scholarly journals Dycrypting tropical forest phenology with coupled remote sensing and field observation

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>

scholarly journals The role of spatial scale and background climate in the latitudinal temperature response to deforestation

2015 ◽  
Vol 6 (2) ◽  
pp. 1897-1937 ◽  
Author(s):  
Y. Li ◽  
N. de Noblet-Ducoudré ◽  
E. L. Davin ◽  
N. Zeng ◽  
S. Motesharrei ◽  
...  
Keyword(s):  
Temperature Change ◽  
Vegetation Change ◽  
Spatial Scales ◽  
Empirical Studies ◽  
Temperature Response ◽  
Shortwave Radiation ◽  
Climate Conditions ◽  
Tropical Regions ◽  
Underlying Mechanisms ◽  
Background Climate

Abstract. Previous modeling and empirical studies have shown that the biophysical impact of deforestation is to warm the tropics and cool the extra-tropics. In this study, we use an earth system model to investigate how deforestation at various spatial scales affects ground temperature, with an emphasis on the latitudinal temperature response and its underlying mechanisms. Results show that the latitudinal pattern of temperature response depends non-linearly on the spatial extent of deforestation and the fraction of vegetation change. Compared with regional deforestation, temperature change in global deforestation is greatly amplified in temperate and boreal regions, but is dampened in tropical regions. Incremental forest removal leads to increasingly larger cooling in temperate and boreal regions, while the temperature increase saturates in tropical regions. The latitudinal and spatial patterns of the temperature response are driven by two processes with competing temperature effects: decreases in absorbed shortwave radiation due to increased albedo and decreases in evapotranspiration. These changes in the surface energy balance reflect the importance of the background climate on modifying the deforestation impact. Shortwave radiation and precipitation have an intrinsic geographical distribution that constrains the effects of biophysical changes and therefore leads to temperature changes that are spatially varying. For example, wet (dry) climate favors larger (smaller) evapotranspiration change, thus warming (cooling) is more likely to occur. Further analysis on the contribution of individual biophysical factors (albedo, roughness, and evapotranspiration efficiency) reveals that the latitudinal signature embodied in the temperature change probably result from the background climate conditions rather than the initial biophysical perturbation.

The past and the future of the tropical forest carbon sink: insights from permanent forest inventory plots

2020 ◽  
Author(s):  
Wannes Hubau ◽  
Simon L. Lewis ◽  
Oliver L. Phillips ◽  
Hans Beeckman ◽  
Keyword(s):  
Tropical Forest ◽  
Tropical Forests ◽  
Carbon Sink ◽  
Forest Carbon ◽  
Policy Implications ◽  
The Past ◽  
Net Zero ◽  
The Future ◽  
Independent Observations ◽  
Vegetation Models

<p>Structurally intact tropical forests sequestered ~1 Pg C yr<sup>-1</sup> over the 1990s and early 2000s, equivalent to ~15% of fossil fuel emissions. Climate-driven vegetation models typically predict that this carbon sink will continue for the remainder of the 21<sup>st</sup> century. However, recent plot inventories from Amazonia show a declining rate of carbon sequestration, potentially signaling an imminent end to the sink. Here we assess whether the African tropical forest sink is also declining.</p><p>Records from 244 multi-census plots across 11 countries reveal that the African tropical forest sink in aboveground live biomass has been stable for three decades, at 0.66 Mg C ha<sup>-1</sup> yr<sup>-1</sup>, from 1985-2015 (95% CI, 0.53-0.79). Thus, the carbon sink responses of Earth’s two largest expanses of tropical forest have diverged over recent decades. A statistical model including CO<sub>2</sub>, temperature, drought, and forest dynamics can account for the trends. Despite the past stability of the African carbon sink, our data and model show that very recently the sink has begun decreasing, and that it will continue to decline in the future.  This implies that the intact tropical forest carbon sink on both continents is set to end decades sooner than even the most extreme vegetation model estimates.</p><p>Published independent observations of inter-hemispheric atmospheric CO<sub>2</sub> concentration indicate increasing carbon uptake into the Northern hemisphere landmass, offsetting a weakening of the tropical forest sink, which reinforces our conclusion that the intact tropical forest carbon sink has already saturated. Nevertheless, continued on-the-ground monitoring of the world’s remaining intact tropical forests will be required to test our prediction that the intact tropical forest carbon sink will continue to decline. Our findings were recently published in Nature (March 2020) and have important policy implications: given tropical forests are likely to sequester less carbon in the future than Earth System Models predict, an earlier date to reach net zero anthropogenic greenhouse gas emissions will be required to meet any given commitment to limit the global heating of Earth.</p>

scholarly journals Detecting tropical forest biomass dynamics from repeated airborne lidar measurements

2013 ◽  
Vol 10 (8) ◽  
pp. 5421-5438 ◽  
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).

scholarly journals Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images

2019 ◽  
Vol 11 (13) ◽  
pp. 1534 ◽  
Author(s):  
John Y. Park ◽  
Helene C. Muller-Landau ◽  
Jeremy W. Lichstein ◽  
Sami W. Rifai ◽  
Jonathan P. Dandois ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Tropical Forests ◽  
Learning Algorithm ◽  
Texture Features ◽  
Absolute Error ◽  
Test Methods ◽  
Image Features ◽  
Leaf Phenology ◽  
Individual Tree ◽  
Individual Trees

Tropical forests exhibit complex but poorly understood patterns of leaf phenology. Understanding species- and individual-level phenological patterns in tropical forests requires datasets covering large numbers of trees, which can be provided by Unmanned Aerial Vehicles (UAVs). In this paper, we test a workflow combining high-resolution RGB images (7 cm/pixel) acquired from UAVs with a machine learning algorithm to monitor tree and species leaf phenology in a tropical forest in Panama. We acquired images for 34 flight dates over a 12-month period. Crown boundaries were digitized in images and linked with forest inventory data to identify species. We evaluated predictions of leaf cover from different models that included up to 14 image features extracted for each crown on each date. The models were trained and tested with visual estimates of leaf cover from 2422 images from 85 crowns belonging to eight species spanning a range of phenological patterns. The best-performing model included both standard color metrics, as well as texture metrics that quantify within-crown variation, with r2 of 0.84 and mean absolute error (MAE) of 7.8% in 10-fold cross-validation. In contrast, the model based only on the widely-used Green Chromatic Coordinate (GCC) index performed relatively poorly (r2 = 0.52, MAE = 13.6%). These results highlight the utility of texture features for image analysis of tropical forest canopies, where illumination changes may diminish the utility of color indices, such as GCC. The algorithm successfully predicted both individual-tree and species patterns, with mean r2 of 0.82 and 0.89 and mean MAE of 8.1% and 6.0% for individual- and species-level analyses, respectively. Our study is the first to develop and test methods for landscape-scale UAV monitoring of individual trees and species in diverse tropical forests. Our analyses revealed undescribed patterns of high intraspecific variation and complex leaf cover changes for some species.

scholarly journals Topography and Traits Modulate Tree Performance and Drought Response in a Tropical Forest

2020 ◽  
Vol 3 ◽  
Author(s):  
Naomi B. Schwartz ◽  
Xiaohui Feng ◽  
Robert Muscarella ◽  
Nathan G. Swenson ◽  
María Natalia Umaña ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Functional Traits ◽  
Tree Growth ◽  
Drought Response ◽  
Species Variation ◽  
Individual Tree ◽  
Average Growth ◽  
Growth And Survival ◽  
Species Responses ◽  
Individual Trees

Predicting drought responses of individual trees in tropical forests remains challenging, in part because trees experience drought differently depending on their position in spatially heterogeneous environments. Specifically, topography and the competitive environment can influence the severity of water stress experienced by individual trees, leading to individual-level variation in drought impacts. A drought in 2015 in Puerto Rico provided the opportunity to assess how drought response varies with topography and neighborhood crowding in a tropical forest. In this study, we integrated 3 years of annual census data from the El Yunque Chronosequence plots with measurements of functional traits and LiDAR-derived metrics of microsite topography. We fit hierarchical Bayesian models to examine how drought, microtopography, and neighborhood crowding influence individual tree growth and survival, and the role functional traits play in mediating species’ responses to these drivers. We found that while growth was lower during the drought year, drought had no effect on survival, suggesting that these forests are fairly resilient to a single-year drought. However, growth response to drought, as well as average growth and survival, varied with topography: tree growth in valley-like microsites was more negatively affected by drought, and survival was lower on steeper slopes while growth was higher in valleys. Neighborhood crowding reduced growth and increased survival, but these effects did not vary between drought/non-drought years. Functional traits provided some insight into mechanisms by which drought and topography affected growth and survival. For example, trees with high specific leaf area grew more slowly on steeper slopes, and high wood density trees were less sensitive to drought. However, the relationships between functional traits and response to drought and topography were weak overall. Species sorting across microtopography may drive observed relationships between average performance, drought response, and topography. Our results suggest that understanding species’ responses to drought requires consideration of the microenvironments in which they grow. Complex interactions between regional climate, topography, and traits underlie individual and species variation in drought response.

scholarly journals Detecting tropical forest biomass dynamics from repeated airborne Lidar measurements

2013 ◽  
Vol 10 (2) ◽  
pp. 1957-1992 ◽  
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).

scholarly journals Long-Term Nitrogen Addition Decreases Soil Carbon Mineralization in an N-Rich Primary Tropical Forest

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 734
Author(s):  
Xiankai Lu ◽  
Qinggong Mao ◽  
Zhuohang Wang ◽  
Taiki Mori ◽  
Jiangming Mo ◽  
...  
Keyword(s):  
Microbial Biomass ◽  
Organic Carbon ◽  
Tropical Forest ◽  
Soil Carbon ◽  
Tropical Forests ◽  
Carbon Mineralization ◽  
N Deposition ◽  
Soil Carbon Mineralization ◽  
N Additions

Anthropogenic elevated nitrogen (N) deposition has an accelerated terrestrial N cycle, shaping soil carbon dynamics and storage through altering soil organic carbon mineralization processes. However, it remains unclear how long-term high N deposition affects soil carbon mineralization in tropical forests. To address this question, we established a long-term N deposition experiment in an N-rich lowland tropical forest of Southern China with N additions such as NH4NO3 of 0 (Control), 50 (Low-N), 100 (Medium-N) and 150 (High-N) kg N ha−1 yr−1, and laboratory incubation experiment, used to explore the response of soil carbon mineralization to the N additions therein. The results showed that 15 years of N additions significantly decreased soil carbon mineralization rates. During the incubation period from the 14th day to 56th day, the average decreases in soil CO2 emission rates were 18%, 33% and 47% in the low-N, medium-N and high-N treatments, respectively, compared with the Control. These negative effects were primarily aroused by the reduced soil microbial biomass and modified microbial functions (e.g., a decrease in bacteria relative abundance), which could be attributed to N-addition-induced soil acidification and potential phosphorus limitation in this forest. We further found that N additions greatly increased soil-dissolved organic carbon (DOC), and there were significantly negative relationships between microbial biomass and soil DOC, indicating that microbial consumption on soil-soluble carbon pool may decrease. These results suggests that long-term N deposition can increase soil carbon stability and benefit carbon sequestration through decreased carbon mineralization in N-rich tropical forests. This study can help us understand how microbes control soil carbon cycling and carbon sink in the tropics under both elevated N deposition and carbon dioxide in the future.

scholarly journals Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro)

2016 ◽  
Vol 9 (11) ◽  
pp. 4227-4255 ◽  
Author(s):  
Bradley O. Christoffersen ◽  
Manuel Gloor ◽  
Sophie Fauset ◽  
Nikolaos M. Fyllas ◽  
David R. Galbraith ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Water Potential ◽  
Plant Traits ◽  
Coupled Model ◽  
Leaf Traits ◽  
Tree Size ◽  
Leaf Mass Per Area ◽  
Stem Water Potential ◽  
Plant Hydraulics ◽  
Individual Trees

Abstract. Forest ecosystem models based on heuristic water stress functions poorly predict tropical forest response to drought partly because they do not capture the diversity of hydraulic traits (including variation in tree size) observed in tropical forests. We developed a continuous porous media approach to modeling plant hydraulics in which all parameters of the constitutive equations are biologically interpretable and measurable plant hydraulic traits (e.g., turgor loss point πtlp, bulk elastic modulus ε, hydraulic capacitance Cft, xylem hydraulic conductivity ks,max, water potential at 50 % loss of conductivity for both xylem (P50,x) and stomata (P50,gs), and the leaf : sapwood area ratio Al : As). We embedded this plant hydraulics model within a trait forest simulator (TFS) that models light environments of individual trees and their upper boundary conditions (transpiration), as well as providing a means for parameterizing variation in hydraulic traits among individuals. We synthesized literature and existing databases to parameterize all hydraulic traits as a function of stem and leaf traits, including wood density (WD), leaf mass per area (LMA), and photosynthetic capacity (Amax), and evaluated the coupled model (called TFS v.1-Hydro) predictions, against observed diurnal and seasonal variability in stem and leaf water potential as well as stand-scaled sap flux. Our hydraulic trait synthesis revealed coordination among leaf and xylem hydraulic traits and statistically significant relationships of most hydraulic traits with more easily measured plant traits. Using the most informative empirical trait–trait relationships derived from this synthesis, TFS v.1-Hydro successfully captured individual variation in leaf and stem water potential due to increasing tree size and light environment, with model representation of hydraulic architecture and plant traits exerting primary and secondary controls, respectively, on the fidelity of model predictions. The plant hydraulics model made substantial improvements to simulations of total ecosystem transpiration. Remaining uncertainties and limitations of the trait paradigm for plant hydraulics modeling are highlighted.

Skill assessment of post-processing methods for ECMWF SEAS5 seasonal forecasts over Europe

2021 ◽  
Author(s):  
Alice Crespi ◽  
Marcello Petitta ◽  
Lucas Grigis ◽  
Paola Marson ◽  
Jean-Michel Soubeyroux ◽  
...  
Keyword(s):  
Large Scale ◽  
Production Management ◽  
Spatial Scales ◽  
Research Field ◽  
Skill Assessment ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Local Climate ◽  
Climate Conditions

<p>Seasonal forecasts provide information on climate conditions several months ahead and therefore they could represent a valuable support for decision making, warning systems as well as for the optimization of industry and energy sectors. However, forecast systems can be affected by systematic biases and have horizontal resolutions which are typically coarser than the spatial scales of the practical applications. For this reason, the reliability of forecasts needs to be carefully assessed before applying and interpreting them for specific applications. In addition, the use of post-processing approaches is recommended in order to improve the representativeness of the large-scale predictions of regional and local climate conditions. The development and evaluation downscaling and bias-correction procedures aiming at improving the skills of the forecasts and the quality of derived climate services is currently an open research field. In this context, we evaluated the skills of ECMWF SEAS5 forecasts of monthly mean temperature, total precipitation and wind speed over Europe and we assessed the skill improvements of calibrated predictions.</p><p>For the calibration, we combined a bilinear interpolation and a quantile mapping approach to obtain corrected monthly forecasts on a 0.25°x0.25° grid from the original 1°x1° values. The forecasts were corrected against the reference ERA5 reanalysis over the hindcast period 1993–2016. The processed forecasts were compared over the same domain and period with another calibrated set of ECMWF SEAS5 forecasts obtained by the ADAMONT statistical method.</p><p>The skill assessment was performed by means of both deterministic and probabilistic verification metrics evaluated over seasonal forecasted aggregations for the first lead time. Greater skills of the forecast systems in Europe were generally observed in spring and summer, especially for temperature, with a spatial distribution varying with the seasons. The calibration was proved to effectively correct the model biases for all variables, however the metrics not accounting for bias did not show significant improvements in most cases, and in some areas and seasons even small degradations in skills were observed.</p><p>The presented study supported the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.</p>

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