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 Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
David A. Coomes ◽  
Daniel Šafka ◽  
James Shepherd ◽  
Michele Dalponte ◽  
Robert Holdaway
Keyword(s):  
New Zealand ◽  
Power Law ◽  
Laser Scanning ◽  
Forest Carbon ◽  
Airborne Laser Scanning ◽  
Canopy Height ◽  
Carbon Density ◽  
Airborne Laser ◽  
Aboveground Carbon Density ◽  
Estimation Models

Abstract Background Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.

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 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

scholarly journals Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-18
Author(s):  
Zhenyu Wu ◽  
Zhaowen Wang ◽  
Ye Yuan ◽  
Jianming Zhang ◽  
Zhangyang Wang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Black Box ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Small Scale ◽  
Model Parameters ◽  
Original Training ◽  
Image Generation ◽  
Adversarial Networks ◽  
Calibration Techniques

Generative adversarial networks (GANs) nowadays are capable of producing images of incredible realism. Two concerns raised are whether the state-of-the-art GAN’s learned distribution still suffers from mode collapse and what to do if so. Existing diversity tests of samples from GANs are usually conducted qualitatively on a small scale and/or depend on the access to original training data as well as the trained model parameters. This article explores GAN intra-mode collapse and calibrates that in a novel black-box setting: access to neither training data nor the trained model parameters is assumed. The new setting is practically demanded yet rarely explored and significantly more challenging. As a first stab, we devise a set of statistical tools based on sampling that can visualize, quantify, and rectify intra-mode collapse . We demonstrate the effectiveness of our proposed diagnosis and calibration techniques, via extensive simulations and experiments, on unconditional GAN image generation (e.g., face and vehicle). Our study reveals that the intra-mode collapse is still a prevailing problem in state-of-the-art GANs and the mode collapse is diagnosable and calibratable in black-box settings. Our codes are available at https://github.com/VITA-Group/BlackBoxGANCollapse .

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
Sign in / Sign up

Export Citation Format

Share Document