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Aim: We aimed to evaluate the vulnerability of the Amazon forest to post‐fire grass invasion under present and future climate scenarios. Location: Amazon Basin. Time period: 1981–2017 and 2070–2099. Major taxa studied: Plants. Methods: We combined a fire–ecosystem model with remote sensing data and empirically‐derived equations to evaluate the effects of a high‐intensity fire (i.e., during an extreme drought) and logging in forest edges on tree canopy, and exotic grass cover under present and unmitigated climate change scenarios. We also contrasted simulated vegetation recovery time (as a function of climate variability) and current fire return intervals to identify areas in which fire–grass feedbacks could lock the system in a grass‐dominated state. Results: Under current climatic conditions, 14% of the Amazon was found to be vulnerable to post‐fire grass invasion, with the south‐eastern Amazon at the highest risk of invasion. We found that under unmitigated climate change, by the end of the century, 21% of the Amazon would be vulnerable to post‐fire grass invasion. In 3% of the Amazon, fire return intervals are already shorter than the time required for grass exclusion by canopy recovery, implying a high risk of irreversible shifts to a fire‐maintained degraded forest grassy state. The south‐eastern region of the Amazon is currently at highest risk of irreversible degradation. Main conclusions: Although resilience is evident in areas with low fire activity, increased fire frequency and intensity could push large Amazon forest areas towards a tipping point, causing transitions to states with low tree and high grass cover. [ABSTRACT FROM AUTHOR]

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The Brazilian Amazon has undergone intense transformations resulting from geopolitical and ideological valuations in the service of productive-competitive integration of its territory in the national and international economy. State and capital affirm an appropriating rationality of nature projected in two vectors of occupation/modernization: techno-industrial and techno-ecological. The undergone problem is inscribed in spatial injustice as counterpart of capitalist expansion regarding land conflicts. The methodology is based on the theoreticalconceptual division of social injustices versus ideology of capitalist modernization of the Amazonian territory. The empirical treatment of the collected datasets unveils land conflicts through information obtained by the Comissão Pastoral da Terra (cpt), the dataluta Project, and the Instituto Brasileiro de Geografia e Estatística (ibge). Thus, theory and empirical practices are unified in a regional dynamic analysis of the Brazilian Amazon in the last 20 years. The study emphasizes that the territorial policies of the State and capitalist forces (multinationals, agribusiness, mining, and logging) promote deforestation, violent conflicts over land and water, slave labor, rural redevelopment, demographic depletion of the affected rural areas, and accelerated precarious urbanization. The expansion of land conflicts and spatial injustices in the Brazilian Amazon results from the modernization of the territory. [ABSTRACT FROM AUTHOR]

• LiDAR-photogrammetry can identify canopy logging damage accurately and estimate AGB. • Canopy loss detected automatically was similar to visually determined loss. • AGB models produced by LiDAR and Hybrid LiDAR-photogrammetric approach were highly correlated. • Previous and post logging AGB estimates were similar to previous LiDAR studies in the Antimary State Forest. Sustainable forest management relies on good knowledge of forest structure obtained from ground surveys combined with remote sensing. Capable of detecting both the forest floor and canopy elements, airborne LiDAR can estimate forest structure parameters with accuracy and precision, but is still difficult to acquire due to the lake of service provider in remote regions of developing countries. Alternatively if ground surface elevations are known (e.g., from LiDAR), they can be tied to a canopy surface model derived from stereo photogrammetry using RGB images from unmanned aerial vehicles (UAV). Here we assessed whether such photogrammetric canopy measurements offer aboveground biomass (AGB) and disturbance impact estimates from logging that are comparable to LiDAR, and whether the use of both in sequence can provide an efficient post-harvest monitoring system. Specifically, through a combination of forest inventory ground plots, airborne LiDAR data, and a UAV-RGB camera system we (i) automatically located and measured canopy disturbance caused by logging, (ii) compared AGB models produced by LiDAR alone and the combination of LiDAR (for terrain elevation model) and RGB-photogrammetry (for forest surface model), and (iii) estimated the AGB stock loss from logging. The study was carried out in the Antimary State forest located in the southwestern Brazilian Amazon. Our results demonstrate that the use of RGB-photogrammetry in regions where the terrain elevation has already been estimated can be an effective way to rapidly identify selective logging and to accurately monitor its impact. [ABSTRACT FROM AUTHOR]

Abstract: This research represents one of the first studies in Amazonia to examine soil moisture and water-use efficiency (WUE) in secondary forest (SF) vegetation regrowing on abandoned pastures subjected to reduced nutrient constraints via a nutrient addition experiment. Extensive forested areas (about 80% of deforestation) have been converted to pastures in Amazonia, which were later abandoned following soil degradation and reduction in grass productivity. Colonization of these areas proceeds through species adapted to adverse edaphic conditions, such as low soil nutrients. Yet there is little data from such environments showing the interaction of soil nutrients and water availability on plant physiological processes. The objective of this study was to test whether three common SF tree species have positive physiological responses, e.g. increased photosynthesis and water-use efficiency, when nutrient limitations are relaxed through fertilization. The experiment was conducted on an abandoned pasture in central Amazonia with 6-year-old secondary vegetation following the application of four treatments: control; +phosphorus (P); +phosphorus and lime (P+Ca); and +phosphorus, lime and gypsum (P+Ca+G). The control had higher mean soil moisture at 140 and 180cm depth at the end of the dry season, indicating that the treatment plots responded positively to fertilizer additions by taking up additional water. Trees of Vismia japurensis and Bellucia grossularioides growing on the fertilized plots had the highest net photosynthesis rates (A) (18.7 and 20.4μmolm−2 s−1, respectively). The three species utilized different strategies with regard to physiological and nutritional response, with V. japurensis, regardless of treatment, using these limiting resources most efficiently to colonize abandoned pastures. Trees growing on the P+Ca but not +P alone plots increased A rates, indicating that Ca is an important limiting nutrient in post-pasture secondary succession. The addition of Ca as ash by burning primary and secondary vegetation could explain the rapid growth and dominance of V. japurensis in abandoned pasture areas in central Amazonia. The efficiency of Vismia to use limiting resources could lead to a restructuring of SF and altered rates of stand-level productivity. [Copyright &y& Elsevier]

• Lidar-derived canopy attributes were useful to characterize different types of forest. • Lidar-derived pre-stratified inventory reduced the sample effort up to 41%. • Lidar data collection costed less than the capital saved by sample reduction. In high biodiversity areas, such as the Amazon, forest inventory is a challenge due to large variations in vegetation structure and inaccessibility. Capturing the full gradient of variability requires the acquisition of a large number of sample plots. Pre-stratified inventory is an efficient strategy that reduces sampling effort and cost. Low-cost remote sensing techniques may significantly expand pre-stratification capacity; however, the simplest option, satellite optical imagery, cannot detect small variations in primary forests. Alternatively, three-dimensional information obtained from airborne laser scanning (ALS, a.k.a. airborne lidar) has been successfully used to estimate structural parameters in tropical forests. Our objective was to assess to what extent forest plot sampling effort could be reduced, while accurately estimating mean vegetation characteristics in the landscape, by stratifying with ALS structural properties, relative to a random, uniformed conventional approach. The study was developed in an 800-ha area of wet Amazonian forest (Acre, Brazil), including portions of palms, bamboo and dense forest. We estimated relevant structural attributes from ALS: canopy height, openness, rugosity and fractions of leaf area index (LAI) along the vertical profile. We clustered vegetation to define heterogeneity into structural types, employing the Ward method and Euclidean distance. Also, principal component analysis was employed to characterize the groups using field and ALS-derived structural attributes. We simulated sampling intensities to estimate the gain in reducing the field efforts based on pre-stratified and non-stratified forest inventory scenarios. The resulting stratification clearly distinguished the forest's structural variation gradient and the vegetation density profile. For a fixed uncertainty of 10% in basal area estimation, the ALS-aided stratified inventory reduced the necessary number of field plots by 41%, relative to simple random sampling. The resulting reduction in sampling effort can offset the cost of ALS data collection, significantly enhancing its financial feasibility. In addition, ALS provides broad-coverage quantifications of basal area (or aboveground carbon stock), canopy structure, and accurate terrain characterization, which have an added value for forest management. [ABSTRACT FROM AUTHOR]

Assessing the persistent impacts of fragmentation on aboveground structure of tropical forests is essential to understanding the consequences of land use change for carbon storage and other ecosystem functions. We investigated the influence of edge distance and fragment size on canopy structure, aboveground woody biomass (AGB), and AGB turnover in the Biological Dynamics of Forest Fragments Project (BDFFP) in central Amazon, Brazil, after 22+ yr of fragment isolation, by combining canopy variables collected with portable canopy profiling lidar and airborne laser scanning surveys with long‐term forest inventories. Forest height decreased by 30% at edges of large fragments (>10 ha) and interiors of small fragments (<3 ha). In larger fragments, canopy height was reduced up to 40 m from edges. Leaf area density profiles differed near edges: the density of understory vegetation was higher and midstory vegetation lower, consistent with canopy reorganization via increased regeneration of pioneers following post‐fragmentation mortality of large trees. However, canopy openness and leaf area index remained similar to control plots throughout fragments, while canopy spatial heterogeneity was generally lower at edges. AGB stocks and fluxes were positively related to canopy height and negatively related to spatial heterogeneity. Other forest structure variables typically used to assess the ecological impacts of fragmentation (basal area, density of individuals, and density of pioneer trees) were also related to lidar‐derived canopy surface variables. Canopy reorganization through the replacement of edge‐sensitive species by disturbance‐tolerant ones may have mitigated the biomass loss effects due to fragmentation observed in the earlier years of BDFFP. Lidar technology offered novel insights and observational scales for analysis of the ecological impacts of fragmentation on forest structure and function, specifically aboveground biomass storage. [ABSTRACT FROM AUTHOR]