Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on the basis of field hyperspectral measurements. A simple index based on the empirical relationships between LAIs and SIs of all available two-waveband combinations from hyperspectral data is developed by considering the difference between reflectance values at 760 and 739 nm (DSI_R760–R739 = R₇₆₀ – R₇₃₉). Among published and newly developed SIs, DSI_R760–R739 exhibited a significant and strong linear relationship with LAI and showed outstanding performance in LAI assessments. The permissible bandwidths for broad-band DSI_R760–R739 investigated using simulated reflectance were 5 nm for both 760 and 739 nm center wavelengths. The results indicate that the linear regression model based on the narrow-band and broad-band DSI_R760–R739 is a simple but accurate method for timely and nondestructive monitoring of LAI.

The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic- and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey—a national plot sampling survey in Japan—was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan.

東北地方を対象に系統抽出された多点の地上調査データとLandsat ETM+データを用いてk近傍法により林分材積を推定した。その結果,本研究対象地における林分材積推定の二乗平均平方根誤差(RMSE)は約120m^3/haであった。この結果を針葉樹人工林の多い九州で行われた先行研究と比較すると約50m^3/ha小さかった。大気・地形効果補正によって林分材積推定値のRMSEはわずかに(約2〜4m^3/ha)減少したが,太陽高度の高い時期のデータを対象としたこともあり,対象時期における大気・地形効果補正の効果は小さかった。