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FOREST RESOURCES WANAGEMENT ›› 2019›› Issue (6): 84-90.doi: 10.13466/j.cnki.lyzygl.2019.06.015

• Scientific Research • Previous Articles     Next Articles

Using Remote Sensing to Conduct Quantitative Study on the Quality of Typical Moso Bamboo Management Area in Southern Collective Forest Area

LIN Lili1,2(), HAO Zhenbang1,3, DAI Shanlin2, YANG Liuqing1,2, LIU Jian1,2,3, YU Kunyong1,3()   

  1. 1. University Key Lab for Geomatics Technology and Optimize Resources Utilization in Fujian Province,Fuzhou 350002
    2. College of Arts,College of Landscape Architecture,Fujian Agriculture and Forestry University,Fuzhou 350002
    3. College of Forestry,Fujian Agriculture and Forestry University,Fuzhou 350002
  • Received:2019-06-28 Revised:2019-09-30 Online:2019-12-28 Published:2020-05-09
  • Contact: YU Kunyong E-mail:fjldd@outlook.com;yuyky@126.com

Abstract:

Based on the SPOT-7 remote sensing image of Dagan town,Shunchang county,Fujian province in 2017,and the data of ground survey of the same period were obtained.In this paper,the main indicators responding to the change of stand quality of Moso bamboo were extracted through principal component analysis.Then combined with the net primary productivity(NPP) and interference index,an evaluation model of stand quality was constructed by analytic hierarchy process(AHP) to quantify the management quality of Moso bamboo.The results showed that the stand quality evaluation of Moso bamboo forest based on SPOT remote sensing image was correlated with mean DBH and stand density.The evaluation results were fitted with the stand quality evaluation in field sample plot survey.The coefficient of determination R2 was 0.757,and the total average accuracy was 83.29%,which indicated that the study was consistent.The overall stand quality of Moso bamboo in villages is Ganshan village >Xianglinchang>Tulong village >Liangfang village >Wufang village.The results show that using the conversion and mining of remote sensing data,combined with topographic factor,net primary productivity of vegetation and interference index,it can effectively monitor the management effect of Moso bamboo resources in southern collective forest area.

Key words: Moso bamboo, stand quality, remote sensing, topographic factor, vegetation index

CLC Number: