L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia

Hamdan, O.; Aziz, H. K.; Hasmadi, I. Mohd

doi:10.1016/j.rse.2014.04.029

Cited by 114 publications

(95 citation statements)

References 32 publications

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“…However our results, also according to Figure 4, do not clearly show the usual 100-150 Mg/ha saturation limit reported in literature [1,57,58]; and it is difficult to understand if at higher AGB levels there is a scarce saturation effect or increased error in model. These results from a single research might be caused also by environmental characteristics of the study areas (e.g., soil and forest structure).…”

Section: Discussioncontrasting

confidence: 94%

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

et al. 2016

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Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R 2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R 2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.

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Section: Discussioncontrasting

confidence: 94%

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

et al. 2016

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“…C correction in combination with 1990206, 1993278, 1995284, 1996159, 1996191, 2001252, 2003290, 2004277, 2005199, 2006266, 2007205, 2008208, 2009290, 2010213, 2011232 L7 ETM+ 1999255, 1999287, 2000258, 2001260, 2002311 BJGS-China L5 TM 1986307, 1988313, 1992212, 1994313, 1997305, 1998228 Japan ALOS PALSAR 2007PALSAR , 2008PALSAR , 2009PALSAR , 2010 2.2.3. ALOS PALSAR Data Acquisition and Pre-Processing ALOS PALSAR as an active microwave sensor using L-band frequency to achieve cloud-free and day-and -night land observation, with a 46-day revisit cycle [52]. The PALSAR image dataset (off-Nadir 34.3˝) used in this study consisted of 10˝ˆ10˝mosaic tiles from a global 25 m resolution PALSAR mosaic from 2007,2008,2009,2010 (Table 2).…”

Section: Field Datamentioning

confidence: 99%

Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data

Shen

Huang

et al. 2016

Remote Sensing

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Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007)(2008)(2009)(2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R 2 of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones.

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“…Although mangroves constitute less than 0.4% of the world's forests [2], they play an important role in providing habitats for thousands of marine and pelagic species, and serving the local communities with food, medicine, fuel and building materials. They also become important in mitigating the impact of climate change by sequestering CO2 (the main greenhouse gas, apart from water vapor) from the atmosphere as they are one of the most carbon-rich forests in the tropics [3][4][5]. They also protect the coastal areas from tidal waves, tsunamis and cyclones [6].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, hyperspectral images are useful in discriminating various mangrove species [17]. In Malaysia mangrove ecosystems have been studied using various remote sensing data for mangrove detection/areal delineation [18][19][20][21], mangrove change detection [22][23][24][25][26][27][28][29], mangrove species classification [21,30,31], and biomass of mangrove forest [5]. Change detection of mangrove areas using satellite data has been conducted in Malaysia at a local scale.…”

Section: Introductionmentioning

confidence: 99%

Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia

et al. 2015

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Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less "salt and pepper effects" compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of OPEN ACCESSRemote Sens. 2015, 7 14361 mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM.

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L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia

Cited by 114 publications

References 32 publications

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data

Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia

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