Automated individual tree crown detection and delineation (ITCD) using remotely sensed data plays an increasingly significant role in efficiently, accurately, and completely monitoring forests. This paper reviews trends in ITCD research from 1990–2015 from several perspectives—data/forest type, method applied, accuracy assessment and research objective—with a focus on studies using LiDAR data. This review shows that active sources are becoming more prominent in ITCD studies. Studies using active data—LiDAR in particular—accounted for 80% of the total increase over the entire time period, those using passive data or fusion of passive and active data comprised relatively small proportions of the total increase (8% and 12%, respectively). Additionally, ITCD research has moved from incremental adaptations of algorithms developed for passive data sources to innovative approaches that take advantage of the novel characteristics of active datasets like LiDAR. These improvements make it possible to explore more complex forest conditions (e.g., closed hardwood forests, suburban/urban forests) rather than a single forest type although most published ITCD studies still focused on closed softwood (41%) or mixed forest (22%). Approximately one-third of studies applied individual tree level (30%) assessment, with only a quarter reporting more comprehensive multi-level assessment (23%). Almost one-third of studies (32%) that concentrated on forest parameter estimation based on ITCD results had no ITCD-specific evaluation. Comparison of methods continues to be complicated by both choice of reference data and assessment metric; it is imperative to establish a standardized two-level assessment framework to evaluate and compare ITCD algorithms in order to provide specific recommendations about suitable applications of particular algorithms. However, the evolution of active remotely sensed data and novel platforms implies that automated ITCD will continue to be a promising technology and an attractive research topic for both the forestry and remote sensing communities.

Objective: This study investigated the relationships between PM_2.5 and 5 criteria air pollutants (SO₂, NO₂, PM₁₀, CO, and O₃) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM_2.5 and 5 air pollutants. Results: The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R² and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R² (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM_2.5. Conclusions: The traditional OLS and GWR are inadequate for describing the non-stationarity of PM_2.5. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial–temporal heterogeneity and possible solutions for modeling the relationships between PM_2.5 and 5 criteria air pollutants for Heilongjiang province, China.

The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-and-play enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images.

Region growing is frequently applied in automated individual tree crown delineation (ITCD) studies. Researchers have developed various rules for initial seed selection and stop criteria when applying the algorithm. However, research has rarely focused on the impact of tree-oriented growth order. This study implemented a marker-controlled region growing (MCRG) algorithm that considers homogeneity, crown size, and shape using airborne laser scanning (ALS) data, and investigated the impact of three growth orders (i.e., sequential, independent, and simultaneous) on tree crown delineation. The study also investigated the benefit of combining ALS data and orthoimagery in treetop detection at both plot and individual tree levels. The results showed that complementary data from the orthoimagery reduced omission error associated with small trees in the treetop detection procedure and improved treetop detection percentage on a plot level by 2%–5% compared to ALS alone. For tree crown delineation, the growth order applied in the MCRG algorithm influenced accuracy. Simultaneous growth yielded slightly higher accuracy (about 2% improvement for producer’s and user’s accuracy) than sequential growth. Independent growth provided comparable accuracy to simultaneous growth in this study by dealing with overlapping pixels among trees according to crown shape. This study provides several recommendations for applying region growing in future ITCD research.

Accurate quantification of individual tree parameters is vital for precise forest inventory and sustainable forest management. However, in dense forests, terrestrial laser scanning (TLS), which can provide accurate and detailed forest structural measurements, is limited to capturing the complete tree structure due to the lack of upper canopy views, resulting in an underestimation of tree height. Combining TLS with unmanned aerial vehicle laser scanning (ULS) is an effective way to overcome this limitation. Thus, it is vital to register multi-platform Light Detection and Ranging (LiDAR) data for various forestry applications. This study proposed three automated and nearly parameter-free optimized coarse-to-fine algorithms (i.e. FPFH-based optimized ICP (F-OICP), RANSAC-based optimized ICP (R-OICP), and NDT-based optimized ICP (N-OICP)) to accurately register TLS and ULS point data for individual tree crown delineation and parameters (diameter at breast height (DBH) and tree height) estimations in different forest types (i.e. coniferous, mixed broadleaf-coniferous, and broadleaf). Results showed that the proposed optimized algorithms had a good registration performance, with an average RMSE of about 8.3 cm for the transformation error; and obtained stable and high accuracies of individual tree crown delineation (ITCD) (F-score: 0.7), DBH (R²: 0.9, RMSE <1.85 cm), and tree height (R²: 0.8, RMSE <0.37 m) estimates for three forest types. F-OICP performed the best in tree height estimation, reducing the RMSE by 48%, 12%, and 12% compared to iterative closest point (ICP), R-OICP, and N-OICP, respectively. Stand type significantly impacted ITCD and individual tree parameter estimations. The ITCD and DBH estimation accuracy of coniferous forests were marginally higher than those of broadleaf forests (F-score: 0.78 vs. 0.78, DBH RMSE: 1.57 vs. 1.74), while those of mixed broadleaf-coniferous forests were the lowest (F-score: 0.71, DBH RMSE: 2.19). The accuracies of tree height estimates in coniferous forests were the highest (R²: 0.87, RMSE: 0.21 m), followed by mixed broadleaf-coniferous (R²: 0.84, RMSE: 0.37 m) and broadleaf (R²: 0.84, RMSE: 0.44 m) forests. This work developed automated, nearly parameter-free, and effective registration algorithms and recommended F-OICP to be the most appropriate for dense forests (i.e. natural secondary forests). The optimized registration algorithms facilitate the ability for the synergistic use of multi-platform LiDAR and offer appealing and promising approaches for future accurate quantification of individual tree parameters, efficient forest inventories, and sustainable forest management.

In recent years, airborne Light Detection and Ranging (LiDAR) that provided three-dimensional forest information has been widely applied in forest inventory and has shown great potential in automatic individual tree crown delineation (ITCD). Usually, ITCD algorithms include treetop detection and crown boundary delineation procedures. In this study, we proposed a novel method called region-based hierarchical cross-section analysis (RHCSA), which combined the two procedures together based on a canopy height model (CHM) derived from airborne LiDAR data for ITCD. This method considers the CHM as a three-dimensional topological surface, simulates stereoscopic scanning from top to bottom using an iterative process, and utilizes the individual crown and vertical structure of crowns to progressively detect individual treetops and delineate crown boundaries. The proposed method was tested in natural forest stands with high canopy densities in Liangshui National Nature Reserve and Maoershan Forest Farm, Heilongjiang Province, China. Its performance was evaluated by an accuracy procedure that considered both the relative position of treetops and overlapped area of crowns. The average overall accuracy achieved was 85.12% for coniferous plots, 83.86% for deciduous plots and 86.44% for coniferous and broad-leaved mixed forest plots. The results revealed that the RHCSA method can detect and delineate individual tree crowns with little influence from forest types and crown size. It could provide technical support for individual tree crown delineation in coniferous, deciduous and mixed forests with high canopy densities.