摘要
本文以福建省漳浦县沿海区域为研究对象,利用2005年的CBERS-02数据和2000年的ETM+数据,进行沿海防护林快速提取研究。通过分析沿海防护林和沿海地区其他典型地物在原始波段、归一化植被指数NDVI和非线性波段比NLBR的光谱特征,提出了适用于不同传感器的沿海防护林快速提取方法。研究发现,综合利用NDVI大于阈值1和NLBR小于阈值2,可以实现沿海防护林的快速提取,而阈值可以根据NDVI和NLBR的散点图确定。该方法对具有绿、红和近红外3个波段的不同传感器数据均有一定的参考价值。研究区2000年~2005年间,沿海防护林减少的面积是增加面积的1.46倍,政府相关部门应该加大对防护林的管理和建设力度,增强沿海地区防御自然灾害的能力。
Coastal protective forest is a green protective screen for disaster prevention and mitigation. For government's management and decision, it's very important to fast obtain the dynamic change of coastal protective forest. In this paper, Zhangpu County of Fujian Province is selected as a study area. Multi-source remote sensing data like CBERS-02 acquired in 2005 and ETM + acquired in 2000 were used. The Normalized Difference Vegetation Index (NDVI) and Non-linear band ratio (NLBR = B4 * B3 / B2, where B2, B3 and B4 are the green, red and near infrared red bands of sensors) were calculated. After analyzing the spectral profile of original bands, NDVI and NLBR of the coastal protective forest and other typical land covers in coastal zone, a method to quickly extract the coastal protective forest for different sensors was developed. We found that jointly using of NDVI and NLBR would effectively acquire the coastal protective forest. NDVI would distinguish vegetation cover from nonvegetation cover, and NLBR would distinguish coastal forest from arable land with vegetation cover. With the rules of NDVI 〉 threshold 1 and NLBR 〈 threshold 2, the coastal protective forests of 2005 and 2000 were acquired, in which the thresholds were determined from the scatter plots of NDVI and NLBR. This method is of reference value to the sensors with similar green, red and near infrared red bands. In the study area, the area of coastal protective forest was 2788. 11hm^2 in 2000, while that was 2462. 28hm^2 in 2005. From 2000 to 2005, the decreased area was 1332. 04 hm^2, which was 1.46 times of that of the increased area. The government agencies should strengthen the management and construction of the coastal protective forest, which will improve the ability to prevent and reduce natural disasters in the coastal zone.
出处
《地球信息科学》
CSCD
2007年第6期100-102,133,134,共5页
Geo-information Science
基金
教育部新世纪优秀人才支持计划(NCET-05-0573)
福建省自然科学基金项目(D0410013)