Remote sensing enables the quantification of tropical deforestation with high spatial resolution. This in-depth mapping has led to substantial advances in the analysis of continent-wide fragmentation of tropical forests. Here we identified approximately 130 million forest fragments in three continents that show surprisingly similar power-law size and perimeter distributions as well as fractal dimensions. Power-law distributions have been observed in many natural phenomena such as wildfires, landslides and earthquakes. The principles of percolation theory provide one explanation for the observed patterns, and suggest that forest fragmentation is close to the critical point of percolation; simulation modelling also supports this hypothesis. The observed patterns emerge not only from random deforestation, which can be described by percolation theory, but also from a wide range of deforestation and forest-recovery regimes. Our models predict that additional forest loss will result in a large increase in the total number of forest fragments-at maximum by a factor of 33 over 50 years-as well as a decrease in their size, and that these consequences could be partly mitigated by reforestation and forest protection.
Aim Estimating the current spatial variation of biomass in the Amazon rain forest is a challenge and remains a source of substantial uncertainty in the assessment of the global carbon cycle. Precise estimates need to consider small‐scale variations of forest structures resulting from local disturbances, on the one hand, and require large‐scale information on the state of the forest that can be detected by remote sensing, on the other hand. In this study, we introduce a novel method that links a forest gap model and a canopy height map to derive the biomass distribution of the Amazon rain forest. Location Amazon rain forest. Methods An individual‐based forest model was applied to estimate the variation of aboveground biomass across the Amazon rain forest. The forest model simulated individual trees; hence, it allowed the direct comparison of simulated and observed canopy heights from remote sensing. The comparison enabled the detection of disturbed forest states and the derivation of a simulation‐based biomass map at 0.16 ha resolution. Results Simulated biomass values ranged from 20 to 490 t (dry mass)/ha across 7.8 Mio km2 of Amazon rain forest. We estimated a total aboveground biomass stock of 76 GtC, with a coefficient of variation of 45%. We found mean differences of only 15% when comparing biomass values of the map with 114 field inventories. The forest model enables the derivation of additional estimates, such as basal area and stem density. Main conclusions Linking a canopy height map with an individual‐based forest model captures the spatial variation of biomass in the Amazon rain forest at high resolution. The study demonstrates how this linkage allows for quantifying the spatial variation in forest structure caused by tree‐level to regional‐scale disturbances. It thus provides a basis for large‐scale analyses on the heterogeneous structure of tropical forests and their carbon cycle.
Precise descriptions of forest productivity, biomass, and structure are essential for understanding ecosystem responses to climatic and anthropogenic changes. However, relations between these components are complex, in particular for tropical forests.We developed an approach to simulate carbon dynamics in the Amazon rainforest including around 410 billion individual trees within 7.8 million km 2 . We integrated canopy height observations from space-borne LIDAR in order to quantify spatial variations in forest state and structure reflecting small-scale to large-scale natural and anthropogenic disturbances.Under current conditions, we identified the Amazon rainforest as a carbon sink, gaining 0.56 GtC per year. This carbon sink is driven by an estimated mean gross primary productivity (GPP) of 25.1 tC ha −1 a −1 , and a mean woody aboveground net primary productivity (wANPP) of 4.2 tC ha −1 a −1 . We found that successional states play an important role for the relations between productivity and biomass. Forests in early to intermediate successional states are the most productive, and woody above-ground carbon use efficiencies are non-linear. Simulated values can be compared to observed carbon fluxes at various spatial resolutions (>40 m). Notably, we found that our GPP corresponds to the values derived from MODIS. For NPP, spatial differences can be observed due to the consideration of forest successional states in our approach.We conclude that forest structure has a substantial impact on productivity and biomass. It is an essential factor that should be taken into account when estimating current carbon budgets or analyzing climate change scenarios for the Amazon rainforest.
Around 30 Mm 3 of sawlogs are extracted annually by selective logging of natural production forests in Amazonia, Earth's most extensive tropical forest. Decisions concerning the management of these production forests will be of major importance for Amazonian forests' fate. To date, no regional assessment of selective logging sustainability supports decision-making. Based on data from 3500 ha of forest inventory plots, our modelling results show that the average periodic harvests of 20 m 3 ha −1 will not recover by the end of a standard 30 year cutting cycle. Timber recovery within a cutting cycle is enhanced by commercial acceptance of more species and with the adoption of longer cutting cycles and lower logging intensities. Recovery rates are faster in Western Amazonia than on the Guiana Shield. Our simulations suggest that regardless of cutting cycle duration and logging intensities, selectively logged forests are unlikely to meet timber demands over the long term as timber stocks are predicted to steadily decline. There is thus an urgent need to develop an integrated forest resource management policy that combines active management of production forests with the restoration of degraded and secondary forests for timber production. Without better management, reduced timber harvests and continued timber production declines are unavoidable.
Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20–43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.
Background: Capturing the response of forest ecosystems to inter-annual climate variability is a great challenge.In this study, we tested the capability of an individual-based forest gap model to display carbon fluxes at yearly and daily time scales. The forest model was applied to a spruce forest to simulate the gross primary production (GPP), respiration and net ecosystem exchange (NEE). We analyzed how the variability in climate affected simulated carbon fluxes at the scale of the forest model.
Tropical forests represent an important pool in the global carbon cycle. Their biomass stocks and carbon fluxes are variable in space and time, which is a challenge for accurate measurements. Forest models are therefore used to investigate these complex forest dynamics. The challenge of considering the high species diversity of tropical forests is often addressed by grouping species into plant functional types (PFTs). We investigated how reduced numbers of PFTs affect the prediction of productivity (GPP, NPP) and other carbon fluxes derived from forest simulations. We therefore parameterized a forest gap model for a specific study site with just one PFT (comparable to global vegetation models) on the one hand, and two versions with a higher amount of PFTs, on the other hand. For an old-growth forest, aboveground biomass and basal area can be reproduced very well with all parameterizations. However, the absence of pioneer tree species in the parameterizations with just one PFT leads to a reduction in estimated gross primary production by 60% and an increase of estimated net ecosystem exchange by 50%. These findings may have consequences for productivity estimates of forests at regional and continental scales. Models with a reduced number of PFTs are limited in simulating forest succession, in particular regarding the forest growth after disturbances or transient dynamics. We conclude that a higher amount of species groups increases the accuracy of forest succession simulations. We suggest using at a minimum three PFTs with at least one species group representing pioneer tree species.
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