JOURNAL OF NATURAL RESOURCES ›› 2018, Vol. 33 ›› Issue (6): 1068-1079.doi: 10.31497/zrzyxb.20170570

• Resource Research Method • Previous Articles     Next Articles

Extraction of Forest Vegetation Information Using GF-1 Imagery

TAO Huan1, LI Cun-jun1, ZHOU Jing-ping1, DONG Xi1, WANG Ai-meng1, LÜ Hong-peng2   

  1. 1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;
    2. Shandong Ruida Harmful Organism Control and Prevention Co., Ltd, Jinan 250101, China
  • Received:2017-06-08 Revised:2017-11-20 Online:2018-06-28 Published:2018-06-28
  • Supported by:
    National Natural Science Foundation of China, No. 41571423; Beijing Academy of Agriculture and Forestry Sciences Youth Research Foundation, No. QNJJ201815. ]

Abstract: Accurate and up-to-date forest vegetation mapping can provide a better understanding of forest resources and support decision-makers in implementing sustainable forest management. Unfortunately, the distribution information of forests plantation with high accuracy and fine spatial resolution is still not yet conveniently available. Remote-sensing technologies are common used in mapping forest vegetation owing to their real-time data acquisition ability. However, extraction of forest vegetation information using single date remote-sensing imagery has been unsuccessful since the existence of similarity in spectrum feature between forest and field crops. Combination of seasonal variations of spectral response and phenological differences between forest vegetation and field crops presents a unique opportunity for forest mapping. Therefore, a method for extracting forest vegetation using multi-temporal GF-1 imagery was proposed and validated in Bengbu City. Based on the phenological changes of forest and dominant field crops in the study area, the whole region was separated into 2 sub-regions (sub-region A and sub-region B), and 5 phases of GF-1 imagery were utilized. Then, 2 sets of decision rules were built and applied to the corresponding sub-regions. In addition, we implemented forest extraction by non-partitioned decision tree for comparative analysis. The results show that the overall accuracy of both partitioned and non-partitioned decision trees are over 85%, which means that decision tree method using multi-temporal GF-1 imagery can acquire good accuracy when extracting forest vegetation in the large scale and mesoscale. Partitioned decision tree achieves overall accuracy of 90.72% and kappa coefficient of 0.81, which are 3.80%-4.65% and 0.07-0.10 higher than the overall accuracy and kappa coefficient of non-partitioned decision tree, respectively. Free GF-1 imageries with a fine spatial resolution, wide coverage, and low revisit period have great potentials in forests extraction which can benefit forestry aviation plant protection.

Key words: aviation plant protection, decision tree, forest vegetation, GF-1, multi-temporal phase

CLC Number: 

  • S771.8