scholarly journals An analysis of forest biomass sampling strategies across scales

2019 ◽  
Author(s):  
Jessica Hetzer ◽  
Andreas Huth ◽  
Thorsten Wiegand ◽  
Hans J. Dobner ◽  
Rico Fischer
Keyword(s):  
Remote Sensing ◽  
South America ◽  
Tropical Forests ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Barro Colorado Island ◽  
South American ◽  
Sampling Strategies ◽  
Minimum Sample ◽  
The Tropics

Abstract. Tropical forests play an important role in the global carbon cycle, as they store a large amount of biomass. To estimate the biomass of a forested landscape, sample plots are often used, assuming that the biomass of these plots represents the biomass of the surrounding forest. In this study, we investigated the conditions under which a limited number of sample plots conform to this assumption. Therefore, minimum sample sizes for predicting the mean biomass of tropical forest landscapes were determined by combining statistical methods with simulations of sampling strategies. We examined forest biomass maps of Barro Colorado Island (50 ha), Panama (50 000 km2), and South America, Africa and Southeast Asia (7 million–15 million km2). The results showed that 100–200 plots (1–25 ha each) are necessary for continental biomass estimations if the sampled plots are spatially randomly distributed. The locations of the current inventory plots in the tropics and the data obtained from remote sensing often do not meet this requirement. Considering the typical aggregation of these plots considerably increase the minimum sample size required. In the case of South America, it can increase to 70 000 plots. To establish more reliable biomass predictions across South American tropical forests, we recommend more spatially randomly distributed inventory plots. If samples are generated by remote sensing, distances of more than 5 km between the measurements increase the reliability of the overall estimate, as they cover a larger area with minimum effort. The use of a combination of remote sensing data and field inventory measurements seems to be a promising strategy for overcoming sampling limitations at larger scales.

scholarly journals An analysis of forest biomass sampling strategies across scales

2020 ◽  
Vol 17 (6) ◽  
pp. 1673-1683
Author(s):  
Jessica Hetzer ◽  
Andreas Huth ◽  
Thorsten Wiegand ◽  
Hans Jürgen Dobner ◽  
Rico Fischer
Keyword(s):  
South America ◽  
Tropical Forests ◽  
Forest Biomass ◽  
Barro Colorado Island ◽  
Point Pattern ◽  
Forest Plot ◽  
Sampling Strategies ◽  
Sampling Intensity ◽  
Reconstruction Methods ◽  
The Mean

Abstract. Tropical forests play an important role in the global carbon cycle as they store a large amount of carbon in their biomass. To estimate the mean biomass of a forested landscape, sample plots are often used, assuming that the biomass of these plots represents the biomass of the surrounding forest. In this study, we investigated the conditions under which a limited number of sample plots conform to this assumption. Therefore, the minimum number of sample sizes for predicting the mean biomass of tropical forest landscapes was determined by combining statistical methods with simulations of sampling strategies. We examined forest biomass maps of Barro Colorado Island (50 ha), Panama (50 000 km2), and South America, Africa, and Southeast Asia (3 × 106–11 × 106 km2). The results showed that around 100 plots (1–25 ha each) are necessary for continent-wide biomass estimations if the sampled plots are randomly distributed. However, locations of current inventory plots often do not meet this requirement, for example, as their sampling design is based on spatial transects among climatic gradients. We show that these nonrandom locations lead to a much higher sampling intensity being required (up to 54 000 plots for accurate biomass estimates for South America). The number of sample plots needed can be reduced using large distances (5 km) between the plots within transects. We also applied novel point pattern reconstruction methods to account for aggregation of inventory plots in known forest plot networks. The results implied that current plot networks can have clustered structures that reduce the accuracy of large-scale estimates of forest biomass if no further statistical approach is applied. To establish more reliable biomass predictions across South American tropical forests, we recommend more spatially randomly distributed inventory plots (minimum: 100 plots) and ensuring that the analyses of inventory plot data consider their spatial characteristics. The precision of forest attribute estimates depends on the sampling intensity and strategy.

scholarly journals Late Pleistocene echimyid rodents (Rodentia, Hystricognathi) from northern Brazil

2016 ◽  
Vol 88 (2) ◽  
pp. 829-845 ◽  
Author(s):  
THAIS M.F. FERREIRA ◽  
ADRIANA ITATI OLIVARES ◽  
LEONARDO KERBER ◽  
RODRIGO P. DUTRA ◽  
LEONARDO S. AVILLA
Keyword(s):  
South America ◽  
Tropical Forests ◽  
Late Pleistocene ◽  
Fossil Record ◽  
South American ◽  
Squamate Reptiles ◽  
Northern Brazil ◽  
Northern South America ◽  
First Time

ABSTRACT Echimyidae (spiny rats, tree rats and the coypu) is the most diverse family of extant South American hystricognath rodents (caviomorphs). Today, they live in tropical forests (Amazonian, coastal and Andean forests), occasionally in more open xeric habitats in the Cerrado and Caatinga of northern South America, and open areas across the southern portion of the continent (Myocastor). The Quaternary fossil record of this family remains poorly studied. Here, we describe the fossil echimyids found in karst deposits from southern Tocantins, northern Brazil. The analyzed specimens are assigned to Thrichomys sp., Makalata cf. didelphoides and Proechimys sp. This is the first time that a fossil of Makalata is reported. The Pleistocene record of echimyids from this area is represented by fragmentary remains, which hinders their determination at specific levels. The data reported here contributes to the understanding of the ancient diversity of rodents of this region, evidenced until now in other groups, such as the artiodactyls, cingulates, carnivores, marsupials, and squamate reptiles.

scholarly journals Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

2014 ◽  
Vol 11 (23) ◽  
pp. 6827-6840 ◽  
Author(s):  
M. Réjou-Méchain ◽  
H. C. Muller-Landau ◽  
M. Detto ◽  
S. C. Thomas ◽  
T. Le Toan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Forest Degradation ◽  
Forest Carbon ◽  
Mitigation Strategies ◽  
Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data

2019 ◽  
Vol 225 ◽  
pp. 77-92 ◽  
Author(s):  
Christine I.B. Wallis ◽  
Jürgen Homeier ◽  
Jaime Peña ◽  
Roland Brandl ◽  
Nina Farwig ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Biomass Productivity ◽  
Montane Forest ◽  
Tropical Montane Forest ◽  
Sensing Data ◽  
Multispectral Remote Sensing

scholarly journals - The genus Stylosanthes (Fabaceae, Papilionoideae, Dalbergieae) in South America.

2017 ◽  
Vol 52 (3) ◽  
pp. 549-585 ◽  
Author(s):  
Ricardo O. Vanni
Keyword(s):  
South America ◽  
World Wide ◽  
Herbarium Specimens ◽  
South American ◽  
Field Observations ◽  
New Synonyms ◽  
Brazilian Species ◽  
The Tropics ◽  
Flora Of Argentina

The papilionoid genus Stylosanthes Sw. includes about 50 spp. distributed world wide in the tropics, approximately half of them grow in South America.The present study focuses on South American Stylosanthes. Based on examinations of herbarium specimens, as well as field observations, a total of 25 taxa (23 spp. and 2 varieties) are here described and identified with a key. Most of the species have been found to be more widely distributed than expected from the previous taxonomic literature, and the genus appears to be mainly concentrated in Brazil and Paraguay. Stylosanthes leiocarpa Vogel is new to the flora of Argentina and the presence of S. nervosa J. F. Macbr. is confirmed in Argentina. The nomenclature of S. guianensis (Aubl.) Sw. is analyzed. Clarifications are made about nine recently described Brazilian species. The names S. hispida Rich. and S. longiseta Micheli are resurrected. Lectotypes or neotypes for eight species and 18 new synonyms are proposed.

scholarly journals From small-scale forest structure to Amazon-wide carbon estimates

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Edna Rödig ◽  
Nikolai Knapp ◽  
Rico Fischer ◽  
Friedrich J. Bohn ◽  
Ralph Dubayah ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
3D Structure ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Small Scale ◽  
Frequency Distributions ◽  
Forest Attributes ◽  
Detailed Interpretation ◽  
Remote Sensing Techniques

Abstract Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20–43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.

scholarly journals Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses

2020 ◽  
Vol 6 (40) ◽  
pp. eaaz8360 ◽  
Author(s):  
Celso H. L. Silva Junior ◽  
Luiz E. O. C. Aragão ◽  
Liana O. Anderson ◽  
Marisa G. Fonseca ◽  
Yosio E. Shimabukuro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tropical Forests ◽  
Forest Fragmentation ◽  
Edge Effect ◽  
Carbon Emissions ◽  
Forest Edges ◽  
Amazonian Forest ◽  
Primary Driver ◽  
The Tropics ◽  
Carbon Losses

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year−1 in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 ± 8 Tg C year−1. Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement’s bold targets.

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