Abstract
Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.
Highlights
Given the explicit advantages of the three “multiple” methods summarized in Table 6, these methods are truly beneficial to improving tree species (TS) classification with various sensors’ data
A total of 231 publications associated with investigating tree species (TS) classification and mapping using various remote sensing sensors’ images were reviewed
(1) During last two decades, most application studies on TS classification were using Very high spatial resolution (VHR) satellite MS data and airborne HS data and light detection and ranging (LiDAR) data. This was just a perfect match during the advent of advanced remote sensing technologies (VHR satellite MS and airborne HS sensors/systems during the late 1990s; the LiDAR technique started in 1960s, its application in remote sensing in TS mapping was most popular after 2000). This is because the properties of high spatial/spectral resolutions and vertical/geometric features are very suitable for TS classification and mapping
Summary
And accurate information on the status and structure of TS and forest species composition is crucial for developing strategies for sustainable management and conservation of artificial and natural resources. Such TS information is needed for many application purposes. Especially satellite remote sensing techniques, have the advantages of overcoming the shortcomings of the traditional methods to rapidly obtain the TS information at a local, regional, or even global scale. Previous research has proved that accurately classifying individual TS and mapping tree canopy and structure using moderate-resolution satellite data are difficult or even impossible [6,7,8,9,10,11]
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