Science topic

Vegetation Mapping - Science topic

Explore the latest questions and answers in Vegetation Mapping, and find Vegetation Mapping experts.
Questions related to Vegetation Mapping
  • asked a question related to Vegetation Mapping
Question
3 answers
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?
Relevant answer
The density clustering with wavelength clustering algorithms and Clustering by Wavelet Analysis may help your work
  • asked a question related to Vegetation Mapping
Question
6 answers
I want to use the daily GPP data from "Fluxnet 2015 data". However, in FLX there are two kinds of daily GPP ("GPP_DT_VUT_MEAN" & "GPP_NT_VUT_MEAN"). I want to know the difference between "Daytime partitioning method" with "from Nighttime partitioning method". And how can I get the daily GPP value. Thank you very much.
  • GPP_DT_VUT (Gross Primary Production, from Daytime partitioning method)
  • GPP_NT_VUT(Gross Primary Production, from Nighttime partitioning method)
I don't know how to get the daily GPP. Thank you very much.
Relevant answer
Answer
There are 40 GPP estimates in FLUXNET2015 dataset for a site. DT (daytime) and NT (nighttime) are two NEE partitioning methods, and CUT and VUT are USTAR (friction velocity) thresholds. You can download daily GPP from https://fluxnet.org/data/fluxnet2015-dataset/. For all GPP estimates, XX_REF is the most representative version according to Pastorello et al. (2020).
  • asked a question related to Vegetation Mapping
Question
4 answers
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
Relevant answer
Answer
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
  • asked a question related to Vegetation Mapping
Question
4 answers
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.
Relevant answer
Answer
The data source for vegetation mapping depends on size (land mass) of the study arearea, as well as the scale of the map. Some of the data sources for processing vegetation maps are listed below
MODIS NDVI images
LANDSAT TM, ETM+ and OLI images
SENTINEL-2 images
SPOT VEGETATION
ASTER L1T
  • asked a question related to Vegetation Mapping
Question
4 answers
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.
  • asked a question related to Vegetation Mapping
Question
22 answers
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.
Relevant answer
Answer
Dear Elizabeth
Thanks for contacting me. The map set of the report is gem as a historical record of the Kalahari. I have donated the set to the ORI in Maun where it is now accessible to you and other interested persons. My hope is that the wildlife and habitat data contained in the maps will be digitised in a GIS and used together with subsequent wildlife censuses and inventories for a landscape history of the Kalahari, relevant for wildlife conservation and tourism.
For a concrete, ambitious project (proposal), I would be delighted to support you with advice and author information as well as second copy, if working from Gaborone with maps in Maun would be a handicap. Please appreciate, the Kalahari and its issues are well-known to me as Namibian SEA officer in the current decade. From my affiliation, you may take my ongoing passion for wildlife and society interfaces.
Please be welcome to consider the RG account as my scientific "facebook".
  • asked a question related to Vegetation Mapping
Question
6 answers
I am looking for a database reporting on vegetation cover/canopy cover of Asian and European cities. Any help will be much appreciated.
Relevant answer
Answer
If you need a database covering cities in both Europe and Asia, that will be hard to find, I think. If you need a sample of some cities, here is a start for The Netherlands: www.boomregister.nl, containing all trees.
  • asked a question related to Vegetation Mapping
Question
4 answers
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?
Relevant answer
Answer
Thank you so much Roberto Filgueiras . I will have a look at this package.
  • asked a question related to Vegetation Mapping
Question
3 answers
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.
Relevant answer
Answer
Hi Omid,
You need to create vegetation indexes with the Spectral Indicies tool.
  • asked a question related to Vegetation Mapping
Question
14 answers
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
Relevant answer
Answer
Quick and easy: http://geocat.kew.org/
  • asked a question related to Vegetation Mapping
Question
3 answers
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.
Relevant answer
Answer
Monitoring NDVI change is an active topic in remote sensing. It maybe include the following concepts.
1. NDVI trend analysis to analyze the NDVI linear trend in the long-term period. Usally, it is represented by the yearly increasing rate.
Suggested method: Theil-Sen median
2. Significant test for trend analysis to determine whether the NDVI trend by Theil-Sen median is statistically significant or not.
Suggested method: Mann-Kendall Test
3. Continuity analysis of NDVI time series to take a measurement of long-term memory of NDVI time series. For example, if NDVI is increasing (by Theil-Sen median) significantly (by Mann-Kendall Test) during 2000-2018, does it keep increasing trend, or oppositely turn to decrease, or fluctuate unpredictably, in future?
Suggested method: Hurst exponent.
Three methods aforementioned can be integrated and get some types of combinations about NDVI status:
I. decreasing significantly (severely) & remaining in future
II. decreasing insignificantly (slightly) & remaining in future
III. unchanged stably & remaining in future
IV. increasing insignificantly (slightly) & remaining in future
V. increasing significantly (markedly) & remaining in future
VI. unpredictable in future
More methods about NDVI time series analysis can be found in the attached.
In addition, you can use more complex method such as EOF(Empirical Orthogonal Function) analysis, also called PCA(Principal components analysis), which determine the spatial and temporal characteristics of time series and often used in climate change research.
Good luck!
  • asked a question related to Vegetation Mapping
Question
10 answers
Field inventory method for above ground biomass calculation for Uttarakhand Himalaya?
Relevant answer
Answer
Find here useful another manual for your purpose.
Sorry for too much late!
  • asked a question related to Vegetation Mapping
Question
3 answers
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)
Relevant answer
Answer
SILAM (Finland) - data for all Europe including Germany аrom various stations and data of satellite remote sensing of the Earth (grass cover)
I cooperate with them
  • asked a question related to Vegetation Mapping
Question
5 answers
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
Relevant answer
Answer
In my oppinion, the CCA results can be evaluated in the following sentences 
Atriplex seems to be an indicator species illustated the higher biodiversity sites. On this context, second species is Haloxylo.  Haloxylo may be another indicator species for higher biodiversity sites
However those species are indicators for higher sites in the case of using I. if u prefer to use 1-D, then Caynodon and Tamarix seems to be better indicator species for diversity.
S, α and D are located in the immediate site of Pulicari in the ordination diagram. It means that Pulicari refers to the sites where species richness is high  but diversity seems to be low. In this case, evenness values should be low in this site of the diagram . With regard to Suadea v, I guess that it is found in the sites where species richness and diversity is low
  • asked a question related to Vegetation Mapping
Question
8 answers
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?
Relevant answer
If you want to understand very well you have to start reading ground filtering algorithms. Every software has their own algorithms and probably with some differences in the final result.
LASTools is a very straightforward application with a friendly interface. Even if you would  like to change some parameters concerning the terrain condition, take it done, (tile the data for covering more area). In case of paid software like ENVI you could get everything at one click, but if you want to modify, knowledge for IDL is needed or pay more :) 
  • asked a question related to Vegetation Mapping
Question
9 answers
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. 
Relevant answer
Answer
1- download modis-ndvi image of your area
2- use "extract by mask" analysis in arcgis to crop the raster of modis
3- use Raster to point analysis
4- change the pixel size to make the rest of the procedure easier
5- use spatial join with the polygon feature of your study area
6- now you can extract a value for each polygon or do any manipulations you want
GL
  • asked a question related to Vegetation Mapping
Question
4 answers
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 
Relevant answer
Answer
@Ron S Mahabir, Yea you can post it to other forums for answers
  • asked a question related to Vegetation Mapping
Question
5 answers
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
Relevant answer
Answer
I found the solution. You must right click on the tree.
  • asked a question related to Vegetation Mapping
Question
13 answers
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!
Relevant answer
Answer
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).
  • asked a question related to Vegetation Mapping
Question
3 answers
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.
Relevant answer
Answer
Which equation are you using to calculate emissivity? Do you have any reference that we can have a look at? Are you using emissivity for Land Surface Temperature (LST) estimation?
If you mean the NDVI based emissivity which Sobrino et al. (2004) talks about in his original research paper (link attached), they use NDVI < 0.2 as bare soil, NDVI > 0.5 as fully vegetated and NDVI values between 0.2 and 0.5 as a mixture of both. However, you should keep in mind that this is specific to a particular location and it may be different for your study area. In any case, have a look at the paper.
  • asked a question related to Vegetation Mapping
Question
5 answers
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?
Relevant answer
Answer
I'll second what Atiqa Khan says. Vegetation indices are band ratios which by their very nature enhance the information content of imagery.  Just looking at an NDVI image you already know which areas are likely vegetated. That's what is meant by spectral enhancement. Because of this  enhancement aspect, you can classify a stand alone NDVI image by determining thresholds or you can add it as an extra input band when classifying multispectral. 
  • asked a question related to Vegetation Mapping
Question
8 answers
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).
Relevant answer
Answer
Hi Patrick,
as an initial introduction, I think "Remote Sensing and GIS for Ecologists using Open Source Software" is a good textbook with also a nice online guide. Please find it here. http://book.ecosens.org/
Note that eCognition also works with LIDAR data or LIDAR-based raster data products, this might be a good way of maximizing the information content of your input data. However, I also warn that unless your classes are very basic and can be recognized by aerial photo interpretation, it will be essential that you collect field data for all the categories you use. Field data has to be well distributed in space across your site, and balanced across categories (approximately the same amount of reference pixels for each class). Maybe if it is a protected area, the already existing local management plan or some biodiversity monitoring or Natura 2000 monitoring, or an existing vegetation map can also be a source of reference data.
If you revert to using manual image interpretation to generate reference data from the same images that you want to classify, you lose the independence of your training and validation data. Any issues specific to your image dataset (camera problems, timing with respect to phenology, atmospheric issues) would also influence your training and validation data then. Reviewers often reject such studies and ask for independent training and validation data, whether from interpretation of remote sensing or from the field.
  • asked a question related to Vegetation Mapping
Question
10 answers
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?
Relevant answer
Answer
Dear Vincent, I suggest that you perform dual-sensor NDVI comparison after radiometric correction (DN to top-of-atmospheric reflectance) or atmospheric correction (to surface reflectance), as suggested by Elif and Prof. Chavez. Recently, I have compared NDVI data from collocated ETM+ and ALI (one of the near-infrared bands resembles the OLI near-infrared band) and concluded that quadratic equation is better to correct for NDVI differences. Linear equation performs poorly for low NDVI values.
  • asked a question related to Vegetation Mapping
Question
3 answers
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?
Relevant answer
Answer
just go through the attachment in these paper they have calculated threshold value empirically 
  • asked a question related to Vegetation Mapping
Question
1 answer
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
Relevant answer
Answer
  • asked a question related to Vegetation Mapping
Question
7 answers
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.
Relevant answer
Answer
The area may not be wetland, but you might look into the wetland mapping by US FWS conducted using Cowardin et al classification, soil series maps of floodplain area may or may not be helpful depending on the accuracy.  Is flooding the only disturbance, or has farming, burning or other activities removed and kept the bottomland trees out?  Are rushes and sedges also present as they are indicators of wetness.  Also water table detail such as from well log, or at least normal water table as indicated by the hydric soil indicators.  The USFS has an inventory method used for groundwater dependent ecosystems that may also apply if looking into methods of sampling, describing, etc.  Is the floodplain cover type natural for conditions, or maintained in grassy condition for habitat or other benefit?  These are some of the questions you may want to consider.  Floodplains are not necessarily uniform, as sediment type, wetness, properties can vary across the floodplain, and may explain some of the vegetation differences, so collaborating with a soil scientist may help.
  • asked a question related to Vegetation Mapping
Question
5 answers
we want to know step wise methodology of LAI.
Relevant answer
Answer
I have experience to determine LAI using surface energy balance method based on multi spectral satellite imaginary. Please have a look at these researchgate links.
  • asked a question related to Vegetation Mapping
Question
10 answers
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
Relevant answer
Answer
There is no unique answer to your question because best time will depend on the growth cycle of the vegetation. As far as marshland is concerned they are by and large perennial vegetation. It may be advisable to select a time when maximum greenness is expected and that is after monsoon in January-February in Chennai.
  • asked a question related to Vegetation Mapping
Question
4 answers
How can I perform object based classification for tree density mapping using world view 2 images?
  • asked a question related to Vegetation Mapping
Question
7 answers
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.
Relevant answer
Answer
Hi Craig, you have to evaluate predicted land use suitability versus empirical data, can take the diffrences about de hectares or kilometers and calculate the accuracy in %.
Regards
  • asked a question related to Vegetation Mapping
Question
5 answers
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! 
Relevant answer
Answer
Hi Rafter,
One tool to detect onset and other parameters of vegetation seasonality, as suggested by Wietske, is TimeSat: http://web.nateko.lu.se/timesat/timesat.asp
As in your study you are using isolated farm sites it is probably better and more simpler to use an available map of vegetation phenology (e.g. http://onlinelibrary.wiley.com/doi/10.1029/2006JG000217/epdf) to choose the month when NDVI should be compared (for example the one corresponding to green peak), instead of collecting the NDVI data and running TimeSat or other software.
All the best,
João
  • asked a question related to Vegetation Mapping
Question
9 answers
Please exclude Atriplex and Salsola species
Relevant answer
Answer
If you could attach to your question, a checklist of all of the native plants that occur in the area you are working in, then I could suggest the top-10 species that would be easiest to try.  Also if you could post a separate list of the exotic plants that are in the area, that would be helpful also.  
Consider ONLY using local native material and exclude any exotics, because they just help destroy whatever remain of the original local ecosystem.  I am doing the same thing right now, that you can see at http://www.ecoseeds.com/kims.html
  • asked a question related to Vegetation Mapping
Question
7 answers
Relevant answer
Answer
 I'm agree with doctor Boori. It's convenient to apply NDVI.
  • asked a question related to Vegetation Mapping
Question
5 answers
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.
Relevant answer
Answer
I think you would want to go with the first approach.  A super helpful paper on the issue that I enjoyed (and which you may have seen already?) is:  Kreft H, Jetz W. 2010. A framework for delineating biogeographical regions based on species distributions. Journal of Biogeography 37:2029-53.
  • asked a question related to Vegetation Mapping
Question
4 answers
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
Relevant answer
Answer
Hi Majid,
if you have your land use as a raster and fishnet (zones) as polygons then an easy way how to summarize the data in the cells is to use Zonal Statistics Tools in ArcGIS. The inputs are your raster and zones, i.e. fishnet cells with unique ID, and you can even choose a method (statistics type) to be computed. 
Regards
Lukas
  • asked a question related to Vegetation Mapping
Question
6 answers
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.
Relevant answer
Answer
Hi Marcos,
Tree cover is not a good indicator of density particularly in the savanna areas. You can have 100% cover from 50cm high seedlings or 5m high trees. Density may vary with soil and particularly moisture condition. With increased tree density you will also find a change in tree form - this may affect your biomass estimate substantially. Unless you have already found the ref below you might want to follow up on the work of Prof. Nico Smit in South Africa, particularly about his work on the BECVOL program. (Smit, N. (2014). BECVOL 3: an expansion of the aboveground biomass quantification model for trees and shrubs to include the wood component. African Journal of Range & Forage Science, 31(2), 179–186. http://doi.org/10.2989/10220119.2013.866161).
I agree with Hein that you are unlikely to find a simple relationship. Do you have other info that might help you with a model?
  • asked a question related to Vegetation Mapping
Question
5 answers
Are there any good methods (relatively accurate) to detect and map water under vegetation using satellite images?
  • asked a question related to Vegetation Mapping
Question
8 answers
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 
Relevant answer
  • asked a question related to Vegetation Mapping
Question
10 answers
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.
Relevant answer
To answer your question, it is possible to model vegetation at your site using data from the orthophoto and DEM, however, it is best to have training sites with observed vegetation data to inform the model.
Image classification using statistical methods  based on spectral signatures (e.g., Maximum Likelihood Classification (MLC)) is probably the most appropriate method for your task. See the attached link for image classification analysis in ArcGIS.
However, I've used regression modeling methods such as Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR) to model single parameters in GIS. If you can determine vegetation community types (e.g., conifer forest, grassland, or deciduous forest) you may be able to model the suitability of the site to these communities one at a time.
Regardless of what sort of field-derived vegetation data you can gather, some sort of additional remotely sensed data would be very useful for your project. Not only can this data be used as a model component (predictor) in regression analysis for habitat suitability, but can also be used for traditional remote-sensing image classification (e.g., Maximum Likelihood Classification (MLC) etc.). You may have some luck using this method with just the orthophoto data (e.g., classify dark green pixels as trees, brown pixels as soil or rock, light green as grass etc.), but assessment for multiple spectra will likely be more useful/accurate. Different species of vegetation have unique and slightly different spectral signatures. If you can find suitable satellite scenes for your study area it may be possible to determine clusters of particular species as well. However, this again, would require some field work to get training data, or at very least, validation data to make sure your modeled product is accurate.
Depending on what software you have access to, their are many solutions for classifying vegetation from remotely sensed data. ArcGIS can manage several methods of classification. Also the Canadian software, PCI Geomatica is excellent for this type of analysis and other remote sensing data manipulation/analysis needs.
Regards,
Matt
  • asked a question related to Vegetation Mapping
Question
3 answers
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.
Relevant answer
Answer
I basically agree with my colleagues. First you must establish a relationship between the porcentje coverage and the number of individuals working on several plots of your study area, then relate coverage and density.
  • asked a question related to Vegetation Mapping
Question
4 answers
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
Relevant answer
Answer
Thank you Matheus. I will look.
  • asked a question related to Vegetation Mapping
Question
3 answers
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? 
Relevant answer
Answer
Hi Charuta,
currently, there are two "slightly" different implementation of REVEALS in R.
Second is available from Petr Kuneš GitHub site: https://github.com/petrkunes/LRA
HTH,
martin
  • asked a question related to Vegetation Mapping
Question
4 answers
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???
Relevant answer
Answer
 Hi 
The main aspect to point out is that you should use time series analysis since vegetations have different phenological behavior. 
A further more subtle point is that you should try to select pure training pixels for each class. To this end, select big farmlands/orchards, preferably the middle of polygon to get ride of mixing problem and MODIS pixel size issue!! 
There are different classifier and all them has their own advantages and disadvantages! In my view, SVM and RF  are better than other. You can get use of feature selection in order to reduce data dimensionality. In your case, some data date may be redundant and consequently reduce classification accuracy. 
In this way, you may draw acceptable results.
Good luck!
  • asked a question related to Vegetation Mapping
Question
2 answers
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.
Relevant answer
Answer
Dear Zhangyan,
Could you sharpen your question?
First of all, I assume you are looking for data on "fractional vegetation cover (FVC)" over land, which is a somewhat ill-defined concept, because it is highly dependent on spatial scale and resolution. Hence you should indicate what overall area you wish to cover, and how much spatial detail you need to have.
Second, you should specify the level of accuracy required. Specifically: What is the largest uncertainty in FVC you can tolerate? Without such a constraint, any answer will be acceptable.
Third, what exactly do you want to "validate"?
Satellite remote sensing techniques may be useful for your problem, but the specifics will depend on your answers to those questions.
Best regards, Michel.
  • asked a question related to Vegetation Mapping
Question
4 answers
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?
Relevant answer
Answer
There are quite a number of papers that have done that.  One of the links below discusses the different sampling designs.
  • asked a question related to Vegetation Mapping
Question
3 answers
I need to estimate how canopy or tree density changed over time? further between QGIS and ARCGIS which is better?
Relevant answer
Answer
Please refer to the following links for some useful resource for tree canopy density mapping using remotely sensed data.
As far as QGIS and ArcGIS are concerned, it really depends upon the scenario of your analysis. You may also consider using both of them in conjunction. Although, QGIS is open source and freely available while ArcGIS comes with different levels of user license.
Hope this helps!
  • asked a question related to Vegetation Mapping
Question
8 answers
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.?
Relevant answer
Answer
There are some natural grasslands, such as wet meadows and areas that burn too frequently to support forests.  Grasslands often have soils high in organic accumulation, unless overgrazed and eroded.  Alot may depend on how far back you go in time.  Your area has a wealth of history, but also land uses I would suppose. 
  • asked a question related to Vegetation Mapping
Question
3 answers
chlorophyll fluorescence, remote sensing of vegetation, plant science 
Relevant answer
Answer
I have used differential equations and kinetic Monte Carlo model to simulate ChlF.
  • asked a question related to Vegetation Mapping
Question
2 answers
Is it advisable to compare BI (Bare soil Index) with the brightness component of Tasseled cap?
Relevant answer
Answer
  • asked a question related to Vegetation Mapping
Question
3 answers
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?
Relevant answer
Answer
 These expressions are made for different sensors.
For the common expression see the links as an example
  • asked a question related to Vegetation Mapping
Question
5 answers
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.
Relevant answer
Answer
Your question is unclear.
Some suggestions:
Sure, NDVI was desighned as vegetation index. TCT Brightness correspond to land surface as a whole. It is completely different feature and used in some cases as a companion index in addition to other TCT features. If you mean TCT Greenness, you should use correlation coefficients for certain samples rather than confusion matrix.
As for TCT brightness itself, you may compare it with,say, panchromatic band of the same satellite sensor if it provide such band.
But I'm confused by the reason...
Maybe the article attached can help.
Good luck!
Maria
  • asked a question related to Vegetation Mapping
Question
2 answers
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
Relevant answer
Answer
 Yes Eric. I noticed that, it should've been "," instead of "?". Anyways, L4 is so easy, I was able to perform it, but I am stuck with the others (L1, L2, and L3), do you have any ideas?
P.S. You've got the old email address for the author, thanks though! I have his recent email, but I wanted to try everything I can before contacting the author.
Thanks! :)
  • asked a question related to Vegetation Mapping
Question
12 answers
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
Relevant answer
Answer
I agree with Paulo about the google search.  Or you may simply refer to the landsat documentation for details (EVI is on page 13).  See below.
  • asked a question related to Vegetation Mapping
Question
2 answers
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?
Relevant answer
Answer
Hi Tayebeh,
k-folds cross validation method is a common validation method that can be used to examine the overall performance of machine learning classifiers. In this method, the dataset is partitioned into k equal subsets. Approximately each subset contains the same proportions of different class labels. Of the k subsets, a single subset is retained as a testing set, and the remaining k-1 subsets as the training set. The cross validation is then repeated k times, until each one of the k subsets was applied exactly once as a testing set. The results can then be averaged to estimate a model predictive performance.
There is no guarantee that using this method will lead the classifiers to show their best performance, however, I think it will show the real capabilities of the classifiers as it uses the full dataset for training and testing (i.e., maximising the use of the dataset).
Hope this explanation will help you.
Kind regards
Ahmed
  • asked a question related to Vegetation Mapping
Question
5 answers
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?
Relevant answer
Answer
Dear all, thank you for your input - I have read the articles and thought about using nearest neighbour analysis before, but since the quadrats are not contiguous this seems impossible. Regarding geostatistics: This might be possible to look at some effects of scal, but I would like to include effdcts of the inhomogenous environment. I will try to improve the formulation of my question to illustrate the problem more precisely.
  • asked a question related to Vegetation Mapping
Question
5 answers
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
Relevant answer
Answer
Dear Ayodeji,
If you are just going to distinguish vegetated pixels from non-vegetated, you may use the threshholding method. Generally positive NDVI values correspong to vegetated pixels, and negative NDVIs correspond to non-vegetated pixels. In practice due to non-linear NDVI relationship with physical plant characteristics, differences in regional plant communities and various study objectives, one have to determine the threshhold values for the aims of certain work. This is not only to be based on NDVI values itself but also on field evaluation and validation data.
The following paper might be also useful
  • asked a question related to Vegetation Mapping
Question
9 answers
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.
Relevant answer
Answer
Dear Pete,
Before you get involved with a whole range of satellite data, some of which are going to be expensive, you may want to go to the field and acquire spectral (preferably hyperspectral) data from the pine forest, the soil and the invasive species. Those spectra should then be ingested into a suitable canopy reflectance model to establish whether there is any objective basis to distinguish between those species on that basis.
And once you are on the field, you may also want to acquire information on the structure of the plant canopies (tree height, crown diameter, DBH, etc.) While both types of vegetation may look "green" and have similar spectral profile, their geometric structure may be sufficiently different that their anisotropic response may be different, and perhaps a better lead to distinguishing between them. Here again, a good plant canopy model should be able to represent the BRDF of these canopies, and indicate a priori whether this might be a worthwhile approach to pursue.
Good luck. Michel.
  • asked a question related to Vegetation Mapping
Question
7 answers
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
Relevant answer
Answer
Dear Saeed,
Greetings and hope you are doing well.
If I understand your question correctly, you should follow these directions:
See the next snapshots.
  • asked a question related to Vegetation Mapping
Question
11 answers
how to differentiate  vegetation using remote sensing?
Relevant answer
Answer
It depends on what type and at what level you want to differentiate the vegetation? Depending on the similarity of the spectral reflectance patterns of the classes and the minimum mapping unit (smallest patches of vegetation type) you would decide on minimum spatial and spectral requirements. Based on those you select your sensor. For us working in wetlands (South Florida Everglades) for some projects Landsat is sufficient for other projects we use World View 2 data and sometimes we include LiDAR and aerial photography. 
  • asked a question related to Vegetation Mapping
Question
3 answers
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.
Relevant answer
Answer
Thanks for  your attached papers. Hayder.
Actually, I care more about no-metric cameras. And height of measurement is at 10~100m. Given that the lens is not far away from  surface objects, the error taken by central projection should be paid more attention.
  • asked a question related to Vegetation Mapping
Question
17 answers
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.
Relevant answer
Answer
A lot of very good suggestions and comments already made and I think that one, or several, of the issues mentioned is the source of your confusion.  Atmospheric and sun elevation corrections, time of year, wet or dry year / season, greened up at one time and not on the other date, and sensor differences (especially 8-bit vs 11-bit between Landsat 5 and Landsat 8) could all be contributing to the differences.  However, as mentioned you can not go by the AVERAGE of the NDVI to determine if there is more or less vegetation cover; this is more of a measure of 'HOW GREEN' the vegetation happens to be at the time of imaging.  My suggestion is to generate an image where a pixel is set to yes vegetated OR not vegetated and then count up the number of pixels in each land cover type.  This will give you the amount of AREA vegetated for each year; then you can use that to decide if there is more vegetation cover or not.
Good Luck,
Pat
  • asked a question related to Vegetation Mapping
Question
12 answers
intend to measure the area occupied by a greeneries at a study site. 
Relevant answer
Answer
I suppose it depends on the size of the site, and how much time and resources you have. I have seen this done many ways. 
1. Aerial photos. If you get a very high resolution photo, you can perhaps use software like Image J to do this. 
2. Random canopy cover "spotting." You could use a frame held at a fixed distance from your eyes, and estimate % canopy cover for each of a set number of random viewpoints, looking upwards. Then average these. 
3. Linear transects. Set a series of transects and then record when vegetation cover begins and ends along the line, and then use that to estimate % cover. 
Many other methods exist -- these are just some I have seen used in the field. 
Patrick.
  • asked a question related to Vegetation Mapping
Question
20 answers
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.
Relevant answer
Answer
Dear Zahra,
Most of the LULCC models available in literature are case specific. Hence, not of much use in other areas, as no model can be taken as standard model. However, since the spatial extend of your area of interest is small, better to use Land change modeler as given in IDRISI. But before using the module, you must create LULC map for two time period having common LULC classification, using high resolution satellite data of the same month or season.
If you are used to statistical analysis and GIS you can develop your own model for land use dynamics in your study area. 
  • asked a question related to Vegetation Mapping
Question
4 answers
Extent of occurrence (EOO) too.
Relevant answer
Answer
Hi Juan
You may refer this paper (see link below) for AAO for animal and plants. Pls also see the reference cited from  Boitani et al. (2007).
  • asked a question related to Vegetation Mapping
Question
15 answers
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.
Relevant answer
Answer
Thank you for your answer. I'm currently working as an independet researcher. My USGS affiliation dates back to 15 years.
  • asked a question related to Vegetation Mapping
Question
8 answers
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.
Relevant answer
Answer
Hi Mounir,
it also all depends on what type of vegetation you mean. Yes, a lot of work on using lidar to look at tree species, biomass etc. has been done. However, there is also the small type of vegetation that is significant. I was involved a few years ago in some work on floodplain characterization with lidar (where I also used object-based image analysis). If you look at some papers by a former colleague who was also involved in the work (Menno Straatsma, see some publications below), you will see that lidar can well be very useful and certainly detailed enough to map vegetation and its variation, density, roughness, etc. very close to eth ground, i.e.. well within 1 m from the ground.
Best regards, Norman
Straatsma, M. W., 2008, Quantitative mapping of hydrodynamic vegetation density of floodplain forests under leaf-off conditions using airborne laser scanning: Photogrammetric Engineering and Remote Sensing, v. 74, no. 8, p. 987-998.
Straatsma, M. W., and Baptist, M. J., 2008, Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing: Remote Sensing of Environment, v. 112, no. 3, p. 1062-1080.
Straatsma, M. W., and Huthoff, F., 2012, Extrapolation error of peak water levels from uncertain floodplain roughness in 2D hydrodynamic models.
Straatsma, M. W., and Middelkoop, H., 2007, Extracting structural characteristics of herbaceous floodplain vegetation for hydrodynamic modeling using airborne laser scanner data: International Journal of Remote Sensing, v. 28, p. 2447-2467.
  • asked a question related to Vegetation Mapping
Question
5 answers
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?
Relevant answer
Answer
I agree the another appropriate method PCA,.
  • asked a question related to Vegetation Mapping
Question
7 answers
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 ????
Relevant answer
Answer
Weighted overlay technique ar applied.
  • asked a question related to Vegetation Mapping
Question
6 answers
Any simple tool for analysing effects of development on vegetation or forest areas?
such as "FragStats" programe.
Relevant answer
Answer
What type of "effects" are you interested in, specifically? If it's just in terms of changes to the land cover (patch area, isolation, fragmentation, etc) FRAGSTATS can be useful if you have before and after rasters of land cover.
  • asked a question related to Vegetation Mapping
Question
3 answers
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?
Relevant answer
Answer
Dear Gamal,
Although NDVI is widely used, it is a very poor estimate of vegetation properties when you require quantitative, reliable, accurate estimates. This is because NDVI values are computed from a trivially simple, rather empirical formula that is not optimized for any particular purpose. Compositing NDVI by selecting the maximum values amongst multiple images has also been shown to introduce significant biases. This product may be sufficient to distinguish vegetated areas from deserts, but remains highly sensitive to all sorts of perturbing factors which are rarely taken into account by analysts, including atmospheric composition, illumination and observation geometry, surface anisotropy, and soil moisture, for instance.
You will get much better and more reliable results using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), which is a physically-based parameter directly related to the productivity of the vegetation and derived from a radiation balance calculation. FAPAR products are derived from detailed analyses of data from various instruments, including MODIS, MISR, MERIS, and they are widely available from multiple Space Agencies.
To get you started in that direction, you may want to visit this web site:
where you'll find publications, reports and data sets.
To answer the rest of your question, please note that the yield of a crop at the end of the growing season is not well predicted by the instantaneous values of remote sensing products. However, it will be well correlated with the integrated value of FAPAR over that season. So, once you have collected FAPAR values in time, the sum of these values is closely related to the yield, and the more the season advances, the more the accumulated FAPAR value becomes a reliable estimator of the final yield.
Lastly, crop yield depends on a large number of factors, including soil composition and texture, temperature and precipitation, radiation, fertilizers and irrigation, seed stock and cultural practices, etc. Some of these factors may be much more important than others, depending on local conditions and constraints. There is no general rule about this.
I hope this helps. Cheers, Michel.
  • asked a question related to Vegetation Mapping
Question
9 answers
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?
Relevant answer
Answer
Dear Dr. Gamal
This distinct feature can be seen in different part of the world and mostly caused by groundwater flow damming along one side of a major lineament , major fracture, and faults, Most of us when teaching environmental geology course show the surface trace of the San Andreas fault which is  conspicuous on the valley floor by the lineup of vegetation along its north side, a result of fault-dammed groundwater flow. 
  • asked a question related to Vegetation Mapping
Question
16 answers
I have an interest in remote sensing of vegetation health; particularly aquatic vegetation. I would like to evaluate the effects of wetland encroachment on surface water and energy fluxes, to derive an indicator for wetland vegetation health.  I want to use data over a time series of about 30 years. I am trying to determine which sensor system is most suitable for my work (Landsat, MODIS or ASTER) as I foresee where data quality might be a limitation given the climatic conditions of the study area (misty for most times of the year). Can anyone with experience in wetland vegetation monitoring give some advice?
Would microwave be the only remote sensing technique I have as an option? Also, is it possible to run imagery from a combination of sensors without risking the credibility of the model? I also want to select a most appropriate ET model, which comes with free and open access.
Thanks in advance for the guidance.
Relevant answer
Answer
Depending on your latitude I recommend Landsat since the images are highly suitable for wetlands.  See this paper on wetland monitoring and analysis I did recently. 
  • asked a question related to Vegetation Mapping
Question
8 answers
I have a good bird community data set and want to relate functional groups etc. to vegetation but, I do not have time for lots of fieldwork to measure veg. characters. Also, what amount of ground-truthing would be necessary for the proposed method.
Relevant answer
Answer
Hi Rion,
You can perform statistical correlation between bird diversity index based on your bird dataset using e.g. Shannon-Wienner and several Vegetation Index calculation e.g. NDVI, TVI, RVI from LANDSAT images. The hypothesis is that bird diversity has a significant correlation with value of Vegetation Index.  
Good luck!
  • asked a question related to Vegetation Mapping
Question
12 answers
On the base of leaves reflectance data obtained by remote sensing method I calculate several vegetation indices (VI), which I found out in literature. Unfortunately no one of authors comments what the differences in the obtained indices  of control and infected plants presents.
Relevant answer
Answer
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.
  • asked a question related to Vegetation Mapping
Question
6 answers
I want to know what are the factors that effect on NDVI values during a year period. 
i mean, not factors which vary spatially let say crop type, but i want to know the factors that affect on the same area over the time. 
Relevant answer
Answer
Plant Phenology.
  • asked a question related to Vegetation Mapping
Question
14 answers
I found an equation but i did not get what the parameters are:
Rate =(1-[(S1-S2)/S1]^1/n)-1
Relevant answer
Answer
Dear Maher,
It's difficult to help you because you do not provide enough information on your problem. If you want to understand that equation specifically, could you please indicate what S1, S2 and n are supposed to be? Also, the equation as written is potentially ambiguous or may have extraneous terms.