scholarly journals Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

2019 ◽  
Vol 11 (6) ◽  
pp. 709 ◽  
Author(s):  
Ekena Rangel Pinagé ◽  
Michael Keller ◽  
Paul Duffy ◽  
Marcos Longo ◽  
Maiza dos-Santos ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Binary Classification ◽  
Forest Degradation ◽  
Selective Logging ◽  
Ecological Impacts ◽  
Airborne Lidar ◽  
Classification Model ◽  
Amazon Forest ◽  
Gap Formation

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.

scholarly journals Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne Lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling

2019 ◽  
Vol 11 (1) ◽  
pp. 92 ◽  
Author(s):  
Danilo Roberti Alves de Almeida ◽  
Scott C. Stark ◽  
Gang Shao ◽  
Juliana Schietti ◽  
Bruce Walker Nelson ◽  
...  
Keyword(s):  
Grain Size ◽  
Leaf Area ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Primary Source ◽  
Airborne Lidar ◽  
Area Index ◽  
Pulse Density ◽  
Optimal Values ◽  
And Function

Airborne Laser Scanning (ALS) has been considered as a primary source to model the structure and function of a forest canopy through the indicators leaf area index (LAI) and vertical canopy profiles of leaf area density (LAD). However, little is known about the effects of the laser pulse density and the grain size (horizontal binning resolution) of the laser point cloud on the estimation of LAD profiles and their associated LAIs. Our objective was to determine the optimal values for reliable and stable estimates of LAD profiles from ALS data obtained over a dense tropical forest. Profiles were compared using three methods: Destructive field sampling, Portable Canopy profiling Lidar (PCL) and ALS. Stable LAD profiles from ALS, concordant with the other two analytical methods, were obtained when the grain size was less than 10 m and pulse density was high (>15 pulses m−2). Lower pulse densities also provided stable and reliable LAD profiles when using an appropriate adjustment (coefficient K). We also discuss how LAD profiles might be corrected throughout the landscape when using ALS surveys of lower density, by calibrating with LAI measurements in the field or from PCL. Appropriate choices of grain size, pulse density and K provide reliable estimates of LAD and associated tree plot demography and biomass in dense forest ecosystems.

scholarly journals Ancient Maya wetland fields revealed under tropical forest canopy from laser scanning and multiproxy evidence

2019 ◽  
Vol 116 (43) ◽  
pp. 21469-21477 ◽  
Author(s):  
Timothy Beach ◽  
Sheryl Luzzadder-Beach ◽  
Samantha Krause ◽  
Tom Guderjan ◽  
Fred Valdez ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Human Impacts ◽  
Population Expansion ◽  
Farming Systems ◽  
Airborne Lidar ◽  
Ancient Maya ◽  
Large Area ◽  
Radiocarbon Dates ◽  
Field Systems

We report on a large area of ancient Maya wetland field systems in Belize, Central America, based on airborne lidar survey coupled with multiple proxies and radiocarbon dates that reveal ancient field uses and chronology. The lidar survey indicated four main areas of wetland complexes, including the Birds of Paradise wetland field complex that is five times larger than earlier remote and ground survey had indicated, and revealed a previously unknown wetland field complex that is even larger. The field systems date mainly to the Maya Late and Terminal Classic (∼1,400–1,000 y ago), but with evidence from as early as the Late Preclassic (∼1,800 y ago) and as late as the Early Postclassic (∼900 y ago). Previous study showed that these were polycultural systems that grew typical ancient Maya crops including maize, arrowroot, squash, avocado, and other fruits and harvested fauna. The wetland fields were active at a time of population expansion, landscape alteration, and droughts and could have been adaptations to all of these major shifts in Maya civilization. These wetland-farming systems add to the evidence for early and extensive human impacts on the global tropics. Broader evidence suggests a wide distribution of wetland agroecosystems across the Maya Lowlands and Americas, and we hypothesize the increase of atmospheric carbon dioxide and methane from burning, preparing, and maintaining these field systems contributed to the Early Anthropocene.

scholarly journals Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ricardo Dalagnol ◽  
Fabien H. Wagner ◽  
Lênio S. Galvão ◽  
Annia S. Streher ◽  
Oliver L. Phillips ◽  
...  
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Tree Mortality ◽  
Structural Changes ◽  
Regional Scale ◽  
Airborne Lidar ◽  
Amazon Forest ◽  
Gap Dynamics ◽  
Relative Height ◽  
Mortality Estimates

AbstractWe report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20–35% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.

scholarly journals Assessment of Tropical Forest Degradation with Canopy Fractional Cover from Landsat ETM+ and IKONOS Imagery

2005 ◽  
Vol 9 (22) ◽  
pp. 1-18 ◽  
Author(s):  
Cuizhen Wang ◽  
Jiaguo Qi ◽  
Mark Cochrane
Keyword(s):  
Tropical Forests ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Selective Logging ◽  
Area Index ◽  
Mato Grosso ◽  
Clear Cut ◽  
Fractional Cover ◽  
Linear Mixture Model

Abstract Tropical forests are being subjected to a wide array of disturbances in addition to outright deforestation. Selective logging is one of the most common disturbances ongoing in the Amazon, which results in significant changes in forest structure and canopy integrity. Assessing forest canopy fractional cover (fc) is one way of measuring forest degradation caused by selective logging. In this study we applied a linear mixture model to a vegetation index domain to map canopy fractional cover in tropical forests in the Amazonian state of Mato Grosso, Brazil. The modified soil adjusted vegetation index (MSAVI) was selected as the optimal vegetation index in the model because it is most linearly related to green canopy abundance up to leaf area index = 4.0. In the canopy fc map derived from the Landsat Enhanced Thematic Mapper Plus (ETM+) image, the fc distribution ranged from 0 to 0.4 in clear-cut areas, higher than 0.8 in undisturbed forests, and a wider range of 0.3–1.0 in degraded forests. The fc map was validated with the 1-m panchromatic sharpened IKONOS image. In the logged forests the ETM+ estimated fc values were clustered along the 1:1 line in the scatterplot with the IKONOS estimated fc and had a squared correlation coefficient (R2) of 0.8.

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 ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Canopy Structure ◽  
Selective Logging ◽  
Amazon Forest ◽  
Survey Sequence

scholarly journals Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

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 ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Change Detection ◽  
Convolutional Neural Networks ◽  
Brazilian Amazon ◽  
Forest Degradation ◽  
Selective Logging ◽  
Amazon Forest ◽  
X Band ◽  
Sar Data

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.

scholarly journals Animal soundscapes reveal key markers of Amazon forest degradation from fire and logging

2021 ◽  
Author(s):  
Danielle I. Rappaport ◽  
Anshuman Swain ◽  
William F. Fagan ◽  
Ralph Dubayah ◽  
Douglas C. Morton
Keyword(s):  
Community Structure ◽  
Communication Networks ◽  
Animal Communication ◽  
Field Studies ◽  
Forest Degradation ◽  
Airborne Lidar ◽  
Amazon Forest ◽  
Acoustic Space ◽  
Animal Communities ◽  
Over Time

AbstractSafeguarding tropical forest biodiversity requires solutions for monitoring ecosystem composition over time. In the Amazon, logging and fire reduce forest carbon stocks and alter tree species diversity, but the long-term consequences for wildlife remain unclear, especially for lesser-known taxa. Here, we combined data from multi-day acoustic surveys, airborne lidar, and satellite timeseries covering logged and burned forests (n=39) in the southern Brazilian Amazon to identify acoustic markers of degradation. Our findings contradict theoretical expectations from the Acoustic Niche Hypothesis that animal communities in more degraded habitats occupy fewer ‘acoustic niches.’ Instead, we found that habitat structure (e.g., aboveground biomass) was not a consistent proxy for biodiversity based on divergent patterns of acoustic space occupancy (ASO) in logged and burned forests. Full 24-hr soundscapes highlighted a stark and sustained reorganization in community structure after multiple fires; animal communication networks were quieter, more homogenous, and less acoustically integrated in forests burned multiple times than in logged or once-burned forests. These findings demonstrate strong biodiversity co-benefits from protecting Amazon forests from recurrent fire activity. By contrast, soundscape changes after logging were subtle and more consistent with community recovery than reassembly. In both logged and burned forests, insects were the dominant acoustic markers of degradation, particularly during midday and nighttime hours that are not typically sampled by traditional field surveys of biodiversity. The acoustic fingerprints of degradation history were conserved across replicate recording locations at each site, indicating that soundscapes offer a robust, taxonomically inclusive solution for tracking changes in community composition over time.Significance StatementFire and logging reduce the carbon stored in Amazon forests, but little is known about how human degradation alters animal communities. We recorded thousands of hours of ecosystem sounds to investigate animal community assembly and the associations between biodiversity and biomass following Amazon forest degradation over time. 24-hr patterns of acoustic activity differed between logged and burned forests, and we observed large and sustained breakpoints in community structure after multiple burns. Soundscape differences among degraded forests were clearest during insect-dominated hours rarely sampled in field studies of biodiversity. These findings demonstrate that acoustic monitoring holds promise for routine biodiversity accounting, even by non-experts, to capture a holistic measure of animal communities in degraded tropical forests and benchmark change over time.

scholarly journals Airborne laser scanning for terrain modeling in the Amazon forest

2018 ◽  
Vol 48 (4) ◽  
pp. 271-279 ◽  
Author(s):  
Mariana Silva ANDRADE ◽  
Eric Bastos GORGENS ◽  
Cristiano Rodrigues REIS ◽  
Roberta Zecchini CANTINHO ◽  
Mauro ASSIS ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Digital Terrain Model ◽  
Horizontal Resolution ◽  
Airborne Laser Scanning ◽  
Amazon Forest ◽  
Vegetation Density ◽  
Airborne Laser ◽  
Pulse Density ◽  
Forested Areas

ABSTRACT Very few studies have been devoted to understanding the digital terrain model (DTM) creation for Amazon forests. DTM has a special and important role when airborne laser scanning is used to estimate vegetation biomass. We examined the influence of pulse density, spatial resolution, filter algorithms, vegetation density and slope on the DTM quality. Three Amazonian forested areas were surveyed with airborne laser scanning, and each original point cloud was reduced targeting to 20, 15, 10, 8, 6, 4, 2, 1, 0.75, 0.5 and 0.25 pulses per square meter based on a random resampling process. The DTM from resampled clouds was compared with the reference DTM produced from the original LiDAR data by calculating the deviation pixel by pixel and summarizing it through the root mean square error (RMSE). The DTM from resampled clouds were also evaluated considering the level of agreement with the reference DTM. Our study showed a clear trade-off between the return density and the horizontal resolution. Higher forest canopy density demanded higher return density or lower DTM resolution.

Urban energetics applications and Geomatic technologies in a Smart City perspective

2015 ◽  
Vol 6 (1) ◽  
pp. 19-29 ◽  
Author(s):  
G. Bitelli ◽  
P. Conte ◽  
T. Csoknyai ◽  
E. Mandanici
Keyword(s):  
Smart City ◽  
Laser Scanning ◽  
Near Infrared ◽  
Three Dimensional ◽  
Energy Sector ◽  
Primary Objective ◽  
Airborne Lidar ◽  
City Management ◽  
Urban Context ◽  
Research Areas

The management of an urban context in a Smart City perspective requires the development of innovative projects, with new applications in multidisciplinary research areas. They can be related to many aspects of city life and urban management: fuel consumption monitoring, energy efficiency issues, environment, social organization, traffic, urban transformations, etc. Geomatics, the modern discipline of gathering, storing, processing, and delivering digital spatially referenced information, can play a fundamental role in many of these areas, providing new efficient and productive methods for a precise mapping of different phenomena by traditional cartographic representation or by new methods of data visualization and manipulation (e.g. three-dimensional modelling, data fusion, etc.). The technologies involved are based on airborne or satellite remote sensing (in visible, near infrared, thermal bands), laser scanning, digital photogrammetry, satellite positioning and, first of all, appropriate sensor integration (online or offline). The aim of this work is to present and analyse some new opportunities offered by Geomatics technologies for a Smart City management, with a specific interest towards the energy sector related to buildings. Reducing consumption and CO2 emissions is a primary objective to be pursued for a sustainable development and, in this direction, an accurate knowledge of energy consumptions and waste for heating of single houses, blocks or districts is needed. A synoptic information regarding a city or a portion of a city can be acquired through sensors on board of airplanes or satellite platforms, operating in the thermal band. A problem to be investigated at the scale A problem to be investigated at the scale of the whole urban context is the Urban Heat Island (UHI), a phenomenon known and studied in the last decades. UHI is related not only to sensible heat released by anthropic activities, but also to land use variations and evapotranspiration reduction. The availability of thermal satellite sensors is fundamental to carry out multi-temporal studies in order to evaluate the dynamic behaviour of the UHI for a city. Working with a greater detail, districts or single buildings can be analysed by specifically designed airborne surveys. The activity has been recently carried out in the EnergyCity project, developed in the framework of the Central Europe programme established by UE. As demonstrated by the project, such data can be successfully integrated in a GIS storing all relevant data about buildings and energy supply, in order to create a powerful geospatial database for a Decision Support System assisting to reduce energy losses and CO2 emissions. Today, aerial thermal mapping could be furthermore integrated by terrestrial 3D surveys realized with Mobile Mapping Systems through multisensor platforms comprising thermal camera/s, laser scanning, GPS, inertial systems, etc. In this way the product can be a true 3D thermal model with good geometric properties, enlarging the possibilities in respect to conventional qualitative 2D images with simple colour palettes. Finally, some applications in the energy sector could benefit from the availability of a true 3D City Model, where the buildings are carefully described through three-dimensional elements. The processing of airborne LiDAR datasets for automated and semi-automated extraction of 3D buildings can provide such new generation of 3D city models.

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