Mapping the Harapan Rainforest - how we did it

By: Jenny Williams - 24/05/2012

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Find out how Kew's GIS unit used remote sensing and field work to produce vegetation maps for the Harapan Rainforest Project.

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We have done a lot of work collecting data in the Harapan Rainforest, in lowland Sumatra. In this blog we present an overview of how all that data is used to produce a vegetation map. 

Setting out

Our focus was to summarize the condition of the Harapan Rainforest. Satellite imagery (taken by the French satellite SPOT in 2009) was used to generate a very basic classified image. These pictures were three years old, but were the most recent high resolution images that we could find of the entire Harapan Rainforest boundary, with low cloud cover (it is very wet and cloudy there).

In general, we view forests in shades of red, as a false colour composite (FCC). This is where the image colours are shifted to allow us to use near infra-red. Healthy vegetation reflects the near infra red (making it look red in the resultant image). The naked eye can discriminate more shades of red than any other colour. 

 

Harapan vegetation mapping infra-red satellite image

Image: Colours in the image represent - Red shades: Vegetation - Cyan shades: Low vegetation/clearings - White/black: No data due to cloud/cloud shadows

In this map, the darker the red the more developed the forest (or at least this is the theory we wanted to test).

A quick classification of the SPOT imagery gave us an indication of vegetation differences that we might be able to identify on the ground within the forest. We were not entirely sure what those differences were and that was why we needed to verify them with ground-based observations.

 

Harapan vegetation mapping infra-red satellite image
Image: Colours were associated with ‘best guess’ vegetation classes (without any ground-truth data). Good forest in red tones, younger forest in blues, and disturbed areas in the greens

In the forest

In the forest we used pre-prepared image analysis maps (such as the classification map above) to guide our daily route of ‘plots’ through the forest. We were looking at the different blocks of colour as an indication of different forest cover levels. We also looked at changes between 2009 and 2011, which would indicate re-growth or forest loss. We loaded these images onto android smart phones (we used the Samsung Galaxy S2 and Samsung Galaxy Note devices, in the field) to view, interact and set interest points using Locus Pro.

Photo of a smart phone 

Image: All the information we gathered in the field was entered into digital forms (ODK collect) on the smart phones.

At each plot we collected the following data:

  • location coordinates
  • environment e.g. hill slope, swamp, etc.
  • percentage forest cover
  • size of trees
  • dominant tree species
  • dominant understory vegetation

Image of rainforest
Image: Roki Afriandi and Purnomo record tree size and species for large trees in the plot

Periodically, we sent data up to the server whenever we had a WiFi connection back at base camp. Back at Kew the data was collated and published on the GIS Unit Expedition Maps webpage. Geotagged tweets as well as our ODK datasheets were mapped in near-real-time.

It was a very successful field campaign and we surveyed over 300 plots assessing detailed canopy measurements and environmental variables with associated geo-locations, photographs and videos.

Back at Kew

Back in the office at Kew we cleaned up the data, filled-in blanks and attached photos taken with our own cameras. The field data was broken into thirds. One third was used to train the satellite imagery into defined forest categories and the last two-thirds to test the classification.

The forest categories we used were: Old Secondary Forest/Disturbed, Young Secondary Forest/Disturbed, Very Young Secondary Forest (Thicket/Disturbed), Scrub, and Cleared. After a few iterations a base map was produced for the area. Our results show that all forest levels showed good potential for regeneration by species identified in the field. There was a lot more intact forest than we had suspected from our initial work with the satellite imagery. See some of the results shown on the map here.

Harapan vegetation mapping infra-red satellite image
Image: Final vegetation map

We are finishing up this phase of the project, but are still updating our online expedition maps with geo-located photographs and our next blog will show you some of the highlights. 

- Jenny -
 


 

Find out more about the project

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1 comment on 'Find out how Kew's GIS unit used remote sensing and field work to produce vegetation maps for the Harapan Rainforest Project.'

Sarah says

28/05/2012 11:38:32 AM | Report abuse

This is marvellous! I studied GIS as a post-grad around 1990 and then worked overseas and I know just how difficult the conditions can be in obtaining data (mind you, our methods were far less high-tech then - even working from old maps in rods and chains!). This brings back many happy memories but I couldn't keep up with the technology overseas and so diversified to horticulture field work only, hence my RHS membership!


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