Harpy Eagle (Harpia harpyja) nest tree selection: Selective logging in Amazon forest threatens Earth's largest eagle

2020 ◽  
Vol 250 ◽  
pp. 108754 ◽  
Author(s):  
Everton B.P. Miranda ◽  
Carlos A. Peres ◽  
Miguel Ângelo Marini ◽  
Colleen T. Downs
2019 ◽  
Vol 11 (6) ◽  
pp. 709 ◽  
Author(s):  
Ekena Rangel Pinagé ◽  
Michael Keller ◽  
Paul Duffy ◽  
Marcos Longo ◽  
Maiza dos-Santos ◽  
...  

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.


2015 ◽  
Vol 45 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Paulo Maurício Lima de Alencastro GRAÇA ◽  
Francisco Dario MALDONADO ◽  
João Roberto dos SANTOS ◽  
Edwin Willem Hermanus KEIZER

Radiometric changes observed in multi-temporal optical satellite images have an important role in efforts to characterize selective-logging areas. The aim of this study was to analyze the multi-temporal behavior of spectral-mixture responses in satellite images in simulated selective-logging areas in the Amazon forest, considering red/near-infrared spectral relationships. Forest edges were used to infer the selective-logging infrastructure using differently oriented edges in the transition between forest and deforested areas in satellite images. TM/Landsat-5 images acquired at three dates with different solar-illumination geometries were used in this analysis. The method assumed that the radiometric responses between forest with selective-logging effects and forest edges in contact with recent clear-cuts are related. The spatial frequency attributes of red/near infrared bands for edge areas were analyzed. Analysis of dispersion diagrams showed two groups of pixels that represent selective-logging areas. The attributes for size and radiometric distance representing these two groups were related to solar-elevation angle. The results suggest that detection of timber exploitation areas is limited because of the complexity of the selective-logging radiometric response. Thus, the accuracy of detecting selective logging can be influenced by the solar-elevation angle at the time of image acquisition. We conclude that images with lower solar-elevation angles are less reliable for delineation of selecting logging.


2008 ◽  
Vol 254 (2) ◽  
pp. 335-349 ◽  
Author(s):  
Alexandre M. Sebbenn ◽  
Bernd Degen ◽  
Vânia C.R. Azevedo ◽  
Marivana B. Silva ◽  
André E.B. de Lacerda ◽  
...  

2021 ◽  
pp. 1-15
Author(s):  
EVERTON B. P. MIRANDA ◽  
CAIO F. KENUP ◽  
CHARLES A. MUNN ◽  
NIKI HUIZINGA ◽  
NICKOLAS LORMAND ◽  
...  

Summary Tourism can be a powerful tool for wildlife conservation if well controlled and responsibly managed. Apex predators constitute particularly attractive subjects for tourism, but simultaneously they may generate conflict with local communities. Harpy Eagles Harpia harpyja are the largest eagle species and are highly sought-after by ecotourists. The last stronghold of the Harpy Eagle is the Amazon Forest, which is being deforested for cattle ranching. We tested methods for developing Harpy Eagle ecotourism as a potential tool to harmonize these issues. Using camera traps, we collected data on timing of Harpy Eagle visits to their nests, as well as on probabilities of viewing an eagle. Harpy Eagles can only be seen predictably during the first 12 of the 30–36 month nest cycle. In nests with nestlings (up to 5–7 months), adults are visible on a daily basis, and this period lasts 16.6% of the nesting cycle, demanding a minimum of 13, 17, and 26 nests to have at least one nest with a nestling on 90%, 95% and 99% of the days. After this 5–7 month window, we found that two and 4.16 days spent at nests afforded high probabilities of sighting a fledgling or adult eagle, respectively. Harpy Eagles were mainly active at the beginning and the end of the day. Activity core lasted 6.5 decimal hours for adults, peaking at 10h00, and 7.45 decimal hours for fledged eagles, peaking at 15h00. Our results demonstrate that Harpy Eagles fit several criteria for a viable wildlife attraction: predictable in activity and location, viewable, and diurnal, even though at the same time they are considered a rarity. In a broader perspective, Harpy Eagle tourism shows every indication of being a significant tool for more robust rainforest conservation.


2019 ◽  
Vol 432 ◽  
pp. 607-611
Author(s):  
Emílio Manabu Higashikawa ◽  
Maria Marcela Ortiz Brasil ◽  
William Ernest Magnusson

2005 ◽  
Vol 9 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Lydia P. Olander ◽  
Mercedes M. Bustamante ◽  
Gregory P. Asner ◽  
Everaldo Telles ◽  
Zayra Prado ◽  
...  

Abstract In the Brazilian Amazon, selective logging is second only to forest conversion in its extent. Conversion to pasture or agriculture tends to reduce soil nutrients and site productivity over time unless fertilizers are added. Logging removes nutrients in bole wood, enough that repeated logging could deplete essential nutrients over time. After a single logging event, nutrient losses are likely to be too small to observe in the large soil nutrient pools, but disturbances associated with logging also alter soil properties. Selective logging, particularly reduced-impact logging, results in consistent patterns of disturbance that may be associated with particular changes in soil properties. Soil bulk density, pH, carbon (C), nitrogen (N), phosphorus (P), calcium (Ca), magnesium (Mg), potassium (K), iron (Fe), aluminum (Al), δ13C, δ15N, and P fractionations were measured on the soils of four different types of logging-related disturbances: roads, decks, skids, and treefall gaps. Litter biomass and percent bare ground were also determined in these areas. To evaluate the importance of fresh foliage inputs from downed tree crowns in treefall gaps, foliar nutrients for mature forest trees were also determined and compared to that of fresh litterfall. The immediate impacts of logging on soil properties and how these might link to the longer-term estimated nutrient losses and the observed changes in soils were studied. In the most disturbed areas, roads and decks, the authors found litter biomass removed and reduced soil C, N, P, particularly organic P, and δ13C. Soils were compacted and often experienced reducing conditions in the deck areas, resulting in higher pH, Ca, and Mg. No increases in soil nutrients were observed in the treefall gaps despite the flush of nutrient-rich fresh foliage in the tree crown that is left behind after the bole wood is removed. Observed nutrient losses are most likely caused by displacement of the litter layer. Increases in soil pH, Ca, and Mg occur in areas with reducing conditions (decks and roads) and may result from Fe reduction, freeing exchange sites that can then retain these cations. Calculations suggest that nutrient inputs from crown foliage in treefall gaps are probably too small to detect against the background level of nutrients in the top soils. The logging disturbances with the greatest spatial extent, skids and gaps, have the smallest immediate effect on soil nutrients, while those with the smallest spatial extent, roads and decks, have the largest impact. The changes observed 3–6 months after logging were similar to those measured 16 yr after logging, suggesting some interesting linkages between the mechanisms causing the immediate change and those maintaining these changes over time. The direct impacts on soil properties appear less important than the loss of nutrients in bole wood in determining the sustainability of selective logging. Medium-to-low intensity selective logging with a sufficiently long cutting cycle may be sustainable in these forests.


FLORESTA ◽  
2020 ◽  
Vol 50 (4) ◽  
pp. 1873
Author(s):  
Juliana Marchesan ◽  
Elisiane Alba ◽  
Mateus Sabadi Schuh ◽  
José Augusto Spiazzi Favarin ◽  
Rudiney Soares Pereira

The tropical forest is characterized by expressive biomass and stores high amounts of carbon, which is an important variable for climate monitoring. Thus, studies aiming to analyze suitable methods to predict biomass are crucial, especially in the tropics, where dense vegetation makes modeling difficult. Thus, the objective of the present study was to estimate aboveground biomass (AGB) in a tropical forest area with selective logging in the Amazon forest using the Random Forest (RF) machine learning algorithm and LiDAR data. For this, 85 sample units were used at Fazenda Cauaxi, in the municipality of Paragominas, Pará State. LiDAR data were collected in 2014 and made available by the Sustainable Landscapes Project. The software R was used for data analysis. Among the LiDAR metrics, the average height was used as it had the greatest significance to compose the model. The model presented a pseudo R² of 0.69 (value obtained by the RF), Spearman's Correlation Coefficient of 0.80, RMSE of 47.05 Mg.ha-1 (19.84%), and Bias of 2.06 Mg.ha-1 (0.87%). With the results, it was possible to infer that the average height metric was enough to estimate AGB in a tropical forest with selective logging, in addition, the RF algorithm the biomass to be estimated, which can be used to assist in monitoring and action management in areas of selective logging and serve as a basis for climate change mitigation policies.


2018 ◽  
Vol 48 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Paulo Henrique da SILVA ◽  
Lucas Rezende GOMIDE ◽  
Evandro Orfanó FIGUEIREDO ◽  
Luis Marcelo Tavares de CARVALHO ◽  
Antônio Carlos FERRAZ-FILHO

ABSTRACT Reduced-impact logging is a well known practice applied in most sustainable forest management plans in the Amazon. Nevertheless, there are still ways to improve the operational planning process. Therefore, the aim of this study was to create an integer linear programming (ILP) to fill in the knowledge gaps in the decision support system of reduced impact logging explorations. The minimization of harvest tree distance to wood log landing was assessed. Forest structure aspects, income and wood production were set in the model, as well as the adjacency constraints. Data are from a dense ombrophylous forest in the western Brazilian Amazon. We applied the phytosociological analysis and BDq method to define the selective logging criteria. Then, ILP models were formulated to allow the application of the constraints. Finally, 32 scenarios (unbalanced forest, UF, and balanced forest, BF) were generated and compared with real executed plans (RE). Robust results were achieved and the expected finding of each scenario was met. The feasibility to integrate ILP models in uneven-aged forest management projects was endorsed. Consequently, the UF and BF scenarios tested were efficient and concise, introducing new advances for forest management plans in the Amazon. The proposed models have a high potential to improve the selective logging activities in the Amazon forest.


2021 ◽  
Vol 500 ◽  
pp. 119648
Author(s):  
Marcus Vinicio Neves d'Oliveira ◽  
Evandro Orfanó Figueiredo ◽  
Danilo Roberti Alves de Almeida ◽  
Luis Claudio Oliveira ◽  
Carlos Alberto Silva ◽  
...  

2021 ◽  
Vol 13 (23) ◽  
pp. 4944
Author(s):  
Tahisa Neitzel Kuck ◽  
Paulo Fernando Ferreira Silva Filho ◽  
Edson Eyji Sano ◽  
Polyanna da Conceição Bispo ◽  
Elcio Hideiti Shiguemori ◽  
...  

It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.


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