Detecting tropical forest biomass dynamics from repeated airborne 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).

Download Full-text

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).

Download Full-text

Releases of carbon to the atmosphere from degradation of forests in tropical Asia

Canadian Journal of Forest Research ◽

10.1139/x91-017 ◽

1991 ◽

Vol 21 (1) ◽

pp. 132-142 ◽

Cited By ~ 43

Author(s):

R. A. Houghton

Keyword(s):

Southeast Asia ◽

Tropical Forest ◽

Tropical Forests ◽

Forest Biomass ◽

Terrestrial Ecosystems ◽

Tropical Asia ◽

South And Southeast Asia ◽

Net Flux ◽

The Difference ◽

Annual Flux

The net annual flux of carbon from south and southeast Asia as a result of changes in the area of forests was calculated for the period 1850 to 1985. The total net flux ranged from 14.4 to 24.0 Pg of carbon, depending on the estimates of biomass used in the calculations. High estimates of biomass, based on direct measurement of a few stands, and low estimates of biomass, based on volumes of merchantable wood surveyed over large areas, differ by a factor of almost 2. These and previous estimates of the release of carbon from terrestrial ecosystems to the atmosphere have been based on changes in the area of forests, or rates of deforestation. Recent studies have shown, however, that the loss of carbon from forests in tropical Asia is greater than would be expected on the basis of deforestation alone. This loss of carbon from within forests (degradation) also releases carbon to the atmosphere when the products removed from the forest burn or decay. Thus, degradation should be included in analyses of the net flux of carbon from terrestrial ecosystems. Degradation may also explain some of the difference between estimates of tropical forest biomass if the higher estimates are based on undisturbed forests and the lower estimates are more representative of the region. The implication of degradation for estimates of the release of carbon from terrestrial ecosystems is explored. When degradation was included in the analyses, the net flux of carbon between 1850 and 1985 was 30.2 Pg of carbon, about 25% above that calculated on the basis of deforestation alone (with high estimates of biomass), and about 110% above that calculated with low estimates of biomass. Thus, lower estimates of biomass for contemporary tropical forests do not necessarily result in lower estimates of flux.

Download Full-text

Dycrypting tropical forest phenology with coupled remote sensing and field observation

10.5194/egusphere-egu21-16202 ◽

2021 ◽

Author(s):

James Ball ◽

Gregoire Vincent ◽

Nicolas Barbier ◽

Ilona Clocher

Keyword(s):

Tropical Forest ◽

Tropical Forests ◽

Spatial Scales ◽

Energy Budgets ◽

Species Variation ◽

Climate Conditions ◽

Vegetation Models ◽

Underlying Mechanisms ◽

Productivity Dynamics ◽

Individual Trees

<p>Tropical forests are integral to the global carbon, water and energy budgets. However, the magnitude of matter and energy fluxes are poorly resolved both spatially and temporally, and the driving underlying mechanisms by which they occur remain unclear poorly described. Specifically, the diversity of foliar phenological patterns and there influence forest fluxes in the tropics has not been properly studied. As a result of these knowledge gaps, dynamic global vegetation models (DGVMs) consistently fail to exhibit observed productivity dynamics and climate-vegetation feedbacks. These shortcomings prevent reliable predictions on the fate and role of tropical forests under changing climate conditions from being made.</p><p>Working at perminant tropical forest fieldsites in French Guiana, we demonstrate that biweekly scans with UAV mounted LiDAR and multispecral sensors can observe subtle phenological changes of individual trees across novel spatial scales. We explore the intra- and inter-species variation in phenological behavoirs and link these dynamics to in-situ flux measurements.</p>

Download Full-text

Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia

10.32920/14669124 ◽

2021 ◽

Author(s):

Yonghua Qu ◽

Ahmed Shaker ◽

Carlos Alberto Silva ◽

Carine Klauberg ◽

Ekena Rangel Pinagé

Keyword(s):

Tropical Forest ◽

Leaf Area Index ◽

Leaf Area ◽

Tropical Forests ◽

Airborne Lidar ◽

Lidar Data ◽

Area Index ◽

Dense Forest ◽

Modis Lai ◽

Airborne Lidar Data

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.

Download Full-text

An analysis of forest biomass sampling strategies across scales

Biogeosciences ◽

10.5194/bg-17-1673-2020 ◽

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.

Download Full-text

Evidence for arrested succession within a tropical forest fragment in Singapore

Journal of Tropical Ecology ◽

10.1017/s0266467411000010 ◽

2011 ◽

Vol 27 (03) ◽

pp. 323-326 ◽

Cited By ~ 12

Author(s):

Gregory R. Goldsmith ◽

Liza S. Comita ◽

Siew Chin Chua

Keyword(s):

Ecosystem Services ◽

Tropical Forest ◽

Tropical Forests ◽

Secondary Forests ◽

Forest Fragment ◽

Landscape Mosaic ◽

Old Growth ◽

Arrested Succession ◽

Old Growth Forests ◽

Tropical Landscape

Secondary forests occupy a growing portion of the tropical landscape mosaic due to regeneration on abandoned pastures and other disturbed sites (Asneret al. 2009). Tropical secondary forests and degraded old-growth forests now account for more than half of the world's tropical forests (Chazdon 2003), and provide critical ecosystem services (Brown & Lugo 1990, Guariguata & Ostertag 2001).

Download Full-text

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.

Download Full-text

Performance of tropical forest seedlings under shade and drought: an interspecific trade-off in demographic responses

Scientific Reports ◽

10.1038/s41598-019-55256-x ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Stefan J. Kupers ◽

Christian Wirth ◽

Bettina M. J. Engelbrecht ◽

Andrés Hernández ◽

Richard Condit ◽

...

Keyword(s):

Tropical Forest ◽

Tropical Forests ◽

Performance Metrics ◽

Barro Colorado Island ◽

First Year ◽

Drought Intensity ◽

Trade Off ◽

Deep Shade ◽

Species Responses ◽

The Relationship

AbstractSeedlings in moist tropical forests must cope with deep shade and seasonal drought. However, the interspecific relationship between seedling performance in shade and drought remains unsettled. We quantified spatiotemporal variation in shade and drought in the seasonal moist tropical forest on Barro Colorado Island (BCI), Panama, and estimated responses of naturally regenerating seedlings as the slope of the relationship between performance and shade or drought intensity. Our performance metrics were relative height growth and first-year survival. We investigated the relationship between shade and drought responses for up to 63 species. There was an interspecific trade-off in species responses to shade versus species responses to dry season intensity; species that performed worse in the shade did not suffer during severe dry seasons and vice versa. This trade-off emerged in part from the absence of species that performed particularly well or poorly in both drought and shade. If drought stress in tropical forests increases with climate change and as solar radiation is higher during droughts, the trade-off may reinforce a shift towards species that resist drought but perform poorly in the shade by releasing them from deep shade.

Download Full-text

Density and diversity of lianas along a chronosequence in a central Panamanian lowland forest

Journal of Tropical Ecology ◽

10.1017/s0266467400001231 ◽

2000 ◽

Vol 16 (1) ◽

pp. 1-19 ◽

Cited By ~ 213

Author(s):

Saara J. Dewalt ◽

Stefan A. Schnitzer ◽

Julie S. Denslow

Keyword(s):

Tropical Forest ◽

Tropical Forests ◽

Relative Abundance ◽

Light Availability ◽

Basal Area ◽

Stand Age ◽

Old Growth ◽

Lowland Forest ◽

Abundance And Diversity

The abundance and diversity of lianas were examined along a tropical forest chronosequence at the Barro Colorado Nature Monument, Panama. Lianas ≥0.5 cm diameter were sampled along transects in two replicated stands in secondary (20, 40, 70 and 100 y after abandonment) and old-growth (>500 y) forests. Ordination of stands based on relative abundance, but not presence-absence, showed a significant separation of stands by age. Lianas were significantly more abundant and diverse (Fisher's α) in younger forests (20 and 40 y) than in older forests (70 and 100 y, and old-growth). The decline in liana abundance with stand age was offset by increased mean basal area per individual, resulting in a relatively constant total basal area and estimated biomass across stand age. The proportions of tendril climbers decreased and stem twiners increased over stand age. Decline in liana abundance and changes in liana composition may be related to changes in support and light availability. Although lianas are recognized as playing an important role in the early secondary sucession of many tropical forests, these results have shown that their important contribution to total basal area and biomass can continue as the forest matures, even as the numbers of established lianas declines.

Download Full-text