@article{oai:jaxa.repo.nii.ac.jp:00022641,
 author = {Thapa, Rajesh Bahadur and 渡邉, 学 and 島田, 政信 and 本岡, 毅 and Thapa, Rajesh Bahadur and Watanabe, Manabu and Shimada, Masanobu and Motohka, Takeshi},
 issue = {7},
 journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
 month = {Mar},
 note = {This study examines the potential of airborne PiSAR-L2 data for estimating forest carbon stocks in central Sumatra. Polarimetric interferometric synthetic aperture radar L-band-2 (PiSAR-L2) is a second-generation airborne sensor developed by JAXA. We acquired full polarimetric data at a fine spatial resolution of 2.5 m during the PiSAR-L2 flight campaign in August 2012. A total of 59 field measurement plots for aboveground forest carbon stocks (AFCSs) were established in same year where AFCS ranged between 4.8 and 253.5; text Mg; text C; textha(exp -1). The plots comprised natural and plantation forests. These plot level field data were used for calibrating and validating AFCS estimation models with the SAR data. Various possibilities including direct sigma naught backscatters and their ratios and various types of textures obtained from HH, HV, and VV polarizations were examined by applying regression modeling. The main indicators used for the selection of best potential models in the calibration phase were textR(exp 2), variable inflation factor (VIF), p-value, and root mean squared errors (RMSEs). The potential models were validated using the leave one out (LOO) method. The results indicated that a simple combination of backscatters and their ratios provides an AFCS estimate with an RMSE of 42.37; text Mg; textC; textha(exp -1) and an textR(exp 2) of 0.65. Inclusion of SAR textural parameters improved the AFCS estimates with an RMSE of 30.93; text Mg; textC; textha(exp -1) and an textR(exp 2) of 0.80. This indicates that the airborne PiSAR-L2 fullpolarimetric data have the potential to estimate forest carbon stocks with an improved accuracy in the tropical region., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA1610010000},
 pages = {3202--3209},
 title = {Examining High-Resolution PiSAR-L2 Textures for Estimating Tropical Forest Carbon Stocks},
 volume = {9},
 year = {2016}
}