Co-registration of GeoTiff images with Full-waveform LiDAR data

In Remote Sensing, combining multiple sensors could be beneficial due to the increased information that could be used to train a classifier. Here, there is a tutorial with some scripts accosiated to help you co-register LiDAR metrics exported from the open source software DASOS [1] with GeoTIFF sattelitte imagery:

 

Link to script: https://github.com/Art-n-MathS/DASOSmetrics_alignmentWithGeoTIFF 

Link to open source software DASOS: https://github.com/Art-n-MathS/DASOS 

The “FOREST” project, with project protocol number “OPPORTUNITY/0916/MSCA/0005”, is co–financed by the European Regional Development Fund and the Republic of Cyprus through the Cyprus Research & Innovation Foundation.

Work Cited:

Miltiadou, M., Grant, M. G., Campbell, N. D., Warren, M., Clewley, D., & Hadjimitsis, D. G. (2019, June). Open source software DASOS: Efficient accumulation, analysis, and visualisation of full-waveform lidar. In Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019) (Vol. 11174, p. 111741M). International Society for Optics and Photonics.

DOI: 10.5194/isprsarchives-XL-7-W3-1257-2015

Python script that creates a copy of the same folders structure

Hello, it has been a while since I last posted something. Today, I wrote a very short script but something that could be useful so I thought it worth sharing and keeping into my blog. So have you ever wanted to copy and paste multiple folders without their content! This is what the following python function with automatically do for you:

import os

def creatFolders(i_inDir,i_outDir):
   cdir=os.getcwd()
   os.chdir(i_inDir)
   dirs=[d[0] for d in os.walk(".")]
   os.chdir(cdir+"/"+i_outDir)
   for d in dirs:
      if not os.path.exists(d):
         os.makedirs(d)
   os.chdir(cdir)

The following scripts returns all the files from a direction with a given extension:

import os

def getFiles(i_inDir,i_ext):
    cdir=os.getcwd()
    os.chdir(i_inDir)
    images=[f for f in os.listdir(".")if f.endswith(i_ext)] 
    print (images)
    print ("Images in Dir")
    print (i_inDir)
    os.chdir(cdir)
    return images

This script was developed as part of the "ASTARTE"(EXCELLENCE/0918/0341) project, which is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Innovation Foundation. | Powered By Copywriter WordPress Theme.

Installation guide for Anaconda, Python, OpenCV and fmask for interpreting Sentinel Images

Download and Install Anaconda available at: https://www.continuum.io/downloads During installation, make sure you include Python in the path:
- Run Anaconda Prompt as an Administrator to be able to install new libraries and run the following commands:

:$ conda install -c conda-forge gdal

Install Jypiter Notebook:

:$ conda install -c conda-forge jupyterlab

Run JypiterLab:

:$ jupyter-lab

Install opencv:
:$ conda install -c menpo opencv
You may need to run the above command using an Administrator Command Prompt


For reading .nc images of Sentinel 3:
:$ conda install -c conda-forge netcdf4


Install fmask from Administrator Command Prompt as follow:
:$ conda config --add channels conda-forge
:$ conda install -c conda-forge python-fmask

or (to be tested)

:$ conda create -n myenv python-fmask
:$ activate myenv


Other dependencies:
:$ conda install -c anaconda scipy
:$ conda install -c anaconda numpy
:$ conda install -c conda-forge matplotlib
:$ conda install scikit-learn


For Doxygen Documentation please install it from here: http://www.stack.nl/~dimitri/doxygen/download.html


Every time you run your scripts, you need to run Anaconda Prompt as an Administrator and run "activate myenv".


Once you activate myenv, check they version of python that you are using. There is a bug in recent anaconda versions and when myenv is activated, your python is automatically updated to 3.6. You may use the following command to downgrade it:
:$ conda install python=2.7.8
Please note that this may be risky, since other applications may depend on Python 3.6.

Acknowledgement

This is part of the H2020 Research Innovation and Staff Exchange project SEO-DWARF with reg. no MSCA-RISE-691071. Website: seo-dwarf.eu

Histograms with R

This is an example of a histogram creation using R that I would like to save in my blog for quickly referring to it when required.

# Run the script using the following command:
# Rscript Histogram.r

# Define an array
arrayA<-c(17.6,16.8,33.6,28,33.6,28,40.8,37.6,38.4,30.4,25.6,23.2,28,16,15.2,24,16.8,32,28,15.2,15.2,28,28.8,15.2,24.8,15.2,14.4,29.6,38.4,19.2,27.2,37.6,15.2,33.6,33.6,28,18.4,17.6,26.4,26.4,36.8,24.8,32,19.2,16,33.6,32,16,16,30.4,16,37.6,16,25.6,27.2,28,24,26.4,26.4,20.8,16.8,26.4,28,32.8,24,15.2,15.2,16,27.2,12,40.8,38.4,40.8,40,15.2,37.6,17.6,17.6,27.2,14.4,15.2,20,19.2,26.4,27.2,14.4,31.2,27.2,28.8,15.2,15.2,14.4,14.4,28.8,24.8,14.4,15.2,14.4,19.2,31.2,18.4,28.8,17.6,17.6,17.6,17.6,17.6,17.6,32,31.2,32,46.4,37.6,40.8,39.2,17.6,17.6,17.6,18.4,17.6,17.6,34.4,34.4,16.8,16.8,15.2,39.2,40.8,29.6,42.4,40.8,40,38.4,42.4,15.2,15.2,16,15.2,34.4,14.4,14.4,14.4,30.4,42.4,48.8,14.4,32,28,28,14.4,14.4,25.6,22.4,29.6,28,31.2,26.4,26.4,25.6,14.4,14.4,14.4,18.4,19.2,19.2,18.4,39.2,15.2,30.4,28.8,33.6,32.8,15.2,33.6,32,32,32.8,31.2,33.6,15.2,24.8,40.8,40.8,39.2,26.4,25.6,18.4,18.4,40.8,37.6,19.2,19.2,37.6,19.2,28.8,28.8,24.8,28,15.2,14.4,31.2,19.2,18.4,19.2,19.2,19.2,32,37.6,14.4,12.8,30.4,15.2,14.4,40,27.2,30.4,38.4,20.8,40,20,20.8,40,41.6,32.8,20.8,20.8,39.2,20.8,20,36,20,19.2,34.4,32,20,20,30.4,26.4,21.6,32,15.2,15.2,28.8,24.8,29.6,17.6,27.2,30.4,33.6,13.6,33.6,35.2,27.2,28,16,15.2,15.2,15.2,28,31.2,38.4,25.6,38.4,29.6,15.2,15.2,32.8,33.6,33.6,26.4,14.4,28.8,34.4,33.6,15.2,32,40,33.6,13.6,15.2,37.6,35.2,14.4,32,15.2,33.6,18.4,22.4,38.4,18.4,36.8,14.4,25.6,14.4,33.6,15.2,26.4,24.8,28,36,39.2,14.4,33.6,11.2,15.2,49.6,35.2,36,46.4,46.4,14.4,46.4,12.8,45.6,15.2,41.6,14.4,41.6,14.4,13.6,37.6,12.8,39.2,41.6,14.4,12.8,13.6,14.4,14.4,14.4,18.4,14.4,32)


# 1. Open jpeg file

jpeg("/home/username/Documents/histArrayA.jpg")


# 2. Create the plot inside the file

hist(arrayA,breaks=seq(0,70,l=70))


# 3. Close and save file

dev.off()

The output of the above script is the following:

Acknowledgments:

The script of this post was written as part of the "FOREST" project with reg. no OPPORTUNITY/0916/0005. The "FOREST" project  is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation.

Reviewers please be kind!

Academia is a very competitive world and it is something that many people, including my self, do not understand when they start a doctorate degree. Competitiveness is OK since we learn how to accept failure and be persistent in getting this work publish. But sometimes I feel disappointed when I receive the comments of the reviewers, not because they rejected my paper, but because they did not even read the entire article and it is clear from their comments! I think more people may resemble to this experience. So, I decided to write this post and ask reviewers to carefully read the entire articles, be kind and encouraging while recommending ways of making the work publishable.

Personally, I am new researcher and I have not written enough papers to fully understand the process and avoid small mistakes. But if reviewers do not help me improve myself then I will never be able to progress my career in academia.

Here are some comments that I found disturbing:
-  This is from a paper that have been rejected before even been reviewed: "a link to bird diversity is offered as motivation, the link to remote sensing is not pursued further in the manuscript". The article was about proposing a new methodology for detecting dead trees from full-waveform LiDAR data. If LiDAR is not about remote sensing then what could it be? By only rephrasing the abstract and submitting it to another high impact journal the article was published with minor corrections, indicating that the editor had not read the article.

- "The LiDAR-specific complications of mapping full-waveform data to scalar volumes are entirely handle by DASOS. It means means that the manuscript oversells its contribution" The reviewer missed the part that DASOS was implemented by the authors of the manuscript to make the research possible!

- "Evaluation of the method is completely insufficient," could have been phrased in a kinder way, especially when the reviewer request comparison with Canny Edge that it included in the article. On top of that the reviewer states "While Canny was considered, its awful results in table 3 indicates that it was implemented incorrectly or using a poor choice of settings." How is it possible for the standard python opencv function for the Canny Edge algorithm to be incorrectly implemented? The reviewer could have request instead an explanation for the bad results, which exists: the Canny Edge includes a smoothing step and the gradient differences are low. Therefore, Canny Edge fails to detect many edges!

- The same reviewer questions the approach used to find k for k-means and that it may not be reliable, but missed the part that the mean shift was also implemented in the article that does not require the number of clusters (k) to be pre-defined. 


Do not get me wrong, there are reviewers that provide constructive feedback that help improve a paper. I just feel that more reviewers should act like that. Behind every article, there is a researcher who spent months reading articles, conducting experiments, stressing with every unexpected results, missing social events hoping that the code will work this time and spending nights overworking to finish with the writing. So be kind to them! I understand that I and every young researcher makes mistakes. My articles usually lack of presentation, but if reviewers are judgmental and badly criticizing my work without even reading the entire articles, then how will I improve myself? 

Course: Overview of LiDAR; system variations, data interpretation & applications

-        Gain an in-depth understanding of LiDAR concepts, systems and algorithms. 

Enrol to Full Course from here: https://www.geo.university/courses/overview-of-lidar-system-variations-data-interpretation-applications?ref=67f4b2

Light Detection And Ranging (LiDAR)

Quickly progress to understand the state-of-art LiDAR research and systems development.

Become an expert on LiDAR data and systems with this step-to-step course, starting from scratch.

This video course gives detailed and broad information about how LiDAR systems works, their usability and interpretation.

According to Wanger et al, LiDAR is a growing technology used in environmental research to collect information about the Earth, such as vegetation and tree species. Earth observation images, acquired from satellites, have been used for years in earth monitoring. In respect to forest monitoring, satellite imagery does not contain information about tree height, diameter at breast height and stem density amongst many other important parameters for monitoring forest health at tree level. In the last couple of decades, LiDAR data acquired from airborne platforms has been increasingly used for forest monitoring, urban planning, archaeology, biodiversity and automated driving. Using this technology, the commercial forestry sector managed a 40% reduction of the expensive fieldwork that cost them millions of dollars annually.

Just listen to these videos and you will become an expert on LiDAR systems in a fraction of time!

Enhance your knowledge of Earth Observation and Remote Sensing along the way!

Check out the curriculum for the detailed contents of this video course!

Curriculum

Chapter 1: Overview of the course (2:56)

Lesson 1:  Overview of the course

Chapter 2: Introduction to LiDAR systems (10:30)

Lesson 2: How LiDAR systems work

Lesson 3: Introduction to discrete and full-waveform LiDAR

Lesson 4: Types according to the way they are carried

Lesson 5: Types according to the way the pulses are emitted

Chapter 3:  Interpretation of LiDAR data (6:50)

Lesson 6: Introduction to your first metrics (Digital Elevation Model, Digital Terrain Model and Canopy Height Model)

Lesson 7: Tree delineation using the Watershed Algorithm

Lesson 8: Further tree delineation approaches

Chapter 4: Full-waveform LiDAR data (11:35)

Lesson 9: Discrete versus full-waveform LiDAR data

Lesson 10: Comparison of data collected using the Leica ALS50_v2 sensor

Lesson 11: Echo Decomposition for peak point extraction

Lesson 12: Voxelisation of full-waveform LiDAR data

Chapter 5: LiDAR file formats (12:13)

Lesson 13: Introduction to binary files

Lesson 14: Discrete LiDAR LAS files formats

Lesson 15: Full-waveform LiDAR LAS file formats

Lesson 16: How to calculate the positions of the waveform samples

Lesson 17: The Pulsewaves file format 

Chapter 6: Sample of available software for interpreting LiDAR data (6:51)

Lesson 18: Sample of available software for interpreting LiDAR data

Chapter 7: Applications of LiDAR data (9:28)

Lesson 19: Biodiversity

Lesson 20: Forest health monitoring

Lesson 21: Urban planning

Lesson 22: Wood trade

Lesson 23: Archaeology

Lesson 24: Automated Driving

Chapter 8: Other Types of LiDAR Systems (2:42)

Lesson 25: Multi-Spectral LiDAR

Lesson 25: Atmospheric LiDAR

Lesson 26: Bathymetric LiDAR

What will you learn?

- Learn how LiDAR systems work

- Gain an in-depth knowledge of various LiDAR systems

- Understand the differences between discrete and full-waveform LiDAR data

- Acquire an understanding of many algorithms used for interpreting LiDAR data

- Learn how to tackle issues using LiDAR in various application areas

- Become aware of various available software able to process LiDAR data

Any prerequisites?

- Basic knowledge of Earth Observation (optional)

- A simple laptop or desktop computer to watch the lectures

Student Profile?

- Undergraduate & Postgraduate students

- PhD/EngD candidates

- Professionals

- Researchers and Academics

- Geospatial Analysts

- Remote Sensing Scientists

A review on the importance of dead wood in forests, with a focus in native Australian Eucalypt forests

Please note that this work is an extended version of the introduction and literature of the following publication:

Miltiadou, M., Campbell, N. D., Gonzalez Aracil, S., Brown, T. and Grant, M. G. (2018), `Detection of dead standing Eucalyptus camaldulensis without tree delineation for managing biodiversity in native Australian forest', International Journal of Applied Earth Observation and Geoinformation 67, 135-147.
Full Paper Available here: https://www.researchgate.net/publication/323398945_Detection_of_dead_standing_Eucalyptus_camaldulensis_without_tree_delineation_for_managing_biodiversity_in_native_Australian_forest

The importance of Dead Wood

The value of dead trees from a biodiversity management perspective is large. Once a tree dies, its woody structure remains for centuries and it contributes to forest regeneration while providing resources for numerous surrounding organisms (Franklin et al., 1987). More than 4000 species inhabit dead wood in Finland (Siitonen, 2001), where an estimate of 1000 species are threatened (Hanski, 2000). These species include animals, birds and other organisms, like fungi. Fungi contributes to wood decaying, formation of hollows and biodiversity, which supports the resilience of our ecosystem (Peterson et al., 1998). Observing the changes of fungal diversity on decaying wood has an increased interest in science (Abrego and Salcedo, 2011) (Stokland and Larsson, 2011) (Lonsdale et al., 2008) in order to ensure the continuous existence of decaying wood in forests.

In Australia, tree hollows play a signi cant role in managing biodiversity (Lindenmayer et al., 1997)
(Bennett et al., 1994). Nearly all arboreal mammals rely on hollows with the exception of the Koala (Phascolarctos cinereus) and perhaps Ringtail Possums (Pseudocheirus peregrinus) that preferentially make a stick nest. Additionally, numerous Australian bird species use hollows for shelters (Gibbons and Lindenmayer, 2002). Nevertheless, Australia has no real hollow creators unlike the northern hemisphere (e.g. Woodpeckers), and therefore it relies predominantly on natural processes of limb breakage, insect and fungal attack when access points are provided through damage caused by wind, storms and re. This kind of hollows takes hundreds of years to form (Wormington and Lamb, 1999).

According to Gibbons et al. (2000), hollows are more likely to exist on dead trees trees or trees in poor physiological condition. In Australia, studies predict shortage of hollows for colonisation in the near future (Lindenmayer and Wood, 2010) (Goldingay, 2009). A sample list of species that rely on hollows, provided by Forestry Corporation of NSW, is depicted at Figure 1. Three of them are threatened (New South Wales Government, 2016). Consequently, automated detection of dead trees plays a substantial role in managing biodiversity.

Figure 1: Some species that uses tree hollows for shelters. The red ones / bold ones are threatened: Kook-
aburra, Sulphur Crested Cockatoo, Corella, Crimson Rosella, Eastern Rosella, Galah, Rainbow Lorikeet,
Musk Lorikeet, Little Lorikeet , Red-winged Parrot, Superb Parrot, Cockatiel, Australian Ringneck (Par-
rot), Red-rumped Parrot, Powerful Owl, Sooty Ow, Barking Owl, Masked Owl, Barn Owl, White-throated
Treecreeper, Hollow Owl, Brush-tailed Possum

As explained above, monitoring dead trees is essential for preserving a resilient ecosystem. Remote
sensing automates the process of monitoring forest and increases the spatial resolution of the monitored area.

Related Work in Remote Sensing

Remote sensing was introduced for automating detection of dead trees since fieldwork is a time consuming task, considering the variance spread of trees and the spatial resolution of the area of interest. From a classification perceptive, the task of identifying dead standing and dead fallen trees is di erent. Fallen trees are identi ed by detecting segments or line-like features on the terrain surface using LiDAR (Polewski et al., 2015) (Mcke et al., 2013). Regarding standing dead trees, their shape (reduced number of leaves or broken branches) (Yao et al., 2012) and light reflectance (less green light illuminated) (Pasher and King, 2009) areimportant factors for identifying them.
Previous work on dead standing trees detection performs single tree crown delineation before health assessment (Yao et al., 2012) (Shendryk, Broich, Tulbure, McGrath, Keith and Alexandrov, 2016). Tree crown delineation is usually done by detecting local maxima from the canopy height model (CHM) and then segmenting trees using the watershed algorithm (Popescu et al., 2003). Improvements has been achieved by introducing markers controlled watershed (Jing et al., 2012) and structural elements of tree crowns with di erent sizes (Hu et al., 2014). Additionally, Popescu and Zhao (2008) analyse the vertical distribution of the LiDAR points in conjunction with the local maximum filtering of CHM.

In the case of Eucalyptus in Australia, tree delineation is a challenge due to their irregular structure and multiple trunk splits. Local maxima filtering, used for tree detection, leads to over-segmentation because each tree trunk split forms a local maxima. Shendryk, Broich, Tulbure and Alexandrov (2016) published an interesting Eucalyptus delineation algorithm that performs segmentation from bottom to top; the trunks point cloud is separated from the leaves and individual trunks are identified before the segmentation. Nevertheless, the density resolution starts from 12 points/m2 and goes up to 36 points/m2 around forested areas. For small research projects capturing this high resolution is reasonable, but for larger areas, the density of the emitted pulses is above the optimal resolution for a cost effective versus quality acquisition (Lovell et al., 2005). Miltiadou et al. (2018) presented an new research direction for forest health assessment without tree delineation that uses 3D windows for extracting structural features and using these structural features to train an object detection system. This works was extended in using multi-scale 3D windows for tackling height differences (Miltiadou et al., 2020). 

References

Abrego, N. and Salcedo, I. (2011), `How does fungal diversity change based on woody debris type? a case study in northern spain', Ekologija 57(3). doi: 10.6001/ekologija.v57i3.1916.

Bennett, A., Lumsden, L. and Nicholls, A. (1994), `Tree hollows as a resource for wildlife in remnant woodlands: spatial and temporal patterns across the northern plains of victoria, australia', Paci c Conservation Biology 1(3), 222-235.

Franklin, J. F., Shugart, H. H. and Harmon, M. E. (1987), `Tree death as an ecological process', BioScience 17(8), 550-556. doi: 10.2307/1310665.

Gibbons, P. and Lindenmayer, D. (2002), `Tree hollows and wildlife conservation in australia', CSIRO Publishing . doi: 10.5860/choice.40-1547.

Gibbons, P., Lindenmayer, D., Barry, S. C. and Tanton, M. (2000), `Hollow formation in eucalypts from temperate forests in southeastern australia', Paci c Conservation Biology 6(3), 218.

Goldingay, R. L. (2009), `Characteristics of tree hollows used by australian birds and bats', Wildlife Research 36(5), 394{409. doi: 10.1071/WR08172.

Hanski, I. (2000), `Extinction debt and species credit in boreal forests: modelling the consequences of di erent approaches to biodiversity conservation', Annales Zoologici Fennici pp. 271-280.

Hu, B., Li, J., Jing, L. and Judah, A. (2014), `Improving the e ciency and accuracy of individual tree
crown delineation from high-density lidar data', International Journal of Applied Earth Observation and Geoinformation 26, 145{15. doi: 0.1016/j.jag.2013.06.003.

Jing, L., Hu, B., Li, J. and Noland, T. (2012), `Automated delineation of individual tree crowns from
lidar data by multi-scale analysis and segmentation', Photogrammetric Engineering & Remote Sensing 78(12), 1275{1284. doi: 10.14358/PERS.78.11.1275.

Lindenmayer, D. B., Cunningham, R. B. and Donnelly, C. F. (1997), `Decay and collapse of trees with hollows in eastern australian forests: Impacts on arboreal marsupials', Ecological Applications 7(2), 625{641. doi: 10.2307/2269526.

Lindenmayer, D. B. and Wood, J. T. (2010), `Long-term patterns in the decay, collapse, and abundance of trees with hollows in the mountain ash (eucalyptus regnans) forests of victoria, southeastern australia', Canadian Journal of Forest Research 40(1), 48{54. doi: 10.1139/X09-185.

Lonsdale, D., Pautasso, M. and Holdenrieder, O. (2008), `Wood-decaying fungi in the forest: conservation needs and management options', European Journal of Forest Research 127(1), 1{22. doi: 10.1007/s10342-007-0182-6.

Lovell, J. L., Jupp, D. L. B., Newnham, G. J., Coops, N. C. and Culvenor, D. S. (2005), `Simulation study for nding optimal lidar acquisition parameters for forest height retrieval', Forest Ecology and Management 214(1), 398{412. doi: 10.1016/j.foreco.2004.07.077.5

Mcke, W., Dek, B., Schroi , A., H. M. and Pfeifer, N. (2013), `Detection of fallen trees in forested areas using small footprint airborne laser scanning data', Canadian Journal of Remote Sensing 139(s1), S32{S40. doi:10.5589/m13-013.

Miltiadou, M., Campbell, N. D., Gonzalez Aracil, S., Brown, T. and Grant, M. G. (2018), `Detection
of dead standing eucalyptus camaldulensis without tree delineation for managing biodiversity in native australian forest', International Journal of Applied Earth Observation and Geoinformation 67, 135{147. doi: https://doi.org/10.1016/j.jag.2018.01.008.

Miltiadou, M., Agapiou, A., Gonzalez Aracil, S., & Hadjimitsis, D. G. (2020). Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations. Forests, 11(2), 161. doi: https://doi.org/10.3390/f11020161

New South Wales Government (2016), Biodiversity conservation act 2016 no 63, Technical report.

Pasher, J. and King, D. J. (2009), `Mapping dead wood distribution in a temperate hardwood for-
est using high resolution airborne imagery', Forest Ecology and Management 258(7), 1536{1548. doi:10.1016/j.foreco.2009.07.009.

Peterson, G., Allen, C. R. and Holling, C. S. (1998), `Ecological resilience, biodiversity, and scale', Ecosystems 1(1), 6{18. doi: 10.1007/s100219900002.

Polewski, P., Yao, W., Heurich, M., Krzystek, P. and Stilla, U. (2015), `Detection of fallen trees in als point clouds using a normalized cut approach trained by simulation', ISPRS Journal of  Photogrammetry and Remote Sensing 105, 252{271. doi: 10.1016/j.isprsjprs.2015.01.010.

Popescu, S. C., Wynne, R. H. and Nelson, R. F. (2003), `Measuring individual tree crown diameter with lidar and assessing its in uence on estimating forest volume and biomass', Canadian journal of remote sensing 29(5), 564{577. doi: 10.5589/m03-027.

Popescu, S. C. and Zhao, K. (2008), `A voxel-based lidar method for estimating crown base height for deciduous and pine trees', Remote sensing of environment 112(3), 767{781. doi: 10.1016/j.rse.2007.06.011.

Shendryk, I., Broich, M., Tulbure, M. G. and Alexandrov, S. V. (2016), `Bottom-up delineation of individual trees from full-waveform airborne laser scans in a structurally complex eucalypt forest', Remote Sensing of Environment 173, 69{83. doi: 10.1016/j.rse.2015.11.008.

Shendryk, I., Broich, M., Tulbure, M. G., McGrath, A., Keith, D. and Alexandrov, S. V. (2016), `Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a foodplain eucalypt forest', Remote Sensing of Environment 187, 202{217. doi: 10.1016/j.rse.2016.10.014.

Siitonen, J. (2001), `Forest management, coarse woody debris and saproxylic organisms: Fennoscandian boreal forests as an example', Ecological bulletins pp. 11{41.