Remote sensing in forestry: current challenges, considerations and directions

Abstract

Remote sensing has developed into an omnipresent technology in the scientific field of forestry and is also increasingly used in an operational fashion. However, the pace and level of uptake of remote sensing technologies into operational forest inventory and monitoring programs varies notably by geographic region. Herein, we highlight some key challenges that remote sensing research can address in the near future to further increase the acceptance, suitability and integration of remotely sensed data into operational forest inventory and monitoring programs. We particularly emphasize three recurrent themes: (1) user uptake, (2) technical challenges of remote sensing related to forest inventories and (3) challenges related to map validation. Our key recommendations concerning these three thematic areas include (1) a need to communicate and learn from success stories in those geographic regions where user uptake was successful due to multi-disciplinary collaborations supported by administrative incentives, (2) a shift from regional case studies towards studies addressing ‘real world’ problems focusing on forest attributes that match the spatial scales and thematic information needs of end users and (3) an increased effort to develop, communicate, and apply best-practices for map and model validation including an effort to inform current and future remote sensing scientists regarding the need for and the functionalities of these best practices. Finally, we present information regarding the use of remote sensing for forest inventory and monitoring, combined with recommendations where possible, and highlighting areas of opportunity for additional investigation.

High-throughput Soxhlet extraction method applied for analysis of leaf lignocellulose and non-structural substances

“…To do this, field samples are collected and subsequently analysed in the laboratory to measure the studied biochemicals. Field data measurements are pivotal in the parametrisation, calibration, validation, and upscaling of remote sensing models [ 19 , 20 ]. However, acquiring ground truth data through fieldwork poses a substantial challenge.…”

Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

“…Remote sensors are carried by three major platform groups: satellite (spaceborne) characterised by the highest land cover rate; airborne, as a primary source for accurate RS data on a state level; and recently, UAS (Unmanned Aerial Systems) platforms, providing the most accurate data and cheapest solutions but with the least spatial coverage. With the rapid elaboration of multiple modern RS systems and new algorithms such as machine and deep learning modelling, RS operations have reached a state-of-the-art status in their application to forestry [20,21].…”

“…Understanding the physiological and biochemical characteristics of infested trees with respect to specific spectral information is critical to successfully applying VI-based ML and DL algorithms [28]. Detailed comparisons of the accuracy and effectiveness of current methods for detecting spruce bark beetle have been undertaken [18,29], and the MS efficiency of UAV/UAS imagery [8,30,31] and the MS efficiency of Sentinel-2 imagery [32,33], including a review of the factors influencing the accuracy of the detection of infestations [28] and current challenges of RS in forestry [20].…”

Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya

“…Remote sensing is widely applied in forest monitoring in many countries globally [16,17]. For example, the Global Forest Watch maintains a near-real-time online system on global forest cover, with its data sourced mainly through remote sensing approaches [18].…”