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The rate at which forest vegetation re-establishes dominance after clearcut harvesting can impact many ecological processes, such as erosion/sedimentation, nutrient and water cycling, carbon storage potential, wildlife habitat, and trophic interactions. Although knowing a forest stand's current state of succession is useful, a clearer understanding...
Contexts in source publication
Context 1
... overall accuracy of the CRP CART model was 46% (Table 4). According to Landis and Koch (1977) a k of 27% suggests ''fair agreement'' between the predicted regrowth classes and test samples. ...
Context 2
... Tau statistic indicates that 27% more pixels were classified correctly than would be expected by random assignment. Ranging from 17 to 79%, the individual class accuracies (Table 4) suggested that the maximum and minimum regrowth classes (i.e., little to no and fast) were predicted with greater accuracy than the classes falling in between (i.e., slow and moderate). ...
Context 3
... overall accuracy of the WCP CART model was 47% (Table 4). A k of 29% suggests ''fair agreement'' between the predicted regrowth classes and the test samples (Landis and Koch, 1977). ...
Context 4
... Tau statistic indicates that 30% more pixels were classified correctly than would be expected by random assignment. Ranging from 29 to 68%, the individual class accuracies (Table 4) suggested that the maximum and minimum regrowth classes (i.e., little to no and fast) were predicted with greater accuracy than the classes falling in between (i.e., slow and moderate). ...
Context 5
... extension of forest regrowth trajectories to the spectral space of Landsat provided the opportunity to more fully investigate the climatic and topographic attributes influencing the rate of forest regrowth following clearcut harvesting in western Oregon. Although the overall accuracies of the CART models were not high in terms of correctly classified test samples (Tables 4 and 5), the resulting classification decision rules provided interesting insights into the geographically referenced environmental attributes influencing forest succes- sion in both ecological provinces. The CRP CART model (Fig. 10) had more decision pathways or terminal nodes (10) than the WCP CART model (6) (Fig. 11), indicating that more favorable growing conditions common to the CRP could possibly result in more complex interactions among plant relevant and physical proxy variables influencing postharvest forest regrowth. ...
Citations
... Irregular and sometimes large temporal gaps, due to, clouds, shadows, and variable sensor swath overlaps, are common in Landsat time series (Egorov et al., 2019). Furthermore, in certain forest systems, post-forest loss regrowth can be rapid, for example, on the order of a decade from clearing to stand re-establishment for fast-growing species in highly productive sites in the U.S. Southeast and Pacific Northwest (Franklin et al., 2002;Schroeder et al., 2007;Huang et al., 2009). Given these issues, and expanding on our previous research , tree cover loss was detected with respect to 9 year periods (mapping within each central 5-year period) on a rolling annual basis. ...
This study describes the generation and comprehensive validation of 30 m Landsat-based annual percent tree cover and forest cover loss products for the conterminous United States (CONUS). The products define (i) forest status with respect to three thematic classes: stable forest, stable non-forest, forest cover loss, (ii) percent tree cover (PTC, 0–100%), (iii) percent tree cover decrease (ΔPTC), and (iv) the Landsat acquisition dates bounding mapped forest cover loss occurrence. Forest was defined, based on the U.S. federal government forested land definition, as 30 m pixels with mapped PTC >10%. Annual products were derived using temporally overlapping 9-year periods (mapping within each central 5-year period) of USGS Landsat Analysis Ready Data (ARD) with reconciliation of the results between periods. The products for 2013 are presented and were validated rigorously by comparison with 1910 30 m independent reference data interpreted from bi-temporal
... Regional climate change can interfere with the forest ecosystem [42,43], especially the interaction with fire, which can cause serious damage to the forest [44]. In the process of studying vegetation recovery after wildfires, climate change, seasonal change, and topographic factors may affect the postfire response [45][46][47]; there was a positive correlation between NDVI and precipitation during the postfire recovery period [48]. Drought may reduce ecosystem resilience; i.e., the ability to recover the predisturbance state [49]. ...
... The slope direction will cause a difference in illumination, and then affect the photosynthesis of vegetation; where the slope is large, the soil loss is often serious, and the nutrient content required for vegetation growth is relatively low. The three topographic factors of elevation, aspect, and slope and the three climatic factors of temperature, precipitation, and wind speed were selected to establish a coupling relationship with the DI [39,45,46]. Considering that the synthetic data for phenology elimination was from July, and July was also the month with the best vegetation growth in the study area, the climatic factor was calculated using the average data of July over the years. ...
The frequency of forest fires is increasing under global climate change, and forest fires can cause devastating disturbances to forest systems and varying degrees of recovery of forest ecosystems after a disaster. Due to the different intensity of forest fires and forest systems, and in particular the fact that forest ecological recovery is influenced by many topographical and climatic factors, the process of postfire vegetation recovery is unclear and must be studied in depth. In this study, the Greater Hinggan Mountain Range was taken as the study area. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat time-series images acquired from 2000 to 2018, this study used the spatiotemporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after forest fires. The vegetation restoration was characterized by disturbance index (DI) values, which eliminated phenological influence. Six types of topography and climatic factors (elevation, aspect, slope; temperature, precipitation, and wind speed) were coupled with DI. Through single-factor analysis of variance and multiple comparison statistical methods, it was found that there was a significant relationship between the six factors and DI, which indicated those factors had a significant impact on the restoration of forest vegetation in burned areas. The results will be useful as a reference for future monitoring and management of forest resources.
... The tree regrowth duration map can be used to evaluate the carbon accumulation related to tree regrowth and assess potential drivers at the national level (Cook-Patton et al. 2020). The dataset can also be used to support related studies on ecological processes, such as erosion/sedimentation, nutrient and water cycling, and wildlife habitat (Schroeder, Cohen, and Yang 2007). We first evaluated the dynamics of tree cover, which showed similar gain and loss of trees in most years, except several years of extensive loss associated with fire ( Figure 4). ...
Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities has increased the demand for information on forest regrowth and recovery following natural and anthropogenic disturbances (e.g. fire, harvest, or thinning). However, a wall-to-wall mapping of the CONUS tree regrowth duration at an annual time interval and 30-m resolution is still challenging. In this study, we utilized the annual land cover products to develop a dataset to quantify forest regrowth duration for CONUS over 1985–2017. The land cover data used to derive the tree regrowth duration map is from the primary land cover product in the U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) collection. The LCMAP product used all available Landsat images to detect disturbances over forest and classify Grass/Shrub to Tree Cover transitions on an annual basis. The average regrowth duration was then calculated for each pixel. The regrowth duration map was validated using human-interpreted annual reference data that were collected independently. The validation results show 1 year of underestimation and 6-year standard deviation of error between the reference data and the regrowth duration map. In southeastern CONUS, where major tree regrowth activities have been observed, our map showed higher accuracy with less than one-year bias and 3.6 years standard deviation of error. Forest in the southeast took around 5 years to recover, which was faster than other regions of CONUS. Many pixels had multiple disturbances during the 33-year study period in the region. The spatial pattern of the tree regrowth indicated intense harvesting activities in this region. The Pacific Northwest coast region was the second main area of tree regrowth, but this region often took multiple decades to recover. Given the increasing interest in forests as nature-based climate solutions, the tree regrowth duration map can be used to assess reforestation activities as well as forest recovery following natural disturbance and harvesting.
... Recently, numerous algorithms for Landsat time series change detection have been proposed, reviewed and widely used (Banskota et al. 2014Cohen et al. 2017;Huang et al. 2009;Kennedy et al. 2010;Zhu 2017, Zhu et al. 2020. A wealth of forest cover change products from local to global scales have been generated from a medium-or high-frequency Landsat time series using these algorithms (Cohen et al. 2016;Czerwinski et al. 2014;Hansen et al. 2016;Margono et al. 2012;Masek et al. 2008;Potapov et al. 2012;Schroeder et al. 2007Schroeder et al. , 2011White et al. 2017). However, these products are either region-specific datasets with high map accuracy or global datasets with high regional uncertainty in map accuracy. ...
Background
Natural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation.
Methods
We used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance, including fire, logging, road construction and insects, using disturbance properties and spectral and topographic variables in the random forest model.
Results
The conventional and area-adjusted overall accuracies (OAs) of the forest disturbance map were 92.3% and 97.70% ± 0.06%, respectively, and the OA of mapping disturbance agents was 85.80%. The estimated disturbed forest area totalled 3313.13 km ² (approximately 2.31% of the total forest area in 1990) from 1990 to 2020, with considerable interannual fluctuations and significant regional differences. The predominant disturbance agent was fire, which comprised approximately 83.33% of the forest area disturbance, followed by logging (12.2%), insects (2.4%) and road construction (2.0%). Massive forest disturbances occurred mainly before 2000, and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value.
Conclusions
This study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR. The findings suggest that China’s logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR, which has implications for enhancing future forest management and biodiversity conservation.
... The proliferation of spaceborne remote sensing datasets and computational capabilities have expanded opportunities to estimate productivity (Running et al., 2004), detect disturbance (Schroeder et al., 2011;Masek et al., 2008;Masek et al., 2013) and assess relative rates of recovery post-disturbance (Schroeder et al., 2007;Madoui et al., 2015;White et al., 2017;White et al., 2018;Cooper et al., 2017). Remote sensing offers considerable advantages in spatial coverage, increased frequency of observations and consistency of datasets over long time series (e.g. ...
... Our results highlight the strong climatic control on rates of forest regrowth across the western U.S. following clearcutting treatments, and this aligns with the results of smaller scale studies. For example, sites with greater annual precipitation and on northerly aspects generally had faster rates of regeneration in two experimental sites located in Oregon (Schroeder et al., 2007). Our results expand on this finding by spanning the climatic extremes of the western U.S., explicitly accounting for the climatic water balance and by spanning various silvicultural methods. ...
Forests are subject to a range of management practices but it is unclear which produce the most rapid rates of regrowth across heterogeneous moisture gradients produced by regional climate and complex terrain. We analyzed recovery rates of satellite derived net primary productivity (NPP) over 27 years for 26,069 individual silvicultural treatments (stands) across the western U.S. at a 30m resolution. Rates of NPP recovery and forest regrowth were on average 116% higher in wet landscapes with lower annual climatic water deficits (8.59 ± 5.07 gC m-2 y-2, median ± inter-quartile range) when compared to dry landscapes (3.97 ± 2.67 gC m-2 y-2). This extensive spatial analysis indicates that hydroclimate is a dominant driver of forest regrowth and that responses can be highly nonlinear depending upon local climate conditions. Differences in silvicultural treatment also strongly controlled rates of regrowth within hydroclimatic settings; microclimates produced by shelterwood treatments maximized regrowth in dry landscapes whereas regrowth following clearcutting was among the fastest in wet landscapes due to enhanced energy availability. Conversely, commercial thinning regrowth rates were insensitive to hydroclimate and relatively consistent across the western U.S. Planting had a differential effect on forest structure and rates of regrowth across hydroclimate with negative effects in wet environments and positive effects in dry environments. In aggregate, this study provides a novel remote sensing approach for characterizing forest regrowth dynamics across climatic gradients and the common treatment options employed.
... For example, they have aimed at producing only yearly products, most commonly an annual forest disturbance map. For example, Landsat time series analyzed with spectral trajectory systems were used to map yearly clearcuts in several regions including western Oregon (Schroeder et al., 2007) Canada (Hermosilla et al., 2015), Finland (White et al., 2018) and Italy . Another example consists in the Global Forest Change map GFC (Hansen et al., 2013). ...
To combat global deforestation, monitoring forest disturbances at sub-annual scales is a key challenge. For this purpose, the new Planetscope nano-satellite constellation is a game changer, with a revisit time of 1 day and a pixel size of 3-m. We present a near-real time forest disturbance alert system based on PlanetScope imagery: the Thresholding Rewards and Penances algorithm (TRP). It produces a new forest change map as soon as a new PlanetScope image is acquired.
To calibrate and validate TRP, a reference set was constructed as a complete census of five randomly selected study areas in Tuscany, Italy. We processed 572 PlanetScope images acquired between 1 May 2018 and 5 July 2019.
TRP was used to construct forest change maps during the study period for which the final user’s accuracy was 86% and the final producer’s accuracy was 92%. In addition, we estimated the forest change area using an unbiased stratified estimator that can be used with a small sample of reference data. The 95% confidence interval for the sample-based estimate of 56.89 ha included the census-based area estimate of 56.19 ha.
... The shortcomings of these systems, which do not fully account for all wood removals at the national level, have been identified at least for Germany (Jochem et al. 2015), Canada (White et al. 2017), and Italy (Chirici et al. 2011). Forest fellings, especially those resulting from clearcutting, are clearly visible from satellite images and readily detected with automated algorithms, particularly when time series or multitemporal data are used, and when the spatial resolution of the data is compatible with the size of the harvested areas (Schroeder et al. 2007). ...
... Studies for the characterizations of regenerating forests with LTS analysis have been conducted primarily in temperate (Kennedy et al. 2007) and boreal forest environments (Olsson 2009), and only a few studies have focused on post-harvest recovery (Schroeder et al. 2007;White et al. 2017;White et al. 2018;White et al. 2019). In Schroeder et al. (2007), trajectories of Landsat spectral signals after clearcut in Douglas fir forests in Oregon (USA) were analyzed. ...
... Studies for the characterizations of regenerating forests with LTS analysis have been conducted primarily in temperate (Kennedy et al. 2007) and boreal forest environments (Olsson 2009), and only a few studies have focused on post-harvest recovery (Schroeder et al. 2007;White et al. 2017;White et al. 2018;White et al. 2019). In Schroeder et al. (2007), trajectories of Landsat spectral signals after clearcut in Douglas fir forests in Oregon (USA) were analyzed. The authors classified as "fast recovery" those classes where the spectral behavior was completely recovered after 12-14 years from the time of clearcut, also highlighting that the rates of forest regrowth after disturbance in western Oregon can be highly variable. ...
Key message
This work analyses the rate of recovery of the spectral signal from clearcut areas of coppice Mediterranean forests using Landsat Time Series (LTS). The analysis revealed a more rapid rate of spectral signal recovery than what was found in previous investigations in boreal and temperate forests.
ContextThe rate of post-disturbance vegetation recovery is an important component of forest dynamics.AimsIn this study, we analyze the recovery of the spectral signal from forest clearcut areas in Mediterranean conditions when the coppice system of forest management is applied.Methods
We used LTS surface reflectance data (1999–2015).We generated an annual reference database of clearcuts using visual interpretation and local forest inventory data, and then derived the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) spectral trajectories for these clearcuts. From these spectral trajectories, we calculated the Years to Recovery or Y2R, the number of years it takes for a pixel to return to within a specified threshold (i.e., 70%, 80%, 90%, 100%) of its pre-disturbance value. Spectral recovery rates were then corroborated using measures of canopy height derived from airborne laser scanning (ALS) data.ResultsThe coppice system is associated with rapid recovery rates when compared to rates of recovery from seeds or seedlings in temperate and boreal forest conditions. We found that the Y2R derived from the spectral trajectories of post-clearcut NBR and NDVI provided similar characterizations of rapid recovery for the coppice system of forest management applied in our study area. The ALS measures of canopy height indicated that the Y2R metric accurately captured the rapid regeneration of coppice systems.Conclusion
The rapid rate of spectral recovery associated with the coppice system is 2–4 years, which contrasts with values reported in boreal and temperate forest environments, where spectral recovery was attained in approximately 10 years. NBR is an effective index for assessing rapid recovery in this forest system.
... Burned-Untreated sites had the smallest vegetation recovery in the west aspect and largest vegetation recovery in north aspect (46% and 64% of pre-fire vegetation, respectively). This is contrary to Schroeder et al. (2007), who used a Landsat analysis and observed that north aspects in Oregon encountered three times the vegetation regrowth compared to other slope aspects. However, in that study, the planting was performed on shallow slopes and low elevations, which was not the case in the Waldo Canyon Fire. ...
Wildfires are becoming more prevalent and are impacting forests, watersheds and important resources. Hydrologic and geomorphic processes following wildfires can include erosion flooding, and degraded water quality. To mitigate these secondary impacts, post-fire restoration treatments can be applied to a burned area to stabilize the land surface or promote vegetative regrowth. This research focuses on wood and straw mulch treatment implemented after the 2012 Waldo Canyon Fire in Colorado (United States) and estimates the spatial and temporal changes in annual and seasonal vegetation after a fire with respect to geomorphic factors. This study highlights the use of satellite-based remote sensing products to investigate the impacts of post-fire rehabilitation treatments on vegetation. Using Enhanced Vegetation Index as a proxy for vegetative growth, vegetation conditions are evaluated with respect to slope, slope aspect, and burn severity to understand the impact of the ground cover treatments on vegetation for five years before and after the fire (2007-2016). Sixty-three burned and untreated sites, forty-nine burned sites treated with wood mulch, and twenty-eight burned sites treated with straw mulch were analyzed. These sites were also compared to two control sites that were unburned and untreated, Hunter's Run and Fountain Creek. Generally, post-fire conditions did not return to pre-fire levels, where average vegetation levels were lower. By the end of the study, burned and untreated sites had larger vegetative levels than burned and treated sites. The vegetation levels of the burned sites were statistically different (α = 0.05) from pre-fire conditions in all areas of treatment. Burned sites treated with wood and straw recovered to 69% and 73% of pre-fire conditions, respectively. This work demonstrates the novel use of remote sensing to observe vegetation after post-fire treatment applications to augment the number of sites and length of time that can be analyzed. The observed change in vegetation conditions also contributes to furthering our understanding of the impacts of post-fire restoration, which is important for post-fire management.
... Most of the studies investigating forest regrowth using Landsat Time Series (LTS) to track forest recovery following wildfire (e.g., Bolton et al., 2015;Franks et al., 2013;Frazier et al., 2018) and less commonly, following harvest Schroeder et al., 2007, White et al., 2017. ...
Within the Copernicus Land Monitoring Services, methodological approaches for change detection and tracking are increasingly being demanded to update existing LC/LU maps and to shorten the timespan between image acquisition and final LC/LU products (e.g. for updating of CORINE LC, Urban Atlas, HRLs, for identification of permanent grasslands, for crop status monitoring, monitoring Natura2000 sites and further applications).
Estimating change from remotely sensed data is not a straightforward approach since time series contain a combination of seasonal, gradual and abrupt changes in addition to noise which originates e.g. from remnant geometric misregistration or atmospheric effects. For monitoring land cover changes on panEuropean level, the availability of optical EO time series is a bottleneck because of data gaps and nonoptimal acquisition time frames due to frequent cloud and snow cover conditions and/or low solar incidence angles, especially in the North of Europe and in Alpine areas. Therefore, for pan-European or global applications, a main requirement is the usage of optical as well as SAR time series to allow a homogeneous wall-to-wall coverage for change monitoring. Depending on the biogeographic region, approaches which are only based on optical data streams (e.g. in Mediterranean areas), and approaches which combine optical and SAR data streams (e.g. in areas with more frequent cloud coverage) can be applied. Therefore, optical and SAR based approaches are benchmarked separately in WP34. In order to
achieve these goals ECoLaSS makes full use of dense time series from High-Resolution (HR) Sentinel-2 optical and/or Sentinel-1 Synthetic Aperture Radar (SAR) data.
... Recent studies have used data from active sensors such as light detecting and ranging (Lidar) to quantify post-disturbance structural characteristics and recovery trajectories (Bolton et al. 2015;Vogeler et al. 2016;Latifi et al. 2016;Bolton et al. 2017;Hill et al. 2017). However, as those approaches are limited in their spatial and temporal extent, it might be beneficial to also utilize data from passive, satellite-borne sensors-such as Landsat-for mapping post-disturbance recovery across extended spatio-temporal scales (Kennedy et al. 2007;Schroeder et al. 2007). Trends in spectral recovery derived from Landsat time series can give valuable insights into the regrowth of vegetation after large-scale disturbances such as clear-cutting and fire in the boreal forests (Frazier et al. 2015;White et al. 2017;Frazier et al. 2018). ...
Context
Recovery from disturbances is a prominent measure of forest ecosystem resilience, with swift recovery indicating resilient systems. The forest ecosystems of Central Europe have recently been affected by unprecedented levels of natural disturbance, yet our understanding of their ability to recover from disturbances is still limited.
Objectives
We here integrated satellite and airborne Lidar data to (i) quantify multi-decadal post-disturbance recovery of two indicators of forest structure, and (ii) compare the recovery trajectories of forest structure among managed and un-managed forests.
Methods
We developed satellite-based models predicting Lidar-derived estimates of tree cover and stand height at 30 m grain across a 3100 km² landscape in the Bohemian Forest Ecosystem (Central Europe). We summarized the percentage of disturbed area that recovered to > 40% tree cover and > 5 m stand height and quantified the variability in both indicators over a 30-year period. The analyses were stratified by three management regimes (managed, protected, strictly protected) and two forest types (beech-dominated, spruce-dominated).
Results
We found that on average 84% of the disturbed area met our recovery threshold 30 years post-disturbance. The rate of recovery was slower in un-managed compared to managed forests. Variability in tree cover was more persistent over time in un-managed forests, while managed forests strongly converged after a few decades post-disturbance.
Conclusion
We conclude that current management facilitates the recovery of forest structure in Central European forest ecosystems. However, our results underline that forests recovered well from disturbances also in the absence of human intervention. Our analysis highlights the high resilience of Central European forest ecosystems to recent disturbances.
