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.

Frequent and intense anthropogenic fires present meaningful challenges to forest management in the boreal forest of China. Understanding the underlying drivers of human-caused fire occurrence is crucial for making effective and scientifically-based forest fire management plans. In this study, we applied logistic regression (LR) and Random Forests (RF) to identify important biophysical and anthropogenic factors that help to explain the likelihood of anthropogenic fires in the Chinese boreal forest. Results showed that the anthropogenic fires were more likely to occur at areas close to railways and were significantly influenced by forest types. In addition, distance to settlement and distance to road were identified as important predictors for anthropogenic fire occurrence. The model comparison indicated that RF had greater ability than LR to predict forest fires caused by human activity in the Chinese boreal forest. High fire risk zones in the study area were identified based on RF, where we recommend increasing allocation of fire management resources.

This study examined how the level of catchment discretization influenced the model parameterization and output uncertainty of the Storm Water Management Model (SWMM) 5.0. Two catchment delineations for a highly urbanized sewershed in Syracuse, New York were developed: (1) the macroscale model containing a minimum required number of subcatchments to retain the original sewer network properties; and (2) the microscale model in which each subcatchment was defined for a unique soil and land-use combination. For both scales, the model parameters were calibrated and the uncertainty of model outputs was quantified using the generalized likelihood uncertainty estimation (GLUE) methodology. Then, calibrated posterior parameter sets were applied at micro- and macroscales individually to a second sewershed, which was also delineated at both micro- and macroscales, to test observed versus simulated flows. The results indicated that the catchment disaggregation level had a great impact on both parameterization and simulation results, and the majority of the parameters were sensitive to the modeling scales. Overall, the posterior parameters calibrated based on the microdelineation resulted in a higher degree of reduction in output uncertainties for both calibrated and validated sewersheds. Hence, it can be argued that the calibrated parameters obtained, based upon the macrodelineation, would result in reduced confidence in simulated runoff for another site unique in its characteristics, whereas the posterior parameters derived from the microdelineation could provide a higher confidence level in terms of parameter transferability for modeling other, particularly ungauged sites.

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.

Individual tree crown delineation (ITCD) is a fundamental and vital component of individual tree-based forest inventory. Region-growing algorithms have been widely developed and applied in ITCD studies. Although individual treetops are typically designated as initial seeds, most region-growing algorithms do not consider tree competition when tree crowns touch each other and have some overlapping area. An agent-based region-growing (ABRG) algorithm was constructed for ITCD using airborne laser scanning (ALS) data considering both growth and competition mechanisms. Three region-growing algorithms were compared for both coniferous and deciduous trees. The algorithms were: (1) marker-controlled region growing with simultaneous growth (MCRG); (2) agent-based region growing with one-way competition (ABRG1W) in which taller trees limit the growth of shorter trees, but shorter trees have no impact on taller trees; and (3) agent-based region growing with two-way competition (ABRG2W) in which taller and shorter trees influence each other. Incorporating competition into the ABRG algorithm resulted in a statistically significant improvement of the accuracy of individual tree delineations for conifers and a statistically significant improvement in the accuracy of plot-level crown area for deciduous trees. For conifers, two-way competition provided more accurate results because of increased competition in coniferous plots. For deciduous plots, the accuracy of one-way competition had a strong correlation (0.92) with relative spacing (an index of competition level). This result is consistent with the one-way competition mechanism because the taller (larger) deciduous trees would limit the growth of small trees as the competition level declines. The degree of improvement in tree crown delineation accuracy by ABRG was related to the characteristics of tree height and density (i.e. distance between trees) in the plots.