Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return (ALSD), and ALS full-waveform (ALSFW), photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems.

Forest field inventory (FFI) data provide valuable reference estimates of aboveground biomass (AGB) at the plot level, forming a basis for developing AGB prediction models that can be scaled to larger extents using predictor variables derived from remote sensing datasets e.g., LiDAR. Historical FFI datasets typically include tree diameter at breast height (DBH) and, in some cases, tree height (Htree). Allometric equations yield more accurate AGB estimates when Htree is incorporated; however, while DBH is commonly recorded, Htree is often partially available or entirely missing from forest field plots. An alternative approach uses DBH as a predictor variable to estimate Htree through Htree – DBH allometric model. In this study, we present a framework to harmonize and incorporate existing yet inconsistent FFI datasets in AGB modeling at the regional scale. We optimized Htree – DBH allometric model based on the previously developed pantropical model of the Western Amazon using existing FFIs data. For this study, we used data from 174 forest field plots each measuring 50 m by 50 m, and coincident with airborne LiDAR data in the Brazilian Legal Amazon (BLA) region, South America. Using existing field-measured Htree, we calibrated the H-DBH model to reflect regional conditions, resulting in an RMSE of a maximum of 6 m for trees with unknown Htree. We then assessed tree height over- and under-estimations by using a 1-m canopy height model (CHM) originating from airborne laser scanning (ALS) as an explicit concurrent unbiased proxy dataset. The results indicate that under tropical forest conditions – BLA region, field measured Htree is generally underestimated when exceeding 30 m, particularly in dense forest canopies. Under-estimation is rarely observed in degraded forests, where over-estimation may occur if forest conditions have changed post-FFI (e.g., due to burning or logging). Following height correction, we applied allometric equations to estimate AGB using simulated GEDI waveform metrics—specifically relative height metrics such as RH5, RH10, RH15, through RH100—as predictor variables, validated against field-measured AGB from FFI data. We evaluated AGB estimates before and after tree height correction, using three machine learning models—Cubist, Random Forest, and XGBoost—to compare performance. Random Forest produced the most accurate AGB estimates in both harmonized and non-harmonized scenarios. This article makes three primary contributions: (a) optimizing the H-DBH allometry model with existing datasets, (b) estimating and harmonizing tree height to address over- and under-estimation issues in FFI data, and (c) evaluating the impact of Htree discrepancies on AGB modeling. The proposed framework provides a baseline for the quantitative use of FFI datasets in AGB modeling, highlighting biases in field datasets and their implications for AGB estimation. For this study, we used data from 174 forest field plots in the BLA region, South America, each measuring 50 m by 50 m. Our findings offer valuable insights for other tropical regions where tree height estimates are challenging, contributing to more reliable AGB quantification.

NASA's Global Ecosystem Dynamics Investigation (GEDI) mission one of the objectives is to estimate global forest aboveground biomass (AGB) using full waveform (WF) LiDAR data. GEDI's relative height (RH) metrics, derived from vertical energy distributions, serve as key predictors in AGB modeling, with energy quantiles ranging from 0 % to 100 %. Despite extensive studies on RH metrics, the selection of optimal RH metrics for AGB estimation remains inconsistent, and using fewer metrics can result in a loss of vertical structural complexity. This study explores the potential of dense sampling of RH metrics (RH5 to RH100, in 5 % increments) to retain forest structural complexity, even across diverse forest regimes. Using noise-free simulated GEDI WF data, we developed machine learning models (Cubist, Random Forest, and XGBoost) to estimate AGB across 174 forest plots in the Brazilian Amazon. Results showed that dense RH sampling outperformed models using fewer recommended RH metrics. Our proposed suite of mean RH (mRH) metrics (R2 = 0.71, RMSE = 65.88 Mg/ha, nRMSE = 0.36) - derived at plot level from an extensive suite of RH metrics (RH5 to RH100, in 5 % increments) at subplot level, and vertical mean RH (vmRH) RH metrics within the 20 % waveform vertical energy distribution (vmRH20, vmRH40, vmRH60, vmRH80, and vmRH100) approach showed similar performance, at the plot level of an average size of 50 m by 50 m. The single vmRH metrics versus plot-level AGB estimates - vmRH80 consistently gives the best results for all ML models and Ordinary Least Square (OLS) regression with R2 ranges from (0.65-0.68), RMSE (53.18 - 70.51) Mg/ha - highest RMSE reported for OLS regression. All model's performances were comparable giving similar RMSE, nRMSE, and coefficient of determination (R2) for derivative RH metrics - mRH and vmRH - compared with the traditional approach of selective RH metrics at GEDI footprint level estimates. The trained model provided AGB estimates at 30 m resolution for entire ALS survey areas of sites (n = 174) in the Brazilian Legal Amazon (BLA) region. Overall, this approach retains GEDI waveform information effectively and offers a scalable solution for regional and potentially global AGB modeling.

The Global Ecosystem Dynamics Investigation (GEDI) is expected to revolutionize the quantification of aboveground carbon at continental scales, through its unprecedented dense vertical observations of vegetation structure. As its primary task, GEDI recently introduced GEDI L4A, the 25 m near-global footprint aboveground biomass density (AGBD) product. As a global mission with significant policy and management applications, it is urgent to conduct a comprehensive evaluation of GEDI L4A and to analyze the factors affecting the product's performance. In this study, the accuracy of GEDI L4A is assessed using co-registered airborne Lidar surveys collected during 2018 similar to 2019 and corresponding AGBD plots at 19 sites of the National Ecological Observatory Network (NEON). The analysis included 11 forest types and spanned 17 eco-climatic domains across the conterminous United States to ensure the representativeness and comprehensiveness of the evaluation result. The interplay of nine factors affecting GEDI L4A is quantified, including the simulated waveform strategy deviation (SWSD) used in GEDI L4A, canopy characteristics (tree height, crown size, and canopy cover), canopy heterogeneity (crown size standard deviation, tree height standard deviation, and tree density), and other factors (forest type and topographic slope). Results show that compared with NEON observations, GEDI L4A generally underestimates the AGBD (Bias: -31.65 Mg/ha), with a moderate relative error exhibited in 14 of 19 sites (%RMSE ranging from 19% to 50%). For half of the forest types, the threshold of the lowest accuracy requirement of AGBD products set by GCOS was met or was close to being met. Broadleaf forests with high AGBD values had the lowest %RMSE (less than 35%), while coniferous forests with low AGBD values had the highest %RMSE (over 50%). Among the different factors considered, the SWSD contributed the most to GEDI L4A's accuracy, with a relative importance of 56.63%, and manifested the indirect impacts of canopy heterogeneity and canopy characteristics. The relative importance of canopy heterogeneity (32.40%) was the second highest after SWSD; it was also much higher than that of canopy characteristics (3.99%). These results indicate the limitation of using only relative heights as predictors in GEDI L4A due to limited representation of horizontal structure and vertical tree complexity within a footprint. The findings in this study are a step forward in GEDI L4A's appropriate application and provide perspectives to aid its improvement.

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

CONTEXT: Maintaining stable and high levels of palm oil production within the current oil palm extent is a potential strategy to relieve deforestation pressure. Recent exponential growth of oil palm plantation area has led to significant carbon emissions and biodiversity loss. However, halting palm oil expansion may have adverse effects on palm oil production and the impact on the palm oil industry has not been well studied. OBJECTIVES: We developed quantitative methods to assess the potential decline of regional oil palm productivity given the landscape-level age-distribution structure on existing oil palm plantation land and evaluated strategies to alleviate this potential decline. These could provide guidance to multiple stakeholders, including mill owners, plantations, farmers, and regional governments, interested in improving and stabilizing palm oil yield. METHODS: We developed a model of palm oil production for Riau, Indonesia, where two decades of extensive industrial and smallholder oil palm expansion has occurred. Using established age-related rates of production, we investigated how palm oil productivity in current plantation areas would change over time if existing oil palm trees were cut and replanted at 25 years. We also designed scenarios based on replanting rates and yield improvements (better management practices or adopting more productive cultivars) to show the different im-pacts of replanting strategies. For each scenario, the production level was projected to 2075. RESULTS AND CONCLUSIONS: We found that the production of fresh fruit bunches from oil palm could fluctuate around 45 million metric tons if all current oil palm was replanted at 25 years with no further planted-area expansion. Replanting 4% annually, with or without yield improvements, would achieve the most stable landscape-level production. With yield improvements, the regional production could be increased to higher levels under any replanting rate. However, improving yields alone would be insufficient to prevent declining productivity trends in current plantation areas. Combining replanting with yield improvements could more quickly overcome potential declines in regional production. The age structure of oil palms in Riau is spatially heterogeneous across different districts, mills and concessions, posing increased challenges for replanting efforts to stabilize landscape-level yields. SIGNIFICANCE: Our study provides important information about the policy implications from different replanting and yield increase strategies on landscape-level palm oil production. Our findings demonstrate the need for collaborations among multiple stakeholders to maintain steady high levels of palm oil production in Riau, however varying stakeholder interests present major challenges for enabling and facilitating collaborations.

The leaf area index (LAI) is a vital parameter for quantifying the material and energy exchange between terrestrial ecosystems and the atmosphere. The Global Ecosystem Dynamics Investigation (GEDI), with its mission to produce a near-global map of forest structure, provides a product of the effective leaf area index (referred to as GEDI LAIe). However, it is unclear about the performance of GEDI LAIe across different temperate forest types and the degree of factors influencing GEDI LAIe performance. This study assessed the accuracy of GEDI LAIe in temperate forests and quantifies the effects of various factors, such as the difference of gap fraction (DGF) between GEDI and discrete point cloud Lidar of the National Ecological Observatory Network (NEON), sensor system parameters, and characteristics of the canopy, topography, and soil. The reference data for the LAIe assessment were derived from the NEON discrete point cloud Lidar, referred to as NEON Lidar LAIe, covering 12 forest types across 22 sites in the Continental United States (the CONUS). Results showed that GEDI underestimated LAIe (Bias: −0.56 m2/m2), with values of the mean absolute error (MAE), root mean square error (RMSE), percent bias (%Bias), and percent RMSE (%RMSE) of 0.70 m2/m2, 0.89 m2/m2, −0.20, and 0.31, respectively. Among forest types, the underestimation of GEDI LAIe in broadleaf forests and mixed forests was generally greater than that in coniferous forests, which showed a moderate error (%RMSE: 0.33~0.52). Factor analysis indicated that multiple factors explained 52% variance of the GEDI LAIe error, among which the DGF contributed the most with a relative importance of 49.82%, followed by characteristics of canopy and soil with a relative importance of 23.20% and 16.18%, respectively. The DGF was a key pivot for GEDI LAIe error; that is, other factors indirectly influence the GEDI LAIe error by affecting the DGF first. Our findings demonstrated that the GEDI LAIe product has good performance, and the factor analysis is expected to shed some light on further improvements in GEDI LAIe estimation.

Ecological protection and high-quality development of the Yellow River Basin (YRB), China, aroused remarkable concerns from China’s Central Government, and has been a major national strategy. The Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a typical dryland with pervasive vegetation restoration through the actions of the ecological projects that have been conducted in recent years. However, how climate changes and human activities, such as land use and land cover (LULC) changes, jointly impact vegetation variations in this region remains poorly understood. Here, using an explainable machine learning technique, we evaluated linkages between the kernel normalized difference vegetation index (kNDVI) and air temperature, precipitation, soil moisture, and LULC changes, and relevant marginal contributions of these four drivers to the observed vegetation changes. The grassland fraction on a pixel level was selected as the quantitative LULC variable due to its key role in regional LULC. We found that interannual kNDVI changes in most areas of this study region were negatively sensitive to temperature, but positively sensitive to precipitation and soil moisture, with area fractions of 71.74%, 96.93%, and 89.33%, respectively. The area fraction of negative kNDVI sensitivity to LULC was roughly equivalent to that of positive kNDVI sensitivity. The contributions of air temperature, precipitation, soil moisture, and LULC to overall kNDVI changes were 21.54%, 33.32%, 32.19%, and 12.95%, respectively. Moisture conditions also play a critical role in vegetation changes, which was reflected by the fluctuating growth of kNDVI as interannual changes in precipitation. Nonetheless, kNDVI changes are also affected by LULC, and LULC became the dominant factor behind the kNDVI anomalies over the grassland restoration regions from barren over the IM-YRB. This research provides theoretical support for dryland vegetation restoration under the influence of climate change.

Protecting the tropical peat swamp forests in Southeast Asia is critical for addressing global sustainability challenges such as climate change and biodiversity loss. However, more than half of these forests have been lost since 1990 due to the rapid expansion of drainage-based agriculture and forestry. Within the oil palm sector, the number of regional smallholder oil palm plantings on peat soils has risen quickly. These activities are challenging to govern and manage, due to their fragmented nature and the numerous farmers involved. It is imperative to understand the spatial distribution and drivers of the smallholder oil palm-related conversion of peat swamp forests. In contrast to existing studies based on farm surveys, we used state-of-art maps of smallholder oil palm plantings, derived from 2019 remote sensing data. Spatial data about socioeconomic and biophysical factors (e.g. mills, roads, water ways, and concessions) was then used to develop logistic regression models to investigate the relative influence of these factors. We show that the spatial patterns of smallholder oil palm plantings are distinct from those of industrial oil palm plantations, revealing the critical roles of roads, especially service roads, residential roads and tracks, in driving smallholder oil palm expansion within peatlands. We found that 90% of smallholder oil palm areas were located within 2 km of roads and 25 km of mills. The mean likelihood of a given land area being converted from peat swamp forests to smallholder oil palm declined rapidly with increasing distance from roads and mills. In addition to roads and mills, land use zones (e.g. the setting of concessions and migration settlements) and other environmental factors (e.g. precipitation and elevation) were identified as important drivers of smallholder oil palm expansion on peatland. Based on these findings, we identify priority regions for the protection of the remaining peat swamp forests in Indonesia and discuss strategies for tackling these sustainability challenges on local and global scales.

The expansion of plantations, such as oil palm, in Indonesia has caused large-scale deforestation. Loss of tropical forest, in particular peatland forest, is a major ecological and environmental threat as well as a source of atmospheric carbon emissions. Understanding the spatio-temporal dynamics of plantation expansion may illuminate pathways to reduce deforestation while maintaining high yields in existing plantations. Beyond mapping forest conversion to plantations, it is also important to understand post-conversion plantation success and crop age. In the case of oil palm, the typical productive lifespan is 25–30 years before replanting or conversion to other land use becomes necessary. Knowledge about the extent of oil palm in different productive growth stages is important for yield estimation and improving management strategies. This study characterizes the land-cover and land-use changes inherent to oil palm plantation expansion and age-structured oil palm dynamics across Riau, the province with the greatest production of oil palm in Indonesia, using a 30 year time-series of Landsat satellite imagery. From 1990 to 2020, Riau lost 4.63 M ha of forest, while oil palm extent grew six-fold, reaching an estimated 3.52 M ha in 2020. Rapid expansion of oil palm plantations in Riau resulted in the predominance of younger age classes (<10 yr-old) and rapidly increasing yields during 2010–2020. Conversion dynamics changed over time such that, after 2014, the <10 yr age class declined by 14%, whereas the 10–20 yr-old (peak yield stage) and ⩾20 yr-old (decline stage) age classes increased by 11% and 3%, respectively. In 28 years of observation (1992–2020), 41% of oil palm planted between 1990 and 1992 underwent at least one cycle of replanting in Riau.

The Upper Guinean Forest region of West Africa, a globally significant biodiversity hotspot, is among the driest and most human-impacted tropical ecosystems. We used Landsat to study forest degradation, loss, and recovery in the forest reserves of Ghana from 2003 to 2019. Annual canopy cover maps were generated using random forests and results were temporally segmented using the LandTrendr algorithm. Canopy cover was predicted with a predicted-observed r(2) of 0.76, mean absolute error of 12.8%, and mean error of 1.3%. Forest degradation, loss, and recovery were identified as transitions between closed (>60% cover), open (15-60% cover) and low tree cover (< 15% cover) classes. Change was relatively slow from 2003 to 2015, but there was more disturbance than recovery resulting in a gradual decline in closed canopy forests. In 2016, widespread fires associated with El Nino drought caused forest loss and degradation across more than 12% of the moist semi-deciduous and upland evergreen forest types. The workflow was implemented in Google Earth Engine, allowing stakeholders to visualize the results and download summaries. Information about historical disturbances will help to prioritize locations for future studies and target forest protection and restoration activities aimed at increasing resilience.

The Global Ecosystem Dynamics Investigation (GEDI), a new spaceborne LiDAR system of the National Aeronautics and Space Administration (NASA), has the potential to revolutionize global measurements of vertical vegetation structure. However, GEDI performance among different forest types and factors influencing GEDI performance needs to be evaluated against similar measurements from existing airborne LiDAR platforms. Ideally, comparisons across diverse forest types will inform future work quantifying biomass or mapping species habitats. Thus, we compared the second version of GEDI L2A product (GEDI V2) with Airborne Observation Platform (AOP) leaf-on LiDAR data across 33 National Ecological Observation Network (NEON) sites. Comparisons were made for ground elevation and relative height (RH) of GEDI with simulated airborne laser scanning (ALS) waveforms from discrete point cloud LiDAR. Results indicated that GEDI V2 obtained high accuracy on ground elevation and RH100 estimations (3σ) with RMSEs of 1.38 m and 2.62 m, respectively. GEDI produced forest height estimations (RH100) for all 12 forest types with a %RMSE below 25%. GEDI RHs were sensitive to ground finding accuracy, and GEDI performance of RH estimation varied from forest profiles of different forest types. For factors influencing GEDI performance, greater than 21% of GEDI RH95 and 33% of ground elevation variations can be explained by land surface attributes, observing sensor system characteristics, and the collection time differences between GEDI and NEON LiDAR. Furthermore, geolocation error remains an essential factor affecting GEDI performance, which varies among forest and land cover types, especially for canopy height estimation. The findings reported here can provide insights to guide and enhance future GEDI-based global forest structure mapping and applications.

Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth's surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks.

Since the late 1980s the Amazon rainforest has been affected by major forest fires every 3?5 years, mainly in the southwestern portion of the region. Besides the reduction of forest biomass and changes in structure and floristic composition, these forest fires favor the expansion of bamboo in forests in the southwestern Amazon. However, we know little about the impact of fire on bamboo expansion and changes in forest structure. The goal of this study is to quantify forest degradation by fire in areas with bamboo in the eastern portion of the state of Acre, Brazil, based upon a combination of forest-inventory and satellite remote-sensing data. The forest fires were defined by remote sensing as those in which the crowns of the trees were directly or indirectly affected by fire to the point that they cause a detectable impact on the optical satellite images in the 1984?2016 period. We measured trees and bamboo in 6 ha distributed in twelve 0.5-ha plots (100 m ? 50 m) in unburned forest, forest burned in 2005, burned forest in 2010 and forest burned in both 2005 and 2010. Our results show change in the structure of the forest with a reduction in the number of live trees as the number of bamboo culms increases after the forest fires. The amount of breakage and damage to the trees by the bamboo culms can double or triple with the expansion of the bamboo after fire impact. Bamboo expansion was identified based on an increase of the proportion of pixels with near-infrared channel reflectance values > 3500. The impact of forest fires resulted in incursion and dominance of bamboo culms over an area of 120,000 ha, changing the forest type of this area to ?bamboo-dominated forest.? Our results clearly show that drought-induced forest fires with anthropogenic sources are capable of shifting the structure of forest in southwestern Amazonia towards bamboo-dominated forest. With future climate scenarios indicating more frequent and extensive droughts due to global warming, which, together with the use of fire for new deforestation and for managing pasture and agricultural fields, can be expected to cause more forests in southwestern Amazonia to be exposed to extensive fires and potential increase in bamboo density.

Nations will reaffirm their commitment to reducing greenhouse gas (GHG) emissions during the 26th United Nations Climate Change Conference (COP26; www.ukcop26.org), in Glasgow, Scotland, in November 2021. Revision of the national commitments will play a key role in defining the future of Earth’s climate. In past conferences, the main target of Amazonian nations was to reduce emissions resulting from land-use change and land management by committing to decrease deforestation rates, a well-known and efficient strategy1,2. However, human-induced forest degradation caused by fires, selective logging, and edge effects can also result in large carbon dioxide (CO2) emissions1,2,3,4,5, which are not yet explicitly reported by Amazonian countries. Despite its considerable impact, forest degradation has been largely overlooked in previous policy discussions5. It is vital that forest degradation is considered in the upcoming COP26 discussions and incorporated into future commitments to reduce GHG emissions.

Ongoing climate change and human conversion of forests to other land uses alter regional evapotranspiration dynamics and, consequently, impact associated hydrological systems in Amazonia. We studied the effects of drought and fragmentation on forest evapotranspiration using the surface energy balance-based model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) for a fragmented forest landscape in Brazil's Amazonian state of Rondonia. Dry season (June-August) forest evapotranspiration estimates were produced for the 2009-2011 period that encompassed the 2010 drought event, one of the extreme droughts in the Amazon. METRIC evapotranspiration data were analyzed in relation to climate (monthly precipitation and cumulative water deficit) and forest fragmentation (edge distance from 100m to 1000m from forest edge and edge density). During the dry season of 2009, pre-drought, forest evapotranspiration did not fall below 110mm/month. However, the 2010 drought year showed a drastic decline in evapotranspiration by 32%, to 75mm/month, from July to August. In 2011, evapotranspiration rates were still depressed with August rates dropping as low as 85mm/month. Forest evapotranspiration dynamics were driven mainly by precipitation and corresponding water deficits in the drier years (2010 and 2011), although evapotranspiration deficits along the edges of forest fragments were locally significant, at the landscape scale. The forests near edges (to 100m) had progressively lower evapotranspiration levels than interior forests as dry seasons progressed and these differences were greatest in the 2010 drought year, reaching almost 5%. Our results suggest that during the driest months, fragmentation exacerbated both the rate and extent of evapotranspiration reductions over forest areas up to 100m from edges, equivalent to similar to 20% of the forested landscape in our study area.

Amazonian tropical rainforest is being converted to other land cover types including crops and pasture. In deforested areas, secondary forest grows after pastures are abandoned, and 'dirty pasture' that has trees and shrubs but is actively used for grazing are also regionally important land cover types following forest conversion. This study describes a multistage process land cover classification method to map primary forest, secondary forest, pasture, pasture with trees, built and water in the Brazilian state of Rondonia. A recently developed Spectral Variability Vegetation Index (SVVI) is tested to discriminate land cover types with differing tree cover amounts. Random Forest classifier (RF) is applied to inputs from a) spectral mixture analysis (SMA), and b) tasselled cap (TC) transformation, both with and without SSVI as an additional input feature. SVVI improved the classification accuracy from 73% (TC) to 85% (TC-SVVI), and TC-SVVI yielded a land cover map with higher accuracy than that from SMA-SVVI (82%). Pasture-with-trees, secondary forest and primary forest were all distinguishable with the SVVI. Pasture-with-trees accounted for 67% of all pastures, demonstrating its importance for regional land cover. This land cover classification workflow with the SVVI index improves the accuracy of mapping heterogeneous tropical land cover types.

In Brazil’s state of Acre, in southwestern Amazonia, wildfires mediated by extreme droughts in 2005 and 2010 affected more than 500,000 ha of forest, causing changes in their structure, species diversity, and aboveground biomass (AGB), and the expansion of bamboo. Our objective was to analyze these changes in an open bamboo forest in Acre after forest fires occurred either in one of the extreme drought years (2005 or 2010) or in both years (2005 + 2010). We sampled 9.75 ha (in 2016 and 2017), distributed in 18 0.5 ha (100 m × 50 m) plots and three 0.25-ha (50 m × 50 m) plots. We identified a strong fire effect on the number of tree individuals per hectare, which declined by 50% if the forest was burned in only one year (2005 or 2010) and by 74% if burned in both years. This was inversely related to the expansion of bamboo stems, which increased in number by 7 to 9 times. Changes in forest structure and species composition after the fire were characterized by a high importance value for pioneer tree species; reductions in the number of trees with logging potential, in the basal area of trees, and in the number of lianas; and an increase in the density of bamboo stems. AGB in the burned forests was 51–73% that of the unburned forest. With the expansion of bamboo, its contribution to AGB increased from 1% in the unburned forest to 27% in the twice-burned forest. These forms of degradation represent serious threats to Amazon forests.