scholarly journals Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 819 ◽  
Author(s):  
Tojal ◽  
Bastarrika ◽  
Barret ◽  
Espeso ◽  
Lopez-Guede ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Tree Height ◽  
National Forest Inventory ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Low Density ◽  
Cloud Data ◽  
Public Data ◽  
The Government ◽  
North Of Spain

Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m2) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass.

scholarly journals Deriving Individual-Tree Biomass from Effective Crown Data Generated by Terrestrial Laser Scanning

2019 ◽  
Vol 11 (23) ◽  
pp. 2793
Author(s):  
Yujie Zheng ◽  
Weiwei Jia ◽  
Qiang Wang ◽  
Xu Huang
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Tree Height ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Crown Height ◽  
Individual Tree ◽  
Cloud Data ◽  
Biomass Equations

Biomass reflects the state of forest management and is critical for assessing forest benefits and carbon storage. The effective crown is the region above the lower limit of the forest crown that includes the maximum vertical distribution density of branches and leaves; this component plays an important role in tree growth. Adding the effective crown to biomass equations can enhance the accuracy of the derived biomass. Six sample plots in a larch plantation (ranging in area from 0.06 ha to 0.12 ha and in number of trees from 63 to 96) at the Mengjiagang forest farm in Huanan County, Jiamusi City, Heilongjiang Province, China, were analyzed in this study. Terrestrial laser scanning (TLS) was used to obtain three-dimensional point cloud data on the trees, from which crown parameters at different heights were extracted. These parameters were used to determine the position of the effective crown. Moreover, effective crown parameters were added to biomass equations with tree height as the sole variable to improve the accuracy of the derived individual-tree biomass estimates. The results showed that the minimum crown contact height was very similar to the effective crown height, and an increase in model accuracy was apparent (with R a 2 increasing from 0.846 to 0.910 and root-mean-square error (RMSE) decreasing from 0.372 kg to 0.286 kg). The optimal model for deriving biomass included tree height, crown length from minimum contact height, crown height from minimum contact height, and crown surface area from minimum contact height. The novelty of the article is that it improves the fit of individual-tree biomass models by adding crown-related variables and investigates how the accuracy of biomass estimation can be enhanced by using remote sensing methods without obtaining diameter at breast height.

scholarly journals Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1101 ◽  
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Erxue Chen ◽  
Xinliang Wei
Keyword(s):  
Aboveground Biomass ◽  
Forest Type ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Estimation Model ◽  
Forest Aboveground Biomass

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.

scholarly journals Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Antonio Montagnoli ◽  
Sara Fusco ◽  
Mattia Terzaghi ◽  
Alan Kirschbaum ◽  
Dirk Pflugmacher ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Lidar Data ◽  
Low Density ◽  
Forest Aboveground Biomass

scholarly journals ABOVEGROUND BIOMASS ESTIMATION IN A TROPICAL FOREST WITH SELECTIVE LOGGING USING RANDOM FOREST AND LIDAR DATA

FLORESTA ◽  
2020 ◽  
Vol 50 (4) ◽  
pp. 1873
Author(s):  
Juliana Marchesan ◽  
Elisiane Alba ◽  
Mateus Sabadi Schuh ◽  
José Augusto Spiazzi Favarin ◽  
Rudiney Soares Pereira
Keyword(s):  
Random Forest ◽  
Tropical Forest ◽  
Aboveground Biomass ◽  
Learning Algorithm ◽  
Important Variable ◽  
Selective Logging ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Average Height ◽  
Amazon Forest

The tropical forest is characterized by expressive biomass and stores high amounts of carbon, which is an important variable for climate monitoring. Thus, studies aiming to analyze suitable methods to predict biomass are crucial, especially in the tropics, where dense vegetation makes modeling difficult. Thus, the objective of the present study was to estimate aboveground biomass (AGB) in a tropical forest area with selective logging in the Amazon forest using the Random Forest (RF) machine learning algorithm and LiDAR data. For this, 85 sample units were used at Fazenda Cauaxi, in the municipality of Paragominas, Pará State. LiDAR data were collected in 2014 and made available by the Sustainable Landscapes Project. The software R was used for data analysis. Among the LiDAR metrics, the average height was used as it had the greatest significance to compose the model. The model presented a pseudo R² of 0.69 (value obtained by the RF), Spearman's Correlation Coefficient of 0.80, RMSE of 47.05 Mg.ha-1 (19.84%), and Bias of 2.06 Mg.ha-1 (0.87%). With the results, it was possible to infer that the average height metric was enough to estimate AGB in a tropical forest with selective logging, in addition, the RF algorithm the biomass to be estimated, which can be used to assist in monitoring and action management in areas of selective logging and serve as a basis for climate change mitigation policies.

scholarly journals Aboveground biomass estimation with airborne hyperspectral and LiDAR data in Tesinske Beskydy Mountains

Beskydy ◽  
2015 ◽  
Vol 8 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Olga Brovkina ◽  
František Zemek ◽  
Tomáš Fabiánek
Keyword(s):  
Aboveground Biomass ◽  
Field Measurements ◽  
Airborne Lidar ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Predicting Model ◽  
The Third ◽  
Study Results ◽  
Airborne Lidar Data ◽  
Forest Aboveground Biomass

The study presents three models for estimation of forest aboveground biomass (AGB) for plot level using different categories of airborne data. The first and the second models estimate AGB from metrics of airborne LiDAR data. The third model estimates AGB from integration of metrics of airborne hyperspectral and LiDAR data. The results are compared with plot level biomass estimated from field measurements. The results show that the best AGB estimate is obtained from the model utilizing a fusion of hyperspectral and LiDAR metrics. Study results expand existing research on the applicability of airborne hyperspectral and LiDAR datasets for AGB assessment. It evidences the efficiency of using a predicting model based on hyperspectral and LiDAR data for study area.

scholarly journals Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

2019 ◽  
Vol 11 (16) ◽  
pp. 1906
Author(s):  
Siqi Li ◽  
Lindi J. Quackenbush ◽  
Jungho Im
Keyword(s):  
New York ◽  
Aboveground Biomass ◽  
Forest Type ◽  
New York State ◽  
Airborne Lidar ◽  
Systematic Sampling ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Sampling Strategies ◽  
York State

Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha−1. The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R2 of 0.26 and RMSE of 70.1 Mg ha−1. The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R2 of 0.40 and an RMSE of 108.2 Mg ha−1 compared to the full lidar coverage model with an R2 of 0.58 and an RMSE of 96.0 Mg ha−1. This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies.

Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density

2006 ◽  
Vol 36 (1) ◽  
pp. 34-47 ◽  
Author(s):  
V Thomas ◽  
P Treitz ◽  
J H McCaughey ◽  
I Morrison
Keyword(s):  
Aboveground Biomass ◽  
Basal Area ◽  
High Density ◽  
Lidar Data ◽  
Low Density ◽  
Entire Study ◽  
Dominant Height ◽  
Forested Areas ◽  
Mixedwood Boreal Forest ◽  
Crown Closure

Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require "scaling-up" from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas.

scholarly journals Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: overcoming problems of high biomass and persistent cloud

2012 ◽  
Vol 9 (1) ◽  
pp. 179-191 ◽  
Author(s):  
E. T. A. Mitchard ◽  
S. S. Saatchi ◽  
L. J. T. White ◽  
K. A. Abernethy ◽  
K. J. Jeffery ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
National Park ◽  
Radar Data ◽  
Optical Data ◽  
Biomass Estimation ◽  
Lidar Data ◽  
High Biomass ◽  
Alos Palsar ◽  
Lope National Park

Abstract. Spatially-explicit maps of aboveground biomass are essential for calculating the losses and gains in forest carbon at a regional to national level. The production of such maps across wide areas will become increasingly necessary as international efforts to protect primary forests, such as the REDD+ (Reducing Emissions from Deforestation and forest Degradation) mechanism, come into effect, alongside their use for management and research more generally. However, mapping biomass over high-biomass tropical forest is challenging as (1) direct regressions with optical and radar data saturate, (2) much of the tropics is persistently cloud-covered, reducing the availability of optical data, (3) many regions include steep topography, making the use of radar data complex, (5) while LiDAR data does not suffer from saturation, expensive aircraft-derived data are necessary for complete coverage. We present a solution to the problems, using a combination of terrain-corrected L-band radar data (ALOS PALSAR), spaceborne LiDAR data (ICESat GLAS) and ground-based data. We map Gabon's Lopé National Park (5000 km2) because it includes a range of vegetation types from savanna to closed-canopy tropical forest, is topographically complex, has no recent contiguous cloud-free high-resolution optical data, and the dense forest is above the saturation point for radar. Our 100 m resolution biomass map is derived from fusing spaceborne LiDAR (7142 ICESat GLAS footprints), 96 ground-based plots (average size 0.8 ha) and an unsupervised classification of terrain-corrected ALOS PALSAR radar data, from which we derive the aboveground biomass stocks of the park to be 78 Tg C (173 Mg C ha−1). This value is consistent with our field data average of 181 Mg C ha−1, from the field plots measured in 2009 covering a total of 78 ha, and which are independent as they were not used for the GLAS-biomass estimation. We estimate an uncertainty of ±25% on our carbon stock value for the park. This error term includes uncertainties resulting from the use of a generic tropical allometric equation, the use of GLAS data to estimate Lorey's height, and the necessity of separating the landscape into distinct classes. As there is currently no spaceborne LiDAR satellite in operation (GLAS data is available for 2003–2009 only), this methodology is not suitable for change-detection. This research underlines the need for new satellite LiDAR data to provide the potential for biomass-change estimates, although this need will not be met before 2015.

scholarly journals Mapping tropical forest biomass with radar and spaceborne LiDAR: overcoming problems of high biomass and persistent cloud

2011 ◽  
Vol 8 (4) ◽  
pp. 8781-8815 ◽  
Author(s):  
E. T. A. Mitchard ◽  
S. S. Saatchi ◽  
L. J. T. White ◽  
K. A. Abernethy ◽  
K. J. Jeffery ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Degradation Mechanism ◽  
National Level ◽  
Radar Data ◽  
Optical Data ◽  
Biomass Estimation ◽  
Lidar Data ◽  
High Biomass ◽  
Alos Palsar

Abstract. Spatially-explicit maps of aboveground biomass are essential for calculating the losses and gains in forest carbon at a regional to national level. The production of such maps across wide areas will become increasingly necessary as international efforts to protect primary forests, such as the REDD+ (Reducing Emissions from Deforestation and forest Degradation) mechanism, come into effect, alongside their use for management and research more generally. However, mapping biomass over high-biomass tropical forest is challenging as (1) direct regressions with optical and radar data saturate, (2) much of the tropics is persistently cloud-covered, reducing the availability of optical data, (3) many regions include steep topography, making the use of radar data complex, (4) while LiDAR data does not suffer from saturation, expensive aircraft-derived data are necessary for complete coverage. We present a solution to the problems, using a combination of terrain-corrected L-band radar data (ALOS PALSAR), spaceborne LiDAR data (ICESat GLAS) and ground-based data. We map Gabon's Lopé National Park (5000 km2) because it includes a range of vegetation types from savanna to closed-canopy tropical forest, is topographically complex, has no recent cloud-free high-resolution optical data, and the dense forest is above the saturation point for radar. Our 100 m resolution biomass map is derived from fusing spaceborne LiDAR (7142 ICESat GLAS footprints), 96 ground-based plots (average size 0.8 ha) and an unsupervised classification of terrain-corrected ALOS PALSAR radar data, from which we derive the aboveground biomass stocks of the park to be 78 Tg C (173 Mg C ha−1). This value is consistent with our field data average of 181 Mg C ha−1, from the field plots measured in 2009 covering a total of 78 ha, and which are independent as they were not used for the GLAS-biomass estimation. We estimate an uncertainty of ± 25 % on our carbon stock value for the park. This error term includes uncertainties resulting from the use of a generic tropical allometric equation, the use of GLAS data to estimate Lorey's height, and the necessity of separating the landscape into distinct classes. As there is currently no spaceborne LiDAR satellite in operation (GLAS data is available for 2003–2007 only), this methodology is not suitable for change-detection. This research underlines the need for new satellite LiDAR data to provide the potential for biomass-change estimates, although this need will not be met before 2015.

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