Vegetation Mapping - Science topic

Greetings: How can I performe the Tasseled Cap Transformation in IDRISI Selva for Landsat 8 Images? because the only options available for that are :MSS, TM & ETM+, and its showing the attached error when I select ETM+

I have field data for random samples from a specific forest, represented by vegetal surveys, where each vegetal survey contains one, two or three dominant vegetal species with geographic coordinates. How can I create a vegetation map using these data on GEE?

I participate in an international research project focusing on the relationship of happiness feelings to environmental conditions. Our hypothesis is that happiness may be valued more in regions with favorable environmental conditions. Among other variables we would like to examine the length of the vegetation period in several world regions. Therefore we are looking for data on the different vegetation period arount the globe that can be processed numerically (all our other variables are processed in SPSS). I found some databases but they are either local or employ a formats that are not accessible to most psychologists. I would appreciate any suggestions about how to find global vegetation data.

I have the vegetation map (raster) as well as the DEM (raster) of a study area. I want to add elevation information to the vegetation map and create a 3D map of the study area using ARCGIS/ARCSCENE 10.8.2. I have seen this done using ARCGIS Pro. However, I am unable to complete the task using ARCGIS 10.8.2. Any suggestions?

I am working on Forest Canopy Density. There is a parameter called "Scaled Shadow Index(SSI)" while computing Forest canopy density. In most of the papers I found that, SSI has been calculated by "Linearly Transforming" Shadow Index. I have computed the Shadow Index. But i am not getting the idea to compute Scaled Shadow Index. Kindly help me out. Moreover, If I am using Landsat 5 and 8 Surface Reflectance Image for FCD Mapping and as the Reflectance value ranges from 0 to 1, is it still mandatory to normalize these Surface Reflectance data before calculating Vegetation Indices?

Hi, I want to realize a project of monitoring of Forest Health status on a large scale in Europe. But I don't are sure of the index that can be better to use for this task. maybe the NDVI is one of the most popular, but I have read something about the NDWI and EVI. What do you think about the better index to assess the health status or the decline of the vegetation canopy of the European forest?

Help me, thank you very much

I would like to get vegetation type details in the MDB and the Murrumbidgee River catchment area to refer it for satellite image analysis. Please also refer any relevant articles. Thank you all in advance.

Hello everyone

I am currently a master student in Nevşehir Hacı Bektaş Veli University, Department of Geography, studying Physical Geography. My main areas of expertise are; İn additıon to geographical analysis, plant geography, data mining, map reduce and hadoop systems, land planning, plant taxonomy, I have been working on social media analytics and social media applications and analysis, social media and geography education in scientific and technical terms. But my main focus is on "Data Mining and Plant Geography modeling". I have a technical research article on this topic in my Research gate research account as full text. Study name "Creatıon of Plant Geography Databases Wıth The Map Reduce Modelıng ın The Clusterıng of the Large Geographıcal Data Sets". This study my Map Reduce and Hadoop systems and algorithms addition to data mining, GIS, plant geography research methods - techniques and various international plant databases, taking advantage of biological databases in Turkey and vegetatıon carried out ın the world, plant geography and so on. I tried to develop a new database model with this latest work that will contribute to the fields. In conclusion, I would especially like to listen and take advantage of the ideas and opinions of my colleagues and teachers working on data mining and geography, plant geography or vegetation, especially among geographers. Thanks to everyone who contributed in advance. Sincereley.

I have obtained a second original report of the CARAP 1980: Countrywide animal and range assessment project, Botswana. DHV/ITC, Amersfoort. 7 vols + maps. The maps include vegetation, wildlife and San distribution maps, valuable for environmental change studies.

I am willing to send the volumes to an interested researcher, archivist or institution in Botswana, Namibia or South Africa in that order of priority.

I am looking for a database reporting on vegetation cover/canopy cover of Asian and European cities. Any help will be much appreciated.

I have searched lots of articles and ducuments, but none of them could get me to know the conversion formula or extract formula of Flag value. Since NDVI3g is the latest version, I assume the old version document is no longer match it. I tried the formula :

(percentile)-floor(double(percentile)*0.1)*10, but it gave me unreasonable values such as 8 or 9 . The flag values are supposed to be 0,1,2 for NDVI3g V1.0.

Could you let me know the correct formula to extract Flag value?

I am trying perform a forest fire fuel analysis using ENVI software. I have defined the wavelenghts.

I am stilling getting an error notification : required indices for vegetation mapping not present.

How can I calculate the Extent of Occurrence (EOO) and Area of Occupancy (AOO) for a threatened plant taxa? I have GPS information for the species. How to estimate EOO and AOO

Dear Researchers,

I want to detect vegetation changes in the permafrost areas and I downloaded AVHRR GIMMS NDVI3g images from this website.

I tried to get some guide from google and youtube how to use these images for vegetation change detection and I did not find any helpful information.

Please share some healthy links and info in this context using the mentioned data sets.

Thanks in Advance.

Regards

Naveed Ahmed.

Field inventory method for above ground biomass calculation for Uttarakhand Himalaya?

Despite Germany is probably the one country in the world where the largest number of vegetation plots (phytosociological relevés) have been sampled, their mobilisation in large vegetation-plot databases is lagging far behind other European countries. This relatively poor electronic data coverage from Germany impedes European studies, e.g. on diversity patterns and their drivers, niches of plant species and assembly rules of plant communities as well as broad-scale consisten vegetation classification, which otherwise are now well possible through the 1.3 million plots stored in the European Vegetation Archive (EVA).

GrassVeg.DE is a new collaborative vegetation-plot database organised by German members of the Eurasian Dry Grassland Group (EDGG) and has just joined the EVA consortium. GrassVeg.DE aims at making grassland relevés from Germany available for fundamental and applied research, both within Germany and internationally, while ensuring that data providers get proper credit and benefit. We collect relevés of grasslands and herblands in the widest sense, i.e. everything except forest, shrubland, segetal and aquatic communities. Phytosociologically, the scope mainly refers to the classes Festuco-Brometea, Koelerio-Corynephoretea (including Sedo-Scleranthetea), Violetea calaminariae, Molinio-Arrhenatheretea, Juncetea maritimi (including Saginetea maritimae), Juncetea trifidi, Elyno-Seslerietea, Carici-Kobresietea, Calluno-Ulicetea (including Nardetea strictae), Loiseleurio-Vaccinetea, Salicetea herbaceae, Trifolio-Geranietea  (including Melampyro-Holcetea), Artemisietea vulgaris (incl. Galio-Urticetea) and Mulgedio-Aconitetea from the territory of Germany. Plot observations from other vegetation classes can be included as well if they form a minor part of a certain contribution.

Contributing data to GrassVeg.DE (and thus to the European and global megadatabases "EVA" and "sPlot"), ensures that your valuable plot data are permanently safeguarded for science, that you will get invitations for co-authorship and citations, while at the same time you become entitled to propose own research projects using the whole GrassVeg.DE and EVA database (and in the future also the sPlot database). That way you can get more out of your data, whether published or unpublished.

The rights and obligations of data contributors and data users of GrassVeg.DE are regulated by Bylaws that ensure a fair balance between both groups. Anyway, data use is restricted to data contributors. GrassVeg.DE is governed by a Custodian and a Deputy Custodian and a Governing Board, elected by the Consortium members (those who contributed the data). You find more information about GrassVeg.DE at our homepage: http://www.bayceer.uni-bayreuth.de/ecoinformatics/en/forschung/gru/html.php?id_obj=139259

If you are interested to join with your data, please contact me (juergen.dengler@uni-bayreuth.de),

Jürgen Dengler

(GrassVeg.DE Custodian)

During a study on the effect of treated sewage effluents on plant cover and soil, I encountered a difficulty in the precise interpretation of the )CCA) diagram, illustrating the relationship between the abundant species and the vegetation characteristics studied for the six sites examined.

From the attached diagram, it looks that all the species examined possess a negative correlation with Suaeda sp. What can we conclude from this observation.

Thank you

I have a lidar file with 5 million points for a forest region in America . I must calculate DEM . for this purpose I have to filter the other object except ground but I don't know how . then for the mature trees I must draw crest elevation . can anybody give me any information about these?

I'm analysing the home ranges of a monkey species. I need to calculate the vegetation cover in the home range of monkeys. I'm using QGIS as my mapping software. 

I am currently studying vegetation changes over Savannas in Africa from 1982 to 2011, and I need to get the Land Cover Map that includes Savannas. MODIS has but its only from 2001 to recent and AVHRR classifications do not explain savanna regions in Africa 

Dear All,

I cannot delete the vegetation (trees) which I have added in the Envi-met project area. I am using the red brackets, but they do not remove the vegetation. Is there another way to delete it?

Thank you,

Stella

Dear all, once I accidentally came across a map (a gradient map in green color) showing the intensity of vegetation ecology studies / or may be it was showing availability of vegetation survey data throughout the world. Now I need this map to show the regions of vegetation data gaps across the globe, but unfortunately I do not exactly know the source of that map. So, I would be grateful, if someone could provide me the source of such a map, which shows the vegetation data gap/or intensity of vegetation survey/ or vegetation data availability  across the globe. Thanks in advance!

in an equation for emissivity  calculation  i should to determine the value which is exactly boundary between soil and vegetation  and the value which is placed between mixture pixels(soil+vegetation) and full vegetation ones, on NDVI image using histogram.

I am currently looking at the dissappearance of wetlands/marshlands in and around Chennai. What would be the best month to get a good look at the vegetation development. December/january after the north east monsoon has hit the area?

Kind regards

Vegetation index as i know is used for estimation with the help of field data, but how are they used in spectral enhancement preprocessing of Spectral image?

I plan to perform an object-based land-cover classification for current vegetation types. The main goal is to create a vegetation map. I am considering to make an object based image segmentation based on 0.2 m orthopfotos and then do the classification step in combination with satellite imagery (because there are more sectral bands).

I worked out a legend (interpretation key) for my classification. I have ground control points of several field surveys. However, I don’t have such data for all classes in my classification key. What would be a general solution to compensate this problem? Is it common to replenish the lack of data simple by image interpretation (for example, CIR)?

I have available Orthophotos (also CIR), satellite imagery (in most cases preprocessed), reference data (LULC and vegetation field data)

Software: eCognition, Erdas imagine, ArcGIS, QGIS,

I know this question is probably too broad and may be difficult to answer. As a beginner I'm interested in basic steps to achieve an acceptable result. What tips can you give for crating the interpretation key and for designing a workflow? I'm also looking for initial literature for this topic (recent rewiew papers, methodology).

I try to study the vegetation change using NDVI from landsat 7 and landsat 8 wherein the maximum NDVI value for NDVI from landsat 7 is 0.8 whereas for the NDVI from Landsat 8 is only 0.6 but practically there is not change in vegetation health or cover. I am using the same month data and working directly on DN values only. how to deal with this inconsistency?

To separate the vegetated area from non-vegetated area we are supposed to use empirically determined threshold value. So how to determine threshold value empirically?

Anyone knows some SAV (submerged aquatic vegetation) discrimination algorithms/methods using remote sensing data? Interested especially in Cystoseira and Zostera, but also in Ulva, Ceramium and Cladophora

Our research project is looking to do a classification of vegetation in a dynamic environment. The environment is an annually flooded floodplain and the dominant vegetation types are annual and perennial grasses. The aim is to create distribution maps of the different grasses at different temporal points during the flood cycle. We have some ground-truthed transect data but would like to extrapolate that to the whole floodplain. The goal is to describe changes in the quality and quantity of vegetation over the years for the whole floodplain and link these to changes in the flooding patters.

We are looking for any guidance on: (i) the methodological approach: (ii) the remote sensing data (we are currently using Landsat); and (iii) how to deal with the potential dynamics in the relationship between the annual growth cycle and spectral signature of the grasses in the floodplain.

we want to know step wise methodology of LAI.

How can I perform object based classification for tree density mapping using world view 2 images?

Hi.

I have developed an index of suitability for a specified land use activity based on a spatial multi criteria analysis approach (continuous raster dataset with values 0-255); additionally, we have empirical data showing where this activity presently occurs and the intensity at which it occurs (present use) (also normalised continuous raster 0-255). The assumption is that this virtual space has not yet been fully exploited, either spatially or in terms of intensity, for this activity. Given this assumption, the problem is how to statistically describe the agreement (or lack of) between the empirical (present use) and predicted suitability datasets? This seems identical to an invasive species habitat modelling problem, where comprehensive presence and absence data may not available (where a habitat has not yet been fully exploited) to evaluate model output (false presences/absences (?)) - any ideas how I could deal with this in my example?

Thanks in advance for any advice.

I'm modeling labor productivity on farm sites spread widely across the US, and I would like to include NDVI (or another vegetation index) as a predictor variable. I'm wondering if there is month during the growing season that makes the most sense for comparing across disparate climate types. Thank you for any suggestions! 

Please exclude Atriplex and Salsola species

1. To use all species available for such mapping, even the correlated species ones, because they have slightly different distributions. Also, it seems to me that using all species available for such mapping will force a cluster in areas with the same species composition, and this is the objective of the mapping.

Or

2. Exclude the correlated species and use just one of them.

If someone has an opinion about that, I would appreciate.

I am hooked up on the second step：

The first step is:

to build up a standard grid frame with vector format and each grid

cell has 1 km width and 1 km length and each cell is identified with

a unique ID. The standard grid frame was generated by FISHNET

module in ArcGIS and geo-referenced to the basin boundary map

at the scale of 1:10,000.

The second step is:

 to use this frame to intersect with the land use data to group the input information into each cell. Finally, the area percentage vector data is transformed into a grid raster to identify the conversion direction and intensification That is what i actually want to do, i understand the first step. I am hooked up on the second step

I have a land use map 5m resolution with trees as one of the classes. Can I use this information as an indicator of tree density? Could I translate tree cover percentage per ha into an estimation of the number of trees per ha? I´m researching in West Africa, but due to the lack of the data, I would also appreciate methods for other savannas of the world.

Are there any good methods (relatively accurate) to detect and map water under vegetation using satellite images?

Dear All,

I am trying to calculate Enhance Vegetation Index (EVI) using LANDSAT 8 images in ERDAS model builder and Arc GIS. But after processing i getting the totally black image. I not able to understand where goes wrong.

For Enhance Vegetation Index (EVI) calculation i am using input as a direct extract image from the rar file.

I have tried to FLOAT (Single, Double) data type as output file but problem still same

So request to all please provide your valuable guidance to complete my task.

Below i have attached model and output screen shot.

I am using ERDAS 2013 and ARC 10.1.

Thank you

Shouvik Jha 

Dear all,

I have orthophoto and DEM. By these data can I classify different types of vegetation using Remote Sensing or GIS?

Many thanks for helping.

I would like to ask about how can i convert shrub cover area percentage (%) in a plot to a density (individu/area)? Although it's hard to obtain individual data of a shrub in field.

Hi all,

I am looking for more information about " Use game engine for exploring vegetation change".

I will be thankful for any help..

My Regard

I am experimenting with some datasets at hand and would like to try REVEALS (Regional Estimates of VEgetation Abundance from Large Sites) model (Sugita 2007) that provides parameters for landscape openness. I extensively searched the internet to see how REVEALS could be accessed, but haven't found much except that it is run as part of the POLLSCAPE which used to be accessed at http://www.geog.ucl.ac.uk/ (according to Sugita 2007 Encyclopedia of Quaternary Science); but now the website has no information about any of the above. 

Can I anyone please help me in terms of identifying the source of this model and explaining the initial steps of its application? 

I have MODIS NDVI data of last year. I want to do crop type classification. Can anyone please suggest me how to do crop type classification by using these data???

I need some ground measured green vegetation fraction data for validation purpose. I searched online and find other vegetation measurements, such as LAI, FPAR and GPP. But no green vegetation fraction data available.

I was thinking to use PhenoCam images to derive green vegetation fraction. Those images are taken from towers at a off-nadir view angles, which is unkown. Since vegetation fraction is dependent on view angles, it is difficult to derive vegetation fraction without view angles.

I have 25 data points which represents woody plants richness. Each one summarizes the value of this variable for the same number of 2mx2m quadrats. Field design was 50mx2m vegetation transects.

Is it meaningful to explore spatial autocorrelation using with Moran's I and variograms?

I need to estimate how canopy or tree density changed over time? further between QGIS and ARCGIS which is better?

I am trying to assess the impact of land-use change in Germany on the ecosystem, and I need values of Potential natural vegetation (PNV) for different indicators, such as soil organic matter, cation exchange capacity, biomass production ... etc.?

chlorophyll fluorescence, remote sensing of vegetation, plant science 

Is it advisable to compare BI (Bare soil Index) with the brightness component of Tasseled cap?

I have downloaded the EVI product directly from USGS, what i have seen in their Guide, is the difference in the formula for the calculation of it.

What could be the reason behind? How does it effect the output?

I have tasseled cap component of Brightness (TBC) and i want to calculate its accuracy with NDVI in ENI using Confusion matrix. How do i find those values from NDVI that are comparable to TBC.

Although, i have used OTSU in MATLAB on NDVI, but i'm confused as NDVI mainly defines vegetation.

Hi all,

I am trying to produce an enhanced MODIS image to detect dust sources. However, this is my first time to deal with MODIS images, and I am on my own. I want to apply the following algorithms from Miller (2003) (Image attached).

My question is how can i set the "Normalization Bounds" especially for L2 in the image above? And for L4 how can I apply the expression in ArcMap?

Regards,

Tarek

I have downloaded Landsat 7 surface reflectance products, How to use that in the calculation of EVI? and What should be the range of values in output

Could you please tell me in the classification methods, such as k-fold cross-validation, Does the repeatability of dataset have a positive role in the better performance of results?

I am searching for a method to conduct spatial vegetation analysis that combines census data from sampled quadrats (regularly spaced grid, which is not contiguous) with a defined point pattern (xy-locations of this complete pattern exists). The possibility to include environmental heterogeneity into the model/analyis is desired. Most point pattern analyses do not seem suitable for this. Any ideas or pointers towards helpful articles?

Please, I am trying to reclassify my NDVI layer into NDVIsoil and NDVIveg so as to calculate my fractional vegetation cover.

I would appreciate help. Thanks

If I wanted to detect a small (up to 3 m in height) invasive plant species growing within an open canopy pine forest (around 50% canopy closure), and differentiate it from other plant species (small tree species, for example), which of the available remotely sensed imagery products would you recommend? Phenology of the plant suggests that it emerges in April, flowers in August, then goes dormant in November. I may have LiDAR, but it is from one point in time - not sure the month yet. We may collect field measurement within sample plots.

Hello

How and using which software can we depict  raster base maps data as a graph or as a distribution curve?

For example two C factor maps (vegetation cover map) of two different dates.

There has been attached an example.

Thanks

how to differentiate  vegetation using remote sensing?

Dear all, I want to discuss how much error caused by central projection of extracting vegetation cover using the method of photograph.  As we all known, the definition of vegetation cover points that parallel projection is necessary for assessing veg cover. However, central projection will bring error for evaluating veg cover. Could anyone recommend some papers about this topic. Thanks a lot.

Using Landsat Images of 2002 and 2014, I tried to investigate the land-cover change between the period of 2002 and 2014. A land cover classification using same images reveals that Urban land-cover in 2002 has increased by more than 35% in 2014 and vegetation cover has reduced significantly. But when I calculated the mean NDVI values by each land cover, the mean values for 2002 are lower/smaller than the mean values for 2014. Ordinarily, the reverse should be the case! Does anybody have an idea why this is so? Am really curious.

intend to measure the area occupied by a greeneries at a study site. 

Hi

Which is the the most recent techniques or methods for Modeling of land use changes?

area: 110 square kilometers ,

Land use: agriculture, urban, pasture, forest, water, desert

Data Source: Maps and satellite images, etc.

Extent of occurrence (EOO) too.

I have used PC-ord and PATTRN for some time ago when preparing my PhD Thesis. I have also tried CANOCO and TWINSPAN. I'm starting to analyse vegetation data of small archipelago. The number of species is 555 and the number of islands is 30.

I will also be using the traditional statistical programs such as Statistics or Sas.

Certainly we have come a long ways since the old days of mapping. Right now 3-D mapping is becoming very popular in many disciplines, so why not in vegetation mapping.

I am planning to assess the impacts of climatic (rainfall, temperature, snow cover) and non-climatic (human and livestock population) factors to vegetation cover in a river basin in Nepal. I saw few papers, some are using PCA and some partial correlation method for identifying the relations? Any suggestions?

Presently available literatures, say that there are 3 indices as follows :

AVI - Advanced Vegetation Index - {(B4+1)(256-B3)(B4-B3)}^(1/3)

S I - Shadow Index - {(256-B2)(256-B3)}^(1/2)

B I - Bare soil index - {B4+B2-B3} / {B4+B2-B3}

but none of them are saying about how these three indices are integrated to get the forest canopy density map. Can anyone help me with this ??? 

Also, In one of the literature it is mentioned that , 

Forest Canopy Density = {sqrt (VD+SSI+1)}-1

but how to determine SSI & VD ????

ie., Scaled shadow index and Vegetation Density ????

Any simple tool for analysing effects of development on vegetation or forest areas?

such as "FragStats" programe.

As is well known, remote sensing is a critical tool for crop monitoring and yield estimation. And typically Normalized Difference Vegetation Index (NDVI) is used as an input to build a regression model or the like to relate a previous productivity of an area and in turn to predict the future yields. Indeed, this regression model was improved by several factors such as soil, rainfall, fertilizers, etc..

What are the main factors that control the yield estimation of crops by relating the NDVI to a previous a area of known productivity?

Lineaments are extractable linear features from satellite and aerial images, which are some how correlated with geological structures such as faults. During the interpretation of the lineaments we may encounter an alignment of the natural vegetation in the dry season in a straight line.

Does the alignment of the natural vegetation in the dry season could be interpreted as a lineament?