全极化雷达数据能够反映目标的全极化散射特征,在森林参数反演中具有较大的应用价值。笔者以南京紫金山国家森林公园为研究对象,以2011年的全极化雷达数据PALSAR和2012年120块野外调查样地为主要信息源,从Pauli和Cloude目标分解特征值、HH(horizontal-horizontal,水平)和HV(horizontal-vertical,水平垂直交互)两种极化状态的后向散射系数、比值植被指数、地形、人为干扰等方面,提取13个因子作为自变量,采用多元线性回归、人工神经网络、K最邻近分类算法、决策与回归树、装袋算法、随机森林6种方法建立遥感估测模型,进行森林蓄积量的估测。研究表明:①在6种遥感估测模型中,随机森林综合性能最高,装袋法次之,多元线性回归最低; ②海拔、坡向等地形因子,以及地物的雷达回波散射特征是影响研究区域森林蓄积量估测的重要变量; ③研究区单位面积蓄积量的空间分布呈现出由里向外逐渐降低的带状分布格局。
Abstract
Forest stock volume is an important indicator for assessing the productivity of ecosystems, and also the basis for analysis of substance circulation in forest ecosystem. Forest stock volume of the different scales area can be estimated based on remote secsing technique, so the spatial distribution and dynamic monitoring of the forest stock volume are significant by using remote sensing techniques. Compared with the traditional optical remote sensing image, the fully polarimetric synthetic aperture radar(PALSAR)is almost unaffected by atmosphere and has the observation capabilities of the whole day and the ability to penetrate clouds, rain and snow. The fully polarimetric SAR image contains more abundant information. Since the fully PALSAR image has a great advantage of being able to obtain the fully polarized scattering attributes of the target object, it is widely used in estimation of forest parameters. In this paper, Zijinshan National Forest Park in Nanjing was chosen as the case study area, while PALSAR image in 2011 and 120 field plots in 2012 were collected as the main information source to estimate scenic forest stock volume. 13 factors including characteristic values extracted from Pauli and Cloude target decomposition, backscattering coefficients of HH and HV, ratio vegetation index, terrain and human disturbances were used to estimate forest parameters of unit stock volume using six remote sensing based models namely multivariate linear regression(MLR), artificial neural network(ANN), K nearest neighbor classification algorithm(KNN), classification and regression tree(CART), bagging(Bagging)and random forest(RF). Research results showed that: ① among the six models, the performance of random forest was the best, followed by bagging method, and multivariate linear regression was the worst; ② terrain factors of DEM(digital elevation model)and aspect, backscattering features of polarization radar echo were important environmental variables which affect unit stock volume; ③ spatial distribution of unit stock volume showed a zonal pattern descending from center to the periphery.
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参考文献
[1] 吕莹莹,任芯雨,李明诗.基于TM/ETM+数据的南京三区域城市森林干扰指数及分析[J].南京林业大学学报(自然科学版),2014, 38(1): 77-82.Doi:10.3969/j.issn.1000-2006.2014.01.014.
Lü Y Y, Ren X Y, Li M S. Assessing forest disturbance patterns over the three forested areas of Nanjing using multi-temporal TM/ETM+imagery[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2014, 38(1):77-82.
[2] 张茂震,王广兴,刘安兴.基于森林资源连续清查资料估算的浙江省森林生物量及生产力[J].林业科学,2009,45(9):13-17.
Zhang M Z, Wang G X, Liu A X. Estimation of forest biomass and net primary production for Zhejiang Province based on continuous forest resources inventory [J]. Scientia Silvae Sinicae, 2009, 45(9): 13-17.
[3] 庞勇.星载干涉雷达和激光雷达数据森林参数反演[D]. 北京:中国科学院遥感应用研究所,2005.
Pang Y. Forest parameters inversion using spaceborne InSAR and Lidar technology [D]. Beijing: Institute of Remote Sensing Applications, Chinese Academy of Sciences, 2005.
[4] 庞勇,李增元,陈尔学,等.激光雷达技术及其在林业上的应用[J].林业科学, 2005, 41(3): 129-136.
Pang Y, Li Z Y, Chen E X, et al. Lidar remote sensing technology and its application in forestry [J]. Scientia Silvae Sinicae, 2005, 41(3): 129-136.
[5] 别强. 基于激光雷达和合成孔径雷达资料的森林参数反演研究[D]. 兰州:兰州大学, 2013.
Bie Q. Study on the estimation method of forest parameters using LiDAR and SAR [D]. Lanzhou: Lanzhou University, 2013.
[6] 徐茂松,李坤,谢酬,等. 极化雷达与光学遥感森林雪灾破坏协同监测[J]. 南京林业大学学报(自然科学版),2014, 38(4): 1-6.Doi:10.3969/j.issn.1000-2006.2014.04.001.
Xu M S, Li K, Xie C, et al. Synergistically monitoring the snowstorm damaged forest with polarimetric SAR and optical remote sensing data[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2014, 38(4): 1-6.
[7] 梁志锋,凌飞龙,汪小钦. L波段SAR与中国东北森林蓄积量的相关性分析[J]. 遥感技术与应用,2013,28(5): 871-878.
Liang Z F, Ling F L, Wang X Q. Correlation analysis between L-band SAR and forest stem volume in northeast China [J]. Remote Sensing Technology and Application, 2013, 28(5): 871-878.
[8] 赵明瑶,刘会云,张晓丽,等. 基于林分结构响应的PALSAR森林结构参数估计[J]. 北京林业大学学报,2015,37(6): 61-69.
Zhao M Y, Liu H Y, Zhang X L, et al. Estimation of forest structural based on stand structure response and PALSAR data [J]. Journal of Beijing Forestry University, 2015, 37(6): 61-69.
[9] 何其钰. 基于目标分解的极化SAR图像对比增强与分类方法研究[D]. 成都:电子科技大学,2011.
He Q Y. The study of image contrast enhancement and classification using SAR data based on target decomposition [D]. Chengdu: University of Electronic Science and Technology, 2011.
[10] Huynen J R. Phenomenological theory of radar targets [J]. Electromagnetic Scatlering, 1978:653-712. Doi:10.1016/B978-0-12-709, 1970.
[11] 黄丽艳,闫巧玲,高添,等. 基于ALSO PALSAR雷达影像的人工林蓄积量估算——以塞罕坝机械林场华北落叶松人工林为例[J]. 生态学杂志,2015,34(9): 2401-2409.
Huang L Y, Yan Q L, Gao T, et al. Estimation on stock volume of plantation forests using ALOS PALSAR images: a case study of Larix principis-rupprechtii plantations in Saihanba Forest Farm [J]. Chinese Journal of Ecology, 2015, 34(9): 2401-2409.
[12] 李文梅,李增元,陈尔学,等. 极化相干层析反演森林垂直结构原理与方法研究[J]. 遥感技术与应用,2014,29(2): 232-239.
Li W M, Li Z Y, Chen R X, et al. Principle and method of forest vertical structure inversion using polarization coherence tomography [J]. Remote Sensing Technology and Application, 2014, 29(2): 232-239.
[13] 张志,田昕,陈尔学,等. 森林地上生物量估测方法研究综述[J]. 北京林业大学学报,2011,33(5): 144-150.
Zhang Z, Tian X, Chen E X, et al. Review of methods on estimating forest above ground biomass [J]. Journal of Beijing Forest University, 2011, 33(5): 144-150.
[14] 王晓宁,徐天蜀,李毅. 合成孔径雷达数据估测森林生物量研究综述[J]. 中南林业调查规划,2011,30(4): 38-42.
Wang X N, Xu T S, Li Y. Review of forest biomass estimation using SAR data [J]. Central South Forest Inventory and Planning, 2011, 30(40): 38-42.
[15] 李明阳,刘米兰,刘敏.森林资源与生态监测的空间平衡抽样方法研究[J].西南林学院学报,2010,34(4): 1-5.
Li M Y, Liu M L, Liu M. Spatially balanced sampling for monitoring forest resources and ecological system [J]. Journal of Southwest Forest University, 2010, 34(4): 1-5.
[16] 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 Gaoligonshan Nature Reserve of Yunnan Province [J]. Forest Resources Management, 2008(1): 69-73.
[17] Cloude S R, Pottier E. A review of target decomposition theorems in radarpolarimetry [J]. IEEE Trans on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
[18] Cloude S R. Target decomposition theorems in radar scattering [J]. Electronics Letters, 1985, 21: 22-24.
[19] Cloude S R. Recent developments in radar polarimetry: a review [J]. IEEE Asia-Pacific Microwave Conference, 1992: 955-958.
[20] Cloude S R, Pottier E. The concept of polarization entropy in optical scattering [J]. Optical Engineering, 1995,34(6): 1599-1610.
[21] 赵立文. 基于目标分解理论的极化SAR图像分类方法研究[D]. 长沙:国防科学技术大学,2007.
Zhao L W. Research on classification methods of polarimetric SAR image based on target decomposition theorem [D]. Changsha: National University of Defense Technology, 2007.
[22] Deng S Q, Katoh M, Guan Q W, et al. Estimating forest aboveground biomass by Combining ALOS PALSAR and WorldView-2 Data: a case study at Purple Mountain National Park, Nanjing, China[J]. Remote Sencing, 2014, 6: 7878-7910.
[23] Breiman L, Friedman J H, Olshen R A, et al. Classification and regression tree [M]. Monterey:Wadsworth International Group, 1984.
[24] Breiman L. Random forests [J]. Machine Learning, 2001, 45: 5-32.
[25] 蒋云娇,胡曼,李明阳,等. 县域尺度森林地上生物量遥感估测方法研究[J]. 西南林业大学学报,2015,35(6): 53-59.
Jiang Y J, Hu M, Li M Y, et al. Remote sensing based estimation of forest aboveground biomass at county level [J]. Journal of Southwest Forestry University,2015,35(6): 53-59.
基金
基金项目:国家自然科学基金项目(31170592)
第一作者:张密芳(458521273@qq.com)。
*通信作者:李明阳(lmy196727@126.com),教授。
引文格式:张密芳,胡曼,李明阳. 基于PALSAR全极化数据的城市森林蓄积量估测[J]. 南京林业大学学报(自然科学版),2016,40(6):56-62.
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