摘要
本研究基于1990年、2000年和2005年遥感影像数据，结合高程、气象与样地调查等数据，运用地理信息系统技术，对桂西北喀斯特区植被碳储量及密度进行了分析。研究结果表明：从1990年到2005年，研究区域植被碳储量与密度呈增加趋势，1990年、2000年和2005年植被碳储量分别为1．03×10^8t、1．41×10^8t和1．63×10^8，植被碳密度分别为14．82t．hm-1、20．38t·hm-2和23．49t-hm，基本与四川省（18．47t．hm-1）、江西省（25．38t．hm-2）植被碳密度值一致；在空间分布上，区域植被储量-9碳密度大致呈现西高东低、中低山（海拔〉500m1高而峰丛洼地（海拔〈500m）低的分布格局，1990年西部大部分县市的植被碳密度为15-22t.hm-2，而中东部大部分县市的植被碳密度为8—15t．hm2；而时间上的变化在空间上表现为，植被碳储量与密度不同程度地表现为低值区的东部增加，高值区的西部减少或轻微增加，典型喀斯特区植被碳密度增加明显（1990年和2005年植被碳储量比例分别为45．54％和51．99％）。研究结果表明，峰丛洼地植被碳储量与密度显著增加，生态环境移民、退耕还林等石漠化治理措施效果显著，有利于增强区域植被碳汇。
The southwest karst area in China could be a key carbon sink after appropriate ecological engineering. Vegetation carbon pool has been noted to be more critical for carbon budgets of ecosystems than shallow soils which have limited carbon saving capacity. However, due to the unique nature of geological settings of karst regions, spatial distributions of vegetation in these regions usually follow three-dimensional characteristics of discontinuity and fragmentation. This has resulted in difficulties in obtaining the belowground portions of vegetation, which have in turn caused a general lack of attention to this issue. Thus vegetation carbon pool has become a limiting factor in the assessment of regional vegetation carbon savings. In this study, we attempted to reveal the spatial distribution characteristics and differentiations of vegetation carbon savings in typical karst regions by analyzing remote sensing images. The spatio-temporal variation of vegetation carbon storage and density in typical karst areas of Northwest Guangxi in China was analyzed using remote sensing images of 1990, 2000 and 2005 in combination with elevation and weather data in geographic information system （GIS） environment. The results showedthat vegetation carbon storage and density in the study area increased for 1990--2005. Vegetation carbon storage increased from 1.03x108t in 1990 to 1.41x108 t in 2000 and then to 1.63x108 t in 2005. The corresponding carbon density was 14.82 t.hm-z in 1990, 20.38 hm-2 in 2000 and 23.49 t.hm-2 in 2005, consistent with vegetation carbon density in Sichuan （18.47 t.hm 2） and Jiangxi （25.38 hm-2） Provinces of China. With regards to spatial distribution of carbon storage and density, high values were noted for the west or high-elevation （elevation 〉 500 m） regions and low values in the east or low-elevation （elevation 〈 500 m） regions of the study area. The carbon density was 15-22 t.hm-2 in most of the western counties of the study area while it was 8-15 t.hm-2 in most of the eastern counties of the study area in 1990. While an explicit increase was noted in the trend of change in spatial distribution of carbon storage and density in low-value areas （the eastern part of the region）, slight decrease or increase was noted in high-value areas （the western part of the region）. Vegetation carbon density obviously increased in typical karst areas with carbon storage to vegetation ratios of 45.54% in 1990 and 51.99% in 2005. The study suggested that vegetation carbon storage and density in cluster and depression regions obviously increased. Also ecosystem conditions accordingly improved due to policy initiatives regarding prevention of rocky desertification such as ecological migration and returning farmlands to forests. This had clearly benefited by enhancing vegetation carbon storage. This paper showed that assessments based on remote sensing images could provide the scientific basis for reducing uncertainty in estimating carbon stocks in karst regions and carbon balance in terrestrial ecosystems. It provided the basis for scientific reference regarding beneficial evaluation and adaptive regulation of rocky desertification in karsts in a comprehensive manner.
出处
《中国生态农业学报》
CAS
CSCD
北大核心
2013年第12期1545-1553,共9页
Chinese Journal of Eco-Agriculture
基金
中国科学院西部行动计划项目(KZCX2-XB3-10)
中国科学院亚热带农业生态研究所青年人才领域前沿项目(ISACX-LYQY-QN-1102)
中国科学院“西部之光”人才培养计划
国家自然科学基金项目(31370484,31000223,41071340)资助
关键词
桂西北
喀斯特
植被碳储量
时空分异
遥感影像
Northwest Guangxi, Karst region, Vegetation carbon storage, Spatio-temporal variation, Remote sensing image
