Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Abstract

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets

“…Low flight altitudes, generally below 150 m above ground result, due to regulatory restrictions on flight rules, in an increased number of collected images than those acquired from a satellite platform or piloted aircraft over the same area [36]. This leads to difficulties in performing in situ at-surface reflectance calibration measurements for all the images acquired by UAVs [37].…”

The Future Is Big—and Small: Remote Sensing Enables Cross-Scale Comparisons of Microbiome Dynamics and Ecological Consequences

“…Coupling microbial and remote sensing data may allow more accurate predictions about how microbiomes may shift in future climate change scenarios ( 9 , 11 , 14 , 56 ) and, in turn, better estimate the biogeochemical, ecosystem-level impacts of such microbial shifts, like carbon and nutrient cycling. We suggest that these approaches will enable more precise predictions of where and when harmful algal blooms ( 56 ) or disease outbreaks ( 13 , 18 ) are likely to occur and their effects on carbon storage or losses across variable landscapes ( 34 , 35 ). Such knowledge may improve conservation and management of natural resources by informing the timing and location of plant restoration activities, coastal development projects, or recreational use of land and waterways.…”

“…Meadows grow in shallow coastal waters; intertidal seagrass can be mapped at low tide by UAVs ( 17 , 31 ), and water correction algorithms allow mapping of subtidal meadows ( 32 , 33 ). We can derive ecosystem-level characteristics such as plant biomass and above-ground carbon stocks from UAVs and satellite measurements in response to disease outbreaks ( 34 , 35 ). Further, UAVs (1-cm resolution) ( Fig.…”

“…With machine learning and statistical modeling, implemented with training data and model validation, we can predict microbial dynamics and their ecological consequences. For example, georeferenced data sets may indicate microbial or chemical predictors of remotely sensed environmental change like algal blooms ( 19 , 26 ), plant productivity or biomass ( 34 , 35 , 46 ), and disease ( 13 , 14 , 18 ). These approaches can be applied to aquatic, marine, and terrestrial ecosystems.…”

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

“…In addition, the UAV-based digital images can be an efficient alternative to data collected with manual fieldwork, which is often arduous, involves destructive sampling, and may provide inconsistent and subjective information. As a result of these strengths, the UAVs are becoming very suitable platforms for agroforestry applications [4,5], such as vegetation characterization and mapping [6], land management [7], environmental monitoring [8], and especially to address diverse precision agriculture objectives [9] and plant phenotypic characterization in breeding programs [10,11].…”