The Sun is shining, birds are singing and most scientists have nothing better to do than jetting around the globe to attend conferences. Yes, it is summer indeed. Here is some advertising for the 2014 Tropical Ecology – Early Carer Meeting of the Bristish Ecological Society. This year the fun is happening in York. As many people seem to be on vacation the deadline for abstracts has been extended to the 14th of July. See the attached Documents ( BES-TEG 2014 Flyer ,BES_TEG Key speakers ) for more information. I will be there as well… .
The British Ecology Society – Tropical Ecology Group (BES-TEG) are organizing a 7th early career meeting scheduled to take place at the University of York, on the 14th and 15th of August 2014. Day one will focus on Ecology and Ecosystem Processes while day two will focus on Practical Applications and Links to Policy; such as conservation, livelihood, policy and development. All early-career researchers, both PhD and Post-Docs, are welcome to present their tropical ecology related research as a poster and/or oral presentation. The deadline for abstract submission has been extended to Monday the 14th of July 2014.
Please find attached the flyer and conference document for detailed information.
It would be appreciated if this flyer circulates within your department. Students are encouraged to come to York for what should be a really interesting few days in August.
An event website has been set up for registration
The start of this year was marked by the publication of two new global datasets for environmental analysis. My impression is that both of those datasets will be of increasing importance in ecological analysis in the future (even though their value for conservation biology has been actively criticized, see Tropek et al. 2014). Thus there is a need to assess the accuracy of their forest loss detection over time and if they are consistent.
The first dataset is the already famous Global Forest Map published by Hansen et al. (2013) in Science end of last year. The temporal span of their dataset goes back from the year 2000 up to the year 2012 and by using only Landsat data in a temporal time-series analysis they got a pretty decent high-resolution land-cover product. Although the resolution of the Hansen dataset is great (30m global average coming from Landsat) Hansen et al. decided to only publish the year 2000 baseline with the forest cover. They provide us with aggregated loss, gain and loss per year layers though, but nevertheless the user has no option to reproduce a similar product for the year 2012.
The other dataset is the combined published result from a 4 year long monitoring by the japanese satellite ALOS-PALSAR. They decided to release a global forest cover map at a 50m spatial resolution, which in contrast to Hansen can be acquired for the whole time-frame of the ALOS-PALSAR mission. It thus has a temporal coverage of the whole globe from the year 2007 until 2010. The data can be acquired on their homepage after getting an account. The ALOS PALSAR data has a nice temporal span and can be downloaded for multiple years, thus in theory allowing to make temporal comparisons and predictions about future land-use trends. However I am a bit concerned about the accuracy of their classifications as I have found multiple errors already in the area I am working in.
Because I am interested in using the ALOS PALSAR dataset in my analysis (how often do you get a nice spatial-temporal dataset of forest cover) I made a comparison between the forest loss detected in my area of interest for both datasets. It should be noted that is a comparison between different satellite sensors as well and not only by classification algorithms. So we are not comparing products from the same data source.
So what is the plan for our comparison:
- We downloaded the whole ALOS PALSAR layers for all years covered of the area around Kilimanjaro in northern Tanzania (N00, E035). We then extracted only the forest cover (Value == 1) and calculate the difference between years to acquire the forest loss for the year 2008,2009 and 2010 respectively.
- From the Google Engine app we downloaded the “loss per year” dataset and cropped it to our area of interest. Furthermore we are only interested in the aggregated Forest loss in the years 2008, 2009 and 2010 which we have available in the ALOS PALSAR dataset. We furthermore resampled the Hansen dataset up to 50m to match up with the ALOS PALSAR resolution.
I haven’t found a fancy way to display this simple comparison, so here comes just the result table. As predicted (if you look at it visually),the ALOS PALSAR algorithm overshots the amount of forest loss a lot.
|Hansen Forest Loss cells||262||304||529|
|ALOS PALSAR Forest Loss cells||26995||24970||16297|
|Equal cells in both||17||30||131|
So which one is right? I personally trust Hansens data a lot more. Especially because I found them to be pretty consistent in my area of study. For me the ALOS PALSAR data is not useable yet until the authors have figured out ways to improve their classification. It can be concluded that users should not forget that those Forest Cover products are ultimately just the result of a big un-supervised algorithm who doesn’t discriminate between right and wrong. Without validation and careful consideration of the observer you might end up having wrong results.
M. Shimada, O. Isoguchi, T. Tadono, and K. Isono, “PALSAR Radiometric and Geometric Calibration,” IEEE Trans. GRS, vol. 47, no. 12, pp.3915-3932, Dec 2009.
M. Shimada and T. Otaki, “Generating Continent-scale High-quality SAR Mosaic Datasets: Application to PALSAR Data for Global Monitoring,” IEEE JSTARS Special Issue on Kyoto and Carbon Initiative, vol. 3, Issue 4, 2010, pp.637-656.
Shimada, M.; Isoguchi, O.; Motooka, T.; Shiraishi, T.; Mukaida, A.; Okumura, H.; Otaki, T.; Itoh, T., “Generation of 10m resolution PALSAR and JERS-SAR mosaic and forest/non-forest maps for forest carbon tracking,” Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International , vol., no., pp.3510,3513, 24-29 July 2011
This is just a quick posting informing all the QGIS interested readers of this blog that I am about to release a new QGIS plugin. It’s name is QSDM (QGIS Species Distribution Modelling) and similar as with LecoS it is particular suited for the practicing ecologists out there. This time i had no plan and interest of coding a graphical interface and thus the whole plugin can only be executed from within the Processing Toolbox (QGIS version > 2.0 ). In my opinion this will be the future of most advanced QGIS plugins anyway.
So what is the idea? Basically QSDM is a plugin taking statistical models for species distribution modeling to QGIS. For now only the famous Maxent is enabled and working, but the ambitious plan is to enable other modeling techniques such as RandomForests and LogisticRegression as well if the user has the necessary libraries enabled.
You might ask what is the advantage of running Maxent from within QGIS? First, you can immediately see the output so it is nice for visual exploration. Second, the QSDM plugin helps you with the formating of your layers and occurrence files. For instance all input raster layers are automatically unified to a common resolution and exported as ESRI .asc files. You simply need to load in your layers and let the tool do the rest. For those of you who want more control (and I really insist that you want to), I also enabled functions to generate a custom parameter file for Maxent and enabled an option to start the Maxent GUI in a new process.
–> I recognize that the easiness of this tool might tempt more people to execute tools without really understanding what they do and how they work. Please be sure what you do and always (!!!) validate the outputs of the tools you use (this includes QSDM). For understanding Maxent parameters I highly recommend reading the attached literature list and this publication!
Other things i implemented in the initial release of QSDM
- Create Species Richness grid
- Creates a new raster containing Species Richness or Endemism of input occurence layer
- Calculate Niche Overlap Statistics
- Can calculate Schoener’s D or Warren’s I based on Hellinger distances for all input layers.
- Range Shift
- Shows the difference between two input prediction layers. For instance for current and likely future conditions.
- Data Transformations
- Makes quick transformations of input raster layers
More is planned, but this depends entirely on my inclination to do so, the time I have available and if it can be useful for my own research as well.
Please remember that the plugin is still experimental. So please don’t be angry if it doesn’t work for you. testing was conducted on QGIS 2.2 stable on my Debian Linux machine and it should hopefully work for Windows as well. But similar as with LecoS i have no opportunity to test the plugin on Mac OS based systems and I also don’t really intend to :-p. Sorry Apple.
- Steven J. Phillips, Robert P. Anderson and Robert E. Schapire, (2006) “Maximum entropy modeling of species geographic distributions” Ecological Modelling, Vol 190/3-4 pp 231-259
- Steven J. Phillips and Miroslav Dudik, (2008) “Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation.” Ecography, Vol 31, pp 161-175
- Jane Elith et al. (2011) “A statistical explanation of MaxEnt for ecologists” Diversity and Distributions, 17, 43–57 DOI: 10.1111/j.1472-4642.2010.00725.x
Another interesting project closely related to PREDICTS is the BIOFRAG Project, which tries to construct a global database of research papers dealing with Forest Fragmentation and its impacts on Biodiversity taxa. One final goal of the BIOFRAG project is the development of a new fragmentation index using watersheds delineation algorithm and fragment descriptors in order to characterize Fragment traits. I am very interested in seeing the final outcome of this approach and maybe I even find the time to implement their algorithm in LecoS for QGIS as soon as it is released. Their database paper, lead authored by Marion Pfeifer, was just released to the public as open-access paper. You can read it in full here.
If you consider of contributing data then more information can be found on the BIOFRAG blog and all researchers involved with forest fragmentation research should consider contributing to them and also to PREDICTS (see here) if you haven’t already done so. And as usual: If you were studying in Africa, then please get in touch with me! I will contact you as soon as I return from my Fieldwork in Kenya and Tanzania at the end of May.
And here are some news from my current field work that is part of my Thesis. After spending some quiet, but exiting days in Nairobi (maybe later more about that) I finally arrived in Wundanyi, Taita Hills, where a substantial part of my work will be conducted along the CHIESA transect. Suited in the coastel area in proximity to Mombasa the Taita Hills are renown for their extraordinary bird diversity and endemic species and as such are considered to be part of the Eastern Arc Mountains Diversity hotspot. The Taita hills encompass a variety of different land-use forms, but the majority of them surely are tropical homegardens as most of the “Taita” people are subsistence farmers growing crops in the highly fertile soil of the mountain slopes. Besides homegardens there are riverine forests in the valleys, shrubland vegetation in the lower altitudes, exotic tree plantations and of course the remaining indigenous forests remaining on the Taita hills mountain tops. Every last forest part is known well and was traditionally protected by the locals as part of their culture. However in the later centuries the remaining forest area became more and more scarcer and even during my visits in some of the forest fragments with the highest biodiversity value (Chawia, Ngangao) I saw frequent signs of fuelwood and timber extraction. Clearly a lack of funding for biodiversity protection seems to be the problem, but also an economic perspective and opportunities such as ecotourism might enhance locals perception if and how these last forest parts should be protected.
My work in the Taita hills is all about birds. Specifically I am conducting avian diversity and abundance assessments along an altitudinal transect encompassing a variety of different land-use systems. Although avian assessments have been conducted in Taita many times before, they were often restricted to the forest fragments and for instance didn’t look at the bird diversity in homegardens in different altitudes. The resulting data will just be used for my thesis as validation dataset, but I am hoping that it has maybe some value on its own as well. Initial results show that especially the homegarden in Taita support quite a high diversity of birds, which is even similar to levels in the remaining forest fragments (although the community is somewhat different and biotic homogenization is likely on-going).
It can be quite challenging to conduct avian research in tropical human-dominated landscapes. Not only do you have to arrange for transport to the specific transect areas and lodging (in my case provided by the University of Helsinki Research station in Wundanyi), but also account for the frequent interruption by children and farmers asking what you are doing. Furthermore it is not an easy task to count birds in for instance a maize or sugarcane plantation due to the limited accessibility and my intention not to damage the farmers crops. Most of the farmers however happily provide access to their land and are very interested in what kind of research this “Mzungu” is doing on their farm. From my own experience here I can tell that the Taita people are very kind and it is a pleasure to work with them on their land. They are very respectful and even walking around late at night or very early in the morning seems to be no problem here (in contrast to for instance Nairobi or Mombasa).
In the end my sampling goes on quite well and much better than I expected. Although it is technically raining season and long heavy rains can be expected every day, the mornings were exceptionally dry and weather was mostly favourable for ornithological research. Generally this time of the year in East Africa is especially interesting for bird assessments as many local bird species are in their breeding plumage and nesting, but also because European migrants are often still around or on their way back to Europe (for instance I saw and heard an European Willow Warbler some days ago). Lets see what else the next weeks will have for be in terms of avian diversity.