Caracterização do cenário dos artigos publicados sobre os produtos florestais não madeireiros, em âmbito nacional e internacional nos últimos 21 anos

Orientador : Msc. Timni VieiraMonografia (especialização) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Curso de Especialização em Gestão FlorestalInclui referênciasResumo : O interesse pelos Produtos Florestais Não Madeireiros (PFNMs) vem crescendo ao longo dos anos, por serem uma fonte de matéria prima. Estes produtos fazem parte da subsistência e são fontes de renda de milhares de populações extrativistas que vivem dentro ou no entorno de florestas. A grande maioria destes produtos é explorada de forma predatória, sem respeito aos aspectos ambientais e ecológicos. Diante disso, foi feita uma revisão bibliográfica dos artigos publicados em revistas de todo o mundo, com base em determinados critérios, sobre PFNMs nos últimos anos como forma de caracterizar o cenário dos estudos e averiguar os tipos de produtos, de enfoques e de metodologias que estão sendo empregados nas pesquisas sobre PFNMs. Cerca de 218 artigos publicados entre 1990 e 2011 foram revisados. Os campos de estudos abarcam, na sua maioria, os países pertencentes ao continente Asiático e Africano, e a América do Sul. Há poucos estudos sobre o entendimento do ciclo e melhoria da produtividade. A maior parte das pesquisas enfoca a caracterização econômica e a forma como o manejo vem ocorrendo dos mesmos PFNMs, sempre com respeito às espécies mais tradicionalmente exploradas por populações tradicionais

Florística e estrutura de um fragmento de Floresta Ombrófila Mista no Planalto Catarinense

O Planalto Catarinense constitui-se em uma importante região estratégica para estudos referentes à conservação, em função da ocorrência de remanescentes de Floresta Ombrófila Mista e por ser uma área de recarga e afloramento do aquífero Guarani. Com o objetivo de avaliar a similaridade florística entre diferentes áreas amostrais e descrever a estrutura do componente arbóreo, foram alocadas, no Parque Natural Municipal de Lages, SC, quatro parcelas permanentes (40 x 40m) e cada uma foi dividida em 16 unidades amostrais de 10 x 10m. Árvores com DAP ≥ 5cm foram mapeadas, marcadas e mensuradas. Os espécimes foram coletados, identificados e depositados em herbário. Foram amostradas 46 espécies distribuídas em 39 gêneros e 27 famílias. As famílias mais ricas em espécies foram Myrtaceae, Lauraceae, Salicaceae e Sapindaceae as quais apresentaram alta densidade, assim como Dicksoniaceae e Clethraceae. Sete espécies somaram mais de 60% do total de indivíduos amostrados. A diversidade específica (H’) foi de 3,05 nats.ind-1 (J’= 0,81). A similaridade entre as parcelas foi de 32 a 44%, indicando baixa semelhança entre as parcelas estudadas. A distribuição espacial da maioria das espécies é classificada como agregada, conforme o índice de Morisita. Esta floresta é considerada rica e diversa, com espécies arbóreas ameaçadas de extinção tais como Araucaria angustifolia e Dicksonia sellowiana. Devido à grande importância ecológica para a flora e fauna local e o processo de fragmentação na região, este remanescente florestal deve ser protegido e conservado, visto que ainda ocorrem interferências antrópicas negativas

Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study From Central Gabon

NASA's Global Ecosystem Dynamic Investigation (GEDI) mission has been designed to measure forest structure using lidar waveforms to sample the earth's vegetation while in orbit aboard the International Space Station. In this paper, we used airborne large-footprint (LF) lidar measurements to simulate GEDI observations from which we retrieved ground elevation, vegetation height, and aboveground biomass (AGB). GEDI-like product accuracy was then assessed by comparing them to similar products derived from airborne small-footprint (SF) lidar measurements. The study focused on tropical forests and used data collected during the NASA and European Space Agency (ESA) AfriSAR ground and airborne campaigns in the Lope National Park in Central Gabon. The measurements covered a gradient of successional stages of forest development with different height, canopy density, and topography. The comparison of the two sensors shows that LF lidar waveforms and simulated waveforms from SF lidar are equivalent in their ability to estimate ground elevation (RMSE = 0.5 m, bias = 0.29 m) and maximum forest height (RMSE = 2.99 m, bias = 0.24 m) over the study area. The difference in the AGB estimated from both lidar instruments at the 1-ha spatial scale is small over the entire study area (RMSE = 6.34 Mg·ha-1, bias = 11.27 Mg·ha-1) and the bias is attributed to the impact of ground slopes greater than 10–20° on the LF lidar measurements of forest height. Our results support the ability of GEDILF lidar to measure the complex structure of humid tropical forests and provide AGB estimates comparable to SF-derived ones

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R2 with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain

Treetop: A Shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists

Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR.The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open-source web-based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting-edge analyses of properties related to forest ecology and management.We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post-hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest.treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open-source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function.The authors thank Nicholas L. Crookston for co‐developing the web‐LiDAR treetop tool, and the two anonymous reviewers for their helpful suggestions on the first version of the manuscript. This study is based on the work supported by the Department of Defence Strategic Environmental Research and Development Program (SERDP) under grants No. RC‐2243, RC19‐1064 and RC20‐1346 and USDA Forest Service (grand No. PRO00031122

Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models esti-mating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems

Co-limitation towards lower latitudes shapes global forest diversity gradients

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers