生态学报2018，Vol.38Issue(4)：1301-1310,10.DOI:10.5846/stxb201701180155
基于WorldView 2影像的矿区植被重建效果评估
Evaluation of vegetation restoration effects in mining areas based on WorldView 2 images
摘要
Abstract
The vegetation coverage fraction reflects the quality of vegetation and is,thus,an important index to evaluate the vegetation restoration effect of reclaimed land in mining areas.This study,considering Antaibao mining areas,evaluated the currency of different vegetation indices using the pixel dichotomy model based on the new band of WoddView 2 (WV 2) images and field-measured data,and then abstracted the vegetation coverage fraction of the study area to indicate the vegetation restoration effect.The results show that (1) with a near-infrared 2 and red-band of WV 2 image,vegetation coverage fraction (Fc2) as calculated by the mixed-pixel method,was the closest to the field-measured values (R2 =0.934,RMSE =0.048).(2) The study areas had been well restored as a whole:the vegetation coverage fraction was moderate-to-high in more than 60％ of the area,while in only 22.09％ of the area was lower than 20％.Vegetation restoration affected by the duration and management measures differed among the plots:in Inner Dump,West Dump,and South Dump,better vegetation restoration was observed with moderate-to-high coverage,at 75.24％,68.35％,and 68.20％,respectively,than in the expanded West Dump (22.29％).(3) The vegetation restoration pattern with the combination of grass,shrub,and trees had the best restoration effect,followed by the inter-planting of grass and trees,while the combination of shrub grass,only grass vegetation,and natural restoration had relatively poor effect.The soil moisture rises with increased vegetation coverage,implying that vegetation status plays an important role in soil surface-water retention.关键词
WorldView 2/植被盖度/安太堡矿/混合像元模型Key words
WorldView 2/vegetation coverage fraction/Antaibao mine/mixed pixel model引用本文复制引用
张泽民,吕昌河,谢苗苗,周伟..基于WorldView 2影像的矿区植被重建效果评估[J].生态学报,2018,38(4):1301-1310,10.基金项目
国家重点研发计划(2017YFC0504401) （2017YFC0504401）
国家自然科学基金项目(41641008) （41641008）
