浏览全部资源
扫码关注微信
纸质出版日期： 2023-09-07 ，
扫 描 看 全 文
李晓雅，田昕，段涛，曹晓明，杨凯捷，卢琦，王锋.2023.融合无人机和卫星影像的温带疏林草原木本和草本植物覆盖度遥感估算.遥感学报，27（9）： 2139-2152
Li X Y，Tian X，Duan T，Cao X M，Yang K J，Lu Q and Wang F. 2023. Estimation of fractional woody and herbaceous vegetation cover in Temperate Sparse Forest Grassland using fusion of UAV and Satellite imagery. National Remote Sensing Bulletin， 27（9）：2139-2152
分布于中国半干旱区的温带疏林草原生态系统，是森林到草原之间过渡的一种生态系统类型，是在独特的气候和地形条件下发育于沙地的地带性顶级植被群落。疏林草原具有乔木、灌木和草本植被混合生长的特点、空间异质性高。植被遥感监测难度大，至今仍是全球范围内最不精确的土地覆盖类型。如何兼顾精度和范围，实现温带疏林草原区域尺度不同类型植物生长状态监测是当前干旱区植被遥感的热点和难点。本研究基于机器学习算法，通过近地面无人机遥感观测平台获取地表植被类型信息构建训练数据集，结合高分辨率卫星影像，建立疏林草原木本、草本植物覆盖度估算模型，实现了由无人机到卫星的温带疏林草原木本和草本植物覆盖度的同步估算，并比较了两种高分辨率卫星影像对疏林草原木本和草本植被覆盖度估算的差异。研究结果表明：（1）利用无人机近地遥感影像能够准确分类地表覆盖类型，为区域温带疏林草原木本、草本植物覆盖度估算模型提供大量精确的训练样本数据；（2）基于机器学习算法，利用高分六号（GF-6）和哨兵二号（Sentinel-2）两种高分辨率卫星影像建立的疏林草原覆盖度模型均可较好的实现木本、草本植物覆盖度的估算。其中，基于GF-6的疏林草原木本、草本覆盖度估计结果与无人机观测值的决定系数分别为0.72和0.66，均方根误差分别为6.76%和10.69%，估算精度分别为46.31%和77.88%；基于Sentinel-2的疏林草原木本、草本覆盖度估计结果与无人机观测值的决定系数分别为0.72和0.81，均方根误差分别为6.53%和8.20%，估算精度分别为54.30%和83.17%；（3）基于Sentinel-2卫星影像的疏林草原木本和草本植物覆盖度估算精度稍高于GF-6卫星，基于两个卫星影像的草本植物覆盖度的估算精度都要显著高于木本植物。本研究为实现疏林草原木本植物和草本植物覆盖度由景观尺度扩展到区域尺度的估测提供了新的思路，从无人机到卫星跨尺度协同观测的方法能够为区域温带疏林草原不同生活型植物生长状况监测提供有效的方法支撑，未来基于多时相的高分辨率卫星数据可进一步实现区域尺度温带疏林草原木本植物和草本植物的动态监测。
China’s temperate sparse forest grassland is a transition ecosystem between forest and grassland and is the top-level ecosystem that evolves under the unique climate and topography of northern China. Sparse forest grassland is characterized by mixed woody and herbaceous vegetation
which are difficult to directly distinguish by remote sensing even when using high-spatial-resolution satellite data. Consequently
mapping Fractional Woody and Herbaceous Vegetation Cover (FWHVC) in this ecosystem is challenging. How to precisely monitor the growth status of woody and herbaceous vegetation in sparse forest grassland on a regional scale is a popular and difficult topic in vegetation remote sensing in dryland.
This study proposed a novel method of FWHVC estimation in temperate sparse forest grassland based on Unmanned Aircraft Vehicle (UAV) aerial images and high-spatial-resolution satellite images (GF-6 and Sentinel-2) via a machine learning algorithm. Training datasets of FWHVC were derived from a very-high-resolution aerial image (2.41 cm/pixel). Meanwhile
this study compared the results of FWHVC estimated from GF-6 and Sentinel-2 images.
The results were as follows (1) The UAV platform could precisely capture the land cover type and provide a large number of reliable training datasets of FWHVC. (2) FWHVC could be estimated well on a regional scale based on both GF-6 and Sentinel-2 high-resolution satellite data through the machine learning algorithm. The FWHVCs derived from GF-6 images and UAV aerial images had determination coefficients
R
2
of 0.72 and 0.66
root-mean-square errors (RMSEs) of 6.76% and 10.69%
and estimated accuracies (EAs) of 46.31% and 77.88%
respectively. The FWHVCs derived from Sentinel-2 images and UAV aerial images had determination coefficients
R
2
of 0.72 and 0.81
RMSEs of 6.53% and 8.20%
and EAs of 54.30% and 83.17%
respectively. (3) The EA of the FWHVC estimated from Sentinel-2 was slightly better than that of GF-6. Meanwhile
the EA of fractional herbaceous vegetation cover estimation was higher than that of woody vegetation cover estimation for both satellite images.
This paper provides a new way to estimate FWHVC in temperate sparse forest grassland on a regional scale by using multisource remote sensing data and a machine learning algorithm. The multiscale approach could provide new methodological support to accurately monitor woody and herbaceous vegetation cover in temperate sparse forest grassland. In the future
FWHVC in sparse forest grassland can be monitored dynamically by utilizing long-term and high-spatial-resolution satellite remote sensing data on a regional scale.
遥感榆树疏林无人机高分六号哨兵二号随机森林分类与回归树
remote sensingElm sparse forest grasslandUnmanned Aerial Vehicle (UAV)GF-6Sentinel-2random forestClassification and Regression Tree(CART)
Adams J B, Smith M O and Johnson P E. 1986. Spectral mixture modeling: a new analysis of rock and soil types at the Viking lander 1 site. Journal of Geophysical Research: Solid Earth, 91(B8): 8098-8112 [DOI: 10.1029/JB091iB08p08098http://dx.doi.org/10.1029/JB091iB08p08098]
Brandt M, Hiernaux P, Tagesson T, Verger A, Rasmussen K, Diouf A A, Mbow C, Mougin E and Fensholt R. 2016. Woody plant cover estimation in drylands from Earth Observation based seasonal metrics. Remote Sensing of Environment, 172: 28-38 [DOI: 10.1016/j.rse.2015.10.036http://dx.doi.org/10.1016/j.rse.2015.10.036]
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32 [DOI: 10.1023/A:1010933404324http://dx.doi.org/10.1023/A:1010933404324]
Breiman L, Friedman J, Olshen R A and Stone C J. 1984. Classification and Regression Trees. Belmont, CA: Wadsworth International Group
Cai D, Li W L, Zhu Y L, Su W L, Zhu G F, Zhao Z G and Wei W. 2019. Remote sensing monitoring of vegetation coverage in Gannan grassland based on multi-source data. Journal of Lanzhou University (Natural Sciences), 55(3): 373-379
蔡栋, 李文龙, 朱亚莉, 苏文亮, 朱高峰, 赵志刚, 魏巍. 2019. 基于多源数据的甘南草地植被覆盖度遥感监测研究. 兰州大学学报(自然科学版), 55(3): 373-379 [DOI: 10.13885/j.issn.0455-2059.2019.03.013http://dx.doi.org/10.13885/j.issn.0455-2059.2019.03.013]
Cai Z L, Miao Z H, Chang X, Liu Y H, Hao G and He L T. 2019. Research on grassland fractional vegetation coverage inversion method based on drone large quadrat data and domestic satellite. Acta Agrestia Sinica, 27(5): 1431-1440
蔡宗磊, 苗正红, 常雪, 刘艳慧, 郝刚, 何龙涛. 2019. 基于无人机大样方数据及国产卫星反演草地植被覆盖度方法研究. 草地学报, 27(5): 1431-1440 [DOI: 10.11733/j.issn.1007-0435.2019.05.040http://dx.doi.org/10.11733/j.issn.1007-0435.2019.05.040]
Chen Q, Li X S, Xiu X M and Yang G B. 2019. Large scale shrub coverage mapping of sandy land at 30 m resolution based on Google Earth Engine and machine learning. Acta Ecologica Sinica, 39(11): 4056-4069
陈黔, 李晓松, 修晓敏, 杨广斌. 2019. 基于Google Earth Engine与机器学习的大尺度30 m分辨率沙地灌木覆盖度估算. 生态学报, 39(11): 4056-4069 [DOI: 10.5846/stxb201811292601http://dx.doi.org/10.5846/stxb201811292601]
Cui Y L, Sun H, Wang G X, Li C J and Xu X Y. 2019. A probability-based spectral unmixing analysis for mapping percentage vegetation cover of arid and semi-arid areas. Remote Sensing, 11(24): 3038 [DOI: 10.3390/rs11243038http://dx.doi.org/10.3390/rs11243038]
DeFries R S, Achard F, Brown S, Herold M, Murdiyarso D, Schlamadinger B and de Souza CJr. 2007. Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environmental Science and Policy, 10(4): 385-394 [DOI: 10.1016/j.envsci.2007.01.010http://dx.doi.org/10.1016/j.envsci.2007.01.010]
González-Roglich M and Swenson J J. 2016. Tree cover and carbon mapping of Argentine savannas: scaling from field to region. Remote Sensing of Environment, 172: 139-147 [DOI: 10.1016/j.rse.2015.11.021http://dx.doi.org/10.1016/j.rse.2015.11.021]
Guo Q H, Hu T Y, Ma Q, Xu K X, Yang Q L, Sun Q H, Li Y M and Su Y J. 2020. Advances for the new remote sensing technology in ecosystem ecology research. Chinese Journal of Plant Ecology, 44(4): 418-435
郭庆华, 胡天宇, 马勤, 徐可心, 杨秋丽, 孙千惠, 李玉美, 苏艳军. 2020. 新一代遥感技术助力生态系统生态学研究. 植物生态学报, 44(4): 418-435 [DOI: 10.17521/cjpe.2019.0206http://dx.doi.org/10.17521/cjpe.2019.0206]
Han D, Wang H Z, Zheng B Y and Wang F. 2018. Vegetation type classification and fractional vegetation coverage estimation for an open elm (Ulmus pumila) woodland ecosystem during a growing season based on an unmanned aerial vehicle platform coupled with decision tree algorithms. Acta Ecologica Sinica, 38(18): 6655-6663
韩东, 王浩舟, 郑邦友, 王锋. 2018. 基于无人机和决策树算法的榆树疏林草原植被类型划分和覆盖度生长季动态估计. 生态学报, 38(18): 6655-6663 [DOI: 10.5846/stxb2018033694http://dx.doi.org/10.5846/stxb2018033694]
Haralick R M. 1979. Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5): 786-804 [DOI: 10.1109/PROC.1979.11328http://dx.doi.org/10.1109/PROC.1979.11328]
Harris A, Carr A S and Dash J. 2014. Remote sensing of vegetation cover dynamics and resilience across southern Africa. International Journal of Applied Earth Observation and Geoinformation, 28: 131-139 [DOI: 10.1016/j.jag.2013.11.014http://dx.doi.org/10.1016/j.jag.2013.11.014]
Herrmann S M, Wickhorst A J and Marsh S E. 2013. Estimation of tree cover in an agricultural parkland of Senegal using rule-based regression tree modeling. Remote Sensing, 5(10): 4900-4918 [DOI: 10.3390/rs5104900http://dx.doi.org/10.3390/rs5104900]
Higginbottom T P, Symeonakis E, Meyer H and van der Linden S. 2018. Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 88-102 [DOI: 10.1016/j.isprsjprs.2018.02.010http://dx.doi.org/10.1016/j.isprsjprs.2018.02.010]
Hunter F D L, Mitchard E T A, Tyrrell P and Russell S. 2020. Inter-seasonal time series imagery enhances classification accuracy of grazing resource and land degradation maps in a savanna ecosystem. Remote Sensing, 12(1): 198 [DOI: 10.3390/rs12010198http://dx.doi.org/10.3390/rs12010198]
James G, Witten D, Hastie T and Tibshirani R. 2013. An Introduction to Statistical Learning. New York: Springer [DOI: 10.1007/978-1-4614-7138-7http://dx.doi.org/10.1007/978-1-4614-7138-7]
Jia K, Yao Y J, Wei X Q, Gao S, Jiang B and Zhao X. 2013. A review on fractional vegetation cover estimation using remote sensing. Advances in Earth Science, 28(7): 774-782
贾坤, 姚云军, 魏香琴, 高帅, 江波, 赵祥. 2013. 植被覆盖度遥感估算研究进展. 地球科学进展, 28(7): 774-782 [DOI: 10.11867/j.issn.1001-8166.2013.07.0774http://dx.doi.org/10.11867/j.issn.1001-8166.2013.07.0774]
Kattenborn T, Lopatin J, Förster M, Braun A C and Fassnacht F E. 2019. UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment, 227: 61-73 [DOI: 10.1016/j.rse.2019.03.025http://dx.doi.org/10.1016/j.rse.2019.03.025]
Latifovic R and Olthof I. 2004. Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote Sensing of Environment, 90(2): 153-165 [DOI: 10.1016/j.rse.2003.11.016http://dx.doi.org/10.1016/j.rse.2003.11.016]
Li X S, Gao Z H, Li Z Y, Bai L N and Wang B Y. 2010. Estimation of sparse vegetation coverage in arid region based on hyperspectral mixed pixel decomposition. Chinese Journal of Applied Ecology, 21(1): 152-158
李晓松, 高志海, 李增元, 白黎娜, 王琫瑜. 2010. 基于高光谱混合像元分解的干旱地区稀疏植被覆盖度估测. 应用生态学报, 21(1): 152-158 [DOI: 10.13287/j.1001-9332.2010.0041http://dx.doi.org/10.13287/j.1001-9332.2010.0041]
Liao X H, Xiao Q and Zhang H. 2019. UAV remote sensing: popularization and expand application development trend. Journal of Remote Sensing, 23(6): 1046-1052
廖小罕, 肖青, 张颢. 2019. 无人机遥感: 大众化与拓展应用发展趋势. 遥感学报, 23(6): 1046-1052 [DOI: 10.11834/jrs.20199422http://dx.doi.org/10.11834/jrs.20199422]
Liu L F, Chen Y H and Li J. 2003. Texture analysis methods used in remote sensing images. Remote Sensing Technology and Application, 18(6): 441-447
刘龙飞, 陈云浩, 李京. 2003. 遥感影像纹理分析方法综述与展望. 遥感技术与应用, 18(6): 441-447 [DOI: 10.11873/j.issn.1004-0323.2003.6.441http://dx.doi.org/10.11873/j.issn.1004-0323.2003.6.441]
Ludwig M, Morgenthal T, Detsch F, Higginbottom T P, Lezama Valdes M, Nauß T and Meyer H. 2019. Machine learning and multi-sensor based modelling of woody vegetation in the Molopo Area, South Africa. Remote Sensing of Environment, 222: 195-203 [DOI: 10.1016/j.rse.2018.12.019http://dx.doi.org/10.1016/j.rse.2018.12.019]
Prasad A M, Iverson L R and Liaw A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9(2): 181-199 [DOI: 10.1007/s10021-005-0054-1http://dx.doi.org/10.1007/s10021-005-0054-1]
Reinhard E, Adhikhmin M, Gooch B and Shirley P. 2001. Color transfer between images. IEEE Computer Graphics and Applications, 21(4): 34-41 [DOI: 10.1109/38.946629http://dx.doi.org/10.1109/38.946629]
Shi Y F, Jin Y H and Jin L S. 2011. Review on elm sparse forest grassland in China. Technology of Soil and Water Conservation, (2): 32-35
史宇飞, 金永焕, 金兰淑. 2011. 国内榆树疏林研究现状. 水土保持应用技术, (2): 32-35 [DOI: 10.3969/j.issn.1673-5366.2011.02.12http://dx.doi.org/10.3969/j.issn.1673-5366.2011.02.12]
Tian F, Brandt M, Liu Y Y, Rasmussen K and Fensholt R. 2017. Mapping gains and losses in woody vegetation across global tropical drylands. Global Change Biology, 23(4): 1748-1760 [DOI: 10.1111/gcb.13464http://dx.doi.org/10.1111/gcb.13464]
Wan C J, Wu X D and Lin X W. 2019. Impact of spatial and temporal scales of remote sensing data on the spatiotemporal change in geographic elements. Journal of Remote Sensing, 23(6): 1064-1077
万昌君, 吴小丹, 林兴稳. 2019. 遥感数据时空尺度对地理要素时空变化分析的影响. 遥感学报, 23(6): 1064-1077 [DOI: 10.11834/jrs.20198327http://dx.doi.org/10.11834/jrs.20198327]
Wang F, Pan X B, Gerlein-Safdi C, Cao X M, Wang S, Gu L H, Wang D F and Lu Q. 2020. Vegetation restoration in Northern China: a contrasted picture. Land Degradation and Development, 31(6): 669-676 [DOI: 10.1002/ldr.3314http://dx.doi.org/10.1002/ldr.3314]
Wang H Z, Han D, Mu Y, Jiang L N, Yao X L, Bai Y F, Lu Q and Wang F. 2019. Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform. Agricultural and Forest Meteorology, 278: 107665 [DOI: 10.1016/j.agrformet.2019.107665http://dx.doi.org/10.1016/j.agrformet.2019.107665]
Yang J, Weisberg P J and Bristow N A. 2012. Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: comparison of vegetation indices and spectral mixture analysis. Remote Sensing of Environment, 119: 62-71 [DOI: 10.1016/j.rse.2011.12.004http://dx.doi.org/10.1016/j.rse.2011.12.004]
Yu S L and Chen H W. 2007. Characteristics and formation causes of temperate sparse forest grassland ecosystem in Inner Mongolia Plateau. Chinese Journal of Ecology, 26(4): 549-554
于顺利, 陈宏伟. 2007. 内蒙古高原温带稀树草原生态系统特征与成因. 生态学杂志, 26(4): 549-554
Yu S L. 2011. Geographical distribution, ecological position, and formation causes of temperate zone sparse forest in China. Science Technology Review, 29(25): 26-29
于顺利. 2011. 中国温带疏林的地理分布、生态地位及成因. 科技导报, 29(25): 26-29 [DOI: 10.3981/j.issn.1000-7857.2011.25.003http://dx.doi.org/10.3981/j.issn.1000-7857.2011.25.003]
Yue J, Mu G J, Tang Z H, Yang X F, Lin Y C and Xu L S. 2020. Remote sensing estimation models for vegetation coverage in desert regions of Xinjiang based on NDVI. Arid Land Geography, 43(1): 153-160
岳健, 穆桂金, 唐自华, 杨雪峰, 林永崇, 徐立帅. 2020. 基于NDVI的新疆荒漠地区植被覆盖度遥感估算经验模型研究. 干旱区地理, 43(1): 153-160 [DOI: 10.12118/j.issn.1000-6060.2020.01.18http://dx.doi.org/10.12118/j.issn.1000-6060.2020.01.18]
Zhang S, Zhao Y J, Bai Y F, Yang L and Sun Z Y. 2019. Remote sensing identification of grassland shrubs using low-altitude unmanned aerial vehicles. Tropical Geography, 39(4): 512-520
张顺, 赵玉金, 白永飞, 杨龙, 孙中宇. 2019. 基于低空无人机的草原灌丛遥感辨识方法. 热带地理, 39(4): 512-520 [DOI: 10.13284/j.cnki.rddl.003147http://dx.doi.org/10.13284/j.cnki.rddl.003147]
Zhang W M, Brandt M, Wang Q, Prishchepov A V, Tucker C J, Li Y M, Lyu H and Fensholt R. 2019. From woody cover to woody canopies: how Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas. Remote Sensing of Environment, 234: 111465 [DOI: 10.1016/j.rse.2019.111465http://dx.doi.org/10.1016/j.rse.2019.111465]
Zhou P C, Cheng G, Yao X W and Han J W. 2021. Machine learning paradigms in high-resolution remote sensing image interpretation. National Remote Sensing Bulletin, 25(1): 182-197
周培诚, 程塨, 姚西文, 韩军伟. 2021. 高分辨率遥感影像解译中的机器学习范式. 遥感学报, 25(1): 182-197 [DOI: 10.11834/jrs.20210164http://dx.doi.org/10.11834/jrs.20210164]
相关作者
相关机构
京公网安备11010802024621