Ah those clouds...

At first glance, it seems the LANDSAT data is the most usable set of information to generate land use maps. Both the 1990 set and the 2000 set cover the whole island. There are a lot of spectral bands to generate signatures from, the 28.5m resolution seems more than adequate for our purpose and there is more than enough literature to prove that LANDSAT data can be used to produce land use maps. Unfortunately, as was mentioned in the previous post, Borneo is plagued with clouds. So we set out on a quest to diversify our datasets. This ongoing quest has taken us all over the web and I made a table to summarize our findings. There is quite a bit of usable stuff out there, but only trials and testing’s will reveal the true value of it. One promising purveyor of such data is the MODIS project. They make a whole bunch of datasets available for free online. Their data is acquired by two satellites, Aqua and Terra. Together, they manage to image the entire earth every one to two days. They publish data numerous times every year for a period spanning from the year 2000 to the present. The datasets produced are not very affected by cloud cover because they are composites of a stack of images taken over several days. For each pixel on the final image, only the “best” pixels are taken into account. The downside with the MODIS data is that the resolution is much coarser than for the LANDSAT data. Some of the higher resolution datasets they produce have a 250m resolution. One interesting dataset is the MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V005. Some of the promising layers are the EVI, the NDVI, and the visible and infrareds bands. At 250m, the resolution might be coarse. On the other hand, I generated an NDVI from the 2000 LANDSAT images and they really seem to show differentiation between the cultivated areas, the forested land and the clearcut areas. My hope is that decent results can be achieved with the MODIS data.

NDVI generated from the 2000 LANDSAT data

NDVI from MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V005
for the same area

Another promising dataset is MODIS/Terra Vegetation Cover Conversion 96-Day L3 Global 250m SIN Grid. Each dataset highlights the areas where change has occurred over the past year in the tree cover. I’m tempted to use several images (there is one dataset produced every three months) to create an animation showing change through time that reflects the transformation of the forest.

Example of MODIS/Terra Vegetation Cover Conversion 96-Day L3 Global 250m SIN Grid
taken from their site

Having talked with Jeff and Rodolphe today, we’re starting to envision the big picture of what is going to be produced from all this. It’s still early to have anything close to a perspective on the final product, but having a coarse estimation of what’s going on with the Borneo forests is definitely a priority. After that, if we can refine some areas of interest, or areas where we have better data, it’s even better. On an other note, using the DEM to figure out (or at least confirm the general assumption) that people are willing to plant palms in more remote areas over time is something that would be worth doing.

Pelleter des nuages

An important issue to be tackled concerning the mapping of Borneo is what to do with the clouds. Borneo is largely covered by rainforest so there are quite a lot of them obstructing the view. So what can be done? I’ll just free style a couple of options here (not all of them are feasible or practical.) Ideally, it would be great to erase all of them. This said, because some of these clouds are quite thick, I’m doubtful even a sophisticated ATCOR algorithm would see through them. Also, I don’t believe it would be relevant to use such an elaborate algorithm here. For one thing, we don’t have information available to use ATCOR reliably. This said, we still need to take into account the atmospheric interference. A more basic approach, using a SMAC like algorithm for instance, would be more suited (ex.: Production of CORINE2000 Land cover data using calibrated LANDSAT 7 ETM satellite image mosaics and digital maps in Finland .) Another way of getting rid of clouds is by comparing multiple images pixel by pixel and keeping the “best” ones. The challenge then becomes finding numerous images of the same areas, ideally taken over a relatively short time frame. In the end, we might have to completely give up on certain areas and concentrate on what we can work with.

On another somewhat related note, I’ve found a new source of data where we have all the bands of the LANDSAT images. Also, the metadata provided enables us to know when each image was taken so we can better take into account both the angle of the satellite and of the sun, something which will turn out quite useful to enhance our images, especially in conjunction with the DEM.