Dr Jonathan Dash, PhD

Dr Jonathan Dash, PhD

Forestry and Remote Sensing Consultant

I’ve always loved forests, technology, and computers and I’ve spent a large part of my career following these passions. Following a degree in Biology and a Masters degree in forest growth and yield modelling in the UK I moved to New Zealand in 2007. I spent the next decade working in the forest sector and as a forestry scientist.

I worked at an innovative company where I was exposed to all aspects of forest measurement and modelling, sampling design, and learned SQL. I worked on research projects including remote sensing and harvesting research and also contributed to the development of New Zealand’s national forest carbon inventory. I then moved to the NZ Forest Research Institute (Scion) as a Forestry and Remote Sensing Scientist. I completed a PhD and helped develop a leading Geomatics team carrying out cutting edge research into forest remote sensing. In my research I used Lidar, photogrammetry, multispectral and hyperspectral sensors and developed deep expertise and coding skills to process these datasets. I also led the development of a state-of-the art heavy lift UAV capability to carry our sensors. I have extensively used satellite imagery and conventional aerial imagery and laser scanning for both research and in the real world.

I have expertise in forest management, remote sensing, natural resource sampling and statistics. I have substantial experience of working in both academic research and commercial forestry where I now turn my analytical skills to solving real world problems for a wide range of clients. I am an expert in sampling design and statistical modelling for forestry and have developed a wide range of forest growth and yield simulation tools for my clients. I am well positioned to offer insight into the accuracy of forest yield prediction systems and identify and implement improvements. I have done this for clients throughout the world.

I have the technical skills to handle all aspects of processing remote sensing data to provide meaningful insights. I have delivered many successful projects including lidar-based forest inventories, satellite monitoring of forest condition and land cover, and mapping of forest risks such as wildfire and forest pathogens.

I now help my clients turn research outputs into value.

Please feel free to take a look through my Publications, Projects, and download my CV for more detail.

If you need help on technical aspects of any project, please contact me.

Download my CV.

Interests
  • Forest Modelling
  • Biometrics
  • Remote Sensing
  • Forest Measurement
  • Statistics
  • Forest Carbon
Education
  • PhD in Forest Remote Sensing, 2020

    University of Canterbury (NZ)

  • MSc in Environmental Forestry (Distinction), 2007

    University of Wales, Bangor (UK)

  • BSc (Hons) Biology, 2005

    University of Sheffield (UK)

Skills

R

Visualisation, data management, package development

Statistics

Biometry, simulation, sampling design

Remote Sensing

Lidar, Satellite, UAV

Experience

 
 
 
 
 
Dash Forest Consulting
Principal
Dash Forest Consulting
Jan 2019 – Present UK

Responsibilities include:

  • Remote sensing projects
  • Biometric modelling
  • Academic research
  • R package development
  • Forest simulation
  • Forest carbon modelling
 
 
 
 
 
Margules Groome BV
Senior Associate Consultant
Margules Groome BV
Jan 2019 – Present UK

Responsibilities include:

  • Biometric modelling
  • Forest inventory sampling design
  • Satellite remote sensing projects
  • Valuation due diligence
  • Lidar-based forest inventory
 
 
 
 
 
Scion (NZ Forest Research Institute)
Senior Scientist
Scion (NZ Forest Research Institute)
Jan 2016 – Jan 2019 New Zealand

Responsibilities include:

  • Remote sensing research
  • Experimental design
  • Project management
  • Forestry research
 
 
 
 
 
Scion (NZ Forest Research Institute)
Remote Sensing Scientist
Scion (NZ Forest Research Institute)
Jan 2013 – Jan 2016 New Zealand

Responsibilities include:

  • Remote sensing research
  • Experimental design
  • UAV capability development
 
 
 
 
 
Interpine Innovations
Consultant
Interpine Innovations
Jan 2010 – Jan 2013 New Zealand

Responsibilities include:

  • Forest inventory design
  • Forest yield analysis
  • Lidar-based forest inventory
  • Forest carbon inventory
  • Harvesting research
 
 
 
 
 
Interpine Innovations
Team Manager/ Analyst
Interpine Innovations
Jan 2008 – Jan 2010 New Zealand

Responsibilities include:

  • Database design and maintenance
  • Team management
  • Forest yield analysis

Selected Publications

Quickly discover relevant content by filtering publications.
(2021). Aboveground Biomass Density Models for NASAs Global Ecosystem Dynamics Investigation GEDI Lidar Mission. Remote Sensing of Environment.

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(2020). Detecting and Mapping Tree Seedlings in UAV Imagery Using Convolutional Neural Networks and Field Verified Data. ISPRS Journal of Photogrammetry and Remote Sensing.

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(2020). Comparison of TanDEM X InSAR Data and High Density ALS for the Prediction of Forest Inventory Attributes in Plantation Forests With Steep Terrain. Remote Sensing of Environment.

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(2019). Stand Density and Genetic Improvement Have Site Specific Effects on the Economic Returns From Pinus Radiata Plantations. Forest Ecology and Management.

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(2019). Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data. Remote Sensing.

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(2018). UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health. Remote Sensing.

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(2018). Comparison of Models Describing Forest Inventory Attributes Using Standard and Voxel Based Lidar Predictors Across a Range of Pulse Densities. International Journal of Applied Earth Observation and Geoinformation.

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(2017). Assessing Very High Resolution UAV Imagery for Monitoring Forest Health During a Simulated Disease Outbreak. ISPRS Journal of Photogrammetry and Remote Sensing.

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(2016). Methods for Estimating Multivariate Stand Yields and Errors Using KNN and Aerial Laser Scanning. Forestry: An International Journal of Forest Research.

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(2016). Spatial Prediction of Optimal Final Stand Density for Even Age Plantation Forests Using Productivity Indices.

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