Estimation of Leaf Loss Rate in Larch Infested with Erannis Jacobsoni Djak Based on Differential Spectral Continuous Wavelet Coefficient
HUANG Xiao-jun1, 2, 3, 4, XIE Yao-wen1*, BAO Yu-hai2, 3, BAO Gang2, 3, QING Song2, 3, BAO Yu-long2, 3, 4
1. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2. College of Geographical Science, Inner Mongolia Normal University, Huhhot 010022, China
3. Inner Mongolia Key Laboratory of Remote Sensing & Geography Information System, Huhhot 010022, China
4. Inner Mongolia Key Laboratory of Disaster and Ecological Security on the Mongolia Plateau, Huhhot 010022, China
Abstract：Defoliation caused by insect pests severely threatens the health and safety of forests; the rapid and accurate acquisition of information regarding leaf loss is of considerable significance to the remote sensing monitoring and estimation of forest pests. Based on this, we conducted spectral measurements of infested trees and tested leaf loss rate estimation owing to larch defoliation caused by Erannis jacobsoni Djak in Mongolia. Differential spectral reflectance (DSR, first derivative of spectral reflectance) and continuous wavelet coefficient of differential spectral reflectance (DSR-CWC, continuous wavelet transform of DSR carried out using 36 mother wavelet basis functions of four wavelet families: biorthogonal, coiflets, daubechies and symlets) were obtained based on the processing of spectral measurement data. The sensitivity of DSR and DSR-CWC with respect to the estimation of leaf loss rate was analyzed, following which the sensitive bands of DSR and DSR-CWC were automatically identified using the Findpeaks (Fp) function of MATLAB and the sensitive features identified. Dimension reduction of the sensitive features was processed using a successive projections algorithm (SPA). Partial least squares regression (PLSR) and support vector machine regression (SVMR) models for estimating leaf loss rate were established based on these sensitive features and their effectiveness was compared with that of stepwise multiple linear regression (SMLR) models. The results showed that: ①DSR-CWC was determined to be more sensitive than DSR to changes in leaf loss rate in infested larch, with more sensitive bands, mainly distributed in three absorption valleys (440～515, 630～760 and 1 420～1 470 nm) and three reflection peaks (516～620, 761～1 000 and 1 548～1 610 nm). This finding reflects the fact that DSR-CWC can enhance spectral reflection and absorption characteristics. ②The use of the combination pattern of Fp and SPA (Fp-SPA) was an effective method for the selection of sensitive spectral features that could not only select these features quickly and objectively but also effectively reduce dimensions. ③The optimal mother wavelet bases for the four wavelet families respectivelywere bior2.4, coif2, db1, and sym6; db1 had the most stable performance and accuracy for leaf loss rate estimation. ④The continuous wavelet transform of DSR could improve the accuracy of leaf loss estimation; db1-PLSR (R2M=0.934 0, RMSEM=0.089 0) exhibited the most obvious improvement, achieving an R2M that was 0.047 5 higher than that of DSR-PLSR and an RMSEM that was 0.024 9 lower than that of DSR-PLSR. ⑤The estimation accuracy of the PLSR and SVMR modelsestablished based on DSR-CWC was either similar to or better than that of the SMLR models. DSR-CWC thus estimated leaf loss rate more effectively than DSR did. It can be seen that DSR-CWC has more potential than DSR in estimating leaf loss rate, and it can provide important reference for remote sensing monitoring of forest pests.
Key words：Erannis jacobsoni Djak; Leaf loss rate of larch; Differential spectral continuous wavelet coefficient; Findpeaks function; Continuous projection algorithm
黄晓君，颉耀文，包玉海，包 刚，青 松，包玉龙. 微分光谱连续小波系数估测雅氏落叶松尺蠖危害下的落叶松失叶率[J]. 光谱学与光谱分析, 2019, 39(09): 2732-2738.
HUANG Xiao-jun, XIE Yao-wen, BAO Yu-hai, BAO Gang, QING Song, BAO Yu-long. Estimation of Leaf Loss Rate in Larch Infested with Erannis Jacobsoni Djak Based on Differential Spectral Continuous Wavelet Coefficient. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2732-2738.
[1] White J, Wulder M, Brooks D, et al. Remote Sensing of Environment, 2005, 96(3-4): 340.
[2] HUANG Xiao-jun, XIE Yao-wen, BAO Yu-hai(黄晓君, 颉耀文, 包玉海). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(3): 905.
[3] Rullansilva C D, Olthoff A E, Delgado d l M J A, et al. Forest Systems, 2013, 22(3): 377.
[4] Wulder M A, Dymond C C, White J C, et al. Forest Ecology & Management, 2006, 221(1-3): 27.
[5] Niemann K O, Quinn G, Stephen R, et al. Canadian Journal of Remote Sensing, 2015, 41 (3): 191.
[6] Hawryło P, Bednarz B, Weżyk P, et al. European Journal of Remote Sensing, 2018, 51(1): 194.
[7] Rahimzadeh-Bajgiran P, Weiskittel A, Kneeshaw D, et al. Forests, 2018, 9 (6): 357.
[8] Spruce J P, Sader S, Ryan R E, et al. Remote Sensing of Environment, 2011, 115(2): 427.
[9] Townsend P A, Singh A, Foster J R, et al. Remote Sensing of Environment, 2012, 119(8): 255.
[10] Adelabu S, Mutanga O, Adam E, et al. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2013, 7(1): 177.
[11] Fassnacht F E, Latifi H, Ghosh A, et al. Remote Sensing of Environment, 2014, 140(1): 533.
[12] Cheng T, Rivard B, Sánchezazofeifa G A, et al. Remote Sensing of Environment, 2010, 114(4): 899.
[13] Lausch A, Heurich M, Gordalla D, et al. Forest Ecology & Management, 2013, 308(4): 76.
[14] Shi Y, Huang W J, González-Moreno P, et al. Remote Sensing, 2018, 10(4): 525.
