[1] |
魏殿生. 造林绿化与气候变化-碳汇问题研究[M]. 北京: 中国林业出版社, 2003. |
[2] |
刘茜, 杨乐, 柳钦火, 等. 森林地上生物量遥感反演方法综述[J]. 遥感学报, 2015, 19(1): 62 − 74.
LIU Qian, YANG Le, LIU Qinhuo, et al. Review of forest above ground biomass inversion methods based on remote sensing technology [J]. J Remote Sensing, 2015, 19(1): 62 − 74. |
[3] |
于艳梅, 徐俊增, 彭世彰, 等. 不同水分条件下水稻光合作用的光响应模型的比较[J]. 节水灌溉, 2012(10): 30 − 33.
YU Yanmei, XU Junzeng, PENG Shizhang, et al. Evaluation of photosynthesis light response model for rice leaf under different water conditions [J]. Water Sav Irrig, 2012(10): 30 − 33. |
[4] |
XIE Yichun, SHA Zongyao, YU Mei, et al. A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China [J]. Ecol Modelling, 2009, 220(15): 1810 − 1818. |
[5] |
POWELL S L, COHEN W B, HEALEY S P, et al. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches [J]. Remote Sensing Environ, 2010, 114(5): 1053 − 1068. |
[6] |
国庆喜, 张锋. 基于遥感信息估测森林的生物量[J]. 东北林业大学学报, 2003, 31(2): 13 − 16.
GUO Qingxi, ZHANG Feng. Estimation of forest biomass based on remote sensing [J]. J Northeast For Univ, 2003, 31(2): 13 − 16. |
[7] |
欧光龙, 胥辉, 王俊峰, 等. 思茅松天然林林分生物量混合效应模型构建[J]. 北京林业大学学报, 2015, 37(3): 101 − 110.
OU Guanglong, XU Hui, WANG Junfeng, et al. Building mixed effect models of stand biomass for Simao pine(Pinus kesiya var. langbianensis) natural forest [J]. J Beijing For Univ, 2015, 37(3): 101 − 110. |
[8] |
姜立春, 李凤日. 混合效应模型在林业建模中的应用[M]. 北京: 科学出版社, 2014. |
[9] |
曾伟生, 唐守正, 夏忠胜, 等. 利用线性混合模型和哑变量模型方法建立贵州省通用性生物量方程[J]. 林业科学研究, 2011, 24(3): 285 − 291.
ZENG Weisheng, TANG Shouzheng, XIA Zhongsheng, et al. Using linear mixed model and dummy variable model approaches to construct generalized single-tree biomass equations in Guizhou [J]. For Res, 2011, 24(3): 285 − 291. |
[10] |
董利虎, 李凤日, 贾炜玮. 基于线性混合效应的红松人工林枝条生物量模型[J]. 应用生态学报, 2013, 24(12): 3391 − 3398.
DONG Lihu, LI Fengri, JIA Weiwei. Linear mixed modeling of branch biomass for Korean pine plantation [J]. Chin J Appl Ecol, 2013, 24(12): 3391 − 3398. |
[11] |
胥喆, 舒清态, 杨凯博, 等. 基于非线性混合效应的高山松林生物量模型研究[J]. 江西农业大学学报, 2017, 39(1): 101 − 110.
XU Zhe, SHU Qingtai, YANG Kaibo, et al. A study on biomass model of Pinus densata forest based on nonlinear mixed effects [J]. Acta Agric Univ Jiangxi, 2017, 39(1): 101 − 110. |
[12] |
胥辉, 张子翼, 欧光龙, 等. 云南省森林生物量和碳储量估算及分布研究[M]. 昆明: 云南科技出版社, 2019. |
[13] |
周小成, 庄海东, 陈铭潮, 等. 面向小班对象的森林资源变化遥感监测方法: 以福建省厦门市为例[J]. 资源科学, 2013, 35(8): 1710 − 1718.
ZHOU Xiaocheng, ZHUANG Haidong, CHEN Mingchao, et al. A method to extract forest cover change by object-oriented classification [J]. Resour Sci, 2013, 35(8): 1710 − 1718. |
[14] |
MOZGERIS G. Estimation and use of continuous surfaces of forest parameters: options for lithuanian forest inventory [J]. Baltic For, 2008, 14(2): 176 − 184. |
[15] |
FERNÁNDEZ-MANSO O, FERNÁNDEZ-MANSO A, QUINTANO C. Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images [J]. Int J Appl Earth Obs Geoinf, 2014, 31(31): 45 − 56. |
[16] |
郎晓雪, 许彦红, 舒清态, 等. 香格里拉市云冷杉林蓄积量遥感估测非参数模型研究[J]. 西南林业大学学报, 2019, 39(1): 146 − 151.
LANG Xiaoxue, XU Yanhong, SHU Qingtai, et al. Nonparametric model for remote sensing estimating the volume of spruce-fir forest in Shangri-La [J]. J Southwest For Univ, 2019, 39(1): 146 − 151. |
[17] |
陆驰, 张加龙, 王爱芸, 等. 基于森林小班的香格里拉市高山松生物量遥感建模[J]. 西南林业大学学报, 2017, 37(3): 158 − 164.
LU Chi, ZHANG Jialong, WANG Aiyun, et al. Building the model on the estimation of Pinus densata’s biomass in Shangri-La City based on forest subcompartment and remote sensing images [J]. J Southwest For Univ, 2017, 37(3): 158 − 164. |
[18] |
董宇. 基于遥感信息估测将乐县森林生物量的研究[D]. 北京: 北京林业大学, 2012.
DONG Yu. Estimate Forest Biomass in Jiangle based on Remote Sensing[D]. Beijing: Beijing Forestry University, 2012. |
[19] |
孙雪莲. 基于Landsat 8-OLI的香格里拉高山松林生物量遥感估测模型研究[D]. 昆明: 西南林业大学, 2016.
SUN Xuelian. Biomass Estimation Model of Pinus densata Forests in Shangri-La City based on Landsat 8-OLI by Remote Sensing[D]. Kunming: Southwest Forestry University, 2016. |
[20] |
岳彩荣. 香格里拉县森林生物量遥感估测研究[D]. 北京: 北京林业大学, 2012.
YUE Cairong. Forest Biomass Estimation in Shangri-La County based on Remote Sensing[D]. Beijing: Beijing Forestry University, 2012. |
[21] |
邱布布, 徐丽华, 张茂震, 等. 基于Landsat OLI和ETM+的杭州城市绿地地上生物量估算比较研究[J]. 西北林学院学报, 2018, 33(1): 225 − 232.
QIU Bubu, XU Lihua, ZHANG Maozhen, et al. Estimation of above-ground biomass of urban green land in Hangzhou based on Landsat OLI and ETM+ data [J]. J Northwest For Univ, 2018, 33(1): 225 − 232. |
[22] |
李春明. 基于两层次线性混合效应模型的杉木林单木胸径生长量模型[J]. 林业科学, 2012, 48(3): 66 − 73.
LI Chunming. Individual tree diameter increment model for Chinese fir plantation based on two-level linear mixed effects models [J]. Sci Silv Sin, 2012, 48(3): 66 − 73. |
[23] |
符利勇, 李永慈, 李春明, 等. 利用2种非线性混合效应模型(2水平)对杉木林胸径生长量的分析[J]. 林业科学, 2012, 48(5): 36 − 43.
FU Liyong, LI Yongci, LI Chunming, et al. Analysis of the basal area for Chinese fir plantation using two kinds of nonlinear mixed effects model(two levels) [J]. Sci Silv Sin, 2012, 48(5): 36 − 43. |
[24] |
PINHEIRO J C, BATES D M. Mixed Effects Models in S and S-Plus[M]. New York: Springer Verlag, 2000. |
[25] |
VONESH E F, CHINCHILLI V M. Linear and Nonlinear Models for the Analysis of Repeated Measurements[M]. New York: Marcel Dekker Inc, 1997. |
[26] |
王磊, 徐晓岭. 统计学[M]. 北京: 人民邮电出版社, 2015. |
[27] |
赵盼盼. 基于Landsat TM和ALOS PALSAR数据的森林地上生物量估测研究[D]. 杭州: 浙江农林大学, 2016.
ZHAO Panpan. Aboveground Forest Biomass Estimation based on Landsat TM and ALOS PALSAR Data[D]. Hangzhou: Zhejiang A&F University, 2016. |
[28] |
周律, 欧光龙, 王俊峰, 等. 基于空间回归模型的思茅松林生物量遥感估测及光饱和点确定[J]. 林业科学, 2020, 56(3): 38 − 47.
ZHOU Lü, OU Guanglong, WANG Junfeng, et al. Light saturation point determination and biomass remote sensing estimation of Pinus kesiya var. langbianensis forest based on spatial regression models [J]. Sci Silv Sin, 2020, 56(3): 38 − 47. |