Forest Extent Research Articles

HISTORIC LAND COVER CHANGE ASSESSMENT OF CHILEAN MEDITERRANEAN COAST: DID FOREST PLANTATIONS REALLY CAUSED FRAGMENTATION?

Abstract. Currently, the Mediterranean coast of Chile has large areas of commercial pine and eucalyptus plantations. Several authors have reported habitat loss and fragmentation of the original “Maulino” native forest due to the expansion of plantations. However, in the Maule coast took place a previous process that affected the native forest. This is the extensive use of forestlands with a moderate slope for the cultivation of wheat. The peak of this process occurred in the second half of the 19th century and meant a great degradation of the soil in the area. In this work, we first look for whether the overexploitation of wheat is the primary cause of the fragmentation of the Maulino forest in the study area. Secondly, we wanted to determine what proportion of the area currently occupied by plantations was previously used for growing wheat. First, using the community of Constitución as a representative area of the central coast we use Species Distribution Modelling to identify the potential original extent of the Maulino forest. Secondly, we created a spatial model to estimate the extension of wheat production using historical data. Finally, we used Landsat-8 images to assess the actual extension of forest plantation. By comparing the three former results, we were able to quantify the effects of wheat production and forest plantation separately, for the first time. The results indicate that 41% of the original native forest was first affected by wheat production and that only 34 % was later replaced by forest plantations. Therefore, the rest of the plantations were installed in places where the forest had previously been cut down for use in wheat production. The main cause of deforestation and forest fragmentation in this landscape was originally the wheat extensive production boosted by the gold rush in California and Australia. Forest commercial plantations further augmented the Maulino forest fragmentation process by significantly reducing its mean patch size and increasing the number of patches.

Critical Ecological Roles, Structural Attributes and Conservation of Old Growth Forest: Lessons From a Case Study of Australian Mountain Ash Forests

Old growth is a critical growth stage in many forest types globally. It has many key ecological roles including biodiversity conservation, carbon storage and the provision of services such as water production. The extent of old growth forest has been declining in many ecosystems around the world, with major ecological and ecosystem service consequences. Important insights about such declines, as well as the structure, function and conservation of old growth forest, can be gained from detailed cross-sectional and longitudinal studies of different age cohorts within a given forest ecosystem. In this review article, we outline key insights into the characteristics of, and threats to old growth forests, using the Mountain Ash (Eucalyptus regnans) forests of the Central Highlands of Victoria, south-eastern Australia as a detailed case study. These forests are dominated by the tallest flowering plants on earth and have been subject to several decades of intense study. These studies show that old growth Mountain Ash forests are characterized by (among other features): giant trees (approaching 100 m tall and sometimes exceeding 20 m in circumference), numerous trees with hollows, an understorey ofAcaciaand rainforest trees, a range of plant and animal species that are rare or absent in younger aged stands, and moist, nutrient-rich soils. The area of old growth Mountain Ash forest has declined to 1.16% of the ∼141,000 ha area occupied by ash-type forests in the Central Highlands region. This is up to 60 times less than it was at the time of European colonization ∼220 years ago. The loss of old growth has major implications for bird, mammal and other biodiversity, as well as for carbon storage and water production for human consumption. The main drivers of old growth decline are recurrent wildfire, widespread clearcutting, and a logging-fire interaction in which cut and then regenerated forests become more flammable and are at significantly elevated risk of burning at high (stand replacing) severity. Climate change is also a driver of old growth decline both through elevating the mortality of large old living trees and underpinning an increase in the frequency of high severity wildfire. These interacting drivers mean that restoring old growth Mountain Ash forest will be an ecological and policy challenge. We argue that a first step must be to cease all commercial logging in the Mountain Ash ecosystem to allow new cohorts of old growth forest to be recruited and thereby expand the extent of the old growth estate. In addition, the Government of Victoria should revert to a past definition of old growth that made it easier for forest to qualify for protection. Given there are high risks of recurrent high-severity wildfire in the existing Mountain Ash forest estate which is dominated by highly flammable young regrowth forest, new technologies (such as the use of drones and satellites) are needed to rapidly detect and then suppress ignitions before fires become large and difficult to control. Mountain Ash forests have provided an important natural laboratory for understanding the dynamics, management and conservation of old growth forest. They have also helped generate some valuable general perspectives likely to be relevant to other forest ecosystems globally. These include: (1) the critical value of multi-facetted cross-sectional and longitudinal studies in quantifying attributes of, and threats to, old growth forest, (2) the need for a carefully crafted definition of old growth that will typically be ecosystem-specific and based on the time required to develop key ecosystem attributes (e.g., large old trees), (3) the importance of rigorous protection measures because poor decisions that result in the loss of old growth now will take prolonged periods to rectify, and (4) setting protection levels that are relative to the existing spatial coverage of remaining old growth and the extent and impacts of stressors driving old growth decline.

Ecological development of mangrove plantations in the Bangladesh Delta

Afforestation is a process used in recovering the global mangrove cover. Succession or development in mangrove communities during mangrove afforestation has rarely been described yet is important for understanding the potential of afforestation in creating diverse and functional mangroves. Since 1966, the Government of Bangladesh has created nearly 280 km2 of plantation mangroves (7.5% of the extent of the Sundarbans mangrove forest and 0.21% of the global mangroves extent) on the Delta of the Ganges, Brahmaputra and Meghna Rivers, located east of the Sundarbans mangroves. Here, we investigated the development of these plantation mangroves, assessing their community structure and species richness over a 40-year chronosequence in comparison to natural mangroves in the same region. Tree communities in the plantations reached a maximum mean aboveground biomass (AGB) of ∼157 ± 13 Mg dry weight ha−1, achieving equivalence in tree aboveground biomass and tree density (255 ± 2 individuals ha−1) to natural mangroves by 20 and 39 years respectively. Our dataset from plantations aged between 10 and 42 years indicated that structural complexity (Ic) was higher in older plantations than in younger plantations. We found that the tree community structure of older mangrove plantations was more diverse in the eastern region of the delta compared to the central and western regions, and the landward plantations were more diverse than the plantations fringing the oceans. One of the originally planted mangrove species, Sonneratia apetala, was dominant in these plantations. However, we recorded 8 mangrove tree species from 5 genera in the plantations, despite only two species (S. apetala and Avicennia officinalis) being planted. Our data indicated that while the tree structure of the established mangrove plantations was similar to natural mangroves within 42 years, equivalent species richness may not be reached for more than 40 years. Therefore, adoption of management to enhance natural successional or development processes in afforestation programs, for example by planting a diverse mix of species, could improve the effectiveness of afforestation in conserving biodiversity.

Soil organic carbon of grassland and bush forest on dryland in Aceh Besar District, Indonesia

Abstract. Abdullah UH, Sufardi S, Syafruddin S, Arabia T. 2022. Soil organic carbon of grassland and bush forest on dryland in Aceh Besar District, Indonesia. Biodiversitas 23: 2594-2600. Soil organic carbon (SOC) is one of the important components that can improve soil conditions and maintain agriculture sustainability. This study was conducted to determine the potential of soil organic carbon in grassland and bush forest in Aceh Besar District. We used purposive random sampling by taking soil samples at various depths with five samples for each land type. The parameters of soil bulk density, % carbon soil, the potential of carbon and carbon stock were analyzed. Soil organic carbon (SOC) analysis followed the Walkley and Black method using analytical guidelines from the Indonesian Soil Research Institute. The results showed that land type and soil depth affected bulk density, % carbon soil, the potential of carbon and carbon stock. Land type affected soil organic carbon in which grasslands had a higher soil carbon than bush forests. In total, total soil carbon stock in the bush forest of Aceh Besar District was higher than that in grassland due to the much larger extent of bush forest in the district. Land use management efforts to improve soil SOC need to be carried out and in collaboration with various parties. Thus, it is necessary to maintain the availability of soil carbon stock for sustainable land management, including agriculture.

Global estimates of the extent and production of macroalgal forests

AbstractAimMacroalgal habitats are believed to be the most extensive and productive of all coastal vegetated ecosystems. In stark contrast to the growing attention on their contribution to carbon export and sequestration, understanding of their global extent and production is limited and these have remained poorly assessed for decades. Here we report a first data‐driven assessment of the global extent and production of macroalgal habitats based on modelled and observed distributions and net primary production (NPP) across habitat types.LocationGlobal coastal ocean.Time periodContemporary.Major taxa studiedMacroalgae.MethodsHere we apply a comprehensive niche model to generate an improved global map of potential macroalgal distribution, constrained by incident light on the seafloor and substrate type. We compiled areal net primary production (NPP) rates across macroalgal habitats from the literature and combined this with our estimates of the global extent of these habitats to calculate global macroalgal NPP.ResultsWe show that macroalgal forests are a major biome with a global area of 6.06–7.22 million km2, dominated by red algae, and NPP of 1.32 Pg C/year, dominated by brown algae.Main conclusionsThe global macroalgal biome is comparable, in area and NPP, to the Amazon forest, but is globally distributed as a thin strip around shorelines. Macroalgae are expanding in polar, subpolar and tropical areas, where their potential extent is also largest, likely increasing the overall contribution of algal forests to global carbon sequestration.

The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results

Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system.

From Forest Dynamics to Wetland Siltation in Mountainous Landscapes: A RS-Based Framework for Enhancing Erosion Control

Human activities have caused a significant change in the function and services that ecosystems have provided to society since historical times. In mountainous landscapes, the regulation of services such as water quality or erosion control has been impacted by land use and land cover (LULC) changes, especially the loss and fragmentation of forest patches. In this work, we develop a Remote Sensing (RS)-based modelling approach to identify areas for the implementation of nature-based solutions (NBS) (i.e., natural forest conservation and restoration) that allow reducing the vulnerability of aquatic ecosystems to siltation in mountainous regions. We used time series Landsat 5TM, 7ETM+, 8OLI and Sentinel 2A/2B MSI (S2) imagery to map forest dynamics and wetland distribution in Picos de Europa National Park (Cantabrian Mountains, northern Spain). We fed RS-based models with detailed in situ information based on photo-interpretation and fieldwork completed from 2017 to 2021. We estimated a forest cover increase rate of 2 ha/year comparing current and past LULC maps against external validation data. We applied this forest gain to a scenario generator model to derive a 30-year future LULC map that defines the potential forest extent for the study area in 2049. We then modelled the distribution of wetlands to identify the areas with the greatest potential for moisture accumulation. We used an S2 mosaic and topography-derived data such as the slope and topographic wetness index (TWI), which indicate terrain water accumulation. Overall accuracy scores reached values of 86% for LULC classification and 61% for wetland mapping. At the same time, we obtained the potential erosion using the NetMap software to identify potential sediment production, transport and deposition areas. Finally, forest dynamics, wetland distribution and potential erosion were combined in a multi-criteria analysis aiming to reduce the amount of sediment reaching selected wetlands. We achieved this by identifying the most suitable locations for the conservation and restoration of natural forests on slopes and in riparian areas, which may reduce the risk of soil erosion and maximise sediment filtering, respectively. The results show a network pattern for forest management that would allow for controlling erosion effects across space and time at three levels: one, by reducing the load that originates upslope in the absence of forest cover; two, by intersecting runoff at watercourses related to sediment transport; and three, by a lack of former barriers, by trapping erosion near to the receiving wetland systems, main river axes and contributing streams. In conclusion, the proposed methodology, which could be transferred to other mountain regions, allows to optimise investment for erosion prevention and wetland conservation by using only very specific areas of the landscape for habitat management (e.g., for NBS implementation).

The interrelated impacts of credit access, market access and forest proximity on livelihood strategies in Cambodia

Livelihood diversification strategies in developing countries are influenced by access to financial credit, to markets and to forests. Understanding their interrelated impacts has important implications for development policy, for market access, credit provision, and forest conservation. Using a survey of 2,417 households in 64 villages in four Provinces in Cambodia and satellite data on forest extent, we test hypotheses and quantify the relative contributions and first-order interactions effects of market and road access, forest access, and formal and informal financial credit access on household expenditures and livelihood incomes. We test hypotheses about their statistical interactions, their relative contributions to incomes, and how their effects differ within and outside the Mekong River floodplain. Market and road access are significant with gross income and expenditures, as well as portfolio shifts to off-farm business activities. Forest access contributes significantly to gross income and expenditures. Credit utilization is significant with gross income, expenditures, off-farm business, and livestock activities. Households below the poverty line use financial credit primarily for consumption and agricultural investments, but above the poverty line for business investment and purchasing assets. Market access and financial credit are more important for incomes in the Mekong floodplain area, while forest access is more important outside it. Using dominance analysis, we find that financial credit contributes more than market or forest access to gross income, expenditures, and livestock income. However, market access is more important than credit for off-farm, on-farm and crop incomes, and forest access contributes more to gross income than formal credit access. We also test for first-order interactions between these effects and find they are statistically significant, confirming a synergistic interaction between credit, market and forest access. Market access improves the impact of credit use, as credit use improves gross and off-farm incomes with improved access to large cities, and improves livestock incomes with improved road access. Forests and roads act synergistically to improve household incomes in areas with good city market access, as forest access contributes more to gross income, expenditures and off-farm incomes in areas with good primary road access, and to on-farm incomes in areas with good secondary road access. Our findings show that market, road, credit and forest effects are interconnected and interdependent, but support each other, and significant interaction effects between forest and market access suggest that policies for poverty reduction and forest conservation should be coordinated with the development of roads to improve potential forest returns.

Sensitivity of headwater streamflow to thawing permafrost and vegetation change in a warming Arctic

Climate change has the potential to impact headwater streams in the Arctic by thawing permafrost and subsequently altering hydrologic regimes and vegetation distribution, physiognomy and productivity. Permafrost thaw and increased subsurface flow have been inferred from the chemistry of large rivers, but there is limited empirical evidence of the impacts to headwater streams. Here we demonstrate how changing vegetation cover and soil thaw may alter headwater catchment hydrology using water budgets, stream discharge trends, and chemistry across a gradient of ground temperature in northwestern Alaska. Colder, tundra-dominated catchments shed precipitation through stream discharge, whereas in warmer catchments with greater forest extent, evapotranspiration (ET) and infiltration are substantial fluxes. Forest soils thaw earlier, remain thawed longer, and display seasonal water content declines, consistent with greater ET and infiltration. Streambed infiltration and water chemistry indicate that even minor warming can lead to increased infiltration and subsurface flow. Additional warming, permafrost loss, and vegetation shifts in the Arctic will deliver water back to the atmosphere and to subsurface aquifers in many regions, with the potential to substantially reduce discharge in headwater streams, if not compensated by increasing precipitation. Decreasing discharge in headwater streams will have important implications for aquatic and riparian ecosystems.

Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning

ABSTRACT Mangrove forests provide vital ecosystem services. The increasing threats to mangrove forest extent and fragmentation can be monitored from space. Accurate spatially explicit quantification of key vegetation characteristics of mangroves, such as leaf area index (LAI), would further advance our monitoring efforts to assess ecosystem health and functioning. Here, we investigated the potential of radiative transfer models (RTM), combined with active learning (AL), to estimate LAI from Sentinel-2 spectral reflectance in the mangrove-dominated region of Ngoc Hien, Vietnam. We validated the retrieval of LAI estimates against in-situ measurements based on hemispherical photography and compared against red-edge NDVI and the Sentinel Application Platform (SNAP) biophysical processor. Our results highlight the performance of physics-based machine learning using Gaussian processes regression (GPR) in combination with AL for the estimation of mangrove LAI. Our AL-driven hybrid GPR model substantially outperformed SNAP (R2 = 0.77 and 0.44 respectively) as well as the red-edge NDVI approach. Comparing two canopy RTMs, the highest accuracy was achieved by PROSAIL (RMSE = 0.13 m2.m−2, NRMSE = 9.57%, MAE = 0.1 m2.m−2). The successful retrieval of mangrove LAI from Sentinel-2 can overcome extensive reliance on scarce in-situ measurements for training seen in other approaches and present a more scalable applicability by relying on the universal principles of physics in combination with uncertainty estimates. AL-based GPR models using RTM simulations allow us to adapt the genericity of RTMs to the peculiarities of distinct ecosystems such as mangrove forests with limited ancillary data. These findings bode potential for retrieving a wider range of vegetation variables to quantify large-scale mangrove ecosystem dynamics in space and time.

Spatio-Temporal Patterns of Carbon Storage Derived Using the InVEST Model in Heilongjiang Province, Northeast China

Carbon storage is an important component of ecosystem services. Under climate warming and human activities, land use/land cover (LULC) have been undergoing tremendous change, leading to spatio-temporal variations in carbon storage. Based on seven series of LULC data and combined with carbon module of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, spatial-temporal changes in LULC types and carbon storage were analyzed and estimated for Heilongjiang Province, Northeast China. Results show varied carbon storage among different types of LULC. Forest and cropland are the dominant LULC types in Heilongjiang Province, Northeast China, accounting for 46–49% (20.90 × 104–22.12 × 104km2) and 30–37% (13.56 × 104–16.70 × 104km2) of the total area. Areal extents of forest, grassland, and unused land declined from 1980 to 2015, by 1.22, 0.84, and 1.11 × 104km2, respectively; while those of cropland and construction land expanded, by 3.14 and 0.08 × 104km2, respectively. From 1980 to 2015, carbon storage displayed consistent change trends with those of LULC types: carbon storage of forest, grassland, and unused land decreased by 236.22, 116.61 and 21.82 × 106 Mg C, respectively; and those of cropland and construction land increased by 414.65 and 0.99 × 106 Mg C, respectively. The total carbon storage in the study region was 6,863.06 × 106–6,907.64 × 106 Mg C, for which the forest, cropland, and grassland were the major contributor (6,778.75 × 106–6,840.57 × 106 Mg C). Due to the conversion of large extents of forest, grassland, and unused land to cropland, which facilitated the formation of carbon sinks and thus enlarged the carbon storage by 45.36 × 106 Mg C from 1980 to 2015. Frequent forest fires, urban expansion, farmland reclamation, and engineering construction were the important factors of changes in the LULC, accelerating permafrost degradation and leading to obvious changes in the total carbon storage in the Heilongjiang Province, Northeast China. Therefore, the estimation of carbon storage in different LULC types can provide important data support and have important implications for evaluation of ecosystem services and carbon cycle.

Utilization of Non-Wood Forest Products as a Basis for Poverty Alleviation and Sustainable Community Development in Guangxi Zhuang Autonomous Region of South China

This paper with its focus on southwest China with special reference to Guangxi Autonomous region (GZAR) has a threefold contribution to the existing literature. First, it summarizes the salient features of the geography and the determinants of poverty among the various regions of GZAR. Second, we draw attention to land use and land cover change over recent decades and their implications. The nature and extent of natural forest and woodlands, from which most non-wood forest products are derived, and eucalyptus plantations that are a major source of revue for GZAR are summarized. Third, policy syndromes in relation to livelihoods, poverty alleviation policies, ecosystem and biodiversity conservation are examined and policy implications discussed. This paper does not aim to provide definitive answers, yet intends to scrutinize the data and re-examine the trends in the light of three key drivers (population increase, climate change and urbanization) This will be done mainly by reviewing the literature but augmented by our field work. There is an attempt to reach a conclusion about recent developments and make suggestions about counter-measures that could be implemented.

Comparisons of regression and machine learning methods for estimating mangrove above-ground biomass using multiple remote sensing data in the red River Estuaries of Vietnam

Currently, remote sensing platforms provide state-of-the-art data for multiple purposes including applications related to coastal wetlands. Mangrove above-ground biomass (MAGB) together with its extent is considered well correlated with the habitats’ environmental and economic values. Above-ground biomass can be estimated by models that integrate remote sensing, field data and statistical information. However, it remains difficult to decide which model and which data offer the best performance for any one study location. Hence, this study aims to assess the spatial change in MAGB over a 45-year period and investigate different approaches to quantify this change through linear and multi linear regression models. Specifically, we test a non-linear model (Multivariate Adaptive Regression Splines; MARS), and non-parametric machine learning models, to predict MAGB using vegetation indices and biophysical variables derived from optical remote sensing data from Sentinel-2, Landsat-8, SPOT-7 and synthetic aperture radar remote sensing data from ALOS-2. The multi linear regression (MLR) and the MARS models were trained by field measured MAGB data to a good level of accuracy (R2 = 0.80 and RMSE = 5.56 Mg ha−1 for MLR and R2 = 0.89, RMSE = 5.42 Mg ha−1 for MARS). These models were subsequently applied to Landsat 2, 5 and 8 time-series images to assess changes in MAGB values and mangrove forest extent over the period 1975 to 2020. To ensure accurate training data for the models, we conducted field work to measure MAGB in 24 plots measured in May 2019. Findings showed that the MARS model generated MAGB values with higher accuracy than linear regression and multi linear regression models. Uses of vegetation indices (Normalized Differenced Vegetation Index, Soil-adjusted Vegetation Index, Green-Normalized Differenced Vegetation Index, Simple Ratio, and Red-edge Simple Ratio) generated MAGB values with accuracy slightly higher than using biophysical variables (Leaf area index, Fraction of Absorbed Radiation, Fractional vegetation cover, and Leaf chlorophyll content). Sentinel-2 and Landsat 8 were effective data sources for MAGB estimates, while SPOT-7 and ALOS-2 produced acceptable MAGB accuracy. Modelling the Landsat time series found an increase in both MAGB values and forest extent over the 1975–2020 period. The MARS model, Sentinel-2, Landsat 8 and vegetation indices are the recommended models and data to use to measure MAGB and could be used to understand changes in MAGB and forest extent at national and regional scales.

Arctic marine forest distribution models showcase potentially severe habitat losses for cryophilic species under climate change.

The Arctic is among the fastest‐warming areas of the globe. Understanding the impact of climate change on foundational Arctic marine species is needed to provide insight on ecological resilience at high latitudes. Marine forests, the underwater seascapes formed by seaweeds, are predicted to expand their ranges further north in the Arctic in a warmer climate. Here, we investigated whether northern habitat gains will compensate for losses at the southern range edge by modelling marine forest distributions according to three distribution categories: cryophilic (species restricted to the Arctic environment), cryotolerant (species with broad environmental preferences inclusive but not limited to the Arctic environment), and cryophobic (species restricted to temperate conditions) marine forests. Using stacked MaxEnt models, we predicted the current extent of suitable habitat for contemporary and future marine forests under Representative Concentration Pathway Scenarios of increasing emissions (2.6, 4.5, 6.0, and 8.5). Our analyses indicate that cryophilic marine forests are already ubiquitous in the north, and thus cannot expand their range under climate change, resulting in an overall loss of habitat due to severe southern range contractions. The extent of marine forests within the Arctic basin, however, is predicted to remain largely stable under climate change with notable exceptions in some areas, particularly in the Canadian Archipelago. Succession may occur where cryophilic and cryotolerant species are extirpated at their southern range edge, resulting in ecosystem shifts towards temperate regimes at mid to high latitudes, though many aspects of these shifts, such as total biomass and depth range, remain to be field validated. Our results provide the first global synthesis of predicted changes to pan‐Arctic coastal marine forest ecosystems under climate change and suggest ecosystem transitions are unavoidable now for some areas.

Maximising resilience to sea-level rise in urban coastal ecosystems through systematic conservation planning

Coastal cities and their natural environments are vulnerable to the impacts of climate change, especially sea-level rise (SLR). Hard coastal defences play a key role in protecting at-risk urban coastal populations from flooding and erosion, but coastal ecosystems also play important roles in the overall sustainability and resilience of cities and urban centres by contributing to coastal protection. Conserving coastal ecosystems and maximising their resilience will ensure that urban coastal communities can continue to benefit from ecosystem services and improve their adaptive capacity to cope with adverse impacts in the future. Using the hyper-urbanised coast of Singapore as a case study, we modelled the resilience of coastal wetlands to SLR and integrated resilience in conservation planning. We found that the responses of coastal habitats to rising sea level vary across the modelling periods. While there is a slight net gain in the extent of mangrove forests and tidal flats by the end of the century due to potential habitat conversion, the existing habitats will experience a loss in coverage. Highly modified coastlines associated with urbanisation impede the ability of existing wetlands to migrate landward, which is a key mechanism for coastal habitats to cope with rising sea levels. Systematic conservation planning can identify sites that are potentially resilient to SLR and incorporate factors that influence an ecosystem’s capability to respond to change. Crucially, the relatively slow rates of SLR and persistence of coastal wetlands during the earlier half of this century present an opportunity to introduce management interventions aimed at enhancing ecosystem resilience.

Spatial-temporal dynamics of mangrove extent in Quang Ninh Province over 33 years (1987–2020): Implications toward mangrove management in Vietnam

Remote sensing technology offers a huge potential to continuously monitor the changes in mangrove forests. Mangrove deforestation has resulted in a loss of coastal ecosystem services and associated economic benefits. Quang Ninh is the third largest mangrove area in Vietnam, but has experienced mangrove deforestation and degradation since 1987. The study used historical Landsat-5/8 and Sentinel-2 data to quantify spatial–temporal dynamics of mangrove extent from 1987 to 2020. The CMRI was used to construct LULC and post-classification comparison was used for change detection analysis over the study period. The overall accuracies of 94.8%, 94.1% 92.4%, and 89.9% were recorded in 2020 (Kappa = 0.917), 2017 (Kappa = 0.906), 2010 (Kappa = 0.872), and 2000 (Kappa = 0.832), respectively. The dynamics of mangrove forests in Quang Ninh were influenced by mangrove deforestation and afforestation. The extent of mangrove forests decreased by 2168.3 ha from 1987 to 2020. Mangrove deforestation was mainly caused by unplanned aquaculture, urban development, and other land uses. However, the root of mangrove deforestation is found due to inappropriate regulations and institutional schemes related to mangrove management. The study suggests that community-based mangrove management model and blue carbon approach are suggested in Quang Ninh and Vietnam.

A framework for a forest ecological base map – An example from Norway

In the management of forest ecosystems, spatial information about the extent, condition and pressures are essential. In the current study, we present a framework for a remote sensing-based forest ecological base map covering Norway. Combining remotely sensed imagery from optical satellite systems such as Sentinel-2 and Landsat provides information about forest ecosystem extent and change over time. Utilizing a national dataset of airborne laser scanning (ALS) data allowed predicting a range of attributes describing forest condition, including naturalness. In total, seven definitions of naturalness were evaluated. Pressures on the forest ecosystems were mapped using a change detection algorithm and satellite data from 1986 to 2020. Change detection is the cornerstone in monitoring and for understanding the pressures on the ecosystems. The predicted forest extent had an overall accuracy of 85 to 89% using Sentinel-2 imagery from 2020 and 71 to 81% using Landsat imagery from 1986. For the forest condition attributes, the explained portion of the variances were >70% for biomass, height and volume and from 21% to 64% for number of stems, crown coverage and a diversity index. Naturalness was classified with accuracies of 77 to 98%, except for age-based definitions. Nevertheless, a large number of false positives were present. Change detection was evaluated in terms of final harvest and was identified with an overall accuracy of 84–92%. The land cover change classification had an overall accuracy of 70–92%. The detailed maps of forest condition and forest pressures were aggregated to a local level using model-based inference, providing estimates of mean values and uncertainty at a scale suitable for ecosystem indicator development. The collection of map layers describing forest extent, condition and pressures form a forest ecological base map important for environmental management.

Global estimates of stress-reflecting indices reveal key climatic drivers of climate-induced forest range shifts

Climate change has the potential to cause forest range shifts at a broad scale and consequently can alter crucial forest functions, including carbon sequestration. However, global-scale projections of future forest range shifts remain challenging because our knowledge of the physiological responses of plants to climatic stress is limited to particular species and is insufficient for wide-range projections, in addition to the uncertainties in the impacts of non-climatic factors, such as wildfire, wind, and insect outbreaks. To evaluate the vulnerability and resilience of forests to climate change, we developed a new empirical approach using climatic indices reflecting physiological stressors on plants. We calculated the global distributions of seven indices based on primary climatic stressors (drought, solar radiation, and temperature) at high resolution. We then modeled the relationship between the seven indices and global forest extent. We found two key stressors driving climate-induced forest range shifts on a global scale: low temperature under high radiation and drought. At high latitudes of the Northern Hemisphere, forest establishment became difficult when the mean temperature was less than approximately 7.2 °C in the highest radiation quarter. Forest sensitivity to drought was more pronounced at mid-latitudes. In areas where the humidity index (ratio of precipitation to potential evapotranspiration) was below 0.45, shrubland and grassland became more dominant than forests. Our results also suggested that the impacts of climate change on global forest range shifts will be geographically biased depending on the areas affected by the key climatic stressors. Potential forest gain was remarkable in boreal regions due to increasing temperature. Potential forest loss was remarkable in current tropical grassland and temperate forest/grassland ecoregions due to increasing drought. Our approach using stress-reflecting indices could improve our ability to detect the roles of climatic stressors on climate-induced forest range shifts.

Talking about trees: the territorial classification of native forests in the Argentinian Chaco

Deforestation represents an important contributor to climate change. For this reason, identifying conditions that enable the adoption of policies halting or reversing this process is crucial to avoid catastrophic climate change. The Argentinian Gran Chaco is a hotspot of deforestation, mainly due to the expansion of capital-intensive agriculture. In Argentina, the introduction of the national forest law (NFL) represents an important step to protect the remaining forests. However, in the Chaco ecoregion, the implementation of the NFL by the different provinces is extremely heterogeneous. Previous research has provided rich descriptions of the dynamics behind the implementation of the NFL. Yet this research, mainly based on qualitative approaches, does not allow for a systematic analysis of the conditions leading to more or less stringent implementations of the NFL. To address this gap, we first combine the socio-ecological systems framework with historical materialism to generate a plausible hypothesis for the heterogeneous implementation of the NFL across the 12 different provinces of the Argentinian Chaco. Specifically, we hypothesise that it is the differences in contextual factors (i.e. differences in forest cover), material/economic conditions (i.e. presence and extent of capital-intensive agriculture) and the strength of pro- and anti-deforestation coalitions, which lead to a heterogeneous territorial classification of native forests across the various provinces. Subsequently, we test the hypothesis by developing thorough case studies via qualitative comparative analysis. This approach allows us to study in a more systematic way the reasons for the observed institutional heterogeneity. The results show that the proportion of native forests characterised as of low conservation value reflects both the environmental context (i.e. the extent of native forests) as well as the material/economic conditions (i.e. the extent of capital-intensive agriculture) and the presence of strong pro-deforestation cultures, expressed via pro-deforestation coalitions.

Mapping forest types over large areas with Landsat imagery partially affected by clouds and SLC gaps

The ecosystem services that forests provide depend on tree species composition. Therefore, it is important to map not only forest extent and its dynamics, but also composition. Open access to Landsat has resulted in considerable improvements in remote sensing methods for mapping tree species, but most approaches fail to perform when there is a shortage of clear observations. Our main goal was to map forest composition with Landsat imagery in various data availability conditions, and to investigate how the missing data, either due to clouds or scan line problems affect classification accuracy. We tested a data driven approach that is based on multi-temporal analysis of the tree species’ spectral characteristics making it applicable to regional-scale mapping even when the gap-free imagery is not available. Our study area consisted of one Landsat footprint (26/28) located in Northern Wisconsin, USA. We selected this area because of numerous tree species (23), heterogenic composition of forests where the majority of stands are mixed, and availability of high-quality reference data. We quantified how classification accuracy at the species level was affected by a) the amount of missing data due to cloud cover and Scanning Line Corrector (SLC) gaps, b) the number of acquisitions, and c) the seasonal availability of images. We applied a decision tree classifier, capable of handling missing data to both single- and a three-year Landsat-7 and Landsat-8 observations. We classified the dominant tree species in each pixel and grouped results to forest stands to match our reference data. Our results show four major findings. First, producer’s and user’s accuracies range from 46.2% to 96.2% and from 59.9% to 93.7%, respectively for the most abundant forest types in the study area (all types covering greater than 2% of the forest area). Second, all tree species were mapped with overall accuracy above 70% even in when we restricted our data set to images having gaps larger than 30% of the study area. Third, the classification accuracy improved with more acquisitions, especially when images were available for the fall, spring, and summer. Finally, producer’s accuracies for pure-stands were higher than those for mixed stands by 10 to 30 percentage points. We conclude that inclusion of Landsat imagery with missing data allows to map forest types with accuracies that previously could be achieved only for those rare years for which several gap-free images were available. The approach presented here is directly applicable to Landsat-like observations and derived products such as seasonal composites and temporal statistics that miss 30% or more of the data for any single date to develop forest composition maps that are important for both forest management and ecology.