Selective Logging Research Articles

Mapping Selective Logging in the Amazon with Artificial Intelligence and Sentinel-2

Mapping Selective Logging in the Amazon with Artificial Intelligence and Sentinel-2

Individual tree mortality: Risks of climate change in the eastern Brazilian Amazon region

The mortality of trees in humid tropical forests plays a fundamental role in understanding forest development, particularly after disturbances such as those caused by logging and extreme weather events. The aim of this study was to evaluate estimates of individual tree mortality following Reduced Impact Logging (RIL) in the Eastern Brazilian Amazon at biennial intervals from 2005 to 2012. RIL is based on operations planning, personnel training, and investments in forest management, and harvesting through RIL must: (a) minimize environmental damage, (b) diminish operation cost by increasing work efficiency, and (c) reduce operational waste. A mortality model was constructed based on the estimation of three distance-independent competition-indices (DII) and five models for predicting the probability of individual tree mortality. The Kolmogorov-Smirnov statistical test was used to determine the most representative model, from which a Neural Network Autoregressive (NNAR) model was constructed to forecast mortality after RIL. Mortality data was correlated with the El Niño–Southern Oscillation (ENSO) and climate (Rainfall, Maximum, Minimum, and Average air temperature). The tested models showed similar and accurate estimates with R2 exceeding 0.90, although underestimation and overestimation trends were observed. The NNAR satisfactorily represented species mortality over the simulated years. The period from 2012 to 2014 was characterized by a Neutral and Weak El Niño event, and exhibited the highest mortality value for a 25 cm DBH (diameter at breast height), the smallest DBH class measured in this study. In the correlation matrix analysis, maximum air temperature showed the highest positive correlation with trees mortality. Despite the challenges in estimating individual tree mortality in tropical forests after selective logging, accurate estimates were achieved using traditional regression techniques and NNAR. These results can support technical and silvicultural decisions regarding forest management in the Eastern Amazon region of Brazil.

Integrative analysis of nutrient dynamics and its hydrological pathways in a tropical montane forest watershed: Implications for landscape management

This study addresses a significant knowledge gap regarding the relationship between nutrient exports and abiotic factors in tropical forested watersheds. Focusing on a tropical forest watershed located on Hainan Island, we investigated the interactions between nitrogen (N) and phosphorus (P) exports and topographic, hydrological, and landscape elements. Using a model-data integration approach, our findings revealed that nutrient exports are predominantly in organic N and P forms, with the values of 15.66 ± 1.87 kg ha−1 yr−1 and 0.26 ± 0.07 kg ha−1 yr−1, respectively. Inorganic N and P were primarily sourced from orchards, while organic N predominantly originates from forest areas. Additionally, lateral flow was identified as the dominant hydrological pathway for nutrient transport, significantly influenced by topographical gradients. A parabolic relationship between organic N export and forest landscape fragmentation was also observed. These findings emphasize the importance of targeted landscape management strategies, such as selective logging, to mitigate N limitation in tropical forest watersheds, advancing the understanding of nutrient export dynamics and providing practical implications for sustainable forest management and conservation efforts.

Long‐Term Impacts of Selective Logging in a Tropical Rainforest in the East Region of Cameroon

ABSTRACTAlthough selective logging has become the primary cause of degradation in many African countries, little is known about its long‐term effects. This study investigated the long‐term effects of selective logging on forest tree species diversity and dynamics in the East Region of Cameroon. Six permanent monitoring plots of 1 ha each in unlogged and logged forests were established in 2005 and a recensus in 2011. Each 1 ha plot was divided into 25, 20 × 20 m quadrats. Each 20 × 20 m quadrat was further divided into 16, 5 × 5 m subquadrats, where the diameter at breast height (DBH) of all trees ≥ 2 cm was measured. There was a decrease in plant species density and richness in all forest types. Sloetiopsis usambarensis was the most important species in the unlogged forests and forests logged 21 years ago. The most important family was Putrangivaceae, Euphorbiaceae and Violaceae in the unlogged forests and forests logged 11 and 21 years ago, respectively. The frequency distribution of stem size classes indicated a reversed J‐shape of tropical forests. The above ground biomass (AGB) recovered more than 50% in all forests, and the unlogged forest had the highest AGB (961.8 Mg/ha). Larger stems indicated a forest at a stage of recovery from disturbance. Silvicultural management should be considered.

Spatial patterns of damage and tree mortality in a selectively logged Atlantic Forest

Selective logging, a common disturbance in mixed-species and uneven-aged forests, can cause substantial collateral stand damage and tree mortality. Here we explore damage patterns and some mechanisms that increase post-harvest tree mortality in a selectively logged subtropical Atlantic Forest in Argentina. We investigate the spatial relations of felled and damaged trees through spatial point pattern analysis and evaluate the relationships between mortality and different endogenous (size - diameter at breast height: DBH; and wood density: WD) and exogenous (damage and neighboring basal area: NBA) factors. The permanent plots were logged in 1999, and the fates of all pre-logging live trees ≥10 cm DBH were evaluated 20 years later. Of the monitored 3973 trees, 381 with damaged concentrated within 10.5 m of felled tree stumps. Over the next twenty years mean mortality was higher and more variable for damaged than undamaged trees (47 % ± 10 % SE and 39 % ± 2 % SE, respectively), and the presence of damage interacted with the other analyzed factors. For undamaged trees, the probability of mortality declined with DBH and NBA but not with WD. For damaged trees, instead, the probability of mortality was related to an interaction between DBH, WD, and NBA. For damaged trees <30 cm DBH, mortality increased with WD and NBA, whereas for damaged trees ≥30 cm DBH, mortality peaked at both extremes of the WD range. For these large trees with low WD, the probability of mortality decreased with NBA, whereas for trees with high WD, the opposite was observed. Our findings suggest that selective logging affects the dynamics of forests by spatially concentrating damage and may alter subsequent tree deaths. This could have, long-term effects on forest structure. Increases in logging intensity would increase overall damage and spatially isolate trees (i.e., lower NBA), rendering them more vulnerable to wind damage and other external factors. Increased tree mortality will reduce forest carbon stocks and thereby jeopardize global efforts to mitigate climate change.

Assessing the potential of species loss caused by deforestation in a mature subtropical broadleaf forest in central China

Deforestation is a major type of land use change to accommodate growing population, especially in developing countries. The risk of diversity loss due to habitat loss can be estimated using the species-area relationship based on abundance of each species. However, deforestation often occurs before there is any understanding of the impact of deforestation on tree diversity. Here, we assessed the potential effect of forest habitat destruction on the loss of species richness in a mature subtropical broadleaf forest in central China. We surveyed and constructed the species-area relationship for 54 400 m2 plots, and simulated habitat loss scenarios by randomly and aggregately sampling plots. Rank-abundance of the 21 tree species was best fitted by the Zipf-Mandelbrot model, and our sample size was sufficient by the criterion of Hill numbers at orders q = 0, 1, and 2. We found that the number of species lost due to habitat loss was well predicted by the random placement species loss-area loss curve, and was lower than that due to aggregated habitat destruction by less than one species. The probability of losing one species reached 40% when losing 16 plots by aggregated sampling, 10 plots fewer than that by random sampling. Moreover, the probability of losing two species was 10–22 % higher by aggregately sampling than that by randomly sampling when losing 17 – 34 plots (0.68 – 1.36 ha). Considering that aggregated deforestation is common in reality, the results imply that the number of tree species lost due to deforestation could be higher than the theoretical estimation. Our study suggests the importance of assessing the impact of deforestation on tree diversity before selective logging in subtropical forests.

The relative importance of local versus regional habitat structure variation on the abundance of two generalist mutualists along a forest disturbance gradient

Mutualistic interactions are key to biodiversity generation and maintenance, being pollination and seed dispersal interactions essential for plant reproduction. However, anthropogenic habitat disturbance can alter these interactions. We studied the regional and local effects of habitat disturbance on two generalist mutualist animals of southern Chile temperate rainforests: the hummingbird Sephanoides sephaniodes and the marsupial Dromiciops gliroides, which perform key roles as pollinator and seed disperser, respectively. We sampled 12 sites corresponding to four disturbance conditions (mature native forest, secondary forest, native forest under selective logging, and exotic forest plantations) in which we estimated the number of S. sephaniodes and D. gliroides records (as a proxy of relative abundance) using camera traps. We measured seven habitat structure variables at each site, known to influence their occurrence. Specifically, we examined S. sephaniodes and D. gliroides abundance variation at (i) a regional scale (among disturbance conditions) and (ii) at a local scale (among sampling sites). Dromiciops gliroides abundance decreased as disturbance increased, but S. sephaniodes abundance was highly variable. At the local scale, S. sephaniodes and D. gliroides abundances were largely variable among sites, influenced by habitat structural features, such as bamboo and shrub cover and woody debris. Our results show that although habitat disturbance may influence mutualist abundances, they can tolerate disturbance if minimum structure and resource requirements are met, but respond idiosyncratically to local conditions. Examining local-scale variability may provide valuable information on ecological processes, which often goes unnoticed when local sites are considered as replicates in landscape-level studies.

Effects of Different Potting Mixture on Early Development of &amp;lt;i&amp;gt;Afzelia quanzesis &amp;lt;/i&amp;gt;Welw. Species Seedlings

Planting native plant species is becoming increasingly important as the world begins to realise the importance of restoring ecosystems, especially since many valuable species are on the brink of extinction due to human over-exploitation. &amp;lt;i&amp;gt;Afzelia quanzensis&amp;lt;/i&amp;gt; Welw. is one of the valuable timber species, highly targeted in uncontrolled selective loggings, hence threatening its existence. This study assessed the effects of different potting mixtures on &amp;lt;i&amp;gt;A. quanzensis&amp;lt;/i&amp;gt; early seed germination, which is an essential step for its mass propagation. Four different potting mixtures: T1 (forest soil, agricultural residues, tobacco, and cow manures), T2 (forest soil, tobacco manure), T3 (forest soil and cow manure), and T4 (forest soil and agricultural residues) were utilised. Employing a completely randomised design with each mixture replicated four times, the study monitored growth parameters, such as leaf count and seedling height, over one month. Results from two-way ANOVA with Turkey’s multiple comparisons test showed that forest soil and cow manure (T3) yielded the tallest plants and highest scores of leaf count. The study provides valuable insights for nursery managers and researchers working on the mass propagation of the &amp;lt;i&amp;gt;A. quanzensis&amp;lt;/i&amp;gt; tree species for plantations in the region.

Soil wetting triggered by selective logging in Bornean lowland tropical rainforests

Aboveground biomass removal and canopy opening by selective logging modifies soil moisture in the main root zone, impacting soil aeration and various biogeochemical processes in tropical production forests. This study investigated the relationship between canopy damage and topsoil (10 cm) moisture in two logged forests in Malaysian Borneo, while simultaneously controlling for logging intensity, time elapsed since historical logging, and spatial autocorrelation. Volumetric soil water content (VSWC), canopy height model (CHM), leaf area index (LAI), and historical logging data were collected from 84 transects placed subjectively in 15 sites exhibiting varying canopies. We generated an index (PC1) quantifying the magnitude of canopy structural degradation from canopy structure metrics (CSM) combining CHM and LAI data within a 20-meter buffer for each transect. PC1 was analyzed for its impact on VSWC across logging periods, and contrasted with topography. Spatial autocorrelation of VSWC was examined regarding to canopy conditions. VSWC was significantly higher in all logged forests (over 0.4 m3 m−3) comparing to non-disturbed forests (0.27 m3 m−3). The immediate wetting could be a result of extracting mature individuals of late-successional species holding large biomass, while the persistent wet condition may be due to retarded canopy and biomass recovery. In the study area, canopy structure was a stronger predictor of soil moisture than topography. The high soil moisture underneath the most degraded canopies presented the largest spatial extent of autocorrelation. This study revealed soil wetting after selective logging in humid tropical forests, driven by reduced transpiration from biomass loss rather than increased evaporative demand resulting from canopy opening. The elevation in soil moisture could have disrupted biogeochemical processes in the below-ground system, which in turn impede forest succession and put stress on the overall vulnerability of disturbed tropical rainforests.

The Influence of Logging-Related Soil Disturbance on Pioneer Tree Regeneration in Mixed Temperate Forests.

The recovery of soil properties and the proper growth of natural tree regeneration are key elements for maintaining forest productivity after selective logging operations. This study was conducted on the soil properties and natural growth of two pioneer seedling species of alder and maple which were on skid trails in the mixed beech forests of northern Iran. To examine the long-term effects, we randomly selected six skid trails, with two replicates established for each of three time periods since last use (10, 20, and 30 years ago). Random plots 4 m × 10 m in size, three plots on each skid trail and six plots on areas without soil compaction (control), were selected. Measurements included the physical and chemical properties of the soil and the growth, and the architectural and qualitative characteristics of the seedlings. The results showed that all the soil properties of the 10- and 20-year-old skid trails were significantly different from the control area (except for the soil moisture in the 20-year-old skid trail). The 30-year-old skid trail showed values of other soil properties which were not significantly different from the control area, except for the amounts of organic matter and soil nitrogen, which was less than the control. The skid trails had a negative effect on all of the growth, qualitative, and architectural indices of seedlings. The characteristics of seedlings were related to soil characteristics and had the highest correlation with the soil penetration resistance (R-value from -0.41 to -0.63 for stem growth, p < 0.05; -0.57 to -0.90 for root growth, p < 0.01; and -0.76 to -0.86 for biomass, p < 0.01). The correlation coefficient between soil penetration resistance and the Dickson quality index of alder and maple seedlings was, respectively, -0.74 and -0.72, p < 0.01. The negative effect of soil compaction on root growth (-27.69% for alder seedlings and -28.08% for maple seedlings) was greater than on stem growth (-24.11% for alder seedlings and -16.27% for maple seedlings). The amount of growth, qualitative, and architectural indices of alder seedlings were higher than that of maple seedlings. Although alder is a better choice as compared to maple seedling in the initial year, the results of our study show that it is recommended to plant both alder and maple on skid trails after logging operations.

Calibrating Tropical Forest Coexistence in Ecosystem Demography Models Using Multi‐Objective Optimization Through Population‐Based Parallel Surrogate Search

AbstractTropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi‐Objective Population‐based Parallel Local Surrogate‐assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size‐ and age‐structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear‐cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40‐year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices.

Inbreeding depression affects the growth of seedlings of an African timber species with a mixed mating reproductive system, Pericopsis elata (Harms) Meeuwen

Selfing or mating between related individuals can lead to inbreeding depression (ID), which can influence the survival, growth and evolution of populations of tree species. As selective logging involves a decrease in the density of congeneric partners, it could lead to increasing biparental inbreeding or self-fertilization, exposing the population to higher ID. We assessed the influence of inbreeding on the growth of a commercial timber species, Pericopsis elata (Fabaceae), which produced about 54% of self-fertilized seedlings in a natural population of the Congo basin. We followed the survival and growth of 540 plants raised in a plantation along a gradient of plant density (0.07–15.9 plants per m2). Parentage analysis allowed us distinguishing selfed and outcrossed seedlings. The annual growth was higher for outcrossed than selfed plants, on average by 10.8% for diameter and 12.9% for height growth. Based on the difference in above ground biomass between selfed and outcrossed seedlings after 41 months, we estimated the level of ID at δ = 0.33, while a lifetime estimate of ID based on the proportions of selfed plants at seedling and adult stages led to δ = 0.7. The level of ID on growth rate did not change significantly with age but tended to vanish under high competition. Pericopsis elata is a particularly interesting model because inbreeding depression is partial, with about 26% of reproducing adults resulting from selfing, contrary to most tropical tree species where selfed individuals usually die before reaching adulthood. Hence, the risks of ID must be considered in the management and conservation of the species.

Detecting selective logging in tropical forests with optical satellite data: an experiment in Peru shows texture at 3 m gives the best results

AbstractSelective logging is known to be widespread in the tropics, but is currently very poorly mapped, in part because there is little quantitative data on which satellite sensor characteristics and analysis methods are best at detecting it. To improve this, we used data from the Tropical Forest Degradation Experiment (FODEX) plots in the southern Peruvian Amazon, where different numbers of trees had been removed from four plots of 1 ha each, carefully inventoried by hand and terrestrial laser scanning before and after the logging to give a range of biomass loss (∆AGB) values. We conducted a comparative study of six multispectral optical satellite sensors at 0.3–30 m spatial resolution, to find the best combination of sensor and remote sensing indicator for change detection. Spectral reflectance, the normalised difference vegetation index (NDVI) and texture parameters were extracted after radiometric calibration and image preprocessing. The strength of the relationships between the change in these values and field‐measured ∆AGB (computed in % ha−1) was analysed. The results demonstrate that: (a) texture measures correlates more with ∆AGB than simple spectral parameters; (b) the strongest correlations are achieved for those sensors with spatial resolutions in the intermediate range (1.5–10 m), with finer or coarser resolutions producing worse results, and (c) when texture is computed using a moving square window ranging between 9 and 14 m in length. Maps predicting ∆AGB showed very promising results using a NIR‐derived texture parameter for 3 m resolution PlanetScope (R2 = 0.97 and root mean square error (RMSE) = 1.91% ha−1), followed by 1.5 m SPOT‐7 (R2 = 0.76 and RMSE = 5.06% ha−1) and 10 m Sentinel‐2 (R2 = 0.79 and RMSE = 4.77% ha−1). Our findings imply that, at least for lowland Peru, low‐medium intensity disturbance can be detected best in optical wavelengths using a texture measure derived from 3 m PlanetScope data.

Human degradation of tropical moist forests is greater than previously estimated

Tropical forest degradation from selective logging, fire and edge effects is a major driver of carbon and biodiversity loss1–3, with annual rates comparable to those of deforestation4. However, its actual extent and long-term impacts remain uncertain at global tropical scale5. Here we quantify the magnitude and persistence of multiple types of degradation on forest structure by combining satellite remote sensing data on pantropical moist forest cover changes4 with estimates of canopy height and biomass from spaceborne6 light detection and ranging (LiDAR). We estimate that forest height decreases owing to selective logging and fire by 15% and 50%, respectively, with low rates of recovery even after 20 years. Agriculture and road expansion trigger a 20% to 30% reduction in canopy height and biomass at the forest edge, with persistent effects being measurable up to 1.5 km inside the forest. Edge effects encroach on 18% (approximately 206 Mha) of the remaining tropical moist forests, an area more than 200% larger than previously estimated7. Finally, degraded forests with more than 50% canopy loss are significantly more vulnerable to subsequent deforestation. Collectively, our findings call for greater efforts to prevent degradation and protect already degraded forests to meet the conservation pledges made at recent United Nations Climate Change and Biodiversity conferences.

Multi-Agent Reinforcement Learning for Stand Structure Collaborative Optimization of Pinus yunnanensis Secondary Forests

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China.

Post-Logging Canopy Gap Dynamics and Forest Regeneration Assessed Using Airborne LiDAR Time Series in the Brazilian Amazon with Attribution to Gap Types and Origins

Gaps are openings within tropical forest canopies created by natural or anthropogenic disturbances. Important aspects of gap dynamics that are not well understood include how gaps close over time and their potential for contagiousness, indicating whether the presence of gaps may or may not induce the creation of new gaps. This is especially important when we consider disturbances from selective logging activities in rainforests, which take away large trees of high commercial value and leave behind a forest full of gaps. The goal of this study was to quantify and understand how gaps open and close over time within tropical rainforests using a time series of airborne LiDAR data, attributing observed processes to gap types and origins. For this purpose, the Jamari National Forest located in the Brazilian Amazon was chosen as the study area because of the unique availability of multi-temporal small-footprint airborne LiDAR data covering the time period of 2011–2017 with five data acquisitions, alongside the geolocation of trees that were felled by selective logging activities. We found an increased likelihood of natural new gaps opening closer to pre-existing gaps associated with felled tree locations (&lt;20 m distance) rather than farther away from them, suggesting that small-scale disturbances caused by logging, even at a low intensity, may cause a legacy effect of increased mortality over six years after logging due to gap contagiousness. Moreover, gaps were closed at similar annual rates by vertical and lateral ingrowth (16.7% yr−1) and about 90% of the original gap area was closed at six years post-disturbance. Therefore, the relative contribution of lateral and vertical growth for gap closure was similar when consolidated over time. We highlight that aboveground biomass or carbon density of logged forests can be overestimated if considering only top of the canopy height metrics due to fast lateral ingrowth of neighboring trees, especially in the first two years of regeneration where 26% of gaps were closed solely by lateral ingrowth, which would not translate to 26% of regeneration of forest biomass. Trees inside gaps grew 2.2 times faster (1.5 m yr−1) than trees at the surrounding non-gap canopy (0.7 m yr−1). Our study brings new insights into the processes of both the opening and closure of forest gaps within tropical forests and the importance of considering gap types and origins in this analysis. Moreover, it demonstrates the capability of airborne LiDAR multi-temporal data in effectively characterizing the impacts of forest degradation and subsequent recovery.

LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert

The Land Use Change Alert (LUCA) dataset was developed for effective and timely monitoring of global forest changes that are mostly associated with human activities. Near- real-time changes of forest land use are mapped at 0.05 ha minimum mapping unit for all forest types across the Earth’s ecoregions, every two weeks. LUCA is based on Sentinel-1 cloud penetrating synthetic aperture radar (SAR) observations to circumvent limitations of optical imagery from pervasive cloud cover over forested areas globally, and especially in the tropics. The methodology is based on a combination of time-series change detection and machine learning analytics to achieve high accuracy of alerts across all ecoregions and landscapes globally with an average area-adjusted users and producers accuracy of 83% and 63%, respectively. The bi-weekly global alert maps capture forest clearing associated with deforestation and industrial timber harvesting, along with forest degradation associated with selective logging, fragmentation, fire, and roads. The product was developed and released publicly through Google Earth Engine to allow for the rapid assessment of land use change activities, quantifying patterns and processes driving forest change and dynamics across forest ecoregions. LUCA is designed to help monitor a variety of emission reduction programs at the local to regional scales and play a key role in implementing regulations on deforestation-free products.

Indicators for monitoring reduced impact logging in the Brazilian amazon derived from airborne laser scanning technology

Monitoring reduced impact logging (RIL) activities in sustainably managed forest areas in the Amazon is a costly and complex, yet crucial endeavor. One viable monitoring option is the use of airborne laser scanning (LiDAR), which enables estimating forest structure parameters over large areas in a reduced timeframe with high accuracy. In this study, we propose and assess the applicability of five monitoring indicators for RIL based on Light Detection and Ranging (LiDAR) data acquisition in areas under forest concession. Five Annual Production Units (APUs) were investigated within the Forest Management Unit (FMU) III of the Jamari National Forest, located in the Southwest of the Brazilian Amazon. These sites were surveyed by LiDAR in 2014 and 2015 (one year after the exploration). Digital Terrain Models (DTMs), Surface Models (DSMs), and Canopy Height Models (CHMs) were generated for each APU. The proposed indicators were: i. Detection and identification of crown removal in dominant and co-dominant trees above 30 m; ii. Gap detection in the forest canopy; iii. Impacts of Reduced Impact Logging on the Understory; iv. Changes in the vertical canopy profile; and v. Affected areas within Permanent Preservation Areas (PPAs) and restricted areas. There was a 3.95% reduction in the occurrence of taller canopies after RIL, and a higher occurrence of small gaps (λ > 1), with λ values (2.34) being higher in the area with the oldest logging history (APU 1). Gini coefficient values in all APUs were below 0.5, indicating a low intensity of disturbances in the forest canopy. The shape (γ) and scale (β) parameters of the understory and canopy were not significantly correlated with variables related to selective logging. Restricted areas were considered for the allocation of roads, trails, log landings, and places with slopes equal to or >15%, and the indices of areas affected by RIL in PPAs and restricted areas were <2%. The proposed indicators using LiDAR data show great potential for monitoring managed areas in the Amazon and can be utilized by concession companies and government oversight.

Environmental correlates of the forest carbon distribution in the Central Himalayas.

Understanding the biophysical limitations on forest carbon across diverse ecological regions is crucial for accurately assessing and managing forest carbon stocks. This study investigates the role of climate and disturbance on the spatial variation of two key forest carbon pools: aboveground carbon (AGC) and soil organic carbon (SOC). Using plot-level carbon pool estimates from Nepal's national forest inventory and structural equation modelling, we explore the relationship of forest carbon stocks to broad-scale climatic water and energy availability and fine-scale terrain and disturbance. The forest AGC and SOC models explained 25% and 59% of the observed spatial variation in forest AGC and SOC, respectively. Among the evaluated variables, disturbance exhibited the strongest negative correlation with AGC, while the availability of climatic energy demonstrated the strongest negative correlation with SOC. Disturbances such as selective logging and firewood collection result in immediate forest carbon loss, while soil carbon changes take longer to respond. The lower decomposition rates in the high-elevation region, due to lower temperatures, preserve organic matter and contribute to the high SOC stocks observed there. These results highlight the critical role of climate and disturbance regimes in shaping landscape patterns of forest carbon stocks. Understanding the underlying drivers of these patterns is crucial for forest carbon management and conservation across diverse ecological zones including the Central Himalayas.

A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon

Understanding direct deforestation drivers at a fine spatial and temporal scale is needed to design appropriate measures for forest management and monitoring. To achieve this, reference datasets with which to design Artificial Intelligence (AI) approaches to classify direct deforestation drivers within areas experiencing forest loss in a detailed, comprehensive and locally-adapted way are needed. This is the case for Cameroon, in the Congo Basin, which has known increasing deforestation rates in recent years. Here, we created an Earth Observation dataset with associated labels to classify detailed direct deforestation drivers in Cameroon, which includes satellite imagery (Landsat and PlanetScope) and auxiliary data on infrastructure and biophysical properties. The dataset provides the following fifteen labels: oil palm, timber, fruit, rubber and other-large scale plantations; grassland/shrubland; small-scale oil palm or maize plantations and other small-scale agriculture; mining; selective logging; infrastructure; wildfires; hunting; and other.