In this paper, we highlighted the tenuous future for mangroves in Myanmar and magnified arguments for greater protection for a critical coastal ecosystem, which is particularly important as Myanmar strives to become more integrated into the regional and global markets for agriculture and aquaculture products. The fate of mangroves in Myanmar will be tied to the effectiveness of conservation policies while under pressure to convert to more lucrative but environmentally harmful land uses.
I have been sharing the coding materials and resources related to my research on my GitHub page but I have not had the chance to blog about these here at all, even briefly. And so, let me make this short introduction on my activities on GitHub, which does seem to show (and share) more of my research activity than in this blog. It’s just been about time too that I updated the ‘mugshot’ on my GitHub page, which used to show an old (younger) photo of me, and now, more appropriately, shows a recent photo of me taken during the wedding of a dear colleague.
Here, we combine Landsat and L-band SAR data for mapping changes in land cover in a tropical biodiversity hotspot undergoing rapid agricultural plantation development and forest conversion.
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.