Explore the vast range of titles available.

Fire as a dominant disturbance has profound implications on the terrestrial carbon cycle. We present the first ever multi-decadal, spatially-explicit, 30 meter assessment of fire regimes across the forested ecoregions of Canada at an annual time-step. From 1985 to 2015, 51 Mha burned, impacting over 6.5% of forested ecosystems. Mean annual area burned was 1,651,818 ha and varied markedly (σ = 1,116,119), with 25% of the total area burned occurring in three years: 1989, 1995, and 2015. Boreal forest types contained 98% of the total area burned, with the conifer-dominated Boreal Shield containing one-third of all burned area. While results confirm no significant national trend in burned area for the period of 1985 to 2015, a significant national increasing trend (α = 0.05) of 11% per year was evident for the past decade (2006 to 2015). Regionally, a significant increasing trend in total burned area from 1985 to 2015 was observed in the Montane Cordillera (2.4% increase per year), while the Taiga Plains and Taiga Shield West displayed significant increasing trends from 2006 to 2015 (26.1% and 12.7% increases per year, respectively). The Atlantic Maritime, which had the lowest burned area of all ecozones (0.01% burned per year), was the only ecozone to display a significant negative trend (2.4% decrease per year) from 1985 to 2015. Given the century-long fire return intervals in many of these ecozones, and large annual variability in burned area, short-term trends need to be interpreted with caution. Additional interpretive cautions are related to year used for trend initiation and the nature and extents of spatial regionalizations used for summarizing findings. The results of our analysis provide a baseline for monitoring future national and regional trends in burned area and offer spatially and temporally detailed insights to inform science, policy, and management.

Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software’s over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.

Mapping and valuing of forest recreation is time-consuming and complex, hampering its inclusion in forest management plans and hence the achievement of a fully sustainable forest management. In this study, we explore the potential of crowdsourced social media data in tackling the mapping and valuing of forest recreation demand. To do so, we assess the relationships between crowdsourced social media data, acquired from over 350,000 Flickr geotagged pictures, and demand for forest recreation in British Columbia (BC) forests. We first identify temporal and spatial trends of forest recreation demand, as well as the countries of origin of BC forests visitors. Second, we estimate the average number of annual recreational visits with a linear regression model calibrated with empirically collected secondary data. Lastly, we estimate recreational values by deriving the average consumer surpluses for the visitors of BC forested provincial parks. We find that annually, on average, over 44 million recreational experiences are completed in BC forests, with peaks during the summer months and during the weekends. Moreover, a crowdsourced travel cost approach allowed us to value the recreational ecosystem service in five forested provincial parks ranging from ~2.9 to ~35.0 million CAN$/year. Our findings demonstrate that social media data can be used to characterize, quantify and map the demand for forest recreation (especially in peri-urban forests), representing a useful tool for the inclusion of recreational values in forest management. Finally, we address the limitations of crowdsourced social media data in the study of forest recreation and the future perspectives of this rapidly growing research field.

Forested ecosystems dominated by trees, wetlands, and lakes occupy more than 65% of Canada's land base. This treed area is dynamic, subject to temporary reductions in area and biomass due to wildfire and timber harvesting, and increases due to successional processes and growth. As such, the net aboveground biomass accumulated over time is a function of multiple, complex factors: standing forests grow and accrue biomass over time, whereas disturbed forests lose biomass, and subsequent regeneration processes result in biomass accrual once again. Knowledge of these processes behind biomass gain and loss is important for a range of considerations including habitat provision, economic opportunities, and exchange of carbon between forests and the atmosphere. Herein, we used a 33 year satellite-derived time series of aboveground biomass estimates for Canada's forested ecosystems to quantify biomass dynamics partitioned by the presence or absence of disturbance, and by disturbance type. Findings suggest that over the analysis period considered (1984-2016), undisturbed forests accounted for accrual of 3.90 Petagrams (Pg) of biomass. In contrast, while occupying ∼75% less area, disturbed forests accounted for a loss of 3.94 Pg biomass. Of this total biomass reduction, 45.4% can be attributed to wildfire, 43.8% to harvesting, 8.3% to non-stand replacing disturbances, and 2.5% to detectable roads and infrastructure development. Following disturbance, an additional 1.32 Pg of biomass were accrued during the analysis period, along with an additional 4.09 Pg in newly treed areas. Overall, Canada's forested ecosystems have realized a net increase in biomass of 5.38 Pg. Results of this analysis demonstrate the decoupling of area disturbed from the resulting biomass consequences by disturbance type, with large areas of wildfire accounting for a change in biomass that is similar to that of forest harvesting, which occurs over a much smaller area of mature and productive forest.

The distribution of different vegetation types is important information for landscape management, especially in the context of tackling global environmental change. Vegetation types can be mapped using satellite and airborne passive remote sensing. However, spectrally similar yet structurally different vegetation types, like different tree-dominated land covers, are often challenging to map using spectral information alone. We examined the potential of vertical vegetation structure acquired in the global ecosystem dynamics investigation (GEDI) mission that harnesses a space-borne waveform lidar sensor in vegetation mapping across a heterogeneous tropical landscape in Cambodia. We extracted 121 waveform metrics from Level-1B and Level-2A data products at 1062 locations across five key vegetation types. After reducing the relative height variables’ dimensionality through simple linear regressions, we developed a Random Forest classifier to predict vegetation classes based on 23 GEDI metrics. We then used this model to classify the vegetation types across more than 77 000 GEDI footprints in the study area. GEDI metrics alone were useful in identifying vegetation types with 81% accuracy. Cropland/grassland class had the highest prediction accuracy (user’s accuracy [UA] = 89%; producer’s accuracy [PA] = 91%), while dry deciduous forest had the lowest accuracy (UA = 73%; PA = 69%). By comparing the GEDI-only classification with an optical-radar map, we found that structural and topographic information from GEDI Level-1B and Level-2A can complement the spectral information in assessing natural habitats that neighbor other vegetation types in a heterogeneous landscape. The highest classification accuracy at the footprint scale was obtained from the combination of GEDI, Sentinel-1, and Sentinel-2 (88.3%). We also demonstrated how wall-to-wall vegetation mapping is possible by combining the three data sources. These findings expand the potential use of GEDI waveform lidar data in supporting the development of policy-relevant maps that depict the distribution of forests together with other vegetation types.

Purpose of Review Three-dimensional (3D) data on forest structure have transformed the level of detail and accuracy of forest information. While these 3D data have primarily been derived from airborne laser scanning (ALS), there has been growing interest in the use of 3D data derived from digital aerial photogrammetry (DAP) and image-matching algorithms. In particular, research and operational forestry communities are interested in using DAP data to update existing ALS-derived enhanced forest inventories. Although DAP depends on accurate terrain information provided by ALS to normalize digital surface models to heights above ground, in an inventory update scenario, DAP data currently have cost advantages over repeat ALS acquisitions. Recent Findings Extensive research across a broad range of forest types has demonstrated that DAP data can provide comparable accuracies to ALS for estimating inventory attributes such as volume, basal area, and height when used in an area-based approach with co-located ground plot information. Summary Herein, we review research relevant to the use of DAP for updating area-based forest inventories in subsequent inventory cycles, highlighting issues and opportunities for DAP data in this context. We examine the use of DAP for area-based forest inventory applications, comparing data inputs, algorithms, and outcomes across numerous studies and forest environments. Lastly, we outline outstanding research gaps that require further inquiry including benchmarking of acquisition parameters and image-matching algorithms.

Ecological monitoring and management require detailed information over broad spatial scales. Historically, such information was often acquired through manual interpretation of aerial photographs. As traditional methods of analyzing aerial photographs can he time-consuming, subjective, and can require well-trained interpreters (who are currently in short supply), new approaches must be explored for collecting this ecological information. First, we discuss the benefits and challenges of using aerial photographs for ecological management. We then examine the eight fundamental characteristics used in photograph interpretation and discuss their ecological relevance. Third, we investigate the feasibility of digital-analysis methods (often used for analysis of satellite imagery) for providing more objective, consistent, and cost-effective results. We end with several examples of how the unique information from aerial photographs can aid in solutions to emerging challenges in ecological research and management, and how they may be further used with supplementary data sets.

Cities are arguably both the cause, and answer, to societies' current sustainability issues. Urbanization is the interplay between a city's physical growth and its socio-economic development, both of which consume a substantial amount of energy and resources. Knowledge of the underlying driver(s) of urban expansion facilitates not only academic research but, more importantly, bridges the gap between science, policy drafting, and practical urban management. An increasing number of researchers are recognizing the benefits of innovative remotely sensed datasets, such as nighttime lights data (NTL), as a proxy to map urbanization and subsequently examine the driving socio-economic variables in cities. We further these approaches, by taking a trans-pacific view, and examine how an array of socio-economic ind0icators of 25 culturally and economically important urban hubs relate to long term patterns in NTL for the past 21 years. We undertake a classic econometric approach-panel causality tests which allow analysis of the causal relationships between NTL and socio-economic development across the region. The panel causality test results show a contrasting effect of population and gross domestic product (GDP) on NTL in fast, and slowly, changing cities. Information derived from this study quantitatively chronicles urban activities in the pan-Pacific region and potentially offers data for studies that spatially track local progress of sustainable urban development goals.

Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope.

Key message Integrating airborne laser scanning and satellite time series data across the forest rotation enhances decision-making in precision forestry. This review supports forest managers by illustrating practical applications of these remote sensing technologies at different stages of intensive forest plantation management—such as site assessment, monitoring, and silviculture—helping improve productivity, sustainability, and operational efficiency. Context Intensively managed forest plantations depend on high-resolution, timely data to guide silviculture and promote sustainability. Aims This review explores how airborne laser scanning (ALS) and satellite time series data support precision forestry across key stages, including site assessment, establishment, monitoring, inventory updates, growth tracking, silvicultural interventions, and harvest planning. Results The review highlights several key applications. ALS-derived digital elevation models and canopy metrics improve site productivity estimation by capturing micro-topographic variables and soil formation factors. Combining ALS with multispectral data enhances monitoring of seedling survival and health, although distinguishing seedlings from non-living components remains a challenge. ALS-based Enhanced Forest Inventories provide spatially detailed forest metrics, while satellite time series and vegetation indices support continuous monitoring of growth and early detection of drought, fire, and pest stress. ALS individual tree detection models offer insights into competition, stand structure, and spatial variability, informing thinning and fertilization decisions by identifying trees under stress or with high growth potential. These models also help mitigate drought and wind damage by guiding density and canopy structure management. ALS terrain data further support harvest planning by optimizing machinery routes and reducing environmental impacts. Conclusion Despite progresses, challenges remain in refining predictive models, expanding remote sensing applications, and developing tools that translate complex data into field operations. A major barrier is the technical expertise needed to interpret spatial data and integrate remote sensing into workflows. Continued research is needed to improve accessibility and operational relevance. High-resolution data still offer strong potential for adaptive management and sustainability.