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Environmental Management

Erschienen in:

26.05.2023

Modelling Green Volume Using Sentinel-1, -2, PALSAR-2 Satellite Data and Machine Learning for Urban and Semi-Urban Areas in Germany

verfasst von: Sebastian Lehmler, Michael Förster, Annett Frick

Erschienen in: Environmental Management | Ausgabe 3/2023

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Abstract

Urban Green Infrastructure (UGI) provides ecosystem services such as cooling of temperatures and is majorly important for climate change adaptation. Green Volume (GV) describes the 3-D space occupied by vegetation and is highly useful for the assessment of UGI. This research uses Sentinel-2 (S-2) optical data, vegetation indices (VIs), Sentinel-1 (S-1) and PALSAR-2 (P-2) radar data to build machine learning models for yearly GV estimation on large scales. Our study compares random and stratified sampling of reference data, assesses the performance of different machine learning algorithms and tests model transferability by independent validation. The results indicate that stratified sampling of training data leads to improved accuracies when compared to random sampling. While the Gradient Tree Boost (GTB) and Random Forest (RF) algorithms show generally similar performance, Support Vector Machine (SVM) exhibits considerably greater model error. The results suggest RF to be the most robust classifier overall, achieving highest accuracies for independent and inter-annual validation. Furthermore, modelling GV based on S-2 features considerably outperforms using only S-1 or P-2 based features. Moreover, the study finds that underestimation of large GV magnitudes in urban forests constitutes the biggest source of model error. Overall, modelled GV explains around 79% of the variability in reference GV at 10 m resolution and over 90% when aggregated to 100 m resolution. The research shows that accurately modelling GV is possible using openly available satellite data. Resulting GV predictions can be useful for environmental management by providing valuable information for climate change adaptation, environmental monitoring and change detection.

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Abdi AM (2020) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience Remote Sens 57(1):1–20. https://doi.org/10.1080/15481603.2019.1650447CrossRef

Adam M, Urbazaev M, Dubois C, Schmullius C (2020) Accuracy assessment of GEDI terrain elevation and canopy height estimates in European temperate forests: influence of environmental and acquisition parameters. Remote Sens 12(23):3948. https://doi.org/10.3390/rs12233948CrossRef

Anderson K, Hancock S, Disney M, Gaston KJ (2016) Is waveform worth it? A comparison of Li DAR approaches for vegetation and landscape characterization. Remote Sens Ecol Conserv 2:5–15. https://doi.org/10.1002/rse2.8

Antropov, O; Rauste, Y; Tegel, K; Baral, Y; Junttila, V; Kauranne, T et al. (2018): Tropical forest tree height and above ground biomass mapping in Nepal using Tandem-X and ALOS PALSAR data. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

Astola H, Häme T, Sirro L, Molinier M, Kilpi J (2019) Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region. Remote Sens Environ 223:257–273CrossRef

Banzhaf E, Kollai H, Kindler A (2020) Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics. Geocarto Int 35(6):623–640. https://doi.org/10.1080/10106049.2018.1524514CrossRef

Braun A (2021) Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations. Open Geosci 13(1):532–569CrossRef

Breiman L (2001) Random Forests. Machine Learning 45:5–32. https://doi.org/10.1023/A:1010933404324

Carreiras J, Melo J, Vasconcelos M (2013) Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data. Remote Sensing 5:1524–1548. https://doi.org/10.3390/rs5041524

Casalegno S, Anderson K, Hancock S, Gaston KJ (2017) Improving models of urban greenspace: from vegetation surface cover to volumetric survey, using waveform laser scanning. Methods Ecol Evol 8(11):1443–1452. https://doi.org/10.1111/2041-210X.12794CrossRef

Castillo JAA, Apan AA, Maraseni TN, Salmo SG (2017) Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS J Photogramm Remote Sens 134:70–85. https://doi.org/10.1016/j.isprsjprs.2017.10.016CrossRef

Chen L, Ren C, Zhang B, Wang Z, Liu M, Man W, Liu J (2021) Improved estimation of forest stand volume by the integration of GEDI LiDAR data and multi-sensor imagery in the Changbai Mountains Mixed forests Ecoregion (CMMFE), northeast China. Int J Appl Earth Observ Geoinf 100:102326. https://doi.org/10.1016/j.jag.2021.102326CrossRef

Copernicus (2018) Corine Land Cover (CLC) 2018. https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018

Degerickx J, Hermy M, Somers B (2020) Mapping functional urban green types using high resolution remote sensing data. Sustainability 12(5):2144. https://doi.org/10.3390/su12052144CrossRef

Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1996) Support Vector Regression Machines. In: M.C. Mozer, M. Jordan, T. Petsche (eds) Advances in Neural Information Processing Systems, vol 9. MIT Press

Lee D-H, Kil S-H, Lee S-B (2021) A Study on Obtaining Tree Data from Green Spaces in Parks Using Unmanned Aerial Vehicle Images: Focusing on Mureung Park in Chuncheon. J People Plants Environ 24:441–450. https://doi.org/10.11628/ksppe.2021.24.4.441

Frampton WJ, Dash J, Watmough G, Milton EJ (2013) Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J Photogramm Remote Sens 82:83–92. https://doi.org/10.1016/j.isprsjprs.2013.04.007CrossRef

Frick A, Tervooren S (2019) A framework for the long-term monitoring of urban green volume based on multi-temporal and multi-sensoral remote sensing data. J Geovis Spat Anal 3 (1). https://doi.org/10.1007/s41651-019-0030-5

Frick, A; Wagner, K; Kiefer, T; Tervooren, S (2020): Wo fehlt Grün? – Defizitanalyse von Grünvolumen in Städten. Unter Mitarbeit von Gotthard Meinel, Ulrich Schumacher, Martin Behnisch und Tobias Krüger

Friedman JH (2001) Greedy function approximation: A gradient boosting machine. Ann Statist 29. https://doi.org/10.1214/aos/1013203451

Friedman JH (2002) Stochastic gradient boosting. Computational Statistics & Data Analysis 38:367–378. https://doi.org/10.1016/S0167-9473(01)00065-2

G Meinel; R Hecht; W Socher (2006): Städtisches Grünvolumen – neuer Basisindikator für die Stadtökologie? Bestimmungsmethodik und Ergebnisbewertung. Online verfügbar unter https://www.researchgate.net/publication/264882710_Stadtisches_Grunvolumen_-_neuer_Basisindikator_fur_die_Stadtokologie_Bestimmungsmethodik_und_Ergebnisbewertung

Gill SE, Handley JF, Ennos AR, Pauleit S (2007) Adapting cities for climate change: The role of the green infrastructure. Built Environ 33(1):115–133. https://doi.org/10.2148/benv.33.1.115CrossRef

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031CrossRef

Großmann; Pohl; Schulze (1983): Grünvolumenzahl und Bodenfunktionszahl in der Landschafts- und Bauleitplanung. In: Schriften der Behörde für Bezirksangelegenheiten (9)

Hecht R, Meinel G, Buchroithner MF (2008) Estimation of urban green volume based on single-pulse LiDAR data. IEEE Trans Geosci Remote Sens 46(11):3832–3840 https://doi.org/10.1109/TGRS.2008.2001771CrossRef

Huang X, Ziniti B, Torbick N, Ducey M (2018) Assessment of Forest above Ground Biomass Estimation Using Multi-Temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 Data. Remote Sensing 10:1424. https://doi.org/10.3390/rs10091424

Huang Y, Yu B, Zhou J, Hu C, Tan W, Hu Z, Wu J (2013) Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images. Front Earth Sci 7:43–54. https://doi.org/10.1007/s11707-012-0339-6

Hunt ER, Daughtry CST, Eitel JUH, Long DS (2011) Remote sensing leaf chlorophyll content using a visible band index. Agron J 103(4):1090–1099. https://doi.org/10.2134/agronj2010.0395CrossRef

IPCC (2021): Summary for Policymakers. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Online verfügbar unter https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM_final.pdf, zuletzt geprüft am 18.01.2022

Jiang Z, Huete A, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ 112(10):3833–3845. https://doi.org/10.1016/j.rse.2008.06.006CrossRef

Kabisch N, Haase D (2014) Green justice or just green? Provision of urban green spaces in Berlin, Germany. Landsc Urban Plan 122:129–139. https://doi.org/10.1016/j.landurbplan.2013.11.016CrossRef

Kaplan G, Rozenstein O (2021) Spaceborne estimation of leaf area index in cotton, tomato, and wheat using Sentinel-2. Land 10(5):505. https://doi.org/10.3390/land10050505CrossRef

Kriegler FJ, Malila WA, Nalepka RF, Richardson W (1969) Preprocessing transformations and their effects on multispectral recognition. Remote Sens Environ VI:97

Lang N, Schindler K, Wegner JD (2019) Country-wide high-resolution vegetation height mapping with Sentinel-2. Remote Sens Environ 233:111347. https://doi.org/10.1016/j.rse.2019.111347CrossRef

Lehmann I, Mathey J, Rößler S, Bräuer A, Goldberg V (2014) Urban vegetation structure types as a methodological approach for identifying ecosystem services – Application to the analysis of micro-climatic effects. Ecol Indic 42:58–72. https://doi.org/10.1016/j.ecolind.2014.02.036CrossRef

Li, Y; Li, M; Li, C; Liu, Z (2020): Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. In: Sci Rep 10 (1). https://doi.org/10.1038/s41598-020-67024-3

Lymburner L, Beggs PJ, Jacobson CR (2000) Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogrammetric Eng Remote Sens 66(2):183–191

Mathey J, Hennersdorf J, Lehmann I, Wende W (2021) Qualifying the urban structure type approach for urban green space analysis – A case study of Dresden, Germany. Ecol Indic 125:107519. https://doi.org/10.1016/j.ecolind.2021.107519CrossRef

Mathey, J; Rößler, S; Lehmann, I; Bräuer, A (2011): Urban Green Spaces: Potentials and Constraints for Urban Adaptation to Climate Change. In: Konrad Otto-Zimmermann (Hg.): Resilient Cities. Dordrecht: Springer Netherlands, S. 479–485

Matzarakis, A (2001): Die thermische Komponente des Stadtklimas. Meteorologisches Institut der Universität Freiburg. Freiburg

Mutanga O, Skidmore AK (2004) Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int J Remote Sens 25(19):3999–4014. https://doi.org/10.1080/01431160310001654923CrossRef

Navarro JA, Algeet N, Fernández-Landa A, Esteban J, Rodríguez-Noriega P, Guillén-Climent ML (2019) Integration of UAV, sentinel-1, and sentinel-2 data for mangrove plantation aboveground biomass monitoring in senegal. Remote Sens 11(1):77. https://doi.org/10.3390/rs11010077CrossRef

Palliwoda J, Banzhaf E, Priess JA (2020) How do the green components of urban green infrastructure influence the use of ecosystem services? Examples from Leipzig, Germany. Landsc Ecol 35(5):1127–1142. https://doi.org/10.1007/s10980-020-01004-wCrossRef

Pham TD, Yokoya N, Xia J, Ha NT, Le NN, Nguyen TTT et al. (2020) Comparison of machine learning methods for estimating mangrove above-ground biomass using multiple source remote sensing data in the red river delta biosphere reserve, Vietnam. Remote Sens 12(8):1334. https://doi.org/10.3390/rs12081334CrossRef

Retief F, Bond A, Pope J, Morrison-Saunders A, King N (2016) Global megatrends and their implications for environmental assessment practice. Environ Impact Assess Rev 61:52–60. https://doi.org/10.1016/j.eiar.2016.07.002CrossRef

Rocha AD, Vulova S, Meier F, Förster M, Kleinschmit B (2022) Mapping evapotranspirative and radiative cooling services in an urban environment. Sustain Cities Soc 85:104051. https://doi.org/10.1016/j.scs.2022.104051CrossRef

Rufin P, Frantz D, Yan L, Hostert P (2021) Operational Coregistration of the Sentinel-2A/B Image Archive Using Multitemporal Landsat Spectral Averages. IEEE Geosci. Remote Sensing Lett 18:712–716. https://doi.org/10.1109/LGRS.2020.2982245

Sabzekar M, Hasheminejad SMH (2021) Robust regression using support vector regressions. Chaos, Solitons & Fractals 144:110738. https://doi.org/10.1016/j.chaos.2021.110738

Senatsverwaltung für Stadtentwicklung, Bauen und Wohnen Berlin (SenSBW) (2022): Blockkarte 1: 5.000 ISU5, Raumbezug Umweltatlas. Online verfügbar unter https://gdi.berlin.de/geonetwork/srv/api/records/0dd101d4-c963-481e-a996-e0b94cc3449b., zuletzt geprüft am 30.11.2022

Solberg S, Hansen EH, Gobakken T, Næssset E, Zahabu E (2017) Biomass and InSAR height relationship in a dense tropical forest. Remote Sens Environ 192:166–175. https://doi.org/10.1016/j.rse.2017.02.010CrossRef

Stumpf A, Michéa D, Malet J-P (2018) Improved co-registration of sentinel-2 and landsat-8 imagery for earth surface motion measurements. Remote Sens 10(2):160. https://doi.org/10.3390/rs10020160CrossRef

Sun Y, Qin Q, Ren H, Zhang T, Chen S (2020) Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From Sentinel-2/MSI Imagery. IEEE Trans. Geosci. Remote Sensing 58:826–840. https://doi.org/10.1109/TGRS.2019.2940826

United Nations, Department of Economic and Social Affairs, Population Division (2018): World Urbanization Prospects The 2018 Revision. Online verfügbar unter https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf, zuletzt geprüft am 18.01.2022

Wagle N, Acharya TD, Kolluru V, Huang H, Lee DH (2020) Multi-temporal land cover change mapping using Google Earth engine and ensemble learning methods. Appl Sci 10(22):8083. https://doi.org/10.3390/app10228083CrossRef

Wang F-M, Huang J-F, Tang Y-L, Wang X-Z (2007) New vegetation index and its application in estimating leaf area index of rice. Rice Sci 14(3):195–203. https://doi.org/10.1016/S1672-6308(07)60027-4CrossRef

Wende W, Huelsmann W, Marty M, Penn-Bressel G, Bobylev N (2010) Climate protection and compact urban structures in spatial planning and local construction plans in Germany. Land Use Policy 27(3):864–868. https://doi.org/10.1016/j.landusepol.2009.11.005CrossRef

Wolch JR, Byrne J, Newell JP (2014) Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’. Landsc Urban Plan 125:234–244. https://doi.org/10.1016/j.landurbplan.2014.01.017CrossRef

Yang X, Tan L, He L (2014) A robust least squares support vector machine for regression and classification with noise. Neurocomputing 140:41–52. https://doi.org/10.1016/j.neucom.2014.03.037CrossRef

Yang, H; Huang, K; Chan, L; King, I; Lyu, MR (2004): Outliers Treatment in Support Vector Regression for Financial Time Series Prediction. In: David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Kleinberg, Friedemann Mattern, John C. Mitchell et al. (Hg.): Neural Information Processing, Bd. 3316. Berlin, Heidelberg: Springer Berlin Heidelberg (Lecture Notes in Computer Science), S. 1260–1265

You L, Jizhen L, Yaxin Q (2011) A new robust least squares support vector machine for regression with outliers. Procedia Eng 15:1355–1360. https://doi.org/10.1016/j.proeng.2011.08.251CrossRef

Zscheischler J, Fischer EM (2020) The record-breaking compound hot and dry 2018 growing season in Germany. Weather Clim Extremes 29:100270. https://doi.org/10.1016/j.wace.2020.100270CrossRef

Titel: Modelling Green Volume Using Sentinel-1, -2, PALSAR-2 Satellite Data and Machine Learning for Urban and Semi-Urban Areas in Germany
verfasst von: Sebastian Lehmler
Michael Förster
Annett Frick
Publikationsdatum: 26.05.2023
Verlag: Springer US
Erschienen in: Environmental Management / Ausgabe 3/2023
Print ISSN: 0364-152X
Elektronische ISSN: 1432-1009
DOI: https://doi.org/10.1007/s00267-023-01826-9

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