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

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.

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

scholarly journals Monitoring tropical forest carbon stocks and emissions using Planet satellite data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ovidiu Csillik ◽  
Pramukta Kumar ◽  
Joseph Mascaro ◽  
Tara O’Shea ◽  
Gregory P. Asner
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Cost Effective ◽  
Carbon Stocks ◽  
Airborne Lidar ◽  
Aboveground Carbon ◽  
Lidar Measurements ◽  
High Resolution Map ◽  
Aboveground Carbon Density ◽  
Planet Satellite

AbstractTropical forests are crucial for mitigating climate change, but many forests continue to be driven from carbon sinks to sources through human activities. To support more sustainable forest uses, we need to measure and monitor carbon stocks and emissions at high spatial and temporal resolution. We developed the first large-scale very high-resolution map of aboveground carbon stocks and emissions for the country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into a random forest machine learning regression workflow, obtaining an R2 of 0.70 and RMSE of 25.38 Mg C ha−1 for the nationwide estimation of aboveground carbon density (ACD). The diverse ecosystems of Peru harbor 6.928 Pg C, of which only 2.9 Pg C are found in protected areas or their buffers. We found significant carbon emissions between 2012 and 2017 in areas aggressively affected by oil palm and cacao plantations, agricultural and urban expansions or illegal gold mining. Creating such a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries will serve as a transformative tool to quantify the climate change mitigation services that forests provide.

scholarly journals Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion

2019 ◽  
Vol 11 (8) ◽  
pp. 928 ◽  
Author(s):  
Tom Swinfield ◽  
Jeremy A. Lindsell ◽  
Jonathan V. Williams ◽  
Rhett D. Harrison ◽  
Agustiono ◽  
...  
Keyword(s):  
Structure From Motion ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Canopy Height ◽  
Training Data ◽  
Small Scale ◽  
Model Parameters ◽  
Carbon Density ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density

Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies (>10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location ( R 2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R 2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR.

scholarly journals Modeling and Mapping Forest Fire Occurrence from Aboveground Carbon Density in Mexico

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 402 ◽  
Author(s):  
Carlos Ivan Briones-Herrera ◽  
Daniel José Vega-Nieva ◽  
Norma Angélica Monjarás-Vega ◽  
Favian Flores-Medina ◽  
Pablito Marcelo Lopez-Serrano ◽  
...  
Keyword(s):  
Forest Fires ◽  
Aboveground Biomass ◽  
Fire Suppression ◽  
Weibull Model ◽  
Management Decision ◽  
Fire Occurrence ◽  
Carbon Density ◽  
Aboveground Carbon ◽  
Fire Activity ◽  
Aboveground Carbon Density

Understanding the spatial patterns of fire occurrence is key for improved forest fires management, particularly under global change scenarios. Very few studies have attempted to relate satellite-based aboveground biomass maps of moderate spatial resolution to spatial fire occurrence under a variety of climatic and vegetation conditions. This study focuses on modeling and mapping fire occurrence based on fire suppression data from 2005–2015 from aboveground biomass—expressed as aboveground carbon density (AGCD)—for the main ecoregions in Mexico. Our results showed that at each ecoregion, unimodal or humped relationships were found between AGCD and fire occurrence, which might be explained by varying constraints of fuel and climate limitation to fire activity. Weibull equations successfully fitted the fire occurrence distributions from AGCD, with the lowest fit for the desert shrub-dominated north region that had the lowest number of observed fires. The models for predicting fire occurrence from AGCD were significantly different by region, with the exception of the temperate forest in the northwest and northeast regions that could be modeled with a single Weibull model. Our results suggest that AGCD could be used to estimate spatial fire occurrence maps; those estimates could be integrated into operational GIS tools for assistance in fire danger mapping and fire and fuel management decision-making. Further investigation of anthropogenic drivers of fire occurrence and fuel characteristics should be considered for improving the operational spatial planning of fire management. The modeling strategy presented here could be replicated in other countries or regions, based on remote-sensed measurements of aboveground biomass and fire activity or fire suppression records.

scholarly journals Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Spatiotemporal Variations ◽  
Estimation Model

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

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