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福建武夷山自然保护区森林碳储量遥感 估测方法与空间分析(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2014年06期
Page:
6-10
Column:
专题报道
publishdate:
2014-12-09

Article Info:/Info

Title:
Estimation and spatial analysis of forest carbon stocks based on remote sensing technology in Wuyi Mountain National Reserve of Fujian province
Article ID:
1000-2006(2014)06-0006-05
Author(s):
LI Mingyang1 WU Jun2* YU Chao1 SHI Yu1
1. College of Forestry, Nanjing Forestry University, Nanjing 210037, China;
2. Ministry of Environmental Protection, Nanjing Institute of Environmental Sciences,Nanjing 210042, China
Keywords:
forest carbon stocks remote sensing-based estimation spatial analysis Wuyi Mountain National Reserve
Classification number :
S757.2
DOI:
10.3969/j.issn.1000-2006.2014.06.002
Document Code:
A
Abstract:
Forest carbon sink is important for mitigating the climate change. Monitoring and quantifying the aboveground forest carbon sink with remote sensing has become a hotspot in the research field of forest remote sensing. Wuyi Mountain National Reserve in Fujian province was chosen as the case study area, while 24 permanent sampling plot data in 2003 and Landsat TM remote sensing images in the same year were collected as the main sources of information to estimate the forest carbon stocks in the study area in 2003 by means of four methods of multiple linear regression, K-nearest neighbor classification, artificial neural networks, and land cover classification, followed by geographically weighed regression and spatial pattern analysis of forest carbon density. Study results show that: ① Among the four models, artificial neural network outperformed others with the highest correlation coefficient, the lowest standard error and the minimum average relative error; ② The difference of carbon density was not very obvious among three functional areas of the reserve, average carbon density was 52.40 t/hm2, totaly 2 785 423 t; ③ Carbon density was negatively correlated with the elevation and aspect, positively correlated with slope, hydrological conditions of forest soil and plant growth conditions; ④ With the decrease of altitude and enhancement of human disturbance, from the core zone, buffer zone to the experimental area,the spatial aggregation of forest carbon density tended to become more weakened and more fragmented.

References

[1] 李怒云. 中国林业碳汇[M]. 北京: 中国林业出版社, 2007.
[2] 杨玉坡. 全球气候变化与森林碳汇作用[J]. 四川林业科技, 2010, 31(1): 14-17. Yang Y P.Global climate change and forest carbon sequestration [J]. Journal of Sichuan Forestry Science and Technology, 2010,31(1):14-17.
[3] 曹吉鑫, 田赟, 王小平, 等. 森林碳汇的估算方法及其发展趋势[J]. 生态环境学报, 2009, 18(5): 2001-2005. Cao J X, Tian Y, Wang X P, et al. Estimation methods of forest sequestration and their prospects [J]. Ecology and Environmental Sciences, 2009, 18(5): 2001-2005.
[4] 徐萍, 徐天蜀. 云南高黎贡山自然保护区森林碳储量估测方法的研究[J]. 林业资源管理, 2008(1): 69-73. Xu P, Xu T S.Research on the estimation method of carbon storage of the forest in Gaoligongshan Nature Reserve of Yunan Province [J].Forest Resource Management, 2008(1): 69-73.
[5] Fang J Y, Chen A P, Peng C H, et al.Changes in forest biomass carbon storage in China between 1949 and 1998 [J]. Science, 2001, 292: 2320-2322.
[6] 王红岩, 高志海, 王琫瑜, 等. 基于SPOT 5遥感影像丰宁县植被地上生物量估测研究[J]. 遥感技术与应用, 2010, 25(5): 639-646. Wang H Y,Gao Z H,Wang F Y, et al. Estimation of vegetation biomass using SPOT 5 satellite images in Fengning County, Hebei Province [J].Remote Sensing Technology and Application, 2010, 25(5): 639-646.
[7] Labrecque S, Fournier R A, Luther J E, et al. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland [J]. Forest Ecology and Management, 2006, 226(1-3): 129-144.
[8] 袁野, 李虎, 刘玉峰. 基于改进型B-P神经网络的西天山云杉林生物量估算[J]. 福建师范大学学报:自然科学版, 2011, 27(2): 124-132. Yuan Y, Li H, Liu Y F. Picea schrenkiana forest biomass estimate in the west Tianshan Mountain based on improved B-P neural network [J].Journal of Fujian Normal University:Natural Science Edition, 2011, 27(2): 124-132.
[9] 浦瑞良,宫鹏,Yang R.应用神经网络和多元回归技术预测森林产量[J].应用生态学报,1999,10(2):129-134. Pu R L, Gong P, Yang R. Forest yield prediction with an artificial neural network and multiple regression [J].Chinese Journal of Applied Ecology, 1999, 10(2):129-134.
[10] Fournier R, Luther J, Guindon L, et al. Mapping aboveground tree biomass at the stand level from inventory information: test cases in Newfoundland and Quebec [J]. Canadian Journal of Forest Research, 2003, 33(10): 1846-1863.
[11] Fotheringham A S, Brunsdon C, Charlton M E. Quantitative Geography: Perspectives on Spatial Data Analysis [M].London: SAGE Publications,2000.
[12] David E. Statistics in Geography [M]. Oxford: Basil Blackwell Ltd, 1985.
[13] 李明阳,张称意,吴军,等.扎龙湿地丹顶鹤生境变化驱动因素分析[J].南京林业大学学报:自然科学版,2012,36(6):76-80. Li M Y, Zhang C Y, Wu J, et al. Drivers analysis of breeding habitat change of red-crowned crane(Grus japonensis)in Zhalong wetland [J].Journal of Nanjing Forestry University:Natural Sciences Edition,2012,36(6):76-80.
[14] Zheng B, Agresti A. Summarizing the predictive power of a generalized linear model [J]. Statistics in Medicine, 2000, 19: 1771-1781.
[15] Luyssaert S, Ciais P, Piao S L, et al. The European carbon balance. Part 3: Forests [J]. Global Change Biology, 2010, 16:1429-1450.

Last Update: 2014-12-31