Impacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence

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 ◽  
...  
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.


2022 ◽  
Vol 505 ◽  
pp. 119945
Author(s):  
Jian Zhang ◽  
Zhaochen Zhang ◽  
James A. Lutz ◽  
Chengjin Chu ◽  
Jianbo Hu ◽  
...  

Author(s):  
Brady S. Hardiman ◽  
Elizabeth A. LaRue ◽  
Jeff W. Atkins ◽  
Robert T. Fahey ◽  
Franklin W. Wagner ◽  
...  

Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy lidar system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10’s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales.


2017 ◽  
Vol 26 (11) ◽  
pp. 963 ◽  
Author(s):  
Michael J. Lacki ◽  
Luke E. Dodd ◽  
Nicholas S. Skowronski ◽  
Matthew B. Dickinson ◽  
Lynne K. Rieske

The extent to which prescribed fires affect forest structure and habitats of vertebrate species is an important question for land managers tasked with balancing potentially conflicting objectives of vegetation and wildlife management. Many insectivorous bats forage for insect prey in forested habitats, serving as the primary predators of nocturnal forest insects, and are potentially affected by structural changes in forests resulting from prescribed fires. We compared forest-stand characteristics of temperate oak–hickory forests, as measured with airborne laser scanning (light detection and ranging, LiDAR), with categorical estimates of burn severity from prescribed fires as derived from Landsat data and field-based Composite Burn Indices, and used acoustic monitoring to quantify activity of insectivorous bats in association with varying degrees of burn severity (unburned habitat, low severity and medium severity). Forest-stand characteristics showed greatest separation between low-severity and medium-severity classes, with gap index, i.e. open-air space, increasing with degree of burn severity. Greater mid-storey density, over-storey density and proportion of vegetation in the understorey occurred in unburned habitat. Activity of bats did not differ with burn severity for high-frequency (clutter-adapted or closed-space foragers) or low-frequency (edge or open-space foragers) bats. Results indicate that differing degrees of burn severity from prescribed fires produced spatial variation in canopy structure within stands; however, bats demonstrated no shifts in activity levels to this variation in canopy structure, suggesting prescribed fire during the dormant season, used as a management practice targeting desired changes in vegetation, is compatible with sustaining foraging habitat of insectivorous bats.


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.


2015 ◽  
Vol 41 (1) ◽  
pp. 169-174 ◽  
Author(s):  
Akira KATO ◽  
Yuma OKITSU ◽  
Nobumitsu TSUNEMATSU ◽  
Tsuyoshi HONJYO ◽  
Tatsuaki KOBAYASHI ◽  
...  

2003 ◽  
Vol 29 (3) ◽  
pp. 388-410 ◽  
Author(s):  
Jean-Michel N Walter ◽  
Richard A Fournier ◽  
Kamel Soudani ◽  
Emmanuel Meyer

Sign in / Sign up

Export Citation Format

Share Document