Automated cropping intensity extraction from isolines of wavelet spectra

Bingwen Qiu, Zhuangzhuang Wang, Zhenghong Tang, Chongcheng Chen, Zhanling Fan, Weijiao Li

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Timely and accurate monitoring of cropping intensity (CI) is essential to help us understand changes in food production. This paper aims to develop an automatic Cropping Intensity extraction method based on the Isolines of Wavelet Spectra (CIIWS) with consideration of intra-class variability. The CIIWS method involves the following procedures: (1) characterizing vegetation dynamics from time-frequency dimensions through a continuous wavelet transform performed on vegetation index temporal profiles; (2) deriving three main features, the skeleton width, maximum number of strong brightness centers and the intersection of their scale intervals, through computing a series of wavelet isolines from the wavelet spectra; and (3) developing an automatic cropping intensity classifier based on these three features. The proposed CIIWS method improves the understanding in the spectral-temporal properties of vegetation dynamic processes. To test its efficiency, the CIIWS method is applied to China's Henan province using 250 m 8 days composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series datasets. An overall accuracy of 88.9% is achieved when compared with in-situ observation data. The mapping result is also evaluated with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data and an overall accuracy of 86.7% is obtained. At county level, the MODIS-derived sown areas and agricultural statistical data are well correlated (r2 = 0.85). The merit and uniqueness of the CIIWS method is the ability to cope with the complex intra-class variability through continuous wavelet transform and efficient feature extraction based on wavelet isolines. As an objective and meaningful algorithm, it guarantees easy applications and greatly contributes to satellite observations of vegetation dynamics and food security efforts.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalComputers and Electronics in Agriculture
Volume125
DOIs
StatePublished - Jul 1 2016

Fingerprint

isogenic lines
wavelet
cropping practice
vegetation dynamics
moderate resolution imaging spectroradiometer
Wavelet transforms
vegetation index
MODIS
vegetation
Satellites
transform
Imaging techniques
methodology
China
Disasters
statistical data
disasters
Feature extraction
Time series
Luminance

Keywords

  • Cropping intensity
  • Intra-class variability
  • MODIS EVI
  • Time-series
  • Wavelet isolines

ASJC Scopus subject areas

  • Forestry
  • Animal Science and Zoology
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture

Cite this

Automated cropping intensity extraction from isolines of wavelet spectra. / Qiu, Bingwen; Wang, Zhuangzhuang; Tang, Zhenghong; Chen, Chongcheng; Fan, Zhanling; Li, Weijiao.

In: Computers and Electronics in Agriculture, Vol. 125, 01.07.2016, p. 1-11.

Research output: Contribution to journalArticle

Qiu, Bingwen ; Wang, Zhuangzhuang ; Tang, Zhenghong ; Chen, Chongcheng ; Fan, Zhanling ; Li, Weijiao. / Automated cropping intensity extraction from isolines of wavelet spectra. In: Computers and Electronics in Agriculture. 2016 ; Vol. 125. pp. 1-11.
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