Much of the work described above entails mapping the location, size and severity of disturbance by forest fire. However, the causes and consequences of spatial variability in post-fire effects become increasingly significant to our understanding of how forest ecosystems respond. The spatial variability of post-fire forests can be characterized by stages of forest succession, forest structure and the regrowth of forest composition since disturbance. Remote sensing of disturbances for the express purpose of quantifying forest patterns enables the extraction of independent variables to predict dependent variables, such as successional stages, stand age, tree diameter and height, biomass, canopy closure, species diversity and other structural parameters over large areas of post-fire forest. The overall review of remote sensing in monitoring the recovery of post-fire vegetation in nature was conducted by Gitas
et al. [
175], and remote sensing of the biophysical parameters of boreal forests was conducted by Lutz
et al. [
176]. The review by Gitas
et al. [
175] discussed remote sensing studies of monitoring post-fire vegetation in general, including tropical, temperate, Mediterranean and boreal ecosystems, while Lutz
et al. [
176] did not focus on studies about monitoring post-fire effects and recovery patterns of boreal forests. Therefore, this section is largely focused on remotely sensed data and methods for monitoring post-fire forest patterns, including successional stages, the attributes of forest structure and the trajectories of forest recovery in regions of post-fire boreal forest. This review will also discuss the data and methods from other ecosystems that can be potentially applied to help understand boreal forest patterns following fire disturbance.
3.3.1. Monitoring Successional Stages
Forest succession is an important ecological process that determines the biophysical, biological and biogeochemical characteristics of forest ecosystems [
177]. Patterns of forest succession vary among fire perimeters depending on many factors, such as site conditions before and after a fire, the extent of the fire and the severity of the fire. In boreal larch forest ecosystems, the size and severity of fires, coupled with climatic condition, are among the principal factors influencing post-fire dynamics and patterns of succession [
82,
178]. For example, three classes of forest successions were determined after climate change and fire in the taiga larch forest in central Siberia, including succession with no replacement of tree species, succession with replacement of a tree species and succession with open larch stands replaced by shrubby tundra [
82]. Furthermore, the length of complete stages of succession in Siberian boreal forests strongly depends on fire intervals and species characteristics, ranging from five years as the first stage of succession, in which pine regenerates successfully, to more than 180 yr as the last stage of succession, represented by old pine stands [
82], and from two years to 90 yr as the first and last stages of succession in larch stands, respectively [
9]. Similarly, the influences of fire severity on the succession of the Siberian larch forest illustrates that during a surface fire with low severity, conditions of pre-fire vegetation can recover after 5–8 yr, while the dominant, pre-fire trees are restored within 7–15 yr and 15–20 yr after moderate and high larch fire severity, respectively [
179]. Pioneer species of herbs and grasses are dominant in the larch forest during the first four years after high fire severity [
179]. Through the periodic measurement of post-fire tree density over several decades, Johnstone
et al. [
180] found that North American boreal tree recruitment occurs within a short (3–10 yr) period after fires. Additionally, the observation of patterns of stand density and composition within five years after fires can be used to predict forest patterns observed two or three decades after fires [
180].
The identification and classification of the successional stages of forests over large areas are challenging to conduct based on field surveys alone. Combining field plots with remotely sensed data, however, provides an alternative approach for monitoring successional stages of forests across large spatial extents [
177] (
Table 6, [
83,
177,
181–
186]). In boreal forest ecosystems, there are very few studies using optical sensors, such as Landsat TM/ETM+, to characterize post-fire patterns of succession [
83,
187–
189]. One known study of burn severity and post-fire succession through remote sensing was conducted by Epting and Verbyla [
83] over a 16-yr post-fire period in interior Alaska. Landsat TM/ETM+ images were used to categorize two successional classes of self-replacement and relay floristic with respect to burn severity and pre-fire vegetation. In this study, both pre- and post-fire images were classified using an unsupervised method of classification to identify six types of vegetation, including closed needle-leaf forest, open needle-leaf forest, needle-leaf woodland, broadleaf forest, mixed forest and shrub land. The strategy for monitoring successional stages by Epting and Verbyla relied on a similar approach with change detection using post-classification maps. Pixels exhibiting the same class of vegetation in both pre- and post-fire images were classified as self-replacement succession, whereas areas that changed classes of vegetation from spruce-dominated to broadleaf-dominated were classified as relay floristic areas [
83]. The result showed that post-fire patterns of succession in Alaskan boreal forests strongly depended on the types of pre-fire vegetation and burn severity. For example, most of the high burn severity areas with closed needle-leaf, open needle-leaf and mixed forest classes shifted to woodland or shrubland, sixteen years after the fire [
83]. However, Epting and Verbyla noted that the study only focused on one burn in the late growing season and that the result may not represent patterns of burn severity and the succession of post-fire vegetation across the Alaskan boreal region. Discrimination of spectral bands and vegetation indices by Landsat TM/ETM+ imagery has been the more commonly used approach to identify the distribution of successional forest stages in temperate regions, compared with boreal regions [
177,
181,
190–
193]. Song
et al. [
181] demonstrated that the temperate conifer forest’s successional stages can be differentiated using a linear regression analysis between the Landsat Tasseled Cap classes of brightness, greenness and stand age. Liu
et al. [
177] found that using forest inventory plot data and Tasseled Cap with two other predictive models, such as decision trees and neural networks, were more successful than linear regression models in predicting a forest’s successional stages. This is due to the fact that these models are not necessary data with normal distributions, and they can eliminate the spectral noise of forest samples. Both Song
et al. [
181] and Liu
et al. [
177] used a chronosequence approach that substitutes space for time, and they concluded that multi-temporal Landsat imagery clearly improved the discrimination of young, mature and old temperate forest stands. This is due to the fact that multi-temporal data can take advantage of annual phenology to improve the classification of different successional stages and pathways [
182]. With the different coverage and the availability of Landsat data from 1972 to the present at local-to-continental scales, these multispectral data are valuable for producing classifications of stages of forest succession at these different scales (
Table 6).
In addition to optical sensors, LiDAR (Light Detection and Ranging) imagery has proven particularly useful to estimate forest structures, as well as to characterize stages of forest succession, since LiDAR measures the three-dimensional arrangement of forest canopies. In Eurasian boreal forests, LiDAR data have been used to separate different types of forest sites in general, ranging from poor (xeric heath forest) to very rich forest [
184], and to identify stands of boreal forests with high herbaceous plant diversity [
185]. As mentioned by Falkowski
et al. [
186], that classifications of forest succession should reflect all potential stages of forest development, the authors successfully used LiDAR height metrics to classify six stages of forest succession across a structurally diverse and mixed-species conifer forest. Six stages of succession were characterized using LiDAR data in conjunction with a non-parametric Random Forest algorithm, resulting in an accuracy greater than 95% overall. The use of nonparametrics, such as the Random Forest classification algorithm, can incorporate a number of continuous and categorical predictors, develop robust predictions and account for uncertainty when mapping successional classes [
186,
194]. The LiDAR-based techniques of data analysis are applicable to the detection of different post-fire stages of succession in boreal forests, because of specifically slow recovery rates of boreal tree saplings combined with extensive shrub regrowth for the first several decades following fires in boreal forests. However, it is noted by Falkowski
et al. [
186] that LiDAR data is associated with very high costs for mapping at regional and continental scales and is not available at all footprints [
186]. To our knowledge, there have been no similar studies, either in general or in sites specific to post-fire environments, conducted in boreal regions to identify
all potential successional stages using LiDAR imagery. A similar approach to Falkowski
et al. [
186] can be conducted to develop and evaluate methods for mapping post-fire forest succession in boreal forest ecosystems (
Table 6).
Spaceborne synthetic aperture radar (SAR) data also provides an alternative approach for monitoring the regrowth of post-fire forests, since backscatter is also sensitive to forest structural parameters. The backscatter coefficient typically increases with forest biomass, and SAR wavelengths are able to differentiate among grass, shrub, young and mature forest stands [
195], commonly represented in secondary forests following fires. Based on the advantage of SAR cross-polarized backscatter, which is more sensitive to forest structure and different stages of forest regrowth, Tanase
et al. [
183] used X-, C- and L-band cross-polarized backscatter to monitor forest regrowth in both Mediterranean and boreal forests affected by fires. The authors found that up to four different stages of post-fire regrowth in boreal forest could be distinguished, whereas five phases of regrowth in Mediterranean forests were discerned using L-band SAR data. However, compared with the NDVI from optical sensor, the reliable differentiation of regrowth phases using NDVI could only be observed for the most recent stages of development (10–20 yr after disturbance), because NDVI responded positively to changes in canopy recovery and then saturated prior to the point where an ecosystem fully recovers from disturbance [
183]. These results suggest the usefulness of SAR data for monitoring forest regrowth after disturbances. However, Kasischke
et al. [
196] reported that L-band microwave backscatter was the most sensitive to variations in aboveground biomass and soil moisture in boreal forests. More specifically, soil moisture did significantly change the correlation between the L-band backscatter and the aboveground biomass typically found in boreal forests that are regenerating, and the influence of soil moisture is dependent on the biomass [
196]. Therefore, the consideration of soil moisture conditions over the study area is important when using SAR backscatter to monitor post-fire forest regrowth and forest structure [
183,
196] (
Table 6).
Generally, the classification of forest successional stages is different from the types of land cover classification, as forest succession involves ecological processes and requires viewing the vegetation community as a continuum rather than as discrete classes [
184,
197,
198]. Classification of forest succession, therefore, not only benefits sustainable forest management, but also effectively quantifies ecological responses and relationships in wildlife habitats [
199,
200]. Additional studies should be performed on whether optical remote sensing data and methods can be confidently applied to monitor post-fire succession of boreal ecosystems, since these data can be archived, historically corresponding to each stage of forest succession. Optical remote sensing data, such as Landsat, AVHRR and MODIS imagery, are also freely accessible online. The capabilities of remote sensing data and methods often used in the literature to identify successional stages in boreal forests and other regions were summarized in
Table 6 as follows.
3.3.2. Measurement of Other Variables in Forest Structure
The structural attributes referring to the spatial arrangement and distribution of a forest are important surrogates to indicate the functional and compositional attributes of forests. The structural attributes used to characterize forest patterns, including successional stages, can be grouped into three main categories: (1) attributes of biophysical and spatial distribution, such as Leaf Area Index (LAI), crown closure/canopy cover, breast height diameter (DBH), height, basal area, volume or biomass, stem/seedling density and stem age/stage of development; (2) species diversity represented by diversity indices, such as the Shannon–Weiner and Simpson indices; and (3) complexity of forest structure (SCI) comprising variations of different spatial attributes, such as DBH, height, basal area and age [
78,
79,
197,
201]. Since post-fire forest environments and forest patterns are significantly different from pre-fire environments, the review of this section will focus more on remote sensing data and methods that have been applied to monitor structural parameters following disturbances in boreal forests. Some studies in other forest ecosystems and undisturbed environments that might be useful for measuring variables in post-fire boreal forests were also included and discussed in this review.
Applications of remote sensing aimed at monitoring structural attributes of forests that were listed above have been driven largely by using empirical models to calibrate remotely sensed data with
in situ data in either boreal or other forest ecosystems (
Table 7, [
8,
28,
112,
187,
196,
202–
213]). For example, regression-based prediction has been a widely accepted approach to mapping regional forest attributes using linear regression [
196,
203,
204,
213], nonlinear regression [
187,
209–
211,
214,
215], partial least squares regression [
216] and regression tree algorithms [
26,
217]. Recently, non-parametric regression approaches, such as Reduced Major Axis (RMA) regression, k-Nearest Neighbor (k-NN), Gradient Nearest Neighbor (GNN) and Random Forest (RF) regressions, have received considerable attention for the estimation of structural forest attributes, because these approaches can account for mapping uncertainty and involve a large number of response variables with analytical and operational flexibility [
28,
209,
218].
Many studies have demonstrated that correlation between independent bands/indices of optical imagery and dependent, site-based forest biophysical variables have a high variation, depending upon derived variables, spectral bands/indices, vegetation properties and disturbance regimes. In post-fire Canadian boreal forest, Zhang
et al. [
187] modeled stand age distribution using a Shortwave Vegetation Index (SWVI) calculated from SPOT VEGETATION imagery. The dated fire scars were based on historic fire data, and the stand age was by pixels of each type of land cover since the fire derived based on the polynomial relationship between stand age and the mean SWVI, with the highest correlation for the mixed coniferous forest (R
2 = 0.53) [
187]. However, the result showed that using SWVI to estimate stand age was limited due to systematic bias with an overestimation of the stand age of about five years for young stands and an underestimation of up to 15 yr for old stands. This index also saturated at different points, depending on the types of land cover and the number of years since the fire. For example, the SWVI of coniferous forests saturated at the value of about 0.25 after 37–42 yr since the fire, while the saturated value of deciduous forest was 0.35 at a post-fire regeneration age less than 10 yr. These findings were similar to Fraser and Li’s [
112] based on a sample of fires across Canada. Fraser and Li [
112] also found that an artificial neural network (ANN) model performed better than multiple regression in predicting the age of regenerating boreal forests after fire. Both studies mentioned that an understanding of burn severity and the spatial heterogeneity of fire disturbance will improve the prediction of forest stand age regenerating after fire with a high level of accuracy. Similarly, some studies were conducted in Siberian boreal regions to monitor forest variables after fires using a moderate resolution dataset [
202,
205,
219]. For example, Chen
et al. [
202] validated the collection 4 MODIS LAI product (MOD15) and Landsat ETM+-derived LAI in different post-fire sites and found that the MODIS LAI product correctly represented the summer site phonologies, but significantly underestimated the LAI value of the site, with a 100-year-old post-fire deciduous forest during the winter period. Compared with the Landsat ETM+-derived LAI, the MODIS LAI overestimated values in the low LAI deciduous forests (LAI < 5) and underestimated values in the high LAI conifer forests (LAI > 6). Additionally, the Landsat ETM+-reduced simple ratio (RSR) significantly improved the LAI prediction, while the Enhanced Vegetation Index (EVI) had the poorest performance in the estimating LAI (R
2 = 0.89 and R
2 = 0.61, respectively). Because of the high variation of the MODIS LAI product, further comparison with field datasets and other LAI products (e.g., CYCLOPES LAI product [
205]) from other boreal forest sites is necessary to improve LAI quantification in disturbed forest landscapes.
Similar to mapping forest succession, some studies mentioned that the inclusion of derived spectral indices, raw spectral bands, biophysical variables and time series data can improve the estimation of structural attributes of post-fire forests. Li
et al. [
217] used Landsat TM and ETM+ to model the height of young forests after fire disturbances in Mississippi, USA, and found that the inclusion of modeled stand age, Landsat raw bands and vegetation indices, such as forestness index (FI), NDVI and NBR, as predictors significantly improved model error over those based only on spectral models, especially with regression tree models rather than step-wise linear regression models. Pflugmacher
et al. [
206] found that live forest biomass estimated by using Landsat time series (LTS) data was of much higher accuracy than that by single-date Landsat data, and LTS models for the estimation of dead biomass above ground performed significantly better than those with either LiDAR data or single-date Landsat data. This suggests that long-term LTS metrics can reveal not only disturbance events, but also recovery progress that directly relates to current forest structure and, thus, finally, improved estimation of current structural attributes of forests. To our knowledge, there has been no evidence in the literature considering those approaches with optical sensors to determine structural attributes in boreal forests following fires.
In addition to optical sensors, a number of studies have been conducted in boreal regions to determine the structural attributes of boreal forests using LiDAR and SAR data (
Table 7). For example, Kasischke
et al. [
196] investigated the use of L-band SAR data for estimating aboveground biomass in post-fire Alaskan boreal forest sites and found that the backscatter from SAR data was a reliable predictor for estimating aboveground biomass using a linear model, especially in the area with highest soil moisture (R
2 = 0.63, RMSE = 3.2 t/ha). The results suggested that the estimation of biomass in post-fire biomes using L-band SAR should account for variations in soil moisture, since soil moisture did change the correlation between L-band microwave backscatter and aboveground biomass in sites with a low level of vegetation regrowth [
196]. Additionally, Magnussen and Wulder [
208] assumed that post-fire forest patterns are typically composed of both post-fire regeneration and elements of pre-fire vegetation, so that the measurement of post-fire structural attributes requires a separation of the burned and unburned structural elements. Consequently, Magnussen and Wulder [
208] successfully separated post-fire and recovered canopy heights from pre-fire canopy heights in Canada’s boreal forests using a sequential statistical procedure with LiDAR data. The study also indicated that the mean regenerated post-fire canopy heights had a positive high correlation with the number of years since the fire.
Although airborne LiDAR and SAR data have been successfully used to derive structural attributes of vegetation, an alternative approach to mapping forest attributes is to use these measurements in combination with remotely sensed optical imagery [
207,
209–
212,
220]. This synergistic use of active and passive sensors provides opportunities to fully characterize the structural attributes and dynamics of forests with regard to disturbance regimes (e.g., burn severity) and pre- and post-fire vegetation types (
Table 7). Wulder
et al. [
210] demonstrated that the overall trend in changes of forest attributes derived from airborne LiDAR height profiles were stable during a five-year period in Canada’s boreal forests without segmented spectral information from Landsat imagery. On the other hand, a local approach for measuring changes in forest attributes over time, using spectrally homogeneous segments derived from Landsat ETM+ data to stratify the LiDAR transect, showed explicit indications of spatial forest growth and depletion. The difference in the magnitude of the changes was greater for degradation, but was less extensive spatially than it was for growth [
210]. Wulder
et al. [
210] suggested that a local approach was more appropriate for characterizing the heterogeneity of dynamic forest ecosystems over broad areas, due to the fact that growth tends to occur incrementally over broad areas, whereas degradations are dramatic and are constrained spatially. Regarding the relationship between post-fire conditions and post-fire forest structure, Wulder
et al. [
211] analyzed forest structure, derived from airborne LiDAR height metrics, and post-fire conditions, as measured in burn severity from Landsat data, and found that the structural attributes of post-fire forests were strongly correlated to post-fire NBR, dNBR and RdNBR. However, there were no marked differences in the performance of post-fire NBR, dNBR and RdNBR for characterizing post-fire effects. The relationship between post-fire structure and post-fire condition was strongly dependent on the types of post-fire vegetation [
211]. These findings were consistent with a recent study by Goetz
et al. [
212] that explored post-fire canopy height in Alaska using the integration of spaceborne LiDAR, MODIS and Landsat imagery. Goetz
et al. [
212] suggested that the consideration of post-fire conditions derived from optical imagery, such as burn severity and types of regenerated vegetation, was important for modeling forest regrowth using spaceborne LiDAR height metrics data acquired from the Geoscience Laser Altimetry System (GLAS) on ICESAT. Similarly, Andersen
et al. [
209] also found that the integration of Landsat TM, LiDAR and SAR data improved significantly in the precision of estimating total biomass in the boreal forests of interior Alaska via nearest-neighbor imputation over the single use of LiDAR data modeling (reduction in relative standard error from 7.3% to 5.1%). This is probably because the inclusion of both spectral and L-band SAR backscatter provided information that contained the two most important attributes of three-dimensional forest structure and species composition in quantifying aboveground biomass [
209].
In general, regarding remote sensing data and methods for modeling forest variables, Powel
et al. [
28] assumed that it is difficult to conclude outright that one modeling technique outperforms the other. The performance of each method depends on measures and scales of validation. As a result of disturbances, the prediction of a forest’s structural parameters also depends upon the nature and level of the disturbances, and so, linkage with accurate maps of forest disturbance might provide a more reliable interpretation of variations in forest structure relative to the process of disturbance and regrowth. In the case of approaches using remote sensing for monitoring boreal forest variables following fire disturbance, this suggests that stratification of post-fire conditions and the inclusion of disturbance regimes, such as fire frequency, burned area and fire/burn severity [
211,
212], in modeling post-fire forest patterns might have the potential to improve the accuracy and interpretation of models.
3.3.3. Tracking Patterns of Forest Recovery after Fire
Remote sensing with time series data offers considerable potential in the trajectory of post-fire forest dynamics, beyond monitoring forest succession and current structural attributes of forests after fires. Many studies have addressed this issue by using moderate-to-low resolution time series NDVI, SAVI, EVI, albedo, NDVI-based Net Primary Productivity (NPP), fraction of absorbed photosynthetically active radiation (fAPAR) and, recently, the vegetation optical depth (VOD) parameter from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) sensor as surrogates representing the recovery of vegetation after fire disturbances in both the Siberian boreal forest [
219,
221] and North American boreal forest [
81,
83,
167,
183,
208,
222–
226] (
Table 8).
With respect to the post-fire recovery of NDVI [
83,
221,
222,
225,
227], Normalized Difference Shortwave Infrared Index (NDSWIR) [
221], NDVI-based NPP [
81] and fAPAR [
219], the observations of the recovery of vegetation from these analyses varied greatly, even in the same eco-region, depending upon data resolution, pre-burn vegetation, burn severity and the temporal and spatial variability of vegetation indices within burned and unburned areas (
Table 8). Kasischke and French [
222] used AVHRR-NDVI time series spanning over three years (1990–1992) to study 14 test sites in Alaska and found that patterns of forest recovery were defined by pre-fire vegetation and the timing of the wildfire during the growing season. The authors found that NDVI increased and reached its maximum after 20–50 yr using the chronosequence approach to observe post-fire succession of three different types of vegetation, since fires depending on species following fires, followed by a decline of NDVI values. Goetz
et al. [
225] used two NDVI time series derived from the Pathfinder AVHRR Land (PAL) and the Global Inventory Modeling and Mapping Studies (GIMMS) AVHRR to investigate the recovery of vegetation after fires in the boreal forests of Canada. Their results indicated that the recovery rates based on NDVI of Canadian boreal forest were different between the PAL and GIMMS datasets, but both were consistently shorter than previous studies, e.g., [
81,
222]. This is probably because the previous studies in North America only emphasized the most impacted pixels within fire perimeters [
225], which might require a longer period to return to pre-fire conditions [
221]. In comparison with North American boreal forest, Cuevas Gonzalez
et al. [
221] found that it took more than 13 yr for the burned Siberian boreal forest to recover fully to pre-fire conditions with respect to NDVI and NDSWIR extracted from MODIS time series data. This recovery rate was longer than the five-year recovery period reported by Goetz
et al. [
225] and the four-year period investigated by Hicke
et al. [
81] in North America. Cuevas Gonzalez
et al. [
221] assumed that the differences in fire regimes and fire types, growing conditions, species composition and data resolution between the Siberian and North American studies might have influenced these results. Similarly, Alcaraz-Segura
et al. [
227] also mentioned that the low resolution data processing (1–8 km observation), such as GIMMS-NDVI data, may introduce a bias that tends to underestimate positive NDVI trends in the Canadian boreal forest.
Surface albedo increases dramatically throughout the first decade after a fire due to the establishment and growth of grasses, shrubs and deciduous broadleaf trees [
224]. Since tree canopies establish and typically succeed from broadleaf to conifer species in boreal forest ecosystems, post-fire forest recovery gradually decreases albedo [
180]. Therefore, monitoring the change in albedo during vegetation’s recovery period following fire could be an alternative approach to determine the effects of fire on post-fire environments and also to understand the trends of vegetation recovery. Jin
et al. [
167] used MODIS data to derive burn severity, albedo and vegetation productivity and then analyzed the dynamics of the recovery of vegetation and albedo during the early stages of succession in Canadian boreal forests. They found that in spring and summer, the albedo increased during the first seven years after the fire and reached higher than the pre-fire level, with the larger increase of post-fire albedo in the site with higher burn severity. These changes of post-fire albedo followed consistently with both EVI changes that recovered to a pre-fire level for 5–8 yr after the fires depending on burn severity classes [
167] and NDVI changes that were often higher than pre-fire levels between five to 15 yr after fires across Canada [
225]. Compared with the result reported by Beck
et al. [
223], Jin
et al. [
167] assumed that spring and summer albedos following fires increase consistently with the development of stand age from early stages of succession to intermediate-aged stands. However, further studies are needed to assess and understand the inter-relationship between albedo change under the impact of fire and the stages of development in post-fire stands, with the inclusion of species composition, forest structural attributes, burn severity and vegetation productivity. A key challenge in this regard might be to quantify the change in magnitude and directionality of albedo and surface energy across successional stages and gradients of burn severity.
Vegetation indices and products derived from them have been the most frequently used tool for monitoring, analyzing and mapping the temporal and spatial dynamics of post-fire environments. However, these remotely sensed data, NDVI for example, usually reach saturation levels prior to the point where an ecosystem fully recovers its maximum biomass after disturbance [
204,
225,
228] (
Table 9). Therefore, tracking recovery patterns in vegetation using vegetation indices might limit and underestimate the rate of recovery after disturbance [
183]. Similar to measuring a forest’s structural attributes and successional stages, SAR and LiDAR data have the potential to track the rate of a forest’s recovery following disturbance [
183,
208], since the signal from these data is sensitive and varied by the forest structure and stages of development [
184,
195] (
Table 8). Magnussen and Wulder [
208] used LiDAR pulse returns to estimate post-fire recovery rates measured by the mean growth rate of canopy height per year since the recorded fire, over burned areas in Canada’s boreal, forest with acceptable levels of precision. The authors also suggested that monitoring the regrowth of canopy height using airborne laser scanner data requires a separation of the burned and unburned structural elements within the perimeter of the fire. Tanaset
et al. [
183] successfully used SAR images to identify regrowth phases in Mediterranean forests and Alaskan boreal forest. The trajectories of forest recovery as seen by L-band HV SAR in both ecosystems showed that L-band backscatter was a negative response to the forest regrowth [
183]. In terms of how the percentage of change following a fire shows the recovery rate, Tanase
et al. [
183] indicated that L-band SAR backscatter provided much longer monitoring intervals of 45–60 yr, compared with the analysis of the NDVI that saturated at about 10–20 yr after disturbance in boreal forest. Since forests may need decades to fully recovery to pre-fire conditions with respect to species composition and forest structure (90 yr for the Siberian larch forest, for example [
9]), the longer intervals for monitoring L-band SAR data would be useful for tracking the rate of forest recovery. However, as noted by Kasischke
et al. [
196], using SAR data to measure biophysical parameters of a forest’s regrowth will require the development of methods to account for variations in soil moisture, particularly in the Alaskan boreal forest.
Even though many current studies have used optical vegetation indices, such as NDVI, to describe forest recovery after disturbances, according to Frolking
et al. [
229], the assumption that the index of vegetation recovery equates to forest recovery may be inappropriate. Buma [
230] examined this hypothesis using MODIS time series data from 2000 to 2010 in the area of burned forest in Colorado’s Routt National Forest, USA, and demonstrated that NDVI is poorly correlated with forest recovery represented by seedling density in burned areas. Therefore, studies on post-fire forest recovery should consider the inclusion of structural forest ground variables, such as seedling recruitment, percent of cover, tree diameter and height, directly to remotely sensed parameters [
34,
175,
230] (
Table 9). To date, however, very few studies in the literature have attempted to tie post-fire ground variables to remotely sensed data with different metrics and spatial scales in either boreal forests or other ecosystems. One example was presented by Roder
et al. [
52], who derived trajectories in post-fire vegetation change by exponential functions with the estimation of green vegetation cover from 20 Landsat MSS, TM and ETM+ (covering 25 yr). These changes in trajectory were used to describe recovery phases following fires in the Ayora region in eastern Spain. In addition to field-based observations, the evaluation of satellite datasets in monitoring post-fire forest recovery should include comparisons of independent observations at the stage of results, for example, comparing detected trends of different optical datasets [
225,
227] and optical and SAR/LiDAR datasets in different regions [
183]. Finally, as noted by some authors (e.g., [
221,
222,
225,
227]), analyzing patterns of vegetation cover in boreal forests using remote sensing data requires the development of approaches to account for variations in spatial and spectral resolution of remotely sensed data, environmental conditions (e.g., clouds and haze, soil moisture, albedo, latitude, topography, climate), vegetation characteristics (e.g., species composition, land cover type, vegetation phenology) and disturbance regimes (e.g., fire and burn severity, fire type, fire frequency). A useful approach might be the stratification of those factors with similar conditions prior to applying remote sensing tools (
Table 9).