收稿：2016-01-22，
纸质出版：2017-01
移动端阅览
植被覆盖变化是生态环境领域的核心研究内容之一，但其估算精度常受到地形效应、土壤背景、大气效应等各种因素影响。以Landsat 8 OLI为遥感数据源，基于像元二分模型，分别利用归一化差值植被指数(NDVI)、经Cosine-C校正的归一化差值植被指数(NDVI)和归一化差值山地植被指数(NDMVI)建立植被覆盖度估算模型，以评估南方丘陵区植被覆盖度的地形效应。结果表明，3种植被覆盖度估算模型均能削弱地形效应，但消除或抑制地形效应影响的能力不同。比较而言，基于NDMVI指数构建的植被覆盖度估算模型的地形效应最小，更适合地形复杂区域的植被覆盖度遥感估算；基于Cosine-C校正的NDVI植被指数构建的植被覆盖度估算模型的地形效应次之，但存在一定的过度校正现象；基于NDVI植被指数构建的植被覆盖度估算模型的地形效应最大，尤其当坡度≥10°时，阴坡植被覆盖度比阳坡明显偏低。
Changes in Fractional Vegetation Cover (FVC) is one of the core research fields in eco-environment assessment. FVC estimation from satellite image is significantly influenced by the terrain
soil
and atmosphere. Among of them
terrain variation can arouse miscalculation of biomass information of similar kind of vegetation
and it is a main bottleneck of quantitative remote sensing of vegetation. Therefore
it is urgent to find a method of FVC estimation that can eliminate or reduce the effects of terrain variation
in order to improve the FVC estimation using the satellite data over mountainous and hilly areas.Taking Landsat 8 OLI image as a data source. Firstly
three vegetation indexes
including the normalized difference vegetation index (NDVI)
Cosine-C corrected NDVI
and the normalized difference mountain vegetation Index (NDMVI)
are estimated. Then FVC are calculated from three vegetation indexes
using a linear unmixing model. Finally
the impact of terrain variation of three estimation methods of FVC are assessed and compared
considering altitude
slope
aspect
and solar incident angle. five altitude ranges
(
i.e.
100—400 m
400—600 m
600—800 m
800—1000 m and ≥1000 m)
72 aspect ranges (from 0 to 360° with a 5° interval)
four slope ranges (
i.e.
0—10°
10°—20°
20°—30° and ≥30°)
and ten solar incident angle ranges (
i.e.
0—10°
10°—20°
…
≥90°) are considered
and the Coefficient of Variation (CV) is used to measure the impacts.The results show that the CV is minimal when the altitude is lower than 400 m
and the CV increases with the increasing altitude in three method. The minimal CV is 0.39%
–8.15% and –3.14% for NDVI-based method
NDVI-C based method and NDMVI-based method
respectively. The CV also increases with increasing slope. The CV of NDVI-based method reaches 11.5% when the slope is larger than 30°
indicating a significant impact of terrain variation. The CV of NDMVI-based method is slightly larger than that of NDVI-C based method when the slope is lower than 10°. The CV of NDMVI is the nearest to 0 when slope ranges from 20° to 30°
indicating a little impact of terrain variation.The terrain effects are reduced for all three VI-based methods. The NDMVI-based method has a best performance
and the terrain variation has little effect on FVC estimate. However
there is over-corrected phenomenon when the Cosine-C is corrected. The NDVI-based method has the poorest result
indicating that it is heavily influenced by the terrain variation when the slope is larger than 10°. The estimated FVC in shady slope is significantly lower than that of sunny slope. Based on the analysis above
it is shown that the NDMVI-based method can effectively eliminate or reduce the effects of terrain variation to improve the FVC estimation from the remote sensing data over mountainous and hilly areas in southern China. However
only Landsat 8 image with spatial resolution of 30 m is employed in this study. The magnitude of terrain effects may also depends on the spatial resolution of image. Therefore
further studies should be focused on terrain correction for high spatial resolution satellite image.
程红芳, 章文波, 陈锋. 2008. 植被覆盖度遥感估算方法研究进展. 国土资源遥感, 75(1): 13–18
Chen H F, Zhang W B and Chen F. 2008. Advances in research on application of remote sensing method to estimating vegetation coverage. Remote Sensing for Land & Resources, 75(1): 13–18
段四波, 阎广建. 2007. 山区遥感图像地形校正模型研究综述. 北京师范大学学报(自然科学版), 43(3): 362–366
Duan S B and Yan G J. 2007. A review of models for topographic correction of remotely sensed images in mountainous area. Journal of Beijing Normal University ( Natural Science ), 43(3): 362–366
胡玉福, 蒋双龙, 刘宇, 李翔, 王钰婷, 陈波. 2014. 基于RS的安宁河上游植被覆盖时空变化研究. 农业机械学报, 45(5): 205–215
Hu Y F, Jiang S L, Liu Y, Li X, Wang Y T and Chen B. 2014. Temporal and spatial variation of vegetation coverage on upper Anning river based on RS. Transactions of the Chinese Society for Agricultural Machinery, 45(5): 205–215
李苗苗, 吴炳方, 颜长珍, 周为峰. 2004. 密云水库上游植被覆盖度的遥感估算. 资源科学, 26(4): 153–159
Li M M, Wu B F, Yan C Z and Zhou W F. 2004. Estimation of vegetation fraction in the upper basin of Miyun reservoir by remote sensing. Resources Sciences, 26(4): 153–159
马娜, 胡云锋, 庄大方, 张学利. 2012. 基于遥感和像元二分模型的内蒙古正蓝旗植被覆盖度格局和动态变化. 地理科学, 32(2): 251–256
Ma N, Hu Y F, Zhuang D F and Zhang X L. 2012. Vegetation coverage distribution and its changes in plan blue banner based on remote sensing data and dimidiate pixel model. Scientia Geograhica Sinica, 32(2): 251–256
Proy C, Tanré D and Deschamps P Y. 1989. Evaluation of topographic effects in remotely sensed data. Remote Sensing of Environment, 30(1): 21–32
Rouse J W, Haas R H and Schell J A. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of Third Earth Resources Technology Satellite 21 Symposium Greenbelt, 1: 48–62
Teillet P M, Guindon B and Goodenough D G. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8(2): 1537–1540
吴见, 刘民士, 李伟涛. 2013. 不同地形条件下植被盖度信息提取技术研究. 植物生态学报, 37(1): 18–25
Wu J, Liu M S and Li W T. 2013. Research on vegetation cover information extraction technologies under different terrain conditions. Chinese Journal of Plant Ecology, 37(1): 18–25
王培娟, 孙睿, 朱启疆, 谢东辉, 陈镜明. 2006. 复杂地形条件下提高BEPS模型模拟能力的途径. 中国图象图形学报, 11(7): 1017–1027
Wang P J, Shun R, Zhu Q J, Xie D H and Chen J M. 2006. Improvement on the abilities of BEPS under accidented Terrain. Journal of Image and Graphics, 11(7): 1017–1027
闻建光, 柳钦火, 肖青, 刘强, 李小文. 2008. 复杂山区光学遥感反射率计算模型. 中国科学(D辑:地球科学), 33(11): 1419–1427
Wen J G, Liu Q H, Xiao Q, Liu Q and Li X W. 2008. Optical remote sensing reflectance calculation model of complicated mountain area. Science in China Series D: Earth Sciences, 38(11): 1419–1427
吴志杰, 徐涵秋. 2011. 卫星影像数据构建山地植被指数与应用分析. 地球信息科学学报, 13(5): 656–663
Wu Z J and Xu H Q. 2011. A new index for vegetation enhancements of mountainous regions based on satellite image data. Journal of Geo-information Science, 13(5): 656–663
徐爽, 沈润平, 杨晓月. 2012. 利用不同植被指数估算植被覆盖度的比较研究. 国土资源遥感, 24(4): 95–100
Xu S, Shen R P and Yang X Y. 2012. A comparative study of different vegetation indices for estimating vegetation coverage based on the Dimidiate Pixel Model. Remote Sensing for Land & Resources, 24(4): 95–100
姚晨,黄微,李元华. 2009. 地形复杂区域的典型植被指数评估. 遥感技术与应用, 24(4): 496–501
Yao C, Huang W and Li Y H. 2009. Evaluation of topographical influence on vegetation indices of rugged terrain. Remote Sensing Technology and Application, 24(4): 496–501
Zribia M, S Le Hegarat-Masclea and Taconeta O. 2003. Derivation of Wild Vegetation Cover Density in Semi-arid Regions: ERS2/SAR Evaluation. International Journal of Remote Sensing, 24(6): 1335–1352
赵善伦, 尹民, 张伟. 2002. GIS支持下的山东省土壤侵蚀空间特征分析. 地理科学, 22(6): 694–699
Zhao S L, Yin M and Zhang W. 2002. An analysis on the spatial features of soil erosion in Shandong province base on GIS. Scientia Geograhica Sinica, 22(6): 694–699
张春玲, 余华, 宫鹏, 居为民. 2009. 武汉市地表亮温与植被覆盖关系定量分析. 地理科学, 29(5): 740–744
Zhang C L, Yu H, Gong P and Ju W M. 2009. Relationship Between Land Brightness Temperature and vegetation abundance in Wuhan city. Scientia Geograhica Sinica, 29(5): 740–744
张宝庆, 吴普特, 赵西宁. 2011. 近30a黄土高原植被覆盖时空演变监测与分析. 农业工程学报, 27(4):287– 293
Zhang B Q, Wu P T and Zhao X N. 2011. Detecting and analysis of spatial and temporal variation of vegetation cover in the Loess Plateau during 1982–2009. Transactions of the CSAE, 27(4):287–293
朱高龙, 柳艺博, 居为民, 陈镜明. 2013. 4种常用植被指数的地形效应评估. 遥感学报, 17(1): 222–234
Zhu G L, Liu Y B, Ju W M and Chen J M. 2013. Evaluation of topographic effects on four commonly used vegetation indices. Journal of Remote Sensing, 17(1): 222–234
张灿, 徐涵秋, 张好, 唐菲, 林中立. 2015. 南方红壤典型水土流失区植被覆盖度变化及其生态效应评估——以福建省长汀县为例. 自然资源学报, 30(6): 917–927
Zhang C, Xu H Q, Zhang H, Tang F and Lin Z L. 2015. Fractional vegetation cover change and its ecological effect assessment in a typical reddish soil region of southeastern china: Changting County, Fujian Province. Journal of Natural Resources, 30(6): 917–927
相关作者
相关机构
京公网安备11010802024621
