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Adaptive forest management strategies require accurate detection of forest disturbance, in various spatial scales. Synthetic Aperture Radar (SAR) data can provide information about the forest attributes, penetrating the canopy at different levels under any weather and lighting conditions. ICEYE consists of the largest constellation of SAR satellites, enabling very high spatial and temporal resolution. In this study, ICEYE data were investigated in the detection of storm damage, in a fir forest with complex topography which was recently hit by the Daniel storm, resulting in severe damage to the forest structure (FS) and alluvium depositions (AD). To identify the best potential for storm damage detection using ICEYE data, an unsupervised change detection approach was employed combining wavelet transform and adaptive thresholds at spatial scales of 0.5 m (R05), 1 m (R1), 2 m (R2) and 3 m (R3). Additionally, two morphological filters were applied in best-performing resolutions to assess the impact of post-processing on the detection accuracy. Finally, FS and AD damage were investigated separately in order to provide detailed information about the detection capabilities of ICEYE. The results showcased that R1 (UA = 32.35, PA = 18.34 and K = 0.31) provided the best detection performance, followed by R05 (UA = 20.62, PA = 20.12 and K = 0.19). Furthermore, the employment of morphological filters slightly increased UA and Kappa metrics in both R1(UA = 33.40 and K = 0.32) and R05 (UA = 25.60 and K = 0.24), suggesting that post-processing is necessary to mitigate false detections. Regarding the investigation of AD and FS damage, it was revealed that post-processed ICEYE data in both R05 and R1 are capable of identifying AD with satisfying accuracy (UA = 47.41, PA = 22.36, K = 0.47), while FS damage detection is more challenging (UA = 10.15%-14.40%, PA = 14.55%-15.11%, and Kappa = 0.10-0.17). Overall, this study demonstrated that ICEYE can be used to detect storm-affected areas in mountainous forest ecosystems, especially in cases where detection with other methods is not feasible. [ABSTRACT FROM AUTHOR]

China leads in the greening of the world, with a nearly doubled increase in its forest area since the 1980 s revealed by the National Forest Inventory (NFI). However, a significant challenge persists in the absence of consistent and reliable remote sensing data that align with the NFI, hindering a comprehensive understanding of the spatiotemporal patterns of terrestrial ecosystem changes driven by afforestation and reforestation efforts over recent decades in China. Moreover, conventional binary thematic maps and land use and land cover (LULC) maps encounter difficulties in providing a thorough assessment of canopy cover at the subpixel level and trees extending beyond officially designated forest boundaries. This limitation creates substantial gaps in our comprehension of their invaluable contributions to ecosystem services. To confront these challenges, this study presents a systematic framework integrating time-series Landsat satellite imagery and random forest-based ensemble learning techniques. This framework aims to generate China's inaugural annual tree cover dataset (CATCD) spanning from 1985 to 2023 at a 30 m spatial resolution. Evaluation against multisource reference data shown high correlations ranging from 0.70 to 0.96 and reasonable RMSE values ranging from 5.6 % to 25.2 %, highlighting the reliability and precision of our approach across different years and data collection methodologies. Our analysis reveals that China's forested area has doubled, expanding from 1.04 million km2 in 1985 to 2.10 million km2 in 2023. Notably, 33 % of this growth can be attributed to a shift from non-forest to forest land categories, primarily observed in the three-north and southwest regions. However, the majority, contributing 67 %, results primarily from crown closure in central and southern China. This realization underscores the limitations of conventional binary thematic maps and LULC maps in accurately quantifying forest gain in China. Furthermore, China's tree population structure has undergone a transformative shift from 83 % forest trees and 17 % non-forest trees in 1985 to 92 % forest trees and 8 % non-forest trees in 2023, signifying a transition from afforestation to established forests. Our study not only enhances the understanding of tree cover variations in China but also provides valuable data for ecological investigations, land management strategies, and assessments related to climate change. [ABSTRACT FROM AUTHOR]

Predicting future conservation needs can help inform conservation management but is subject to uncertainty. We measured deforestation rates during 2015–2017 for 114 protected areas in Madagascar, linked deforestation to the status of protection according to IUCN categories I–VI, used recent deforestation rates to extrapolate forest cover over 2017–2050 and linked the size of forest blocks to the projected persistence of lemur subpopulations. In the six IUCN categories for protected areas in Madagascar the median size of forest blocks is 9–37 km2 and median annual deforestation rates range from 0.02% in the single IUCN category III site to 0.19% in category II and 1.95% in category VI sites. In 2017, 40% of all forest blocks within protected areas were < 10 km2, and this is projected to increase to 45% in 2050. Apart from these small forest fragments, the modal site of forest blocks was 160–320 km2 in 2017, and this is projected to decrease to 80–160 km2 in 2050. The range of > 50% of all lemur species exclusively contains forest blocks of < 10 km2. The modal size of forest blocks > 10 km2 is predicted to remain at 120 km2 until 2050. Although uncertainty remains, these analyses provide hope that forest blocks within the protected areas of Madagascar will remain large enough to maintain lemur subpopulations for most species until 2050. This should allow sufficient time for the implementation of effective conservation measures.

Snow and watershed models typically do not account for forest structure and shading; therefore, they display substantial uncertainty when attempting to account for forest change or when comparing hydrological response between forests with varying characteristics. This study collected snow water equivalent (SWE) measurements in a snow-dominated forest in Colorado, the United States, with variable canopy structure. The SWE measurements were integrated with 1 m Lidar derived canopy structure metrics and incoming solar radiation to create empirical SWE offset equations for four canopy structure groupings (forest gaps, south-facing forest edges, north-facing forest edges, and the interior forest) that varied in size compared to an open area. These simple equations indirectly integrate terrain shading and canopy shading and were able to estimate 40 to 70% of SWE variation in a heterogenous forested environment. The equations were then applied to a snow melt model with a 100 m grid size by applying the area-weighted average of SWE offsets from the four canopy structure groupings in each model cell. This tiled model configuration allowed for the model to better represent the subgrid heterogeneity of a forest environment that can be seen through an ensemble or range of potential outputs rather than a singular estimate. [ABSTRACT FROM AUTHOR]

Predicting future conservation needs can help inform conservation management but is subject to uncertainty. We measured deforestation rates during 2015–2017 for 114 protected areas in Madagascar, linked deforestation to the status of protection according to IUCN categories I–VI, used recent deforestation rates to extrapolate forest cover over 2017–2050 and linked the size of forest blocks to the projected persistence of lemur subpopulations. In the six IUCN categories for protected areas in Madagascar the median size of forest blocks is 9–37 km2 and median annual deforestation rates range from 0.02% in the single IUCN category III site to 0.19% in category II and 1.95% in category VI sites. In 2017, 40% of all forest blocks within protected areas were < 10 km2, and this is projected to increase to 45% in 2050. Apart from these small forest fragments, the modal site of forest blocks was 160–320 km2 in 2017, and this is projected to decrease to 80–160 km2 in 2050. The range of > 50% of all lemur species exclusively contains forest blocks of < 10 km2. The modal size of forest blocks > 10 km2 is predicted to remain at 120 km2 until 2050. Although uncertainty remains, these analyses provide hope that forest blocks within the protected areas of Madagascar will remain large enough to maintain lemur subpopulations for most species until 2050. This should allow sufficient time for the implementation of effective conservation measures. [ABSTRACT FROM AUTHOR]

The success and rate of forest regeneration following disturbance has implications for sustainable forest management, climate change mitigation, and biodiversity, among others. Systematic monitoring of forest regeneration over large and often remote areas of the boreal forest is challenging. The use of remotely sensed data to characterize post-disturbance recovery in the boreal forest has been an active research topic for more than 30 years. Innovations in sensors, data policies, curated data archives, and increased computational power have enabled new insights into the characterization of post-disturbance forest recovery, particularly following stand-replacing disturbances. Landsat time series data have emerged as an important data source for post-disturbance forest recovery assessments, with Landsat’s 40-year archive of 30-m resolution data providing consistent observations on an annual time step and enabling retrospective capacity to establish spatially explicit recovery baselines. The application of remote sensing for monitoring post-disturbance forest recovery is a rapidly growing area of research globally; however, despite the large amount of disturbance and the disproportionate effects of climate change in the boreal, the boreal biome is relatively underrepresented in the remote sensing forest recovery literature. Herein, the past and present contributions of optical time series and airborne laser scanning data to the characterization of forest recovery in boreal forests are highlighted, and future research priorities are identified.

The soil respiration (Rs) of forests is a major component of global Rs, yet few studies have focused on it. This study aimed to estimate global forest Rs and its changes at a resolution of 30 m via an artificial neural network (ANN) model. Five input candidates representing forest type, climatic, soil, and geographical information, as well as 1472 satisfactory forest Rs records, were used to build the ANN model and evaluate the model performance via a 10‐fold cross‐validation scheme. Global forest change data sets were used to accurately define the extent of forests and their changes, which was achievable because of the dynamic information and high resolution (30 m) of the data sets. The results indicate that the average annual global forest Rs from 2000 to 2020, as estimated by the optimal ANN model with an r2 value of 0.67 and a root‐mean‐square error of 252.6 g C m−2 yr−1, was 46.24 ± 5.86 Pg C yr−1. From 2001 to 2019, the average theoretical annual global forest Rs loss was 0.22 ± 0.06 Pg C yr−1 due to an average forest loss area of 23.4 million ha yr−1. In addition, the annual Rs theoretically increased by 0.75 Pg C in 2012 due to a global forest gain area of 80.5 million from 2001 to 2012. The presented data sets of global forest Rs and its changes can provide an accurate benchmark for discussing the carbon cycle and climate change at global to regional scales, even when operating over a small forest area (i.e., dozens of ha), which is a scale that has been ignored in other global Rs studies. Plain Language Summary: This paper discusses the soil respiration (written as Rs) of global forests, which has rarely been the focus of other Rs studies. Soil respiration is defined as the amount of Carbon Dioxide released from the soil surface into the atmosphere. The goal of this study was to estimate global forest Rs and its changes at a resolution of 30 m using the artificial intelligence model. The model had five input candidates representing forest type, climatic, soil, and geographical information. Additionally, 1472 satisfactory forest Rs records were used to build the model and evaluate its performance via a cross‐validation scheme. Global forest change data sets have a high 30 m‐resolution and provide dynamic information, and were thus chosen to accurately define the extent of forests and their changes in this research. This results show that the estimated average annual global forest Rs from 2000 to 2020 was 46.24 ± 5.86 quadrillion gram of carbon. The data sets produced by this study is able to act as an accurate benchmark used to discuss the carbon cycle and climate change at global to regional scales, and they can even make it possible to operate over a smaller forest area, such as dozens of ha. Key Points: 30‐m resolution global data sets of forest soil respiration and its changes from 2000 to 2020 are providedThe average annual global forest soil respiration from 2000 to 2020 was 46.24 ± 5.86 Pg C yr−1An accurate benchmark is provided for discussing the carbon cycle and climate change at global to regional scales, even over a small forest area (i.e., dozens of ha) [ABSTRACT FROM AUTHOR]

Mangroves are ecosystems within the intertidal zone of tropical and subtropical coasts; they offer ecosystem services such as protection from coastal erosion and storms and flood control, act as carbon sinks and are also sources of income by providing various forest products. However, their cover is rapidly disappearing worldwide, which makes the diagnosis and monitoring of the state of these important ecosystems, as well as their restoration and conservation, a challenge. Remote sensing is a promising technique that provides accurate and efficient results in the mapping and monitoring of these ecosystems. The Landsat sensor provides the most used medium-resolution images for this type of study. The main objective of this article is to provide an updated review of the main remote sensing techniques, specifically Landsat satellite imagery, used in the detection of changes and mapping of mangrove forests, as well as a review of climatic and/or chemical factors related to changes in the spatial distribution of these ecosystems. [ABSTRACT FROM AUTHOR]

Abstract The soil respiration (Rs) of forests is a major component of global Rs, yet few studies have focused on it. This study aimed to estimate global forest Rs and its changes at a resolution of 30 m via an artificial neural network (ANN) model. Five input candidates representing forest type, climatic, soil, and geographical information, as well as 1472 satisfactory forest Rs records, were used to build the ANN model and evaluate the model performance via a 10‐fold cross‐validation scheme. Global forest change data sets were used to accurately define the extent of forests and their changes, which was achievable because of the dynamic information and high resolution (30 m) of the data sets. The results indicate that the average annual global forest Rs from 2000 to 2020, as estimated by the optimal ANN model with an r2 value of 0.67 and a root‐mean‐square error of 252.6 g C m−2 yr−1, was 46.24 ± 5.86 Pg C yr−1. From 2001 to 2019, the average theoretical annual global forest Rs loss was 0.22 ± 0.06 Pg C yr−1 due to an average forest loss area of 23.4 million ha yr−1. In addition, the annual Rs theoretically increased by 0.75 Pg C in 2012 due to a global forest gain area of 80.5 million from 2001 to 2012. The presented data sets of global forest Rs and its changes can provide an accurate benchmark for discussing the carbon cycle and climate change at global to regional scales, even when operating over a small forest area (i.e., dozens of ha), which is a scale that has been ignored in other global Rs studies.

Social forestry schemes are now being implemented in numerous state forest areas in Indonesia, aiming to reduce deforestation and improve the community's livelihood. However, spatial monitoring in the social forestry area is still limited to see how the implementation progresses. The present study aimed to identify the change of forest taking a case in Pati Forest Farmer Communities (KTH Pati) social forestry area from 1996 to 2022 using the LandTrendr algorithm based on Normalized Burn Ratio (NBR) value of Landsat image series. The results detected forest loss and gain covering an area of 453.97 ha and 494.18 ha, respectively. Two main reasons causing the forest loss are the country's financial and political situation from 1997 to 2003 and the harvest of forest plantations in 2017-2018. However, it was found that the study area had a positive forest gain with the current continuous growth of 292.32 ha (20.16% of the total area). Even though the social forestry policy has not significantly shown a positive impact on forest growth, spatial monitoring through remote sensing can be a great tool for observing the progress. [ABSTRACT FROM AUTHOR]

Background. Pre-fire fuels, topography, and weather influence wildfire behaviour and firedriven ecosystem carbon loss. However, the pre-fire characteristics that contribute to fire behaviour and effects are often understudied for wildfires because measurements are difficult to obtain. Aims. This study aimed to investigate the relative contribution of pre-fire conditions to fire energy and the role of fire advancement direction in fuel consumption. Methods. Over 15 years, we measured vegetation and fuels in California mixed-conifer forests within days before and after wildfires, with co-located measurements of active fire behaviour. Key results. Pre-fire litter and duff fuels were the most important factors in explaining fire energy and contributed similarly across severity categories. Consumption was greatest for the forest floor (litter and duff; 56.8 Mg ha-1) and 1000-h fuels (36.0 Mg ha-1). Heading fires consumed 13.2 Mg ha-1 more litter (232%) and 24.3 Mg ha-1 more duff (202%) than backing fires. Remotely sensed fire severity was weakly correlated (R2 = 0.14) with fuel consumption. Conclusions. 1000-h fuels, litter, and duff were primary drivers of fire energy, and heading fires consumed more fuel than backing fires. Implications. Knowledge of how consumption and fire energy differ among contrasting types of fire behaviours may inform wildfire management and fuels treatments. [ABSTRACT FROM AUTHOR]

Forests and the dynamic changes play decisive roles in soil erosion prevention, biodiversity conservation and climate warming mitigation, and are crucial for China to achieve carbon neutrality because of the ability for carbon uptake. China's forests experienced a substantial change over the past century, primarily since the forest conservation and restoration programs were implemented. In this paper, the progress of China's forest change and the similarities and differences of the spatio-temporal patterns from multi-dimension coupling with ground surveys and satellite observations were reviewed comprehensively, and the drivers of forest change were discussed. Our review reported that: 1) China's forests experienced an extensive forest gain/restoration in vegetation greenness, forest cover, and aboveground biomass since the 1970s. 2) Changes in various forest parameters suggested distinctive spatiotemporal patterns in China. Consistent hotspots of forest gain clustered in the Loess Plateau and the southwestern China from most studies, while controversial conclusions about forest loss/degradation were found in the spatial distribution. 3) The geographic discrepancy of forest changes may be related to climate change, human activities, and forest disturbances. Variations in forest dynamics are prerequisites to exploring the effects of natural conditions and forestry policies on forest cover, and strengthening understanding of the carbon cycle. These findings can provide valuable insights for various ecological services in further studies and a scientific basis for forest management and policy-making in China. [ABSTRACT FROM AUTHOR]

Globally, the loss of forest vegetation is a significant concern due to the crucial roles that forests play in the Earth's system, including the provision of ecosystem services, participation in biogeochemical cycles, and support for human well-being. Forests are especially critical in mountains environments, where deforestation can lead to accelerated biodiversity loss, soil erosion, flooding, and reduced agricultural productivity, as well as increased poverty rates. In response to these problems, China has implemented a series of ecological restoration programs aimed at restoring forests. However, there is a lack of knowledge as to whether the forest cover is increasing or decreasing, as well as the relative roles played by natural and human factors in forest change. Here, we aim to address these issues by analyzing the pattern and process of the forest changes in Guizhou province, a typical mountainous karst area with a fragile environment in southwestern China, between 1980 and 2018, and evaluating the extent to which these forest changes were influenced by natural and anthropogenic driving forces. Using a temporal sequence of satellite images and a Markov model, we found that the forest cover increased by 468 km2, and that over 33% of the cropland in Guizhou province was converted into forest between 1980 and 2018, with the most significant increases in the forest cover occurring in Qiandongnan. Through correlation analyses and generalized linear model (GLM) regression, we demonstrate that management factors exerted a more significant positive impact on the forest cover than climate change. While the mean annual precipitation and temperature were mostly stable during the period studied, the effects of population and gross domestic product (GDP) on the forest changes weakened, and the influence of land-use change markedly increased. These findings provide valuable information for resource managers engaging in forest protection, deforestation prevention, and ecological restoration in similar regions. [ABSTRACT FROM AUTHOR]

Context: Forest disturbance and management can significantly affect water quality. The understanding of the quantitative relationships between forest change/forest landscape patterns and water quality is critical for designing forest management practices to ensure a sustainable clean water supply. Objectives: We quantified the relationships between (1) forest cover and (2) forest landscape patterns and water quality parameters. We also assessed contributing factors (i.e., climate zones, seasonality, watershed properties, forest types, and watershed context) to the variations in the above-mentioned relationships. Methods: A meta-analysis based on published case studies around the globe was conducted to address the stated objectives. Results: (1) Forest cover significantly affects the selected water quality parameters with higher forest cover rates overall improving water quality. (2) Surprisingly, the forest-water quality relationship is more controlled by watershed properties (i.e., slope, watershed size) and forest characteristics than by climate, except dissolved oxygen. The mixtures of coniferous and broadleaved forests improve the positive effects of forest cover on TN and TP compared to monocultures, while natural forests have significantly positive effects on phosphorus parameters compared to planted forests. (3) Forest landscape fragmentation causes negative effects on all selected forms of nitrogen (i.e., TN, nitrate, and ammonia). (4) The regulating effects of forests are influenced by the land use contexts in a watershed where agricultural lands significantly degrade water quality. Conclusions: Forests can have positive effects on water quality. Deforestation and forest landscape fragmentation lead to declines in water quality. However, there are large variations in water quality responses depending on the selected water quality parameters, watershed properties, forest types, climate, and land use contexts. [ABSTRACT FROM AUTHOR]

Forests in California have changed dramatically during the 20th century. Shifts in forest structure including densification, declines in large trees and tree basal area have altered the function, productivity, and resilience of modern day forests. Attributing these changes to specific drivers is increasingly important for effective management of healthy and productive forests. Previous studies focus on climatic (temperature, precipitation, climatic water deficit), disturbance (fire), geomorphological (topography, soil types), and anthropogenic (logging, fire suppression) drivers, but few studies evaluate large scale change in forest structure across land ownership type. In this paper, we investigate 20th century changes to forest structure across six land ownership classes in California. We compare historical and contemporary forest structural data and find that declines in large trees and increases in forest density are consistent across the state. This pattern is most pronounced on private timberlands, which experience up to 400% regional increases in small tree (

Forests in California have changed dramatically during the 20th century. Shifts in forest structure including densification, declines in large trees and tree basal area have altered the function, productivity, and resilience of modern day forests. Attributing these changes to specific drivers is increasingly important for effective management of healthy and productive forests. Previous studies focus on climatic (temperature, precipitation, climatic water deficit), disturbance (fire), geomorphological (topography, soil types), and anthropogenic (logging, fire suppression) drivers, but few studies evaluate large scale change in forest structure across land ownership type. In this paper, we investigate 20th century changes to forest structure across six land ownership classes in California. We compare historical and contemporary forest structural data and find that declines in large trees and increases in forest density are consistent across the state. This pattern is most pronounced on private timberlands, which experience up to 400% regional increases in small tree (

Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017–2021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources. [ABSTRACT FROM AUTHOR]

Understanding terrestrial water storage (TWS) dynamics and associated drivers (e.g., climate variability, vegetation change, and human activities) across climate zones is essential for designing water resources management strategies in a changing environment. This study estimated TWS anomalies (TWSAs) based on the corrected Gravity Recovery and Climate Experiment (GRACE) gravity satellite data and derived driving factors for 214 watersheds across six climate zones in China. We evaluated the long-term trends and stationarities of TWSAs from 2004 to 2014 using the Mann–Kendall trend test and Augmented Dickey-Fuller stationarity test, respectively, and identified the key driving factors for TWSAs using the partial correlation analysis. The results indicated that increased TWSAs were observed in watersheds in tropical and subtropical climate zones, while decreased TWSAs were found in alpine and warm temperate watersheds. For tropical watersheds, increases in TWS were caused by increasing water conservation capacity as a result of large-scale plantations and the implementation of natural forest protection programs. For subtropical watersheds, TWS increments were driven by increasing precipitation and forestation. The decreasing tendency in TWS in warm temperate watersheds was related to intensive human activities. In the cold temperate zone, increased precipitation and soil moisture resulting from accelerated and advanced melting of frozen soils outweigh the above-ground evapotranspiration losses, which consequently led to the upward tendency in TWS in some watersheds (e.g., Xiaoxing'anling mountains). In the alpine climate zone, significant declines in TWS were caused by declined precipitation and soil moisture and increased evapotranspiration and glacier retreats due to global warming, as well as increased agriculture activities. These findings can provide critical scientific evidence and guidance for policymakers to design adaptive strategies and plans for watershed-scale water resources and forest management in different climate zones. [ABSTRACT FROM AUTHOR]

Observational evidence suggests that forests in the Northern Alps are changing at an increasing rate as a consequence of climate change. Yet, it remains unclear whether the acceleration of forest change will continue in the future, or whether downregulating feedbacks will eventually decouple forest dynamics from climate change. Here we studied future forest dynamics at Berchtesgaden National Park, Germany by means of a process‐based forest landscape model, simulating an ensemble of 22 climate projections until the end of the 21st century. Our objectives were (i) to assess whether the observed acceleration of forest dynamics will continue in the future, (ii) to analyze how uncertainty in future climate translates to variation in future forest disturbance, structure, and composition, and (iii) to determine the main drivers of future forest dynamics. We found that forest dynamics continue to accelerate in the coming decades, with a trend towards denser, structurally more complex and more species rich forests. However, changes in forest structure leveled off in the second half of the 21st century regardless of climate scenario. In contrast, climate scenarios caused trajectories of tree species change to diverge in the second half of the 21st century, with stabilization under RCP 2.6 and RCP 4.5 scenarios and accelerated loss of conifers under RCP 8.5. Disturbance projections were 3 to 20 times more variable than future climate, whereas projected future forest structure and composition varied considerably less than climate. Indirect effects of climate change via alterations of the disturbance regime had a stronger impact on future forest dynamics than direct effects. Our findings suggest that dampening feedbacks within forest dynamics will decelerate forest change in the second half of the 21st century. However, warming beyond the levels projected under RCP 4.5 might profoundly alter future forest disturbance and composition, challenging conservation efforts and ecosystem service supply. [ABSTRACT FROM AUTHOR]

Mapping and monitoring forest with time-series remote sensing methodologies requires reference data constructed in a sampling framework. Few worldwide reference data sets with time-series information are currently available for large areas. We produced a global forest reference set at 30 m with time-series information from 2000 to 2020 based on a stratified random sampling scheme. All available Landsat, high-resolution Google Earth images and other relative land cover/land change products were used in a visual interpretation approach. This reference dataset contains 10339 sample units (6252 persisting forest sample units, 2049 change sample units and 2038 persisting non-forest sample units) attributed with annual forest/non-forest information from 2000 to 2020. The results of our analysis highlight many cases of undetected forest change due to definition and other factors, and suggest that change of forest canopy cover could be monitored instead of land cover types. The reference set can be potentially used for many large-area applications such as validation of forest mapping projects or as auxiliary data for forest monitoring analysis. And this reference set will be available online (). [ABSTRACT FROM AUTHOR]

Latin America and the Caribbean (LAC) contain more tropical high-biodiversity forest than the remaining areas of the planet combined, yet experienced more than a third of global deforestation during the first decade of the 21st century. While drivers of forest change occur at multiple scales, we examined forest change at the municipal and national scales integrated with global processes such as capital, commodity, and labor flows. We modeled multi-scale socioeconomic, demographic, and environmental drivers of local forest cover change. Consistent with LAC's global leadership in soy and beef exports, primarily to China, Russia, the US, and the EU, national-level beef and soy production were the primary land use drivers of decreased forest cover. National level gross domestic product (GDP), migrant worker remittances and foreign investment, along with municipal-level temperature and area, were also significantly related to reduced forest cover. This challenges forest transition frameworks, which theorize that rising GDP and intensified agricultural production should be increasingly associated with forest regrowth. Instead, LAC forest change was linked to local, national, and global demographic, dietary and economic transitions, resulting in massive net forest cover loss. This suggests an urgent need to reconcile forest conservation with mounting global demand for animal protein. [ABSTRACT FROM AUTHOR]

Forests in Mongolia yield low productivity and are vulnerable to disturbances from drought, fire, pests, and illegal logging. Such forests can quickly lose their ecological balance. Logging activities in these areas are limited in monitoring and controls. This study assesses two different logging operations for their natural regeneration capacity by comparing the composition of the soil, soil organisms, physical and chemical properties, and forest cover change after the completion of logging operations. The logging operations were analyzed in two different regions, the Khartsai and Tariakhtai threshold in Selenge soum, Bulgan province. A skyline logging operation was undertaken on Khartsai threshold in 1983 and a tractor logging operation (clear-cutting) on Tariakhtai threshold in 1987. After the completion of the logging, the forests were naturally regenerated. In 2002, soil samples were collected and soil organisms and physical and chemical properties were examined. Satellites images were also used to evaluate forest cover changes after the end of the logging operations. Significant differences in the naturally regenerated tree species in the skyline logging, tractor logging, and natural forest areas were observed. Average tree ring growth was 0.9 mm in the skyline logging site, 0.6 mm in the tractor logging site, and 1.2 mm in the natural forest. Based on forest cover changes observed in satellite images, the density of naturally regenerated tree species in the natural forest area was higher than that in the skyline logging area. In contrast, the latter recorded a higher density than that in the tractor logging area. Therefore, processing of satellite images of forest cover changes with high-resolution data provides valuable information for the local forest community and helps decision-makers in their further actions.

Globally, the loss of forest vegetation is a significant concern due to the crucial roles that forests play in the Earth’s system, including the provision of ecosystem services, participation in biogeochemical cycles, and support for human well-being. Forests are especially critical in mountains environments, where deforestation can lead to accelerated biodiversity loss, soil erosion, flooding, and reduced agricultural productivity, as well as increased poverty rates. In response to these problems, China has implemented a series of ecological restoration programs aimed at restoring forests. However, there is a lack of knowledge as to whether the forest cover is increasing or decreasing, as well as the relative roles played by natural and human factors in forest change. Here, we aim to address these issues by analyzing the pattern and process of the forest changes in Guizhou province, a typical mountainous karst area with a fragile environment in southwestern China, between 1980 and 2018, and evaluating the extent to which these forest changes were influenced by natural and anthropogenic driving forces. Using a temporal sequence of satellite images and a Markov model, we found that the forest cover increased by 468 km2, and that over 33% of the cropland in Guizhou province was converted into forest between 1980 and 2018, with the most significant increases in the forest cover occurring in Qiandongnan. Through correlation analyses and generalized linear model (GLM) regression, we demonstrate that management factors exerted a more significant positive impact on the forest cover than climate change. While the mean annual precipitation and temperature were mostly stable during the period studied, the effects of population and gross domestic product (GDP) on the forest changes weakened, and the influence of land-use change markedly increased. These findings provide valuable information for resource managers engaging in forest protection, deforestation prevention, and ecological restoration in similar regions.

Due to favourable afforestation policies in the past few decades, China has become the nation with the greatest afforestation area in the world. However, there are few specific distribution maps of forest change at regional and national level in China. Here, we applied all available Landsat time series images in the growing seasons from 1990 to 2018 on the Google Earth Engine platform to track annual forest changes in China. Our validation indicates that the overall accuracy reached 89%. From 2000 to 2016, most forest change in China occurred on the east side of the Line of Huanyong Hu (a line linking Aihui in Northeast China and Tengchong in Southwest China dividing China into east and west parts). Forest growth is the major change type in China with a forest-area expansion of 494,317 ± 22,830 km2. Meanwhile, there have also been some forest losses mostly scattered in southern and northeastern China. The total loss of the forest area was 52,856 ± 3366 km2. Thus, from 2000 to 2016, the net increase in forest area in China was 441,461 ± 26,196 km2, which is approximately 4.6% of the terrestrial area of China, almost equivalent to the entire Thailand. Among these 16 years, there have also been some forest areas recovered or replanted after disturbances. Meanwhile, afforestation achievements and effectiveness have also met the great risk that some afforested areas are eventually destroyed back to previous non-forest land again. The areas for these two change types are 43,728 ± 6574 km2 and 18,457 ± 2396 km2, respectively. The results indicated great afforestation achievements, but there was also some loss of forest resources and threats to consolidating afforestation achievements in China. The results of this study offer greater detail of forest change, especially afforestation, and can provide useful information for forest management and ecological restoration evaluation. Additionally, the forest change result will be available online (). [ABSTRACT FROM AUTHOR]

Forest loss is occurring at alarming rates across the globe. The pine rockland forests of Andros, The Bahamas, likely represent some of the largest stands of Bahamian subspecies of Caribbean pine in the world. Given the unique species that inhabit these pine forests, such as the endemic and critically endangered Bahama Oriole, monitoring habitats on Andros is crucial to inform conservation planning. We developed a 2019 land classification map to assess the status of nine terrestrial habitats on Andros. Our Random Forest classification model predicted habitat classes with high overall accuracy. Caribbean pine was the dominant land class making up roughly one-third of the total terrestrial area. Whereas much of the pine forest area was found as small patches, most were close to other patches of pine suggesting isolation of forest patches is low. We compared our known intact forest areas to recent forest loss identified by the Hansen et al. Global Forest Change product and assessed areas of habitat disturbance in high-resolution imagery. Our results suggest that this global map overpredicted forest loss on Andros. The small degree of true forest loss on Andros was driven mostly by anthropogenic activity. A cross-tabulation of the Hansen forest loss with fire data showed that understory fires were frequently associated with falsely classified deforestation. Given the threats of climate change to this open forest type—intensifying fire regimes, strengthening hurricanes, and sea level rise—monitoring changes in open forest extent is a critical task across the Caribbean region and the world. [ABSTRACT FROM AUTHOR]

The rate of tropical deforestation is increasing globally, and the fragmentation of remaining forests is particularly high in arable landscapes of West Africa. As such, there is an urgent need to map and monitor these remnant forest patches/fragments and so identify their multiple benefits and values. Indeed, recognizing their existence will help ensure their continued provision of ecosystem services while facilitating their conservation and sustainable use. The aim of this study is therefore to inventory and characterise the current extent and change of remnant forest patches of West Africa, using multi-source remote sensing products, time-series analyses, and ancillary datasets. Specifically, we collate and analyse descriptive and change metrics to provide estimates of fragment size, age, biophysical conditions, and relation to social-ecological change drivers, which together provide novel insights into forest fragment change dynamics for over four decades. We map forests patches outside protected areas with a tree cover ≥30%, a tree height of ≥5 m, an area ≥1 km2 and ≤10 km2. Appended to each patch are descriptive and change dynamics attributes. We find that most fragments are small, secondary forest patches and these cumulatively underwent the most forest loss. However, on average, larger patches experience more loss than smaller ones, suggesting that small patches persist in the landscape. Primary forest patches are scarce and underwent fewer losses, as they may be less accessible. In 1975 most patches were mapped as secondary, degraded forests, savanna, woodland, and mangrove, and relatively few comprised cropland, settlements, and agriculture, suggesting that new forest patches rarely emerged from arable land over the past 45 years (1975–2020), but rather are remnants of previously forested landscapes. Greening is widespread in larger secondary fragments possibly due to regrowth from land abandonment and migration to urban areas. Forest loss and gain are greater across fragments lying in more modified landscapes of secondary forests, while forest loss increases with distance to roads. Finally, larger forest patches harbour a denser tree cover and higher trees as they may be less impacted by human pressures. The number and extent of West African forest patches are expected to further decline, with a concurrent heightening of forest fragmentation and accompanying edge effects. Lacking any conservation status, and subject to increasing extractive demands, their protection and sustainable use is imperative.

The coverage of global forest cover is most essential for soil health, climate, water cycle and air quality. The condition can be improved by reforestation and afforestation to some extent but cannot be restored to full range. The satellite images used for this analysis of forest change detection are of planet scope having 3 m resolution. The target of this research is to detect the forest cover change using remote sensing and GIS. For the identification of forest covers various techniques like segmentation and classification are used. The best classification is by Normalized Difference Vegetation Index (NDVI) which identifies the health of vegetation along with the changes in the different parameters. After using NDVI some values generated will detect the areas of forest changes with the amount of change. And with the help of this research it is observed that out of total forest area i.e. 76740.32 hectares, the negative forest change is reduced to 29.44% due to degradation of forests maybe due to fires, deforestation, leaf shedding etc. [ABSTRACT FROM AUTHOR]

Riparian forests are at risk from the cumulative effects of human and natural disturbance. We assessed the magnitude and rate of forest loss adjacent to freshwater features in the province of British Columbia, Canada, from 2000 to 2021. We used Global Forest Change data to quantify percent tree cover lost annually across six ecologically and topographically diverse ecosections containing 361,370 streams, lakes, and wetlands. We stratified the aquatic features into large and small size classes within and outside parks. We hypothesized that cumulative forest loss would be greater outside of parks as a consequence of human-caused and natural disturbance. Also, we hypothesized greater rates of forest loss around small aquatic features that generally receive less protection outside of parks. Loss of riparian forest was consistently greater outside of parks (median losses of 3.20% versus 1.29%), and adjacent to small streams (median = 3.31%) and wetlands regardless of size (median = 3.10%). Rates of cumulative riparian forest loss were nonlinear, increasing substantially in 2013. Though the annual riparian forest losses were small, the cumulative losses from 2000 to 2021 were substantial. We anticipate the continued loss of riparian forest as climate changes and natural disturbances intensify. We recommend further investigation of the implications of these observed trends for ecosystem values that are found within or adjacent to riparian forest across managed and unmanaged landscapes. • We assessed the rate of loss of riparian forest in British Columbia, Canada. • Increased frequency of disturbance reduced riparian forest over a 21-year period. • Riparian forest loss was greater across managed landscape than in protected areas. • Wetlands and small streams experienced the greatest rate of loss of riparian forest. [ABSTRACT FROM AUTHOR]

Vegetation change can significantly alter evapotranspiration (ET), an important component of the terrestrial water balance, and consequently influences other hydrological processes. With no direct measurement techniques available at large spatial scales, the accurate estimation of ET under changing forest landscapes and climate is challenging. In this study, we used an improved method based on Fuh's equation (a functional form of the Budyko framework) to investigate ET responses to cumulative forest disturbance and climate in the snow‐dominated interior of British Columbia, Canada. First, we divided the study region into three distinct climate groups, and then related the watershed parameter m in Fuh's equation to vegetation change (represented by cumulative equivalent clearcut area (CECA)) and watershed properties, with independent calibration and validation watersheds. The validated relationships were used to examine regional ET variations (∼380,000 km2). Our results showed that ET in moderate climates had the highest sensitivity to CECA, characterized by wetness index between 1 and 2 (the ratio of precipitation to potential evapotranspiration). ET in dry climates (wetness index <1) was also significantly related to CECA, but with reduced sensitivity compared to the moderate climates. Wetter climates (wetness index >2) did not show a significant relationship with CECA, suggesting an insensitivity of ET to forest change. Simulations under future climate and vegetation disturbance scenarios demonstrated that ET would be further decreased in most of the study region. Forest management should consider reducing disturbance in the dry to moderate climate areas to mitigate negative impacts on hydrological processes and functions. Key Points: We related the watershed parameter m in Fuh's equation to forest disturbance for estimating evapotranspiration (ET) under changing vegetation and climateForest disturbance significantly decreased ET in most parts of the study regionET was most sensitive to forest disturbance in moderate climates, with reduced sensitivity in dry areas, and no sensitivity in wet areas [ABSTRACT FROM AUTHOR]

Questions: How have species composition and diversity in the herb and bryophyte layer of spruce forest communities developed on unlimed plots and on plots with varying liming intensity under the conditions of decreasing sulphur (S) deposition and unabated high nitrogen (N) deposition? Location: Spruce stands in the Erzgebirge, East Germany. Methods: We repeated herb and bryophyte layer surveys on 36 quasi‐permanent plots after approx. 23 years (1989/1991 vs 2012/2014). Over this period, the study area experienced a sharp decline in S deposition and repeated liming of the spruce stands. Our plots represent a gradient of liming from zero to four applications. We analyse the change in species composition and species diversity depending on the liming intensity using Ellenberg indicator values (EIV), correspondence analysis, indicator species analysis and diversity parameters (alpha diversity, Lennon similarity). Results: On the unlimed plots, the species composition of the herb layer remained unchanged over the study period, despite the decrease in the coverage of Calamagrostis villosa and Deschampsia flexuosa and in the total coverage of the herb layer. In contrast, the bryophyte layer showed a significant increase in coverage and species richness with the decrease in S deposition. Liming led to a sharp increase in species richness in the herb layer, due to the appearance of disturbance indicators. The mean EIVs for acidity and nitrogen (EIV‐R and EIV‐N) also increased significantly. All observed effects increased with increasing liming intensity. We also noticed an increase in species richness over time in the bryophyte layer on the limed plots. However, this was less pronounced than on the unlimed plots, and decreased with increasing liming intensity. This was because the appearance of disturbance indicators or of typical species for the Calamagrostio villosae‐Piceetum was partially compensated for by the disappearance of acid indicators as a result of liming. The species turnover was thus much higher in the bryophyte layer than in the herb layer. Conclusions: In contrast to the herb layer, the bryophyte layer proves to be a sensitive indicator of changes in air pollution. The effects of greatly reduced S deposition on the bryophyte layer outweigh those of continuous N deposition in the study area. Liming acts as a disturbance in both the herb layer and the bryophyte layer. This influence increases with increasing liming intensity. Our results do not support a general application of liming in large, contiguous forest areas. Rather, this measure should be applied in a differentiated manner. [ABSTRACT FROM AUTHOR]

Activity systems determine the land-use strategies that ultimately shape landscapes. Identifying the reasoning behind the decision making on land use strategies can help to understand how humans contribute to shaping landscapes and to design appropriate land management plans. We studied changes in land-use strategies employed by rural communities, as well as their impact on the landscape in Atzalan, Mexico. For this, we conducted interviews in households distributed throughout the municipality, documenting their activities, resources, and motivations related to land use. We also compared local perceptions of landscape change with that detected by remote sensing analysis. Our results indicate that the activity system in Atzalan features a multiplicity of economic strategies with traditional activities at their core, as well as other more intensified systems such as monocultures and pastures. However, traditional activities are largely giving way to intensified land uses, leading to landscape homogenization. The spatial and temporal distribution of the dominant land covers throughout the municipality reflects a dependency on the environmental context, social structure and land accessibility. People are aware of changes in the landscape and recognize their role in these transformations, mostly due to impacts on the forest and crop cover. Simplification of the activity system has been reflected in the homogenization of the landscape and could prevent households from maintaining diversified strategies of multiple uses of resources. [ABSTRACT FROM AUTHOR]

Landslides are common disturbances in forests around the world, and a major threat to human life and property. Landslides are likely to become more common in many areas as storms intensify. Forest vegetation can improve hillslope stability via long, deep rooting across and through failure planes. In the U.S. Rocky Mountains, landslides are infrequent but widespread when they do occur. They are also extremely understudied, with little known about the basic vegetation recovery processes and rates of establishment which restabilize hills. This study presents the first evaluation of post‐landslide vegetation recovery on forested landslides in the southern Rocky Mountains. Six years after a major landslide event, the surveyed sites have very little regeneration in initiation zones, even when controlling for soil coverage. Soils are shallower and less nitrogen rich in initiation zones as well. Rooting depth was similar between functional groups regardless of position on the slide, but deep‐rooting trees are much less common in initiation zones. A lack of post‐disturbance tree regeneration in these lower elevation, warm/dry settings, common across a variety of disturbance types, suggests that complete tree restabilization of these hillslopes is likely to be a slow or non‐existent, especially as the climate warms. Replacement by grasses would protect against shallow instabilities but not the deeper mass movement events which threaten life and property. [ABSTRACT FROM AUTHOR]

Deforestation processes driven by anthropogenic activities lead to the loss of biodiversity and the alteration of ecological systems. In this paper, we examined the land use / land cover change trends from 2000 to 2020 in the Sudano-Guinean region of Benin, and analysed the driving factors of those changes. The adopted method is based on the diachronic analysis of the land cover through the use of remote sensing data specifically, Landsat satellite images of 2000 and 2020. The results showed that natural forest vegetation and Savannah strongly decreased, whereas farmland and the built-up area increased. Indeed, forest land lost 3.22%, 2.01%, and 1.57%, in Bante, Glazoué, and Ouèssè, respectively. The intensification of human activities was identified as the proximate driver while population growth, a growing demand for agricultural and forest products coupled with improved infrastructure were identified as the underlying drivers of deforestation. To achieve sustainable management goal for forest resources in Benin, the introduction of alternative sources of energy, sustainable farming practices, diversification of income sources, the promotion of community participation, should be implemented. Deforestation processes driven by anthropogenic activities lead to the loss of biodiversity and the alteration of ecological systems. In this paper, we examined the land use / land cover change trends from 2000 to 2020 in the Sudano-Guinean region of Benin, and analysed the driving factors of those changes. The adopted method is based on the diachronic analysis of the land cover through the use of remote sensing data specifically, Landsat satellite images of 2000 and 2020. The results showed that natural forest vegetation and Savannah strongly decreased, whereas farmland and the built-up area increased. Indeed, forest land lost 3.22%, 2.01%, and 1.57%, in Bante, Glazoué, and Ouèssè, respectively. The intensification of human activities was identified as the proximate driver while population growth, a growing demand for agricultural and forest products coupled with improved infrastructure were identified as the underlying drivers of deforestation. To achieve sustainable management goal for forest resources in Benin, the introduction of alternative sources of energy, sustainable farming practices, diversification of income sources, the promotion of community participation, should be implemented. [ABSTRACT FROM AUTHOR]

Aim: At the elevational limit of forest distribution, montane forests show diverse responses to environmental change with upward shifts, increased tree density and lateral expansion reported. To enable informed analysis of the consequences forest advance will have on montane biodiversity, we quantify changes in the area and elevation of the tree line ecotone and identify how patterns of forest advance are modified by topography and over time. Location: Central Mountain Range, Taiwan. Time period: 1963–2016. Major taxa studied: Montane Forests. Methods: Changes in the area and elevation of montane forest at the tree line ecotone were quantified using a stratified random sample of aerial photography captured in 1963, 1980, 2001 and 2016. Weighted estimates of habitat area and elevation for each time step were used to quantify the influence of slope aspect and inclination on tree line ecotone change and identify how the rate of habitat change varies over time. Results: Non‐forest area declined by 29% between 1963 and 2016 driven by a 295.0 ha increase in forest area within the study region. Despite no change in mean forest elevation, the mean elevation of establishing forest has increased at a rate of 2.17 m/yr. Changes in forest area and elevation are spatially variable, driven by the complex montane topography. East and south facing slopes show the largest gains in forest area and 0–20° slopes show an increasing rate of forest establishment up to 2016, while slopes facing west or with incline > 46° show negligible change. Main conclusions: Climate‐linked montane forest expansion in the Central Mountain Range in Taiwan is dominated by infilling rather than increases in forest elevation. Forest expansion has significantly reduced non‐forest habitat area in this endemic species‐rich region. However, considerable terrain‐dependent variation in forest advance occurs, offering the potential that non‐forest species will continue to persist at high elevations with reduced population size. [ABSTRACT FROM AUTHOR]

Island ecosystems are particularly susceptible to climate change and human activities. The change of land use and land cover (LULC) has considerable impacts on island ecosystems, and there is a critical need for a free and open-source tool for detecting land cover fluctuations and spatial distribution. This study used Google Earth Engine (GEE) to explore land cover classification and the spatial pattern of major land cover change from 1990 to 2019 on Haitan Island, China. The land cover classification was performed using multiple spectral bands (RGB, NIR, SWIR), vegetation indices (NDVI, NDBI, MNDWI), and tasseled cap transformation of Landsat images based on the random forest supervised algorithm. The major land cover conversion processes (transfer to and from) between 1990 and 2019 were analyzed in detail for the years of 1990, 2000, 2007, and 2019, and the overall accuracies ranged from 88.43% to 91.08%, while the Kappa coefficients varied from 0.86 to 0.90. During 1990–2019, other land, cultivated land, sandy land, and water area decreased by 30.70%, 13.63%, 3.76%, and 0.95%, respectively, while forest and built-up land increased by 30.94% and 16.20% of the study area, respectively. The predominant land cover was other land (34.49%) and cultivated land (26.80%) in 1990, which transitioned to forest land (53.57%) and built-up land (23.07%) in 2019. Reforestation, cultivated land reduction, and built-up land expansion were the major land cover change processes on Haitan Island. The spatial pattern of forest, cultivated land, and built-up land change is mainly explained by the implementation of a 'Grain for Green Project' and 'Comprehensive Pilot Zone' policy on Haitan Island. Policy and human activities are the major drivers for land use change, including reforestation, population growth, and economic development. This study is unique because it demonstrates the use of GEE for continuous monitoring of the impact of reforestation efforts and urbanization in an island environment. [ABSTRACT FROM AUTHOR]

Latin America and the Caribbean (LAC) contain more tropical high-biodiversity forest than the remaining areas of the planet combined, yet experienced more than a third of global deforestation during the first decade of the 21st century. While drivers of forest change occur at multiple scales, we examined forest change at the municipal and national scales integrated with global processes such as capital, commodity, and labor flows. We modeled multi-scale socioeconomic, demographic, and environmental drivers of local forest cover change. Consistent with LAC’s global leadership in soy and beef exports, primarily to China, Russia, the US, and the EU, national-level beef and soy production were the primary land use drivers of decreased forest cover. National level gross domestic product (GDP), migrant worker remittances and foreign investment, along with municipal-level temperature and area, were also significantly related to reduced forest cover. This challenges forest transition frameworks, which theorize that rising GDP and intensified agricultural production should be increasingly associated with forest regrowth. Instead, LAC forest change was linked to local, national, and global demographic, dietary and economic transitions, resulting in massive net forest cover loss. This suggests an urgent need to reconcile forest conservation with mounting global demand for animal protein.

Forest cover change can be detected with high precision using 3D geospatial data and semi-automatic analyses of Remote Sensing data. The aim of our study, performed in Gorce National Park in Poland, was to generate a land use land cover (LULC) map and use it to analyse forest cover change. The study area is a subalpine forest region that has been affected by bark beetle and wind disturbances. The Geographic Object-Based Image Analysis approach was used for classification, with Colour Infrared orthophotos and normalized Digital Surface Models generated using image-matching approach. Gathered results showed that dominating LULC class is coniferous forests (3380 ha; 47% of study area), when second largest class is deciduous forests (2204 ha; 30%). The dead Norway spruce stands (465.5 ha; 6.5%) showed significant increase comparing to 114.1 ha mapped in 1997.

Abstract Climate warming in the western United States is causing changes to the wildfire regime in mixed‐conifer forests. Rising temperatures, longer fire seasons, increased drought, as well as fire suppression and changes in land use, have led to greater and more severe wildfire activity, all contributing to altered forest composition over the past century. To understand future interactions among climate, wildfire, and vegetation in a fire‐prone landscape in the southern Blue Mountains of central Oregon, we used a spatially explicit forest landscape model, LANDIS‐II, to simulate forest and fire dynamics under current management practices and two projected climate scenarios. The results suggest that wildfires will become more frequent, more extensive, and more severe under projected climate than contemporary climate. Furthermore, projected climate change generated a 20% increase in the number of extreme fire years (years with at least 40,000 ha burned). This caused large shifts in tree species composition, characterized by a decline in the sub‐alpine species (Abies lasiocarpa, Picea engelmannii, Pinus albicaulis) and increases in lower‐elevation species (Pinus ponderosa, Abies grandis), resulting in forest homogenization across the elevational gradient. This modeling study suggests that climate‐driven increases in fire activity and severity will make high‐elevation species vulnerable to decline and will reduce landscape heterogeneity. These results underscore the need for forest managers to actively consider climate change, altered fire regimes, and projected declines in sub‐alpine species in their long‐term management plans.

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.

India is home of the largest remaining population of the Asian elephant (Elephas maximus L.) in the South and Southeast Asia. The forest loss and fragmentation is the main threat to the long-term survival of Asian elephants. In the present study, we assessed forest loss and fragmentation in the major elephant ranging provinces in India, viz., north-eastern, north-western, central, and southern since the 1930s. We quantified forest cover changes by generating and analyzing forest cover maps of 1930, 1975, and 2013, whereas fragmentation of contiguous forest areas was quantified by applying landscape metrics on the temporal forest cover maps. A total of 21.49% of the original forest cover was lost from 1930 to 1975, while another 3.19% forest cover was lost from 1975 to 2013 in the elephant ranges in India. The maximum forest loss occurred in the southern range (13,084 km2) followed by north-eastern (10,188 km2), central (5614 km2), and north-western (4030 km2) elephant ranges in the past eight decades. The forests in the central range were the most fragmented followed by southern, north-eastern, and north-western elephant ranges. The forest fragmentation in the southern range occurred at the fastest rate than central, north-eastern, and north-western ranges. The core forest areas shrunk by 39.6% from 1930 to 2013. The causative factors of forest change and situation of elephant-human conflict have been discussed. Study outcomes would be helpful in planning effective conservation strategies for Asian elephants in India. [ABSTRACT FROM AUTHOR]

Climate warming in the western United States is causing changes to the wildfire regime in mixed-conifer forests. Rising temperatures, longer fire seasons, increased drought, as well as fire suppression and changes in land use, have led to greater and more severe wildfire activity, all contributing to altered forest composition over the past century. To understand future interactions among climate, wildfire, and vegetation in a fire-prone landscape in the southern Blue Mountains of central Oregon, we used a spatially explicit forest landscape model, LANDIS-II, to simulate forest and fire dynamics under current management practices and two projected climate scenarios. The results suggest that wildfires will become more frequent, more extensive, and more severe under projected climate than contemporary climate. Furthermore, projected climate change generated a 20% increase in the number of extreme fire years (years with at least 40,000 ha burned). This caused large shifts in tree species composition, characterized by a decline in the sub-alpine species (Abies lasiocarpa, Picea engelmannii, Pinus albicaulis) and increases in lowerelevation species (Pinus ponderosa, Abies grandis), resulting in forest homogenization across the elevational gradient. This modeling study suggests that climate-driven increases in fire activity and severity will make high-elevation species vulnerable to decline and will reduce landscape heterogeneity. These results underscore the need for forest managers to actively consider climate change, altered fire regimes, and projected declines in sub-alpine species in their long-term management plans. [ABSTRACT FROM AUTHOR]

This study identified spatial patterns of forest change in Hoa Binh, Vietnam, from 2005 to 2017 by integrating Landsat data and Geographic Information System (GIS) data. A Multiple Logistic Regression (MLR) model was adapted to analyse drivers of three types of forest change, including (i) deforestation and forest degradation, (ii) forest regrowth and (iii) plantation expansion. The results reveal that accessibility and local economic development were determinants of forest cover dynamics during the integration and globalization period and that increased population pressure and poverty were no longer the main factors in forest cover transition. Several key policies in this time period positively affected forest cover change, triggering private sector participation in developing the forest economy by including households and small and medium enterprises in particular. The findings of this study can provide learning points for the implementation of sustainable forest management policies in other Vietnamese provinces or in neighbouring countries. This research has provided an effective tool for visualizing human‐forest ecosystem interaction, and this tool can be applied to subsequent studies to determine causes of forest cover change at the provincial level in Vietnam. [ABSTRACT FROM AUTHOR]

Forest change is a major environmental problem worldwide. Forest streams, with their large aquatic–terrestrial interface and strong dependence on terrestrially derived organic matter, are highly sensitive to forest changes. Fast-wood plantations can be particularly threatening if they markedly differ from native forests. Eucalyptus plantations, in particular, cover large areas worldwide (> 20 million ha, mostly from 35°S to 35°N), but their effects on stream functioning have been addressed mostly in the Iberian Peninsula, which limits generalization to other regions. We assessed the effect of eucalyptus plantations on total (microbial decomposers and macroinvertebrates; in coarse mesh bags) and microbial-driven (in fine mesh bags) leaf litter decomposition by comparing streams flowing through native forests and eucalyptus plantations in seven regions in the Iberian Peninsula, Central Africa and South America. We found an overall significant inhibition of total litter decomposition by 23%. The effect did not significantly differ across regions, although a significant inhibition was found for Spain (− 41%), South Brazil (− 31%) and Uruguay (− 36%) (Portugal had a marginally nonsignificant inhibition by 50%) but not for other regions, suggesting that the effects of plantations in temperate climates are mediated through effects on macroinvertebrate communities. Contrarily, the overall effect for microbial-driven litter decomposition was non-significant, but it significantly differed across regions with a significant stimulation in Central Brazil (110%) and Uruguay (32%), and nonsignificant effects for other regions (Kenya had a marginally nonsignificant inhibition by 48%), suggesting that functional redundancy among microbial communities is not general and effects can occur if plantations induce changes in nutrient availability, solar irradiation or litter characteristics. [ABSTRACT FROM AUTHOR]

European forests are among the most extensively studied ecosystems in the world, yet there are still debates about their recent dynamics. We modeled the changes in tree canopy height across Europe from 2001 to 2021 using the multidecadal spectral data from the Landsat archive and calibration data from Airborne Laser Scanning (ALS) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidars. Annual tree canopy height was modeled using regression tree ensembles and integrated with annual tree canopy removal maps to produce harmonized tree height map time series. From these time series, we derived annual tree canopy extent maps using a ≥ 5 m tree height threshold. The root-mean-square error (RMSE) for both ALS-calibrated and GEDI-calibrated tree canopy height maps was ≤4 m. The user's and producer's accuracies estimated using reference sample data are ≥94% for the tree canopy extent maps and ≥ 80% for the annual tree canopy removal maps. Analyzing the map time series, we found that the European tree canopy extent area increased by nearly 1% overall during the past two decades, with the largest increase observed in Eastern Europe, Southern Europe, and the British Isles. However, after the year 2016, the tree canopy extent in Europe declined. Some regions reduced their tree canopy extent between 2001 and 2021, with the highest reduction observed in Fennoscandia (3.5% net decrease). The continental extent of tall tree canopy forests (≥ 15 m height) decreased by 3% from 2001 to 2021. The recent decline in tree canopy extent agrees with the FAO statistics on timber harvesting intensification and with the increasing extent and severity of natural disturbances. The observed decreasing tree canopy height indicates a reduction in forest carbon storage capacity in Europe. • European tree canopy extent and height change 2001–2021 mapped with Landsat archive. • Continental multidecadal tree height mapping supported by lidar calibration data. • Tree canopy extent increased in Europe by 1%, decreased in Fennoscandia. • Tall forests (height ≥ 15 m) lost 3% of their area. • The annual tree canopy removal area increased by 18% from 2001–2011 to 2012–2021. [ABSTRACT FROM AUTHOR]

• An IEU-Net model and high-resolution images are reliable for urban forest mapping. • Afforestation and deforestation exerted a net cooling and warming. • Two landscape-type conversions displayed greater cooling and weaker warming. • A robust and accurate land surface temperature feedback framework was proposed. Accurate characterization of urban forest change can help quantify its impact on the urban thermal environment. Taking Hangzhou City and Zhaoqing City, China as two cases, we proposed a deep learning-based approach to extract forest/non-forest types using China's Gaofen data and the labels it generates. We then explored the impacts of urban forest patterns on land surface temperature (LST). Our results indicate that Gaofen-1/6 images performed well for urban forest/non-forest mapping with an accuracy of over 90%. We found that afforestation and deforestation exerted a net cooling of -0.62±0.30°C and -1.23±0.26°C and warming of 1.71±0.16°C and 1.29±0.14°C in Hangzhou and Zhaoqing, respectively. Balancing the LST changes of both afforestation and deforestation found total warming due to deforestation, especially in Hangzhou. Latitudinal reduced cooling and enhanced warming were found from Zhaoqing to Hangzhou, which can be explained by strong evaporative effects and inconsistent albedo effects. Moreover, a more pronounced cooling when other types converted to landscape core type and a weaker warming when landscape core type converted to other types were detected, especially in Zhaoqing. This study highlights the importance of designing rational afforestation plans, avoiding deforestation activities, and reducing forest fragmentation in delineating effective climate mitigation strategies. [ABSTRACT FROM AUTHOR]

Forests are important oxygen sources and carbon sinks. They provide a series of ecosystem services that are crucial to eco-island sustainability, such as the protection of soils, conservation of biodiversity, and development of the eco-tourism, etc. Interestingly, Chongming eco-island is located in the borderlands between fresh- and saltwater environments, where the Yangtze River meets the East China Sea. Most forests in Chongming island are therefore man-made and very vulnerable to the ecological environment mixing of freshwater streams and rivers with salty ocean tides, and are affected by climate and human activity. Estimating and monitoring forest change within an estuary is required for the sustainable management of forest resources and forest-based eco-tourism. Most optical satellites are unsuitable for continuous forest mapping due to cloud cover and their relatively low spatial and temporal resolution. Here, using Sentinel-1 satellite carrying an imaging C-band synthetic aperture radar, radar vegetation index was derived to detect the forest dynamics on the island. Furthermore we quantified forest area change in three well known and the most strictly protected and representative areas, namely Dongping National Forest Park, Dongtan National Wetland Park, and Xisha National Wetland Park, in the Chongming eco-island over the past five years at 10-metre resolution. We recorded the early and mid summers when the forest canopies grew to the peak in the study areas. The planted forest in Dongping National Forest Park grew an area of 7.38 hectares from 2015 to 2019, and disappeared from an area of almost 2.59 hectares in 2018. The man-made forest of Xisha National Wetland Park increased at an area of almost 20.19 hectares over the past five years. The forest in Dongtan National Wetland Park increased to an area of almost 2.12 hectares in the period of 2015–2017 and 2018–2019. However, from 2017 to 2018, the area of planted forests in Dongtan National Wetland Park decreased by 1.35 hectares. This study shows man-made forest change can be measured and that evidence can be collected to show how the forest is altered by human activities, and informs forest management decision-making for Chongming eco-island.