SPOTing long-term changes in vegetation over short-term variability

被引:17
|
作者
Bao, Nisha [1 ,2 ]
Lechner, Alex [2 ]
Fletcher, Andrew [2 ]
Erskine, Peter [2 ]
Mulligan, David [2 ]
Bai, Zhongke [1 ]
机构
[1] China Univ Geosci, Sch Land Sci & Technol, Beijing, Peoples R China
[2] Univ Queensland, Ctr Mined Land Rehabil, Brisbane, Qld, Australia
关键词
remote sensing; change detection; rehabilitation; mining sites; NDVI; SPOT; NDVI TIME-SERIES; ESTIMATING ABOVEGROUND BIOMASS; LAND-COVER CHANGES; SOIL-MOISTURE; SEMIARID BOTSWANA; JHARIA COALFIELD; INDEX; CLASSIFICATION; AREA; DYNAMICS;
D O I
10.1080/17480930.2012.710505
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study investigates change detection in the vegetation cover of a closed gold mine to assess whether the rehabilitated vegetation responds in a similar manner to the surrounding environment. Rehabilitation took place in seven rehabilitation areas within the mine site. SPOT scenes covering the site were acquired at six time periods from September 2004 to September 2005, and annually in the dry season from 2004 to 2010, except for 2008. Normalized difference vegetation index, soil-adjusted vegetation index and transformed soil-adjusted vegetation index were tested to estimate the percentage vegetation cover (PVC) using a linear regression model. The results showed higher PVC during wet season and lower PVC during the dry season in the native vegetation surrounding the mine site. However, temporal and spatial patterns of PVC in rehabilitated and native areas were similar only in the TD40ha rehabilitation area, one of the seven rehabilitation areas. This area was the first to be rehabilitated and had the most intensive rehabilitation effort using tube stock planting in March 1998. The seasonal variability showed a high correlation with an r(2) value of 0.77 in TD40ha rehabilitation area in tailings dam, which was similar to the native area with an r(2) value of 0.82. The findings of this study suggest that it is important for monitoring programs to take into account seasonal variation and environmental covariates, such as rainfall in order to successfully assess patterns in vegetation condition over time.
引用
收藏
页码:2 / 24
页数:23
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