scholarly journals Size and frequency of natural forest disturbances and the Amazon forest carbon balance

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Fernando D.B. Espírito-Santo ◽  
Manuel Gloor ◽  
Michael Keller ◽  
Yadvinder Malhi ◽  
Sassan Saatchi ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Large Scale ◽  
Carbon Sink ◽  
Remote Sensing Data ◽  
Biomass Accumulation ◽  
Airborne Lidar ◽  
Small Scale ◽  
Amazon Forest ◽  
Natural Forests ◽  
Landscape Scales

Abstract Forest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we characterize the frequency distribution of disturbance events in natural forests from 0.01 ha to 2,651 ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of ~1.28 Pg C y−1 over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of ~0.01 Pg C y−1, and that the largest-scale disturbances as a result of blow-downs only account for losses of ~0.003 Pg C y−1. Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink.

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 Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations

2020 ◽  
Vol 72 (1) ◽  
pp. 110-125
Author(s):  
Gustavo Fluminense Carneiro ◽  
Matheus Pinheiro Ferreira ◽  
Carlos Frederico de Sá Volotão
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Natural Forests ◽  
Planted Forests ◽  
Sensing Data ◽  
Eucalyptus Forest ◽  
Eucalyptus Plantations

It is challenging to map the spatial distribution of natural and planted forests based on satellite images because of the high correlation among them. This investigation aims to increase accuracies in classifications of natural forests and eucalyptus plantations by combining remote sensing data from multiple sources. We defined four vegetation classes: natural forest (NF), planted eucalyptus forest (PF), agriculture (A) and pasture (P), and sampled 410,251 pixels from 100 polygons of each class. Classification experiments were performed by using a random forest algorithm with images from Landsat-8, Sentinel-1, and SRTM. We considered four texture features (energy, contrast, correlation, and entropy) and NDVI. We used F1-score, overall accuracy and total disagreement metrics, to assess the classification performance, and Jeffries–Matusita (JM) distance to measure the spectral separability. Overall accuracy for Landsat-8 bands alone was 88.29%. A combination of Landsat-8 with Sentinel-1 bands resulted in a 3% overall accuracy increase and this band combination also improved the F1-score of NF, PF, P and A in 2.22%, 2.9%, 3.71%, and 8.01%, respectively. The total disagreement decreased from 11.71% to 8.71%. The increase in the statistical separability corroborates such improvement and is mainly observed between NF-PF (11.98%) and A-P (45.12%). We conclude that combining optical and radar remote sensing data increased the classification accuracy of natural and planted forests and may serve as a basis for large-scale semi-automatic mapping of forest resources.

scholarly journals Analysis of the Impacts of Environmental Factors on Rat Hole Density in the Northern Slope of the Tienshan Mountains with Satellite Remote Sensing Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4709
Author(s):  
Haiyang Shi ◽  
Qun Pan ◽  
Geping Luo ◽  
Olaf Hellwich ◽  
Chunbo Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Small Scale ◽  
Driving Mechanism ◽  
Hole Density ◽  
Northern Slope ◽  
Sensing Data

Understanding the impacts of environmental factors on spatial–temporal and large-scale rodent distribution is important for rodent damage prevention. Investigating rat hole density (RHD) is one of the most effective methods to obtain the intensity of rodent damage. However, most of the previous field surveys or UAV-based remote sensing methods can only evaluate small-scale RHD and its influencing factors. However, these studies did not consider large-scale temporal and spatial heterogeneity. Therefore, we collected small-scale and in situ measurement records of RHD on the northern slope of the Tien Shan Mountains in Xinjiang (NTXJ), China, from 1982 to 2015, and then used correlation analysis and Bayesian network (BN) to analyze the environmental impacts on large-scale RHD with satellite remote sensing data such as the GIMMS NDVI product. The results show that the built BN can better quantify causality in the environmental mechanism modeling of RHD. The NDVI and LAI data from satellite remote sensing are important to the spatial–temporal RHD distribution and the mapping in the future. In regions with an elevation higher than 600 m (UPR) and lower than 600 m (LWR) of NTXJ, there are significant differences in the driving mechanism patterns of RHD, which are dependent on the elevation variation. In LWR, vegetation conditions have a weaker impact on RHD than UPR. It is possibly due to the Artemisia eaten by the dominant species Lagurus luteus (LL) in UPR being more sensitive to precipitation and temperature if compared with the Haloxylon ammodendron eaten by the Rhombomys opimus (RO) in LWR. In LWR, grazing intensity is more strongly and positively correlated to RHD than UPR, possibly due to both winter grazing and RO dependency on vegetation distribution; moreover, in UPR, sheep do not feed Artemisia as the main food, and the total vegetation is sufficient for sheep and LL to coexist. Under the different conditions of water availability of LWR and UPR, grazing may affect the ratio of aboveground and underground biomass by photosynthate allocation, thereby affecting the distribution of RHD. In extremely dry years, the RHD of LWR and UPR may have an indirect interactive relation due to changes in grazing systems.

scholarly journals Resource availability and disturbance shape maximum tree height across the Amazon

2020 ◽  
Author(s):  
Eric Gorgens ◽  
Matheus Henrique Nunes ◽  
Tobias Jackson ◽  
David Coomes ◽  
Michael Keller ◽  
...  
Keyword(s):  
High Resolution ◽  
Environmental Variables ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Brazilian Amazon ◽  
Airborne Lidar ◽  
Maximum Height ◽  
Amazon Forest ◽  
Secondary Forests ◽  
Growth And Survival

AbstractThe factors shaping the distribution of giant tropical trees are poorly understood, despite its importance as a link between evolutionary biology and ecosystem biogeochemistry. The recent discovery of clusters of trees over 80 metres tall in the Guiana Shield region of the Amazon rainforest challenges the current understanding of the factors controlling the growth and survival of giant trees. The new discovery led us to revisit the question: what determines the distribution of the tallest trees of the Amazon?Here, we used high-resolution airborne LiDAR (Light Detection and Ranging) surveys to measure canopy height across 282,750 ha of primary old-growth and secondary forests throughout the entire Brazilian Amazon to investigate the relationship between the occurrence of giant trees and the environmental factors that influence their growth and survival. Our results suggest that the factors controlling where trees grow extremely tall are distinct from those controlling their longevity. Trees grow taller in areas with high soil clay content (> 42%), lower radiation (< 130 clear days per year) and wind speeds, avoiding alluvial areas (elevations higher than 40 m a.s.l), and with an optimal precipitation range of 1,500 to 2,500 mm yr-1. We then used an envelope model to determine the environmental conditions that support the very tallest trees (i.e. over 70 m height). We found that, as opposed to the myriad of interacting factors that control the maximum height at a large scale, wind speed had by far the largest influence on the distribution of these sentinel trees, and explained 67% of the probability of finding trees over 70 m in the Brazilian Amazon forest.The high-resolution pan-Amazon LiDAR data showed that environmental variables that drive growth in height are fundamentally different from environmental variables that support their survival. While precipitation and temperature seem to have lower importance for their survival than expected from previous studies, changes in wind and radiation regimes could reshape our forested biomes. This should be carefully considered by policy-makers when identifying important hotspots for the conservation of biodiversity in the Amazon.

scholarly journals Cirrus Mammatus Properties Derived from an Extended Remote Sensing Dataset

2006 ◽  
Vol 63 (2) ◽  
pp. 712-725 ◽  
Author(s):  
Likun Wang ◽  
Kenneth Sassen
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
Cloud Base ◽  
Doppler Velocity ◽  
Small Scale ◽  
Length Scales ◽  
Atmospheric Remote Sensing

Abstract The first quantitative and statistical evaluation of cirrus mammatus clouds based on wavelet analysis of remote sensing data is made by analyzing the University of Utah Facility for Atmospheric Remote Sensing (FARS) 10-yr high-cloud dataset. First, a case study of cirrus mammata combining a high-resolution lidar system and a W-band Doppler radar is presented, yielding an assessment of the thermodynamic environment and dynamic mechanisms. Then, 25 cirrus mammatus cases selected from the FARS lidar dataset are used to disclose their characteristic environmental conditions, and vertical and length scales. The results show that cirrus mammata occur in the transition zone from moist (cloudy) to dry air layers with weak wind shear, which suggests that cloud-induced thermal structures play a key role in their formation. Their maximum vertical and horizontal length scales vary from 0.3 to 1.1 km and 0.5 to 8.0 km, respectively. It is also found that small-scale structures develop between the large-scale protuberances. The spectral slopes of the lidar-returned power and mean radar Doppler velocity data extracted from the cirrus cloud-base region further indicate the presence of developed three-dimensional, locally isotropic, homogeneous turbulence generated by buoyancy. Finally, comparisons of anvil and cirrus mammata are made. Although both are generated in a similar environment, cirrus mammata generally do not form fallout fronts like their anvil counterparts, and so do not have their smooth and beautiful outlines.

scholarly journals The sustainability assessment of CO2 capture, utilization and storage (CCUS) and the conversion of cropland to forestland program (CCFP) in the Water–Energy–Food (WEF) framework towards China’s carbon neutrality by 2060

2021 ◽  
Vol 80 (14) ◽  
Author(s):  
Yachen Xie ◽  
Zhengmeng Hou ◽  
Hejuan Liu ◽  
Cheng Cao ◽  
Jiaguo Qi
Keyword(s):  
Crop Production ◽  
Large Scale ◽  
Sustainability Assessment ◽  
Carbon Sink ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Gas Production ◽  
Plantation Forest ◽  
Carbon Neutrality ◽  
And Storage

AbstractThe global warming induced by the emission of greenhouse gases, especially the carbon dioxide, has become the global climate and environmental issues. China has been working in the CO2 emission reduction and carbon sinks with the purpose of becoming the carbon-neutral country by 2060. The CO2 capture, utilization and storage (CCUS) technologies and the reforestation technology represented by the Conversion of Cropland to Forestland Program (CCFP) have great potential for sinking CO2 emission. However, the trade-off among CCFP, CCS/CCUS and Water-Energy-Food (WEF) nexus are not well evaluated. In this paper, the remote-sensing data are collected and used to evaluate the sustainability of CCFP by analyzing the variation of land use and land cover (LULC), crop production, etc. The results show that 13.29% of the cropland in 2001 vanished and converted to grassland (8.3%), mosaic cropland (3%) and urban land (0.98%) in 2019, demonstrating that the CCFP is successful in both WEF nexus and carbon sink. The total crop production has increased around 50% between 2001 and 2019, implying that the CCFP will not lead to the food risk during the conversion of croplands into other types of land in China. A sustainable implementation of CCFP and other environmental Payments for Ecosystem Services (PES) policies in 2019–2060 could reach an estimated total growth of 7.462 billion m3 in comparison of that in 2018 and the total plantation forest stock of about 10.852 billion m3 in 2060, with a corresponding minimum CO2 sink of 2.90 billion tons in 2060. The estimated peak of net equivalent CO2 emissions before 2030 is about 11.0 billion tons and could not be reduced to zero by 2060 without the large-scale application of the CCS/CCUS technologies as geological sequestration of CO2. Besides, the application of CCS/CCUS can be beneficial for WEF, e.g., through replacing the water by CO2 during energy production, especially in the shale gas production in the regions with high water risks in China. In one word, CCS/CCUS and CCFP are two decided pathways of carbon sequestration and should be systematically applied to achieve China’s carbon neutrality by 2060.

scholarly journals Sea-ice deformation in a coupled ocean–sea-ice model and in satellite remote sensing data

2017 ◽  
Vol 11 (4) ◽  
pp. 1553-1573 ◽  
Author(s):  
Gunnar Spreen ◽  
Ron Kwok ◽  
Dimitris Menemenlis ◽  
An T. Nguyen
Keyword(s):  
Sea Ice ◽  
Power Law ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Satellite Observations ◽  
Small Scale ◽  
Grid Spacing ◽  
Model Solutions ◽  
Realistic Representation ◽  
The Right

Abstract. A realistic representation of sea-ice deformation in models is important for accurate simulation of the sea-ice mass balance. Simulated sea-ice deformation from numerical simulations with 4.5, 9, and 18 km horizontal grid spacing and a viscous–plastic (VP) sea-ice rheology are compared with synthetic aperture radar (SAR) satellite observations (RGPS, RADARSAT Geophysical Processor System) for the time period 1996–2008. All three simulations can reproduce the large-scale ice deformation patterns, but small-scale sea-ice deformations and linear kinematic features (LKFs) are not adequately reproduced. The mean sea-ice total deformation rate is about 40 % lower in all model solutions than in the satellite observations, especially in the seasonal sea-ice zone. A decrease in model grid spacing, however, produces a higher density and more localized ice deformation features. The 4.5 km simulation produces some linear kinematic features, but not with the right frequency. The dependence on length scale and probability density functions (PDFs) of absolute divergence and shear for all three model solutions show a power-law scaling behavior similar to RGPS observations, contrary to what was found in some previous studies. Overall, the 4.5 km simulation produces the most realistic divergence, vorticity, and shear when compared with RGPS data. This study provides an evaluation of high and coarse-resolution viscous–plastic sea-ice simulations based on spatial distribution, time series, and power-law scaling metrics.

scholarly journals Lennukilidari ja spektraalse kaugseireandmestiku kasutamine metsa peamiste takseertunnuste hindamiseks Aegviidu katsealal / Estimation of main forest inventory variables from spectral and airborne lidar data in Aegviidu test site, Estonia

2012 ◽  
Vol 56 (1) ◽  
pp. 27-41 ◽  
Author(s):  
Mait Lang ◽  
Johannes Anniste ◽  
Tauri Arumäe
Keyword(s):  
Standard Error ◽  
Forest Inventory ◽  
Remote Sensing Data ◽  
Test Site ◽  
Airborne Lidar ◽  
Stem Volume ◽  
Lidar Data ◽  
Height Distribution ◽  
Understorey Vegetation ◽  
Airborne Lidar Data

Abstract Field measurements from 450 sample plots, airborne lidar data and spectral images from Aegviidu, Estonia, 15 by 15 km test site were used to analyse options to estimate main forest inventory variables using remote sensing data. Up to 7 m random error in location of 15 m radius sample plots within homogeneous stands causes usually about 0.5 m standard deviation in lidar pulse return height distribution percentiles. Forest mean height can be predicted with linear relationship from 80th percentile of lidar pulse return height distribution. Upper percentiles of pulse return height distribution are not significantly affected by omitting returns from ground and forest understorey vegetation. Total stem volume in forest can be predicted by using 80th percentile, 25th percentile and canopy cover as model arguments with less than 70 m3 ha-1 standard error. Best species specific stem volume models had 10 m3 ha-1 smaller standard error.

scholarly journals A Lagrangian Analysis of Pockets of Open Cells over the Southeast Pacific

2021 ◽  
Author(s):  
Kevin M. Smalley ◽  
Matthew D. Lebsock ◽  
Ryan Eastman ◽  
Mark Smalley ◽  
Mikael Witte
Keyword(s):  
Boundary Layer ◽  
Large Scale ◽  
Large Body ◽  
Remote Sensing Data ◽  
Small Scale ◽  
Control Group ◽  
Lagrangian Analysis ◽  
Liquid Water Path ◽  
Closed Cell ◽  
Southeast Pacific

Abstract. Pockets of open cells (POCs) have been shown to develop within closed-cell stratocumulus (StCu) and a large body of evidence suggests that the development of POCs result from changes in small-scale processes internal to the boundary layer rather than large-scale forcings. Precipitation is widely viewed as a key process important to POC development and maintenance. In this study, GOES-16 satellite observations are used in conjunction with MERRA-2 winds to track and compare the microphysical and environmental evolution of two populations of closed-cell StCu selected by visual inspection over the southeast Pacific Ocean: one group that transitions to POCs and another control group that does not. The high spatio-temporal resolution of the new GOES-16 data allows for a detailed examination of the temporal evolution of POCs in this region. We find that POCs tend to develop near the coast, last tens of hours, are larger than 104 km2, and often (88 % of cases) do not re-close before they exit the StCu deck. Most POCs are observed to form at night and tend to exit the StCu during the day when the StCu is contracting in area. Relative to the control trajectories, POCs have systematically larger effective radii, lower cloud drop number concentrations, comparable conditional in-cloud liquid water path, and a higher frequency of more intense rainfall. Meanwhile, no systematic environmental differences other than boundary-layer height are observed between POC and control trajectories. These results support the consensus view regarding the importance of precipitation on the formation and maintenance of POCs and demonstrate the utility of modern geostationary remote sensing data in evaluating POC lifecycle.

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