Synthesizing Spectral and Field Observations of Post-fire Conifer Recovery in Dry Conifer Forests

Assessing spectral measures of post-harvest forest recovery with field plot data

ContextRecent large and high-severity wildfires have burned vast areas of coniferous forests throughout Western North America. These burned landscapes are recovering amid increasingly frequent climate extremes, such as drought. We need to understand how post-fire climate extremes and other ecological drivers (such as fire impacts) influence patterns and trends of coniferous recovery.ObjectivesWe worked at a landscape scale (> 400,000 hectares) to investigate the association between distinct post-fire forest recovery and ecological drivers in dry sub-boreal forests. We created structural recovery groups distinct in patterns and trends of coniferous cover and density and then modeled their association with ecological drivers.MethodsWe used Landsat time-series data to identify unique spectral recovery, which we grouped based on post-fire regrowth and stocking estimates. Remotely Piloted Aircraft light detection and ranging (lidar) provided structural estimates 5–21 years post-fire. We modeled the association between structural recovery groups and ecological drivers with random forests. For each category of drivers (site conditions, climate, climate anomalies, pre-fire composition, and fire impacts), we used individual models to identify important drivers. We then incorporated the most important drivers in a global model to highlight the drivers that were important across categories.ResultsInitial spectral trends indicated longer-term differences in structural forest recovery. Climate anomalies (such as post-fire extremes in temperature and precipitation) and pre-fire basal area best predicted observed structural groupings—abnormally cold and dry summers after the fire were associated with slow conifer establishment. Comparatively, areas with a higher pre-fire basal area maintained a mixed canopy of deciduous and coniferous stems.ConclusionsAt a landscape scale, post-fire climate conditions best predicted structural forest recovery, suggesting management plans should be adaptable to the conditions experienced post fire.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10980-025-02266-y.

Extensive high-severity fires are creating large shrubfields in many dry conifer forests of the interior western USA, raising concerns about forest-to-shrub conversion. This study evaluates the role of disturbance in shrubfield formation, maintenance and succession in the Jemez Mountains, New Mexico. We compared the environmental conditions of extant Gambel oak (Quercus gambelii) shrubfields with adjoining dry conifer forests and used dendroecological methods to determine the multi-century fire history and successional dynamics of five of the largest shrubfields (76–340 ha). Across the study area, 349 shrubfields (5–368 ha) occur in similar topographic and climate settings as dry conifer forests. This suggests disturbance, rather than other biophysical factors, may explain their origins and persistence. Gambel oak ages and tree-ring fire scars in our sampled shrubfields indicate they historically (1664–1899) burned concurrently with adjoining conifer forests and have persisted for over 115 years in the absence of fire. Aerial imagery from 1935 confirmed almost no change in sampled shrubfield patch sizes or boundaries over the twentieth century. The largest shrubfield we identified is less than 4% the size of the largest conifer-depleted and substantially shrub-dominated area recently formed in the Jemez following extensive high-severity wildfires, indicating considerable departure from historical patterns and processes. Projected hotter droughts and increasingly large high-severity fires could trigger more forest-to-shrub transitions and maintain existing shrubfields, inhibiting conifer forest recovery. Restoration of surface fire regimes and associated historical forest structures likely could reduce the rate and patch size of dry conifer forests being converted to shrubfields.

Two peat bogs were studied in the Bory Dolnośląskie, the forest complex in Lower Silesia (Poland). An Instorf drill was used to collect two peat profiles from the deepest places. The macroremains analysis showed that after the initiation of peat-forming processes phytocoenoses responsible for the deposition of transition sphagnum peat were developed at both locations. Later on, the development of both peat bogs differed. The smaller peat bog continued to develop, whereas the big bog was shifted to ombrotrophic water regime. Therefore, phytocoenoses accumulated 1.3 m of <em>Sphagnum</em> peat. The peat-forming process was initiated at different times in both sites. For the smaller peat bog, it took place during the Atlantic period, while in the case of the larger peat bog – several thousand years later. The first identified forest phytocoenoses in the Atlantic period are mesophilic multi-species deciduous forests. Dry coniferous forests and mixed birch-pine forests grew in dry habitats. Riparian forests occupied lower grounds. In the Subboreal period, the oak–hazel communities initially developed and mixed coniferous forests were partially replaced by light oak forests. The encroachment of spruce, fir, hornbeam, and beech resulted in the development of dry ground forests, including beech–fir woods. The importance of riparian forests increased, whereas in dry grounds pine and mixed coniferous forests continued to occur. In the Subatlantic period, the transformations in forest communities were associated with the spread of hornbeam, beech, and fir and thereby vast fertile habitats were colonized by dry ground communities and beech woods. Pine and mixed forests as well as riparian forests were of lesser importance. Pollen records from the last 500 years showed the clear presence of humans. It was evident from the presence of cereal and weed pollen and from the disturbances in the pollen records caused by peat extraction in the Middle Ages.

Wildfire is a key driver of forest dynamics in boreal forests; however, the annual area burned in boreal forests is highly variable, with increasing wildfire activity documented over the past half century. Post-fire recovery has important implications for carbon balance, and for the wide range of ecosystem goods and services provisioned by the boreal forest. Monitoring post-fire recovery is challenging given the vast and often inaccessible areas impacted, coupled with the marked variability in post-fire conditions and recovery processes. Field assessments of recovery are constrained in space and time, whereas remotely sensed data are spatially explicit, cover large areas, and can retrospectively provide assessments of pre- and post-fire conditions and establish spectral recovery baselines. However, there is a need to link spectral measures of recovery to manifestations of post-fire forest recovery on the ground. Understanding how different forest characteristics influence spectral recovery is key to the successful application of spectral recovery metrics for forest management applications in different forest environments. Herein, using a synthesis of plot data for the North American boreal forest, we assess the influence of pre- and post-fire field-measured characteristics on spectral recovery rates, with a focus on stem density and composition in Canadian boreal forests. Plots that experienced rapid spectral recovery (as measured using Landsat-derived NBR time series data) were associated with a transition from conifer to broadleaf or were broadleaf pre-fire. Plots that experienced a decrease in stem density took significantly longer to spectrally recover than plots that experienced no change in stem density. Plots that had not yet spectrally recovered by the end of the time series were associated with higher elevations, drier sites, greater pre-fire basal area, and had the greatest change magnitude. Our results emphasize that recovery is a process that is highly variable and that knowledge of pre-fire condition is important for characterizing and interpreting measures of post-fire spectral recovery for forest management applications. Remotely sensed time series data are uniquely able to provide information on both pre- and post-fire condition. Our analysis of pre- and post-fire field measures provided novel insights regarding the influence of forest composition and changes in stem density on measures of post-fire spectral recovery in boreal forests.

Forests globally are experiencing unprecedented levels of disturbances, negatively impacting ecosystem functioning and services. Ecosystem restoration (ER) is a global priority to counteract and reverse the effects of disturbances, highlighted by initiatives such as the UN Decade for ER and the Convention on Biological Diversity 30x30 target.    With increased investments in ER, more effective monitoring is required. Conventionally, ER monitoring relies on field surveys which are costly and infeasible for large or remote restoration sites. Recent advances in remote sensing technologies are seeing this technology increasingly being used to evaluate impacts of natural disturbances on forest ecosystems. Previous research has demonstrated strong correlations between remotely sensed spectral data and the recovery of forest ecosystems post-disturbance. These remote sensing recovery monitoring methods have relied on pre-disturbance status to assess recovery progress. However, increasingly multidisciplinary initiatives and ER management in practice require more flexibility in defining recovery targets. Additionally, ER practitioners face barriers to use remote sensing technology due to computational demands and complexity of time series analysis.  To address these issues, the Pioneer Earth Observation apPlications for the Environment (PEOPLE) ER project, funded by the European Space Agency, developed spectral-recovery, an open-source, flexible, remote sensing tool to support monitoring of vegetation recovery in forested ecosystems. Written in the open-source Python programming language, the spectral-recovery package provides simple computational methods for analyzing Sentinel-2 or Landsat satellite data time series, with straightforward interfaces that allow users to select from a variety of spectral indices and recovery metrics to monitor recovery trends and trajectories over time. To facilitate the integration of the tool with existing ER practices, users have the flexibility to determine recovery targets using either a historic method, based on the restoration site's historical conditions, or a reference method, which uses reference sites for target conditions. The tool produces raster layers for each index and recovery metric, along with recovery trajectory graphs for each restoration site. This allows for flexible post-tool analysis and mapping visualizations. In this presentation, the potential of this tool is demonstrated via case studies in Canada and Europe of detecting and quantifying forest recovery from wildfire verified by using airborne laser scanning (ALS) data. Results in the Canada case study found that 84% of the tool's estimated recovered area also had met structural recovery targets of height and/or cover, supporting the use of the spectral-recovery tool to monitor, quantify, and map post-disturbance forest recovery at multiple scales. The tool’s ability to provide wall-to-wall recovery estimates over entire restoration sites or landscapes enables the comparison of various restoration activities over time and space through continuous monitoring and consistent metrics, addressing the most prevalent limitations of current ER monitoring efforts.   The spectral-recovery tool is openly available via Github with demonstration notebooks and documentation, and is presented as an important tool for monitoring forest recovery, and assisting European and other countries in monitoring commitments under international agreements, EU policies, and at national level. 

Forest transitions (FT) have been observed in many developed countries and more recently in the developing world. However, our knowledge of FT from tropical regions is mostly derived from case studies from within a particular country, making it difficult to generalize findings across larger regions. Here we overcome these difficulties by conducting a recent (2001-2010) satellite-based analysis of trends in forest cover across Central America, stratified by biomes, which we related to socioeconomic variables associated with human development. Results show a net decrease of woody vegetation resulting from 12,201 km(2) of deforestation of moist forests and 6,825 km(2) of regrowth of conifer and dry forests. The Human Development Index was the socioeconomic variable best associated with forest cover change. The least-developed countries, Nicaragua and Guatemala, experienced both rapid deforestation of moist forests and significant recovery of conifer and dry forests. In contrast, the most developed countries, Panama and Costa Rica, had net woody vegetation gain and a more stable forest cover configuration. These results imply a good agreement with FT predictions of forest change in relation to socioeconomic development, but strong asymmetry in rates and directions of change largely dependent upon the biome where change is occurring. The FT model should be refined by incorporating ecological and socioeconomic heterogeneity, particularly in multicountry and regional studies. These asymmetric patterns of forest change should be evaluated when developing strategies for conserving biodiversity and environmental services.

Grizzly bear selection of recently harvested forests is dependent on forest recovery rate and landscape composition

Forest transition and socio-economic development in India and their implications for forest transition theory

Contemporary wildfires are more severe compared to the historical reference period in western US dry conifer forests

Temporal and spatial patterns of fire regime disruption were reconstructed in conifer forests of western North America from information on pre-disruption and disrupted mean fire intervals (MFIs) of 498 dendrochronology-based fire chronologies. We identified the conifer forest types most affected by MFI shift and the influence of land category designation on MFI change. We also mapped the years of the MFI shift, the last fire recorded, and disrupted/pre-disruption MFI ratios. Fire cessation and longer MFIs predominated in most fire chronologies and conifer forest types. MFI was significantly higher in most conifer forest types, with most differences in dry conifer forests. MFI shift occurred mainly before the designation of protected, federal, social-property, and private areas. MFI shift began in 1829 in the United States of America Southwest, a region subjected to prolonged fire exclusion and with the highest disrupted/pre-disruption MFI ratios. Fire regime disruption moved gradually into the Pacific Northwest and the Sierra Nevada until reaching the northern conifer forests of Canada and Alaska. In contrast, one-third of fire chronologies in Mexican conifer forests retained pre-disruption MFIs. Our findings allowed us to identify areas with MFIs outside of their natural variability, with prolonged fire exclusion, or with intact fire regimes.

Differences in throughfall and net precipitation between soybean and transitional tropical forest in the southern Amazon, Brazil

The scale of forest transition: Amazonia and the Atlantic forests of Brazil

Multiple drivers and pathways to China's forest transition

Large-scale forest monitoring benefits greatly from change detection analysis based on remote sensing data because it enables characterizing forest dynamics of disturbance and recovery by detecting both gradual and abrupt changes on Earth’s surface. In this study, two of the main disturbances occurring in Mediterranean forests, harvesting operations and forest fires, were analyzed through the analysis of Landsat Times Series images in a case study in Central Italy (Tuscany region). Disturbances were characterized based on their distinct temporal behaviors before and after the event: a period of 20 years (1999–2018) was used to extract and analyze at pixel level spectral trajectories for each disturbance and produce descriptive temporal trends of the phenomena. Recovery metrics were used to characterize both short- (5 years) and long-term aspects of recovery for harvested and burned areas. Spectral, recovery, and trend analysis metrics were then used with the Random Forest classifier to differentiate between the two disturbance classes and to investigate their potential as predictors. Among spectral bands, the Landsat SWIR 1 band proved the best to detect areas interested by harvesting, while forest fires were better detected by the SWIR 2 band; among spectral indices, the NBR scored as the best for both classes. On average, harvested areas recovered faster in both short- and long-term aspects and showed less variability in the magnitude of the disturbance event and recovery rate over time. This tendency is confirmed by the results of the classifier, which obtained an overall accuracy of 98.6%, and identified the mean of the post-disturbance values of the trend as the best predictor to differentiate between disturbances.