Tropical forest mapping using a combination of optical and microwave data of ALOS

Hoan, Nguyen Thanh; Tateishi, Ryosuke; Alsaaideh, Bayan; Ngigi, Thomas G.; Alimuddin, Ilham; Johnson, Brian R.

doi:10.1080/01431161.2012.709329

Cited by 19 publications

(16 citation statements)

References 15 publications

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“…The major advantage of all SAR systems is the weather-and daylight-independency of the system [20,23,24]. In addition, numerous studies have also reported that long-wavelength (e.g., L and P bands) SAR is more appropriate than short-wavelength (e.g., X and C bands) SAR for forest biomass estimations [25,26]. In this case, the detected radiation is mostly due to backscattering from the branches and stems of the trees, and thus L-and P-band SAR should respond characteristically to forest volume and biomass [22,27,28].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China

et al. 2014

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Enhanced methods are required for mapping the forest aboveground biomass (AGB) over a large area in Chinese forests. This study attempted to develop an improved approach to retrieving biomass by combining PALSAR (Phased Array type L-band Synthetic Aperture Radar) and WorldView-2 data. A total of 33 variables with potential correlations with forest biomass were extracted from the above data. However, these parameters had poor fits to the observed biomass. Accordingly, the synergies of several variables were explored to identify improved relationships with the AGB. Using principal component analysis and multivariate linear regression (MLR), the accuracies of the biomass estimates obtained using PALSAR and WorldView-2 data were improved to approximately 65% to 71%. In addition, using the additional dataset developed from the fusion of FBD (fine beam dual-polarization) and WorldView-2 data improved the performance to 79% with an RMSE (root mean square error) of 35.13 Mg/ha when using the MLR method. Moreover, a further improvement (R 2 = 0.89, relative RMSE = 17.08%) was obtained by combining all the variables mentioned above. For the purpose of comparison with MLR, a neural network approach was also used to estimate the biomass. OPEN ACCESSRemote Sens. 2014, 6 7879 However, this approach did not produce significant improvements in the AGB estimates. Consequently, the final MLR model was recommended to map the AGB of the study area. Finally, analyses of estimated error in distinguishing forest types and vertical structures suggested that the RMSE decreases gradually from broad-leaved to coniferous to mixed forest. In terms of different vertical structures (VS), VS3 has a high error because the forest lacks undergrowth trees, while VS4 forest, which has approximately the same amounts of stems in each of the three DBH (diameter at breast height) classes (DBH > 20, 10 ≤ DBH ≤ 20, and DBH < 10 cm), has the lowest RMSE. This study demonstrates that the combination of PALSAR and WorldView-2 data is a promising approach to improve biomass estimation.

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Section: Introductionmentioning

confidence: 99%

“…However, most studies of these studies have mainly focused on temperate and tropical forests [15,26,33,34,38,47,48]. Estimating forest biomass in China has mainly been achieved by conducting national forest inventories using the sample plotting method every five years.…”

Section: Introductionmentioning

confidence: 99%

Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China

et al. 2014

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“…High quality and time-specific optical images (e.g., ALOS AVNIR-2, Landsat, and MODIS) have been combined with PALSAR FBD data to identify and monitor local land cover types (e.g., forests) (Bagan et al, 2012;Dong et al, 2013Dong et al, , 2012bHoan et al, 2013;Lehmann et al, 2012), or large area of forests at 500-m spatial resolution (Sheldon et al, 2012). Most of these studies were carried out at local hot spots in tropical regions, and highlight the potential of combining PALSAR and optical data for mapping forests.…”

Section: Introductionmentioning

confidence: 99%

Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI

Qin

Xiao

Dong

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Fusion of multi-resolution and/or multi-sensor remote sensing (RS) imagery has been shown to result in higher classification accuracy in many past studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15], so classification-oriented image fusion is an important research topic. Satellite data from the Landsat series is commonly-used for land use/land cover (LULC) classification in RS, and Landsat 4/5/7/8 have image bands that vary in terms of spatial resolution, as detailed in [16].…”

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confidence: 99%

Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7(7), 8368–8390

Johnson

2015

Remote Sensing

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Much remote sensing (RS) research focuses on fusing, i.e., combining, multi-resolution/multi-sensor imagery for land use/land cover (LULC) classification. In relation to this topic, Sun and Schulz [1] recently found that a combination of visible-to-near infrared (VNIR; 30 m spatial resolution) and thermal infrared (TIR; 100-120 m spatial resolution) Landsat data led to more accurate LULC classification. They also found that using multi-temporal TIR data alone for classification resulted in comparable (and in some cases higher) classification accuracies to the use of multi-temporal VNIR data, which contrasts with the findings of other recent research [2]. This discrepancy, and the generally very high LULC accuracies achieved by Sun and Schulz (up to 99.2% overall accuracy for a combined VNIR/TIR classification result), can likely be explained by their use of an accuracy assessment procedure which does not take into account the multi-resolution nature of the data. Sun and Schulz used 10-fold cross-validation for accuracy assessment, which is not necessarily inappropriate for RS accuracy assessment in general. However, here it is shown that the typical pixel-based cross-validation approach results in non-independent training and validation data sets when the lower spatial resolution TIR images are used for classification, which causes classification accuracy to be overestimated.

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Tropical forest mapping using a combination of optical and microwave data of ALOS

Cited by 19 publications

References 15 publications

Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China

Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China

Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI

Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7(7), 8368–8390

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