scholarly journals MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY

2018 ◽  
Vol IV-3 ◽  
pp. 13-20 ◽  
Author(s):  
Reginald Jay Labadisos Argamosa ◽  
Ariel Conferido Blanco ◽  
Alvin Balidoy Baloloy ◽  
Christian Gumbao Candido ◽  
John Bart Lovern Caboboy Dumalag ◽  
...  
Keyword(s):  
Random Forest ◽  
Principal Components ◽  
Mangrove Forest ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Forest Mapping ◽  
Ground Biomass ◽  
Sar Data ◽  
Volumetric Objects ◽  
Occurrence Matrix

Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23&amp;thinsp;cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75&amp;thinsp;cm to 7.5&amp;thinsp;cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a&amp;minus;c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r<sup>2</sup> of 0.79 and an RMSE of 0.44&amp;thinsp;Mg using only four features, namely, &amp;sigma;<sup>&amp;deg;</sup><sub><i>VH</i></sub> GLCM variance, &amp;sigma;<sup>&amp;deg;</sup><sub><i>VH</i></sub> GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.

scholarly journals Impact of tidal height on characteristics of ALOS PALSAR measurements to estimate above ground biomass of mangrove forest in Indonesia

2018 ◽  
Author(s):  
Ketut Wikantika
Keyword(s):  
Mangrove Forest ◽  
Impact Analysis ◽  
Time Series Data ◽  
Tidal Height ◽  
Series Data ◽  
Above Ground Biomass ◽  
Large Area ◽  
Alos Palsar ◽  
Ground Biomass ◽  
The Impact

Mangrove has the most carbon rich forests in the tropics. Mapping and monitoring biomass of mangrove forest is very important to manage ecosystem and field survey of mangrove biomass and productivity is very difficult due to muddy soil condition, heavy weight of the wood, very large area and tidal effect on mangrove area. Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) is available for identification and monitoring mangrove forest. The objective of this research is to investigate the impact of tidal height on characteristics of HH and HV derived from ALOS PALSAR for estimation above ground biomass of mangrove forest. Methodology consists of collecting of tidal height data in the study area, ALOS-PALSAR time series data, region of interest (ROI) on mangrove forest, characterization of HH and HV and impact analysis of tidal height on HH and HV. The result of this research has showed the impact of tidal height on characteristics HH and HV on mangrove forest types derived from ALOS-PALSAR and proposed the model for estimation aboveground biomass of mangrove forest.

scholarly journals Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

2018 ◽  
Vol 10 (9) ◽  
pp. 1355 ◽  
Author(s):  
Luciana Pereira ◽  
Luiz Furtado ◽  
Evlyn Novo ◽  
Sidnei Sant’Anna ◽  
Veraldo Liesenberg ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Floodplain Forest ◽  
Single Frequency ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Area Index ◽  
Ground Biomass ◽  
L Band ◽  
Sar Data

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.

Modelling Above Ground Biomass of Indian Mangrove Forest Using Dual-pol SAR Data

2020 ◽  
pp. 100457
Author(s):  
Bhumika Vaghela ◽  
Sanid Chirakkal ◽  
Deepak Putrevu ◽  
Hitesh Solanki
Keyword(s):  
Mangrove Forest ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Sar Data

Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest

2021 ◽  
Vol 21 ◽  
pp. 100462
Author(s):  
Sadhana Yadav ◽  
Hitendra Padalia ◽  
Sanjiv K. Sinha ◽  
Ritika Srinet ◽  
Prakash Chauhan
Keyword(s):  
Random Forest ◽  
Tropical Forests ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
X Band

scholarly journals Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices

2021 ◽  
Vol 13 (12) ◽  
pp. 2339
Author(s):  
Haibo Yang ◽  
Fei Li ◽  
Wei Wang ◽  
Kang Yu
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Tuber Formation ◽  
Growth Stages ◽  
Spectral Indices ◽  
Above Ground Biomass ◽  
Full Spectrum ◽  
Ground Biomass ◽  
Tuber Bulking ◽  
Input Variables

Spectral indices rarely show consistency in estimating crop traits across growth stages; thus, it is critical to simultaneously evaluate a group of spectral variables and select the most informative spectral indices for retrieving crop traits. The objective of this study was to explore the optimal spectral predictors for above-ground biomass (AGB) by applying Random Forest (RF) on three types of spectral predictors: the full spectrum, published spectral indices (Pub-SIs), and optimized spectral indices (Opt-SIs). Canopy hyperspectral reflectance of potato plants, treated with seven nitrogen (N) rates, was obtained during the tuber formation and tuber bulking from 2015 to 2016. Twelve Pub-SIs were selected, and their spectral bands were optimized using band optimization algorithms. Results showed that the Opt-SIs were the best input variables of RF models. Compared to the best empirical model based on Opt-SIs, the Opt-SIs based RF model improved the prediction of AGB, with R2 increased by 6%, 10%, and 16% at the tuber formation, tuber bulking, and for across the two growth stages, respectively. The Opt-SIs can significantly reduce the number of input variables. The optimized Blue nitrogen index (Opt-BNI) and Modified red-edge normalized difference vegetation index (Opt-mND705) combined with an RF model showed the best performance in estimating potato AGB at the tuber formation stage (R2 = 0.88). In the tuber bulking stage, only using optimized Nitrogen planar domain index (Opt-NPDI) as the input variable of the RF model produced satisfactory accuracy in training and testing datasets, with the R2, RMSE, and RE being 0.92, 208.6 kg/ha, and 10.3%, respectively. The Opt-BNI and Double-peak nitrogen index (Opt-NDDA) coupling with an RF model explained 86% of the variations in potato AGB, with the lowest RMSE (262.9 kg/ha) and RE (14.8%) across two growth stages. This study shows that combining the Opt-SIs and RF can greatly enhance the prediction accuracy for crop AGB while significantly reduces collinearity and redundancies of spectral data.

scholarly journals ESTIMASI BIOMASSA PERMUKAAN DAN STOK KARBON PADA TEGAKAN POHON Avicennia marina DAN Rhizophora mucronata DI PERAIRAN PESISIR OEBELO KABUPATEN KUPANG

2016 ◽  
Vol 16 (2) ◽  
pp. 163 ◽  
Author(s):  
Glucklich Manafe ◽  
Michael Riwu Kaho ◽  
Fonny Risamasu
Keyword(s):  
Coastal Area ◽  
Forest Floor ◽  
Mangrove Forest ◽  
Co2 Sequestration ◽  
Avicennia Marina ◽  
Research Data ◽  
Nursery Ground ◽  
Above Ground Biomass ◽  
Mangrove Species ◽  
Ground Biomass

Mangrove forest has an important function for living thing especially in the ocean and coastal area. Besides as feeding and nursery ground, mangrove forest is also has a function as carbon sinker. The utilizing of mangrove forest as a corbon sinker is one of ways to reduce CO2 in atmosphere. Mangrove forest in Oebelo village has a capability to utilize as carbon sinker. The aim of this research was to estimate above ground biomass and carbon reserve from two mangrove species Avicennia marina and Rhizopora mucronata in coastal area of Oebelo Village. In this research data was collected from diameter breast high and litter from forest floor. Alometric was used to estimate the above ground biomass. After data collected, analysis would continue with t test to know the different between these two species.The result showed A. marina and R. mucronata were different, the highest biomass, carbon reserve and CO2 sequestration were in A.marina respectively 118.80 Mg.ha-1, 54.65 Mg.ha-1, 200.37 Mg.ha-1 and R. mucronata respectively 28.90 Mg.ha-1, 13.30 Mg.ha-1, 48.75 Mg.ha-1. The result for litter biomass and carbon reserve showed there was no different between these tow species.

Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data

2016 ◽  
Vol 10 (4) ◽  
pp. 046003 ◽  
Author(s):  
Wang Li ◽  
Zheng Niu ◽  
Zengyuan Li ◽  
Cheng Wang ◽  
Mingquan Wu ◽  
...  
Keyword(s):  
Coniferous Forest ◽  
Airborne Lidar ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Ground Biomass

scholarly journals Detecting tropical selective logging with SAR data requires a time series approach

2020 ◽  
Author(s):  
MG Hethcoat ◽  
JMB Carreiras ◽  
DP Edwards ◽  
RG Bryant ◽  
S Quegan
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Forest Degradation ◽  
Selective Logging ◽  
Forest Monitoring ◽  
Free Access ◽  
Alos Palsar ◽  
Lowland Tropical Forest ◽  
Time Series Analyses ◽  
Sar Data

AbstractSelective logging is the primary driver of forest degradation in the tropics and reduces the capacity of forests to harbour biodiversity, maintain key ecosystem processes, sequester carbon, and support human livelihoods. While the preceding decade has seen a tremendous improvement in the ability to monitor forest disturbances from space, advances in forest monitoring have almost universally relied on optical satellite data from the Landsat program, whose effectiveness is limited in tropical regions with frequent cloud cover. Synthetic aperture radar (SAR) data can penetrate clouds and have been utilized in forest mapping applications since the early 1990s, but no study has exclusively used SAR data to map tropical selective logging. A detailed selective logging dataset from three lowland tropical forest regions in the Brazilian Amazon was used to assess the effectiveness of SAR data from Sentinel-1, RADARSAT-2 and PALSAR-2 for monitoring tropical selective logging. We built Random Forest models in an effort to classify pixel-based differences in logged and unlogged areas. In addition, we used the BFAST algorithm to assess if a dense time series of Sentinel-1 imagery displayed recognizable shifts in pixel values after selective logging. Random Forest classification with SAR data (Sentinel-1, RADARSAT-2, and ALOS-2 PALSAR-2) performed poorly, having high commission and omission errors for logged observations. This suggests little to no difference in pixel-based metrics between logged and unlogged areas for these sensors. In contrast, the Sentinel-1 time series analyses indicated that areas under higher intensity selective logging (> 20 m3 ha−1) show a distinct spike in the number of pixels that included a breakpoint during the logging season. BFAST detected breakpoints in 50% of logged pixels and exhibited a false alarm rate of approximately 10% in unlogged forest. Overall our results suggest that SAR data can be used in time series analyses to detect tropical selective logging at high intensity logging locations within the Amazon (> 20 m3 ha−1). These results have important implications for current and future abilities to detect selective logging with freely available SAR data from SAOCOM 1A, the planned continuation missions of Sentinel-1 (C and D), ALOS PALSAR-1 archives (expected to be opened for free access in 2020), and the upcoming launch of NISAR.

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