Determination of tropical deforestation rates and related carbon losses from 1990 to 2010

Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar. We found that lost forests are special cases, and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography. Thus, using biomass mapping, we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions. Here, using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests, we found that deforestation in the 2000s in Latin America, one of the severely deforested regions, mainly occurred in forests with a significantly lower carbon stock density than typical mature forests. Deforestation areas with carbon stock densities lower than 20.0, 50.0, and 100.0 Mg C/ha accounted for 42.1%, 62.0%, and 83.3% of the entire deforested area, respectively. The average carbon stock density of lost forests was only 49.13 Mg C/ha, which challenges the current knowledge on the carbon stock density of lost forests (with a default value 100 Mg C/ha according to the Intergovernmental Panel on Climate Change Tier 1 estimates, or approximately 112 Mg C/ha used in other studies). This is demonstrated over both the entire region and the footprints of the spaceborne lidar. Consequently, our estimate of carbon loss from deforestation in Latin America in the 2000s was 253.0 ± 21.5 Tg C/year, which was considerably less than existing remote-sensing-based estimates, namely 400-600 Tg C/year. This indicates that forests in Latin America were most likely not a net carbon source in the 2000s compared to established carbon sinks. In previous studies, considerable effort has been devoted to rectify the underestimation of carbon sinks; thus, the overestimation of carbon emissions should be given sufficient consideration in global carbon budgets. Our results also provide solid evidence for the necessity of renewing knowledge on the role of tropical forests in the global carbon budget in the future using observations from new space missions.

Contrasting carbon cycle along tropical forest aridity gradients in West Africa and Amazonia

Tropical forests cover large areas of equatorial Africa and play a substantial role in the global carbon cycle. However, there has been a lack of biometric measurements to understand the forests' gross and net primary productivity (GPP, NPP) and their allocation. Here we present a detailed field assessment of the carbon budget of multiple forest sites in Africa, by monitoring 14 one-hectare plots along an aridity gradient in Ghana, West Africa. When compared with an equivalent aridity gradient in Amazonia, the studied West African forests generally had higher productivity and lower carbon use efficiency (CUE). The West African aridity gradient consistently shows the highest NPP, CUE, GPP, and autotrophic respiration at a medium-aridity site, Bobiri. Notably, NPP and GPP of the site are the highest yet reported anywhere for intact forests. Widely used data products substantially underestimate productivity when compared to biometric measurements in Amazonia and Africa. Our analysis suggests that the high productivity of the African forests is linked to their large GPP allocation to canopy and semi-deciduous characteristics.

A biomass map of the Brazilian Amazon from multisource remote sensing

The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha-1, a mean of 174 Mg ha-1, and a standard deviation of 102 Mg ha-1. This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions.

Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D'Ivoire

Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to take valid and coordinated countermeasures to combat climate change. This article discusses how artificial intelligence in agriculture can help to reduce costs, especially in developing countries such as Côte d'Ivoire, employing only low-cost or open-source tools, from hardware to software and open data. We developed machine learning models for two tasks: the first is improving agricultural farming cultivation, and the second is water management. For the first task, we used deep neural networks (YOLOv5m) to detect healthy plants and pods of cocoa and damaged ones only using mobile phone images. The results confirm it is possible to distinguish well the healthy from damaged ones. For actions at a larger scale, the second task proposes the analysis of remote sensors, coming from the GRACE NASA Mission and ERA5, produced by the Copernicus climate change service. A new deep neural network architecture (CIWA-net) is proposed with a U-Net-like architecture, aiming to forecast the total water storage anomalies. The model quality is compared to a vanilla convolutional neural network.

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.

Land use-land cover change in the Tano basin, Ghana and the implications on sustainable development goals

The changes in land use and land cover (LULC) contribute to carbon emissions that lead to climate change and global warming. Obtaining information on LULC change is of necessity to ensure land transformation planning, and anthropogenic and natural impact evaluation. The objective of this study is to analyze the historical changes in LULC in the Tano River Basin of Ghana, to provide scientific information for decision-making on achieving sustainable development. A supervised classification of Landsat images (1986, 2010, and 2020) using the Random Forest algorithm was performed, as well as a post-classification comparison of LULC maps in terms of area and size. A "from-to" matrix was used to identify the changes in LULC that occurred between the periods 1986-2010, 2010-2020, and 1986-2020. The results show an overall classification accuracy of 88.9, 88.5, and 88% for LULC maps for the years 1986, 2010, and 2020, respectively. Between the period 1986 and 2020, there is a conversion of the greater part of dense forest to open forest, and subsequently to settlement and cropland as a major historical LULC change pattern in the Tano basin. Cropland and Settlement increased at the rate of 24.8 km/yr and 1.5 km/yr respectively, while Dense forest and Open forest decreased at the rate of 298.4 km/yr and 173.9 km/yr over the period 1986 to 2020. The study outputs are not only useful for formulating and implementing national policies and programs but also, can contribute to assessing and monitoring progress towards attaining Sustainable Development Goal 13 (climate action).

Community evaluation of forest and REDD+ governance quality in the Democratic Republic of the Congo

The Democratic Republic of Congo (DRC) has over 100 million Ha of forest and has significant potential to benefit from these forests, including through REDD+ if they are managed effectively. Effective governance of forest landscapes is essential for environmental management and equitable harnessing of ecosystem service benefits for communities. Poor governance, political instability, and capacity limitations in the DRC are widely highlighted. However, there have been few, if any, attempts to evaluate forest governance in the DRC, especially at the community level. This paper reports a community-level evaluation of forest governance in the DRC, using a survey method. The results suggest that REDD+ projects have the ability to improve forest governance as perceived by the community. The research shows that building the right capacity, consulting and accessing the needs of the community and building long-term projects and partnerships a key success factors. These findings and the novel approach to supporting communities to evaluate their governance are applicable to similar community-level forest governance contexts.

Tropical forests post-logging are a persistent net carbon source to the atmosphere

Logged and structurally degraded tropical forests are fast becoming one of the most prevalent land-use types throughout the tropics and are routinely assumed to be a net carbon sink because they experience rapid rates of tree regrowth. Yet this assumption is based on forest biomass inventories that record carbon stock recovery but fail to account for the simultaneous losses of carbon from soil and necromass. Here, we used forest plots and an eddy covariance tower to quantify and partition net ecosystem CO₂ exchange in Malaysian Borneo, a region that is a hot spot for deforestation and forest degradation. Our data represent the complete carbon budget for tropical forests measured throughout a logging event and subsequent recovery and found that they constitute a substantial and persistent net carbon source. Consistent with existing literature, our study showed a significantly greater woody biomass gain across moderately and heavily logged forests compared with unlogged forests, but this was counteracted by much larger carbon losses from soil organic matter and deadwood in logged forests. We estimate an average carbon source of 1.75 ± 0.94 Mg C ha^-1 yr^-1 within moderately logged plots and 5.23 ± 1.23 Mg C ha^-1 yr^-1 in unsustainably logged and severely degraded plots, with emissions continuing at these rates for at least one-decade post-logging. Our data directly contradict the default assumption that recovering logged and degraded tropical forests are net carbon sinks, implying the amount of carbon being sequestered across the world's tropical forests may be considerably lower than currently estimated.

Quasi-experimental analysis of new mining developments as a driver of deforestation in Zambia

Mining is a vital part of the global, and many national, economies. Mining also has the potential to drive extensive land cover change, including deforestation, with impacts reaching far from the mine itself. Understanding the amount of deforestation associated with mining is important for conservationists, governments, mining companies, and consumers, yet accurate quantification is rare. We applied statistical matching, a quasi-experimental methodology, along with Bayesian hierarchical generalized linear models to assess the impact on deforestation of new mining developments in Zambia from 2000 to present. Zambia is a globally significant producer of minerals and mining contributes ~ 10% of its gross domestic product and ~ 77% of its exports. Despite extensive deforestation in mining impacted land, we found no evidence that any of the 22 mines we analysed increased deforestation compared with matched control sites. The extent forest lost was therefore no different than would likely have happened without the mines being present due to other drivers of deforestation in Zambia. This suggests previous assessments based on correlative methodologies may overestimate the deforestation impact of mining. However, mining can have a range of impacts on society, biodiversity, and the local environment that are not captured by our analysis.

Droughts and deforestation: Does seasonality matter?

Extreme weather events, particularly droughts, have strong impacts on the livelihoods of populations in rural areas. In a context of low access to insurance and credit markets, households respond to such shocks by implementing different risk-management strategies, which in turn are likely to have an impact on the environment, in particular through land-use changes and deforestation. This paper contributes to the emerging literature on the links between droughts and deforestation: (1) distinguishing responses to previously experienced droughts versus current droughts, and (2) disentangling the time of the agricultural season at which droughts occur. We show that deforestation declines whenever a drought occurs during the growing season, while it increases whenever a drought occurs during the harvesting season. These impacts are mitigated within protected areas and are exacerbated in more accessible locations, i.e., areas within 4 hours of travel time of main/major cities. By contrast, deforestation outcomes following droughts that occur during the planting season depend on whether the crop considered is maize or cassava.

Comparing Community Needs and REDD+ Activities for Capacity Building and Forest Protection in the Équateur Province of the Democratic Republic of Congo

Primary forests are essential ecosystems that can play a key role in mitigating climate change. REDD+ is designed to help countries and communities secure benefits for avoiding deforestation but has faced significant implementation challenges. There are substantial potential benefits for REDD+ in the Democratic Republic of Congo (DRC), where shifting agriculture is the major cause of deforestation. However, implementation requires significant capacity building in a number of sectors and at a number of levels. This paper explores how well the capacity building activities within the DRC REDD+ strategy are aligned with the capacity needs identified by provincial government stakeholders and local communities in the Équateur province of the DRC, identified through workshops and surveys. The research suggests that while many technical capacity needs identified by stakeholders could be potentially addressed by the REDD+ strategy, there are number of systemic capacity needs that are unlikely to be addressed. Failure to address these needs risks undermining any implementation of REDD+. The results suggest that education and training in governance and management, as well as fundamental education in sustainability, are key capacity needs that REDD+ may need to incorporate. The results also provide further evidence that REDD+ projects need to be long-term and take into account the local context and needs in order to be effective.

Long-term monitoring reveals widespread and severe declines of understory birds in a protected Neotropical forest

Long-term studies on the population dynamics of tropical resident birds are few, and it remains poorly understood how their populations have fared in recent decades. Here, we analyzed a 44-y population study of a Neotropical understory bird assemblage from a protected forest reserve in central Panama to determine if and how populations have changed from 1977 to 2020. Using the number of birds captured in mist nets as an index of local abundance, we estimated trends over time for a diverse suite of 57 resident species that comprised a broad range of ecological and behavioral traits. Estimated abundances of 40 (∼70%) species declined over the sampling period, whereas only 2 increased. Furthermore, declines were severe: 35 of the 40 declining species exhibited large proportional losses in estimated abundance, amounting to ≥50% of their initial estimated abundances. Declines were largely independent of ecology (i.e., body mass, foraging guild, or initial abundance) or phylogenetic affiliation. These widespread, severe declines are particularly alarming, given that they occurred in a relatively large (∼22,000-ha) forested area in the absence of local fragmentation or recent land-use change. Our findings provide robust evidence of tropical bird declines in intact forests and bolster a large body of literature from temperate regions suggesting that bird populations may be declining at a global scale. Identifying the ecological mechanisms underlying these declines should be an urgent conservation priority.

Are Global Environmental Uncertainties Inevitable? Measuring Desertification for the SDGs

Continuing uncertainty about the present magnitudes of global environmental change phenomena limits scientific understanding of human impacts on Planet Earth, and the quality of scientific advice to policy makers on how to tackle these phenomena. Yet why global environmental uncertainties are so great, why they persist, how their magnitudes differ from one phenomenon to another, and whether they can be reduced is poorly understood. To address these questions, a new tool, the Uncertainty Assessment Framework (UAF), is proposed that builds on previous research by dividing sources of environmental uncertainty into categories linked to features inherent in phenomena, and insufficient capacity to conceptualize and measure phenomena. Applying the UAF shows that, based on its scale, complexity, areal variability and turnover time, desertification is one of the most inherently uncertain global environmental change phenomena. Present uncertainty about desertification is also very high and persistent: the Uncertainty Score of a time series of five estimates of the global extent of desertification shows limited change and has a mean of 6.8, on a scale from 0 to 8, based on the presence of four conceptualization uncertainties (terminological difficulties, underspecification, understructuralization and using proxies) and four measurement uncertainties (random errors, systemic errors, scalar deficiencies and using subjective judgment). This suggests that realization of the Land Degradation Neutrality (LDN) Target 15.3 of the UN Sustainable Development Goal (SDG) 15 (“Life on Land”) will be difficult to monitor in dry areas. None of the estimates in the time series has an Uncertainty Score of 2 when, according to the UAF, evaluation by statistical methods alone would be appropriate. This supports claims that statistical methods have limitations for evaluating very uncertain phenomena. Global environmental uncertainties could be reduced by devising better rules for constructing global environmental information which integrate conceptualization and measurement. A set of seven rules derived from the UAF is applied here to show how to measure desertification, demonstrating that uncertainty about it is not inevitable. Recent review articles have advocated using ‘big data’ to fill national data gaps in monitoring LDN and other SDG 15 targets, but an evaluation of a sample of three exemplar studies using the UAF still gives a mean Uncertainty Score of 4.7, so this approach will not be straightforward.

Southeast Asian protected areas are effective in conserving forest cover and forest carbon stocks compared to unprotected areas

Protected areas aim to conserve nature, ecosystem services, and cultural values; however, they have variable success in doing so under high development pressure. Southeast Asian protected areas faced the highest level of human pressure at the turn of the twenty-first century. To estimate their effectiveness in conserving forest cover and forest carbon stocks for 2000–2018, we used statistical matching methods to control for the non-random location of protected areas, to compare protection against a matched counterfactual. We found Southeast Asian protected areas had three times less forest cover loss than similar landscapes without protection. Protected areas that had completed management reporting using the Management Effectiveness Tracking Tool (METT) conserved significantly more forest cover and forest carbon stocks than those that had not. Management scores were positively associated with the level of carbon emissions avoided, but not the level of forest cover loss avoided. Our study is the first to find that METT scores could predict the level of carbon emissions avoided in protected areas. Given that only 11% of protected areas in Southeast Asia had completed METT surveys, our results illustrate the need to scale-up protected area management effectiveness reporting programs to improve their effectiveness for conserving forests, and for storing and sequestering carbon.

Global sensitivities of forest carbon changes to environmental conditions

The responses of forest carbon dynamics to fluctuations in environmental conditions at a global scale remain elusive. Despite the understanding that favourable environmental conditions promote forest growth, these responses have been challenging to observe across different ecosystems and climate gradients. Based on a global annual time series of aboveground biomass (AGB) estimated from radar satellites between 1992 and 2018, we present forest carbon changes and provide insights on their sensitivities to environmental conditions across scales. Our findings indicate differences in forest carbon changes across AGB classes, with regions with carbon stocks of 50-125 MgC ha^-1 depict the highest forest carbon gains and losses, while regions with 125-150 MgC ha^-1  have the lowest forest carbon gains and losses in absolute terms. Net forest carbon change estimates show that the arc-of-deforestation and the Congo Basin were the main hotspots of forest carbon loss, while a substantial part of European forest gained carbon during the last three decades. Furthermore, we observe that changes in forest carbon stocks were systematically positively correlated with changes in forest cover fraction. At the same time, it was not necessarily the case with other environmental variables, such as air temperature and water availability at the bivariate level. We also used a model attribution method to demonstrate that atmospheric conditions were the dominant control of forest carbon changes (56% of the total study area) followed by water-related (29% of the total study area) and vegetation (15% of the total study area) conditions. Regionally, we find evidence that carbon gains from long-term forest growth covary with long-term carbon sinks inferred from atmospheric inversions. Our results describe the contributions from the atmosphere, water-related and vegetation conditions to forest carbon changes and provide new insights into the underlying mechanisms of the coupling between forest growth and the global carbon cycle.

Patterns of forest cover loss in the terrestrial Key Biodiversity Areas in the Philippines: critical habitat conservation priorities

The Philippines, home to over 20,000 endemic species of plants and animals, is facing a biodiversity crisis due to the constant decrease of forest cover. The Key Biodiversity Area (KBA) approach was developed to conserve species threatened with extinction using a site-based conservation strategy to select globally important sites using threshold-based criteria for species irreplaceability and vulnerability. This study investigates the applicability of remotely sensed data through geospatial analysis to quantify forest cover loss of the 101 terrestrial KBAs in the country between 2001 and 2019. Results showed that the study sites had 4.5 million hectares (ha) of forest in the year 2000. However, these sites have lost about 270,000 ha of forest in nearly two decades, marking a steady decline with an annual deforestation rate of 14,213 ha per year in these terrestrial KBAs. The majority of the study sites (58) had a high percentage of forest loss (>3.13%), and these should be prioritized for conservation. By the year 2030, it is forecast that a total of 331 thousand ha of forest will be lost unless there is a transformational change in the country’s approach to dealing with deforestation. The results of this study provide relevant data and information in forest habitat in near real-time monitoring to assess the impact and effectiveness of forest governance and approaches within these critical habitats.

Routes of Soil Uses and Conversions with the Main Crops in Brazilian Cerrado: A Scenario from 2000 to 2020

The Brazilian Savannah, also known as Cerrado Biome, is a hotspot for Brazilian biodiversity. The hypothesis tested in this study is that there are diverse routes of soil uses for agriculture production in Cerrado, derived mainly from areas with pasture (natural and planted) due to the decrease in Cerrado deforestation in the last 20 years (from 2000 to 2020). The aim of this study was (i) to determine the profile of crop production in Brazilian Cerrado; (ii) to demonstrate the routes of soil uses during the last 20 years; (iii) to demonstrate the increase of soybean and corn production in Cerrado. The design of the study was based on data of (i) the accumulation of biomass and carbon in Cerrado; (ii) production area and yield of corn, soybean, coffee, sugarcane, cotton, and pasture (natural and planted); (iii) Cerrado deforestation. Results showed that the vegetation of Cerrado promotes a higher accumulation of biomass and carbon on the subsurface, followed by accumulation in the surface, deadwood, and litter. In the last 20 years, there has been a reduction of 75% in deforestation and an increase of 66% in crop areas and 78% in crop yield. However, there was no clear reduction in deforestation specifically in the Matopiba region. In Minas Gerais/MG, Goiás/GO, and Mato Grosso/MT, there were higher productions of coffee/MG, soybean/MT, corn/MT, sugarcane/MG-GO, and cotton/MT. Planted pasture (and not natural pasture) covered the larger areas, representing 75% of the total area with pasture. The low routes of soil uses from deforestation to (i) planted pasture and (ii) crop production explained the decrease in deforestation. The increases in yield and crop areas are explained by the routes from pasture (planted and natural) to agriculture. Our results provided clear insights that programs of Cerrado preservation should continue the decrease of deforestation with the sustainable development in agriculture, mainly in the Matopiba region where there was no clear decrease in deforestation in the last 20 years.

Quantification of blue carbon in seagrass ecosystems of Southeast Asia and their potential for climate change mitigation

Seagrasses have the ability to contribute towards climate change mitigation, through large organic carbon (C_org) sinks within their ecosystems. Although the importance of blue carbon within these ecosystems has been addressed in some countries of Southeast Asia, the regional and national inventories with the application of nature-based solutions are lacking. In this study, we aim to estimate national coastal blue carbon stocks in the seagrass ecosystems in the countries of Southeast Asia including the Andaman and Nicobar Islands of India. This study further assesses the potential of conservation and restoration practices and highlights the seagrass meadows as nature-based solution for climate change mitigation. The average value of the total carbon storage within seagrass meadows of this region is 121.95 ± 76.11 Mg ha^-1 (average ± SD) and the total C_org stock of the seagrass meadows of this region was 429.11 ± 111.88 Tg, with the highest C_org stock in the Philippines (78%). The seagrass meadows of this region have the capacity to accumulate 5.85-6.80 Tg C year^-1, which accounts for $214.6-249.4 million USD. Under the current rate of decline of 2.82%, the seagrass meadows are emitting 1.65-2.08 Tg of CO₂ year^-1 and the economic value of these losses accounts for $21.42-24.96 million USD. The potential of the seagrass meadows to the offset current CO₂ emissions varies across the region, with the highest contribution to offset is in the seagrass meadows of the Philippines (11.71%). Current national policies and commitments of nationally determined contributions do not include blue carbon ecosystems as climate mitigation measures, even though these ecosystems can contribute up to 7.03% of the countries' reduction goal of CO₂ emissions by 2030. The results of this study highlight and promote the potential of the southeast Asian seagrass meadows to national and international agencies as a practical scheme for nature-based solutions for climate change mitigation.

Emerging reporting and verification needs under the Paris Agreement: How can the research community effectively contribute?

Greenhouse gas (GHG) emission inventories represent the link between national and international political actions on climate change, and climate and environmental sciences. Inventory agencies need to include, in national GHG inventories, emission and removal estimates based on scientific data following specific reporting guidance under the United Nation Framework Convention on Climate Change (UNFCCC) and the Paris Agreement, using the methodologies defined in the Intergovernmental Panel on Climate Change (IPCC) Guidelines. Often however, research communities and inventory agencies have approached the problem of climate change from different angles and by using terminologies, metrics, rules and approaches that do not always match. This is particularly true dealing with "Land Use, Land-Use Change and Forestry" (LULUCF), the most challenging among the inventory sectors to deal with, mainly because of high level of complexity of its carbon dynamics and the difficulties in disaggregating the fluxes between those caused by natural and anthropogenic processes. In this paper, we facilitate the understanding by research communities of the current (UNFCCC) and future (under the Paris Agreement) reporting requirements, and we identify the main issues and topics that should be considered when targeting improvement of the GHG inventory. In relation to these topics, we describe where and how the research community can contribute to producing useful inputs, data, methods and solutions for inventory agencies and policy makers, on the basis of available literature. However, a greater effort by both communities is desirable for closer cooperation and collaboration, for data sharing and the understanding of respective and common aims.

Height–diameter allometry in African monodominant forest close to mixed forest

African monodominant forests are frequently formed by Gilbertiodendron dewevrei (De Wild.) J. Leonard and commonly found close to mixed forests. However, previous studies have ignored differences between these two forest types in height–diameter allometry, which is extremely important for aboveground biomass (AGB) estimates. This study aims to evaluate the performance of height–diameter models and their effects on height attributes and AGB estimations in African monodominant and mixed forests. Four 1-ha plots divided in 16 subplots (0.25 ha) were installed in each forest type in northern Republic of Congo. We measured diameter of all trees ≥ 10 cm diameter for each subplot and we measured the height of 264 trees over a large range of 7–64 m in two forest types. There was a significant difference in height–diameter allometry between two forest types and trees were taller and had greater AGB in monodominant forests than in mixed forests. Two height–diameter models from the literature generated the lowest error values when predicting tree height and AGB in mixed forests, whereas no model derived from the literature was appropriate for monodominant forests. The variation in height–diameter allometry between monodominant and mixed forests influences AGB estimates that have practical implications for carbon monitoring.

Changes in global terrestrial live biomass over the 21st century

Live woody vegetation is the largest reservoir of biomass carbon, with its restoration considered one of the most effective natural climate solutions. However, terrestrial carbon fluxes remain the largest uncertainty in the global carbon cycle. Here, we develop spatially explicit estimates of carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show that live biomass has removed 4.9 to 5.5 PgC year^-1 from the atmosphere, offsetting 4.6 ± 0.1 PgC year^-1 of gross emissions from disturbances and adding substantially (0.23 to 0.88 PgC year^-1) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink.

The role of fire in global forest loss dynamics

Fires, among other forms of natural and anthropogenic disturbance, play a central role in regulating the location, composition and biomass of forests. Understanding the role of fire in global forest loss is crucial in constraining land-use change emissions and the global carbon cycle. We analysed the relationship between forest loss and fire at 500 m resolution based on satellite-derived data for the 2003-2018 period. Satellite fire data included burned area and active fire detections, to best account for large and small fires, respectively. We found that, on average, 38 ± 9% (± range) of global forest loss was associated with fire, and this fraction remained relatively stable throughout the study period. However, the fraction of fire-related forest loss varied substantially on a regional basis, and showed statistically significant trends in key tropical forest areas. Decreases in the fraction of fire-related forest loss were found where deforestation peaked early in our study period, including the Amazon and Indonesia while increases were found for tropical forests in Africa. The inclusion of active fire detections accounted for 41%, on average, of the total fire-related forest loss, with larger contributions in small clearings in interior tropical forests and human-dominated landscapes. Comparison to higher-resolution fire data with resolutions of 375 and 20 m indicated that commission errors due to coarse resolution fire data largely balanced out omission errors due to missed small fire detections for regional to continental-scale estimates of fire-related forest loss. Besides an improved understanding of forest dynamics, these findings may help to refine and separate fire-related and non-fire-related land-use change emissions in forested ecosystems.

Forests, Farms, and Fallows: The Dynamics of Tree Cover Transition in the Southern Part of the Uluguru Mountains, Tanzania

Forests and woodlands remain under threat in tropical Africa due to excessive exploitation and inadequate management interventions, and the isolated success stories of tree retention and tree cover transition on African agricultural land are less well documented. In this study, we characterize the status of tree cover in a landscape that contains forest patches, fallows, and farms in the southern part of Uluguru Mountains. We aimed to unveil the practices of traditional tree fallow system which is socially acceptable in local settings and how it provides a buffering effects to minimize forest disturbances and thus represents an important step towards tree cover transition. We assessed land cover dynamics for the period of 1995 to 2020 and compared tree stocking for forest patches, fallows, and farms. We found that tree biomass carbon stocks were 56 ± 5 t/ha in forest patches, 33 ± 7 t/ha in fallows, and 9 ± 2 t/ha on farms. In terms of land cover, farms shrank at intensifying rates over time for the entire assessment period of 1995–2020. Forest cover decreased from 1995–2014, with the reduction rate slowing from 2007–2014 and the trend reversing from 2014–2020, such that forest cover showed a net increase across the entire study period. Fallow consistently and progressively increased from 1995–2020. We conclude that traditional tree fallows in the study site remain a significant element of land management practice among communities, and there appears to be a trend towards intensified tree-based farming. The gains in fallowed land represent an embracing of a traditional land management system that supports rotational and alternate uses of cropping space as well as providing a buffering effect to limit over-exploitation of forests. In order to maximize tree cover and carbon stocks in the farm landscape, this well-known traditional tree fallow system can be further optimized through the incorporation of additional innovations.

Carbon loss and removal due to forest disturbance and regeneration in the Amazon

The forest carbon flux is the difference between the total carbon loss from deforestation, forest degradation, and natural disturbance and removal of atmospheric CO₂ due to photosynthetic activity. The Amazon rainforest accounts for approximately a quarter of global emissions from land use change, due in part to its' immense size, carbon storage, and recent history of land use change. Large area estimates of carbon exchange in forests are highly uncertain, however, which reflects the pervasive challenges in estimating carbon flux parameters, such as disturbance area and forest carbon pools. In this study, we use a new dataset with characterized uncertainty on deforestation, degradation, and natural disturbances in the Amazon Ecoregion to estimate carbon loss from disturbance and removals from regeneration at biennial intervals from 1996 to 2017. Using the gain-loss approach to estimating carbon flux in a Monte Carlo analysis we found that carbon loss from degradation and deforestation averaged 0.23 (±0.09) Pg C biennium^-1 and 0.34 (±0.16) Pg C biennium^-1, respectively. While deforestation contributed the most to carbon loss overall, there were two biennial periods in which degradation and natural disturbance resulted in more carbon loss. Regeneration partially offset these emissions, but our results show that loss is occurring much more rapidly than removal, resulting in a total net carbon loss of 4.86 to 5.32 Pg C over the study period. With the compounding effect of drought and fires in addition to continued deforestation it appears certain that forest disturbance in the Amazon will continue to be a significant factor in the terrestrial carbon cycle.

Assessing Accuracy of Land Cover Change Maps Derived from Automated Digital Processing and Visual Interpretation in Tropical Forests in Indonesia

This study assessed the accuracy of land cover change (2000–2018) maps compiled from Landsat images with either automated digital processing or with visual interpretation for a tropical forest area in Indonesia. The accuracy assessment used a two-stage stratified random sampling involving a confusion matrix for assessing map accuracy and by estimating areas of land cover change classes and associated uncertainty. The reference data were high-resolution images from SPOT 6/7 and high-resolution images finer than 5 m obtained from Open Foris Collect Earth. Results showed that the map derived from automated digital processing had lower accuracy (overall accuracy 73–77%) compared to the map based on visual interpretation (overall accuracy 80–84%). The automated digital processing map error was in differentiating between native forest and plantation areas. While the visual interpretation map had a higher accuracy, it did not consistently differentiate between native forest and shrub areas. Future improvement of the digital map requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.

Changes in diversity and community assembly of jumping spiders (Araneae: Salticidae) after rainforest conversion to rubber and oil palm plantations

Rainforest conversion into monoculture plantations results in species loss and community shifts across animal taxa. The effect of such conversion on the role of ecophysiological properties influencing communities, and conversion effects on phylogenetic diversity and community assembly mechanisms, however, are rarely studied in the same context. Here, we compare salticid spider (Araneae: Salticidae) communities between canopies of lowland rainforest, rubber agroforest (“jungle rubber”) and monoculture plantations of rubber or oil palm, sampled in a replicated plot design in Jambi Province, Sumatra, Indonesia. Overall, we collected 912 salticid spider individuals and sorted them to 70 morphospecies from 21 genera. Salticid richness was highest in jungle rubber, followed by rainforest, oil palm and rubber, but abundance of salticids did not differ between land-use systems. Community composition was similar in jungle rubber and rainforest but different from oil palm and rubber, which in turn were different from each other. The four investigated land-use systems differed in aboveground plant biomass, canopy openness and land use intensity, which explained 12% of the observed variation in canopy salticid communities. Phylogenetic diversity based on ~850 bp 28S rDNA fragments showed similar patterns as richness, that is, highest in jungle rubber, intermediate in rainforest, and lowest in the two monoculture plantations. Additionally, we found evidence for phylogenetic clustering of salticids in oil palm, suggesting that habitat filtering is an important factor shaping salticid spider communities in monoculture plantations. Overall, our study offers a comprehensive insight into the mechanisms shaping communities of arthropod top predators in canopies of tropical forest ecosystems and plantations, combining community ecology, environmental variables and phylogenetics across a land-use gradient in tropical Asia.

Statistical physics approaches to the complex Earth system

Global warming, extreme climate events, earthquakes and their accompanying socioeconomic disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, multiple interactions and complex structures of the Earth system, the understanding and, in particular, the prediction of such disruptive events represent formidable challenges to both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge of the Earth system, including climate extreme events, earthquakes and geological relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as critical phenomena, network theory, percolation, tipping points analysis, and entropy can be applied to complex Earth systems. Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics concepts and theories can be useful in the field of Earth system science.

Impact of Ex-Closure in above and below Ground Carbon Stock Biomass

Globally, there is a serious issue in carbon stock due to high deforestation and the loss of land, limited carbon storage pools in aboveground and underground forests in different regions, and increased carbon emissions to the atmosphere. This review paper highlights the impact of exclosures on above and below ground carbon stocks in biomass as a solution to globally curb carbon emissions. The data has been analyzed dependent on the Intergovernmental Panel on Climate Change (IPCC) guidelines, the Food and Agriculture Organization (FAO) Forest Resource Assessment report (FRA, 2020), and scientific journal publications mostly from the last decade, to show the research results of carbon stock and the impact of exclosures, particularly the challenges of deforestation and erosion of land and opportunities of area exclosures to provide a general outlook for policymakers. Overall, the world’s forest regions are declining, and although the forest loss rate has slowed, it has still not stopped sufficiently because the knowledge and practice of exclosures are limited. The global forest loss and carbon stock have decreased from 7.8 million ha/yr to 4.7 million ha/yr and from 668 gigatons to 662 gigatons respectively due to multiple factors that differ across the regions. However, a move toward natural rehabilitation and exclosures to reduce the emissions of Greenhouse Gas (GHGs) is needed. In the global production of carbon, the exclosure of forests plays an important role, in particular for permanent sinks of carbon.

Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales

Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.

Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.

Policies Drive Sub-National Forest Transitions in Vietnam

Vietnam has seemingly been able to shortcut the forest transition (FT) by quickly moving to the reforestation phase. Provincial-level forest cover and socio-economic trends are, however, not necessarily compatible with a standard FT framework. This article compares forest cover change and associated policy reforms in two provinces. Bac Kan is one of the poorest provinces in Vietnam, and has, after years of deforestation and forest degradation, expanded its forest cover during the past two decades. In contrast, Lam Dong province has higher GDP and population density, but has had high deforestation linked to expansion of perennial crops. This is contrary to what could be expected from a conventional FT hypothesis. Land use dynamics in Vietnam is heavily driven by its historical heritage related to the independence from French rule and heavy state-control and collectivization, and its more recent shift to “market-led socialism” (doi moi), involving export promotion, decentralization and land tenure reforms. The Vietnam experience shows that policies can trump the typical FT patterns linked to general development trends and structural changes, and that the typical FT-trajectory is not unavoidable. Yet, these policies have not primarily been guided by forest concerns, but should be viewed as a side effect of the doi moi policies pursuing economic growth and of the devolution of rights and decision-making.

Spatial Agreement among Vegetation Disturbance Maps in Tropical Domains Using Landsat Time Series

Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, and detecting disturbances, many methods have been well-studied and successfully implemented. However, factors such as the vegetation type, input data, and change detection method can significantly alter the outcomes of a disturbance-detection study. We evaluated the spatial agreement of disturbance maps provided by the Breaks For Additive Season and Trend (BFAST) algorithm, evaluating seven spectral indices in three distinct vegetation domains in Brazil: Atlantic forest, savanna, and semi-arid woodland, by assessing levels of agreement between the outputs. We computed individual map accuracies based on a reference dataset, then ranked their performance, while also observing their relationships with specific vegetation domains. Our results indicated a low rate of spatial agreement among index-based disturbance maps, which itself was minimally influenced by vegetation domain. Wetness indices produced greater detection accuracies in comparison to greenness-related indices free of saturation. The normalized difference moisture index performed best in the Atlantic forest domains, yet performed poorest in semi-arid woodland, reflecting its specific sensitivity to vegetation and its water content. The normalized difference vegetation index led to high disturbance detection accuracies in the savanna and semi-arid woodland domains. This study offered novel insight into vegetation disturbance maps, their relationship to different ecosystem types, and corresponding accuracies. Distinct input data can produce non-spatially correlated disturbance maps and reflect site-specific sensitivity. Future research should explore algorithm limitations presented in this study, as well as the expansion to other techniques and vegetation domains across the globe.

Hkakabo Razi landscape as one of the last exemplar of large contiguous forests

Deforestation and forest degradation around the world endanger the functioning of ecosystems, climate stability, and conservation of biodiversity. We assessed the spatial and temporal dynamics of forest cover in Myanmar’s Hkakabo Razi Landscape (HRL) to determine its integrity based on forest change and fragmentation patterns from 1989 to 2016. Over 80% of the HRL was covered by natural areas, from which forest was the most prevalent (around 60%). Between 1989 and 2016, forest cover declined at an annual rate of 0.225%. Forest degradation occurred mainly around the larger plains of Putao and Naung Mung, areas with relatively high human activity. Although the rate of forest interior loss was approximately 2 to 3 times larger than the rate of total forest loss, forest interior was prevalent with little fragmentation. Physical and environmental variables were the main predictors of either remaining in the current land-cover class or transitioning to another class, although remaining in the current land cover was more likely than land conversion. The forests of the HRL have experienced low human impact and still constitute large tracts of contiguous forest interior. To ensure the protection of these large tracts of forest, sustainable forest policies and management should be implemented.

Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests

Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high-resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED-2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the eastern Amazon as initial conditions to ED-2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥66%) experienced water stress with declines in ET (up to 34%) and GPP (up to 35%) and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multiyear droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are driven by not only climate and deforestation but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.

An Assessment of Global Forest Change Datasets for National Forest Monitoring and Reporting

Global Forest Change datasets have the potential to assist countries with national forest measuring, reporting and verification (MRV) requirements. This paper assesses the accuracy of the Global Forest Change data against nationally derived forest change data by comparing the forest loss estimates from the global data with the equivalent data from Guyana for the period 2001–2017. To perform a meaningful comparison between these two datasets, the initial year 2000 forest state needs first to be matched to the definition of forest land cover appropriate to a local national setting. In Guyana, the default definition of 30% tree cover overestimates forest area is by 483,000 ha (18.15%). However, by using a tree canopy cover (i.e., density of tree canopy coverage metric) threshold of 94%, a close match between the Guyana-MRV non-forest area and the Global Forest Change dataset is achieved with a difference of only 24,210 ha (0.91%) between the two maps. A complimentary analysis using a two-stage stratified random sampling design showed the 94% tree canopy cover threshold gave a close correspondence (R2 = 0.98) with the Guyana-MRV data, while the Global Forest Change default setting of 30% tree canopy cover threshold gave a poorer fit (R2 = 0.91). Having aligned the definitions of forest for the Global Forest Change and the Guyana-MRV products for the year 2000, we show that over the period 2001–2017 the Global Forest Change data yielded a 99.34% overall Correspondence with the reference data and a 94.35% Producer’s Accuracy. The Guyana-MRV data yielded a 99.36% overall Correspondence with the reference data and a 95.94% Producer’s Accuracy. A year-by-year analysis of change from 2001–2017 shows that in some years, the Global Forest Change dataset underestimates change, and in other years, such as 2016 and 2017, change is detected that is not forest loss or gain, hence the apparent overestimation. The conclusion is that, when suitably calibrated for percentage tree cover, the Global Forest Change datasets give a good first approximation of forest loss (and, probably, gains). However, in countries with large areas of forest cover and low levels of deforestation, these data should not be relied upon to provide a precise annual loss/gain or rate of change estimate for audit purposes without using independent high-quality reference data.

A Hierarchical Clustering Method for Land Cover Change Detection and Identification

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis.

Assessing Typhoon-Induced Canopy Damage Using Vegetation Indices in the Fushan Experimental Forest, Taiwan

Cyclonic windstorms profoundly affect forest structure and function throughout the tropics and subtropics. Remote sensing techniques and vegetation indices (VIs) have improved our ability to characterize cyclone impacts over broad spatial scales. Although VIs are useful for understanding changes in forest cover, their consistency on detecting changes in vegetation cover is not well understood. A better understanding of the similarities and differences in commonly used VIs across disturbance events and forest types is needed to reconcile the results from different studies. Using Landsat imagery, we analyzed the change between pre- and post-typhoon VI values (ΔVIs) of four VIs for five typhoons (local name of cyclones in the North Pacific) that affected the Fushan Experimental Forest of Taiwan. We found that typhoons varied in their effect on forest canopy cover even when they had comparable trajectories, wind speeds, and rainfall. Most VIs measured a decrease in forest cover following typhoons, ranging from −1.18% to −19.87%; however, the direction of ΔVI–topography relationships varied among events. All typhoons significantly increased vegetation heterogeneity, and ΔVI was negatively related to pre-typhoon VI across all typhoons. Four of the five typhoons showed that more frequently affected sites had greater VI decreases. VIs ranged in their sensitivity to detect typhoon-induced changes in canopy coverage, and no single VI was most sensitive across all typhoons. Therefore, we recommend using VIs in combination—for example Normalized Difference Infrared Index (NDII) and Enhanced Vegetation Index (EVI), when comparing cyclone-disturbance-induced changes in vegetation cover among disturbances and across forests.

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.