The main goal of national forest programs is to lead and steer forest policy development and implementation processes in an inter-sectoral way (FAO 2006
). National forest monitoring systems contribute to forest programs through monitoring forest changes and forest services over time (FAO 2013
). To do so, they generally collect and analyze forest-related data and provide knowledge and recommendations at regular intervals. The collection of forest-related data and their analyses have continually evolved with technological and computational advances (Kleinn 2002
). For instance, ground measurements, such as diameter or height measurements, which were typically measured with measuring tape or forest compasses and relascopes, are now enhanced with new technologies, such as laser range finders. Furthermore, remote sensing is being increasingly used to improve ground sampling strategies (Maniatis and Mollicone 2010
), to calculate forested land area and area changes (INPE 2006
; INPE 2008
; Hansen et al. 2013
), and to detect many variables of interest such as forest fires, pest outbreaks, or trees outside forests (Barducci et al. 2002
). The use of remotely sensed data together with ground-based observations has gained a lot of attention for estimating greenhouse gas emissions and removals associated with forests, particularly in the context of REDD+ (GOFC-GOLD 2010
; GFOI 2014
). During the last decades, the amount of information collected during forest inventories has thus grown rapidly and has, in turn, improved our ability to survey and manage many services such as biodiversity, carbon sequestration, or recreation. However, national forest monitoring approaches remain very heterogeneous from one country to another, and many national systems have still not taken the full advantage of newly operational technologies, despite an increasing availability of free, or at least less costly, data. This is probably because the use of these technologies to assess forest structural properties is, for the most part, used by only a few specialists and is largely confined to the research sector. The objective of this paper is to raise awareness by presenting, in a comprehensible way, some existing and promising technologies for supporting national forest monitoring.
The number of approaches to estimating forest-related variables from field data, from remote sensing, or from a combination of the two is striking. A good illustration of the variety of the approaches is the Food and Agriculture Organization of the United Nations (UN-FAO) Forest Resources Assessment (FAO 2010
) that report 90 variables in all types of forests occurring in 233 countries, with region- or country-specific approaches, variables, and efforts. The data compilation by the UN-FAO thus constitutes a challenge, as it should follow a standardized format and methodology. To overcome this obstacle, the UN-FAO recently used a systematic sample of the free Landsat satellite imagery to report estimates of forest land area and area changes for forest, other wooded land and other land for the period 1990–2005 (FAO and JRC 2012
). This approach has the merit of providing critical information about land use changes in a globally standardized way but overlooks the continuous and intrinsic variability of forest structure within strata (Réjou-Méchain et al. 2014
). To account for such variability, recent works have relied on remote sensing signals to model in a continuous way the spatial and temporal variation of forest cover or forest carbon stocks (Asner et al. 2010
; Saatchi et al. 2011
; Baccini et al. 2012
; Hansen et al. 2013
; Achard et al. 2014
). However, huge discrepancies have been shown both between these different maps (Mitchard et al. 2013
) and between these maps and the national estimates (Achard et al. 2014
). Such discrepancies may be explained by differences in the definitions of forests, in the forest and land classification systems, in the approaches used to analyze the satellite imagery, or by the field inventory data used, e.g., Hansen et al. (2013
) focused on tree cover canopy, while FAO and JRC (2012
) focused on forest land use and change. A clear challenge to improve estimates of forest cover, carbon stocks, and dynamics is thus to effectively combine different top-down and ground-up approaches, a recommendation made by the United Nations Framework on Climate Change Convention in the context of reducing emissions from deforestation and forest degradation (REDD+) (UNFCCC 2009
). However, the combination of field and remote sensing information requires an appropriate use of definitions and descriptors at all levels. Countries themselves decide what level of detail or classification scheme they wish to use, leading to the abovementioned huge heterogeneity among national forest systems. Using the FAO Land Cover Classification System (Di Gregorio and Jansen 2005
) to label the various identified land cover classes is suggested by Global Forest Observations Initiative (2014
) as a promising option ensuring homogeneity between different country-specific legends and maps.
This article introduces some newly operational technological tools and approaches that may considerably improve national forest monitoring systems. This overview of forestry technologies and methods is the result of an extensive literature survey and was initiated by discussions held during the “Regional Technical Workshop on Tree Volume and Biomass Allometric Equations in South and Central America” in Costa Rica, on May 21–24, 2013. We firstly introduce some useful technologies in the context of forest monitoring and then discuss how these new technologies can be integrated when monitoring national forests.
Henry M, Réjou-Méchain M, Jara M, Wayson C, Piotto D, Westfall J, Fuentes J, Guier F, Lombis H, López E, Lara R, Rojas K, Del Águila Pasquel J, Montoya Á, Vega J, Galo A, López O, Marklund L, Milla F, de Jesús Návar Cahidez J, Malavassi E, Pérez J, Zea C, García L, Pons R, Sanquetta C, Scott C, Zapata-Cuartas M, Saint-André L 2015. An overview of existing and promising technologies for national forest monitoring. Ann. For. Sci.: 1-10. 10.1007/s13595-015-0463-z.