Object-based image analysis and machine-learning classification were applied to multispectral camera array data acquired by a small rotating blade unmanned aerial vehicle (UAV) over a hardwood forest in eastern Ontario. White birch, aspen, and two species of maple were surveyed in the field. Images were segmented and the resulting objects were visually confirmed to correspond with the sampled tree crowns. Following the application of machine-learning classification using the Random Forest algorithm, an independent validation sample of 23 tree crowns was, overall, approximately 78% correct. Aspen and birch were the most distinct species; the two maples appeared to be confused with each other and with immature trees and understory shrubs. Classification accuracy, commission errors, and variable importance were interpreted to be consistent with experience documented in aerial photointerpretation selection and elimination keys for northern hardwoods. Additional tests are recommended to more fully analyse the accuracy of deciduous tree species classification using digital analysis of high spatial resolution multispectral UAV imagery.

The use of multispectral cameras deployed on unmanned aerial vehicles (UAVs) in land cover and vegetation mapping applications continues to improve and receive increasing recognition and adoption by resource management and forest survey practitioners. Comparisons of different camera data and platform performance characteristics are an important contribution in understanding the role and operational capability of this technology. In this article, object-based classification accuracies for different cover types and vegetation species of interest in central Ontario were examined using data from three UAV-based multispectral cameras. Five land-cover classes (forest, shrub, herbaceous, bare soil, and built-up) were determined to be up to 95% correct overall with calibrated multispectral Parrot Sequoia digital camera data compared to independent field observations. The levels of classification accuracy decreased approximately 10–15% when spectrally less capable consumer-grade RGB sensors were used. Multispectral Parrot Sequoia classification accuracy was approximately 89% when more detailed vegetation classes, including individual deciduous tree species, shrub communities and agricultural crops, were analysed. Additional work is suggested in the use of such UAV multispectral and point cloud data in ash tree discrimination to support emerald ash borer infestation detection and management, and in analysis of functional and structural vegetation characteristics (e.g. leaf area index).

Land cover characteristics remain of particular interest to the monitoring and reporting communities, and approaches for generating annual maps of land cover informed by change information derived from long time series are critically needed. In this study, we demonstrate and verify the utility of disturbance and recovery metrics derived from annual Landsat time series to inform the classification of annual land cover over a > 1.2 million hectare forest management area in the Boreal Mixedwood Region of northern Ontario, Canada. Annual land cover maps were generated, producing temporally informed products and compared to the established approach of using single-date spectral variables and indices. The Random Forest (RF) classification algorithm was used to classify land cover annually between 1990 and 2010, followed by the application of an annual temporal filter to remove illogical land cover transitions. Change detection in the study area had an overall accuracy of 92.47%. The use of time series metrics in the classification of land cover improved overall accuracy by 6.38% compared to single-date results. Using a separate independent reference sample, the RF classification approach combined with postclassification transition filtering resulted in an overall classification accuracy of 87.98%. The use of annual change and trend information to guide land cover, which is further informed by logical land cover transition rules, points to the creation of efficient, robust, and reliable land cover products in a transparent and operational fashion.

In this study we examined forest disturbance, largely via forest harvest, over three decades in a coastal temperate forest on Vancouver Island, British Columbia, Canada. We analysed how disturbance history relates to current canopy structural conditions by interpreting the relationship between light detection and ranging (lidar) derived canopy structure and forest disturbance trajectories derived from Landsat images to assess if a particular stand structural condition is to result based on disturbance histories. The lidar data were obtained in 2004, and are used to relate forest structural conditions at the end of the Landsat time series (1972–2004), essentially providing for a measure of resultant structure emerging from the spectral trends captured. Correlation analysis was applied between lidar-derived canopy structure (canopy cover and height) and Landsat spectral indices, such as the Tasseled Cap Angle (TCA), which showed a strong correlation coefficient (r = 0.86) with canopy cover. TCA was then use... Dans cette étude, nous examinons la perturbation des forêts, liée en grande partie à l'exploitation forestière, pendant trois décennies dans une forêt tempérée côtière sur l'ile de Vancouver, Colombie-Britannique, Canada. Nous avons analysé comment l'historique des perturbations est lié aux conditions structurelles du couvert forestier actuel par l'interprétation de la relation entre la structure du couvert forestier dérivée du lidar («light detection and ranging») et les trajectoires des perturbations forestières dérivées d'images Landsat pour évaluer si la condition structurelle d'un peuplement résulte de l'historique des perturbations. Les données lidar ont été obtenues en 2004, et sont utilisées pour relier les conditions structurelles forestières à la fin de la série temporelle Landsat (1972–2004), fournissant essentiellement une mesure de la structure résultante des tendances spectrales capturées. Une analyse de corrélation a été appliquée entre la structure du couvert forestier dérivée du lidar (la...

In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include “logging”, “plantation”, “shifting cultivation”, “urban expansion”, “water invasion”, “recovery”, “other change”, and “no change”. The overall accuracy of the attribution model at the patch and area levels was 84.7% and 96.0%, respectively. The estimated disturbance area from the attribution model accounted for 10.0% of the study area. The largest disturbance agent was found to be logging (59.8%), followed by water invasion (14.6%). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments.