Journal of Remote Sensing / 2021 / Article / Tab 1

Review Article

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

Table 1

Summary of focuses of existing major review papers related to this review for tree species (TS) classification.

Authors (year)TitleFocus/objective

Fassnacht et al. (2016) [21]Review of studies on tree species classification from remotely sensed dataQuantify general trends on TS classification in remote sensing studies; provide a detailed overview on the current methods for TS classification with typical sensor types; identify gaps and future trends for TS classification using modern remote sensing data
Yin and Wang (2016) [22]How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: a reviewProvide a review of techniques and methods for individual tree study using remote sensing data; summarize key factors that need to be considered to evaluate individual tree level forest inventory products; discuss existing problems and possible solutions in individual tree studies
Koenig and Höfle (2016) [23]Full-waveform airborne laser scanning in vegetation studies—a review of point cloud and waveform features for tree species classificationIdentify frequently used full-waveform airborne laser scanning-based point cloud and waveform features for TS classification; compare and analyze features and their characteristics for specific tree species detection; discuss limiting and influencing factors on feature characteristics and TS classification
Li et al. (2019) [24]Remote sensing in urban forestry: recent applications and future directionsSummarize recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multisource, multitemporal, and multiscale inputs; discuss the potential of remote sensing to improve the reliability and accuracy of mapping urban forests
Wang et al. (2019) [18]A review: individual tree species classification using integrated airborne LiDAR and optical imagery with a focus on the urban environmentOffer a review of the potential of LiDAR data to improve the accuracy of urban TS mapping and classification, fused with optical sensors’ data; discuss some future considerations for improving urban TS classification
Michałowska and Rapiński (2021) [25]A review of tree species classification based on airborne LiDAR data and applied classifiersProvide a review of the TS classification literature, data collected by LiDAR; evaluate the most efficient group of LiDAR-derived features in TS classification; identify the most useful classification algorithm to improve TS species discrimination