow should be released from the dam so that it can maintain the EMC-B condition, which should not be less than the EMC-C condition in worst case.

Flow-Duration Curve (FDC) Method

The flow-duration curve is the plot between the percentages of time that flow in a stream is likely to equal or exceed some specified value of interest. The basic time unit used in preparing a flow-duration curve will greatly affect the nature of the curve. For most studies, mean daily discharges are used. These will give a steep curve. When the mean flow over a long period is used (such as mean monthly flow), the resulting curve will be flatter due to normalization of short-term peaks by averaging with intervening smaller flows during a month. The extreme discharge values are averaged out more and more, as the time period gets larger (e.g., for a flow duration-curve based on annual flows at a long-record station). The shape of a flow-duration curve in its upper and lower regions is particularly significant in evaluating the river and basin characteristics. The shape of the curve in the high-flow region indicates the type of flood regime the river basin is likely to have; whereas, the shape of the low-flow region characterizes the ability of the river basin to sustain low flows during dry seasons. A very steep curve (high flows for short periods) would be expected due to rainfall in small watersheds, as the watershed behave non-linearly to the rainfall-runoff transformation process. The floods due to snowmelt, which last for several days, or regulation of floods with reservoir storage, will generally result in a much flatter curve near the upper limit. In the low-flow region, an intermittent stream would exhibit periods of no flow; whereas, a very flat curve indicates that moderate flows are sustained throughout the year due to natural or artificial stream flow regulation, or due to a large groundwater capacity which sustains the base flow to the stream.

In this study, the available 10-daily averaged observed discharge time series in a particular month for different years are ranked using the Weibull plotting position method. The flow-duration curve for that specific month is represented by a table of flow values (percentiles) covering the entire range of probabilities of occurrence corresponding to 17 fixed percentage points: 0.01, 0.1, 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99, 99.9, and 99.99%. These percentage points ensure that the entire range of flows is adequately accounted for. The flow-duration curve developed using the historical data is termed as the reference class. For estimating the Environmental Flow (E-flow) requirement for different categories of EMC, the flow duration curve is shifted by one step, two steps, three steps, and four steps for EMC-A, EMC-B, EMC-C, and EMC-D, respectively. The E-flow is computed based on the 90% dependable flow of this FDC for different EMCs. The E-flow thus computed for different months is routed along the river reached downstream by using the MIKE 11 Hydrodynamic Model (DHI-Water & Environment, 2012) to simulate the spatial distribution of discharge, flow depth, and flow velocity at different sections of the river downstream under no lateral flow condition.

Habitat Simulation Method

The main issue with the FDC method is a limited justification of the permissible FDC shifts per EMC. The currently accepted step of a shift (one FDC table point per class) is based on limited calibration of the proposed method. It is very difficult to evaluate the results when there are no ecological data available to confirm or deny the suitability of the estimated EF. It is, in principle, possible to collect some limited hydraulic information for rivers and examine the characteristics of the available habitat (water depth, wetted area and velocity, for example) under different flow conditions (natural and FDC shifting method recommendations). This is not, however, a real substitute for scientific information on the relationships between ecological characteristics and flow.

Keeping this in view, a further assessment is to be carried out using habitat simulation and hydraulic modelling. Habitat requirements have to be established in different seasons for baseline data collected .

From baseline data, habitat requirement are to be established to protect fishes in the river limbs. Dependency of the local population on rivers for other needs e.g. domestic requirement, irrigation, navigation, commercial fisheries. is not there.

Flow modeling has to be carried out for different release scenarios in different seasons using Mike11. Flow simulations generally to be carried out for 10%, 15%, 20%, 25%, 30%, 40%, 50% and 100% releases of the average discharge in 90% dependable year of the flow series. Annual series may be further divided into Monsoon, Lean and pre & post monsoon period. Flow release recommendations are to be made using this approach to ensure habitat requirements are met with throughout the year.

MIKE 11 Hydrodynamic Model

The MIKE11 hydrodynamic model (DHI-Water & Environment, 2012) is widely used world-wide to model the 1-D flood wave movement in rivers under steady and unsteady flow conditions. This model solves the de Saint-Venant equations of continuity and momentum using six-point Abbot numerical scheme. The Saint-Venant equations of continuity and momentum are to be followed.

 The MIKE11 model uses the E-flow as the upstream boundary condition, which is then routed along the river downstream to estimate the discharge, flow depth and velocity at different sections of the river reach at different time levels. MIKE11 is accepted by US Federal Emergency Management Agency for use in the National Flood Insurance Program.

  Comments

The Report on Estimation of Environment Flows Requirement for tropical rivers like India typically may include the following;

 Baseline Data, broadly given below, may be elaborated.

 Secondary sources based on literature covering fish fauna of rivers rivers

 Primary sources – Based on sampling and analysis covering physico-chemical and biological characteristics

 ·         Physico–chemical Characteristics (water and sediment)                                                     

·         Periphyton, Phytoplankton and zooplankton                                                                     ·         Macro-Invertebrates (benthic) Fish Fauna

 ·         Fish species sampled during different seasons

·         Catch Per Unit Effort

·         Size/s of fish caught during sampling in different season

·         Habitat Characteristics of sites where sampling was conducted like substrate, river depth, flow velocity, etc.

 The data from the seasonal sampling on water quality and biological features (phytoplankton, zooplankton, macro-benthos, fishes, etc) are to be included in the report along with discussion on seasonal trend in parameters and correlation, if any, between water quality and biological parameters of key species.

 Establishing habitat requirement based on Ecological and Social needs in different seasons – Lean (Winter), Monsoon, pre and post monsoon, i.e., Flow Depth, Width, Velocity, Turbidity, Flood Peaks, Intermittent Wetting of Riparian vegetation during high flows, etc. needs to be elaborated in a report.

 Meeting of habitat requirement through environment flow may also be elaborated in a report.

 ·        

·                                                                                

·         Spills from dam during monsoon to be assessed

·         

Hydrology

MIKE 11 has been used for hydro-dynamic modelling and several runs for different discharges must  be made with number of inputs .

 Maximum number of river cross-sections on river limbs need to be used for modelling (at least 9-10 sections on each limb). The use of data for several sections would facilitate assessment of critical stretch.

 . The entire river reach needs to be represented in the model set up using all the cross sections provided.

             The model output should include flow depth, velocity of flow and top flow width at each cross section of the study reach for 10%, 15%, 20%, 25%, 30%, 40%, 50% & 100% flow of the average of leanest 4 months, 4 monsoon months & other 4 months in 90% dependable year.

 The flow simulation results, viz., depth of flow, velocity and top flow width of all the three seasons need to be correlated with the respective riverine fauna to estimate the environmental flow requirements.

Dear Amr Saleem, The end of my post missed so trying to proceed:

Hydrological cycle in all water bodies (including groundwaters) is very important factor,  not only flash floods caused in abundant precipitation. Evaporation drived by temperature dynamics  and other factors is also valuable too. Note that exact parameters had to be taken into account  depend on certain location. Very important point is a scale. Drivers of short-term (seasonal) dynamics usually differ from annual, decimal trends  as well as dymamic character itself. human impact including land use is most unpredictable factor. Remotely sensed (satellite) data help in estimation of most referred factors (direct or ondirect) for model verification. This is suggested in accordance to our experience. Honestly speaking, the models you suggest are really complicated   I suddenly found the reference I was giong to share: https://www.researchgate.net/publication/270769761_Spatially_explicit_estimation_of_soil-water_resources_by_coupling_of_an_eco-hydrological_model_with_remote_sensing_data_in_the_Weihe_River_Basin_of_China

Kathleen, I don't think you should be focusing on temperature as a limiting factor for plant growth in Southern Africa. The climate of Southern Africa is almost the complete opposite to the European climate. Southern Africa is warm and dry whereas Europe is cool and wet. In Europe temperatures drop below the minimum required for plant growth during the winter (the length of this period obviously depends on altitude/latitude etc), while moisture is plentiful. In Southern Africa temperatures probably do not fall below the minimum required for plant growth at any time of the year, while rainfall is relatively low and unpredictable and drought is common. Therefore I think you should be considering rainfall (or moisture availability) as your main environmental factor limiting plant growth.

Apologies in advance if I am telling you things you already know. If you suspect the spatial correlation to be present in your data, the first thing to do is plot a variogram or bubble plot, there are several R packages that allow you to do this (spatial, gstat, geoR). This will allow you to either visualise how the semi variance of the model changes with distance (variogram) or how the residuals of your model are positioned in space (bubble plot). In both cases if you see clear patterns, you have spatial autocorrelation. 

Accounting for heteroscedasticity can be done in several ways including adding more explanatory variables (which may explain the patterns of variance), random effects, or variance structures

Without knowing more about your data is it hard to give a definite answer on what is best. But a generalised least squares regression (GLS) allows you to incorporate both a spatial correlation structure and variance structure (to account for heteroscedasticity), so it might be a good place to start in your case.

Beyond that, there are other mixed effects regressions which could work, but the specification of variance/correlation structures becomes more complex.

Hope this is helpful.

Arshad your question is really articulative. Shall we take  it  this way , whether or not soil C:N ratio affect the leaf  C:N ratio ? . My answer will big affirmative. Any change in concentration of soil C will bring simultaneous change in N status , considering the C:N ratio   of  soil as a constant . Such  soil C:N ratio  will trigger  the better microbial diversity plus the higher buffering capacity of the soil , thereby , maintain the better nutrient supply to above ground plant parts  .

Dear Amber,

Here are a couple of  papers available on RG and another on ncbi for your reference

1. Applications of the IUCN Red List: towards a global barometer for plant diversity

N Brummitt, SP Bachman, J Moat - Endangered Species …, 2008

2. Modelling bryophyte distribution based on ecological information for extent of occurrence assessment ,C Sérgio, R Figueira, D Draper, R Menezes… 2007 - Elsevier

3.Supporting Red List threat assessments with GeoCAT: geospatial conservation assessment tool by S Bachman, J Moat, AW Hill, J de Torre, B Scott - ZooKeys, 2011 - ncbi.nlm.nih.gov.sci-hub.org

Good luck!

You could also try PAST - it has ordination and diversity calculations, among other things, and the creator of the program is constantly updating it and always available to answer questions and assist people in using the program.

Here is the URL: http://folk.uio.no/ohammer/past/

I agree the another appropriate method PCA,.

 Edward Greene's study of the California poppy ecotypes has been reprinted, and the most interesting part is that he created a taxonomic key to sort out the 116 ecotypes that he describes in detail.   If you go to Amazon and look for Pittonia Volume 5 1902-1905, the paperback reprint is under $30.  

And the simplicity of the perennial grasses and how quickly you can conduct a common garden study to view the fixed ecotypic variations, by using a common garden study is like putting on magic glasses, and be able to see evolution in progress.

a strong site of african plants is http://www.africanplants.senckenberg.de/root/index.php?page_id=76

Hi,

The NDVI has been around for over 40 years, has a simple formula and requires only red and NIR data, all of which contribute to its ongoing popularity. However, it is highly sensitive to soil background and atmospheric effects, while the EVI was designed to be much less sensitive to these extraneous effects and, therefore, to provide a better quantification of vegetation abundance and physiological activity. The paper below reviews the development of some of the most important post-NDVI indices and provides a modified EVI equation that does not require blue band data:

http://vip.arizona.edu/documents/dataviewer/EVI2_Paper_Huete.pdfI hope it helps.

JP

Dear Svetla,

The issue raised in your last message (detecting pathogens) is rather different, and much more specific, than your original question (meaning of vegetation indices). As pointed out by Rafael, vegetation indices are rather arbitrary (and largely obsolete) combinations of spectral bands that exhibit at best limited correlations with plant properties collected in field campaigns. And when such relations have been established, they are generally applicable only to the geographical area and time periods of those campaigns.

Coming back to your much more pointed question on the pathogens: the first thing to do would be to establish whether the presence or the effect of these pathogens has any noticeable impact on the bidirectional spectral reflectance of the affected canopies (hence in either the color or the anisotropy of the plant canopy), at the spatial scales and resolutions sampled by space borne instruments. If the canopy reflectance does not change as a result of the pathogen attack (or if the change is too small to be noticeable from space), then remote sensing is not an appropriate approach for this problem, irrespective of what index or other 'trick' you may want to use...

Assuming now that the reflectance of plants is indeed sufficiently affected by pathogens to be measurable, then the next step would be to characterize those changes, and to design a method to highlight those particular changes (not any generic change in plant properties). Hence, you should develop your own approach to address this particular problem: there is no reason to believe that any odd generic vegetation index is relevant or optimized for your specific interest.

To explore these ideas further, you may want to look at this paper:

Verstraete, M. M. and B. Pinty (1996) 'Designing optimal spectral indices for remote sensing applications', IEEE Transactions on Geoscience and Remote Sensing, 34, 1254–1265.

Good luck with your investigations. Michel.

cross-cultural differences,  chi-square, Fishers test, RRI,etc.

Jean, Check out this paper by a colleague Christian Marks

This is focused on how changing the flood regime changes species composition within floodplain forest communities

I suggest you should be considered all available environment gradients 

Dear Ripu,

please note, that most plants of D. umbrosa are purple too. The colour itself is not sufficient to discriminate.  Shape and measures of the single floral and vegetative organs are to considered too in any case.

Furthermore I would like to correct a mistake in the wording of "ochroleica" in my above commentary, which must be ochroleuca.

Best wishes, Richard

Hi

General information about Royal paulownia (Paulownia tomentosa) Tree:

To Zaal,

Thank you very much, I strongly approve of your opinion.

It was an obvious phenomenon that  the sheep like to eat the tussock more than the deep-rooted grass in the experimental area. I believe that your hypothesis is convincing.

I appreciated very much.

Thanks - that makes sense. The permutation just wasn't stable at the default number of iterations.

Hi Frank,

Theoretically, if some trees are rooted in deeper/wetter soil than their neighbors, you would expect to see relatively higher predawn water potentials, provided that the plant is in equilibrium with the soil. It would be necessary to verify soil moisture profiles. In practice, such an approach tends to be problematic. You might also consider the use of stable isotopes. Here is a paper you may find helpful.

Google Scholar produced this list:

It is always better to use your own wetlands with historical and present data in order to establish an índex correctly or at least to have reference conditions from your own cases

Dear Olga,

I am not specialised on vegetation on steppe, however, have rather large experience with R. Soó's publications. The problems are following: 1) Soó wrote large amount of papers and sometimes he published more papers during one year. As many of his papers are witten in Hungarian, it is sometimes difficult for other people to estimate in which of the papers could be published particular description. Additionally, my impression from Soó's papers is that he was able to place anything anywhere, and thus e.g. some aquatic syntaxa were described in the paper focused mainly on halophilous vegetation;

2) Many of the Soó's descriptions are invalid, mainly as nomina nuda according to the article 2b of ICPN  (there are many cases when the association name is only mentioned in the text, without any relevé or table of relevés). However, some of the names which were just mentioned in some of his older papers we  documented by relevé(s) in some newer publication. It is one of the sources of the confusions because some of the authors (especially those before the first version of the International Code of Phytosociological Nomenclature publication) used the year of invalid publication in syntaxa names, some other authors the year of valid publication.... and some other made type error in the date and thus many of the associations are given with very variable years of description. Even Soó himself, when refered to his own papers, often gave several various dates or he even did was not sure about the year of publication of the associations he should describe!

3) Some of the valid descriptions are documented just by a single relevé which is so well hidden in the text that many people did not find it and considered the descriptions as invalid (e.g. the case of species poor vegetation like Lemnetum minoris or Potametum crispi associations).

May be some of the colleagues who will join your question will know accurately where the description of Stipetum lessingianae was validly published for the first time. If no, than I recommend you to check all the papers published in the years which appear in various forms of the name, i.e. besides 1948 also 1927, any may be also some later papers, if you would not be successful. The paper from 1927 is probably Soó R. von (1927): Geobotanische Monographie von Kolozsvár (Klausenburg). A studium könyvkiadó R. T. Bizománya, Budapest. 

For 1946 I do not have any idea but will try to check some sources of references when I will be in my office.

From the newer papers there is important

Soó, R. (1964): A magyar flóra és vegetació rendszertani-növenyföldrajzikézikönyve I. (Synopsis systematico-geobotanica florae vegetationisque Hungariae I). - Budapest 589 S. I think there are not valid descriptions of associations, just list of syntaxa, however, there are references to older Soó's papers and the names have authors and years of publication, thus potentially there can be some new information for you. Of course, there is couple of other papers. As in the following weeks I must go through them (search of aquatic syntaxa), I can check them for Stipetum lessingianae too (unfortunately, the papers are not available in my institution and thus I can not manage it immediately).

If you hurry on the correct reference of Soó's paper, you can try to contact someone from Hungary here on ResearchGate directly (by sending message), e.g. Róbert Pál, Zoltán Botta Dukát or Attila Lengyel.

Good luck in your search and best regards!

Katerina

Dear Dr. Windey,

My observations also are the same as of you. However, recently came across a paper which stated a positive relationship between growth rate and invasive potential of tree species.

با عرض سلام دفتر اقتصاد محیط زیست سازمان حفاطت محیط زیست در تابستان 1390 یک نشست یک روزه در این مورد برگزار کرده که گزارش از کارهای تحقیقاتی انجام شده را ارایه کرده

2/ خانم دکتر زهرا عابدی سرپرست یک گروه تحقیقاتی در این زمینه بوده اند

3/ نام خانم مونا نظیری را جستجو فارسی کنید

4 در SID نام SEEA را جستجو لفرمتیید مقلات زیادی را مییابید

موفق باشید

Blue carbon is the carbon captured by the world's oceans and coastal ecosystems. The carbon captured by living organisms in oceans is stored in the form of biomass and as sediments from mangroves, salt marshes and sea-grasses. Soil organic carbon has been contributed both by microbial degradation of litters or detritus and sewage effluent, aquaculture run off, industrial wastes as well. Here to consider it under the domain of blue carbon the biogenic part preferably those obtain by microbial degradation of coastal vegetation should have certain % over the carbon being contributed by other means...

Hi Dr. Arastoo - Perhaps some of the following or the authors may help:

.

Baker, Barry; Viglizzo, Ernesto F. 1998? 9. Rangeland and Livestock. In: UNEP/IVM Handbook 34 p.

Climate change and sustainable livelihood of rural people in Mongolia. Preliminary report..67 p.

Colpaert, Alfred et al. 2003. Reindeer Pasture Biomass Assessment Using Satellite Remote Sensing. Arctic 56(2): 147-158.

El-Tantawi, Attia Mahmoud Mohamed. 1966. Climate Change in Libya and Desertification of Jifara Plain Using Geographical Information System and Remote Sensing Techniques. Dissertation. 261 p.

Eniolorunda, Nathaniel Bayode 2014. Climate Change Analysis and Adaptation: The Role of Remote Sensing (Rs) and Geographical Information System (Gis). International Journal Of Computational Engineering Research 04()1)

Fang, Xiuqin et al. 2014. Analysis of vegetation dynamics and climatic variability impacts on greenness across Canada using remotely sensed data from 2000 to 2009. SPIE Digital Library.

FAO. 2008. Climate change adaptation and mitigation in the food and agriculture sector. Technical background document from the expert consultation held on 5 to 7 March 2008. 17 p.

Feenstra, Jan F. et al. eds.1998. Handbook on Methods for Climate Change Impact Assessment and Adaptation Strategies. Version 2.0. 464 p.

Gavazov, K. S., et al. 2013. Dynamics of forage production in pasture-woodlands of the Swiss Jura Mountains under projected climate change scenarios. Ecology and Society 18(1): 38.

Gunlycke, Naemi; Tuomaala, Anja. 2011. Detecting forest degradation in Marakwet district, Kenya, using remote sensing and GIS. Seminar series nr 200. 78 p.

Hartman, Ingrid et al. 2009 The Impact of Climate Change on Pastoral Societies of Somaliland. Candlelight for Health, Education & Environment Report. 62 p.

Maynard, N.G. et al. 2008. Eurasian Reindeer Pastoralism in a Changing Climate: Indigenous Knowledge & NASA Remote Sensing. 26 p.

McKeon, G.M et al. 2009. Climate change impacts on Australia’s rangeland livestock carrying capacity: A review of challenges. Report for Land & Water Australia Senior Research Fellowship (QNR46). 78 p.

Projected impacts of climate changes on agriculture. NSW.

Rakonczai, J. et al. 2008. Climate change and changing landscape – a comparative evaluation on Chinese and Hungarian sample areas. Journal of Env. Geogr. 1(1-2): 23-30.

Renchin, Tsolom et al. 2009. Land Use Change Analysis in Uvurkhnagai Province (Mongolia). ASPRS 2009 Annual Conference. 8 p.

Singh, Sudhir Kumar. 2010. Temporal monitoring impact of land use and land cover change in Shiwalik hills of Punjab, India using satellite remote sensing. Geospatial World.

The Bridge Fund. 2012. Climate Change Impacts on the Tibetan Plateau. 39 p.

Tolba, Mostafa K.; Saab. Najib W. 2009. Arab Environment: Climate Change - Impact of Climate Change on Arab Countries. 2009 report of the Arab forum for environment and development. 181 p.

Traore, S.; Owiyo, T. 2013. Dirty droughts causing loss and damage in Northern Burkina Faso. Int. J. Global Warming, 5(4):498–513.

Weber, Keith T. et al. 2013. Forecasting Rangeland Condition with GIS in Southeastern Idaho Rangeland Ecology and Management 66(1): 82-87. Abstract.

Weber, Keith T.; Davis, Kerynn. Eds. 2010. Final Report: Forecasting Rangeland Condition with GIS in Southeastern Idaho (NNG06GD82G) 189 p.

Weber, Keith T.; Gokhale, Bhushan S. 2010. Effect of Grazing Treatment on Soil Moisture in Semiarid Rangelands. In: Final Report: Forecasting Rangeland Condition with GIS in Southeastern Idaho. 14 p.

White, David H.; Howden, S. Mark. 2010? Climate and its effects on crop productivity and Management. EOLSS. 9 p.

For spatial mathematics, including environmental modeling, the kriging estimation method (as well most methods in geostatistics) relies on finding the weights for it's samples trought the solution of A*w=B system, the kriging system. The criteria for the solution is that the model of anisotropy given by the user is followed in the estimation.

In the same procedure it is very common to use rotation matrices to make compatible two different spatial entities of data (point and grid or mesh, for example).

Also I believe linear algebra is commonly used in most physical engines, including scientific DEM methods to calculate force, velocity, etc., by means of direction. In fact is so common that most probably the same is used in robotics.

I've actually googled it, (http://commons.bcit.ca/math/examples/robotics/linear_algebra/) and in here you can see they use algebra to handle several different coordinate systems in the same machine.

@Sandra,

Try this !

Arcmap - Add point layer - Arctoolbox - Analysis tools - Proximity - Point Distance - Add your point layer into "input and near" boxes - define your output location for saving the the .dbf file - click OK.

You can open your .dbf file using excel.

Hope it helps

Raoof