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The Relevance of Forest Structure for Biomass and Productivity in Temperate Forests: New Perspectives for Remote Sensing

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Abstract

Forests provide important ecosystem services such as carbon sequestration. Forest landscapes are intrinsically heterogeneous—a problem for biomass and productivity assessment using remote sensing. Forest structure constitutes valuable additional information for the improved estimation of these variables. However, survey of forest structure by remote sensing remains a challenge which results mainly from the differences in forest structure metrics derived by using remote sensing compared to classical structural metrics from field data. To understand these differences, remote sensing measurements were linked with an individual-based forest model. Forest structure was analyzed by lidar remote sensing using metrics for the horizontal and vertical structures. To investigate the role of forest structure for biomass and productivity estimations in temperate forests, 25 lidar metrics of 375,000 simulated forest stands were analyzed. For the lidar-based metrics, top-of-canopy height arose as the best predictor for describing horizontal forest structure. The standard deviation of the vertical foliage profile was the best predictor for the vertical heterogeneity of a forest. Forest structure was also an important factor for the determination of forest biomass and aboveground wood productivity. In particular, horizontal structure was essential for forest biomass estimation. Predicting aboveground wood productivity must take into account both horizontal and vertical structures. In a case study based on these findings, forest structure, biomass and aboveground wood productivity are mapped for whole of Germany. The dominant type of forest in Germany is dense but less vertically structured forest stands. The total biomass of all German forests is 2.3 Gt, and the total aboveground woody productivity is 43 Mt/year. Future remote sensing missions will have the capability to provide information on forest structure (e.g., from lidar or radar). This will lead to more accurate assessments of forest biomass and productivity. These estimations can be used to evaluate forest ecosystems related to climate regulation and biodiversity protection.

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References

  • Asner GP, Mascaro J (2014) Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric. Remote Sens Environ 140:614–624

    Article  Google Scholar 

  • Bohn FJ, Huth A (2017) The importance of forest structure to biodiversity–productivity relationships. R Soc Open Sci 4:160521

    Article  Google Scholar 

  • Bohn FJ, Frank K, Huth A (2014) Of climate and its resulting tree growth: simulating the productivity of temperate forests. Ecol Model 278:9–17

    Article  Google Scholar 

  • Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320:1444–1449

    Article  Google Scholar 

  • Boncina A (2000) Comparison of structure and biodiversity in the Rajhenav virgin forest remnant and managed forest in the Dinaric region of Slovenia. Glob Ecol Biogeogr 9:201–211

    Article  Google Scholar 

  • Cazcarra-Bes V, Tello-Alonso M, Fischer R, Heym M, Papathanassiou K (2017) Monitoring of forest structure dynamics by means of L-band SAR tomography. Remote Sens 9:1229

    Article  Google Scholar 

  • Dănescu A, Albrecht AT, Bauhus J (2016) Structural diversity promotes productivity of mixed, uneven-aged forests in southwestern Germany. Oecologia 182:319–333

    Article  Google Scholar 

  • del Río M, Pretzsch H, Alberdi I, Bielak K, Bravo F, Brunner A, Condés S, Ducey MJ, Fonseca T, von Lüpke N, Pach M, Peric S, Perot T, Souidi Z, Spathelf P, Sterba H, Tijardovic M, Tomé M, Vallet P, Bravo-Oviedo A (2016) Characterization of the structure, dynamics, and productivity of mixed-species stands: review and perspectives. Eur J For Res 135:23–49

    Article  Google Scholar 

  • Dieler J, Uhl E, Biber P, Müller J, Rötzer T, Pretzsch H (2017) Effect of forest stand management on species composition, structural diversity, and productivity in the temperate zone of Europe. Eur J For Res 136:739–766

    Article  Google Scholar 

  • Disney M (2018) Terrestrial Li DAR: a three-dimensional revolution in how we look at trees. New Phytol. https://doi.org/10.1111/nph.15517

    Google Scholar 

  • Dobbertin M (2002) Influence of stand structure and site factors on wind damage—comparing the storms Vivian and Lothar. For Snow Landsc Res 77:187–205

    Google Scholar 

  • Dubayah RO, Sheldon SL, Clark DB, Hofton MA, Blair JB, Hurtt GC, Chazdon RL (2010) Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. J Geophys Res Biogeosci 115:G00E09

    Article  Google Scholar 

  • Exbrayat J-F, Bloom AA, Carvalhais N, Fischer R, Huth A, MacBean N, Williams M (2019) Understanding the land carbon cycle with space data: current status and prospects. Surv Geophys. https://doi.org/10.1007/s10712-019-09506-2

    Google Scholar 

  • Falkowski MJ, Hudak AT, Crookston NL, Gessler PE, Uebler EH, Smith AMS (2010) Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data. Can J For Res 40:184–199

    Article  Google Scholar 

  • Ferraz A, Saatchi S, Mallet C, Meyer V (2016) Lidar detection of individual tree size in tropical forests. Remote Sens Environ 183:318–333

    Article  Google Scholar 

  • Fischer R, Bohn F, Dantas de Paula M, Dislich C, Groeneveld J, Gutiérrez AG, Kazmierczak M, Knapp N, Lehmann S, Paulick S, Pütz S, Rödig E, Taubert F, Köhler P, Huth A (2016) Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests. Ecol Model 326:124–133

    Article  Google Scholar 

  • Fischer R, Knapp N, Bohn F, Huth A (2019) Remote sensing measurements of forest structure types for ecosystem service mapping. In: Schröter M, Bonn A, Klotz S, Seppelt R, Baessler C (eds) Atlas of ecosystem services: drivers, risks, and societal responses. Springer, Cham, pp 63–67

    Chapter  Google Scholar 

  • Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, Helkowski JH, Holloway T, Howard EA, Kucharik CJ, Monfreda C, Patz JA, Prentice IC, Ramankutty N, Snyder PK (2005) Global consequences of land use. Science 309:570–574

    Article  Google Scholar 

  • Frazer GW, Magnussen S, Wulder MA, Niemann KO (2011) Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sens Environ 115:636–649

    Article  Google Scholar 

  • Getzin S, Fischer R, Knapp N, Huth A (2017) Using airborne LiDAR to assess spatial heterogeneity in forest structure on Mount Kilimanjaro. Landsc Ecol 32:1881–1894

    Article  Google Scholar 

  • Grace J, Mitchard E, Gloor E (2014) Perturbations in the carbon budget of the tropics. Glob Change Biol 20:3238–3255

    Article  Google Scholar 

  • Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853

    Article  Google Scholar 

  • Hardiman BS, Bohrer G, Gough CM, Vogel CS, Curtis PS (2011) The role of canopy structural complexity in wood net primary production of a maturing northern deciduous forest. Ecology 92:1818–1827

    Article  Google Scholar 

  • Harding DJ, Lefsky MA, Parker GG, Blair JB (2001) Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests. Remote Sens Environ 76:283–297

    Article  Google Scholar 

  • Houghton RA, Lawrence KT, Hackler JL, Brown S (2001) The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Glob Change Biol 7:731–746

    Article  Google Scholar 

  • Hurtt GC, Dubayah R, Drake J, Moorcroft PR, Pacala SW, Blair JB, Fearon MG (2004) Beyond potential vegetation: combining lidar data and a height-structured model for carbon studies. Ecol Appl 14:873–883

    Article  Google Scholar 

  • Hurtt GC, Fisk J, Thomas RQ, Dubayah R, Moorcroft PR, Shugart HH (2010) Linking models and data on vegetation structure. J Geophys Res Biogeosci. https://doi.org/10.1029/2009JG000937

    Google Scholar 

  • Ishii HT, Tanabe S, Hiura T (2004) Exploring the relationships among canopy structure, stand productivity, and biodiversity of temperate forest ecosystems. For Sci 50:342–355

    Google Scholar 

  • Jetz W, Cavender-Bares J, Pavlick R, Schimel D, Davis FW, Asner GP, Guralnick R, Kattge J, Latimer AM, Moorcroft P, Schaepman ME, Schildhauer MP, Schneider FD, Schrodt F, Stahl U, Ustin SL (2016) Monitoring plant functional diversity from space. Nat Plants 2:16024

    Article  Google Scholar 

  • Knapp N, Fischer R, Huth A (2018a) Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states. Remote Sens Environ 205:199–209

    Article  Google Scholar 

  • Knapp N, Huth A, Kugler F, Papathanassiou K, Condit R, Hubbell SP, Fischer R (2018b) Model-assisted estimation of tropical forest biomass change: a comparison of approaches. Remote Sens 10:731

    Article  Google Scholar 

  • Köhler P, Huth A (2010) Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests. Biogeosciences 7:2531–2543

    Article  Google Scholar 

  • Lefsky MA (2010) A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophys Res Lett 37:L15401

    Article  Google Scholar 

  • Lefsky MA, Harding D, Cohen WB, Parker G, Shugart HH (1999) Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sens Environ 67:83–98

    Article  Google Scholar 

  • Liang JJ, Crowther TW, Picard N, Wiser S, Zhou M, Alberti G, Schulze ED, McGuire AD, Bozzato F, Pretzsch H, de-Miguel S, Paquette A, Hérault B, Scherer-Lorenzen M, Barrett CB, Glick HB, Hengeveld GM, Nabuurs GJ, Pfautsch S, Viana H, Vibrans AC, Ammer C, Schall P, Verbyla D, Tchebakova N, Fischer M, Watson JV, Chen HYH, Lei XD, Schelhaas MJ, Lu HC, Gianelle D, Parfenova EI, Salas C, Lee E, Lee B, Kim HS, Bruelheide H, Coomes DA, Piotto D, Sunderland T, Schmid B, Gourlet-Fleury S, Sonké B, Tavani R, Zhu J, Brandl S, Vayreda J, Kitahara F, Searle EB, Neldner VJ, Ngugi MR, Baraloto C, Frizzera L, Balazy R, Oleksyn J, Zawiła-Niedźwiecki T, Bouriaud O, Bussotti F, Finér L, Jaroszewicz B, Jucker T, Valladares F, Jagodzinski AM, Peri PL, Gonmadje C, Marthy W, O’Brien T, Martin EH, Marshall AR, Rovero F, Bitariho R, Niklaus PA, Alvarez-Loayza P, Chamuya N, Valencia R, Mortier F, Wortel V, Engone-Obiang NL, Ferreira LV, Odeke DE, Vasquez RM, Lewis SL, Reich PB (2016) Positive biodiversity–productivity relationship predominant in global forests. Science 354:196

    Article  Google Scholar 

  • Lu D, Chen Q, Wang G, Liu L, Li G, Moran E (2016) A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int J Digit Earth 9:63–105

    Article  Google Scholar 

  • Malhi Y, Wood D, Baker TR, Wright J, Phillips OL, Cochrane T, Meir P, Chave J, Almeida S, Arroyo L, Higuchi N, Killeen TJ, Laurance SG, Laurance WF, Lewis SL, Monteagudo A, Neill DA, Vargas PN, Pitman NCA, Quesada CA, Salomão R, Silva JNM, Lezama AT, Terborgh J, Martinez RV, Vinceti B (2006) The regional variation of aboveground live biomass in old-growth Amazonian forests. Glob Change Biol 12:1107–1138

    Article  Google Scholar 

  • Müller S, Ammer C, Nüsslein S (2000) Analyses of stand structure as a tool for silvicultural decisions—a case study in a Quercus petraea—Sorbus torminalis stand. Forstwiss Cent 119:32–42

    Article  Google Scholar 

  • Palace MW, Sullivan FB, Ducey MJ, Treuhaft RN, Herrick C, Shimbo JZ, Mota-E-Silva J (2015) Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data. Remote Sens Environ 161:1–11

    Article  Google Scholar 

  • Pan YD, Birdsey RA, Fang JY, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala SW, McGuire AD, Piao SL, Rautiainen A, Sitch S, Hayes D (2011) A large and persistent carbon sink in the world’s forests. Science 333:988–993

    Article  Google Scholar 

  • Peck JE, Zenner EK, Brang P, Zingg A (2014) Tree size distribution and abundance explain structural complexity differentially within stands of even-aged and uneven-aged structure types. Eur J For Res 133:335–346

    Article  Google Scholar 

  • Pereira HM, Ferrier S, Walters M, Geller GN, Jongman RHG, Scholes RJ, Bruford MW, Brummitt N, Butchart SHM, Cardoso AC, Coops NC, Dulloo E, Faith DP, Freyhof J, Gregory RD, Heip C, Hoft R, Hurtt G, Jetz W, Karp DS, McGeoch MA, Obura D, Onoda Y, Pettorelli N, Reyers B, Sayre R, Scharlemann JPW, Stuart SN, Turak E, Walpole M, Wegmann M (2013) Essential biodiversity variables. Science 339:277–278

    Article  Google Scholar 

  • Pettorelli N, Wegmann M, Skidmore A, Mücher S, Dawson TP, Fernandez M, Lucas R, Schaepman ME, Wang T, O’Connor B, Jongman RHG, Kempeneers P, Sonnenschein R, Leidner AK, Böhm M, He KS, Nagendra H, Dubois G, Fatoyinbo T, Hansen MC, Paganini M, de Klerk HM, Asner GP, Kerr JT, Estes AB, Schmeller DS, Heiden U, Rocchini D, Pereira HM, Turak E, Fernandez N, Lausch A, Cho MA, Alcaraz-Segura D, McGeoch MA, Turner W, Mueller A, St-Louis V, Penner J, Vihervaara P, Belward A, Reyers B, Geller GN (2016) Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sens Ecol Conserv 2:122–131

    Article  Google Scholar 

  • Pommerening A (2002) Approaches to quantifying forest structures. Forestry 75:305–324

    Article  Google Scholar 

  • Pretzsch H (2009) Forest dynamics, growth and yield. Springer, Berlin

    Book  Google Scholar 

  • Pretzsch H, del Río M, Schütze G, Ammer C, Annighöfer P, Avdagic A, Barbeito I, Bielak K, Brazaitis G, Coll L, Drössler L, Fabrika M, Forrester DI, Kurylyak V, Löf M, Lombardi F, Matović B, Mohren F, Motta R, den Ouden J, Pach M, Ponette Q, Skrzyszewski J, Sramek V, Sterba H, Svoboda M, Verheyen K, Zlatanov T, Bravo-Oviedo A (2016) Mixing of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) enhances structural heterogeneity, and the effect increases with water availability. For Ecol Manag 373:149–166

    Article  Google Scholar 

  • Ranson KJ, Sun G, Knox RG, Levine ER, Weishampel JF, Fifer ST (2001) Northern forest ecosystem dynamics using coupled models and remote sensing. Remote Sens Environ 75:291–302

    Article  Google Scholar 

  • Reineke LH (1933) Perfecting a stand-density index for even-aged forests. J Agric Res 46:627–638

    Google Scholar 

  • Rödig E, Cuntz M, Heinke J, Rammig A, Huth A (2017) Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: linking remote sensing, forest modelling and field inventory. Glob Ecol Biogeogr 26:1292–1302

    Article  Google Scholar 

  • Rödig E, Cuntz M, Rammig A, Fischer R, Taubert F, Huth A (2018) The importance of forest structure for carbon fluxes of the Amazon rainforest. Environ Res Lett 13:054013

    Article  Google Scholar 

  • Saatchi SS, Houghton RA, Alvala RCDS, Soares JV, Yu Y (2007) Distribution of aboveground live biomass in the Amazon basin. Glob Change Biol 13:816–837

    Article  Google Scholar 

  • Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard ETA, Salas W, Zutta BR, Buermann W, Lewis SL, Hagen S, Petrova S, White L, Silman M, Morel A (2011) Benchmark map of forest carbon stocks in tropical regions across three continents. Proc Natl Acad Sci USA 108:9899–9904

    Article  Google Scholar 

  • Schall P, Gossner MM, Heinrichs S, Fischer M, Boch S, Prati D, Jung K, Baumgartner V, Blaser S, Böhm S, Buscot F, Daniel R, Goldmann K, Kaiser K, Kahl T, Lange M, Müller J, Overmann J, Renner SC, Schulze ED, Sikorski J, Tschapka M, Türke M, Weisser WW, Wemheuer B, Wubet T, Ammer C (2018a) The impact of even-aged and uneven-aged forest management on regional biodiversity of multiple taxa in European beech forests. J Appl Ecol 55:267–278

    Article  Google Scholar 

  • Schall P, Schulze E-D, Fischer M, Ayasse M, Ammer C (2018b) Relations between forest management, stand structure and productivity across different types of Central European forests. Basic Appl Ecol 32:39–52

    Article  Google Scholar 

  • Shugart HH (2003) A theory of forest dynamics. The Blackburn Press, Caldwell

    Google Scholar 

  • Shugart HH, Saatchi S, Hall FG (2010) Importance of structure and its measurement in quantifying function of forest ecosystems. J Geophys Res Biogeosci. https://doi.org/10.1029/2009JG000993

    Google Scholar 

  • Shugart HH, Asner GP, Fischer R, Huth A, Knapp N, Le Toan T, Shuman JK (2015) Computer and remote-sensing infrastructure to enhance large-scale testing of individual-based forest models. Front Ecol Environ 13:503–511

    Article  Google Scholar 

  • Shugart HH, Wang B, Fischer R, Ma J, Fang J, Yan X, Huth A, Armstrong AH (2018) Gap models and their individual-based relatives in the assessment of the consequences of global change. Environ Res Lett 13:033001

    Article  Google Scholar 

  • Simard M, Pinto N, Fisher JB, Baccini A (2011) Mapping forest canopy height globally with spaceborne lidar. J Geophys Res Biogeosci 116:G04021

    Article  Google Scholar 

  • Snyder M (2010) What is forest stand structure and how is it measured? North Woodl 64:15

    Google Scholar 

  • Stark SC, Leitold V, Wu JL, Hunter MO, de Castilho CV, Costa FRC, McMahon SM, Parker GG, Shimabukuro MT, Lefsky MA, Keller M, Alves LF, Schietti J, Shimabukuro YE, Brandao DO, Woodcock TK, Higuchi N, de Camargo PB, de Oliveira RC, Saleska SR (2012) Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. Ecol Lett 15:1406–1414

    Article  Google Scholar 

  • Tello M, Pardini M, Papathanassiou K, Fischer R (2014) Towards forest structure characteristics retrieval from SAR tomographic profiles. In: Electronic proceedings EUSAR 2014; 10th European conference on synthetic aperture radar, 03–05 June 2014, Berlin, Germany. VDE Verlag, Berlin, pp 1425–1428

  • Tello M, Cazcarra-Bes V, Fischer R, Papathanassiou K (2018) Multiscale forest structure estimation from SAR tomography. In: Electronic proceedings EUSAR 2018; 12th European conference on synthetic aperture radar, 04–07 June, 2018, Aachen, Germany. VDE Verlag, Berlin, pp 600–603

  • Tews J, Brose U, Grimm V, Tielbörger K, Wichmann MC, Schwager M, Jeltsch F (2004) Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr 31:79–92

    Article  Google Scholar 

  • Thuenen-Institut (2015) Dritte Bundeswaldinventur - Basisdaten (Stand 20.03.2015)

  • Wiegand T, He F, Hubbell SP (2013) A systematic comparison of summary characteristics for quantifying point patterns in ecology. Ecography 36:92–103

    Article  Google Scholar 

  • Young BD, D’Amato AW, Kern CC, Kastendick DN, Palik BJ (2017) Seven decades of change in forest structure and composition in Pinus resinosa forests in northern Minnesota, USA: comparing managed and unmanaged conditions. For Ecol Manag 395:92–103

    Article  Google Scholar 

  • Zenner EK, Hibbs DE (2000) A new method for modeling the heterogeneity of forest structure. For Ecol Manag 129:75–87

    Article  Google Scholar 

Download references

Acknowledgements

This study originates from the workshop “Space-based Measurement of Forest Properties for Carbon Cycle Research” at the International Space Science Institute in Bern during November 2017. We thank the Thünen Institute for providing the German national forest inventory data. We also want to thank Hans Pretzsch, Peter Biber and Michael Heym (TUM) for their input on forest structure and structure metrics. Kostas Papathanassiou, Victor Cazcarra-Bes, Matteo Pardini and Marivi Tello Alonso (DLR) gave useful insights into linking forest structure and remote sensing. We also thank the anonymous reviewers for their insightful comments and suggestions. This study was part of the HGF-Helmholtz-Alliance “Remote Sensing and Earth System Dynamics” HA-310 under the funding reference RA37012. NK was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) under the funding reference 50EE1416. FB was funded by the Deutsche Forschungsgemeinschaft (DFG) within the research unit FOR1246 (Kilimanjaro ecosystems under global change: linking biodiversity, biotic interactions and biogeochemical ecosystem processes). HHS was funded by NASA grants 14-TE14-0085 and 16-ESUSPI-16-0015.

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Authors and Affiliations

  1. Department of Ecological Modeling, Helmholtz Centre for Environmental Research GmbH – UFZ, Permoserstasse 15, 04318, Leipzig, Germany

    Rico Fischer, Nikolai Knapp, Friedrich Bohn & Andreas Huth

  2. Institute for Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, Kreuzeckbahnstrasse 19, 82467, Garmisch-Partenkirchen, Germany

    Friedrich Bohn

  3. Department of Environmental Sciences, University of Virginia, Charlottesville, VA, 22903, USA

    Herman H. Shugart

  4. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany

    Andreas Huth

  5. Institute of Environmental Systems Research, University of Osnabrück, Barbarastr. 12, 49076, Osnabrück, Germany

    Andreas Huth

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  1. Rico Fischer
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Correspondence to Rico Fischer.

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Appendices

Appendix 1: Estimation of Forest Attributes Using Structural Information

1.1 Describing Forest Structure from Field Data

The study examined a total of 13 field-based metrics to describe forest structure, which are listed in the following. Forest structure was described, for example, by basal area BA [m2], which is the sum of all tree basal area values BAi of a forest stand:

$${\text{BA}} = \sum \limits_{i} {\text{BA}}_{i} = \sum \limits_{i} \frac{\pi }{4}d_{i} ^{2} ,$$

where di (m) is the stem diameter of a tree i (in total n trees in a stand). Alternative metrics to describe the horizontal and vertical structures of a forest stand are:

  • standard deviation of stem diameters: \({\text{SD}}_{\text{DBH}} = \sqrt {\frac{1}{n - 1}\sum \limits_{i} \left( {d_{i} - \bar{d}} \right)^{2} }\)

  • coefficient of variation of all stem diameters: \({\text{CV}}_{\text{DBH}} = \frac{{{\text{SD}}_{\text{DBH}} }}{{\overline{{d_{i} }} }}\)

  • skewness of the diameter distribution: \({\text{Skew}}_{\text{DBH}} = \frac{{\frac{1}{n} \cdot \sum \nolimits_{i = 1}^{n} \left( {d_{i} - \bar{d}} \right)^{3} }}{{\left( {\frac{1}{n} \cdot \sum \nolimits_{i = 1}^{n} \left( {d_{i} - \bar{d}} \right)^{2} } \right)^{{\frac{3}{2}}} }}\)

  • Gini coefficient of the diameter distribution: \({\text{Gini}}_{\text{DBH}} = \frac{{2\sum \nolimits_{i} i \cdot d_{i} }}{{n\sum \nolimits_{i} d_{i} }} - \frac{n + 1}{n}\), where di is the sorted list of stem diameters.

\(\bar{d}\) is the mean diameter of all trees within a stand. The same metrics can be calculated also for the tree height distribution (where Hi (m) is the height of a tree) or basal area distribution. Especially for the tree height distribution, we have calculated further metrics.

  • maximum height: \(H_{\hbox{max} } = \hbox{max} \left( {H_{i} } \right)\)

  • mean height: \(H_{\text{mean}} = \frac{1}{n}\sum \nolimits_{i} H_{i}\)

  • quadratic mean height: \(H_{{{\text{quad}} \cdot {\text{mean}}}} = \sqrt {\frac{1}{n}\sum\nolimits_{i} {H_{i}^{{2_{i} }} } }\)

  • Lorey’s height: \(H_{\text{Lorey's}} = \frac{{\sum \nolimits_{i} H_{i} \cdot {\text{BA}}_{i} }}{{\sum \nolimits_{i} {\text{BA}}_{i} }}.\)

1.2 Describing Forest Structure from Remote Sensing Data

Estimating forest structure from remote sensing is more challenging as remote sensing data are not tree-based as in the field-based case. This study examined a total of 25 remote sensing-based metrics to describe forest structure. The basis for most metrics is the lidar-derived canopy height model (CHM) with a spatial resolution of 1 m × 1 m. In this study, we described horizontal structure for each 20 m × 20 m forest stand mainly by the mean top-of-canopy height TCH (m), which is the mean of the canopy height model (CHM):

$${\text{TCH}} = \frac{{\sum \nolimits_{i = 1}^{n} P_{{{\text{CHM}},i}} }}{n},$$

where PCHM,i is the forest height of the CHM in pixel i and n is the number of pixels. Alternative metrics based on the CHM are:

  • maximum height: \(H_{\hbox{max} } = {\hbox{max} } \left( {P_{{{\text{CHM}},i}} } \right)\)

  • quadratic TCH: \({\text{QTCH}} = \sqrt {\frac{{\sum \nolimits_{i = 1}^{n} P_{{{\text{CHM}},i}}^{2} }}{n}}\)

  • relative height of the CHM: \({\text{RH}}_{q} = {\text{quantile}}_{q} \left( {P_{{{\text{CHM}},i}} } \right)\)

It is also possible to calculate the standard deviation, the coefficient of variation and the skewness of the CHM (functions are described above in the field-based section). In this study, we considered further advanced metrics based on the CHM:

  • Shannon index of the CHM: \({\text{Shannon}}_{\text{CHM}} = - \sum \limits_{i = 1}^{{i_{\hbox{max} } }} {\text{CHM}}\left( {h_{i} } \right) \cdot \ln \left( {{\text{CHM}}\left( {h_{i} } \right)} \right),\)

  • with CHM (hi) being the CHM profile value (pixel count) in bin i. CHM (hi) has to be > 0, and CHM (hi) = 0 is ignored,

  • Kurtosis of the CHM: \({\text{Kurtosis}}_{\text{CHM}} = n \cdot \frac{{\sum \nolimits_{i = 1}^{n} \left( {P_{{{\text{CHM}},i}} - \overline{{P_{\text{CHM}} }} } \right)^{4} }}{{\left( {\sum \nolimits_{i = 1}^{n} \left( {P_{{{\text{CHM}},i}} - \overline{{P_{\text{CHM}} }} } \right)^{2} } \right)^{2} }},\)

with n being the total pixel number, PCHM,i the value of pixel i and \(\overline{{P_{\text{CHM}} }}\) the mean value of the CHM (which is the same as TCH),

  • the p–h ratio of the CHM: \(P:H_{{{\text{CHM}}}} = \frac{{h\left( {\mathop {{\text{max}}}\limits_{{i\epsilon \left[ {1,i_{{{\text{max}}}} } \right]}} \left( {{\text{CHM}}\left( {h_{i} } \right)} \right)} \right)}}{{\mathop {{\text{max}}}\limits_{{i\epsilon \left[ {1,i_{{{\text{max}}}} } \right]}} \left( {h_{i} } \right)}}\),

with CHM (hi) being the pixel count in height bin hi and imax is the highest height bin.

Another class of metrics calculates the fractional canopy cover above a certain threshold × (m) using the CHM: \({\text{FCC}}_{x} = \frac{{\sum \nolimits_{{h_{i} = x}}^{{h_{\hbox{max} } }} {\text{CHM}}\left( {h_{i} } \right)}}{{\sum \nolimits_{{h_{i} = 0}}^{{h_{\hbox{max} } }} {\text{CHM}}\left( {h_{i} } \right)}},\) with CHM (hi) the count of CHM pixels in height bin hi and × the height threshold to distinguish canopy from gap.

Instead of using the CHM as the basic information for calculating all these lidar metrics, we have used the vertical foliage profile (VFP) for a second class of metrics. All the above-described metrics can be calculated using the VFP. For this reason, the VFP was divided into 1-m height classes. This height classes can now be used in the equations described above by replacing the CHM. The generation of a VFP profile from a CHM is described below.

1.3 Calculating the Vertical Foliage Profile from a CHM

The vertical foliage profile (VFP) was reconstructed from the CHM profile at 1 m vertical resolution following the approach described by Harding et al. (2001).

$${\text{VFP}}\left( {h_{i} } \right) = \frac{1}{k*\Delta h}*\ln \left( {\frac{{P\left( {h_{i} } \right)}}{{P\left( {h_{i + 1} } \right)}}} \right),$$

with k being the light extinction coefficient, Δh the width of one height bin and P(hi) the value of the cumulative CHM profile in height bin hi. The method reconstructs the vertical leaf profile by giving more weight to lower parts of the profile. All pixels below 5 m height were regarded as ground and the light extinction coefficient was set to 0.3 which has been shown to result in good LAI estimations (Getzin et al. 2017).

1.4 Estimation of Forest Biomass and Productivity Using Forest Structure

See Figs. 9 and 10.

Fig. 9
figure 9

Relationship between observed biomass and estimated biomass derived by three different approaches (see Table 1). Each point represents one of 375,000 forest stands from the forest factory data set. The observed biomass have been derived by summing up the biomass values of all trees in the 20 m × 20 m stand. The estimated biomass was determined using the structural information for each forest stand. a Estimation of biomass using only information from the horizontal structural index TCH (AGB = 9.49 * TCH1.22, r2 = 0.90), b using the vertical structural index SDVFP (AGB = 34.77 * SD 0.48VFP , r2 = 0.01) and c using the vertical and horizontal structural index (AGB = 7.55 * TCH1.20 * SD 0.23VFP , r2 = 0.90). A comparison of the estimated biomass values for the different approaches is shown in Fig. 6a

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Fig. 10
figure 10

Relationship between observed and estimated aboveground woody productivity (AWP) for 375,000 forest stands (forest factory data set). Each dot represents one forest stand. a Estimation of productivity using only the horizontal structural index TCH (AWP = 1.68 * TCH0.31, r2 = 0.14), b only the vertical structural index SDVFP (AWP = 4.03 * SD −0.34VFP , r2 = 0.09) and c using the vertical and horizontal structural index (AWP = 2.55 * TCH0.34 * SD −0.39VFP , r2 = 0.31). A comparison of the estimated productivity values with the different approaches is shown in Fig. 6b

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Appendix 2: Analysis of the German Forest Inventory Data Set

All analyses so far referred to the forest factory data set. This Appendix reproduces all analyses with the empirical BWI data set. For each forest stand of the BWI data set, a virtual lidar campaign was carried out and the remote sensing-based metrics were then calculated.

See Figs. 11, 12, 13, 14, 15, 16 and 17.

Fig. 11
figure 11

Remote sensing-based estimation of the forest structure using the BWI data set. Each dot represents one stand of the BWI. The figure shows the estimate of a the horizontal forest structure (basal area) from lidar using top-of-canopy height and b the vertical forest structure (tree height heterogeneity) from lidar using the standard deviation of the vertical foliage profile

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Fig. 12
figure 12

Overview of all correlations between field-based structural metrics and remote sensing-based metrics based only on the BWI data set. Numbers and gray scale indicate the coefficient of determination. All structural metrics are explained in Appendix 1

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Fig. 13
figure 13

Role of forest structure for biomass, derived from the BWI data set (more than 45,000 field plots, 20 m × 20 m). Forest structure is estimated from remote sensing. As horizontal forest structure descriptor the top-of-canopy height (TCH) was used and as vertical structure descriptor the standard deviation of the vertical foliage profile (SDVFP). Shown is the mean aboveground biomass in relation to the forest structure classes. Error bars indicate the standard deviation

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Fig. 14
figure 14

Relationship between observed biomass and estimated biomass. Each point represents one forest stand from the forest inventory data set BWI. The observed biomass has been taken from the BWI data set. The estimated biomass values were determined using different approaches (cf. Table 1) and information on forest structure. a Estimation of biomass using only the horizontal structural index TCH, b only the vertical structural index SDVFP and c using the vertical and horizontal structural index. A comparison of the estimated values with the different approaches is shown in Fig. 15

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Fig. 15
figure 15

Histogram for forest biomass estimates for Germany based on the BWI data set. The biomass was estimated using three different approaches: H—horizontal structure (blue), V—vertical structure (green) and H + V—horizontal and vertical structures (red). The histogram was compared with the measured values from the BWI data set (black line)

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Fig. 16
figure 16

Forest structure of Germany over different gradients. Mean value of the horizontal structure (TCH) from a south to north, c west to east in Germany and e over the altitudinal gradient. Mean value of the vertical structure (SDVFP) from a south to north, c west to east in Germany and e over the altitudinal gradient. The structure values correspond to Fig. 7 (forest structure maps of Germany)

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Fig. 17
figure 17

Histograms of structural metrics for forests outside of national parks and inside national parks. As horizontal forest structure descriptor, the top-of-canopy height (TCH, left) was used, and as vertical structure descriptor, the standard deviation of the vertical foliage profile (SDVFP, right) was used

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Fischer, R., Knapp, N., Bohn, F. et al. The Relevance of Forest Structure for Biomass and Productivity in Temperate Forests: New Perspectives for Remote Sensing. Surv Geophys 40, 709–734 (2019). https://doi.org/10.1007/s10712-019-09519-x

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