Blogs began as virtual notebooks, places people kept track of the things that caught their interest. And the flood of information makes it hard to keep track of anything on a regular basis. So why not challenge yourself?
Pick up an atlas and you’ll find vegetation maps of some sort which delineate different vegetation types (biomes, life zones, or something else of the sort). At broad scales you’ll see things like savannas and rainforests, at finer scales you’ll see things like oak-hickory forests. Everywhere you look, you’ll see vegetation mapped with what appears to be a high degree of confidence. But ever stop to wonder what those colourful areas on the map come from?
Traditionally there were two ways to map vegetation – ground surveys and aerial surveys. In the 19th century people like Charles Flauhert at Montpellier began mapping and classifying vegetation. People like Robert Smith brought this work back to Britain; after his death, his brother William Gardner Smith continued his work. W.G. Smith was one of the organising members of the British Vegetation Committee (together with Arthur Tansley and others), which later gave rise to the British Ecological Society. They used field surveys to map different vegetation types, and standardised sampling procedures to characterise the different forms of vegetation. In America, E. Lucy Braun, Edgar Transeau and William S. Cooper played a similar pioneering role in vegetation mapping.
The availability of aerial surveys, starting in the 1920s and 30s, added another dimension to vegetation surveys. Now, it was possible to collect data over a large area, but the quality of the data was low – you could probably distinguish forest from grassland, farmland from urban. As aerial photography developed it was possible to extract more information about vegetation – coniferous forest from deciduous forest, open forest from closed forest, even distinguish younger successional forest by the roughness of the canopy (younger forests tend to be dominated by even-aged stands of the same species, so the canopy is made up of relatively similar trees. Older forests are more uneven in age and species composition, leading to a canopy that varied more in height and colour.) Things began to take off as additional sensors, many of them space-based, became available. By measuring additional wavelengths, outside the visible spectrum, and by actually collecting quantitative data about the absolute reflectivity in each band, it was possible to develop much more structured data from remotely sensed data. Some of these sensors can estimate the amount of chlorophyll in vegetation, while others can measure vegetation structure.
In the March 2013 issue of Biotropica, Sebastián Martinuzzi, William Gould and others used LIDAR data to classify dry forest1 in Guánica, Puerto Rico, into “forest type” (semi-deciduous forest, semi-evergreen forest, scrub forest, dwarf forest and mesquite forest, a “relatively homogeneous stand of Prosopis pallida with a dense herbaceous understory”) and beyond that, into successional stage: mid-secondary forest with a logging2 past, late secondary forest with a logging past, late-secondary forest with an agricultural past, and ‘primary’ forest (undisturbed for more than 90 years). This part of their analysis they restricted to semi-deciduous forest areas. Since LIDAR penetrates the forest canopy, it can identity the underlying land surface (and thus, elevation and topography), and the height not only of the forest canopy, but also of various layers in the canopy.
It’s hard to explain how appealing the idea of being able to map and age these forests using remote sensing can be. I’ve mapped forest cover using aerial photographs – first you map current forest cover, then you map historical forest cover, and use that to estimate areas of “older growth” forest. Not only is it tedious, there’s also a risk of ending up with subjective results. If an area was cut-over and regrew in the interval between two images, you may never noticed it. And if an area lost all its understorey without losing its canopy, you’d probably be unaware.
While avoiding subjectivity in that regard, this approach added a different sort of subjectivity.
We used GPS surveys and visual interpretation of 1-m spatial resolution color aerial photos supported by expert knowledge, for a total of 83 sample locations. All samples represented an area of at least 30 m by 30 m of the same forest type (to coincide with the geospatial grain size used by this study), and were separated by >60 m (consistent with Agosto Diaz 2008).
As far as I know, the definitions of the different forest types in Guánica Forest are subjective. They are somewhat self-evident, as long as you stay in the park (and avoid certain grey areas), but I’m not aware of any formal delineations of these vegetation types. Granted, there’s nothing unique about that, but having poked around in Guánica Forest, uncertain (at times) whether I was in one forest type or another…I’m a tad bothered.
Perhaps the thing that interested me most was this
The most important predictors in the LiDAR canopy model included the median absolute deviation of vegetation heights (HMAD), the 90th percentile of vegetation heights (H90th), and the percent of returns >1.0 m (CDENSITY2; Table 3).
Canopy closure is a useful, but incomplete predictor of forest type. The most closed canopies tend to be in ravines and arroyos (semi-evergreen forest), but certain young secondary forests could also have a closed canopy (like the Prosopis forest they mentioned). But when you combine that with with the variation in vegetation height, you can probably separate out the species-poor secondary forests dominated by Prosopis or Leucaena leucocephala, since these forests lack structural diversity – most of the trees are the same age and belong to just a single species. This has me wondering…
- Martinuzzi, Sebastián, William A. Gould, Lee A. Vierling, Andrew T. Hudak, Ross F. Nelson, and Jeffrey S. Evans. 2013. Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR. Biotropica 45(2): 135-146
- Logging in this case means harvest for fence posts and charcoal production; this process left the rootstocks intact and allowed rapid coppice regeneration.
- Lugo, Ariel E., Jose A. Gonzalez-Liboy, Barbara Cintron, and Ken Dugger . 1978. Structure, productivity, and transpiration of a subtropical dry forest in Puerto Rico. Biotropica, 10: 278–291.