The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of pre- and post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions.

Post-fire forest regeneration is strongly influenced by abiotic and biotic heterogeneity in the pre- and post-fire environments, including fire regimes, species characteristics, landforms, hydrology, regional climate, and soil properties. Assessing these drivers is key to understanding the long-term effects of fire disturbances on forest succession. We evaluated multiple factors influencing patterns of variability in a post-fire boreal Larch (Larix sibirica) forest in Siberia. A time-series of remote sensing images was analyzed to estimate post-fire recovery as a response variable across the burned area in 1996. Our results suggested that burn severity and water content were primary controllers of both Larch forest recruitment and green vegetation cover as defined by the forest recovery index (FRI) and the fractional vegetation cover (FVC), respectively. We found a high rate of Larch forest recruitment in sites of moderate burn severity, while a more severe burn was the preferable condition for quick occupation by vegetation that included early seral communities of shrubs, grasses, conifers and broadleaf trees. Sites close to water and that received higher solar energy during the summer months showed a higher rate of both recovery types, defined by the FRI and FVC, dependent on burn severity. In addition to these factors, topographic variables and pre-fire condition were important predictors of post-fire forest patterns. These results have direct implications for the post-fire forest management in the Siberian boreal Larch region.

The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in high northern latitudes is expensive and restricted to a few locations due to limited accessibility to most places along rivers from shorelines, remote sensing techniques are a suitable approach for monitoring. This study developed a RADARSAT-2 based method to monitor the spatio-temporal variation of ice covers, as well as ice types during the freeze-up period, along the main channel of the Slave River Delta in the Northwest Territories of Canada. The spatio-temporal variation of ice covers along the river was analyzed using the backscatter-based coefficient of variation (CV) in the 2013–2014 and 2014–2015 winters. As a consequence of weather and flow conditions, the ice cover in the 2013–2014 winter had the higher variation than the 2014–2015 winter, particularly in the potential areas of flooded/cracked ice covers. The river sections near active channels (e.g., Middle Channel and Nagle Channel), Big Eddy, and Great Slave Lake also yielded higher intra-annual variation of ice cover characteristics during the winters. With the inclusion of backscatter and texture analysis from RADARSAT-2 data, four water and ice cover classes consisting of open water, thermal ice, juxtaposed ice, and consolidated ice, were discriminated in the images acquired between November and March in both the studied winters. In addition to river geomorphology and climatic conditions such as river width, sinuosity or air temperature, the fluctuation of water flows during the winter has a significant impact on the variation of ice cover as well as the formation of different ice types in the Slave River. The RADARSAT-2 based monitoring algorithm can also be applied to other river systems in high latitude ecosystems to annually monitor their river-ice variation and formation during the freeze-up and ice cover progression period.

Inaccuracies in topographic data can be a significant source of error and uncertainty for hydrologic, atmospheric-land exchange and climate change modeling. This study was conducted to examine how different data sources and techniques used to generate digital elevation models (DEM) influence DEM accuracy. Using the interferometric synthetic aperture radar (InSAR) technique, the system acquisition parameters and the topographic characteristics are important factors in determining DEM quality. ICESat elevation footprints and highly accurate ground control points (based on GPS measurements) are used as independent references to validate the accuracy of DEMs. The TanDEM-X DEM shows the highest accuracy among all studied DEM products, with the root mean square error (RMSE) of 2.9 m, followed by 3.3 m for Canadian digital elevation data, 5.2 m for ASTER-GDEM, and 5.5 m for ALOS-PALSAR DEM. The RMSEs of the RADARSAT-2 based InSAR DEM and global GTOPO30 are considerably higher than the other DEMs, 24 m and 36 m, respectively. The interferograms of RADARSAT-2 and ALOS-PALSAR data acquired in early winter resulted in higher DEM accuracy compared with the summer image pairs, even with their lower InSAR coherence. This is attributed to the higher importance of perpendicular baseline and height of ambiguity and potentially the significant influence of atmospheric phase delay in DEM generation. We also compared the accuracy of DEMs of different land cover types and their representation of the land-water surface. The results show that vegetated areas of coniferous and broadleaf trees (i.e., white and black spruce, trembling aspen, balsam poplar) seem to be the main sources of error in all DEMs. The river slopes derived from the TanDEM-X, ASTER, and ALOS-PALSAR DEMs show patterns consistent with the river bed slope from our river bathymetry survey. Further studies are necessary to evaluate the performance of those different DEMs in earth surface modeling as well as to improve the quality of high error DEMs such as RADARSAT-2 data in this study. The Slave River Delta in the Northwest Territories of Canada was used as a test case.

The Slave River is a northern river in Canada, with ice being an important component of its flow regime for at least half of the year. During the spring breakup period, ice jams and ice-jam flooding can occur in the Slave River Delta, which is of benefit for the replenishment of moisture and sediment required to maintain the ecological integrity of the delta. To better understand the ice jam processes that lead to flooding, as well as the replenishment of the delta, the one-dimensional hydraulic river ice model RIVICE was implemented to simulate and explore ice jam formation in the Slave River Delta. Incoming ice volume, a crucial input parameter for RIVICE, was determined by the novel approach of using MODIS space-born remote sensing imagery. Space-borne and air-borne remote sensing data were used to parameterize the upstream ice volume available for ice jamming. Gauged data was used to complement modeling calibration and validation. HEC-RAS, another one-dimensional hydrodynamic model, was used to determine ice volumes required for equilibrium jams and the upper limit of ice volume that a jam can sustain, as well as being used as a threshold for the volumes estimated by the dynamic ice jam simulations using RIVICE. Parameter sensitivity analysis shows that morphological and hydraulic properties have great impacts on the ice jam length and water depth in the Slave River Delta.

Assessing burn severity is critical for understanding both the short- and long-term effects of fire disturbance on forest ecosystems. This study proposed a methodology to reconstruct burn severity from the Landsat imagery at different time lags after a fire (≤18 years) in Siberian larch (Larix sibirica) forest. The estimated accuracy of the burn severity models we developed indicated strong effects of forest recovery, image acquisition date and remote sensing predictors on the burn severity assessment. In the first several years after the fire, the dNBR (differenced Normalized Burn Ratio) was the most important remotely sensed index for assessing burn severity, followed by the dNDMI (differenced Normalized Difference Moisture Index) and dNDVI (differenced Normalized Difference Vegetation Index). However, the dNDMI was more important than the dNBR and dNDVI in explaining burn severity when larch forest regrowth dominated. The overall accuracy of the classification and regression tree models showed a decrease in accuracy from 83% to 62% depending on the lag times of burn severity assessment. The high severity class had the lowest omission and commission errors, followed by the low and moderate classes among lag times. Our evaluation of model transferability and thresholds of burn severity index demonstrates the advantage of the proposed methodology for rapid assessment of fire effects in boreal larch forest that will assist in understanding the complex relationships among forest fires and ecological processes in Eurasian boreal ecosystems.