Cartography involves the drawing of maps, which in fact is both a science and an art. I’ve come to learn in the course of my years of study and professional experience that mapmaking is not as easy as it seems. Maps tell a story. And for maps to effectively tell that story, the mapmaker should think not only about the main subject of the map, but should also give due consideration to the various map elements such as their position and balance across the map, as well as the accuracy of the measurements, and the styling of these elements which involve, for example, the choice of colors, line weights, among other considerations.
Here’s another suite of software tools that land change scientists and geospatial analysts should have in their toolbox: Open Foris.
Open Foris is a set of free and open-source software tools designed to facilitate flexible and efficient data collection, analysis, and reporting for environmental monitoring such as forest inventories, climate change reporting, socio-economic surveys, biodiversity assessments, land use/cover change assessments, among others [1]. This initiative, resulting from the collaborative efforts of numerous public and private institutions, is hosted by the Food and Agriculture Organisation of the United Nations.
A fantastic opportunity for land change science studies in the near immediate future is the growing utility of cloud computing geospatial analysis platforms such as Google Earth Engine. Combined with the ever-increasing availability of earth observation datasets, these kinds of technologies are expected to facilitate more regional- to global-scale analyses, as well as in-depth local-scale investigations, of land system changes.
Since the latter part of last year, I have been using Earth Engine first hand for my land change analyses.
A recent study mapped the distribution and drivers of global mangrove forest change from 1996 to 2010 [1]. The study, published in PLoS ONE by members of the ALOS Kyoto & Carbon Initiative led by the Japan Aerospace Exploration Agency, investigated the drivers of mangrove land use and land cover change across the tropics using time-series L-band synthetic aperture radar sensors, particularly JER-1 SAR and ALOS/PALSAR mosaic data.
One of the major findings showed that Southeast Asia contained the largest proportion of mangrove forests globally (33.