Forest Recovery Research Articles

Low-carbon agricultural technologies improve forage and feed production in the Caatinga biome, Brazil: Characteristics, comparison, effects of climate change, resilience, local development, and food security

Adjustments in Brazilian livestock are necessary to minimize greenhouse gas (GHG) emissions, since the largest source of methane comes from ruminants’ enteric fermentation, and of carbon from deforestation. Low-carbon agriculture technologies (LCAT) contribute to mitigating these emissions and this study evaluates the role of these technologies on ruminant forage production in Caatinga. A Strength, Weakness, Opportunity, and Threats analysis was used to elucidate the main features, followed by an Analytic Hierarchical Process, ranking the LCAT, and a risk analysis. Integrated Crop-Livestock-Forest System (ICLFS) is the most recommended technology, followed by Sustainable Forest Management (SFM) and Recovery of Degraded Areas with Pastures (RDA-P). The results can aid in the choice of the LCAT to be implemented by the smallholder in Caatinga, demonstrating the need to strengthen rural technical assistance, so that there is a real benefit to the producer and the environment.

Forest Fire Severity and Koala Habitat Recovery Assessment Using Pre- and Post-Burn Multitemporal Sentinel-2 Msi Data

Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in Australia in 2019/2020, mostly areas dominated by eucalypts, which provide koala habitats. We studied the impact of fire and three subsequent years of recovery on a property in South-East Queensland, Australia. A classified Differenced Normalised Burn Ratio (dNBR) calculated from pre- and post-burn Sentinel-2 scenes encompassing the local study area was used to assess regional impact of fire on koala-habitat forest types. The geometrically structured composite burn index (GeoCBI), a field-based assessment, was used to classify fire severity impact. To detect lower levels of forest recovery, a manual classification of the multitemporal dNBR was used, enabling the direct comparison of images between recovery years. In our regional study area, the most suitable koala habitat occupied only about 2%, and about 10% of that was burnt by wildfire. From the five koala habitat forest types studied, one upland type was burnt more severely and extensively than the others but recovered vigorously after the first year, reaching the same extent of recovery as the other forest types. The two alluvial forest types showed a negligible fire impact, likely due to their sheltered locations. In the second year, all the impacted forest types studied showed further, almost equal, recovery. In the third year of recovery, there was almost no detectable change and therefore no more notable vegetative growth. Our field data revealed that the dNBR can probably only measure the general vegetation present and not tree recovery via epicormic shooting and coppicing. Eucalypt foliage growth is a critical resource for the koala, so field verification seems necessary unless more-accurate remote sensing methods such as hyperspectral imagery can be implemented.

Assessing the effectiveness of vegetation indices in detecting forest disturbances in the southeast Amazon

Amazon forests are becoming increasingly vulnerable to disturbances such as droughts, fires, windstorms, logging, and forest fragmentation, all of which lead to forest degradation. Nevertheless, quantifying the extent and severity of disturbances and their cumulative impact on forest degradation remains a significant challenge. In this study, we combined multispectral data from Landsat sensors with hyperspectral data from the Earth Observing-One (Hyperion/EO-1) sensor to evaluate the efficacy of multiple vegetation indices in detecting forest responses to disturbances in an experimentally burned forest in southeastern Amazonia. Our experimental area was adjacent to an agricultural field and consisted of three 50-ha treatments – an unburned Control, a plot burned every three years, and a plot burned annually from 2004 to 2010. All plots were monitored to assess vegetation recovery after fire disturbance. These areas were also affected by three drought events (2007, 2010, and 2016) over the study period. We evaluated a total of 18 Vegetation Indices (VI), one unique to Landsat, 12 unique to Hyperion/EO-1, and five commons to both satellites (i.e., 6 total from Landsat and 17 from Hyperion). We used linear models (LM) to evaluate how changes in ground observations of forest structure (biomass, leaf area index [LAI], and litter production) associated with fire were captured by the two VIs most sensitive to forest degradation. Our results indicate that the Plant Senescence Reflectance Index (PSRI) derived from Hyperion/EO-1 was the most sensitive to vegetation changes associated with forest fires, increasing by 94% in burned vs. unburned forests. Of the Landsat-derived VIs, we found that the Green-Red Normalized Difference (GRND) were the most sensitive to forest degradation by fire, showing a marked decline (87%) in the burned plots compared with the unburned Control. However, compared to PSRI, the GRND was a better predictor of changes associated with fire, both in the forest interior or forest edge, for the three ground variables: biomass stocks (r2 = 0.5–0.8), LAI (r2 = 0.8–0.9), and litter production (r2 = 0.4–0.7). This study demonstrate that VIs can detect forest responses to fire and other disturbances over time, highlighting the relative strengths of each VI. In doing so, it shows how the integration of multispectral and hyperspectral data can be useful for monitoring tropical forest degradation and recovery. Moreover, it provides valuable insights into the limitations of existing approaches, which can inform the design of next-generation sensors for global forest monitoring.

Restricted recovery of Eisenia bicyclis kelp forest following sea urchin removal on the Pacific coast of Tohoku, Japan

Little information is available on the destruction of Eisenia bicyclis kelp forests (Phaeophyceae: Laminariales) due to overgrazing by sea urchins. In Shizugawa Bay, on the northeastern Pacific coast of Japan, overgrazing by the sea urchin Mesocentrotus nudus caused the destruction of E. bicyclis forests between 2014 and 2015. This study investigated the recovery of an adult kelp forest in relation to sea urchin densities through the continuous removal of sea urchins at 3 permanent experimental transects spanning 100 m2 for 3.8 yr from September 2015 to June 2019. The relationship between the densities of sea urchins and kelp juveniles was also analyzed. Estimates of the threshold density and biomass of sea urchins required to trigger the phase shift from an E. bicyclis forest to a barren were 15.5 ind. m-2 and 712.0 g m-2, respectively. The removal of sea urchins expanded the offshore kelp growth area. However, an increase in adult kelp following the return of juveniles was restricted to the nearshore half of the transects. The successful recruitment of kelp juveniles, followed by the recovery of adult kelp forest, requires reduction of the density of M. nudus of 40-50 mm diameter to 0 or 1 ind. m-2. Reducing sea urchin densities in autumn, when many move from barrens to invade kelp forests, is crucial to ensure the survival of kelp juveniles. This study highlights the presence of an intensified positive feedback mechanism of the barren state due to high grazing intensity and a weakened Oyashio Current.

Study on the Evolutionary Characteristics of Post-Fire Forest Recovery Using Unmanned Aerial Vehicle Imagery and Deep Learning: A Case Study of Jinyun Mountain in Chongqing, China

Forest fires pose a significant threat to forest ecosystems, with severe impacts on both the environment and human society. Understanding the post-fire recovery processes of forests is crucial for developing strategies for species diversity conservation and ecological restoration and preventing further damage. The present study proposes applying the EAswin-Mask2former model based on semantic segmentation in deep learning using visible light band data to better monitor the evolution of burn areas in forests after fires. This model is an improvement of the classical semantic segmentation model Mask2former and can better adapt to the complex environment of burned forest areas. This model employs Swin-Transformer as the backbone for feature extraction, which is particularly advantageous for processing high-resolution images. It also includes the Contextual Transformer (CoT) Block to better capture contextual information capture and incorporates the Efficient Multi-Scale Attention (EMA) Block into the Efficiently Adaptive (EA) Block to enhance the model’s ability to learn key features and long-range dependencies. The experimental results demonstrate that the EAswin-Mask2former model can achieve a mean Intersection-over-Union (mIoU) of 76.35% in segmenting complex forest burn areas across different seasons, representing improvements of 3.26 and 0.58 percentage points, respectively, over the Mask2former models using ResNet and Swin-Transformer backbones, respectively. Moreover, this method surpasses the performance of the DeepLabV3+ and Segformer models by 4.04 and 1.75 percentage points, respectively. Ultimately, the proposed model offers excellent segmentation performance for both forest and burn areas and can effectively track the evolution of burned forests when combined with unmanned aerial vehicle (UAV) remote sensing images.

The Impact of Ecological Restoration on Soil Quality in Humid Region Forest Habitats: A Systematic Review

Ecological restoration is widely recognized as an essential technique for addressing soil degradation, biomass decline, and biodiversity loss. Improving and maintaining soil quality is critical to ensuring environmental sustainability and successful forest recovery. This systematic review aimed to assess the impact of ecological forest restoration efforts on soil quality in humid regions, as well as to compare the effectiveness of various ecological restoration strategies on soil quality indicators. Subsequently, a systematic search on various databases (e.g., Scopus and Google Scholar) yielded 696 records, of which 28 primary studies met the inclusion criteria. The results emphasized that chemical and physical soil properties are the key indicators for assessing ecosystem performance during forest restoration. The most commonly measured parameters were soil carbon, nitrogen, phosphorus, pH, bulk density, and soil porosity. It was shown that the restoration process required a longer duration to reach a comparable level of recovery as seen in mature forests, particularly in terms of fully restoring soil quality. Additionally, it has been noted that prior land use influences the length of time needed for soil quality recovery. In planted sites, soil quality may keep improving as the site ages, though it tends to stabilize after a certain period.

Accuracy assessment of LAI, PAI and FCOVER from Sentinel-2 and GEDI for monitoring forests and their disturbance in Central Germany

ABSTRACT Forest monitoring benefits from biophysical parameters such as Leaf Area Index (LAI), Plant Area Index (PAI) and fractional vegetation cover (FCOVER) and can be obtained from optical and LiDAR remote sensing, such as Sentinel-2 (S2) and the Global Ecosystem Dynamics Investigation (GEDI). While GEDI-derived products consider all phyto-elements, those from S2 refer to green elements only. Apart from individual accuracies, systemic deviations among products are thus expectable. However, products from S2 and GEDI lack inter-comparison. We evaluated S2 and GEDI-derived LAI, PAI and FCOVER with digital hemispherical photography observations (DHP) in a forest disturbance hotspot in Germany across various forest conditions, including vital stands, standing deadwood and clearings. We found moderate to high agreement with in-situ data, with highest accuracy for S2-derived LAI (R2 = 0.54) and GEDI-derived FCOVER (R2 = 0.73). Agreements between S2 and GEDI products were low, which we attribute to systematic influences of woody components, GEDI’s limitations in sloped terrain, and saturation of optical signals in dense canopy. In conclusion, findings suggest that while GEDI is effective in dense canopies, S2 products are beneficial for monitoring forest recovery. We also see potential for synergistic use in monitoring standing deadwood for habitat mapping and fire risk assessment.

Effects of salvage logging after forest fire on Siberian larch regeneration and ecosystem carbon stocks at the drought limit of the boreal forest in Mongolia

Post-fire salvage logging is widely applied in Mongolia's boreal forests with the intent to prevent intact forests from logging. The rationale behind this approach is the assumption that the additional disturbance caused by the removal of standing deadwood after stand-replacing fire is of no further significance for the already heavily disturbed ecosystem. However, while there is a global debate on effects of salvage logging for regeneration success, biodiversity, and soil health, little evidence has been collected from strongly drought-limited southern boreal forests of Central Asia. Comparing sites with and without salvage logging, we investigated forests of Siberian larch (Larix sibirica) ca. 20 years after stand-replacing fire and asked whether postfire salvage logging affected regeneration density, terminal shoot length and radial stem increment, ecosystem carbon stock densities, and reduced organic layer depth and compacted the soil. The biomass of the larch regeneration was significantly reduced by salvage logging, while tree growth was not affected. The ecosystem carbon stock density of burnt forest without salvage logging was 202 Mg C ha−1 and thus even in the lower range of intact larch forests from Mongolia, whereas burnt forests with salvage logging had organic carbon stock densities (104 Mg C ha−1) that were lower than those of unburned grasslands in the forest-steppe. These results show that removing deadwood from burnt forest is not insignificant, but has the potential to delay forest recovery and strongly reduces organic carbon storage. However, we did not find significant reductions in soil organic carbon stocks or soil compaction. Nonetheless, our findings raise the question of whether careful management of intact forests (especially by selective felling under a continuous-cover forestry regime) would be a more ecologically sustainable alternative than post-fire salvage logging.

Forest Management Assessment Using LASSO and the Critical List of Variables for Sustaining the Commons: Four Cases in the Monarch Reserve

In the Monarch Reserve, four cases were selected for exploratory analysis using the critical list of variables for sustaining the commons and LASSO regression to determine which of these are most influential in the successful maintenance of forest cover over a decade. Forest recovery from degraded to dense forest is found to be inversely related to resource size, consistent with theory. However, in four other categories of forest change, the size of the resource is less influential, though it is still a significant, variable with a positive relationship to forest cover change which is inconsistent with theory. The analysis also showed that less overlap, greater poverty and lower dependence on the resource are associated both with forest gain and with mitigation of forest loss, which is contrary to theory and most research. Also associated with poverty in the LASSO regression are homogeneity of identity and ease of enforcement of rules. These show a negative relation to transitions from non-forest to dense forest, with low coefficients. This is inconsistent with theory. Possible reasons for the divergence from theory are discussed in detail, as is the utility of this approach for assessment and monitoring.

Satellite measurement of forest disturbance, recovery and deposit distribution following explosive volcanic eruptions

The characteristics and extent of forest damage, and the subsequent patterns of recovery, reflect the intensity of an explosive volcanic eruption and have the potential to be a novel proxy for eruption magnitude and impact. Using satellite measurements of vegetation damage and recovery patterns, following the 2015 explosive eruption of Calbuco, Chile, we assess the impact on surrounding temperate forests and how areas impacted by different deposit types recover post-eruption. The Calbuco eruption resulted in tephra deposition over hundreds of square kilometres, pyroclastic flows extending 6 km and lahars extending 15 km. We explore NDVI derived from optical imagery (June 2013–May 2023) as well as radar backscatter and phase coherence (October 2014–June 2023) through time series analysis, clustering and estimation of recovery timescales to find patterns in forest disturbance and recovery. We find that forest damage and recovery correspond primarily with deposit type, thickness and dispersal directions. The thickest tephra deposits (> 40 cm) correlate with the most vegetation loss, so our vegetation impact maps allow us to refine the spatial mapping of tephra fall-deposit isopachs to give a revised eruption volume of 0.28 km3. Vegetation recovery rates relate to initial impact type and intensity, but also local topography, aspect and altitude. Our results demonstrate a novel application of optical and radar satellite remote sensing to determine eruption extents and magnitudes through vegetation disturbance. We show that measuring vegetation disturbance, particularly in remote and densely vegetated environments, can help refine field-based analyses in inaccessible or intensely damaged zones.

Recurrent wildfires alter forest structure and community composition of terra firme Amazonian forests

Abstract Wildfires associated with land-use and climate change have considered a key driver to the Amazon forest collapse. However, achieving a detailed understanding of how human-related disturbances impact forest successional trajectories needs comprehensive information spanning forest strata. Here, we investigate the impact of recurrent wildfires on forest structure, species diversity, and composition, making a comprehensive assessment of the regenerating, understory, and canopy tree communities in a sustainable use reserve in the eastern Amazon. Plant communities were described across 16 forest stands (old-growth, burned once and twice) sampling a total of 3620 individuals and 326 tree and palm species. Wildfires affected all attributes of forest structure. Aboveground biomass decreased by 44% in forest burned once, and 71% in twice-burned forest stands. Forest canopy was the most affected strata after the second fire, with a 44%-decrease compared to unburned forest. The same pattern emerged for basal area, which decreased by an average of 27.5% after the first fire and 53.8% following the second fire event. Overall, plant communities experienced a 50%-loss of species richness after two fires, including both dominant and rare species. Plant communities also became more dissimilar as fire events accumulated, with 58%–61% increase in species dissimilarity following two fires events. As wildfires reoccured, the old-growth forests of our focal landscape were converted into a mosaic of regenerating forest stands dominated by local short-lived pioneers (i.e. low-biomass early-regenerating forest stands) and a few tree species less sensitive to fire. Our findings highlight the urgent need to secure a resilient future for Amazonian forests with actions needed to support local livelihoods whilst reducing the prevalence of ignitions sources and allowing forest recovery.

Effects of Climate, Soil, Topography and Disturbance on Liana Prevalence.

Lianas (woody vines and climbing monocots) are increasing in abundance in many tropical forests with uncertain consequences for forest functioning and recovery following disturbances. At a global scale, these increases are likely driven by disturbances and climate change. Yet, our understanding of the environmental variables that drive liana prevalence at regional scales is incomplete and geographically biased towards Latin America. To address this gap, we present a comprehensive study evaluating the combined effects of climate, soil, disturbance and topography on liana prevalence in the Australian Wet Tropics. We established 31 20 × 20 m vegetation plots along an elevation gradient in low disturbance (canopy closure ≥ 75%) and high disturbance (canopy closure ≤ 25%) forest stands. In these plots, all tree and liana (defined as all woody dicot vines and climbing monocots, i.e., rattans) stems ≥ 1 cm DBH were measured and environmental data were collected on climate, soil and topography. Generalised linear models were used with multi-model averaging to quantify the relative effects of the environmental variables on measures of liana prevalence (liana-tree basal area ratio, woody vine basal area and stem density and rattan stem density). Liana prevalence decreased with elevation but increased with disturbance and mean annual precipitation. The increase in the liana-tree ratio with precipitation was more pronounced for highly disturbed sites. Like other tropical regions, disturbance is an important driver of liana prevalence in Australian rainforests and appears to interact with climate to increase liana-tree ratios. The observed increase in liana-tree ratio with precipitation contrasts findings from elsewhere but is confounded by correlated changes in elevation and temperature, which highlights the importance of regional studies. Our findings show that forests with high disturbance and climatic conditions favourable to lianas are where lianas most likely to outcompete trees and impede forest recovery.

Assessing Vulnerability to Cyclone Hazards in the World’s Largest Mangrove Forest, The Sundarbans: A Geospatial Analysis

The Sundarbans is the world’s largest contiguous mangrove forest with an area of about 10,000 square kilometers and shared between Bangladesh and India. This world-renowned mangrove forest, located on the lower Ganges floodplain and facing the Bay of Bengal, has long served as a crucial barrier, shielding southern coastal Bangladesh from cyclone hazards. However, the Sundarbans mangrove ecosystem is now increasingly threatened by climate-induced hazards, particularly tropical cyclones originating from the Indian Ocean. To assess the cyclone vulnerability of this unique ecosystem, using geospatial techniques, we analyzed the damage caused by past cyclones and the subsequent recovery across three salinity zones, i.e., Oligohaline, Mesohaline, and Polyhaline. Our study also examined the relationship between cyclone intensity with the extent of damage and forest recovery. The findings of our study indicate that the Polyhaline zone, the largest in terms of area and with the lowest elevation, suffered the most significant damage from cyclones in the Sundarbans region, likely due to its proximity to the most cyclone paths. A correlation analysis revealed that cyclone damage positively correlated with wind speed and negatively correlated with the distance of landfall from the center of the Sundarbans. With the expectation of more extreme weather events in the near future, the Sundarbans mangrove forest faces a potentially devastating outlook unless both natural protection processes and human interventions are undertaken to safeguard this critical ecosystem.

Abalone recruitment patterns before and after sea urchin barrens formation in northern California: incorporating climate change

ABSTRACT Understanding the recruitment dynamics of invertebrates in kelp forests is critical to informing climate-ready restoration. Here we examine abalone and sea urchin recruitment (3–20 mm in size) patterns in northern California across a period of drastic change. Annual surveys were conducted before, during and after the MHW (2014–2016), the loss of a major predatory sea star (2012–2016) and the collapse of a bull kelp forest in 2014. Divers surveyed artificial reef recruitment modules (n = 12) over 20 years in an area that once supported dense bull kelp, Nereocystis leutkeana, forests and the world's largest recreational abalone fishery. From 2016 to 2022, we tracked the decline of red abalone, Haliotis rufescens, recruitment and the rise of purple sea urchin, Strongylocentrotus purpuratus, recruitment. Adult densities of purple sea urchins increased as did newly settled sea urchins (<3 mm), while adult and newly settled red abalone declined. Eight years after the kelp forest collapse, red abalone recruitment remained low and sea urchin recruitment continued to increase. Recruitment patterns can inform both abalone restoration targets and sea urchin dynamics as part of a more holistic kelp forest recovery plan that is responsive to climate change drivers.

Beyond the surface: microbiological and biochemical attributes as indicators of soil quality in Atlantic Forest ecosystem

The objective of this study was to determine the biochemical and microbiological attributes in areas with different vegetation covers (mature forest, secondary forest, and sugar cane monoculture) and seasonal changes, and to compare the sensitivity of these attributes types (mycorrhizal, enzymatic, and carbon attributes) in response to changes in vegetation cover and season. Soil samplings were conducted over two years (one per semester, during the dry and wet periods of 2007 and 2008) in three types of areas: (I) mature forest; (II) secondary forest; and (III) sugar cane crop. The enzymatic activity of fluorescein diacetate, dehydrogenase, saccharase, basal carbon respiration, the carbon content of microbial biomass, the microbial coefficient, total and easily extracted glomalin soil-related proteins, mycorrhizal colonization, the number of glomerospores, total organic carbon, fulvic and humic acid content, and the fulvic/humic acid ratio were analyzed. In general, mycorrhizal parameters were more sensitive in distinguishing areas with different types of vegetation cover. Additionally, carbon stocks in areas of mature and secondary forest were higher compared to areas cultivated with sugar cane. The study reveals that mycorrhizal properties, such as root colonization and GRSP fractions, are more sensitive indicators of soil health than soil C attributes, providing a cost-effective alternative for evaluating soil health in the Atlantic Forest. The confirmed hypothesis supports the effectiveness of microbiological activity and C dynamics in discerning significant changes between sugar cane cultivation and forested areas. The study underscores the vital role of conserving forested areas, serving as crucial carbon reservoirs, and emphasizes the potential for enhanced sustainability through conservation practices and rapid soil function recovery in secondary forests within the Atlantic Forest.

A global analysis of the effects of forest thinning on soil N stocks and dynamics

Forest thinning is a critical management measure for changing the forest community structure and improving the soil microclimate and nitrogen (N) cycle. However, differences in forest types, thinning intensity, recovery time, and climate conditions make the accumulation of soil N under forest thinning uncertain; especially the soil N accumulation rate is still lacking. This study systematically discusses the effects of forest thinning on the rate of soil N stock change (RNC) and analyzes the relationship between the RNC and the rate of soil carbon (C) stock change (RCC) by synthesizing 387 global data points. Forest thinning significantly increased the soil N stock, with the RNC being +0.07 Mg/ha yr−1. The RNCs in coniferous and mixed forests (+0.07 and +0.09 Mg/ha yr−1, respectively) were higher than that in broadleaf forest (−0.03 Mg/ha yr−1). Heavy forest thinning inhibited soil N accumulation; however, light forest thinning and long-term recovery were more conducive to improving it. The RNC decreased with increasing precipitation and temperature under forest thinning. Additionally, the RNC was positively correlated with the RCC. Overall, heavy forest thinning decreases the soil N accumulation rate in the global forest ecosystem; conversely, long-term recovery increases it. Reducing the forest thinning intensity and extending the recovery time can accelerate soil N accumulation and thus reduce greenhouse gas emissions.

Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests

AbstractWestern North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.

FBA-DPAttResU-Net: Forest burned area detection using a novel end-to-end dual-path attention residual-based U-Net from post-fire Sentinel-1 and Sentinel-2 images

Forest burned area (FBA) detection using remote sensing (RS) data is critical for timely forest management and recovery attempts after wildfires. This study introduces a dual-path attention residual-based U-Net (DPAttResU-Net), a novel end-to-end deep learning (DL) model tailored for FBA detection using dual-source post-fire Sentinel-1 (S1) and Sentinel-2 (S2) satellite RS imagery. To better distinguish FBAs from other land cover types, DPAttResU-Net incorporates a dual-pathway structure to exploit complementary geometrical/physical and spectral features from S1 and S2, respectively. An integral component in the proposed architecture is the channel-spatial attention residual (CSAttRes) block, which emphasizes salient features through the channel and spatial attention modules, thus improving the burned area feature representation. To compare DPAttResU-Net to state-of-the-art DL models, experiments were conducted on benchmark FBA datasets collected over 12 areas, where ten datasets were used as training data and two datasets were used to test the trained DL models. The experimental results demonstrate the high proficiency of the proposed deep model in meticulously delineating FBAs. In further detail, DPAttResU-Net, with a PFN of 17.96 (%) in the first case and an IoU of 89.31 (%) in the second case, outperformed the existing U-Net-based models. Accordingly, through dual-path integration and attention mechanisms, DPAttResU-Net contributes to accurately identifying FBAs by preserving their geometrical details, making it a promising tool for post-wildfire forest management.

Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method.

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.

The changes in diversity of vegetation and foliar stable isotopes during the terrestrial plant succession of a subtropical forest and their ecological implications

The disturbed forest system can be restored through successional processes, where changes in the diversity of plant species serves as key indicators of processes. Meanwhile plant water use efficiency and nitrogen dynamics are important factors influencing photosynthesis-water coupling and nutrient stress in plants, respectively, during forest recovery. Analyzing these factors is crucial for understanding forest recovery and promoting sustainable ecosystem development. In this study, traditional vegetation surveys and biodiversity indices, along with foliar stable carbon and nitrogen isotope analyses, as well as nitrogen content analyses, were conducted in a subtropical forest with various successional stages following human and natural disturbances in Sichuan province, China. These analyses were used to assess species diversity patterns, evaluate water use efficiency, and assess nitrogen dynamics in primary, secondary, and artificial forests. The results showed distinct stage-specific differences in succession among forest types. Primary forests exhibited the highest successional stage but were limited by water stress and high nitrogen competition, with the highest δ13C values (i.e., −29.0 ‰), lowest δ15N values (i.e., −3.6 ‰), and the lowest nitrogen contents (i.e., 3.6 %) on average. Artificial forests had the lowest species diversity due to species dominance and nitrogen competition, with the lowest δ13C values (i.e., −29.7 ‰), relatively low nitrogen contents (i.e., 4.0 %), and high δ15N values (i.e., −1.8 ‰). Secondary forests, with longer recovery periods, reached higher successional stage due to sufficient nitrogen and water, which is indicated by the highest δ15N values and nitrogen contents (i.e., −1.7 ‰ and 4.5 %), and the relatively low δ13C values (i.e., −29.4 ‰), leading to accelerated vegetation growth. This study highlights not only the importance of analyzing plant diversity, but also the material cycles, especially water use efficiency, and nitrogen dynamics, to understanding forest recovery. These findings can support effective conservation and sustainable development of subtropical forest ecosystems.