scholarly journals Effects of Forest Canopy Structure on Forest Aboveground Biomass Estimation using Landsat Imagery

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chao Li ◽  
Mingyang Li ◽  
Kotaro Iizuka ◽  
Jie Liu ◽  
Keyi Chen ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Forest Canopy ◽  
Landsat Imagery ◽  
Canopy Structure ◽  
Biomass Estimation ◽  
Forest Aboveground Biomass

scholarly journals Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

2016 ◽  
Vol 8 (6) ◽  
pp. 469 ◽  
Author(s):  
Panpan Zhao ◽  
Dengsheng Lu ◽  
Guangxing Wang ◽  
Chuping Wu ◽  
Yujie Huang ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Spectral Reflectance ◽  
Landsat Imagery ◽  
Biomass Estimation ◽  
Forest Aboveground Biomass

scholarly journals Tropical PeatLand Forest Biomass Estimation Using Polarimetric Parameters Extracted from RadarSAT-2 Images

Land ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 193
Author(s):  
Mirza Waqar ◽  
Rahmi Sukmawati ◽  
Yaqi Ji ◽  
Josaphat Sri Sumantyo
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Index ◽  
Forest Biomass ◽  
Canopy Structure ◽  
Acquisition Time ◽  
Biomass Estimation ◽  
Temporal Consistency ◽  
Volume Scattering ◽  
Tropical Peatland ◽  
Forest Aboveground Biomass

This paper was aimed at estimating the forest aboveground biomass (AGB) in the Central Kalimantan tropical peatland forest, Indonesia, using polarimetric parameters extracted from RadarSAT-2 images. Six consecutive acquisitions of RadarSAT-2 full polarimetric data were acquired and polarimetric parameters were extracted. The backscattering coefficient ( σ o ) for HH, HV, VH, and VV channels was computed respectively. Entropy (H) and alpha ( α ) were computed using eign decomposition. In order to understand the scattering behavior, Yamaguchi decomposition was performed to estimate surface scattering ( γ s u r f ) and volume scattering ( γ v o l ) components. Similarly following polarimetric indices were computed; Biomass Index (BMI), Canopy Structure Index (CSI), Volume Scattering Index (VSI), Radar Vegetation Index (RVI) and Pedestal Height ( p h ). The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence, and suitability for forest aboveground biomass estimation across rainy and dry conditions. Regression analysis was performed between referenced biomass measurements and polarimetric parameters; VSI, H, RVI, p h , and γ v o l were found significantly correlated with AGB. Biomass estimation was carried out using significant models. Resultant models were validated using field-based AGB measurements. Validation results show a significant correlation between measured and referenced biomass measurements with temporal consistency over the acquisition time period.

scholarly journals Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 914
Author(s):  
Adeel Ahmad ◽  
Hammad Gilani ◽  
Sajid Rashid Ahmad
Keyword(s):  
High Resolution ◽  
Literature Review ◽  
Satellite Imagery ◽  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Independent Variables ◽  
Non Linear ◽  
Multiple Variables ◽  
Forest Aboveground Biomass

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

scholarly journals ESTIMATION OF REGIONAL FOREST ABOVEGROUND BIOMASS COMBINING ICESAT-GLAS WAVEFORMS AND HJ-1A/HSI HYPERSPECTRAL IMAGERIES

2016 ◽  
Vol XLI-B7 ◽  
pp. 731-737
Author(s):  
Yanqiu Xing ◽  
Sai Qiu ◽  
Jianhua Ding ◽  
Jing Tian
Keyword(s):  
Carbon Cycle ◽  
Aboveground Biomass ◽  
Forest Canopy ◽  
Field Investigation ◽  
Support Vector ◽  
Laser Altimeter ◽  
Spectral Bands ◽  
Spectral Imager ◽  
Regional Forest ◽  
Forest Aboveground Biomass

Estimation of forest aboveground biomass (AGB) is a critical challenge for understanding the global carbon cycle because it dominates the dynamics of the terrestrial carbon cycle. Light Detection and Ranging (LiDAR) system has a unique capability for estimating accurately forest canopy height, which has a direct relationship and can provide better understanding to the forest AGB. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) is the first polarorbiting LiDAR instrument for global observations of Earth, and it has been widely used for extracting forest AGB with footprints of nominally 70&thinsp;m in diameter on the earth's surface. However, the GLAS footprints are discrete geographically, and thus it has been restricted to produce the regional full coverage of forest AGB. To overcome the limit of discontinuity, the Hyper Spectral Imager (HSI) of HJ-1A with 115 bands was combined with GLAS waveforms to predict the regional forest AGB in the study. Corresponding with the field investigation in Wangqing of Changbai Mountain, China, the GLAS waveform metrics were derived and employed to establish the AGB model, which was used further for estimating the AGB within GLAS footprints. For HSI imagery, the Minimum Noise Fraction (MNF) method was used to decrease noise and reduce the dimensionality of spectral bands, and consequently the first three of MNF were able to offer almost 98% spectral information and qualified to regress with the GLAS estimated AGB. Afterwards, the support vector regression (SVR) method was employed in the study to establish the relationship between GLAS estimated AGB and three of HSI MNF (i.e. <i>MNF1</i>, <i>MNF2</i> and <i>MNF3</i>), and accordingly the full covered regional forest AGB map was produced. The results showed that the adj.R<sup>2</sup> and RMSE of SVR-AGB models were 0.75 and 4.68&thinsp;t&thinsp;hm<sup>&minus;2</sup> for broadleaf forests, 0.73 and 5.39&thinsp;t&thinsp;hm<sup>&minus;2</sup> for coniferous forests and 0.71 and 6.15&thinsp;t&thinsp;hm<sup>&minus;2</sup> for mixed forests respectively. The full covered regional forest AGB map of the study area had 0.62 of accuracy and 11.11&thinsp;t&thinsp;hm<sup>&minus;2</sup> of RMSE. The study demonstrated that it holds great potential to achieve the full covered regional forest AGB distribution with higher accuracy by combing LiDAR data and hyperspectral imageries.

scholarly journals Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin

2020 ◽  
Vol 12 (18) ◽  
pp. 2926
Author(s):  
Pierre Migolet ◽  
Kalifa Goïta
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Aboveground Biomass ◽  
Fourier Transforms ◽  
Canopy Structure ◽  
Principal Component ◽  
Congo Basin ◽  
Biomass Estimation ◽  
Mean Square Errors

The present study developed methods using remote sensing for estimation of total dry aboveground biomass (AGB) of oil palm in the Congo Basin. To achieve this, stem diameters at breast height (DBH, 1.3 m) and stem heights were measured in an oil palm plantation located in Gabon (Congo Basin, Central Africa). These measurements were used to determine AGB in situ. The remote sensing approach that was used to estimate AGB was textural ordination (FOTO) based upon Fourier transforms that were applied, respectively, to PlanetScope and FORMOSAT-2 satellite images taken from the area. The FOTO method is based on the combined use of two-dimensional (2D) Fast Fourier Transform (FFT) and Principal Component Analysis (PCA). In the context of the present study, it was used to characterize the variation in canopy structure and to estimate the aboveground biomass of mature oil palms. Two types of equations linking FOTO indices to in situ biomass were developed: multiple linear regressions (MLR); and multivariate adaptive spline regressions (MARS). All best models developed yielded significant results, regardless of whether they were derived from PlanetScope or from FORMOSAT-2 images. Coefficients of determination (R2) varied between 0.80 and 0.92 (p ≤ 0.0005); and relative root mean-square-errors (%RMSE) were less than 10.12% in all cases. The best model was obtained using MARS approach with FOTO indices from FORMOSAT-2 (%RMSE = 6.09%).

scholarly journals ESTIMATION OF REGIONAL FOREST ABOVEGROUND BIOMASS COMBINING ICESAT-GLAS WAVEFORMS AND HJ-1A/HSI HYPERSPECTRAL IMAGERIES

2016 ◽  
Vol XLI-B7 ◽  
pp. 731-737 ◽  
Author(s):  
Yanqiu Xing ◽  
Sai Qiu ◽  
Jianhua Ding ◽  
Jing Tian
Keyword(s):  
Carbon Cycle ◽  
Aboveground Biomass ◽  
Forest Canopy ◽  
Field Investigation ◽  
Support Vector ◽  
Laser Altimeter ◽  
Spectral Bands ◽  
Spectral Imager ◽  
Regional Forest ◽  
Forest Aboveground Biomass

Estimation of forest aboveground biomass (AGB) is a critical challenge for understanding the global carbon cycle because it dominates the dynamics of the terrestrial carbon cycle. Light Detection and Ranging (LiDAR) system has a unique capability for estimating accurately forest canopy height, which has a direct relationship and can provide better understanding to the forest AGB. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) is the first polarorbiting LiDAR instrument for global observations of Earth, and it has been widely used for extracting forest AGB with footprints of nominally 70&thinsp;m in diameter on the earth's surface. However, the GLAS footprints are discrete geographically, and thus it has been restricted to produce the regional full coverage of forest AGB. To overcome the limit of discontinuity, the Hyper Spectral Imager (HSI) of HJ-1A with 115 bands was combined with GLAS waveforms to predict the regional forest AGB in the study. Corresponding with the field investigation in Wangqing of Changbai Mountain, China, the GLAS waveform metrics were derived and employed to establish the AGB model, which was used further for estimating the AGB within GLAS footprints. For HSI imagery, the Minimum Noise Fraction (MNF) method was used to decrease noise and reduce the dimensionality of spectral bands, and consequently the first three of MNF were able to offer almost 98% spectral information and qualified to regress with the GLAS estimated AGB. Afterwards, the support vector regression (SVR) method was employed in the study to establish the relationship between GLAS estimated AGB and three of HSI MNF (i.e. &lt;i&gt;MNF1&lt;/i&gt;, &lt;i&gt;MNF2&lt;/i&gt; and &lt;i&gt;MNF3&lt;/i&gt;), and accordingly the full covered regional forest AGB map was produced. The results showed that the adj.R&lt;sup&gt;2&lt;/sup&gt; and RMSE of SVR-AGB models were 0.75 and 4.68&thinsp;t&thinsp;hm&lt;sup&gt;&minus;2&lt;/sup&gt; for broadleaf forests, 0.73 and 5.39&thinsp;t&thinsp;hm&lt;sup&gt;&minus;2&lt;/sup&gt; for coniferous forests and 0.71 and 6.15&thinsp;t&thinsp;hm&lt;sup&gt;&minus;2&lt;/sup&gt; for mixed forests respectively. The full covered regional forest AGB map of the study area had 0.62 of accuracy and 11.11&thinsp;t&thinsp;hm&lt;sup&gt;&minus;2&lt;/sup&gt; of RMSE. The study demonstrated that it holds great potential to achieve the full covered regional forest AGB distribution with higher accuracy by combing LiDAR data and hyperspectral imageries.

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.

Forest Aboveground Biomass Estimation with GEDI and ICESat-2 in Boreal Forests

2021 ◽  
Author(s):  
Laura Duncanson ◽  
Amy Neuenschwander ◽  
Carlos Alberto Silva ◽  
Paul Montesano ◽  
Eric Guenther ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Boreal Forests ◽  
Biomass Estimation ◽  
Forest Aboveground Biomass
Sign in / Sign up

Export Citation Format

Share Document