scholarly journals Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3330
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Emilio Moran ◽  
Mateus Batistella
Keyword(s):  
Carbon Stock ◽  
Brazilian Amazon ◽  
Airborne Lidar ◽  
Coefficient Of Determination ◽  
Lidar Data ◽  
Carbon Density ◽  
Modis Data ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density ◽  
Airborne Lidar Data

Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.

scholarly journals Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in a Western Amazonian Forest Using Airborne LiDAR

2015 ◽  
Vol 8 (1) ◽  
pp. 9 ◽  
Author(s):  
Patricio Molina ◽  
Gregory Asner ◽  
Mercedes Farjas Abadía ◽  
Juan Ojeda Manrique ◽  
Luis Sánchez Diez ◽  
...  
Keyword(s):  
Density Estimation ◽  
Airborne Lidar ◽  
Spatially Explicit ◽  
Carbon Density ◽  
Estimation Model ◽  
Amazonian Forest ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density

Estimating aboveground carbon density across forest landscapes of Hawaii: Combining FIA plot-derived estimates and airborne LiDAR

2018 ◽  
Vol 424 ◽  
pp. 323-337 ◽  
Author(s):  
R. Flint Hughes ◽  
Gregory P. Asner ◽  
James A. Baldwin ◽  
Joseph Mascaro ◽  
Lori K.K. Bufil ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Carbon Density ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density

Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal

2015 ◽  
Vol 38 ◽  
pp. 280-291 ◽  
Author(s):  
Yogendra K. Karna ◽  
Yousif Ali Hussin ◽  
Hammad Gilani ◽  
M.C. Bronsveld ◽  
M.S.R. Murthy ◽  
...  
Keyword(s):  
Tree Species ◽  
Carbon Stock ◽  
Species Level ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals ESTIMATING CARBON STOCK CHANGES OF MANGROVE FORESTS USING SATELLITE IMAGERY AND AIRBORNE LiDAR DATA IN THE SOUTH SUMATRA STATE, INDONESIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 705-709 ◽  
Author(s):  
Y. Maeda ◽  
A. Fukushima ◽  
Y. Imai ◽  
Y. Tanahashi ◽  
E. Nakama ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Tree Species ◽  
Carbon Stock ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Total Biomass ◽  
Mangrove Forests ◽  
The South ◽  
Spatial Volume ◽  
Airborne Lidar Data

The purposes of this study were 1) to estimate the biomass in the mangrove forests using satellite imagery and airborne LiDAR data, and 2) to estimate the amount of carbon stock changes using biomass estimated. The study area is located in the coastal area of the South Sumatra state, Indonesia. This area is approximately 66,500 ha with mostly flat land features. In this study, the following procedures were carried out: (1) Classification of types of tree species using Satellite imagery in the study area, (2) Development of correlation equations between spatial volume based on LiDAR data and biomass stock based on field survey for each types of tree species, and estimation of total biomass stock and carbon stock using the equation, and (3) Estimation of carbon stock change using Chronological Satellite Imageries. The result showed the biomass and the amount of carbon stock changes can be estimated with high accuracy, by combining the spatial volume based on airborne LiDAR data with the tree species classification based on satellite imagery. Quantitative biomass monitoring is in demand for projects related to REDD+ in developing countries, and this study showed that combining airborne LiDAR data with satellite imagery is one of the effective methods of monitoring for REDD+ projects.

scholarly journals ESTIMATING CARBON STOCK CHANGES OF MANGROVE FORESTS USING SATELLITE IMAGERY AND AIRBORNE LiDAR DATA IN THE SOUTH SUMATRA STATE, INDONESIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 705-709
Author(s):  
Y. Maeda ◽  
A. Fukushima ◽  
Y. Imai ◽  
Y. Tanahashi ◽  
E. Nakama ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Tree Species ◽  
Carbon Stock ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Total Biomass ◽  
Mangrove Forests ◽  
The South ◽  
Spatial Volume ◽  
Airborne Lidar Data

The purposes of this study were 1) to estimate the biomass in the mangrove forests using satellite imagery and airborne LiDAR data, and 2) to estimate the amount of carbon stock changes using biomass estimated. The study area is located in the coastal area of the South Sumatra state, Indonesia. This area is approximately 66,500 ha with mostly flat land features. In this study, the following procedures were carried out: (1) Classification of types of tree species using Satellite imagery in the study area, (2) Development of correlation equations between spatial volume based on LiDAR data and biomass stock based on field survey for each types of tree species, and estimation of total biomass stock and carbon stock using the equation, and (3) Estimation of carbon stock change using Chronological Satellite Imageries. The result showed the biomass and the amount of carbon stock changes can be estimated with high accuracy, by combining the spatial volume based on airborne LiDAR data with the tree species classification based on satellite imagery. Quantitative biomass monitoring is in demand for projects related to REDD+ in developing countries, and this study showed that combining airborne LiDAR data with satellite imagery is one of the effective methods of monitoring for REDD+ projects.

scholarly journals Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data

2015 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Qi Chen ◽  
Dengsheng Lu ◽  
Michael Keller ◽  
Maiza dos-Santos ◽  
Edson Bolfe ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Brazilian Amazon ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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