Forest Monitoring

The need to be able to accurately monitor forest cover and quality is crucial to understanding the costs of deforestation. Forest monitoring is defined by the International Union of Forest Research Organizations (IUFRO) as the regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Without robust statistics, understanding the loss of biodiversity and reduction of carbon sequestration capacity that results from deforestation becomes much more difficult. With the wide range of definitions for what counts as forest and diversity of methods of measuring forest cover, national data collection methods have never been standardized. 

 

In the past, foresters would use field and aerial surveys to collect forest cover data, and aerial photography was used for plot-based analysis of forest stocks. With the advent of satellite imaging technology, it is much more common to use remote sensing techniques to monitor forest data, in particular tropical deforestation. Researchers have often used the US Geological Survey’s Landsat satellite program and Worldwide Reference System to provide satellite imagery data to analyze forest trends. Through a combination of satellite imagery and forest inventory data, and supercomputing technology, a Yale-led study estimated that there are more than 3 trillion trees on Earth. Through projections of global tree density, the study estimated that over 15 billion trees are cut down each year - as in this study, information from satellite imagery can help resource managers more accurately determine the costs of land-use changes and economic benefits of forest ecosystem services.

The Landsat satellite platforms carry three main sensors, including the Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+), and improvements in data sharing through the Internet has decreased the costs of using satellite imagery. Landsat imagery can help locate and map out logging transportation networks, and identify burn scars where clearing has taken place - it is typically used to distinguish between forest and non-forest land cover, and to monitor land-use changes. However, one challenge with Landsat imagery is that it cannot penetrate cloud cover, and so researchers also rely on satellite platforms that can provide radar imagery as well.  

Independent verifications of forest statistics are also necessary and useful, as government interpretations of satellite imagery are not always reliable due to political agendas or lack of capacity. In areas like Indonesia, where tropical deforestation is widespread across a large geographic area, it is difficult to detect illegal logging from satellite imagery. Nevertheless, to detect changes over large areas, especially in more remote regions, satellite imagery provides an important resource in understanding and monitoring issues of deforestation. Other satellite technologies like radar and the light-based LiDAR imaging systems are able to complement optical imaging systems like Landsat. 

In regards to ground-based monitoring systems, traditional methods of measuring tree volume can be very time consuming and costly. Relevant data include tree height, diameter at base height, and stem density, which cannot be captured by satellite imaging. Ground-based monitoring is particularly important to REDD+ monitoring, reporting, and verification of tree carbon stocks. LiDAR imaging systems can also be used on the ground, and can measure and record physical characteristics (i.e. of a forest stand) by emitting a laser and measuring the time it takes to reflect back. When resources for such high-tech monitoring is unavailable, manual methods of forest monitoring are required - some studies have concluded that participatory forest monitoring programs, which involve local communities using either their customary or culturally appropriate data collection methods, or conventional scientific methods, can substitute for professional data collectors. Whether or not such programs should use advanced technology like GPS, GIS systems, and online tools is debated by practitioners and academics, as some perspectives see the use of such technology as unsustainable, while others see it as necessary for communicating information. Forest monitoring on the ground provides information that complements data collected via satellite, allowing scientists and communities to measure and observe changes in forest health and cover. Read through to learn more about forest monitoring in the Amazon Basin and the Congo Basin


Sources: 

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., … & Bradford, M. A. (2015). Mapping tree density at a global scale. Nature.

Fry, B. P. (2011). Community forest monitoring in REDD+: the ‘M’in MRV?. Environmental Science & Policy, 14(2), 181-187. 

Fuller, D. O. (2006). Tropical forest monitoring and remote sensing: A new era of transparency in forest governance?. Singapore Journal of Tropical Geography, 27(1), 15-29. 

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Hopkinson, C., Chasmer, L., Young-Pow, C., & Treitz, P. (2004). Assessing forest metrics with a ground-based scanning lidar. Canadian Journal of Forest Research, 34(3), 573-583. 

Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850-853. 

Holck, M. H. (2008). Participatory forest monitoring: an assessment of the accuracy of simple cost–effective methods. Biodiversity and Conservation, 17(8), 2023-2036. 

Lim, K., Treitz, P., Wulder, M., St-Onge, B., & Flood, M. (2003). LiDAR remote sensing of forest structure. Progress in Physical Geography, 27(1), 88-106. 

Sader, S. A., Hayes, D. J., Hepinstall, J. A., Coan, M., & Soza, C. (2001). Forest change monitoring of a remote biosphere reserve. International Journal of Remote Sensing, 22(10), 1937-1950.