scholarly journals Estimating Mangrove Above-Ground Biomass Loss Due to Deforestation in Malaysian Northern Borneo between 2000 and 2015 Using SRTM and Landsat Images

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1018
Author(s):  
Charissa J. Wong ◽  
Daniel James ◽  
Normah A. Besar ◽  
Kamlisa U. Kamlun ◽  
Joseph Tangah ◽  
...  
Keyword(s):  
Mangrove Forest ◽  
Field Measurements ◽  
Tree Level ◽  
Mangrove Forests ◽  
Above Ground Biomass ◽  
Estimation Model ◽  
Landsat Images ◽  
Ground Biomass ◽  
Elevation Data ◽  
Srtm Data

Mangrove forests are highly productive ecosystems and play an important role in the global carbon cycle. We used Shuttle Radar Topography Mission (SRTM) elevation data to estimate mangrove above-ground biomass (AGB) in Sabah, Malaysian northern Borneo. We developed a tree-level approach to deal with the substantial temporal discrepancy between the SRTM data and the mangrove’s field measurements. We predicted the annual growth of diameter at breast height and adjusted the field measurements to the SRTM data acquisition year to estimate the field AGB. A canopy height model (CHM) was derived by correcting the SRTM data with ground elevation. Regression analyses between the estimated AGB and SRTM CHM produced an estimation model (R2: 0.61) with a root mean square error (RMSE) of 8.24 Mg ha−1 (RMSE%: 5.47). We then quantified the mangrove forest loss based on supervised classification of multitemporal Landsat images. More than 25,000 ha of mangrove forest had disappeared between 2000 and 2015. This has resulted in a significant decrease of about 3.96 million Mg of mangrove AGB in Sabah during the study period. As SRTM elevation data has a near-global coverage, this approach can be used to map the historical AGB of mangroves, especially in Southeast Asia, to promote mangrove carbon stock conservation.

scholarly journals Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1690 ◽  
Author(s):  
Tianyu Hu ◽  
YingYing Zhang ◽  
Yanjun Su ◽  
Yi Zheng ◽  
Guanghui Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Dimensional Structure ◽  
Coefficient Of Determination ◽  
Free Access ◽  
Mangrove Forests ◽  
Estimation Model ◽  
Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

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.

scholarly journals Allometric equations to estimate carbon pool in soil and above-ground biomass in mangrove forests in Southeast Mexico

2013 ◽  
Author(s):  
J. J. Guerra-Santos ◽  
R. M. Cerón ◽  
J. G. Cerón ◽  
A. Alderete-Chávez ◽  
D. L. Damián-Hernández ◽  
...  
Keyword(s):  
Carbon Pool ◽  
Allometric Equations ◽  
Mangrove Forests ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Southeast Mexico

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 Structure and biomass accumulation of natural mangrove forest at Gazi Bay, Kenya

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
JAMES G. KAIRO ◽  
MICHAEL NJOROGE GITHAIGA ◽  
KIPLAGAT KOTUT ◽  
FRANCIS KARIUKI
Keyword(s):  
Forest Structure ◽  
Mangrove Forest ◽  
Biomass Accumulation ◽  
Shoot Biomass ◽  
Total Biomass ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Significant Difference ◽  
Below Ground ◽  
Gazi Bay

Abstract. Githaiga MN, Kotut K, Kariuki F, Kairo JG. 2019. Structure and biomass accumulation of natural mangrove forest at Gazi Bay, Kenya. Bonorowo Wetlands 9: 18-32. The goal of this study was to determine the forest structure and estimate biomass accumulation above and below ground in the mangrove forest of Gazi Bay. The western, middle, and eastern forest blocks of the Gazi Bay mangrove forest were investigated for forest structure, whereas the western forest block was determined for biomass accumulation. To calculate below-ground biomass accumulation, in-growth cores of 80 cm long, 20 cm broad, and 60 cm deep were employed. Above-ground biomass accumulation was calculated using data on tree height and stem diameter at breast height (DBH-130). Leaf phenology was observed by tagging shoots. At the start, environmental variables were measured every four months for a year across four mangrove species zones. The linear regeneration sampling approach was used to determine the composition and distribution pattern of natural regeneration (LRS). Salinity revealed a strong negative connection with above-ground biomass accumulation among the soil environment characteristics studied. Sonneratia alba had the highest biomass accretion rate of 10.5 1.9 t ha-1 yr-1 among the four forest zones. Rhizophora mucronata (8.5 0.8 t ha-1 yr-1), Avicennia marina (5.2 1.8 t ha-1 yr-1), and Ceriops tagal (2.6 1.5 t ha-1 yr-1) were the next most abundant species. Above-ground and below-ground biomass accumulation differed significantly among zones (F (3, 8) = 5.42, p = 0.025) and (F (3, 8) = 16.03, p = 0 001), respectively. There was a significant difference in total biomass accumulation across zones (F (3, 8) =15.56, p = 0.001). For the entire forest, a root : shoot biomass accumulation ratio of 2 : 5 was calculated. This study's findings provide more accurate estimates of mangrove carbon capture and storage, which can be used in carbon credit discussions in the emerging carbon market.

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.

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