基于高分2号遥感数据估测中亚热带天然林木本植物物种多样性
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摘要
【目的】探索高分2号遥感数据与中亚热带天然林木本植物物种Shannon-Wiener多样性指数、Simpson多样性指数和Pielou均匀性指数之间的关系,为森林经营管理和保护策略提供参考。【方法】提取高分2号多光谱数据的原始波段、植被指数、纹理特征和全色波段纹理特征,使用随机森林算法筛选变量并对3种多样性指数进行建模,设置不同纹理提取窗口来寻找最优窗口。【结果】基于随机森林算法的RFE冗余变量去除方法可从众多遥感变量中快速选择对模型精度具有显著贡献的少量变量。多光谱数据3×3窗口纹理特征、全色数据7×7窗口纹理特征和植被指数结合的特征集对3种多样性指数具有较好估测结果,其决定系数(R~2)和均方根误差(RMSE)分别为0.47和0.300(Shannon-Wiener多样性指数)、0.53和0.042 (Simpson多样性指数)、0.61和0.051 (Pielou均匀性指数)。植被指数中类胡萝卜素反射率指数与3种多样性指数具有显著相关关系。【结论】高分2号遥感数据中的植被指数和纹理特征可有效估测研究区森林木本植物物种多样性。类胡萝卜素反射率指数可体现森林中类胡萝卜素相对于叶绿素的含量,在秋冬季节作为反映常绿树种和落叶树种分布的指数,对森林木本植物物种多样性估测具有最大贡献。使用星载遥感数据预测的多样性和均匀性指数分布可有效监测森林木本植物物种多样性变化。
【Objective】 In this paper, we explored the possibility of defining relationships between remote sensing features that come from GF-2 and species diversity indices for subtropical forest in Gutian Mountain of Zhejiang Province, China.【Method】 We extracted the reflectance values, spectral indices, texture from GF-2 data. Random forest models were used to select variables and estimate species diversity indices. We compared the texture values that come from different window size to find the best window size for species diversity estimation.【Result】 Based on the random forest(RF),recursive feature elimination(RFE)was used to find small subsets of features with high discrimination levels on data sets, which provide good performance for species diversity modeling. For the multispectral(MSS)data, the best window size is 3×3, and for the panchromatic(Pan)data, the best window size is 7×7. Both texture features and spectral indices were selected for species diversity modeling and the Carotenoid reflectance index provided the best performance.The determinate coefficient and RMSE for three species diversity are 0.47 and 0.300(Shannon-Wiener diversity index),0.53 and 0.042(Simpson diversity index),0.61 and 0.051(Pielou evenness index),respectively.【Conclusion】 The result demonstrated that the GF-2 data can be used to model tree species diversity effectively. Predictive map derived from the presented method ology can help evaluate spatial aspects and monitor tree species diversity of the studied forest and facilitate the evaluation of forest management and conservation strategies.
【Objective】 In this paper, we explored the possibility of defining relationships between remote sensing features that come from GF-2 and species diversity indices for subtropical forest in Gutian Mountain of Zhejiang Province, China.【Method】 We extracted the reflectance values, spectral indices, texture from GF-2 data. Random forest models were used to select variables and estimate species diversity indices. We compared the texture values that come from different window size to find the best window size for species diversity estimation.【Result】 Based on the random forest(RF),recursive feature elimination(RFE)was used to find small subsets of features with high discrimination levels on data sets, which provide good performance for species diversity modeling. For the multispectral(MSS)data, the best window size is 3×3, and for the panchromatic(Pan)data, the best window size is 7×7. Both texture features and spectral indices were selected for species diversity modeling and the Carotenoid reflectance index provided the best performance.The determinate coefficient and RMSE for three species diversity are 0.47 and 0.300(Shannon-Wiener diversity index),0.53 and 0.042(Simpson diversity index),0.61 and 0.051(Pielou evenness index),respectively.【Conclusion】 The result demonstrated that the GF-2 data can be used to model tree species diversity effectively. Predictive map derived from the presented method ology can help evaluate spatial aspects and monitor tree species diversity of the studied forest and facilitate the evaluation of forest management and conservation strategies.
引文
胡正华, 于明坚, 丁炳扬,等. 2003.古田山国家级自然保护区常绿阔叶林类型及其群落物种多样性研究. 应用与环境生物学报, 9(4):341-345.(Hu Z H,Yu M J,Ding B Y, et al. 2003. Types of evergreen broad-leaved forests and their species diversity in Gutian Mountain National Nature Reserve.Chinese Journal of Applied and Environmental Biology, 9(4):341-345.[in Chinese])
金毅. 2015. 冰雪灾害对古田山常绿阔叶林短期动态的影响——群落结构变化、幼苗更新与林窗干扰效应. 杭州:浙江大学博士学位论文.(Jin Y. 2015. Impacts of ice storm on the short-term dynamics of broad-leaved forest in Gutianshan: community structure alternation, seeding regeneration, and effects of forest gap disturbance.Hangzhou:PhD thesis of Zhejiang University.[in Chinese])
任学敏, 杨改河, 王得祥,等. 2012. 环境因子对巴山冷杉-糙皮桦混交林物种分布及多样性的影响. 生态学报, 32(2), 605-613.(Ren X M, Yang G H, Wang D X, et al. 2012. Effects of environmental factors on species distribution and diversity in an Abiesfargesii-Betula utilis mixed forest.Acta Ecologica Sinica, 32(2): 605-613. [in Chinese])
解潍嘉, 黄侃, 李瑞平,等. 2015. 应用高分辨率卫星数据估算阔叶红松林乔木多样性. 北京林业大学学报, 37(3):24-30.(Xie W J, Huang K, Li R P, et al. 2015. Applying high-resolution satellite images to estimate tree diversity of mixed broadleaf-korean pine forest.Journal of Beijing Forestry University, 37(3):24-30.[in Chinese])
徐文婷, 吴炳方. 2005. 遥感用于森林生物多样性监测的进展. 生态学报, 25(5):1199-1204.(Xu W T, Wu B F. 2005. Progress on measuring forest biodiversity with remote sensing technique. Acta Ecologica Sinica,25(5):1199-1204.[in Chinese])
徐学红, 于明坚, 胡正华,等. 2005. 浙江古田山自然保护区甜槠种群结构与动态. 生态学报, 25(3):645-653.(Xu X H, Yu M J, Hu Z H, et al. 2005. The structure and dynamics of Castanopsis eyrei population in Gutian Mountain Natural Reserve in Zhejiang, East China.Acta Ecologica Sinica,25(3):645-653.[in Chinese])
于明坚, 胡正华, 余建平,等. 2001. 浙江古田山自然保护区森林植被类型. 浙江大学学报:农业与生命科学版, 27(4):375-380.(Yu M J, Hu Z H, Yu J P, et al. 2001.Forest vegetation types in Gutianshan Natural Reserve in Zhejiang. Journal of Zhejiang University:Agriculture and Life Sciences, 27(4):375-380.[in Chinese])
祝燕, 赵谷风, 张俪文,等. 2008. 古田山中亚热带常绿阔叶林动态监测样地——群落组成与结构. 植物生态学报, 32(2):262-273.(Zhu Y, Zhao G F, Zhang L W, et al. 2008. Community composition and structure of Gutianshan forest dynamic plot in a mid-subtropical evergreen broad-leaved forest, east-China. Journal of Plant Ecology, 32(2):262-273.[in Chinese])
Bai F, Sang W, Axmacher J. 2011. Forest vegetation responses to climate and environmental change: a case study from Changbai Mountain, NE China. Forest Ecology and Management, 262(11): 2052-2060.
Belgiu M, Drgut L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31.
Birth G S, McVey G R. 1968. Measuring the color of growing turf with a reflectance spectrophotometer1. Agronomy Journal, 60(6): 640-643.
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32.
Cayuela L, Benayas J M R, Justel A, et al. 2006. Modelling tree diversity in a highly fragmented tropical montane landscape. Global Ecology and Biogeography, 15(6): 602-613.
Chambers J Q, Asner G P, Morton D C, et al. 2007. Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends in Ecology & Evolution, 22(8): 414-423.
Dalmayne J, M?ckel T, Prentice H C, et al. 2013. Assessment of fine-scale plant species beta diversity using World View-2 satellite spectral dissimilarity. Ecological Informatics, 18: 1-9.
Evans K L, Warren P H, Gaston K J. 2005. Species-energy relationships at the macroecological scale: a review of the mechanisms. Biological Reviews, 80(1): 1-25.
Fricker G A, Wolf J A, Saatchi S S, et al. 2015. Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing. Ecological Applications, 25(7): 1776-1789.
Gairola S, Proche? ?, Rocchini D. 2013. High-resolution satellite remote sensing: a new frontier for biodiversity exploration in Indian Himalayan forests. International Journal of Remote Sensing, 34(6): 2006-2022.
Gamon J A, Surfus J S. 1999. Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 143(1): 105-117.
Gaston K J. 2000. Global patterns in biodiversity. Nature, 405(6783): 220-227.
Gillespie T W, Saatchi S, Pau S, et al. 2009. Towards quantifying tropical tree species richness in tropical forests. International Journal of Remote Sensing, 30(6): 1629-1634.
Gitelson A A, Merzlyak M N. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5): 689-692.
Gitelson A A, Zur Y, Chivkunova O B, et al. 2007. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75(3): 272-281.
Granitto P M, Furlanello C, Biasioli F, et al. 2006. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems, 83(2): 83-90.
Hashemi S A, Chai M M F, Bayat S. 2013. An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran. Arabian Journal of Geosciences, 6(9): 3363-3369.
Hortal J, Lobo J M. 2005. An ED-based protocol for optimal sampling of biodiversity. Biodiversity and Conservation, 14(12): 2913-2947.
Huete A. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309.
Huete A, Didan K, Miura T, et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1): 195-213.
Kaboli M, Guillaumet A, Prodon R. 2006. Avifaunal gradients in two arid zones of central Iran in relation to vegetation, climate, and topography. Journal of Biogeography, 33(1): 133-144.
Kalbi S, Fallah A, Hojjati S M. 2014. Using and comparing two nonparametric methods (CART and RF) and SPOT-HRG satellite data to predictive tree diversity distribution. Nusantara Bioscience, 6(1): 57-62.
Levin N, Shmida A, Levanoni O, et al. 2007. Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices. Diversity and Distributions, 13(6): 692-703.
Loarie S R, Joppa L N, Pimm S L, et al. 2007. Satellites miss environmental priorities. Trends in Ecology & Evolution, 22(12): 630-632.
Mohammadi J, Shataee S, Babanezhad M. 2011. Estimation of forest stand volume, tree density and biodiversity using Landsat ETM+data, comparison of linear and regression tree analyses. Procedia Environmental Sciences, 7: 299-304.
Oindo B O, Skidmore A K, de Salvo P. 2003. Mapping habitat and biological diversity in the Maasai Mara ecosystem. International Journal of Remote Sensing, 24(5): 1053-1069.
Pal M. 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1): 217-222.
Pinty B, Verstraete M M. 1992. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101(1): 15-20.
Regniers O, Bombrun L, Guyon D, et al. 2015. Wavelet-based texture features for the classification of age classes in a maritime pine forest. IEEE Geoscience and Remote Sensing Letters, 12(3): 621-625.
Rocchini D. 2007. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sensing of Environment, 111(4): 423-434.
Rocchini D, Balkenhol N, Carter G A, et al. 2010. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecological Informatics, 5(5): 318-329.
Rocha A V, Shaver G R. 2009. Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agricultural and Forest Meteorology, 149(9), 1560-1563.
Rouse W, Haas R H, Deering D W. 1974. Monitoring vegetation systems in the great Plains with ERTS. NASA Special Publication, 351: 309.
Saatchi S, Buermann W, ter Steege H, et al. 2008. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sensing of Environment, 112(5): 2000-2017.
Sripada R P, Heiniger R W, White J G, et al. 2006. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal, 98(4): 968-977.
Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150.
金毅. 2015. 冰雪灾害对古田山常绿阔叶林短期动态的影响——群落结构变化、幼苗更新与林窗干扰效应. 杭州:浙江大学博士学位论文.(Jin Y. 2015. Impacts of ice storm on the short-term dynamics of broad-leaved forest in Gutianshan: community structure alternation, seeding regeneration, and effects of forest gap disturbance.Hangzhou:PhD thesis of Zhejiang University.[in Chinese])
任学敏, 杨改河, 王得祥,等. 2012. 环境因子对巴山冷杉-糙皮桦混交林物种分布及多样性的影响. 生态学报, 32(2), 605-613.(Ren X M, Yang G H, Wang D X, et al. 2012. Effects of environmental factors on species distribution and diversity in an Abiesfargesii-Betula utilis mixed forest.Acta Ecologica Sinica, 32(2): 605-613. [in Chinese])
解潍嘉, 黄侃, 李瑞平,等. 2015. 应用高分辨率卫星数据估算阔叶红松林乔木多样性. 北京林业大学学报, 37(3):24-30.(Xie W J, Huang K, Li R P, et al. 2015. Applying high-resolution satellite images to estimate tree diversity of mixed broadleaf-korean pine forest.Journal of Beijing Forestry University, 37(3):24-30.[in Chinese])
徐文婷, 吴炳方. 2005. 遥感用于森林生物多样性监测的进展. 生态学报, 25(5):1199-1204.(Xu W T, Wu B F. 2005. Progress on measuring forest biodiversity with remote sensing technique. Acta Ecologica Sinica,25(5):1199-1204.[in Chinese])
徐学红, 于明坚, 胡正华,等. 2005. 浙江古田山自然保护区甜槠种群结构与动态. 生态学报, 25(3):645-653.(Xu X H, Yu M J, Hu Z H, et al. 2005. The structure and dynamics of Castanopsis eyrei population in Gutian Mountain Natural Reserve in Zhejiang, East China.Acta Ecologica Sinica,25(3):645-653.[in Chinese])
于明坚, 胡正华, 余建平,等. 2001. 浙江古田山自然保护区森林植被类型. 浙江大学学报:农业与生命科学版, 27(4):375-380.(Yu M J, Hu Z H, Yu J P, et al. 2001.Forest vegetation types in Gutianshan Natural Reserve in Zhejiang. Journal of Zhejiang University:Agriculture and Life Sciences, 27(4):375-380.[in Chinese])
祝燕, 赵谷风, 张俪文,等. 2008. 古田山中亚热带常绿阔叶林动态监测样地——群落组成与结构. 植物生态学报, 32(2):262-273.(Zhu Y, Zhao G F, Zhang L W, et al. 2008. Community composition and structure of Gutianshan forest dynamic plot in a mid-subtropical evergreen broad-leaved forest, east-China. Journal of Plant Ecology, 32(2):262-273.[in Chinese])
Bai F, Sang W, Axmacher J. 2011. Forest vegetation responses to climate and environmental change: a case study from Changbai Mountain, NE China. Forest Ecology and Management, 262(11): 2052-2060.
Belgiu M, Drgut L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31.
Birth G S, McVey G R. 1968. Measuring the color of growing turf with a reflectance spectrophotometer1. Agronomy Journal, 60(6): 640-643.
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32.
Cayuela L, Benayas J M R, Justel A, et al. 2006. Modelling tree diversity in a highly fragmented tropical montane landscape. Global Ecology and Biogeography, 15(6): 602-613.
Chambers J Q, Asner G P, Morton D C, et al. 2007. Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends in Ecology & Evolution, 22(8): 414-423.
Dalmayne J, M?ckel T, Prentice H C, et al. 2013. Assessment of fine-scale plant species beta diversity using World View-2 satellite spectral dissimilarity. Ecological Informatics, 18: 1-9.
Evans K L, Warren P H, Gaston K J. 2005. Species-energy relationships at the macroecological scale: a review of the mechanisms. Biological Reviews, 80(1): 1-25.
Fricker G A, Wolf J A, Saatchi S S, et al. 2015. Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing. Ecological Applications, 25(7): 1776-1789.
Gairola S, Proche? ?, Rocchini D. 2013. High-resolution satellite remote sensing: a new frontier for biodiversity exploration in Indian Himalayan forests. International Journal of Remote Sensing, 34(6): 2006-2022.
Gamon J A, Surfus J S. 1999. Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 143(1): 105-117.
Gaston K J. 2000. Global patterns in biodiversity. Nature, 405(6783): 220-227.
Gillespie T W, Saatchi S, Pau S, et al. 2009. Towards quantifying tropical tree species richness in tropical forests. International Journal of Remote Sensing, 30(6): 1629-1634.
Gitelson A A, Merzlyak M N. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5): 689-692.
Gitelson A A, Zur Y, Chivkunova O B, et al. 2007. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75(3): 272-281.
Granitto P M, Furlanello C, Biasioli F, et al. 2006. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems, 83(2): 83-90.
Hashemi S A, Chai M M F, Bayat S. 2013. An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran. Arabian Journal of Geosciences, 6(9): 3363-3369.
Hortal J, Lobo J M. 2005. An ED-based protocol for optimal sampling of biodiversity. Biodiversity and Conservation, 14(12): 2913-2947.
Huete A. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309.
Huete A, Didan K, Miura T, et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1): 195-213.
Kaboli M, Guillaumet A, Prodon R. 2006. Avifaunal gradients in two arid zones of central Iran in relation to vegetation, climate, and topography. Journal of Biogeography, 33(1): 133-144.
Kalbi S, Fallah A, Hojjati S M. 2014. Using and comparing two nonparametric methods (CART and RF) and SPOT-HRG satellite data to predictive tree diversity distribution. Nusantara Bioscience, 6(1): 57-62.
Levin N, Shmida A, Levanoni O, et al. 2007. Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices. Diversity and Distributions, 13(6): 692-703.
Loarie S R, Joppa L N, Pimm S L, et al. 2007. Satellites miss environmental priorities. Trends in Ecology & Evolution, 22(12): 630-632.
Mohammadi J, Shataee S, Babanezhad M. 2011. Estimation of forest stand volume, tree density and biodiversity using Landsat ETM+data, comparison of linear and regression tree analyses. Procedia Environmental Sciences, 7: 299-304.
Oindo B O, Skidmore A K, de Salvo P. 2003. Mapping habitat and biological diversity in the Maasai Mara ecosystem. International Journal of Remote Sensing, 24(5): 1053-1069.
Pal M. 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1): 217-222.
Pinty B, Verstraete M M. 1992. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101(1): 15-20.
Regniers O, Bombrun L, Guyon D, et al. 2015. Wavelet-based texture features for the classification of age classes in a maritime pine forest. IEEE Geoscience and Remote Sensing Letters, 12(3): 621-625.
Rocchini D. 2007. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sensing of Environment, 111(4): 423-434.
Rocchini D, Balkenhol N, Carter G A, et al. 2010. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecological Informatics, 5(5): 318-329.
Rocha A V, Shaver G R. 2009. Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agricultural and Forest Meteorology, 149(9), 1560-1563.
Rouse W, Haas R H, Deering D W. 1974. Monitoring vegetation systems in the great Plains with ERTS. NASA Special Publication, 351: 309.
Saatchi S, Buermann W, ter Steege H, et al. 2008. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sensing of Environment, 112(5): 2000-2017.
Sripada R P, Heiniger R W, White J G, et al. 2006. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal, 98(4): 968-977.
Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150.
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