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Estimation of leaf area index from high resolution ZY-3 satellite imagery in a catchment dominated by Larix principis-rupprechtii, northern China

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Abstract

Leaf area index (LAI) is a key factor that determines a forest ecosystem’s net primary production and energy exchange between the atmosphere and land surfaces. LAI can be measured in many ways, but there has been little research to compare LAI estimated by different methods. In this study, we compared the LAI results from two different approaches, i.e., the dimidiate pixel model (DPM) and an empirical statistic model (ESM) using ZY-3 high-accuracy satellite images validated by field data. We explored the relationship of LAI of Larix principis-rupprechtii Mayr plantations with topographic conditions. The results show that DPM improves the simulation of LAI (r = 0.86, RMSE = 0.57) compared with ESM (r = 0.62, RMSE = 0.79). We further concluded that elevation and slope significantly affect the distribution of LAI. The maximum peak of LAI appeared at an aspect of east and southeast at an elevation of 1700–2000 m. Our results suggest that ZY-3 can satisfy the needs of quantitative monitoring of leaf area indices in small-scale catchment areas. DPM provides a simple and accurate method to obtain forest vegetation parameters in the case of non-ground measurement points.

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References

  • Asner GP, Scurlock J, Hicke JA (2003) Global synthesis of leaf area index observations: implications for ecological and remote sensing studies. Global Ecol Biogeogr 12(3):191–205

    Article  Google Scholar 

  • Bicheron P, Marc L (1999) Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sens Environ 67:251–266

    Article  Google Scholar 

  • Chason JW, Baldocchi DD, Huston MA (1991) A comparison of direct and indirect methods for estimating forest canopy leaf area. Agr Forest Meteorol 57(1–3):107–128

    Article  Google Scholar 

  • Chen JM, Black TA (1992) Defining leaf area index for non flat leaves. Plant, Cell Environ 15:421–429

    Article  Google Scholar 

  • Chen JM, Cihlar J (1996) Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens Environ 55(2):153–162

    Article  Google Scholar 

  • Chen X, Vierling L, Deering D, Conley A (2005) Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study. Int J Remote Sens 26(24):5433–5451

    Article  Google Scholar 

  • Colombo R, Bellingeri D, Fasolini D, Marino CM (2003) Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sens Environ 86(1):120–131

    Article  Google Scholar 

  • De Bruijn A, Gustafson EJ, Sturtevant BR, Foster JR, Miranda BR, Lichti NI, Jacobs DF (2014) Toward more robust projections of forest landscape dynamics under novel environmental conditions: embedding PnET within LANDIS-II. Ecol Model 287:44–57

    Article  Google Scholar 

  • Fang HL, Liang SL (2003) Retrieving leaf area index with a neural network method: simulation and validation. IEEE T Geosci Remote 41:2052–2062

    Article  Google Scholar 

  • Fang L, Wang M, Li D, Pan J (2014) CPU/GPU near real-time preprocessing for ZY-3 satellite images: relative radiometric correction, MTF compensation, and geocorrection. Isprs J Photogramm 87:229–240

    Article  Google Scholar 

  • Fassnacht KS, Gower ST, MacKenzie MD, Nordheim EV, Lillesand TM (1997) Estimating the leaf area index of north central Wisconsin forests using the Landsat Thematic Mapper. Remote Sens Environ 2(61):229–245

    Article  Google Scholar 

  • Freeman A, Durden SL (1998) A three-component scattering model for polarimetric SAR data. Ieee T Geosci Remote 36:963–973

    Article  Google Scholar 

  • Ganguly S, Schull MA, Samanta A, Shabanov V, Milesi C, Nemani R (2008) Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: theory. Remote Sens Environ 112:4333–4343

    Article  Google Scholar 

  • Gao S, Niu Z, Huang N, Hou X (2013) Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2. Int J Appl Earth Obs 24:1–8

    Article  Google Scholar 

  • Gao P, Niu X, Wang B, Zheng Y (2015) Land use changes and its driving forces in hilly ecological restoration area based on gis and rs of northern china. Scientific Reports 5:11038

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Houborg R, Boegh E (2008) Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data. Remote Sens Environ 112(1):186–202

    Article  Google Scholar 

  • Houborg R, McCabe M, Cescatti A, Gao F, Schull M, Gitelson A (2015) Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC). Remote Sens Environ 159:203–221

    Article  Google Scholar 

  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1):195–213

    Article  Google Scholar 

  • Kitaoka S, Koike T (2004) Invasion of broad-leaf tree species into a larch plantation: seasonal light environment, photosynthesis and nitrogen allocation. Physiol Plantarum 121(4):604–611

    Article  CAS  Google Scholar 

  • Koetz B, Baret F, Poilve H, Hill J (2005) Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sens Environ 95:115–124

    Article  Google Scholar 

  • Li X, Zhang Y, Bao Y, Luo J, Jin X, Xu X, Song X, Yang G (2014) Exploring the best hyperspectral features for LAI estimation using partial least squares regression. Remote Sensing 6(7):6221–6241

    Article  Google Scholar 

  • Liu Q, Liang SL, Xiao Z, Fang H (2014) Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data. Remote Sens Environ 145:25–37

    Article  Google Scholar 

  • Los SO (1999) A global multi-year biophysical land surface data set from NOAA AVHRR data. IEEE Int Geosci Remote Sensing 2:1087–1089

    Google Scholar 

  • Majasalmi T, Rautiainen M, Stenberg P, Lukeš P (2013) An assessment of ground reference methods for estimating LAI of boreal forests. Forest Ecol Manag 292:10–18

    Article  Google Scholar 

  • Meier IC, Leuschner C (2008) Leaf size and leaf area index in Fagus sylvatica forests: competing effects of precipitation, temperature, and nitrogen availability. Ecosystems 11(5):655–669

    Article  CAS  Google Scholar 

  • Moran MS, Maas SJ, Pinter PJ (1995) Combining remote sensing and modeling for estimating surface evaporation and biomass production. Remote Sens Rev 3(12):335–353

    Article  Google Scholar 

  • Ni W, Sun G, Ranson KJ, Pang Y, Zhang Z, Yao W (2015) Extraction of ground surface elevation from ZY-3 winter stereo imagery over deciduous forested areas. Remote Sens Environ 159:194–202

    Article  Google Scholar 

  • Novelli A, Tarantino E, Fratino U, Iacobellis V, Romano G, Gentile F (2016) A data fusion algorithm based on the Kalman filter to estimate leaf area index evolution in durum wheat by using field measurements and MODIS surface reflectance data. Remote Sens Lett 7(5):476–484

    Article  Google Scholar 

  • Olivas PC, Oberbauer SF, Clark DB, Clark DA, Ryan MG, O’Brien JJ, Ordonez H (2013) Comparison of direct and indirect methods for assessing leaf area index across a tropical rain forest landscape. Agr Forest Meteorol 177:110–116

    Article  Google Scholar 

  • Pfeifer M, Gonsamo A, Disney M, Pellikka P, Marchant R (2012) Leaf area index for biomes of the Eastern Arc Mountains: landsat and SPOT observations along precipitation and altitude gradients. Remote Sens Environ 118:103–115

    Article  Google Scholar 

  • Qi Y, Li F, Liu Z, Jin G (2014) Impact of understorey on overstorey leaf area index estimation from optical remote sensing in five forest types in northeastern China. Agr Forest Meteorol 198:72–80

    Article  Google Scholar 

  • Rouse JW, Haas RH, Hell JA, Deering DW, Harlan JC (1974) Monitoring the vernal advancement of retrogradation of natural vegetation, NASA/GSFC, Type III, Final Report, Greenbelt, MD, p. 371

  • Sainte-Marie J, Saint-André L, Nouvellon Y, Laclau JP, Roupsard O, le Maire G, Delpierre N, Henrot A, Barrandon M (2014) A new probabilistic canopy dynamics model (SLCD) that is suitable for evergreen and deciduous forest ecosystems. Ecol Model 290:121–133

    Article  Google Scholar 

  • Schleppi P, Thimonier A, Walthert L (2011) Estimating leaf area index of mature temperate forests using regressions on site and vegetation data. Forest Ecol Manag 261(3):601–610

    Article  Google Scholar 

  • Schlerf M, Atzberger C (2006) Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sens Environ 100(3):281–294

    Article  Google Scholar 

  • Soudani K, Francois C, le Maire G, Le Dantec V, Dufrene E (2006) Comparative analysis of IKONOS, SPOT, and ETM + data leaf area index estimation in temperature coniferous and deciduous forest stands. Remote Sens Environ 103(4):497

    Article  Google Scholar 

  • Stocker TF, Qin D, Plattner G, Tignor MM, Al E (2013) IPCC. Cambridge University Press, New York

    Google Scholar 

  • Strozzi T, Kääb A, Frauenfelder R (2004) Detecting and quantifying mountain permafrost creep from in situ, airborne and spaceborne remote sensing methods. Int J Remote Sens 25:2919–2931

    Article  Google Scholar 

  • Tang H, Brolly M, Zhao F, Strahler AH, Schaaf CL, Ganguly S, Zhang G, Dubayah R (2014) Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: a case study in Sierra National Forest, CA. Remote Sens Environ 143:131–141

    Article  Google Scholar 

  • Taugourdeau S, le Maire G, Avelino J, Jones JR, Ramirez LG, Jara Quesada M, Charbonnier F, Gómez-Delgado F, Harmand J, Rapidel B, Vaast P, Roupsard O (2014) Leaf area index as an indicator of ecosystem services and management practices: an application for coffee agroforestry. Agric Ecosys Environ 192:19–37

    Article  Google Scholar 

  • Tillack A, Clasen A, Kleinschmit B, Förster M (2014) Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices. Remote Sens Environ 141:52–63

    Article  Google Scholar 

  • Toda M, Yokozawa M, Emori S, Hara T (2010) More asymmetric tree competition brings about more evapotranspiration and less runoff from the forest ecosystems: a simulation study. Ecol Model 221(24):2887–2898

    Article  Google Scholar 

  • Verrelst J, Rivera JP, Veroustraete F, Muñoz-Marí J, Clevers JG, Camps-Valls G, Moreno J (2015) Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods–A comparison. Isprs J Photogramm 108:260–272

    Article  Google Scholar 

  • Verstraete MM, Pinty B (1996) Designing optimal spectral indexes for remote sensing applications. Ieee T Geosci Remote 34:1254–1265

    Article  Google Scholar 

  • Verstraete MM, Pinty B, Dickinson RE (1990) A physical model of the bidirectional reflectance of vegetation canopies: 1 Theory. J Geophys Res 95(D8):11755–11765

    Article  Google Scholar 

  • Viana H, Aranha J, Lopes D, Cohen WB (2012) Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecol Model 226:22–35

    Article  Google Scholar 

  • Vina A, Chen X, Yang W, Liu W, Li Y, Ouyang Z, Liu J (2013) Improving the efficiency of conservation policies with the use of surrogates derived from remotely sensed and ancillary data. Ecol Indic 26:103–111

    Article  Google Scholar 

  • Wang Q, Adiku S, Tenhunen J, Granier A (2005) On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens Environ 94(2):244–255

    Article  Google Scholar 

  • Wang T, Zhang G, Li D, Tang X, Jiang Y, Pan H, Zhu X, Fang C (2014) Geometric accuracy validation for ZY-3 satellite imagery. IEEE Geosci Remote Sens Lett 11(6):1168–1171

    Article  Google Scholar 

  • Wang X, Bi H, Song Q, Lu S (2015) Influence of forest coverage on basin runoff in China’s loess plateau. Pol J Environ Stud 24(2):743–750

    Google Scholar 

  • Watson D (1947) Comparative physiological studies in the growth of field crops I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Ann Bot-London 11(1):41–76

    Article  CAS  Google Scholar 

  • Weiss M, Baret F, Smith GJ, Jonckheere I, Coppin P (2004) Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling. Agr Forest Meteorol 121(1–2):37–53

    Article  Google Scholar 

  • White K, Pontius J, Schaberg P (2014) Remote sensing of spring phenology in northeastern forests: a comparison of methods, field metrics and sources of uncertainty. Remote Sens Environ 148:97–107

    Article  Google Scholar 

  • Wu J, Wang D, Bauer ME (2007) Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies. Field Crop Res 102(1):33–42

    Article  Google Scholar 

  • Yang G, Pu R, Zhang J, Zhao C, Feng H, Wang J (2013) Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas. Isprs J Photogramm 77:79–93

    Article  Google Scholar 

  • Zhang X, Liao C, Li J, Sun Q (2013) Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data. Int J Appl Earth Obs 21:506–512

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the China Center for Resources Satellite Data and Application for providing the ZY-3 data sets. We gratefully acknowledge the support of the Taiyue Forestry Bureau and the Haodifang Forestry Centre for fieldwork. The English language was revised by Pengjun Cheng and Dr. Gerrit Hazenberg. We also thank all those who provided helpful suggestions and comments on improving the quality of this manuscript.

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Authors and Affiliations

  1. College of Forestry, Beijing Forestry University, Beijing, 100083, China

    Tian Wang, Fengfeng Kang, Hairong Han, Xiaoqin Cheng, Jiang Zhu & Wensong Zhou

  2. Department of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, 213032, Jiangsu, China

    Tian Wang

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  1. Tian Wang
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  2. Fengfeng Kang
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  3. Hairong Han
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  4. Xiaoqin Cheng
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  5. Jiang Zhu
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Corresponding authors

Correspondence to Hairong Han or Xiaoqin Cheng.

Additional information

Project funding: The study was supported by the National Forestry Public Welfare Professional Scientific Research Project (No. 201404213), and the National Key Research and Development Program of China (No. 2016YFD0600205).

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Corresponding editor: Hu Yanbo.

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Wang, T., Kang, F., Han, H. et al. Estimation of leaf area index from high resolution ZY-3 satellite imagery in a catchment dominated by Larix principis-rupprechtii, northern China. J. For. Res. 30, 603–615 (2019). https://doi.org/10.1007/s11676-018-0617-6

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  • DOI: https://doi.org/10.1007/s11676-018-0617-6

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