Combination of SPOT-5 and ALOS PALSAR images in estimating aboveground biomass of lowland Dipterocarp forest

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

doi:10.1088/1755-1315/18/1/012016

Cited by 9 publications

(9 citation statements)

References 18 publications

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“…This result showed a slightly better performance than that reported by Hamdan et al [70] who estimated AGB by fitting regression models to reflectance data (corrected by ToA) from the SPOT-5 and ALOS PALSAR (R 2 = 0.803; RMSE = 32.6 Mg¨ha´1). Furthermore, in a study in southwest Colorado (USA), a lower correlation for biomass prediction (r = 0.86, RMSE = 45.6 Mg¨ha´1) with data corresponding to vegetation indices and texture analysis in Landsat TM images corrected by ToA radiometric correction was obtained [18].…”

Section: Discussioncontrasting

confidence: 56%

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

et al. 2016

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Solar radiation is affected by absorption and emission phenomena during its downward trajectory from the Sun to the Earth's surface and during the upward trajectory detected by satellite sensors. This leads to distortion of the ground radiometric properties (reflectance) recorded by satellite images, used in this study to estimate aboveground forest biomass (AGB). Atmospherically-corrected remote sensing data can be used to estimate AGB on a global scale and with moderate effort. The objective of this study was to evaluate four atmospheric correction algorithms (for surface reflectance), ATCOR2 (Atmospheric Correction for Flat Terrain), COST (Cosine of the Sun Zenith Angle), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) and 6S (Second Simulation of Satellite Signal in the Solar), and one radiometric correction algorithm (for reflectance at the sensor) ToA (Apparent Reflectance at the Top of Atmosphere) to estimate AGB in temperate forest in the northeast of the state of Durango, Mexico. The AGB was estimated from Landsat 5 TM imagery and ancillary information from a digital elevation model (DEM) using the non-parametric multivariate adaptive regression splines (MARS) technique. Field reference data for the model training were collected by systematic sampling of 99 permanent forest growth and soil research sites (SPIFyS) established during the winter of 2011. The following predictor variables were identified in the MARS model: Band 7, Band 5, slope (β), Wetness Index (WI), NDVI and MSAVI2. After cross-validation, 6S was found to be the optimal model for estimating AGB (R 2 = 0.71 and RMSE = 33.5 Mg¨ha´1; 37.61% of the average stand biomass). We conclude that atmospheric and radiometric correction of satellite images can be used along with non-parametric techniques to estimate AGB with acceptable accuracy.

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Section: Discussioncontrasting

confidence: 56%

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

et al. 2016

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“…This result is echoed by Hamdan et al. () and Attarchi and Gloaguen (), who both used spaceborne L‐band radar to estimate biomass in tropical and temperate forests respectively. This suggests that integrating or fusing multispectral and radar imagery could provide an important opportunity to improve biomass monitoring in the absence of a spaceborne LiDAR sensor.…”

Section: Benefits For Ecology Biodiversity Research and Conservationmentioning

confidence: 87%

“…First, imagery from different sensors can be used as separate predictors to estimate a parameter of interest. This includes using multispectral and radar imagery jointly in a classification algorithm (Haack, Solomon, Bechdol, & Herold, 2002;Naidoo, Mathieu, Main, Wessels, & Asner, 2016) or a statistical model (Hamdan, Hasmadi, & Aziz, 2014;Poggio & Gimona, 2017;van der Wal & Herman, 2007). This is commonly referred to as decision-level fusion.…”

Section: Introductionmentioning

confidence: 99%

Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science

Bühne

Pettorelli

2017

Methods Ecol Evol

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The availability and accessibility of multispectral and radar satellite remote sensing (SRS) imagery are at an unprecedented high. These data have both become standard source of information for investigating species ecology and ecosystems structure, composition and function at large scales. Since they capture complementary aspects of the Earth's surface, synergies between these two types of imagery have the potential to greatly expand research and monitoring opportunities. However, despite the benefits of combining multispectral and radar SRS data, data fusion techniques, including image fusion, are not commonly used in biodiversity monitoring, ecology and conservation. To help close this application gap, we provide for the first time an overview of the most common SRS data fusion techniques, discussing their benefits and drawbacks, and pull together case studies illustrating the added value for biodiversity research and monitoring. Integrating and fusing multispectral and radar images can significantly improve our ability to assess the distribution as well as the horizontal and vertical structure of ecosystems. Additionally, SRS data fusion has the potential to increase opportunities for mapping species distribution and community composition, as well as for monitoring threats to biodiversity. Uptake of these techniques will benefit from more effective collaboration between remote sensing and biodiversity experts, making the reporting of methodologies more transparent, expanding SRS image processing capacity and promoting widespread open access to satellite imagery. In the context of a global biodiversity crisis, being able to track subtle changes in the biosphere across adequate spatial and temporal extents and resolutions is crucial. By making key parameter estimates derived from SRS data more accurate, SRS data fusion promises to become a powerful tool to help address current monitoring needs, and could support the development of essential biodiversity variables.

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“…An experiment has found that the backscattering intensity interacted only with trees of dbh larger than 15 cm. These stands are actually dominating the higher canopies, which gave the best response to backscatter in HV polarization of L-band [13]. Other factors-such as orientation of the forest, polarimetry, incidence angle, and crown structure-also play important role in the estimated biomass [36,37].…”

Section: Non-linear Regressionmentioning

confidence: 99%

Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia

2017

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Space borne synthetic aperture radar (SAR) data have become one of the primary sources for aboveground biomass (AGB) estimation of forests. However, studies have indicated that limitations occur when a single sensor system is employed, especially in tropical forests. Hence, there is potential for improving estimates if two or more different sensor systems are used. Studies on integrating multiple sensor systems for estimation of AGB over Malaysia's tropical forests are scarce. This study investigated the use of PALSAR-2 L-band and Sentinel-1A C-band SAR polarizations to estimates the AGB over 5.25 million ha of the lowland, hill, and upper hill forests in Peninsular Malaysia. Polarized images, i.e., HH-HV from PALSAR-2 and VV-VH from Sentinel-1A have been utilized to produce several variables for predictions of the AGB. Simple linear and multiple linear regression analysis was performed to identify the best predictor. The study concluded that although limitations exist in the estimates, the combination of all polarizations from both PALSAR-2 and Sentiel-1A SAR data able to increase the accuracy and reduced the root means square error (RMSE) up to 14 Mg ha −1 compared to the estimation resulted from single polarization. A spatially distributed map of AGB reported the total AGB within the study area was about 1.82 trillion Mg of the year 2016.

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Combination of SPOT-5 and ALOS PALSAR images in estimating aboveground biomass of lowland Dipterocarp forest

Cited by 9 publications

References 18 publications

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science

Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia

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