Remote sensing is a powerful set of technologies that has enabled Land Change Science (LCS) to develop as a field. It is a major tool for LCS and its use in distinguishes LCS from other environmental change disciplines. It empowers researcher to ask big questions, questions that are not possible to answer without a complete global view of the world or an extensive availability of data. Questions like ‘What types of lands are being converted for agricultural use?’ (Gibbs et al, 2009; Koh et al. 2010), or ‘How many times a year do smallholders crop their fields?’ (Jain et. al.,2013), or even ‘What was the global land cover like through history?’ (Ramankutty and Foley, 1999).
Remote sensing can provides global datasets, which LCS researchers can exploit to discover the mechanisms driving land change. Gibbs et al. (2009) is an example of a question that would not be able to be answered without remote sensing. Through the utilizing of randomly sampled Landsat imagery, which was classified and measured for land change from forested to agricultural lands, they were able to determine that the global demand for agricultural products is being supplied through converting of tropical forest land to farms and pastures. They found that between 1980 and 2000 more than 55% of new agricultural land came at the expense of intact forests, and another 28% came from disturbed forests. This question on such a large scale could not have been answered without the use of remote sensing.
The Gibbs et al. (2009) study would not had been possible to do without satellite imagery spanning 3 decades, and this show the importance of not only global but continuous collection of remotely sensed data. Many environmental models are limited by the availability of land use data. Remote sensing satellites missions like Landsat Thematic Mapper (TM) have only around since 1982 (Markham et al. 2004), limiting the scope of data available. For this reason backcasting land use is important, like in the work of Ramankutty & Foley (1999). In this study the authors estimated landuse over three centuries by backcasting 1992 land cover data, reconstructing historical croplands based off of databases and land cover models. This dataset can only be seen as an estimate, and some of the land covers can be debated, but it provides a reasonable predication of what the world has looked like in term of human use over the last three centuries.
Although remote sensing can answer the big questions, it can also have applications on the smaller scale like in Jain et al. (2013) study on cropping intensity of smallholder farms. This paper showed the possibilities of overcoming limits in spatial and spectral resolution through the integration of multiple sensors. Answering a question from space about smallholders crop intensity, or how many crops they plant per year, seems like a impossible feat, but the development of such tools are important in assessing food security, especially since about half rural populations accesses food through small holding means. The Jain et al. (2003) pushed remote sensing to the limits finding creative ways to overcome challenges by integrating multiple data sources over different methods. Some issues like climatic patterns, or cloud cover due to monsoonal season where impossible to overcome. It shows that LCS is never going to happen in a controlled environment, since the data collected is from the world, there will always be challenges with it’s application.
Remote sensing is an amazing technique that has made and will continue to make a difference in LCS research. It is plagued with challenges in data collection, intermittent availability, and difference is spectral and spatial resolutions, but its potential at opening our eyes to how the world works is immense. Remote sensing technology is still relatively cutting edge and young compared to many techniques, but as it continues to develop, our understanding of the world will develop as well.
Works Cited
Gibbs, H. K., Ruesch, A. S., Achard, F., Clayton, M. K., Holmgren, P., Ramankutty, N., & Foley, J. A. (2010). Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proceedings of the National Academy of Sciences, 107(38), 16732-16737.
Jain, M., Mondal, P., DeFries, R. S., Small, C., & Galford, G. L. (2013). Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sensing of Environment, 134, 210-223.
Koh, L. P., Miettinen, J., Liew, S. C., & Ghazoul, J. (2011). Remotely sensed evidence of tropical peatland conversion to oil palm. Proceedings of the National Academy of Sciences, 108(12), 5127-5132.
Markham, B. L., Storey, J. C., Williams, D. L., & Irons, J. R. (2004). Landsat sensor performance: history and current status. Geoscience and Remote Sensing, IEEE Transactions on, 42(12), 2691-2694.
Ramankutty, N., & Foley, J. A. (1999). Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global biogeochemical cycles, 13(4), 997-1027.
No comments:
Post a Comment