Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery

Abstract Habitat monitoring can be used to evaluate the potential occurrence and distribution of plant diseases and pests in a region. Here, we present a new method for monitoring crop diseases and pests based on Worldview 2 and Landsat 8 satellite data. As a case study, the method was applied to wheat fields in Zhou Jiazhuang, Jinzhou City, Hebei Province. Crop growth indices (GNDVI and VARI red-edge ) and environmental features (Wetness, Greenness, and LST) were used describe habitat, and an independent t -test was used to evaluate the performances of these five features in representing crop diseases and pests. Field measurements were used to evaluate the validity of the method. An FLDA model incorporating both vegetation and environmental indices was more accurate for monitoring crop disease and pest occurrence compared with a model based on vegetation indices alone (71% vs. 82% accuracy). Future work should include multiple forms of information (e.g., meteorological data and web sensor networks) to further improve regional-scale monitoring of crop diseases and pests.

[1]  Han Xiu.  The application of remote sensing and GIS in monitoring East Asia locust hazards , 2003 .

[2]  Qingxi Tong,et al.  Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance , 2014 .

[3]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[4]  S. Branford.  A threat to global food security. , 2010 .

[5]  B. Rivard,et al.  Spectroscopic determination of leaf water content using continuous wavelet analysis , 2011 .

[6]  S. Ustin,et al.  Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing , 2003 .

[7]  Wenjiang Huang,et al.  Monitoring Powdery Mildew of Winter Wheat by Using Moderate Resolution Multi-Temporal Satellite Imagery , 2014, PloS one.

[8]  P. R. Scott,et al.  Plant disease: a threat to global food security. , 2005, Annual review of phytopathology.

[9]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[10]  Z. Niu,et al.  Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging , 2007, Precision Agriculture.

[11]  K. Bryceson,et al.  Use of Remotely-Sensed Data in the Australian Plague Locust Commission , 1993 .

[12]  Wenjiang Huang,et al.  Integrating Remotely Sensed and Meteorological Observations to Forecast Wheat Powdery Mildew at a Regional Scale , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Zhihao Qin,et al.  Detection of rice sheath blight for in-season disease management using multispectral remote sensing , 2005 .

[14]  Soizik Laguette,et al.  Remote sensing applications for precision agriculture: A learning community approach , 2003 .

[15]  Minzan Li,et al.  Spectral difference analysis and airborne imaging classification for citrus greening infected trees , 2012 .

[16]  Clayton C. Kingdon,et al.  Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario , 2008 .

[17]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .