Historically, the main purpose behind forest management was for the production of timber; through increasing growth yields and optimising harvesting for the maximum commercial return (Wulder, Hall et al. 2005). While this commercial element remains to some degree in the majority of forest and woodland management activities, in more recent times the underlying justification for forest and woodland management has shifted to include assessment of wildlife potential, recreation (both current and potential use), aesthetics (internal and external), biological and conservation requirements (Watson 2006). This development means that the basic forest inventory
assessment of growth patterns and tree volumes no longer provides the most suitable data for the management task. Operational level monitoring now has to include sufficient information on forest structure, the success of silvicultural intervention methods, habitat, biodiversity, hydrology and soil profiling, as well as the long-term planning of the entire production cycle (Jennings, Brown et al. 1999, Wulder, Hall et al. 2005).
As a result, many variables must be measured within trees or in forest stands, for the purposes of understanding growth patters or enabling the prediction of future yields, or for understanding the type and availability of habitat within a given area. (Strahler, Jupp et al. 2008) describe that for a great number of forest inventory and management applications, the measurement of vegetation structure is essential to aid better understanding of the tree stock attributes. Latifi, Fassnacht et al. (2015), highlight that the expansive forests of central Europe are still monitored with conventional, large- scale terrestrial inventories, where the operational management of the forests is considered challenging by the rapid environmental changes as a response to natural disturbances and many “multilayer silvicultural systems” that are in use throughout the region.
Whatever the justification is for measuring trees, for a long time, simple, easy-to- reach measurements have been taken from trees, and these are used as the input variables into a wide range of allometric equations, looking to find predictive relationships. For example, the standard measurement of tree (or top) height and DBH (Dassot, Constant et al. 2011). Several studies show however, that frequently there are significant errors in the use of allometric equations in the measurement of trees, volume estimates or for other forestry applications that require later rectification
(Dassot, Constant et al. 2011, Ahmed, Siqueira et al. 2013, Mugasha, Mwakalukwa et al. 2016). Typical GR tree measurements taken at established plots or individual trees, used for the determination of tree characteristics, include the readily accessible features of the tree; species, location, overall height, DBH, crown height, crown extent, stem density at plot or location, and a general site description (Lovell, Jupp et al. 2003). It is believed that issues with large scale environmental management are being overcome with the increased use of Light Detection and Ranging (LiDAR) RS techniques. It is believed that this will reduce issues of what are considered to be time- costly, manual, field based methodologies that when sampled can only provide rough estimates of stand attributes, and cannot account for large amounts of variability in site terrain and vegetation changes (Gorrod and Keith 2009, Dassot, Constant et al. 2011, Hamraz, Contreras et al. 2016).
Lindberg, Holmgren et al. (2012), also advise taking detailed GR observations of control trees, as this data is typically geo-referenced and frequently considered a data ‘certainty’ upon which many environmental and vegetation models are based (McNellie, Oliver et al. 2015). Valbuena (2014) and Lovell, Jupp et al. (2003) also highlight that the majority of ALS applications for forestry investigations will require a combination of additional data sources, in particular field measurement or GR data, and for complex investigations, even co-registered combinations of ALS with terrestrial laser scanning (TLS) and acquired GR data are shown to be effective (Hauglin, Lien et al. 2014).
2.2.1
Plot Establishment for Site Surveys
The connection between undertaking forest surveying and establishing specific site areas or plots is well established in practice, as in particular, ALS LiDAR becomes ever more frequently used in forest inventory applications (Hauglin, Lien et al. 2014).
To acquire accurate geolocation information about survey plot location or the biophysical properties of the vegetation within them, i.e. GR data, it is important to determine the field information with high accuracy as the GR data will serve as a cross reference to the RS data. Subsequently, this requirement leads to the use of high- gain global navigation satellite system (GNSS) or global positioning system (GPS) to provide this information for plot level or stand level data acquisition (Latifi, Fassnacht et al. 2015). In a comparative study of characterising forest structure through the combination of ALS LiDAR, RapidEye satellite imagery and auxiliary environmental GR data, Dash, Watt et al. (2016) describe an extensive set of field measurements were taken from a network of 493 field plots (0.06 ha), across a total area of 180,000 ha (surveyed area 29.58 ha/0.01% of total area). The plot centres were located with high accuracy GPS and corrected using permanent, local, differential base stations. Common forest and tree attributes were recorded at each plot, specifically attributes that are regularly used in forest management and inventory operations e.g. DBH, tree height etc. Approximately 12% of these plots (60 sites), were randomly selected and used for later model validation purposes (Dash, Watt et al. 2016).