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Background information to give context to the studyCanopy gaps are the most evident manifestation of how disturbances disrupt forest landscapes. The size distribution and return frequency of gaps, and subsequent recovery processes, determine whether the old-growth state can be reached.The aim or research questionWe used remote sensing metrics to compare the disturbance regime of four Amazon regions based on the size distribution of gaps, their dynamics and geometric characteristics.A brief summary of the methodology usedWe assessed gap dynamics at four sites in the central, central eastern, southeastern, and northeastern regions of the Brazilian Amazon using repeated airborne laser scanning surveys. We developed a novel analysis to quantify four possible stages of gap dynamics: formation, expansion, persisting and recovering. For that, we overlapped layers of gap locations from two consecutive airborne laser scanning surveys.Key results with some significance measuresThe gap fraction in our study sites varied between 1.26% to 7.84%. All the sites have similar proportion of gaps among size classes. What notably changed between sites was not the gap size-distribution, but the relative importance of stages of gap dynamics. Growing and persisting rates were greatest in the site with the stronger seasonal variation in climate, lower annual precipitation, higher mean wind speed and higher solar radiation.The conclusions, which address the main aimsThe concept of stability reflects the tendency of a system to quickly return to a position of equilibrium when disturbed. We showed that gap dynamics varied among sites, with one example of low recovery rate contrasted to three other sites with faster recovery. Our results support that such as assessing the size distribution of gaps, investigating their return frequency and severity is crucial for understanding forest dynamics at the landscape and regional scales.

Airborne laser scanning (ALS) can support the management of complex natural forests. The thesis presents two studies, one focuses on a small scale ALS assessment that was used to qualify emergent trees and to assess how many commercial trees can be found in order to improve forest management operational plans. The second study demonstrates the usefulness of a large-scale ALS sampling assessment to evaluate the distribution of canopy gaps across the Amazonian region in Brazil. In the first study (chapter 1) by using the emergent canopy model from ALS-Lidar we classified 66 individuals as having potential for commerce, from which 58 individuals presented the best stem quality for logging, which represented more than seven high quality commercial trees per hectare. We propose that ALS operational planning can be used to more efficiently direct field surveys without the need for a full census that would reduce field work in the initial stages of management. In the chapter 2, we mapped the variation of the power-law scale coefficient (α) function, widely used to describe the gap size-frequency distribution in different forests around the world. We observed a large-scale Northwest to Southeast pattern in (α higher proportion of large gaps in the Southeast), which aligns with recent work on tree mortality rates. We also explained the relationship of these canopy gaps with forest structure and environmental variables. The proportion of large gaps in the forest canopy varied substantially over the Brazilian Amazon as a result of canopy structure and disturbance rates. Scenarios of climate change is a point of concern since increasing extreme weather events may therefore increase the proportion of large gaps in currently intact forests, causing them to resemble human modified forests. Keywords: Emergent crowns, Forest dynamic, Gap size distribution, LiDAR, Selective logging, Sustainable forest management, Tropical rainforest O escaneamento laser aerotransportado (ALS) pode apoiar o manejo de florestas naturais complexas. A tese apresenta dois estudos, o primeiro engloba uma avaliação em pequena escala onde o ALS foi usado para qualificar árvores emergentes e para avaliar quantas árvores comerciais podem ser encontradas para melhorar os planos operacionais de manejo florestal. O segundo estudo demonstra a utilidade de uma amostragem em larga escala usando ALS para avaliar a distribuição de clareiras na região amazônica no Brasil. No primeiro estudo (capítulo 1) usando o modelo de dossel emergente do ALS-Lidar 66 indivíduos foram classificados como tendo potencial para comércio, dos quais 58 indivíduos apresentaram as melhores qualidades de fuste para exploração, o que representou mais de sete árvores comerciais de alta qualidade por hectare. Os resultados permitiram propor que o planejamento operacional com dados ALS pode ser usado para direcionar de forma mais eficiente os trabalhos de campo sem a necessidade de um censo completo, o que reduziria o esforço em campo nos estágios iniciais de gestão. No capítulo 2, nós mapeamos a variação do coeficiente de escala (α) da função power-law, amplamente utilizada para descrever a distribuição de clareiras em diversas florestas. Observamos assim, um padrão de grande escala na variação dos α de Noroeste a Sudeste (maior proporção de grandes clareiras no Sudeste), o que se alinha com trabalhos recentes sobre taxas de mortalidade de árvores. Também explicamos a relação dessas clareiras do dossel com a estrutura da floresta e variáveis ambientais. A proporção de grandes clareiras no dossel florestal variou substancialmente na Amazônia brasileira como resultado da estrutura do dossel e das taxas de perturbação. Cenários de mudanças climáticas são um ponto de preocupação, pois o aumento de eventos climáticos extremos pode, portanto, aumentar a proporção de grandes clareiras em florestas atualmente intactas, fazendo com que elas se assemelhem a florestas modificadas pelo homem.

Canopy gaps are openings in the forest canopy resulting from branch fall and tree mortality events. The geographical distribution of large canopy gaps may reflect underlying variation in mortality and growth processes. However, a lack of data at the appropriate scale has limited our ability to study this relationship until now. We detected canopy gaps using a unique LiDAR dataset consisting of 650 transects randomly distributed across 2500 km(2) of the Brazilian Amazon. We characterized the size distribution of canopy gaps using a power law and we explore the variation in the exponent, alpha. We evaluated how the alpha varies across the Amazon, in response to disturbance by humans and natural environmental processes that influence tree mortality rates. We observed that South-eastern forests contained a higher proportion of large gaps than North-western, which is consistent with recent work showing greater tree mortality rates in the Southeast than the Northwest. Regions characterized by strong wind gust speeds, frequent lightning and greater water shortage also had a high proportion of large gaps, indicating that geographical variation in alpha is a reflection of underlying disturbance processes. Forests on fertile soils were also found to contain a high proportion of large gaps, in part because trees grow tall on these sites and create large gaps when they fall; thus, canopy gap analysis picked up differences in growth as well as mortality processes. Finally, we found that human-modified forests had a higher proportion of large gaps than intact forests, as we would expect given that these forests have been disturbed. Synthesis. The proportion of large gaps in the forest canopy varied substantially over the Brazilian Amazon. We have shown that the trends can be explained by geographical variation in disturbance and growth. The frequency of extreme weather events is predicted to increase under climate change, and changes could lead to greater forest disturbance, which should be detectable as an increased proportion of large gaps in intact forests.

The factors shaping the distribution of giant tropical trees are poorly understood, despite its importance as a link between evolutionary biology and ecosystem biogeochemistry. The recent discovery of clusters of trees over 80 metres tall in the Guiana Shield region of the Amazon rainforest challenges the current understanding of the factors controlling the growth and survival of giant trees. The new discovery led us to revisit the question: what determines the distribution of the tallest trees of the Amazon?Here, we used high-resolution airborne LiDAR (Light Detection and Ranging) surveys to measure canopy height across 282,750 ha of primary old-growth and secondary forests throughout the entire Brazilian Amazon to investigate the relationship between the occurrence of giant trees and the environmental factors that influence their growth and survival. Our results suggest that the factors controlling where trees grow extremely tall are distinct from those controlling their longevity. Trees grow taller in areas with high soil clay content (> 42%), lower radiation (< 130 clear days per year) and wind speeds, avoiding alluvial areas (elevations higher than 40 m a.s.l), and with an optimal precipitation range of 1,500 to 2,500 mm yr-1. We then used an envelope model to determine the environmental conditions that support the very tallest trees (i.e. over 70 m height). We found that, as opposed to the myriad of interacting factors that control the maximum height at a large scale, wind speed had by far the largest influence on the distribution of these sentinel trees, and explained 67% of the probability of finding trees over 70 m in the Brazilian Amazon forest.The high-resolution pan-Amazon LiDAR data showed that environmental variables that drive growth in height are fundamentally different from environmental variables that support their survival. While precipitation and temperature seem to have lower importance for their survival than expected from previous studies, changes in wind and radiation regimes could reshape our forested biomes. This should be carefully considered by policy-makers when identifying important hotspots for the conservation of biodiversity in the Amazon.

Very few studies have been devoted to understanding the digital terrain model (DTM) creation for Amazon forests. DTM has a special and important role when airborne laser scanning is used to estimate vegetation biomass. We examined the influence of pulse density, spatial resolution, filter algorithms, vegetation density and slope on the DTM quality. Three Amazonian forested areas were surveyed with airborne laser scanning, and each original point cloud was reduced targeting to 20, 15, 10, 8, 6, 4, 2, 1, 0.75, 0.5 and 0.25 pulses per square meter based on a random resampling process. The DTM from resampled clouds was compared with the reference DTM produced from the original LiDAR data by calculating the deviation pixel by pixel and summarizing it through the root mean square error (RMSE). The DTM from resampled clouds were also evaluated considering the level of agreement with the reference DTM. Our study showed a clear trade-off between the return density and the horizontal resolution. Higher forest canopy density demanded higher return density or lower DTM resolution. RESUMO São poucos os estudos dedicados a entender a criação de modelo digital de terreno (MDT) para florestas amazônicas. O MDT tem uma importante função quando o escaneamento laser aerotransportado é usado para estimar a biomassa da vegetação. Examinamos a relação da densidade de pulsos, resolução espacial, algoritmos de filtragem, densidade da vegetação e inclinação do terreno com a qualidade do MDT. Três áreas de floresta amazônica foram sobrevoadas usando LiDAR aerotransportado. Cada nuvem de dados original teve sua densidade reduzida objetivando 20; 15; 10; 8; 6; 4; 2; 1; 0,75; 0,5 e 0,25 pulsos por metro quadrado, utilizando um processo de reamostragem aleatória. Os MDTs das nuvens reamostradas foram comparados com o MDT de referência, produzido a partir da nuvem original, calculando o desvio pixel a pixel e resumindo-o por meio do erro padrão da estimativa (RMSE). Os MDTs das nuvens reamostradas também foram avaliados quanto ao nível de correspondência com o MDT de referência. Houve uma clara compensação entre densidade de pontos e resolução horizontal. Dosséis mais densos exigem uma maior densidade de retornos, ou MDT com menor resolução.

The objective of this study was to model the potential effect of future climate change on the distribution of a tree species indicator of Cerrado. For the modeling, we used 488 occurrence points of the species and also bioclimatic variables corresponding to 2050 and 2070, for the more optimistic and pessimistic scenarios. All generated models were classified as consistent, getting an area under curve higher than 0.90. The current modeling of Connarus suberosus showed that 88% of the area with a high probability of species occurrence is inside the Cerrado domain. Future projections suggest losses in the environmental suitability area around 40.8% and 44.8% in the optimistic scenario, 61.6% and 81.6% in the pessimistic scenario considering 2050 and 2070, respectively. Furthermore, we found a tendency of the C. suberosus to move in the Atlantic Forest direction. This modeling is an alert that the C. suberosus will suffer from future climate change.