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Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information. The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes. Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities.

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For more than three decades, major efforts in sampling and analyzing tree diversity in South America have focused almost exclusively on trees with stems of at least 10 and 2.5 cm diameter, showing highest species diversity in the wetter western and northern Amazon forests. By contrast, little attention has been paid to patterns and drivers of diversity in the largest canopy and emergent trees, which is surprising given these have dominant ecological functions. Here, we use a machine learning approach to quantify the importance of environmental factors and apply it to generate spatial predictions of the species diversity of all trees (dbh ≥ 10 cm) and for very large trees (dbh ≥ 70 cm) using data from 243 forest plots (108,450 trees and 2832 species) distributed across different forest types and biogeographic regions of the Brazilian Amazon. The diversity of large trees and of all trees was significantly associated with three environmental factors, but in contrasting ways across regions and forest types. Environmental variables associated with disturbances, for example, the lightning flash rate and wind speed, as well as the fraction of photosynthetically active radiation, tend to govern the diversity of large trees. Upland rainforests in the Guiana Shield and Roraima regions had a high diversity of large trees. By contrast, variables associated with resources tend to govern tree diversity in general. Places such as the province of Imeri and the northern portion of the province of Madeira stand out for their high diversity of species in general. Climatic and topographic stability and functional adaptation mechanisms promote ideal conditions for species diversity. Finally, we mapped general patterns of tree species diversity in the Brazilian Amazon, which differ substantially depending on size class.

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The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r 2 = .35 [.12, .51] (mean [min, max]) than for tree density r 2 = .23 [.05, .42]. The best predictive performance when mapping tree height was for broadleaf and coniferous-broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for analytical tools that help us exploit the full potential of the Mexican forest inventory datasets.

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Amazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree-level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat-derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha-1 year-1 during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest-based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods.

Los bosques amazónicos son reservorios y sumideros de carbono, contribuyendo a la mitigación del cambio climático. Si bien experimentan perturbaciones, el efecto de estas en la biomasa y biodiversidad a través del tiempo no ha sido evaluado a gran escala. En este estudio, evaluamos el grado de perturbación forestal reciente en la Amazonía peruana y los efectos de las perturbaciones, condiciones ambientales y actividad antrópica sobre la biomasa y la biodiversidad en bosques perturbados. Los datos de biomasa aérea y riqueza de especies forestales provenientes de 1,840 subparcelas del Inventario Nacional Forestal y de Fauna Silvestre (INFFS) se analizaron en conjunto con la información de detección de cambios de cobertura forestal derivadas de perturbaciones detectadas a partir de series de tiempo de índices de diferencia de humedad normalizados (NDMI) a partir de imágenes Landsat. Nuestros resultados muestran un claro efecto negativo de la intensidad de las perturbaciones sobre la riqueza de especies arbóreas. Este efecto también fue observado en los valores de recuperación de biomasa aérea y riqueza de especies arbóreas hacia niveles no perturbados, así como en la recuperación de la composición florística. El tiempo transcurrido desde la perturbación tuvo un efecto mayor sobre la biomasa aérea que sobre la riqueza de especies. Mientras el tiempo desde una perturbación forestal tuvo un efecto positivo sobre la biomasa área, se observó un pequeño efecto negativo sobre la riqueza de especies. Estimamos que aproximadamente el 15% de los bosques en la Amazonía peruana han experimentado una perturbación al menos una vez desde 1984, y que, tras esta, han aumentado en biomasa aérea en una tasa de 4.7 Mg ha−1 año−1 durante los primeros 20 años posteriores al evento de perturbación. Además, el efecto positivo de la cubierta forestal circundante fue evidente tanto para la biomasa aérea como para su recuperación hacia niveles no perturbados, así como para los valores de riqueza de especies. La accesibilidad a bosques tuvo un efecto negativo en la recuperación de la composición de especies hacia niveles no perturbados. Recomendamos que los esfuerzos de mitigación de cambio climático basados en bosques tengan en cuenta las perturbaciones forestales mediante el análisis integrado de información de inventarios forestales con métodos de teledetección.

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While the Brazilian National Forest Inventory (NFI) is in progress, there is a growing demand to understand the effect of cluster size on the accuracy and precision of forest-attribute estimation. We aimed to find the minimum cluster size (in area) to estimate merchantable volume (MV) with the same accuracy and precision as the estimates derived from the original cluster of 8,000 m2. We used data from an inventory carried out in a forest unit (Bom Futuro National Forest) in the southwestern Brazilian Amazon, where 22 clusters were distributed as a two-stage sampling design. Three products were evaluated: (i) MV of trees with a diameter at breast height (DBH) ≥ 20 cm (P1); (ii) MV of trees with DBH ≥ 50 cm (P2); and (iii) MV of commercial species with DBH ≥ 50 cm and stem quality ‘level 1 or ‘level 2 (P3). We assessed ten scenarios in which the cluster size was reduced from 8,000 m2 to 800 m2. The accuracy of P1, P2 and P3 was highly significantly lower for reductions < 2,400 m². The precision was more sensitive to variations in cluster size, especially for P2 and P3. Minimum cluster sizes were ≥ 2,400 m² to estimate P1, ≥ 4,800 m² to estimate P2, and ≥ 7,200 m² to estimate P3. We concluded that it is possible to reduce the cluster size without losing the accuracy and precision given by the original NFI cluster. A cluster of 2,400 m² provides estimates as accurate as the original cluster, regardless of the evaluated product.(AU)

Enquanto o Inventário Florestal Nacional Brasileiro (IFN) está em andamento, há uma demanda crescente para entender o efeito da área do conglomerado sobre a exatidão e precisão da estimativa de atributos florestais. O objetivo deste estudo foi determinar a área mínima de um conglomerado para estimar o volume comercial (VC) com a mesma acurácia e precisão que as estimativas derivadas do conglomerado original de 8.000 m². A base de dados é proveniente de um inventário realizado em uma unidade florestal (Floresta Nacional do Bom Futuro) no sudoeste da Amazônia brasileira, onde 22 conglomerados foram distribuídos em um desenho amostral em dois estágios. Foram avaliados três produtos: (i) VC de árvores com diâmetro à altura do peito (DAP) ≥ 20 cm (P1); (ii) VC de árvores com DAP ≥ 50 cm (P2); e (iii) VC de espécies comerciais com DAP ≥ 50 cm e qualidade de fuste ‘nível 1 ou ‘nível 2 (P3). O estudo avaliou dez cenários em que a área do conglomerado foi reduzida de 8.000 a 800 m². A acurácia de P1, P2 e P3 foi significativamente menor para reduções < 2.400 m². A precisão foi mais sensível à variação no tamanho do conglomerado, sobretudo para P2 e P3. Os tamanhos mínimos de conglomerado foram ≥ 2.400 m² para estimar P1, ≥ 4.800 m² para estimar P2 e ≥ 7.200 m² para estimar P3. Concluímos que é possível reduzir a área do conglomerado sem perder acurácia e precisão do conglomerado original do IFN. Um conglomerado de 2.400 m² fornece estimativas com a mesma acurácia que o conglomerado original, independentemente do produto avaliado.(AU)

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A list of species of Cerambycinae (Cerambycidae) collected in Iguaçu National Park (PNI) during the period from March 2012 to December 2018 is provided. A total of 135 species were sampled using different collection methods, all recorded for the first time in the PNI and including 21 new distribution records for the state of Paraná, and two new records for Brazil. The geographic distribution and material examined are stated for each species. A photograph of each newly recorded species is also provided.

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This paper presents four case studies in which forest data catalysed shifts in public policy and corporate activities. Brazil greatly reduced deforestation during the period between 2005 and 2014; Cameroon introduced a structured forest concessions regime; Viet Nam achieved their forest transition; and corporate operations around the world invested in supply chain management to alleviate deforestation concerns. We break the problem-solving required for these achievements into four steps: problem recognition, proposal and choice of solution, putting the solution into effect, and monitoring results. At each of these steps, we consider the relevant forest data. Data helped place issues on policymaker agendas, supported reaching sound decisions and enabled quantitative targets. Policy instruments for implementing change were built around available data and forest monitoring helped evaluate progress. The details of these successes can be an inspiration to those interested in improving collection of data on forests that can effectively support decision-making and better policies. There have been impressive recent improvements to many developing countries' national forest monitoring capabilities. The successful examples of data application presented and evaluated here provide insight into how these new data can be effectively leveraged.

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Abstract This work is an inventory of ferns and lycophytes of remnants in Amazônia Maranhense. The data provided are the results of collections made from 2010-2011 and 2016-2019 in various municipalities of Maranhão in the Amazonian part of the state. Sixty-four species of ferns and lycophytes, in 36 genera and 18 families, were recorded. Of these, 24 species are new records for Maranhão and four are new records for the Northeast Region of Brazil. The most representative family was Pteridaceae (23 species), followed by Polypodiaceae (7 species). The predominant life forms were terrestrial (37 species) and epiphytic (15 species); the latter is the highest number of epiphytic species recorded for a fern and lycophyte inventory in Maranhão. The data support the need for collecting ferns and lycophytes in Amazônia Maranhense to better understand the flora of these groups in the state.

Resumo Neste trabalho apresentamos um inventário de Samambaias e Licófitas ocorrentes em remanescentes da Amazônia Maranhense. Os dados aqui presentes são resultados de coletas entre os anos de 2010-2011 e 2016-2019 em vários municípios do Maranhão inseridos nesta porção amazônica do estado. Registrou-se um total de 64 espécies de Samambaias e Licófitas, distribuídos em 36 gêneros e 18 famílias. Vinte e quatro espécies são novas ocorrências para o Maranhão, com 4 destas relatadas pela primeira vez para a região Nordeste do Brasil. A família mais representativa foi Pteridaceae (23 espécies), seguida por Polypodiaceae (7 espécies). As formas de vida predominantes foram terrícolas com 37 espécies e epífitas com 15 espécies, sendo este número de epífitas o maior registrado em um inventário de Samambaias e Licófitas do Maranhão. Estes dados reforçam a necessidade de coletas destes grupos de plantas na Amazônia Maranhense de forma a caracterizar amplamente a flora de Samambaias e Licófitas do Maranhão.

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BACKGROUND: Ecotone has been defined as "a multi-dimensional environmentally stochastic interaction zone between ecological systems with characteristics defined in space and time, and by the strength of the interaction" (Hufkens et al. 2009). This is a known concept to define transitional zones between two or more ecological communities, ecosystems or biotic regions. Ecotone forests, dispersed in northern Brazilian Amazonia, are natural formations which have been largely affected by anthropogenic impacts, such as deforestation and fire. Maracá Ecological Station, State of Roraima, Brazil, is a protected area with extensive representations of ecotone forests in this region of the Amazonia. Forest inventories and floristic surveys are important as they extend our knowledge (1) of forest structure and tree species composition and (2) of tree and palm species ecology in this region of the Amazonia. Both improve our ability to predict changes in plant diversity, considering the future scenarios of climate change in comparison with previous surveys performed in Maracá. NEW INFORMATION: We present a forest inventory carried out in 129 plots (10 m x 50 m; 6.45 ha in total) dispersed in a grid (5 km x 5 km) located in a forest zone ecotone in the eastern part of Maracá Ecological Station. All stems (tree + palm) with diameter at breast height ≥ 10 cm were recorded, identified and measured. A total of 3040 stems were recorded (tree = 2815; palm = 225), corresponding to 42 botanic families and 140 identified species. Seven families and 20 genera contained unidentified taxa (12.2%). Sapotaceae (735 stems; 10 species), Leguminosae (409; 24) and Rubiaceae (289; 12) were the most abundant families. Peltogyne gracilipes Ducke (Leguminosae), Pradosia surinamensis (Eyma) T.D.Penn. (Sapotaceae) and Ecclinusa guianensis Eyma (Sapotaceae) were the species with the highest importance value index (~ 25%). The dominance (m2 ha-1) of these species corresponds to > 36% of the total value observed in the forest inventory. Our dataset provides complementary floristic and structure information on tree and palm in Maracá, improving our knowledge of this Amazonian ecotone forest.

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Remote sensing is revolutionizing the way in which forests studies are conducted, and recent technological advances, such as Structure from Motion (SfM) photogrammetry from Unmanned Aerial Vehicle (UAV), are providing more efficient methods to assist in REDD (Reducing Emissions from Deforestation and forest Degradation) monitoring and forest sustainable management. The aim of this work was to develop and test a methodology based on SfM from UAV to generate high quality Digital Terrain Models (DTMs) on teak plantations (Tectona grandis Linn. F.) situated in the Coastal Region of Ecuador (dry tropical forest). UAV overlapping images were collected using a DJI Phantom 4 Advanced© quadcopter during the dry season (leaf-off phenological stage) over 58 teak square plots of 36 m side belonging to three different plantations located in the province of Guayas (Ecuador). A workflow consisting of SfM absolute image alignment based on field surveyed ground control points, very dense point cloud generation, ground points filtering and outlier removal, and DTM interpolation from labeled ground points, was accomplished. A very accurate Terrestrial Laser Scanning (TLS) derived ground points were employed as ground reference to estimate the UAV-SfM DTM vertical error in each reference plot. The plot-level obtained DTMs presented low vertical bias and random error (-3.1 cm and 11.9 cm on average, respectively), showing statistically significant greater error in those reference plots with basal area and estimated vegetation coverage above 15 m2/ha and 60%, respectively. To the best of the authors' knowledge, this is the first study aimed at monitoring of teak plantations located in dry tropical forests from UAV images. It provides valuable information that recommends carrying out the UAV image capture during the leaf-off season to obtain UAV-SfM derived DTMs suitable to serve as ground reference in supporting teak plantations inventories.

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Nutrient concentrations in tree biomass components may vary with the component considered, age and genetic material and its measurement is important to understand forestry nutrient management. Due to it, above ground biomass production and nutrient accumulation and its potential removal through thinning in two species of five-year-old Eucalyptus with were evaluated in the southern of Brazil. Treatments were arranged in a randomized block design with three replications in a 2 x 4 factorial scheme, being 2 species (E. grandis and E. urophylla) and four diametric classes (C1: 3.0 - 8.8; C2: 8.9 - 14.7; C3: 14.8 - 20.6; C4: 20.7 - 26.5 cm). Average annual accumulation of dry matter in both species was of 16.9 Mg ha-¹. Biomass was mainly allocated in the stem (wood and bark). Trees’ canopy (branches and leaves) represent the smallest fractions, with only 14,3% of the total biomass. E. grandis showed highest values of wood and leaf compartments. The highest content of N and K were found in the leaves, with N levels of 33.2 and 30.5 g.Kg-¹ respectively to E. grandis and E. urophylla. Trees bark represent an important Ca and Mg reserve to the development of the plant, with values of 18.02 and 3.24 g.kg-¹ respectively. The average concentration of Ca, N, K, Mg and P in the total biomass of the two species was of 528, 305, 200, 128 and 30 kg ha-¹.

As concentrações de nutrientes nos componentes da biomassa arbórea podem variar em função do componente, da idade da plantação e o material genético, e sua mensuração é importante para entender o manejo florestal de nutrientes do solo. A produção de biomassa da parte aérea, o acúmulo de nutrientes e o seu potencial de remoção em duas espécies de Eucalyptus com cinco anos de idade foram avaliados no sul do Brasil. Os tratamentos foram dispostos em delineamento de blocos casualizados com três repetições, em esquema fatorial 2 x 4, sendo duas espécies (E. grandis e E. urophylla) e quatro classes diamétricas (C1: 3,0 - 8,8; C2: 8,9 - 14,7; C3: 14,8 - 20,6; C4: 20,7 - 26,5 cm). O acúmulo médio anual de matéria seca em ambas as espécies foi de 16,9 Mg ha-¹. A biomassa foi alocada principalmente no caule (madeira e casca). O dossel das árvores (ramos e folhas) representa as menores frações, com apenas 14,3% da biomassa total. E. grandis apresentou maiores valores de madeira e compartimentos foliares. Os maiores teores de N e K foram encontrados nas folhas, com níveis de N de 33,2 e 30,5 g.Kg-¹, respectivamente, para E. grandis e E. urophylla. A casca das árvores representa uma importante reserva de Ca e Mg para o desenvolvimento da planta, com valores de 18,02 e 3,24 g.kg-¹, respectivamente. As concentrações médias de Ca, N, K, Mg e P na biomassa total das duas espécies foram de 528, 305, 200, 128 e 30 kg ha-¹.

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Nutrient concentrations in tree biomass components may vary with the component considered, age and genetic material and its measurement is important to understand forestry nutrient management. Due to it, above ground biomass production and nutrient accumulation and its potential removal through thinning in two species of five-year-old Eucalyptus with were evaluated in the southern of Brazil. Treatments were arranged in a randomized block design with three replications in a 2 x 4 factorial scheme, being 2 species (E. grandis and E. urophylla) and four diametric classes (C1: 3.0 - 8.8; C2: 8.9 - 14.7; C3: 14.8 - 20.6; C4: 20.7 - 26.5 cm). Average annual accumulation of dry matter in both species was of 16.9 Mg ha-¹. Biomass was mainly allocated in the stem (wood and bark). Trees canopy (branches and leaves) represent the smallest fractions, with only 14,3% of the total biomass. E. grandis showed highest values of wood and leaf compartments. The highest content of N and K were found in the leaves, with N levels of 33.2 and 30.5 g.Kg-¹ respectively to E. grandis and E. urophylla. Trees bark represent an important Ca and Mg reserve to the development of the plant, with values of 18.02 and 3.24 g.kg-¹ respectively. The average concentration of Ca, N, K, Mg and P in the total biomass of the two species was of 528, 305, 200, 128 and 30 kg ha-¹.(AU)

As concentrações de nutrientes nos componentes da biomassa arbórea podem variar em função do componente, da idade da plantação e o material genético, e sua mensuração é importante para entender o manejo florestal de nutrientes do solo. A produção de biomassa da parte aérea, o acúmulo de nutrientes e o seu potencial de remoção em duas espécies de Eucalyptus com cinco anos de idade foram avaliados no sul do Brasil. Os tratamentos foram dispostos em delineamento de blocos casualizados com três repetições, em esquema fatorial 2 x 4, sendo duas espécies (E. grandis e E. urophylla) e quatro classes diamétricas (C1: 3,0 - 8,8; C2: 8,9 - 14,7; C3: 14,8 - 20,6; C4: 20,7 - 26,5 cm). O acúmulo médio anual de matéria seca em ambas as espécies foi de 16,9 Mg ha-¹. A biomassa foi alocada principalmente no caule (madeira e casca). O dossel das árvores (ramos e folhas) representa as menores frações, com apenas 14,3% da biomassa total. E. grandis apresentou maiores valores de madeira e compartimentos foliares. Os maiores teores de N e K foram encontrados nas folhas, com níveis de N de 33,2 e 30,5 g.Kg-¹, respectivamente, para E. grandis e E. urophylla. A casca das árvores representa uma importante reserva de Ca e Mg para o desenvolvimento da planta, com valores de 18,02 e 3,24 g.kg-¹, respectivamente. As concentrações médias de Ca, N, K, Mg e P na biomassa total das duas espécies foram de 528, 305, 200, 128 e 30 kg ha-¹.(AU)

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A list of species of Lamiinae (Cerambycidae) collected in Iguaçu National Park (PNI) during the period from March 2012 to December 2015, using different collection methods, is provided. A total of 134 species were sampled, all recorded for the first time in the PNI and including 38 new distribution records for the state of Paraná, and one new record for Brazil. The geographic distribution and material examined are stated for each species. Each new record also accompanied by an illustration of the species.

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Este trabalho teve como objetivo avaliar o ajuste de modelos de taper com e sem casca para o clone GG100 em um plantio comercial localizado no sul do estado do Tocantins. Foram cubadas 25 árvores em cujos dados se ajustou cinco modelos de taper, sendo três representando os mais utilizados no Brasil e dois representando os modelos pouco testados no país. Na avaliação dos modelos de taper, adotou-se os critérios estatísticos: erro padrão da estimativa, coeficiente de determinação ajustado, desvio médio, soma de quadrados do resíduo relativo, resíduo percentual e análise da distribuição de resíduos. Concluiu-se por um modelo do tipo expoente-forma para descrever o perfil do tronco com e sem casca de árvores individuais do clone GG100.(AU)

The objective of this work was to evaluate the adjustment of taper models with and without bark for clone GG100 in a commercial plantation located in the southern state of Tocantins. Twenty-five trees were cubed in which data were fitted five taper models, three of which were the most used in Brazil and two representing the poorly tested models in the country. In the evaluation of the taper models, the following statistical criteria were adopted: standard error of the estimate, adjusted coefficient of determination, mean deviation, sum of squares of the relative residue, percentage residue, and analysis of the distribution of residues. It was concluded by an exponent-shape type model to describe the trunk profile with and without bark of individual clones of clone GG100.(AU)

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Este trabalho teve como objetivo avaliar o ajuste de modelos de taper com e sem casca para o clone GG100 em um plantio comercial localizado no sul do estado do Tocantins. Foram cubadas 25 árvores em cujos dados se ajustou cinco modelos de taper, sendo três representando os mais utilizados no Brasil e dois representando os modelos pouco testados no país. Na avaliação dos modelos de taper, adotou-se os critérios estatísticos: erro padrão da estimativa, coeficiente de determinação ajustado, desvio médio, soma de quadrados do resíduo relativo, resíduo percentual e análise da distribuição de resíduos. Concluiu-se por um modelo do tipo expoente-forma para descrever o perfil do tronco com e sem casca de árvores individuais do clone GG100.

The objective of this work was to evaluate the adjustment of taper models with and without bark for clone GG100 in a commercial plantation located in the southern state of Tocantins. Twenty-five trees were cubed in which data were fitted five taper models, three of which were the most used in Brazil and two representing the poorly tested models in the country. In the evaluation of the taper models, the following statistical criteria were adopted: standard error of the estimate, adjusted coefficient of determination, mean deviation, sum of squares of the relative residue, percentage residue, and analysis of the distribution of residues. It was concluded by an exponent-shape type model to describe the trunk profile with and without bark of individual clones of clone GG100.

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A list of species of the Prioninae, Parandrinae and Lepturinae (Cerambycidae) and Disteniidae sampled in the Iguaçu National Park (PNI) from March 2012 to December 2015 using different collection methods, is presented. Twenty species were recorded, all of which are registered for the first time for the PNI, including three Parandrinae species represent new distribution records for the state of Paraná. Details of distribution, the material examined and an illustration are given for each species.

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ABSTRACT Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

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ABSTRACT Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

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BACKGROUND: To understand the impacts of global changes on future community compositions, knowledge of community dynamics is of crucial importance. To improve our knowledge of community composition, biomass stock and maintenance of gallery forests in the Brazilian Cerrado, we provide two datasets from the 0.5 ha Corrego Fazendinha Gallery Forest Dynamics Plot and the Corrego Fundo Gallery Forest Dynamics Plot situated in the Bom Despacho region, Minas Gerais, Southeastern Brazil. NEW INFORMATION: We report diameter at breast height, basal area and height measurements of 3417 trees and treelets identified during three censuses in both areas.

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This study aimed to map the stem biomass of an even-aged eucalyptus plantation in southeastern Brazil based on canopy height profile (CHPs) statistics using wall-to-wall discrete return airborne laser scanning (ALS), and compare the results with alternative maps generated by ordinary kriging interpolation from field-derived measurements. The assessment of stem biomass with ALS data was carried out using regression analysis methods. Initially, CHPs were determined to express the distribution of laser point heights in the ALS cloud for each sample plot. The probability density function (pdf) used was the Weibull distribution, with two parameters that in a secondary task, were used as explanatory variables to model stem biomass. ALS metrics such as height percentiles, dispersion of heights, and proportion of points were also investigated. A simple linear regression model of stem biomass as a function of the Weibull scale parameter showed high correlation (adj.R2 = 0.89). The alternative model considering the 30th percentile and the Weibull shape parameter slightly improved the quality of the estimation (adj.R2 = 0.93). Stem biomass maps based on the Weibull scale parameter doubled the accuracy of the ordinary kriging approach (relative root mean square error = 6 % and 13 %, respectively).

Assuntos