It is recognised that RS has contributed greatly to the understanding of the landscape, and in particular within forest and landscape management (Takao, Priyadi et al. 2010). The use of the high-end RS technologies and methods; specific software, specialised equipment, and unique analyses, have been applied widely at the research or strategy
level. Yet there remains a significant difference between the gathering and creation of sophisticated data, and the tactical application of RS generated insights to aid the end user at the practical, operations level (Takao, Priyadi et al. 2010). In an earlier US report for the White House Office of Science and Technology Policy, Peterson, Resetar et al. (1999) recognised that although existing monitoring and surveying capabilities existed, specifically programs that relied upon a combination of ground and aerial RS observations, they failed to meet the ever more complex operational requirements that large-scale environmental management needs. This was particularly so for end-user managers who were attempting to meet environmental policy requirements in an increasingly complex policy framework.
Peterson, Resetar et al. (1999) state that although RS technologies, in particular SLS, were not the all saving panacea that could solve all operational requirements, there were some elements of RS, such as the ability to capture low-cost imagery, that were operationally beneficial. However, further drawbacks were identified as being the associated increased in costs with the subsequent requirement for RS data processing and analysis, which were perceived as being prohibitively expensive for forest management operations. Nevertheless, the Peterson, Resetar et al. (1999) report concludes that should these requirements be met, then there would be a wide desire to develop an appropriate strategic vision for the use of RS across the US forest management industries. Subsequently, Wulder, Hall et al. (2005) report that the use of RS in the forest management sector has progressively increased, principally due to the better integration of optical elements of RS (e.g. aerial imagery, aerial LiDAR), improved database repositories and the wider use of GIS technologies. Furthermore, there has also been a sector wide implementation of technology that meets the needs of forest managers (Wulder, Hall et al. 2005). While the initial vision of the Peterson,
Resetar et al. (1999) requirements appear to be slowly being met, there remains the need to keep the development of RS techniques, methodologies and applications relevant for the specific needs of the end user. An additional benefit of RS approaches, is that RS is widely seen as a valid alternative to traditional destructive investigations where in order to fully account for biomass increment trees were frequently felled, or had parts of the tree removed and modelled, and plant material was collected and measured (Jonckheere, Fleck et al. 2004)
2.9.1
Predicted Remote Sensing Trends
Accurately predicting the future with any amount of certainty is an impossible task. Nevertheless, horizon scanning forecasts generally anticipate an increase in the use of RS techniques and methodologies worldwide, and in particular an exponential growth in the use of LiDAR in its varied forms; SLS, ALS, TLS, MLS etc. (Tehrany, Kumar et al. 2017). Within recent years there has been a shift towards the development of LiDAR techniques and scanning equipment focussing on improving measurement techniques, instrument function, accuracy and precision (Telling, Lyda et al. 2017). However, there is also the desire to enable the longer-term installation of LiDAR sensors for automated or semi-automated remote monitoring of environmental change. Unfortunately, the unavoidable problem of accessing high cost equipment is largely prohibitive. However, as a result of recent and ongoing developments in the automotive and MLS sectors, the availability of hard wearing scanners with the potential for permanent or semi-permanent installation is expected to be available within the near future (Telling, Lyda et al. 2017).
Concurrently, there are indications that different methodological approaches to 3D investigations are becoming more prevalent over studies that utilise LiDAR as the main data capture method. Image processing that utilises Structure from Motion
(SfM) can create 3D models that are comparable to TLS LiDAR, and, that have the advantage of generally being more cost effective and easy to use. Therefore, for some 3D vegetation modelling investigations, it is believed that SfM would have more potential future applications (Fonstad, Dietrich et al. 2013). A significant limitation of SfM, however, is that this image-based approach cannot penetrate vegetation canopies due to shading, and can only produce a surface 3D model. It is expected that future developments in this area would likely see a fusion between TLS and SfM approaches, where high resolution models can be created using the most advantageous features from both systems (Telling, Lyda et al. 2017).
Until relatively recent technological developments, LiDAR research has most often used satellites, fixed or rotary wing aircraft, mobile or terrestrial platforms to conduct data collection. A study by Jaakkola, Hyyppä et al. (2017) suggests that there is an opportunity to further develop the use of UAVs with lightweight laser scanners, such as the Puck LITE, for the acquisition of LiDAR data in a forest environment. This is largely due to the ongoing miniaturisation of this type of scanning technology and additional payload capability of UAV platforms (Wargo, Church et al. 2014).
Once operational problems such as errors in the direct estimation of tree parameters i.e. DBH, or sensor issues such as high levels of beam divergence and range issues can be overcome, Jaakkola, Hyyppä et al. (2017) suggest that there is the opportunity for the UAV scanning of the inside of tree canopies. There will also be the potential for the calibration of UAV scans with other larger scale airborne RS data acquisition campaigns. This future vision includes operations where UAV LiDAR forest data collection is completed with the integration of UAVs and automated piloting systems (Jaakkola, Hyyppä et al. 2017). At the time of writing, Jaakkola, Hyyppä et al. (2017)
assert that this technology is currently under development with a leading robotic systems company. These ongoing developments suggest that it is accepted that there is great potential in the remote sensing of trees, and that the research community will continue to develop new methods and technologies for the investigation and classification of trees in the environment. Ultimately leading to more informed and data driven environmental management.