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Received：04 January 2019，
Published：07 December 2020
移动端阅览
林龄结构信息能够有效反映区域森林群落不同生长阶段的固碳能力，对于评估森林生态系统的健康状况具有重要意义。本研究以中国温带典型优势树种落叶松林为研究对象，分别选择其芽萌动期、展叶期和落叶期时段的Sentinel-2影像，采用多元线性回归（MLR）、随机森林（RF）、支持向量机回归（SVR）、前馈反向传播神经网络（BP）以及多元自适应回归样条（MARS）等5种方法依次构建落叶松林龄反演模型。通过相关性分析首先确定最佳遥感反演物候期，并在此基础上根据相关性差异筛选出5个最优特征变量用于模型反演，分别为冠层含水量（CWC），归一化水体指数（NDWI），叶面积指数（LAI），光合有效辐射吸收率（FAPAR）和植被覆盖度（FVC）。研究结果表明，展叶期为落叶松林最佳遥感反演物候期。除植被衰减指数（PSRI）以及落叶期的NDVI、RVI外，落叶松林龄与各指标之间均呈负相关关系，其中与冠层含水量（CWC）的相关性最高，pearson相关系数达到-0.74（
p
<
0.01）。此外，不同模型反演结果表明，随机森林模型（RF）为最佳落叶松林龄估测模型，其平均决定系数
R
2
和平均均方根误差RMSE分别为0.89和2.91 a；多元线性回归模型（MLR）的林龄估测结果最差，其平均决定系数
R
2
和平均均方根误差RMSE仅为0.57和5.69 a，非线性模型能更好的解释林龄与建模变量之间的关系。
The information of forest age structure can effectively reflect the carbon sequestration capacity of regional forest communities at different growth stages. This way is important for assessing the health status of forest ecosystems. In this study
the typical dominant tree species
Larix gmelinii
forest in temperate zone of China is selected as the object
and Sentinel-2 images of its bud germination period
elongating period of leaf
and defoliation period are selected. The retrieval model of
Larix gmelinii
stand age is constructed using Multiple Linear Regression (MLR)
Random Forest (RF)
support vector regression
feedforward back propagation neural network
and multiple adaptive regression spline. The optimal phenophase of remote sensing retrieval is first determined through correlation analysis. On this basis
five optimal characteristic variables
namely
Canopy Water Content (CWC)
normalized difference water index
leaf area index
fraction of absorbed photosynthetically active radiatio
and fractional vegetation cover
are selected for model retrieval according to the difference in correlation. Results show that the elongating period of leaf is the optimal remote sensing retrieval phenophase. Except for the plant senescence reflectance index and NDVI and RVI in defoliation period
a negative correlation exists between the stand age of
Larix gmelinii
and each index
among which the correlation between the stand age and (CWC is the closest
and the correlation coefficient of Pearson reaches -0.74 (
p
<
0.01). The results of different model retrievals indicate that RF model is the best model for estimating the age of
Larix gmelinii
and its average coefficient of determination (
R
2
) and mean Root Mean Square Error (RMSE) are 0.89 and 2.91 a
respectively. MLR is the worst for estimating
Larix gmelinii
forest age
and its average
R
2
and RMSE are 0.57 and 5.69 a
respectively. Nonlinear models can better explain the relationship between stand age and modeling variables.
Birth G S and McVey G R . 1968 . Measuring the color of growing turf with a reflectance spectrophotometer . Agronomy Journal , 60 ( 6 ): 640 - 643 [ DOI: 10.2134/agronj1968.00021962006000060016x http://dx.doi.org/10.2134/agronj1968.00021962006000060016x ]
Champion I , Dubois-Fernandez P , Guyon D and Cottrel M . 2008 . Radar image texture as a function of forest stand age . International Journal of Remote Sensing , 29 ( 6 ): 1795 - 1800 [ DOI: 10.1080/01431160701730128 http://dx.doi.org/10.1080/01431160701730128 ]
Chen B Q , Cao J H , Wang J K , Wu Z X , Tao Z L , Chen J M , Yang C and Xie G S . 2012 . Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data: a case study in Hainan Island, China . Forest Ecology and Management , 274 : 222 - 230 [ DOI: 10.1016/j.foreco.2012.01.033 http://dx.doi.org/10.1016/j.foreco.2012.01.033 ]
Chen L Y . 2015 . Adaptive Regression Model and Application of Multivariate Smoothing Splines . Tangshan : North China University of Science and Technology
陈立宇 . 2015 . 多元光滑样条自适应回归模型及其应用 . 唐山 : 华北理工大学
Cutler A , Cutler D R and Stevens J R . 2011 . Random forests . Machine Learning , 45 ( 1 ): 157 - 176
Dai L M , Jia J , Yu D P , Lewis B J , Zhou L , Zhou W M , Zhao W and Jiang L H . 2013 . Effects of climate change on biomass carbon sequestration in old-growth forest ecosystems on Changbai Mountain in Northeast China . Forest Ecology and Management , 300 : 106 - 116 [ DOI: 10.1016/j.foreco.2012.06.046 http://dx.doi.org/10.1016/j.foreco.2012.06.046 ]
Dai M , Zhou T , Yang L L and Jia G S . 2011 . Spatial pattern of forest ages in China retrieved from national-level inventory and remote sensing imageries . Geographical Research , 30 ( 1 ): 172 - 184
戴铭 , 周涛 , 杨玲玲 , 贾根锁 . 2011 . 基于森林详查与遥感数据降尺度技术估算中国林龄的空间分布 . 地理研究 , 30 ( 1 ): 172 - 184 [ DOI: 10.11821/yj2011010017 http://dx.doi.org/10.11821/yj2011010017 ]
Eggers J J , Bauml R , Tzschoppe R and Girod B . 2003 . Scalar costa scheme for information embedding . IEEE Transactions on Signal Processing , 51 ( 4 ): 1003 - 1019 [ DOI: 10.1109/TSP.2003.809366 http://dx.doi.org/10.1109/TSP.2003.809366 ]
Filippi A M , Güneralp İ and Randall J . 2014 . Hyperspectral remote sensing of aboveground biomass on a river meander Bend using multivariate adaptive regression splines and stochastic gradient boosting . Remote Sensing Letters , 5 ( 5 ): 432 - 441 [ DOI: 10.1080/2150704X.2014.915070 http://dx.doi.org/10.1080/2150704X.2014.915070 ]
Fiorella M and Ripple W J . 1993 . Analysis of conifer forest regeneration using Landsat Thematic Mapper data . Photogrammetric Engineering and Remote Sensing , 59 ( 9 ): 1383 - 1388
Fraser R H and Li Z . 2002 . Estimating fire-related parameters in boreal forest using SPOT VEGETATION . Remote Sensing of Environment , 82 ( 1 ): 95 - 110 [ DOI: 10.1016/S0034-4257(02)00027-5 http://dx.doi.org/10.1016/S0034-4257(02)00027-5 ]
Friedman J H . 1991 . Multivariate adaptive regression splines . The Annals of Statistics , 19 ( 1 ): 1 - 67 [ DOI: 10.1214/aos/1176347963 http://dx.doi.org/10.1214/aos/1176347963 ]
Gao B C . 1996 . NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space . Remote Sensing of Environment , 58 ( 3 ): 257 - 266 [ DOI: 10.1016/S0034-4257(96)00067-3 http://dx.doi.org/10.1016/S0034-4257(96)00067-3 ]
Gitelson A A , Gritz Y and Merzlyak M N . 2003 . Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves . Journal of Plant Physiology , 160 ( 3 ): 271 - 282 [ DOI: 10.1078/0176-1617-00887 http://dx.doi.org/10.1078/0176-1617-00887 ]
Grant L . 1987 . Diffuse and specular characteristics of leaf reflectance . Remote Sensing of Environment , 22 ( 2 ): 309 - 322 [ DOI: 10.1016/0034-4257(87)90064-2 http://dx.doi.org/10.1016/0034-4257(87)90064-2 ]
Güneralp İ , Filippi A M and Randall J . 2014 . Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling . International Journal of Applied Earth Observation and Geoinformation , 33 : 119 - 126 [ DOI: 10.1016/j.jag.2014.05.004 http://dx.doi.org/10.1016/j.jag.2014.05.004 ]
Gunn S R . 1998 . Support Vector Machines for Classification and Regression . University of Southampton : 1 - 28 [ DOI: 10.1039/b918 972f http://dx.doi.org/10.1039/b918972f ]
Haywood A and Stone C . 2017 . Estimating large area forest carbon stocks- a pragmatic design based strategy . Forests , 8 ( 4 ): 99 [ DOI: 10.3390/f8040099 http://dx.doi.org/10.3390/f8040099 ]
He L M , Chen J M , Pan Y D , Birdsey R and Kattge J . 2012 . Relationships between net primary productivity and forest stand age in U.S . forests. Global Biogeochemical Cycles , 26 ( 3 ):
GB 3009 [ DOI: 10.1029/2010gb003942 http://dx.doi.org/10.1029/2010gb003942 ]
Jensen J R , Qiu F and Ji M H . 1999 . Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data . International Journal of Remote Sensing , 20 ( 14 ): 2805-2822(18 ) [ DOI: 10.1080/0143116 99211804 http://dx.doi.org/10.1080/014311699211804 ]
Jiang W Y and Chen F L . 2005 . Dictionary of Resources and Environment Law . Beijing : China Legal Publishing House
江伟钰 , 陈方林 . 2005 . 资源环境法词典 . 北京 : 中国法制出版社
Jiao L C . 1990 . Neural Network System Theory . Xi’an : Xidian University Press
焦李成 . 1990 . 神经网络系统理论 . 西安 : 西安电子科技大学出版社
Ju W Z , Wang X J and Sun Y J . 2011 . Age structure effects on stand biomass and carbon storage distribution of Larix olgensis plantation . Acta Ecologica Sinica , 31 ( 4 ): 1139 - 1148
巨文珍 , 王新杰 , 孙玉军 . 2011 . 长白落叶松林龄序列上的生物量及碳储量分配规律 . 生态学报 , 31 ( 4 ): 1139 - 1148
Kimes D S , Nelson R F , Salas W A and Skole D L . 1999 . Mapping secondary tropical forest and forest age from SPOT HRV data . International Journal of Remote Sensing , 20 ( 18 ): 3625 - 3640 [ DOI: 10.1080/014311699211246 http://dx.doi.org/10.1080/014311699211246 ]
Knipling E B . 1970 . Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation . Remote Sensing of Environment , 1 ( 3 ): 155 - 159 [ DOI: 10.1016/S0034-4257(70)80021-9 http://dx.doi.org/10.1016/S0034-4257(70)80021-9 ]
Lal R . 2008 . Carbon sequestration . Philosophical Transactions of the Royal Society B : Biological Sciences , 363 ( 1492 ): 815 - 830 [ DOI: 10.1098/rstb.2007.2185 http://dx.doi.org/10.1098/rstb.2007.2185 ]
Li D Q , Ju W M , Fan W Y and Gu Z J . 2014 . Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons . Journal of Applied Remote Sensing , 8 ( 1 ): 083670 [ DOI: 10.1117/1.JRS.8.083670 http://dx.doi.org/10.1117/1.JRS.8.083670 ]
Li F , Li M Z , Shi Z L , Jiang H Y and An J P . 2018 . Estimates stand age distribution based on forest survey and remote sensing data . Forest Engineering , 34 ( 2 ): 30 - 34
李凡 , 李明泽 , 史泽林 , 姜宏宇 , 安金鹏 . 2018 . 基于遥感数据与森林详查估算林龄的空间分布 . 森林工程 , 34 ( 2 ): 30-34 [ DOI: 10.3969/j.issn.1006-8023.2018.02.007 http://dx.doi.org/10.3969/j.issn.1006-8023.2018.02.007 ]
Li Q , Zhu J H , Feng Y and Xiao W F . 2018 . Carbon storage and carbon sequestration potential of the forest in China . Climate Change Research , 14 ( 3 ): 287 - 294
李奇 , 朱建华 , 冯源 , 肖文发 . 2018 . 中国森林乔木林碳储量及其固碳潜力预测 . 气候变化研究进展 , 14 ( 3 ): 287 - 294 [ DOI: 10.12006/j.issn.1673-1719.2017.102 http://dx.doi.org/10.12006/j.issn.1673-1719.2017.102 ]
Li R , Li C J , Xu X G , Wang J H , Yang X D , Huang W J and Pan Y C . 2009 . Winter wheat yield estimation based on support vector machine regression and multi-temporal remote sensing data . Transactions of the Chinese Society of Agricultural Engineering , 25 ( 7 ): 114 - 117
黎锐 , 李存军 , 徐新刚 , 王纪华 , 杨小冬 , 黄文江 , 潘瑜春 . 2009 . 基于支持向量回归(SVR)和多时相遥感数据的冬小麦估产 . 农业工程学报 , 25 ( 7 ): 114 - 117 [ DOI: 10.3969/j.issn.1002-6819.2009.07.021 http://dx.doi.org/10.3969/j.issn.1002-6819.2009.07.021 ]
Li X H . 2013 . Using “random forest” for classification and regression . Chinese Bulletin of Entomology , 50 ( 4 ): 1190 - 1197
李欣海 . 2013 . 随机森林模型在分类与回归分析中的应用 . 应用昆虫学报 , 50 ( 4 ): 1190 - 1197 [ DOI: 10.7679/j.issn.2095-1353.2013.163 http://dx.doi.org/10.7679/j.issn.2095-1353.2013.163 ]
Litvak M , Miller S , Wofsy S C and Goulden M . 2003 . Effect of stand age on whole ecosystem CO 2 exchange in the Canadian boreal forest. Journal of Geophysical Research: Atmospheres, 108 (D 3 ): 8225 [ DOI: 10.1029/2001JD000854 http://dx.doi.org/10.1029/2001JD000854 ]
Liu L . 2005 . Support Vector Machine and Its Application in Remote Sensing Image Processing . Hefei : University of Science and Technology of China
刘莉 . 2005 . 支持向量机及其在遥感图像处理中的应用 . 合肥 : 中国科学技术大学) [ DOI: 10.7666/d.y730870 http://dx.doi.org/10.7666/d.y730870 ]
Liu R , Miao Q G , Huang B M , Song J F and Debayle J . 2016 . Improved road centerlines extraction in high-resolution remote sensing images using shear transform, directional morphological filtering and enhanced broken lines connection . Journal of Visual Communication and Image Representation , 40 : 300 - 311 [ DOI: 10.1016/j.jvcir.2016.06.024 http://dx.doi.org/10.1016/j.jvcir.2016.06.024 ]
Liu Z Y , Zhang J and Chen L . 2017 . The latest change in the Qinghai-Tibetan Plateau vegetation index and its relationship with climate factors . Climatic and Environmental Research , 22 ( 3 ): 289 - 300
刘振元 , 张杰 , 陈立 . 2017 . 青藏高原植被指数最新变化特征及其与气候因子的关系 . 气候与环境研究 , 22 ( 3 ): 289 - 300 [ DOI: 10.3878/j.issn.1006-9585.2017.14247 http://dx.doi.org/10.3878/j.issn.1006-9585.2017.14247 ]
Louis J . 2016 . Sentinel 2 MSI-level 2A Product Definition . Paris : European Space Agency : 4
Main-Knorn M , Pflug B , Louis J , Debaecker V , Müller-Wilm U and Gascon F . 2017 . Sen2Cor for Sentinel-2//Proceedings of the SPIE 10427 Image and Signal Processing for Remote Sensing XXIII . Warsaw : SPIE : 3 [ DOI: 10.1117/12.2278218 http://dx.doi.org/10.1117/12.2278218 ]
Meng X Y . 2006 . Holzmesslehre. Beijing: China Forestry Publishing House, 2006 (孟宪宇 . 2006 . 测树学 . 北京: 中国林业出版社)
Merzlyak M N , Gitelson A A , Chivkunova O B and Rakitin V Y U . 1999 . Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening . Physiologia Plantarum , 106 ( 1 ): 135 - 141 [ DOI: 10.1034/j.1399-3054.1999.106119.x http://dx.doi.org/10.1034/j.1399-3054.1999.106119.x ]
Miao Z L , Shi W Z , Zhang H and Wang X X . 2013 . Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines . IEEE Geoscience and Remote Sensing Letters , 10 ( 3 ): 583 - 587 [ DOI: 10.1109/LGRS.2012.2214761 http://dx.doi.org/10.1109/LGRS.2012.2214761 ]
Muller-Wilm U , Devignot O and Pessiot L . 2016 . Sen2Cor Configuration and User Manual. S2-PDGS-MPC-L2 A-SUM-V 2 . 3 Issue: 01 . European Space Agency
Shen W J and Li M S . 2017 . Mapping disturbance and recovery of plantation forests in southern China using yearly Landsat time series observations . Acta Ecologica Sinica , 37 ( 5 ): 1438 - 1449
沈文娟 , 李明诗 . 2017 . 基于长时间序列Landsat影像的南方人工林干扰与恢复制图分析 . 生态学报 , 37 ( 5 ): 1438 - 1449 ） [ DOI: 10.5846/stxb201510142074 http://dx.doi.org/10.5846/stxb201510142074 ]
Sivanpillai R , Smith C T , Srinivasan R , Messina M G and Wu X B . 2006 . Estimation of managed loblolly pine stand age and density with Landsat ETM+ data. Forest Ecology and Management, 223( 1 / 3 ): 247 - 254 [ DOI: 10.1016/j.foreco.2005.11.013 http://dx.doi.org/10.1016/j.foreco.2005.11.013 ]
Suratman M N , Bull G Q , Leckie D G , Lemay V M , Marshall P L and Mispan M R . 2004 . Prediction models for estimating the area, volume, and age of rubber ( Hevea brasiliensis ) plantations in Malaysia using Landsat TM data . International Forestry Review , 6 ( 1 ): 1 - 12 [ DOI: 10.1505/ifor.6.1.1.32055 http://dx.doi.org/10.1505/ifor.6.1.1.32055 ]
Tauxe G M , MacWilliam D , Boyle S M , Guda T and Ray A . 2013 . Targeting a dual detector of skin and CO 2 to modify mosquito host seeking . Cell , 155 ( 6 ): 1365 - 1379 [ DOI: 10.1016/j.cell.2013.11.013 http://dx.doi.org/10.1016/j.cell.2013.11.013 ]
Tucker C J . 1979 . Red and photographic infrared linear combinations for monitoring vegetation . Remote Sensing of Environment , 8 ( 2 ): 127 - 150 [ DOI: 10.1016/0034-4257(79)90013-0 http://dx.doi.org/10.1016/0034-4257(79)90013-0 ]
Van der Linden S , Rabe A , Held M , Jakimow B , Leitão P J , Okujeni A , Schwieder M , Suess S and Hostert P . 2015 . The EnMAP-Box—a toolbox and application programming interface for EnMAP data processing . Remote Sensing , 7 ( 9 ): 11249 - 11266 [ DOI: 10.3390/rs70911249 http://dx.doi.org/10.3390/rs70911249 ]
Wang X C , Wang C K and Yu G R . 2008 . Spatio-temporal patterns of forest carbon dioxide exchange based on global eddy covariance measurements . Science in China Series D : Earth Sciences , 51 ( 8 ): 1129 - 1143
王兴昌 , 王传宽 , 于贵瑞 . 2008 . 基于全球涡度相关的森林碳交换的时空格局 . 中国科学 D辑 : 地球科学 , 38 ( 9 ): 1092 - 1102
Watson R T , Noble I R , Bolin B , Ravindranath N H , Verardo D J and Dokken D J . 2000 . Land Use, Land-Use Change, and Forestry . Cambridge University Press : 375
Weiss M and Baret F . 2016 . S2 Toolbox Level 2 Products: LAI, FAPAR, FCOVER . INRA [ http://step.esa.int/docs/extra/ATBD_ S2ToolBox_L2B_V1.1.pdf http://step.esa.int/docs/extra/ATBD_S2ToolBox_L2B_V1.1.pdf ]
Wu W N and Wan T . 2013 . Progress of dating methods of tree age . Journal of Green Science and Technology , ( 7 ): 152 - 155
吴斡宁 , 万涛 . 2013 . 浅谈树木年龄测定方法的研究进展. 绿色科技, (7): 152 - 155 [DOI: 10.3969/j.issn.1674-9944.2013.07.072]
Wulder M A , Skakun R S , Kurz W A and White J C . 2004 . Estimating time since forest harvest using segmented Landsat ETM+ imagery. Remote Sensing of Environment, 93( 1 / 2 ): 179 - 187 [ DOI: 10.1016/j.rse.2004.07.009 http://dx.doi.org/10.1016/j.rse.2004.07.009 ]
Xu K J , Tian Q J , Yue J B and Tang S F . 2018 . Forest tree species identification and its response to spatial scale based on multispectral and multi-resolution remotely sensed data . Chinese Journal of Applied Ecology , 29 ( 12 ): 3986 - 3994
徐凯健 , 田庆久 , 岳继博 , 唐少飞 . 2018 . 基于多光谱影像的森林树种识别及其空间尺度响应 . 应用生态学报 , 29 ( 12 ): 3986 - 3994 [ DOI: 10.13287/j.1001-9332.201812.011 http://dx.doi.org/10.13287/j.1001-9332.201812.011 ]
Yang B , Li D , Wang L and Chen C . 2017 . Retrieval of surface vegetation biomass information and analysis of vegetation feature based on Sentinel-2A in the upper of Minjiang River . Science and Technology Review , 35 ( 21 ): 74 - 80
杨斌 , 李丹 , 王磊 , 陈财 . 2017 . 基于Sentine1-2A岷江上游地表生物量反演与植被特征分析 . 科技导报 , 35 ( 21 ): 74-80 [ DOI: 10.3981/j.issn.1000-7857.2017.21.009 http://dx.doi.org/10.3981/j.issn.1000-7857.2017.21.009 ]
Yu H N , Lee W K , Son Y , Kwak D , Nam K , Kim M , Byun J , Lee S and Kwon T . 2013 . Estimating carbon stocks in Korean forests between 2010 and 2110: a prediction based on forest volume-age relationships . Forest Science and Technology , 9 ( 2 ): 105 - 110 [ DOI: 10.1080/21580103.2013.801174 http://dx.doi.org/10.1080/21580103.2013.801174 ]
Zhang Q F , Pavlic G , Chen W J , Latifovic R , Fraser R and Cihlar J . 2004 . Deriving stand age distribution in boreal forests using SPOT VEGETATION and NOAA AVHRR imagery. Remote Sensing of Environment, 91( 3 / 4 ): 405 - 418 [ DOI: 10.1016/j.rse.2004.04.004 http://dx.doi.org/10.1016/j.rse.2004.04.004 ]
Zhang Z W . 2013 . Investigation and observation of growth and annual cycle phenology of four Larix species . Anhui Agricultural Science Bulletin , 19 ( 15 ): 106 - 107
张志伟 . 2013 . 4种落叶松的生长与年周期物候的调查观测 . 安徽农学通报 , 19 ( 15 ): 106 - 107 [ DOI: 10.3969/j.issn.1007-7731.2013.15.057 http://dx.doi.org/10.3969/j.issn.1007-7731.2013.15.057 ]
Zhou F Y . 2017 . Relationships among DBH, crown width and stand age of Pinus sylvestris var. mongolica plantation in sand land. Protection Forest Science and Technology, (2): 19-21 (周凤艳 . 2017 . 沙地樟子松人工林林木胸径、冠幅等生长指标与林龄相关性研究. 防护林科技, (2): 19 - 21 ) [DOI: 10.13601/j.issn.1005-5215.2017.02.007]
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