Aboveground biomass and carbon of the highly diverse Atlantic Forest in Brazil: comparison of alternative individual tree modeling and prediction strategies

The factors determining individual tree growth response are examined during the 4 years following thinning in experiments in even-aged, 8- or 12-year-old regrowth Eucalyptusregnans F. Muell. forest at two sites in southern Australia. At one site, a vigorous understorey dominated by a sedge developed after the thinning. At that site, light-use efficiency by the trees was unaffected by thinning and the aboveground biomass production by the trees in the thinned stand was substantially less than that in the unthinned stand. At the other site, little understorey developed, light-use efficiency by trees in the thinned stand was greater than that in the unthinned stand, and aboveground biomass production was unaffected by thinning even though the leaf weight of the thinned stand was far below that of the unthinned stand. Where the understorey developed, it was concluded that it competed successfully with the trees for water, thereby reducing production in the thinned stand when compared with the unthinned stand. The individual tree growth response that occurred in the thinned stand at that site appeared to be due soley to the extra light available to individual trees following the canopy opening. Where the understorey did not develop, it was concluded that individual tree growth response was due not only to the extra light available to individual trees but also to the increased availability of belowground resources, most probably soil water. Application of a pre-existing stand growth model suggested that at that site the tendency for increased growth resulting from extra water availability in the thinned stand was just balanced by decreased growth due to lower radiation absorption by the reduced canopy, so that net production was unaffected by thinning.

Download Full-text

IMPACTS OF TREE HEIGHT-DBH ALLOMETRY ON LIDAR-BASED TREE ABOVEGROUND BIOMASS MODELING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-625-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 625-628

Author(s):

R. Fang

Keyword(s):

Aboveground Biomass ◽

Vertical Structure ◽

Model Performance ◽

Tree Height ◽

Allometric Model ◽

Individual Tree ◽

Tree Diameter ◽

Lidar Measurements ◽

Major Variable ◽

Model Residuals

Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate sensitivity of lidar-based AGB model to the decreasing strength of height-dbh relationship using a Monte Carlo simulation approach. Striking decrease in <i>R</i><sup>2</sup> and increase in relative RMSE were found in lidar-based AGB model, as the variance of height-dbh model residuals grew. I, therefore, concluded that individual tree height-dbh model residuals fundamentally introduce errors to lidar-AGB models.

Download Full-text

TREE COMMUNITY COMPOSITION AND ABOVEGROUND BIOMASS IN A SECONDARY ATLANTIC FOREST, SERRA DO MAR STATE PARK, SÃO PAULO, BRAZIL

CERNE ◽

10.1590/01047760201622042242 ◽

2016 ◽

Vol 22 (4) ◽

pp. 501-514 ◽

Cited By ~ 7

Author(s):

Nidia Mara Marchiori ◽

Humberto Ribeiro da Rocha ◽

Jorge Yoshio Tamashiro ◽

Marcos Pereira Marinho Aidar

Keyword(s):

Atlantic Forest ◽

Aboveground Biomass ◽

Sao Paulo ◽

Shannon Index ◽

Forest Composition ◽

São Paulo ◽

Heterogeneous Distribution ◽

Serra Do Mar ◽

Secondary Stage ◽

History Of

ABSTRAT Projects involving floristic-phytosociological surveys are becoming increasingly frequent and is a very important tool to access the biodiversity, status of succession, biomass and carbon storage, guiding conservation and management strategies. These studies are particularly important in Atlantic Forest, which is considered a hotspot in terms of biodiversity, endemism and impacts. São Paulo State lost more than 80% of original forest and, nowadays, remains only isolated patches with a variety stage of succession and history of use. The aim of this study was to characterize the structure, composition and biomass of the woody plant community in a Montane Ombrophilous Dense Forest, Serra do Mar State Park. All trees with DBH ≥ 4.8 cm were sampled in 1 ha plot area, totaling 1,704 individuals belonging to 38 botanical families and 143 species. The highest species richness was found in the Myrtaceae and Lauraceae families, and the greatest value of abundance and Importance (IV) was observed in the Arecaceae and Euphorbiaceae. The Shannon index (H’) was 3.7 nats.ind.-1 and the Pielou’s evenness index (J) 0.7, characterizing a very diverse community with heterogeneous distribution of individuals by species. The aboveground biomass was 166.3 Mg.ha-1, similar to others studies in Atlantic forests. The forest composition, biomass and the history of land use indicate a middle secondary stage of regeneration, but evolving to a more mature condition.

Download Full-text

Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods

Remote Sensing ◽

10.3390/rs11080949 ◽

2019 ◽

Vol 11 (8) ◽

pp. 949 ◽

Cited By ~ 1

Author(s):

Salim Malek ◽

Franco Miglietta ◽

Terje Gobakken ◽

Erik Næsset ◽

Damiano Gianelle ◽

...

Keyword(s):

Active Learning ◽

Data Collection ◽

Aboveground Biomass ◽

Field Data ◽

Random Selection ◽

Support Vector ◽

Lidar Data ◽

Individual Tree ◽

Field Data Collection ◽

Collection Cost

Light detection and ranging (lidar) data are nowadays a standard data source in studies related to forest ecology and environmental mapping. Medium/high point density lidar data allow to automatically detect individual tree crowns (ITCs), and they provide useful information to predict stem diameter and aboveground biomass of each tree represented by a detected ITC. However, acquisition of field data is necessary for the construction of prediction models that relate field data to lidar data and for validation of such models. When working at ITC level, field data collection is often expensive and time-consuming as accurate tree positions are needed. Active learning (AL) can be very useful in this context as it helps to select the optimal field trees to be measured, reducing the field data collection cost. In this study, we propose a new method of AL for regression based on the minimization of the field data collection cost in terms of distance to navigate between field sample trees, and accuracy in terms of root mean square error of the predictions. The developed method is applied to the prediction of diameter at breast heights (DBH) and aboveground biomass (AGB) of individual trees by using their height and crown diameter as independent variables and support vector regression. The proposed method was tested on two boreal forest datasets, and the obtained results show the effectiveness of the proposed selecting strategy to provide substantial improvements over the different iterations compared to a random selection. The obtained RMSE of DBH/AGB for the first dataset was 5.09 cm/95.5 kg with a cost equal to 8256/6173 m by using the proposed multi-objective method of selection. However, by using a random selection, the RMSE was 5.20 cm/102.1 kg with a cost equal to 28,391/30,086 m. The proposed approach can be efficient in order to get more accurate predictions with smaller costs, especially when a large forest area with no previous field data is subject to inventory and analysis.

Download Full-text

Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal

Remote Sensing ◽

10.3390/rs11010077 ◽

2019 ◽

Vol 11 (1) ◽

pp. 77 ◽

Cited By ~ 36

Author(s):

José Antonio Navarro ◽

Nur Algeet ◽

Alfredo Fernández-Landa ◽

Jessica Esteban ◽

Pablo Rodríguez-Noriega ◽

...

Keyword(s):

Aboveground Biomass ◽

Auxiliary Information ◽

Field Measurements ◽

Cost Effective ◽

Point Clouds ◽

Support Vector ◽

Monitoring Methods ◽

Individual Tree ◽

Entire Study ◽

Sentinel 2

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurements of AGB may be a challenge because of their remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund Oceanium project that monitors 10,000 ha of mangrove plantations. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric support vector regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2, and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m, respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in young mangrove plantations.

Download Full-text