The Challenges of Sustainable Forest Operations in Amazonia

DeArmond, Daniel; Rovai, Andre; Suwa, Rempei; Higuchi, Niro

doi:10.1007/s40725-023-00210-4

Cited by 4 publications

(3 citation statements)

References 98 publications

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“…We define HLU as any of Croplands, Urban and Built-up Lands, and Cropland/ Natural Vegetation Mosaics. Thus, to avoid misinterpreting HLU changes after 2020 (DeArmond et al, 2023;INPE, 2023) as resilience changes, we only analyze pre-2020 years. In particular, Wang et al (2024) recently showed that anthropogenic influence highly affects resilience in the ARF.…”

Section: Grid Cell Selectionmentioning

confidence: 99%

Spatial Correlation Increase in Single‐Sensor Satellite Data Reveals Loss of Amazon Rainforest Resilience

Blaschke,

Nian,

Bathiany

et al. 2024

Earth's Future

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The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna‐like state. Whilst previous work has suggested that forest resilience has declined in recent decades, that work was based only on local resilience indicators, and moreover was potentially biased by the employed multi‐sensor and optical satellite data and undetected anthropogenic land‐use change. Here, we show that the average correlation between neighboring grid cells' vegetation time series, which is referred to as spatial correlation, provides a more robust resilience indicator than local estimations. We employ it to measure resilience changes in the ARF, based on single‐sensor Vegetation Optical Depth data under conservative exclusion of human activity. Our results show an overall loss of resilience until around 2019, which is especially pronounced in the southwestern and northern Amazon for the time period from 2002 to 2011. The results from the reliable spatial correlation indicator suggest that in particular the southwest of the ARF has experienced pronounced resilience loss over the last two decades.

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Section: Grid Cell Selectionmentioning

confidence: 99%

Spatial Correlation Increase in Single‐Sensor Satellite Data Reveals Loss of Amazon Rainforest Resilience

Blaschke,

Nian,

Bathiany

et al. 2024

Earth's Future

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“…The use of these machines offers several advantages, such as a lower energy use in operations and lower soil disturbance, a rather low cycle time in some operational contexts, and acceptable productivity [11]. These features make sledge yarders good candidates for ensuring the economic and environmental sustainability of forest operations [12,13]. Among their disadvantages is the fact that all the workers undertake their activities outdoors, commonly in rough terrain, and at the expense of a high physical effort, particularly when considering cable work [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Simulation Study of Noise Exposure in Sledge-Based Cable Yarding Operations

Borz,

Mederski,

Bembenek

2024

Forests

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Ensuring the safety of forestry workers is a key challenge, particularly when working with partly mechanized harvesting systems. Cable yarding is typically used in steep terrain timber harvesting. For long-distance extraction, one of the few alternatives is to use sledge yarders, but these machines may expose workers to high doses of noise. The goal of this study was to model haulers’ exposure to noise in sledge-based cable yarding operations, based on a simulation approach that considered variable factors such as the yarding distance, lateral yarding distance, and average skyline height. Taken into consideration were 165 scenarios developed by examining the variation in yarding distance (500 to 1500 m, with a step of 100 m), lateral yarding distance (10 to 50 m, with a step of 10 m), and average skyline height above the ground (10, 15, and 20 m). The simulations assumed an 8-h working day with a break of 1 h. The models and statistics published by other studies were used to calculate the time consumption and number of work cycles completed within a working day. These data were used to compute the equivalent exposure to noise (LAeq) for each scenario, as well as for those work elements that were likely to expose the haulers to noise the most. The presented findings indicated that (i) the exposure to noise was higher than 100 dB(A), irrespective of variation in the considered factors; (ii) the trend in exposure was characterized by polynomials in relation to the extraction distance, and the magnitude of exposure was consistently affected by variation in the considered factors; and (iii) without hearing protection, the empty and loaded turns exposed workers to noise over the permissible limits. These findings strongly suggest the use of hearing protection when working in close proximity to sledge-based cable yarding operations. The methods proposed in this study in the form of simulation may help benchmark other forest operations.

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“…Addressing tropical forest loss presents enormous and diverse challenges (DeArmond et al 2023). In the last 5 years, Brazil lost approximately 8.56 million hectares of native vegetation.…”

Section: Introductionmentioning

confidence: 99%

Impact of surface quality on the identification of tropical wood species using benchtop and portable NIR instruments

Huancas,

Medeiros,

Dias

et al. 2024

Preprint

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Near-infrared (NIR) spectroscopy combined with multivariate analysis has proven to be a fast and efficient method for identifying wood species. Despite significant technical advances in recent years, challenges remain that limit its application in field conditions, particularly the influence of sample surface preparation on the performance of classification models. This study aimed to evaluate the impact of wood surface quality on the performance of NIR instruments in identifying tropical wood species. Wood samples were collected from fields and log yards and prepared using different tools. NIR spectra were recorded using portable and benchtop NIR instruments on the transverse surfaces of wood specimens subjected to five treatments: (1) field conditions (untreated), (2) chainsaw, (3) circular saw, (4) bandsaw, and (5) sandpaper. Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were performed using the NIR signatures. Spectra collected from surfaces prepared with a circular saw and sandpaper showed clearer groupings in the PCA score plot, facilitating the identification of distinct wood species. Cross-validated PLS-DA models showed high success rates, with classification accuracies ranging from 95.3% to 99.2% for untreated, circular saw, bandsaw, and sanded surfaces. Wood surfaces prepared with a chainsaw yielded lower classification accuracies: 88.7% for benchtop and 92.8% for portable NIR sensors. These results highlight the potential of NIR spectroscopy for classifying tropical woods, even when surface quality varies.

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The Challenges of Sustainable Forest Operations in Amazonia

Cited by 4 publications

References 98 publications

Spatial Correlation Increase in Single‐Sensor Satellite Data Reveals Loss of Amazon Rainforest Resilience

Spatial Correlation Increase in Single‐Sensor Satellite Data Reveals Loss of Amazon Rainforest Resilience

A Simulation Study of Noise Exposure in Sledge-Based Cable Yarding Operations

Impact of surface quality on the identification of tropical wood species using benchtop and portable NIR instruments

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