Monitoring Damage Caused by Pantana phyllostachysae Chao to Moso Bamboo Forests Using Sentinel-1 and Sentinel-2 Images

Pantana phyllostachysae Chao (PPC) is one of the deadliest defoliators of Moso bamboo. Accurately locating and evaluating PPC damage is essential for the management of bamboo forests. Moso bamboo has a unique biennial growth cycle, consisting of the on-year period (bamboo shoots are incubated and then produced) and the off-year period (old leaves are dropped and then new leaves are grown, and no bamboo shoots are produced in the coming year). The similar physiological characteristics of off-year bamboo and damaged on-year bamboo create difficulties in monitoring PPC damage using remote sensing data. In this study, we synergistically used Sentinel-1, Sentinel-2, and field inventory data to construct machine learning (extreme gradient boosting, XGBoost) models monitoring PPC damage. The results show that the single-time observation feature-based model (using images from October) outperformed the double-time observation feature-based model (using the differences between remote sensing signals from October and February or April) due to the interference from other disturbance agents (e.g., logging and weeding). The overall accuracy (OA) values of the single-time observation feature-based model were at least 3% and 10% higher than those for double-time observation feature-based models for on- and off-year samples, respectively. With the consideration of the on- and off-year phenological differences, OA was improved by over 4%. The model without differentiation of the phenological difference tended to underestimate the damaged area of on-year bamboo and overestimate that of off-year bamboo. We also found that the responses of optical and SAR (synthetic aperture radar) features to PPC damage were different. The optical features increased or decreased with increasing damage severity. SAR features decreased significantly at the initial stage of PPC damage and then changed marginally with the increase in damage severity. The addition of SAR features to optical features improved the model performance, mainly for healthy and mildly damaged samples. The methodology developed in this study provides technical and theoretical support for the pest monitoring of bamboo forests using remote sensing data.

[1]  Xiaohang Li,et al.  Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms , 2022, Sensors.

[2]  Zhanghua Xu,et al.  Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection , 2022, Forests.

[3]  P. Gong,et al.  Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine , 2022, Remote. Sens..

[4]  Ronghua Ma,et al.  Landsat observations of chlorophyll-a variations in Lake Taihu from 1984 to 2019 , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Weimin Ju,et al.  Monitoring the Severity of Pantana phyllostachysae Chao on Bamboo Using Leaf Hyperspectral Data , 2021, Remote. Sens..

[6]  J. Hicke,et al.  Detecting subtle change from dense Landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance , 2021 .

[7]  Petr Lukes,et al.  Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2 , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Weiliang Fan,et al.  Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data , 2021 .

[9]  Daniel Berveiller,et al.  Potential of C-band Synthetic Aperture Radar Sentinel-1 time-series for the monitoring of phenological cycles in a deciduous forest , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[10]  A. Weiskittel,et al.  Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery , 2021 .

[11]  蒋馥根，孙华，李成杰，马开森，陈松，龙江平，任蓝翔 JIANG Fugen.  Retrieving the forest aboveground biomass by combining the red edge bands of Sentinel-2 and GF-6 , 2021, Acta Ecologica Sinica.

[12]  D. Lu,et al.  Examining phenological variation of on-year and off-year bamboo forests based on the vegetation and environment monitoring on a New Micro-Satellite (VENµS) time-series data , 2020, International Journal of Remote Sensing.

[13]  S. Orlović,et al.  Physiological responses of two pedunculate oak (Quercus robur L.) families to combined stress conditions – drought and herbivore attack , 2020, Šumarski list.

[14]  Nikrooz Bagheri,et al.  Application of aerial remote sensing technology for detection of fire blight infected pear trees , 2020, Comput. Electron. Agric..

[15]  D. Lu,et al.  Mapping Moso bamboo forest and its on-year and off-year distribution in a subtropical region using time-series Sentinel-2 and Landsat 8 data , 2019, Remote Sensing of Environment.

[16]  Federico Filipponi,et al.  Sentinel-1 GRD Preprocessing Workflow , 2019, Proceedings.

[17]  Z. Çakır,et al.  Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage , 2019, Applied Sciences.

[18]  Nicholas C. Coops,et al.  Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[19]  Hang Chen,et al.  Photosynthetic traits and antioxidative defense responses of Pinus yunnanensis after joint attack by bark beetles Tomicus yunnanensis and T. minor , 2018, Journal of Forestry Research.

[20]  Shoba Periasamy,et al.  Significance of dual polarimetric synthetic aperture radar in biomass retrieval: An attempt on Sentinel-1 , 2018, Remote Sensing of Environment.

[21]  Feng Zhao,et al.  Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements , 2018, Remote Sensing of Environment.

[22]  Xiaojun Xu,et al.  Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data , 2018, Annals of Forest Science.

[23]  C. Peng,et al.  The response of the net primary production of Moso bamboo forest to the On and Off-year management: A case study in Anji County, Zhejiang, China , 2018 .

[24]  Patrick Hostert,et al.  Remote sensing of forest insect disturbances: Current state and future directions , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[25]  Lufeng Mo,et al.  Development of the BIOME-BGC model for the simulation of managed Moso bamboo forest ecosystems. , 2016, Journal of environmental management.

[26]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[27]  David Zhang,et al.  Feature selection and analysis on correlated gas sensor data with recursive feature elimination , 2015 .

[28]  Johannes Breidenbach,et al.  Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data , 2013, Remote. Sens..

[29]  Lars M. H. Ulander,et al.  Impact and modeling of topographic effects on P-band SAR backscatter from boreal forests , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[30]  J. Goswami,et al.  Bamboo resources mapping using satellite technology. , 2010 .

[31]  M. Berenbaum,et al.  Indirect effects of insect herbivory on leaf gas exchange in soybean , 2005 .

[32]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..