High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle

Quantitative evaluation of the global impacts of human land modification on raptors

Raptors are threatened by anthropogenic land modifications, but targeted quantitative assessment of these impacts is lacking. We conducted the first global quantitative evaluation of the impacts of human-modified land on raptors. We used eBird data from 2001 to 2020 on 425 raptor species and occupancy models to assess the impacts of human-modified land on raptor distribution. The mean spatiotemporal correlations of human settlement, cropland, and pasture with raptor occupancy probability were -0.048 (SE 0.031), -0.134 (0.032), and -0.145 (0.032), respectively. The mean sensitivity of raptor occupancy probability to settlement, cropland, and pasture was -5.760 (2.266), -3.128 (1.540), and -2.402 (1.551), respectively. The occupancy probability of raptors with a large body mass was more negatively correlated with cropland (phylogenetic generalized least squares regressions: slope = -0.052 [SE 0.022], t = -2.335, df = 1, 407, p = 0.020, λ = 0.006) and more positively correlated with pasture (slope = 0.047 [0.022], t = 2.118, df = 1, 407, p = 0.035, λ = 0.013). The occupancy probability of raptors with a more extensive range size was more positively correlated with cropland (slope = 0.002 [0.004], t = 0.399, df = 1, 407, p < 0.001, λ = 0.000). Raptors that prefer open habitats were more positively correlated with cropland (analysis of variance: F = 3.424, df = 2, p = 0.034, λ = 0.000) and pasture (F = 6.577, df = 2, p = 0.002, λ = 0.000). In Africa and South America, where raptor species are most abundant, raptor occupancy probability decreased over 20 years, most likely due to habitat fragmentation associated with human land modification. Although raptors with different ecological characteristics had different responses to human land modification, the impacts of settlement, cropland, and pasture on mean raptor occupancy probability were negative, regardless of space and time.

Land cover changes and carbon dynamics in Central India's dry tropical forests: A 25-year assessment and nature-based eco-restoration approaches

Anthropogenic land use and land cover changes are major drivers of environmental degradation and declining soil health across heterogeneous landscapes in Central India. To examines the land cover changes and spatio-temporal variations in forest carbon stock and soil organic carbon (SOC) over the past 25 years in central India. Geospatial techniques, coupled with ground measurements were employed to detect changes in land cover, carbon stocks in vegetation, and soil carbon in various vegetation types. The results indicate that forested areas have decreased, while agriculture and habitation have expanded between 1997 and 2022. Vegetation C stocks varied significantly (P < 0.05) from 39.42 to 139.95 Mg ha-1 and the SOC varied from 7.02 to 17.98 Mg ha-1 under different soil profiles across vegetation types, which decreased with soil depth, while the pH and bulk density increased. The maximum bulk density in the soil was found at a depth of 40-60 cm (lower profile) in Bamboo Brake, while the minimum was observed under Dense Mixed Forest at a depth of 0-20 cm (top profile). The topsoil profile contributed 33.6%-39%, the middle profile (20-40 cm) was 33.6%-34.4%, and the lower profile was 26.5%-30.8% of soil organic carbon. The study site has experienced rapid carbon losses due to changes in land cover, such as illegal expansion of agriculture, encroachments into forest fringes, and activities like selective logging and overgrazing, which have degraded dense forests. The ecological engineering of degraded ecosystems poses a great challenge and application of complex biological, mechanical and engineering measures is highly cumbersome, expensive, uneconomical and practically not feasible for upscaling. Nevertheless, proposed nature-based solutions mimic natural reparation and processes provide sustainable interventions for the reclamation of ruined landscapes besides improving ecological integrity and rendering many co-benefits to ecosystems and human societies.

Ecosystem carbon storage considering combined environmental and land-use changes in the future and pathways to carbon neutrality in developed regions

Assessing the carbon storage capacity of terrestrial ecosystems is crucial for land management and carbon reduction policymaking. There is still a knowledge gap regarding how ecosystem carbon storage will be impacted by combined environmental and land-use factors and their spatial-temporal changes, especially in developed regions where urbanization has slowed down. This study investigated how developed regions in subtropical and tropical areas might increase carbon storage and achieve carbon neutrality, using Guangdong Province in South China as an example. Based on the sustainable development assumption, three land-management scenarios were developed and simulated for 2020-2060 using the Patch-generating Land Use Simulation model. Without considering disturbance and natural losses, carbon storage was estimated by net ecosystem productivity (NEP)-the difference between net primary productivity (NPP) and heterotrophic respiration (HR). NPP was predicted using an artificial neural network model trained by historical NPP data and 16 environmental and land-use variables. HR was predicted using soil respiration models from previous research. Based on the balance between carbon storage and emissions, we predicted the allowable fossil fuel consumption to achieve net-zero CO2 emissions in 2060. The results show that Guangdong's total carbon storage changes from 73.7 MtC in 2020 to 70.6-74.8 MtC in 2060 under different scenarios. Nonlinear relationships exist between the carbon stored and the areas of different land-use types. Topography, temperatures, and land-use configurations jointly lead to significantly varied carbon storage between croplands and between forests in space and time. Protecting and regenerating forests in subtropical areas and forest edges is more effective than afforestation in lowland tropical areas for storing carbon. Net-zero CO2 emissions rely more on reducing emissions than land management. To achieve this, the proportion of fossil energy in total energy consumption should be lowered from 75.5 % in 2020 to ~25 % in 2060.

Biodiversity and carbon conservation under the ecosystem stability of tropical forests

Although efforts to protect high levels of biodiversity and carbon storage can greatly increase the effectiveness of species loss and climate change mitigation, there is evidence indicating a trade-off scenario for their conservation at regional scale. Decisions making in trade-off scenarios can be supported by including information on the ecosystem stability of tropical forests (i.e., the ability of the ecosystem to maintain its function over time). Forest stability may affect biodiversity integrity and the residence time of carbon stored in tree biomass. Here, we assess the stability of old-growth forests' productivity by analyzing a 19-year time series of the Normalized Difference Vegetation Index (NDVI). We also used geoprocessing tools to analyze the overlap among forest-specialist vertebrate species richness, carbon density, and stability of old-growth forest throughout the Brazilian Atlantic Forest. We used model selection to find environmental predictors of the stability of primary productivity and build a predictive map of potential stability. Then, we overlapped maps of potential stability, species richness of forest-specialist vertebrates, and carbon density to identify hotspot areas of biodiversity and carbon density occurring at highest and lowest potential stability. We found that forest stability increases from north to south along the Atlantic Forest. High biodiversity occurs mainly at low stability while high carbon stock at high stability. Spatial overlap of the hotspots, where conservation co-benefits high biodiversity and carbon stock, occurs mostly at high stability in a large area along part of the coast and in smaller inland areas of the southern region. Most of the hotspots with low stability for biodiversity, carbon stock and combination of both are found in unprotected areas. Hence, the strategic mitigation of species loss and carbon emissions lies in three approaches: prioritizing forest protection in unprotected hotspots; implementing forest management practices in protected hotspots with low stability; and enforcing a comprehensive regime of protection and management in hotspots that exhibit low stability. Focused on forest stability, these approaches involve ecosystem-based planning offering Brazil's government effective strategies to fulfill its commitments in biodiversity conservation and carbon emission reduction.

Impacts of landscape patterns on plant species diversity at a global scale

Landscape patterns are important drivers of biodiversity. Owing to differences in vegetation types, sampling methods, diversity measures, spatial scales, and landscape levels, the impact of landscape patterns on biodiversity remains widely debated. Using a global standardized plant community database and land use and land cover maps at 30-m resolution, for the period 1990-2017, we calculated plant species α- and β-diversity, and landscape metrics at patch- and landscape-levels, and discerned the direct and indirect impacts of landscape patterns on plant species diversity based on environmental factors, namely climate, spatial features, and human disturbance. We found that landscape patterns exhibited the main indirect effects, whereas climate factors exhibited dominant direct effects on plant α-diversity via the direct effects of patch patterns and functional traits. With respect to β-diversity, landscape-level patterns exerted more direct than indirect effects. These effects are strongly dependent on scale. Landscape- and patch-level patterns had opposite effects on plant diversity, depending on their composition and spatial structure, demonstrating that their effects could be mediated by one another. The adaptation of plants to landscape patterns is mainly through variations in leaf area, plant height, specific leaf area, stem density, seed biomass, and other seed-dispersal traits, which vary across vegetation types. Our findings highlight the importance of functional traits and diversity in understanding the mechanism by which landscape patterns influence plant species diversity; accordingly, we recommend balancing the spatial structure of patch- and landscape-level patterns to enhance variation in functional traits, and, ultimately, to maintain global plant diversity.

Global forest fragmentation change from 2000 to 2020

A comprehensive quantification of global forest fragmentation is urgently required to guide forest protection, restoration and reforestation policies. Previous efforts focused on the static distribution patterns of forest remnants, potentially neglecting dynamic changes in forest landscapes. Here, we map global distribution of forest fragments and their temporal changes between 2000 and 2020. We find that forest landscapes in the tropics were relatively intact, yet these areas experienced the most severe fragmentation over the past two decades. In contrast, 75.1% of the world's forests experienced a decrease in fragmentation, and forest fragmentation in most fragmented temperate and subtropical regions, mainly in northern Eurasia and South China, declined between 2000 and 2020. We also identify eight modes of fragmentation that indicate different recovery or degradation states. Our findings underscore the need to curb deforestation and increase connectivity among forest fragments, especially in tropical areas.

Giant panda-focused conservation has limited value in maintaining biodiversity and carbon sequestration

Biodiversity and climate are interconnected through carbon. Drivers of climate change and biodiversity loss interact in complex ways to produce outcomes that may be synergistic, and biodiversity loss and climate change reinforce each other. Prioritizing the conservation of flagship and umbrella species is often used as a surrogate strategy for broader conservation goals, but it is unclear whether these efforts truly benefit biodiversity and carbon stocks. Conservation of the giant panda offers a paradigm to test these assumptions. Here, using the benchmark estimates of ecosystem carbon stocks and species richness, we investigated the relationships among the giant panda, biodiversity, and carbon stocks and assessed the implications of giant panda conservation for biodiversity and carbon-focused conservation efforts. We found that giant panda density and species richness were significantly positively correlated, while no correlation was found between giant panda density and soil carbon or total carbon density. The established nature reserves protect 26 % of the giant panda conservation region, but these areas contain <21 % of the ranges of other species and <21 % of total carbon stocks. More seriously, giant panda habitats are still facing high risks of habitat fragmentation. Habitat fragmentation is negatively correlated with giant panda density, species richness, and total carbon density. The ongoing giant panda habitat fragmentation is likely to cause an additional 12.24 Tg C of carbon emissions over 30 years. Thus, giant panda-focused conservation efforts have effectively prevented giant panda extinction but have been less effective in maintaining biodiversity and high‑carbon ecosystems. It is urgent for China to contribute to the development of an effective and representative national park system that integrates climate change issues into national biodiversity strategies and vice versa in dealing with the dual environmental challenges of biodiversity loss and climate change under a post-2020 framework.

Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century

The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

Diversified land conversion deepens understanding of impacts of rapid rubber plantation expansion on plant diversity in the tropics

Understanding the status and changes of plant diversity in rubber (Hevea brasiliensis) plantations is essential for sustainable plantation management in the context of rapid rubber expansion in the tropics, but remains very limited at the continental scale. In this study, we investigated plant diversity from 10-meter quadrats in 240 different rubber plantations in the six countries of the Great Mekong Subregion (GMS)-where nearly half of the world's rubber plantations are located-and analyzed the influence of original land cover types and stand age on plant diversity using Landsat and Sentinel-2 satellite imagery since the late 1980s. The results indicate that the average plant species richness of rubber plantations is 28.69 ± 7.35 (1061 species in total, of which 11.22 % are invasive), approximating half the species richness of tropical forests but roughly double that of the intensively managed croplands. Time-series satellite imagery analysis revealed that rubber plantations were primarily established in place of cropland (RP_C, 37.72 %), old rubber plantations (RP_ORP, 27.63 %), and tropical forests (RP_TF, 24.12 %). Plant species richness in RP_TF (34.02 ± 7.62) was significantly (p < 0.001) higher than that in RP_ORP (26.41 ± 7.02) and RP_C (26.34 ± 5.37). More importantly, species richness can be maintained for the duration of the 30-year economic cycle, and the number of invasive species decreases as the stand ages. Given diverse land conversions and changes in stand age, the total loss of species richness due to rapid rubber expansion in the GMS was 7.29 %, which is far below the traditional estimates that only consider tropical forest conversion. In general, maintaining higher species richness at the earliest stages of cultivation has significant implications for biodiversity conservation in rubber plantations.

Do Environmental Regulations Facilitate a Low-Carbon Transformation in China's Resource-Based Cities?

Resource-based cities (RBCs) are not only important for ensuring national resource and energy security, but they also face serious ecological and environmental problems. To achieve China's carbon peaking and neutrality goals in the coming years, RBCs' achievement of a low-carbon transformation has become increasingly significant. The core of this study is an investigation as to whether governance, including environmental regulations, can facilitate the low-carbon transformation of RBCs. Based on RBC data from 2003 to 2019, we establish a dynamic panel model to research the influence and mechanism of environmental regulations on low-carbon transformation. We found that China's environmental regulations facilitate a low-carbon transformation in RBCs. Mechanism analysis identified that the environmental regulations facilitate the low-carbon transformation in RBCs by strengthening foreign direct investment, enhancing green technology innovation and promoting industrial structure upgrading. Heterogeneity analysis found that the environmental regulations play a greater role in facilitating the low-carbon transformation of RBCs in regions with more developed economies and less dependence on resources. Our research provides theoretical and policy implications for environmental regulations for the low-carbon transformation of RBCs in China, applicable to other resource-based areas.

Declining Amazon biomass due to deforestation and subsequent degradation losses exceeding gains

In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011-2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom-up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon.

Multiscale predictors of small tree survival across a heterogeneous tropical landscape

Uncertainties about controls on tree mortality make forest responses to land-use and climate change difficult to predict. We tracked biomass of tree functional groups in tropical forest inventories across Puerto Rico and the U.S. Virgin Islands, and with random forests we ranked 86 potential predictors of small tree survival (young or mature stems 2.5-12.6 cm diameter at breast height). Forests span dry to cloud forests, range in age, geology and past land use and experienced severe drought and storms. When excluding species as a predictor, top predictors are tree crown ratio and height, two to three species traits and stand to regional factors reflecting local disturbance and the system state (widespread recovery, drought, hurricanes). Native species, and species with denser wood, taller maximum height, or medium typical height survive longer, but short trees and species survive hurricanes better. Trees survive longer in older stands and with less disturbed canopies, harsher geoclimates (dry, edaphically dry, e.g., serpentine substrates, and highest-elevation cloud forest), or in intervals removed from hurricanes. Satellite image phenology and bands, even from past decades, are top predictors, being sensitive to vegetation type and disturbance. Covariation between stand-level species traits and geoclimate, disturbance and neighboring species types may explain why most neighbor variables, including introduced vs. native species, had low or no importance, despite univariate correlations with survival. As forests recovered from a hurricane in 1998 and earlier deforestation, small trees of introduced species, which on average have lighter wood, died at twice the rate of natives. After hurricanes in 2017, the total biomass of trees ≥12.7 cm dbh of the introduced species Spathodea campanulata spiked, suggesting that more frequent hurricanes might perpetuate this light-wooded species commonness. If hurricane recovery favors light-wooded species while drought favors others, climate change influences on forest composition and ecosystem services may depend on the frequency and severity of extreme climate events.

Facilitating Reforestation Through the Plant Microbiome: Perspectives from the Phyllosphere

Tree planting and natural regeneration contribute to the ongoing effort to restore Earth's forests. Our review addresses how the plant microbiome can enhance the survival of planted and naturally regenerating seedlings and serve in long-term forest carbon capture and the conservation of biodiversity. We focus on fungal leaf endophytes, ubiquitous defensive symbionts that protect against pathogens. We first show that fungal and oomycetous pathogen richness varies greatly for tree species native to the United States (n = 0-876 known pathogens per US tree species), with nearly half of tree species either without pathogens in these major groups or with unknown pathogens. Endophytes are insurance against the poorly known and changing threat of tree pathogens. Next, we review studies of plant phyllosphere feedback, but knowledge gaps prevent us from evaluating whether adding conspecific leaf litter to planted seedlings promotes defensive symbiosis, analogous to adding soil to promote positive feedback. Finally, we discuss research priorities for integrating the plant microbiome into efforts to expand Earth's forests.

Understanding climate change impacts on biome and plant distributions in the Andes: Challenges and opportunities

AIM

Climate change is expected to impact mountain biodiversity by shifting species ranges and the biomes they shape. The extent and regional variation in these impacts are still poorly understood, particularly in the highly biodiverse Andes. Regional syntheses of climate change impacts on vegetation are pivotal to identify and guide research priorities. Here we review current data, knowledge and uncertainties in past, present and future climate change impacts on vegetation in the Andes. Location: Andes. Taxon: Plants.

METHODS

We (i) conducted a literature review on Andean vegetation responses to past and contemporary climatic change, (ii) analysed future climate projections for different elevations and slope orientations at 19 Andean locations using an ensemble of model outputs from the Coupled Model Intercomparison Project 5, and (iii) calculated changes in the suitable climate envelope area of Andean biomes and compared these results to studies that used species distribution models.

RESULTS

Future climatic changes (2040-2070) are projected to be stronger at high-elevation areas in the tropical Andes (up to 4°C under RCP 8.5), while in the temperate Andes temperature increases are projected to be up to 2°C. Under this worst-case scenario, temperate deciduous forests and the grasslands/steppes from the Central and Southern Andes are predicted to show the greatest losses of suitable climatic space (30% and 17%-23%, respectively). The high vulnerability of these biomes contrasts with the low attention from researchers modelling Andean species distributions. Critical knowledge gaps include a lack of an Andean wide plant checklist, insufficient density of weather stations at high-elevation areas, a lack of high-resolution climatologies that accommodates the Andes' complex topography and climatic processes, insufficient data to model demographic and ecological processes, and low use of palaeo data for distribution modelling.

MAIN CONCLUSIONS

Climate change is likely to profoundly affect the extent and composition of Andean biomes. Temperate Andean biomes in particular are susceptible to substantial area contractions. There are, however, considerable challenges and uncertainties in modelling species and biome responses and a pressing need for a region-wide approach to address knowledge gaps and improve understanding and monitoring of climate change impacts in these globally important biomes.

The effect of elevated CO2 on aboveground and belowground carbon allocation and eco-physiology of four species of angiosperm and gymnosperm forest trees

Although atmospheric CO2 concentration ([CO2]) continues to rise, the question of how tree carbon (C) allocation is affected by this change remains. Studies show that C assimilation increases under elevated CO2 (eCO2). Yet, no detailed study has determined the fate of the surplus C, i.e., its compartment and physiological process allocation, nor in multiple species together. In this project, we grew 2-year-old saplings of four key Mediterranean tree species (the conifers Cupressus sempervirens L. and Pinus halepensis Mill., and the broadleaf Quercus calliprinos Webb. and Ceratonia siliqua L.) to [CO2] levels of 400 or 700 p.p.m. for 6 months. We measured the allocation of C to below and aboveground growth, respiration, root exudation, storage and leaf litter. In addition, we monitored intrinsic water-use efficiency (WUE), soil moisture, soil chemistry and nutrient uptake. Net assimilation, WUE and soil nitrogen uptake significantly increased at eCO2 across the four species. Broadleaf species showed soil water savings, which were absent in conifers. All other effects were species-specific: Cupressus had higher leaf respiration, Pinus had lower starch in branches and transiently higher exudation rate and Quercus had higher root respiration. Elevated CO2 did not affect growth or litter production. Our results are pivotal to understanding the sensitivity of tree C allocation to the change in [CO2] when water is abundant. Species-specific responses should be regarded cautiously when predicting future changes in forest function in a higher CO2 world.

A global map of planting years of plantations

Plantation is an important land use type that differs from natural forests and affects the economy and the environment. Tree age is one of the key factors used to quantify the impact of plantations. However, there is a lack of datasets explicitly documenting the planting years of global plantations. Here we used time-series Landsat archive from 1982 to 2020 and the LandTrendr algorithm to generate global maps of planting years based on the global plantation extent products in Google Earth Engine (GEE) platform. The datasets developed in this study are in a GeoTIFF format with 30-meter spatial resolution by recording gridded specie types and planting years of global plantations. The derived dataset could be used for yield prediction of tree crops and social and ecological cost-benefit analysis of plantations.

Measurement(s)	planting years of plantations
Technology Type(s)	remote sensing and LandTrendr algorithm
Sample Characteristic - Organism	forest

The Effects of Environmental Changes on Plant Species and Forest Dependent Communities in the Amazon Region

We review the consequences of environmental changes caused by human activities on forest products and forest-dependent communities in the Amazon region—the vast Amazonas River basin and the Guiana Shield in South America. We used the 2018 and 2021 Intergovernmental Panel on Climate Change reports and recent scientific studies to present evidence and hypotheses for changes in the ecosystem productivity and geographical distribution of plants species. We have identified species associated with highly employed forest products exhibiting reducing populations, mainly linked with deforestation and selective logging. Changes in species composition along with a decline of valuable species have been observed in the eastern, central, and southern regions of the Brazilian Amazon, suggesting accelerated biodiversity loss. Over 1 billion native trees and palms are being lost every two years, causing economic losses estimated between US$1–17 billion. A decrease in native plant species can be abrupt and both temporary or persistent for over 20 years, leading to reduced economic opportunities for forest-dependent communities. Science and technology investments are considered promising in implementing agroforestry systems recovering deforested and degraded lands, which could engage companies that use forest products due to supply chain advantages.

Space-Time Dynamics of Land Use in the Municipality of Goianésia Do Pará, Brazil

Hydroelectric energy generates more than 50% of all renewable electricity in the world. The Amazon is home to a large part of these ventures, promoted as a strategy of energy independence in order to reduce greenhouse gas emissions in the countries of the region. However, these hydroelectric plants lead to changes in land cover, fragmentation, degradation, and loss of tropical forests. This article analyzes the spatial pattern of alterations in the land cover of the municipality of Goianésia do Pará, one of the seven municipalities affected by the artificial lake of the Tucuruí hydroelectric plant. This case study integrates remote sensing and landscape metrics to identify, quantify, and spatialize the loss of tropical forest within the municipality by using satellite images of the TM-Landsat 5, ETM+-Landsat 7 and OLI-Landsat 8 sensors. The results show that the average deforestation rates were high in the first two periods: 1984–1988 (23,101.2 ha per year) and 1988–1999 (13,428.6 ha per year). However, this rate drastically fell in the last period because, by 2010, more than 60% of the territory was already deforested, which shows the consolidation of the municipality’s deforestation process.

Forest fragmentation impacts the seasonality of Amazonian evergreen canopies

Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial. Here, we use terrestrial LiDAR surveys every two weeks spanning wet and dry seasons in Central Amazonia to show that plant phenology varies strongly across vertical strata in old-growth forests, but is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we find that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests lost plant material. In contrast, the understory greened up with increased light availability driven by the upper canopy loss, alongside increases in solar radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures in forest edges exacerbated the upper canopy losses of large trees throughout the dry season, whereas the understory in these light-rich environments was less dependent on the altered upper canopy structure. Our findings reveal a strong influence of edge effects on phenological controls in wet forests of Central Amazonia.

Even evergreen tropical forests can have seasonal dynamics, which may be sensitive to disturbance. Here, the authors combine high-resolution remote sensing observations and microclimate data to show that forest fragmentation impacts canopy phenology dynamics in the Amazon forest.

JA, Sanchez‐Azofeifa GA, De Deurwaerder HPT, Krishna Moorthy SM, Schnitzer SA, Marvin DC, Longo M, Liu C, Broadbent EN, Almeyda Zambrano AM, Muller‐Landau HC, Detto M, Verbeeck H. Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (-30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (-19%) and ecosystem (-7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco

Vegetation structure is a key component in assessing habitat quality for wildlife and carbon storage capacity of forests. Studies conducted at global scale demonstrate the increasing pressure of the agricultural frontier on tropical forest, endangering their continuity and biodiversity within. The Paraguayan Chaco has been identified as one of the regions with the highest rate of deforestation in South America. Uninterrupted deforestation activities over the last 30 years have resulted in the loss of 27% of its original cover. The present study focuses on the assessment of vegetation structure characteristics for the complete Paraguayan Chaco by fusing Sentinel-1, -2 and novel spaceborne Light Detection and Ranging (LiDAR) samples from the Global Ecosystem Dynamics Investigation (GEDI). The large study area (240,000 km2) calls for a workflow in the cloud computing environment of Google Earth Engine (GEE) which efficiently processes the multi-temporal and multi-sensor data sets for extrapolation in a tile-based random forest (RF) regression model. GEDI-derived attributes of vegetation structure are available since December 2019, opening novel research perspectives to assess vegetation structure composition in remote areas and at large-scale. Therefore, the combination of global mapping missions, such as Landsat and Sentinel, are predestined to be combined with GEDI data, in order to identify priority areas for nature conservation. Nevertheless, a comprehensive assessment of the vegetation structure of the Paraguayan Chaco has not been conducted yet. For that reason, the present methodology was developed to generate the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index and Foliage-Height-Diversity-Index. The complex ecosystems of the Paraguayan Chaco ranging from arid to humid climates can be described by canopy height values from 1.8 to 17.6 m and canopy covers from sparse to dense (total canopy cover: 0 to 78.1%). Model accuracy according to median R2 amounts to 64.0% for canopy height, 61.4% for total canopy cover, 50.6% for Plant-Area-Index and 48.0% for Foliage-Height-Diversity-Index. The generated maps of vegetation structure should promote environmental-sound land use and conservation strategies in the Paraguayan Chaco, to meet the challenges of expanding agricultural fields and increasing demand of cattle ranching products, which are dominant drivers of tropical forest loss.

Elevated growth and biomass along temperate forest edges

Fragmentation transforms the environment along forest edges. The prevailing narrative, driven by research in tropical systems, suggests that edge environments increase tree mortality and structural degradation resulting in net decreases in ecosystem productivity. We show that, in contrast to tropical systems, temperate forest edges exhibit increased forest growth and biomass with no change in total mortality relative to the forest interior. We analyze >48,000 forest inventory plots across the north-eastern US using a quasi-experimental matching design. At forest edges adjacent to anthropogenic land covers, we report increases of 36.3% and 24.1% in forest growth and biomass, respectively. Inclusion of edge impacts increases estimates of forest productivity by up to 23% in agriculture-dominated areas, 15% in the metropolitan coast, and +2% in the least-fragmented regions. We also quantify forest fragmentation globally, at 30-m resolution, showing that temperate forests contain 52% more edge forest area than tropical forests. Our analyses upend the conventional wisdom of forest edges as less productive than intact forest and call for a reassessment of the conservation value of forest fragments.

Accelerated forest fragmentation leads to critical increase in tropical forest edge area

Large areas of tropical forests have been lost through deforestation, resulting in fragmented forest landscapes. However, the dynamics of forest fragmentation are still unknown, especially the critical forest edge areas, which are sources of carbon emissions due to increased tree mortality. We analyzed the changes in forest fragmentation for the entire tropics using high-resolution forest cover maps. We found that forest edge area increased from 27 to 31% of the total forest area in just 10 years, with the largest increase in Africa. The number of forest fragments increased by 20 million with consequences for connectivity of tropical landscapes. Simulations suggest that ongoing deforestation will further accelerate forest fragmentation. By 2100, 50% of tropical forest area will be at the forest edge, causing additional carbon emissions of up to 500 million MT carbon per year. Thus, efforts to limit fragmentation in the world’s tropical forests are important for climate change mitigation.

Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

Drivers of carbon stocks in forest edges across Europe

Forests play a key role in global carbon cycling and sequestration. However, the potential for carbon drawdown is affected by forest fragmentation and resulting changes in microclimate, nutrient inputs, disturbance and productivity near edges. Up to 20% of the global forested area lies within 100 m of an edge and, even in temperate forests, knowledge on how edge conditions affect carbon stocks and how far this influence penetrates into forest interiors is scarce. Here we studied carbon stocks in the aboveground biomass, forest floor and the mineral topsoil in 225 plots in deciduous forest edges across Europe and tested the impact of macroclimate, nitrogen deposition and smaller-grained drivers (e.g. microclimate) on these stocks. Total carbon and carbon in the aboveground biomass stock were on average 39% and 95% higher at the forest edge than 100 m into the interior. The increase in the aboveground biomass stock close to the edge was mainly related to enhanced nitrogen deposition. No edge influence was found for stocks in the mineral topsoil. Edge-to-interior gradients in forest floor carbon changed across latitude: carbon stocks in the forest floor were higher near the edge in southern Europe. Forest floor carbon decreased with increasing litter quality (i.e. high decomposition rate) and decreasing plant area index, whereas higher soil temperatures negatively affected the mineral topsoil carbon. Based on high-resolution forest fragmentation maps, we estimate that the additional carbon stored in deciduous forest edges across Europe amounts to not less than 183 Tg carbon, which is equivalent to the storage capacity of 1 million ha of additional forest. This study underpins the importance of including edge influences when quantifying the carbon stocks in temperate forests and stresses the importance of preserving natural forest edges and small forest patches with a high edge-to-interior surface area.

Long-term (1990-2019) monitoring of forest cover changes in the humid tropics

Accurate characterization of tropical moist forest changes is needed to support conservation policies and to quantify their contribution to global carbon fluxes more effectively. We document, at pantropical scale, the extent and changes (degradation, deforestation, and recovery) of these forests over the past three decades. We estimate that 17% of tropical moist forests have disappeared since 1990 with a remaining area of 1071 million hectares in 2019, from which 10% are degraded. Our study underlines the importance of the degradation process in these ecosystems, in particular, as a precursor of deforestation, and in the recent increase in tropical moist forest disturbances (natural and anthropogenic degradation or deforestation). Without a reduction of the present disturbance rates, undisturbed forests will disappear entirely in large tropical humid regions by 2050. Our study suggests that reinforcing actions are needed to prevent the initial degradation that leads to forest clearance in 45% of the cases.

Deriving Tree Size Distributions of Tropical Forests from Lidar

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

Long-term forest degradation surpasses deforestation in the Brazilian Amazon

Although deforestation rates in the Brazilian Amazon are well known, the extent of the area affected by forest degradation is a notable data gap, with implications for conservation biology, carbon cycle science, and international policy. We generated a long-term spatially quantified assessment of forest degradation for the entire Brazilian Amazon from 1992 to 2014. We measured and mapped the full range of activities that degrade forests and evaluated the relationship with deforestation. From 1992 to 2014, the total area of degraded forest was 337,427 square kilometers (km²), compared with 308,311 km² that were deforested. Forest degradation is a separate and increasing form of forest disturbance, and the area affected is now greater than that due to deforestation.

Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon

Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.

The impact of anthropogenic disturbances on the genetic diversity of terrestrial species: A global meta-analysis

Human activities are primarily responsible for habitat loss and changes in natural environments around the world. It has been suggested that populations inhabiting human-modified landscapes experience reduced gene flow, inbreeding depression, and loss of alleles due to genetic drift. However, empirical evidence shows the contrasting effects of anthropogenic disturbances on the genetic diversity of species. We performed a meta-analysis of 61 studies that compared the genetic diversity of plant and/or animal populations in disturbed and more preserved areas (316 paired comparisons) to investigate the genetic responses to different disturbance types. There is a negative effect (effect size: -0.45; 95% confidence interval: -0.61, -0.29) of disturbances on genetic diversity, in which the most detrimental effects are caused by the loss of connectivity and forest cover. The methodological approach can explain part of the heterogeneity among the genetic responses detected by primary studies: (a) studies using the number of effective alleles did not detect genetic erosion, while all other indices revealed negative responses to disturbances; and (b) only studies performed with transferred or a combination of transferred and specific microsatellites detected negative responses. The effects on animal populations are more detrimental than in plant populations. Only plant species with a shrub life form, self-incompatible reproductive systems, and biotic pollination and seed dispersal showed negative responses to disturbances. Despite heterogeneity among studies, there is an overall negative effect of disturbances on genetic diversity, which indicates that remaining populations inhabiting human-modified landscapes have reduced evolutionary potential and are prone to local extinction.

Examining land surface phenology in the tropical moist forest eco-zone of South America

Using leaf area index (LAI) data from 1981 to 2014 in the tropical moist forest eco-zone of South America, we extracted start (SOS) and end (EOS) dates of the active growing season in forest and savanna at each pixel. Then, we detected spatiotemporal characteristics of SOS and EOS in the two vegetation types. Moreover, we analyzed relationships between interannual variations of SOS/EOS and climatic factors, and simulated SOS/EOS time series based on preceding mean air temperature and accumulated rainfall. Results show that mean SOS and EOS ranged from 260 to 330 day of year (DOY) and from 150 to 260 DOY across the study region, respectively. From 1981 to 2014, SOS advancement is more extensive than SOS delay, while EOS advancement and delay are similarly extensive. For most pixels of forest and savanna in tropical moist forest eco-zone, preceding rainfall correlates predominantly negatively with SOS but positively with EOS, while the relationship between preceding temperature and phenophases is location-specific. In addition, preceding rainfall is more extensive than preceding temperature in simulating SOS, while both preceding rainfall and temperature play an important role for simulating EOS. This study highlights the reliability of using LAI data for long-term phenological analysis in the tropical moist forest eco-zone.

Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year^-1 in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 ± 8 Tg C year^-1 Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement's bold targets.

Global Significance of Mangrove Blue Carbon in Climate Change Mitigation

Mangrove forests store and sequester large area-specific quantities of blue carbon (Corg). Except for tundra and peatlands, mangroves store more Corg per unit area than any other ecosystem. Mean mangrove Corg stock is 738.9 Mg Corg ha−1 and mean global stock is 6.17 Pg Corg, which equates to only 0.4–7% of terrestrial ecosystem Corg stocks but 17% of total tropical marine Corg stocks. Per unit area, mangroves sequester 179.6 g Corg m−2a−1 and globally about 15 Tg Corg a−1. Mangroves sequester only 4% (range 1.3–8%) of Corg sequestered by terrestrial ecosystems, indicating that mangroves are a minor contributor to global C storage and sequestration. CO2 emissions from mangrove losses equate to 0.036 Pg CO2-equivalents a−1 based on rates of C sequestration but 0.088 Pg CO2-equivalents a−1 based on complete destruction for conversion to aquaculture and agriculture. Mangrove CO2 emissions account for only 0.2% of total global CO2 emissions but 18% of CO2 emissions from the tropical coastal ocean. Despite significant data limitations, the role of mangrove ecosystems in climate change mitigation is small at the global scale but more significant in the tropical coastal ocean and effective at the national and regional scale, especially in areas with high rates of deforestation and destruction.

Global Significance of Mangrove Blue Carbon in Climate Change Mitigation (Version 1)

Mangrove forests store and sequester large area-specific quantities of blue carbon (Corg). Except for tundra and peatlands, mangroves store more Corg per unit area than any other ecosystem. Mean mangrove Corg stock is 738.9 Mg Corg ha−1 and mean global stock is 6.17 Pg Corg, which equates to only 0.4–7% of terrestrial ecosystem Corg stocks but 17% of total tropical marine Corg stocks. Seagrasses sequester more Corg per unit area than mangroves (179.6 g Corg m−2·a−1) but twice the Corg sequestered by mangroves globally (15 Tg Corg a−1). Mangroves sequester only 4% (range 1.3–8%) of Corg sequestered by terrestrial ecosystems, indicating that mangroves are a minor contributor to global C storage and sequestration. CO2 emissions from mangrove losses equate to 0.036 Pg CO2-equivalents a−1 based on rates of C sequestration but 0.088 Pg CO2-equivalents a−1 based on complete destruction for conversion to aquaculture and agriculture. Mangrove CO2 emissions account for only 0.2% of total global CO2 emissions but 18% of CO2 emissions from the tropical coastal ocean. Despite significant data limitations, the role of mangrove ecosystems in climate change mitigation is globally insignificant but may be more significant and effective at the national and regional scale.

Reconstructing Long Term High Andean Forest Dynamics Using Historical Aerial Imagery: A Case Study in Colombia

High Andean forests are biodiversity hotspots that also play key roles in the provisioning of vital ecosystem services for neighboring cities. In past centuries, the hinterland of Andean fast-growing cities often experienced a dramatic decline in forested areas, but there are reports that forest cover has been recovering recently. We analyzed aerial imagery spanning the years 1940 to 2007 from nine administrative localities in the Eastern Andean Cordillera of Colombia in order to elucidate precise patterns of forest vegetation change. To this aim, we performed image object-based classification by means of texture analysis and image segmentation. We then derived connectivity metrics to investigate whether forest cover trajectories showed an increase or decrease in fragmentation and landscape degradation. We observed a forest cover recovery in all the examined localities, except one. In general, forest recovery was accompanied by an increase in core habitat areas. The time scale of the positive trends identified partially coincides with the creation of protected areas in the region, which very likely furthered the recovery of forest patches. This study unveils the long-term dynamics of peri-urban high Andean forest cover, providing valuable information on historical vegetation changes in a highly dynamic landscape.

Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model-data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB₉₅ ) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB₉₅ , particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.

Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots

Global terrestrial biodiversity hotspots (GBH) represent areas featuring exceptional concentrations of endemism and habitat loss in the world. Unfortunately, geospatial data of natural habitats of the GBHs are often outdated, imprecise, and coarse, and need updating for improved management and protection actions. Recent developments in satellite image availability, combined with enhanced machine learning algorithms and computing capacity, enable cost-efficient updating of geospatial information of these already severely fragmented habitats. This study aimed to develop a more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, and to update the knowledge on its spatial extent, level of fragmentation, and conservation status. We tested 1023 model possibilities stemming from a combination of Sentinel-1 (S1) and Sentinel-2 (S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series, and topographic information, using random forest modelling approach. We compared the best CCEF model with existing spatial forest products from the EAM through independent accuracy assessment. Finally, the CCEF model was used to estimate the fragmentation and conservation coverage of the EAM. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296,000 ha of Eastern Arc Forests (EAF) left. Furthermore, acknowledging small forest fragments (1–10 ha) implies that the EAFs are more fragmented than previously considered. Currently, the official protection of EAFs is disproportionally targeting well-studied mountain blocks, while less known areas and small fragments are underrepresented in the protected area network. Thus, the generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans to balance this situation. The results highlight that spatial texture of S2, seasonality, and topography are the most important variables describing the EAFs, while spatial texture of S1 increases the model performance slightly. All in all, our work demonstrates that recent developments in Earth observation allows significant enhancements in mapping, which should be utilized in areas with outstanding biodiversity values for better forest and conservation planning.

Increasing fragmentation of forest cover in Brazil's Legal Amazon from 2001 to 2017

Persistent forest loss in the Brazilian Legal Amazon (BLA) is responsible for carbon emission, reduction of ecosystem services, and loss of biodiversity. Combining spatial data analysis with high spatial resolution data for forest cover and forest loss, we quantified the spatial and temporal patterns of forest dynamics in the BLA. We identified an alarming trend of increasing deforestation, with especially high rates in 2016 and 2017. Moreover, the creation of forest cover fragments is faster than ever due to decreasing size and dispersion of forest loss patches. From 2001 to 2017, the number of large forest loss patches decreased significantly, accompanied by a reduction in the size of these patches. Enforcement of field inspections and of initiatives to promote forest conservation will be required to stop this trend.

Carbon declines along tropical forest edges correspond to heterogeneous effects on canopy structure and function

Nearly 20% of tropical forests are within 100 m of a nonforest edge, a consequence of rapid deforestation for agriculture. Despite widespread conversion, roughly 1.2 billion ha of tropical forest remain, constituting the largest terrestrial component of the global carbon budget. Effects of deforestation on carbon dynamics in remnant forests, and spatial variation in underlying changes in structure and function at the plant scale, remain highly uncertain. Using airborne imaging spectroscopy and light detection and ranging (LiDAR) data, we mapped and quantified changes in forest structure and foliar characteristics along forest/oil palm boundaries in Malaysian Borneo to understand spatial and temporal variation in the influence of edges on aboveground carbon and associated changes in ecosystem structure and function. We uncovered declines in aboveground carbon averaging 22% along edges that extended over 100 m into the forest. Aboveground carbon losses were correlated with significant reductions in canopy height and leaf mass per area and increased foliar phosphorus, three plant traits related to light capture and growth. Carbon declines amplified with edge age. Our results indicate that carbon losses along forest edges can arise from multiple, distinct effects on canopy structure and function that vary with edge age and environmental conditions, pointing to a need for consideration of differences in ecosystem sensitivity when developing land-use and conservation strategies. Our findings reveal that, although edge effects on ecosystem structure and function vary, forests neighboring agricultural plantations are consistently vulnerable to long-lasting negative effects on fundamental ecosystem characteristics controlling primary productivity and carbon storage.

How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review

In the context of the climate and biodiversity crisis facing our planet, tropical forests playing a key role in global carbon flux and containing over half of Earth’s species are important to preserve. They are today threatened by deforestation but also by forest degradation, which is more difficult to study. Here, we performed a systematic review of studies on moist tropical forest degradation using remote sensing and fitting indicators of forest resilience to perturbations. Geographical repartition, spatial extent and temporal evolution were analyzed. Indicators of compositional, structural and regeneration criteria were noted as well as remote sensing indices and metrics used. Tropical moist forest degradation is not extensively studied especially in the Congo basin and in southeast Asia. Forest structure (i.e., canopy gaps, fragmentation and biomass) is the most widely and easily measured criteria with remote sensing, while composition and regeneration are more difficult to characterize. Mixing LiDAR/Radar and optical data shows good potential as well as very high-resolution satellite data. The awaited GEDI and BIOMASS satellites data will fill the actual gap to a large extent and provide accurate structural information. LiDAR and unmanned aerial vehicles (UAVs) form a good bridge between field and satellite data. While the performance of the LiDAR is no longer to be demonstrated, particular attention should be brought to the UAV that shows great potential and could be more easily used by local communities and stakeholders.

The fate of tropical forest fragments

Tropical forest fragmentation results in habitat and biodiversity loss and increased carbon emissions. Here, we link an increased likelihood of tropical forest loss to decreasing fragment size, particularly in primary forests. The relationship holds for protected areas, albeit with half the rate of loss compared with all fragments. The fact that disturbance increases as primary forest fragment size decreases reflects higher land use pressures and improved access for resource extraction and/or conversion in smaller fragments. Large remaining forest fragments are found in the Amazon and Congo Basins and Insular Southeast Asia, with the majority of large extent/low loss fragments located in the Amazon. Tropical areas without large fragments, including Central America, West Africa, and mainland Southeast Asia, have higher loss within and outside of protected areas. Results illustrate the need for rigorous land use planning, management, and enforcement in maintaining large tropical forest fragments and restoring regions of advanced fragmentation.

On the Management of Large-Diameter Trees in China’s Forests

Large-diameter trees have mainly been used for timber production in forestry practices. Recently, their critical roles played in biodiversity conservation and maintenance of ecosystem functions have been recognized. However, current forestry policy on the management of large-diameter trees is weak. As China is the biggest consumer of large-diameter timbers, how to maintain sustainable large-diameter timber resources as well as maximize ecological functions of the forests is a critical question to address. Here we summarize historical uses, distribution patterns, and management strategies of large-diameter trees in China. We found that large-diameter trees are mainly distributed in old-growth forests. Although China’s forest cover has increased rapidly in the past decades, large-diameter trees are rarely found in plantation forests and secondary forests. We suggest that knowledge of large-diameter trees should be widely disseminated in local forestry departments, especially their irreplaceable value in terms of biodiversity conservation and ecosystem functions. Protection of large-diameter trees, especially those in old-growth forests, is critical for sustainable forestry. To meet the increasing demand of large-diameter timbers, plantation forests and secondary forests should apply forest density management with thinning to cultivate more large-diameter trees.

Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China

Accurate acquisition of the spatiotemporal distribution of urban forests and fragmentation (e.g., interior and intact regions) is of great significance to contributing to the mitigation of climate change and the conservation of habitat biodiversity. However, the spatiotemporal pattern of urban forest cover changes related with the dynamics of interior and intact forests from the present to the future have rarely been characterized. We investigated fragmentation of urban forest cover using satellite observations and simulation models in the Nanjing Laoshan Region of Jiangbei New Area, Jiangsu, China, during 2002–2023. Object-oriented classification-based land cover maps were created to simulate land cover changes using the cellular automation-Markov chain (CA-Markov) model and the state transition simulation modeling. We then quantified the forest cover change by the morphological change detection algorithm and estimated the forest area density-based fragmentation patterns. Their relationships were built through the spatial analysis and statistical methods. Results showed that the overall accuracies of actual land cover maps were approximately 83.75–92.25% (2012–2017). The usefulness of a CA-Markov model for simulating land cover maps was demonstrated. The greatest proportion of forest with a low level of fragmentation was captured along with the decreasing percentage of fragmented area from 81.1% to 64.1% based on high spatial resolution data with the window size of 27 pixels × 27 pixels. The greatest increase in fragmentation (3% from 2016 to 2023) among the changes between intact and fragmented forest was reported. However, intact forest was modeled to have recovered in 2023 and restored to 2002 fragmentation levels. Moreover, we found 58.07 km2 and 0.35 km2 of interior and intact forests have been removed from forest area losses and added from forest area gains. The loss rate of forest interior and intact area exceeded the rate of total forest area loss. However, their approximate ratio (1) implying the loss of forest interior and intact area would have slight fragmentation effects on the remaining forests. This analysis illustrates the achievement of protecting and restoring forest interior; more importantly, excessive human activities in the surrounding area had been avoided. This study provides strategies for future forest conservation and management in large urban regions.

Determining a Threshold to Delimit the Amazonian Forests from the Tree Canopy Cover 2000 GFC Data

Open global forest cover data can be a critical component for Reducing Emissions from Deforestation and Forest Degradation (REDD+) policies. In this work, we determine the best threshold, compatible with the official Brazilian dataset, for establishing a forest mask cover within the Amazon basin for the year 2000 using the Tree Canopy Cover 2000 GFC product. We compared forest cover maps produced using several thresholds (10%, 30%, 50%, 80%, 85%, 90%, and 95%) with a forest cover map for the same year from the Brazilian Amazon Deforestation Monitoring Project (PRODES) data, produced by the National Institute for Space Research (INPE). We also compared the forest cover classifications indicated by each of these maps to 2550 independently assessed Landsat pixels for the year 2000, providing an accuracy assessment for each of these map products. We found that thresholds of 80% and 85% best matched with the PRODES data. Consequently, we recommend using an 80% threshold for the Tree Canopy Cover 2000 data for assessing forest cover in the Amazon basin.

Greenhouse gas footprints of palm oil production in Indonesia over space and time

Palm oil, the most widely used vegetable oil, is one of the largest drivers of greenhouse gas (GHG) emissions from global land use and land cover change. Here, we provide fine-resolution (100 m × 100 m) estimates of GHG footprints of current (2015) and potential future scenarios (2030) of crude palm oil (CPO) production in Indonesia. The current estimated average GHG footprint excluding production on Java is 5.7 t CO₂ eq t^-1 CPO; ranging from 0.7 t CO₂ eq t^-1 CPO in Hulu Sungai Tengah, Kalimantan to 26.0 t CO₂ eq t^-1 CPO in Pontianak, Kalimantan, and these vast differences are only discernible at fine spatial scales. The future GHG footprint of Indonesian CPO could be reduced by 42% without compromising increased output by limiting expansion to non-forest and non-peat land. Our fine-scale analysis provides a spatial screening approach to inform new oil palm concessions and sourcing decisions, before more cost-intensive patch analysis and carbon stock assessments are conducted.