Equations to estimate tree gaps in a precision forest management area the amazon based on crown morphometry

ABSTRACT The precision forest management technique still has much to be improved with the incorporation of forest biometric techniques and forest profiling with airborne LIDAR. When planning the cutting of a tree in forest management, the volume to be produced for industry is estimated but not the area impacted by removal of the tree. The objective of the present study was to develop equations for the Amazon rainforest that are able to estimate the impact area of gaps from harvesting individual dominant and co-dominant trees based on the canopy morphology obtained through forest profiling. On two separate occasions profiles were made in an annual forest-production unit in the Antimary State Forest (FEA) in the state of Acre, Brazil. The first was done a few days before the start of logging in 2010 and the second was done after completion of harvest activities in 2011. With field measurements and processing of the cloud of LIDAR points, dendrometric and morphometric variables were obtained for the canopy in order to develop equations for estimating gap areas. After evaluation of the explanatory variables with the highest correlation with gap area, the method used considered all possible models and included 2-4 parameters. The explanatory variables that best represent the impact of clearings are volume of the crown (VCop) and crown-projection area (APC). Ten equations were selected, of which two were chosen for use; these had R2 aj > 75% and Syx <23%. The good fit of the equations demonstrates the potential use of LIDAR to obtain information for estimating in advance the gaps in the forest cover that will be created from harvesting trees of different sizes

MANEJO DE Amburana cearensis var. acreana NO ESTADO DO ACRE, BRASIL

This work has as its objectives: a) to assess the geographical distribution and population structure of Amburana cearensis var. acreana; b) to calculate sustainable cutting rates, according to stipulated cutting cycles, and c) to simulate the projected recovery potential in volume based on the calculated cutting rate. It was used data from sustainable forest management plans, and the results will contribute for future decisions about its endangered condition. The results did not corroborate the information that Amburana cearensis var. acreana is endangered in Acre state. However the management sustainability will only be feasible if considered the ideal remaining population structure and the estimative of the optimal cutting rate according to the cutting cycle.Os objetivos desse trabalho foram: a) analisar a distribui\ue7\ue3o geogr\ue1fica e a estrutura populacional de Amburana cearensis var. acreana; b) calcular taxas de corte sustent\ue1veis baseado em ciclos de corte estipulados e c) simular a recupera\ue7\ue3o potencial em volume baseado na taxa de corte calculada. Foram usados dados de planos de manejo florestal sustent\ue1vel, e os resultados contribuir\ue3o para as tomadas de decis\ue3o futuras sobre sua condi\ue7\ue3o de esp\ue9cie amea\ue7ada. Os resultados n\ue3o corroboram a informa\ue7\ue3o de que a Amburana cearensis var. acreana est\ue1 amea\ue7ada no Estado do Acre. Entretanto, o manejo sustent\ue1vel dessa esp\ue9cie s\uf3 ser\ue1 poss\uedvel se for considerada a estrutura para a popula\ue7\ue3o remanescente ideal e a estimativa da taxa \uf3tima de corte, considerando o ciclo de corte vigente

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Dados LiDAR e análise orientada a objeto no monitoramento de manejo florestal

Although selective logging activity has declined significantly in the past decade, it is still an important land use that affects biological diversity and carbon stocks in the Brazilian Amazon. We present initial results of the application of an object-oriented analysis of airborne small-footprint lidar data as a tool for monitoring forest management in the Antimary State Forest in Acre. For a trial area logged within a few months prior to data acquisition, we processed the lidar returns to calculate the relative density of returns between 0 and 1 m height. The resulting product highlights areas with bare-ground or minimal ground vegetation allowing us to visualize the network of roads and skid trails beneath the forest canopy. The product was segmented and classified into two categories: (1) roads and skid trails and (2) and all other. The resulting classification was compared to a reference data set developed by visual interpretation and validated by GPS ground control points. We found that 67.0% of the areas were correctly classified by our technique demonstrating the potential of this tool. In the future we hope to minimize the uncertainty in the classification by inclusion of more parameters into the decision rules for automated segmentation.Pages: 6171-617

Optimal strategies of ecosystem services provision for Amazonian production forests

Data and source R codes to support the article "Optimal strategies of Ecosystem Services provision for Amazonian production forests", published in Environmental Research Letters. The main code "main_Rscript_figshare.R" must be opened and run with the statistical software R ; it calls and opens other codes and data included in this repository