An analysis of forest biomass sampling strategies across scales

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.

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An analysis of forest biomass sampling strategies across scales

10.5194/bg-2019-277 ◽

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.

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Stochastically driven adult–recruit associations of tree species on Barro Colorado Island

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2014.0922 ◽

2014 ◽

Vol 281 (1790) ◽

pp. 20140922 ◽

Cited By ~ 21

Author(s):

Stephan Getzin ◽

Thorsten Wiegand ◽

Stephen P. Hubbell

Keyword(s):

Seed Dispersal ◽

Tree Species ◽

Community Dynamics ◽

Dispersal Limitation ◽

Habitat Associations ◽

Barro Colorado Island ◽

Point Pattern ◽

Forest Plot ◽

Pattern Forming ◽

Spatial Independence

The spatial placement of recruits around adult conspecifics represents the accumulated outcome of several pattern-forming processes and mechanisms such as primary and secondary seed dispersal, habitat associations or Janzen–Connell effects. Studying the adult–recruit relationship should therefore allow the derivation of specific hypotheses on the processes shaping population and community dynamics. We analysed adult–recruit associations for 65 tree species taken from six censuses of the 50 ha neotropical forest plot on Barro Colorado Island (BCI), Panama. We used point pattern analysis to test, at a range of neighbourhood scales, for spatial independence between recruits and adults, to assess the strength and type of departure from independence, and its relationship with species properties. Positive associations expected to prevail due to dispersal limitation occurred only in 16% of all cases; instead a majority of species showed spatial independence (≈73%). Independence described the placement of recruits around conspecific adults in good approximation, although we found weak and noisy signals of species properties related to seed dispersal. We hypothesize that spatial mechanisms with strong stochastic components such as animal seed dispersal overpower the pattern-forming effects of dispersal limitation, density dependence and habitat association, or that some of the pattern-forming processes cancel out each other.

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Detecting tropical forest biomass dynamics from repeated airborne lidar measurements

Biogeosciences ◽

10.5194/bg-10-5421-2013 ◽

2013 ◽

Vol 10 (8) ◽

pp. 5421-5438 ◽

Cited By ~ 65

Author(s):

V. Meyer ◽

S. S. Saatchi ◽

J. Chave ◽

J. W. Dalling ◽

S. Bohlman ◽

...

Keyword(s):

Tropical Forest ◽

Spatial Scale ◽

Tropical Forests ◽

Spatial Scales ◽

Forest Biomass ◽

Airborne Lidar ◽

Barro Colorado Island ◽

Accurate Estimation ◽

Old Growth ◽

Lidar Measurements

Abstract. Reducing uncertainty of terrestrial carbon cycle depends strongly on the accurate estimation of changes of global forest carbon stock. However, this is a challenging problem from either ground surveys or remote sensing techniques in tropical forests. Here, we examine the feasibility of estimating changes of tropical forest biomass from two airborne lidar measurements of forest height acquired about 10 yr apart over Barro Colorado Island (BCI), Panama. We used the forest inventory data from the 50 ha Center for Tropical Forest Science (CTFS) plot collected every 5 yr during the study period to calibrate the estimation. We compared two approaches for detecting changes in forest aboveground biomass (AGB): (1) relating changes in lidar height metrics from two sensors directly to changes in ground-estimated biomass; and (2) estimating biomass from each lidar sensor and then computing changes in biomass from the difference of two biomass estimates, using two models, namely one model based on five relative height metrics and the other based only on mean canopy height (MCH). We performed the analysis at different spatial scales from 0.04 ha to 10 ha. Method (1) had large uncertainty in directly detecting biomass changes at scales smaller than 10 ha, but provided detailed information about changes of forest structure. The magnitude of error associated with both the mean biomass stock and mean biomass change declined with increasing spatial scales. Method (2) was accurate at the 1 ha scale to estimate AGB stocks (R2 = 0.7 and RMSEmean = 27.6 Mg ha−1). However, to predict biomass changes, errors became comparable to ground estimates only at a spatial scale of about 10 ha or more. Biomass changes were in the same direction at the spatial scale of 1 ha in 60 to 64% of the subplots, corresponding to p values of respectively 0.1 and 0.033. Large errors in estimating biomass changes from lidar data resulted from the uncertainty in detecting changes at 1 ha from ground census data, differences of approximately one year between the ground census and lidar measurements, and differences in sensor characteristics. Our results indicate that the 50 ha BCI plot lost a significant amount of biomass (−0.8 Mg ha−1 yr−1 ± 2.2(SD)) over the past decade (2000–2010). Over the entire island and during the same period, mean AGB change was 0.2 ± 2.4 Mg ha−1 yr−1 with old growth forests losing −0.7 Mg ha−1 yr−1 ± 2.2 (SD), and secondary forests gaining +1.8 Mg ha yr−1 ± 3.4 (SD) biomass. Our analysis suggests that repeated lidar surveys, despite taking measurement with different sensors, can estimate biomass changes in old-growth tropical forests at landscape scales (>10 ha).

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Detecting tropical forest biomass dynamics from repeated airborne Lidar measurements

Biogeosciences Discussions ◽

10.5194/bgd-10-1957-2013 ◽

2013 ◽

Vol 10 (2) ◽

pp. 1957-1992 ◽

Cited By ~ 12

Author(s):

V. Meyer ◽

S. S. Saatchi ◽

J. Chave ◽

J. Dalling ◽

S. Bohlman ◽

...

Keyword(s):

Tropical Forest ◽

Tropical Forests ◽

Spatial Scales ◽

Forest Biomass ◽

Airborne Lidar ◽

Barro Colorado Island ◽

Accurate Estimation ◽

Prediction Errors ◽

Old Growth ◽

Lidar Measurements

Abstract. Reducing uncertainty of terrestrial carbon cycle depends strongly on the accurate estimation of changes of global forest carbon stock. However, this is a challenging problem from either ground surveys or remote sensing techniques in tropical forests. Here, we examine the feasibility of estimating changes of tropical forest biomass from two airborne Lidar measurements acquired about 10 yr apart over Barro Colorado Island (BCI), Panama from high and medium resolution airborne sensors. The estimation is calibrated with the forest inventory data over 50 ha that was surveyed every 5 yr during the study period. We estimated the aboveground forest biomass and its uncertainty for each time period at different spatial scales (0.04, 0.25, 1.0 ha) and developed a linear regression model between four Lidar height metrics and the aboveground biomass. The uncertainty associated with estimating biomass changes from both ground and Lidar data was quantified by propagating measurement and prediction errors across spatial scales. Errors associated with both the mean biomass stock and mean biomass change declined with increasing spatial scales. Biomass changes derived from Lidar and ground estimates were largely (36 out 50 plots) in the same direction at the spatial scale of 1 ha. Lidar estimation of biomass was accurate at the 1 ha scale (R2 = 0.7 and RMSEmean = 28.6 Mg ha−1). However, to predict biomass changes, errors became comparable to ground estimates only at about 10-ha or more. Our results indicate that the 50-ha BCI plot lost a~significant amount of biomass (−0.8 ± 2.2 Mg ha−1 yr−1) over the past decade (2000–2010). Over the entire island and during the same period, mean AGB change is −0.4 ± 3.7 Mg ha−1 yr−1. Old growth forests lost biomass (−0.7 ± 3.5 Mg ha−1 yr−1), whereas the secondary forests gained biomass (+0.4 ± 3.4 Mg ha−1 yr−1). Our analysis demonstrates that repeated Lidar surveys, even with two different sensors, is able to estimate biomass changes in old-growth tropical forests at landscape scales (>10 ha).

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Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations

Remote Sensing ◽

10.3390/rs13112231 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2231

Author(s):

Débora Souza Alvim ◽

Júlio Barboza Chiquetto ◽

Monica Tais Siqueira D’Amelio ◽

Bushra Khalid ◽

Dirceu Luis Herdies ◽

...

Keyword(s):

Carbon Monoxide ◽

South America ◽

Aerosol Optical Depth ◽

Tropical Forests ◽

Optical Depth ◽

Eastern China ◽

Central Africa ◽

Central China ◽

Stratospheric Chemistry ◽

Tropical Areas

The scope of this work was to evaluate simulated carbon monoxide (CO) and aerosol optical depth (AOD) from the CAM-chem model against observed satellite data and additionally explore the empirical relationship of CO, AOD and fire radiative power (FRP). The simulated seasonal global concentrations of CO and AOD were compared, respectively, with the Measurements of Pollution in the Troposphere (MOPITT) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite products for the period 2010–2014. The CAM-chem simulations were performed with two configurations: (A) tropospheric-only; and (B) tropospheric with stratospheric chemistry. Our results show that the spatial and seasonal distributions of CO and AOD were reasonably reproduced in both model configurations, except over central China, central Africa and equatorial regions of the Atlantic and Western Pacific, where CO was overestimated by 10–50 ppb. In configuration B, the positive CO bias was significantly reduced due to the inclusion of dry deposition, which was not present in the model configuration A. There was greater CO loss due to the chemical reactions, and shorter lifetime of the species with stratospheric chemistry. In summary, the model has difficulty in capturing the exact location of the maxima of the seasonal AOD distributions in both configurations. The AOD was overestimated by 0.1 to 0.25 over desert regions of Africa, the Middle East and Asia in both configurations, but the positive bias was even higher in the version with added stratospheric chemistry. By contrast, the AOD was underestimated over regions associated with anthropogenic activity, such as eastern China and northern India. Concerning the correlations between CO, AOD and FRP, high CO is found during March–April–May (MAM) in the Northern Hemisphere, mainly in China. In the Southern Hemisphere, high CO, AOD, and FRP values were found during August–September–October (ASO) due to fires, mostly in South America and South Africa. In South America, high AOD levels were observed over subtropical Brazil, Paraguay and Bolivia. Sparsely urbanized regions showed higher correlations between CO and FRP (0.7–0.9), particularly in tropical areas, such as the western Amazon region. There was a high correlation between CO and aerosols from biomass burning at the transition between the forest and savanna environments over eastern and central Africa. It was also possible to observe the transport of these pollutants from the African continent to the Brazilian coast. High correlations between CO and AOD were found over southeastern Asian countries, and correlations between FRP and AOD (0.5–0.8) were found over higher latitude regions such as Canada and Siberia as well as in tropical areas. Higher correlations between CO and FRP are observed in Savanna and Tropical forests (South America, Central America, Africa, Australia, and Southeast Asia) than FRP x AOD. In contrast, boreal forests in Russia, particularly in Siberia, show a higher FRP x AOD correlation than FRP x CO. In tropical forests, CO production is likely favored over aerosol, while in temperate forests, aerosol production is more than CO compared to tropical forests. On the east coast of the United States, the eastern border of the USA with Canada, eastern China, on the border between China, Russia, and Mongolia, and the border between North India and China, there is a high correlation of CO x AOD and a low correlation between FRP with both CO and AOD. Therefore, such emissions in these regions are not generated by forest fires but by industries and vehicular emissions since these are densely populated regions.

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A comparison of soil sampling procedures used to monitor soil fertility in permanent pastures

Soil Research ◽

10.1071/sr9840081 ◽

1984 ◽

Vol 22 (1) ◽

pp. 81 ◽

Cited By ~ 16

Author(s):

DK Friesen ◽

GJ Blair

Keyword(s):

Sampling Method ◽

Nutrient Status ◽

Temporal Variations ◽

Cluster Sampling ◽

Soil Test ◽

Sampling Strategies ◽

Grazed Pasture ◽

The Mean ◽

Sampling Procedures ◽

Bray 1

Soil testing programs are often brought in disrepute by unexplained variability in the data. The deposition of dung and urine onto grazed pasture brings about marked variation in the chemical status of soils which contributes to this variability. A study was undertaken to compare a range of sampling procedures to estimate Colwell-P, Bray-1 P, bicarbonate K and pH levels in adjacent low and high P status paddocks. The sampling strategies used consisted of 75 by 50 m grids; whole and stratified paddock zig-zag and cluster (monitor plot) samplings. Soil test means for the various parameters did not vary among sampling methods. The number of grid samples required to estimate within 10% of the mean varied from 121 for Bray-1 P down to 1 for soil pH. Sampling efficiencies were higher for cluster sampling than for whole paddock zig-zag path sampling. Stratification generally did not improve sampling efficiency in these paddocks. Soil test means declined as sampling depth increased, but the coefficient of variation remained constant for Colwell-P and pH. The results indicate that cluster sampling (monitor plots) is the most appropriate procedure for estimating the nutrient status of grazed pastures. This sampling method enables a more accurate measure to be taken of the nutrient status of a paddock and should allow more reasonable estimates to be made of the temporal variations in soil test.

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Absence of Last Glacial Maximum Records in Lowland Tropical Forests

Quaternary Research ◽

10.1006/qres.1997.1953 ◽

1998 ◽

Vol 49 (2) ◽

pp. 233-237 ◽

Cited By ~ 104

Author(s):

Marie-Pierre Ledru ◽

Jacques Bertaux ◽

Abdelfettah Sifeddine ◽

Kenitiro Suguio

Keyword(s):

South America ◽

Tropical Forests ◽

Last Glacial Maximum ◽

Sedimentation Rate ◽

Glacial Maximum ◽

Radiocarbon Dates ◽

Last Glacial ◽

The Last Glacial Maximum ◽

Pollen Records ◽

The Last Glacial

Environmental conditions of the lowland tropical forests during the last glacial maximum (LGM) between ca 20,000 and 18,000 14C yr B.P., are reevaluated in terms of dating control and lithology analyzed in seven pollen records from South America. The reevaluation shows that probably in none of the published records are LGM sediments present or abundant. This conclusion is based on the occurrence of abrupt lithologic changes coupled with changes in sedimentation rate interpolated from radiocarbon dates. These findings suggest that the LGM was represented probably by a hiatus of several thousand years, indicative of drier climates than before or after.

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Phylogenetics, classification, and biogeography of the Neotropical forest lizards (Squamata, Diploglossidae)

Zootaxa ◽

10.11646/zootaxa.4974.2.1 ◽

2021 ◽

Vol 4974 (2) ◽

pp. 201-257

Author(s):

MOLLY SCHOOLS ◽

S. BLAIR HEDGES

Keyword(s):

South America ◽

Tropical Forests ◽

Morphological Data ◽

Undescribed Species ◽

Caribbean Islands ◽

Neotropical Forest ◽

The Family ◽

Middle America ◽

America South ◽

Early Cenozoic

Lizards of the family Diploglossidae occur in moist, tropical forests of Middle America, South America, and Caribbean islands. Our analyses based on new molecular and morphological data indicate that the widely distributed genera Celestus Gray, 1839 and Diploglossus Wiegmann, 1834 are paraphyletic. We restrict the former to Caribbean islands and the latter to South America and Caribbean islands. We assign species in Middle America, formerly placed in Celestus and Diploglossus, to Advenus gen. nov., Mesoamericus gen. nov., and Siderolamprus Cope, 1861. We assign species on Caribbean islands, formerly placed in Celestus, to Caribicus gen. nov., Comptus gen. nov., Celestus, Panolopus Cope, 1862, Sauresia Gray, 1852, and Wetmorena Cochran, 1927. Our phylogenetic tree supports three major clades in the family: Celestinae subfam. nov. (Advenus gen. nov., Caribicus gen. nov., Comptus gen. nov., Celestus, Panolopus, Sauresia, and Wetmorena), Diploglossinae (Diploglossus and Ophiodes Wagler, 1828), and Siderolamprinae subfam. nov. (Mesoamericus gen. nov. and Siderolamprus). Our timetree indicates that the diploglossid lineage originated in the early Cenozoic and established three major centers of diversification in the Americas: Middle America (siderolamprines and one celestine), South America (diploglossines), and Caribbean islands (celestines and diploglossines). The majority of threatened species are on Caribbean islands, with the major threats being deforestation and predation by the introduced mongoose. Molecular and morphological data indicate that there are many undescribed species in this family of lizards.

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

Anais da Academia Brasileira de Ciências ◽

10.1590/0001-3765201620150288 ◽

2016 ◽

Vol 88 (2) ◽

pp. 829-845 ◽

Cited By ~ 3

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.

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Taxonomic revision of Amorimia W.R. Anderson (Malpighiaceae)

Hoehnea ◽

10.1590/2236-8906-47/2017 ◽

2018 ◽

Vol 45 (2) ◽

pp. 238-306 ◽

Cited By ~ 1

Author(s):

Rafael Felipe de Almeida

Keyword(s):

South America ◽

Tropical Forests ◽

Taxonomic Revision ◽

Identification Key ◽

Risk Assessments ◽

Distribution Maps ◽

Seasonally Dry ◽

Seasonally Dry Tropical Forests ◽

Dry Tropical Forests

ABSTRACT The taxonomic revision of Amorimia (Malpighiaceae) is presented, including typifications, and descriptions for all accepted species. The genus is endemic to Seasonally Dry Tropical Forests and Rainforests of South America, and its species can be distinguished by morphological details of leaves, indumenta, inflorescences, flowers, and fruits. This study includes an identification key for the subgenera and species of Amorimia, illustrations, distribution maps, conservation risk assessments, and comments on ecology, nomenclature, and taxonomy for all species. Additionally, I provide a key to differentiate Amorimia from the remaining genera of the Malpighioid clade.

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