nach oben

Environmental Earth Sciences

Erschienen in:

01.05.2021 | Original Article

The application of EO-1 Hyperion hyperspectral data to estimate the GPP of temperate forest in Changbai Mountain, Northeast China

verfasst von: Yuan Zhang, Anzhi Wang, Fenghui Yuan, Dexin Guan, Jiabing Wu

Erschienen in: Environmental Earth Sciences | Ausgabe 9/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Flux tower is a link between ground measurements and large-scale remote sensing data. A large number of remote sensing model methods are used to estimate the regional scale Gross Primary Productivity (GPP) based on this principle. In this study, Vegetation Photosynthesis Model (VPM) and Vegetation Indexes (VIs) were used to estimate the GPP based on Earth Observing 1 (EO-1) Hyperion hyperspectral data in Changbai Mountain temperate forest. Result shows that the two different types of remote sensing input data of the VPM has similar result at the same level. For different time scope, 3-day flux data can better match remote sensing data. For different footprint, the effect of 500, 1000, 1500 m almost no difference in our area. Among the comparison of the four types of VIs, Bands Ratio (BR), Bands Subtraction (BS) and Bands Difference (BD) have a higher correlation significant than Single Band (SB). 457 nm is the optimum band for SB. The best bands combination of BR, BS, and BD mainly focus on near infrared region. Our research shows that for VPM, and other Light Use Efficiency (LUE) remote sensing model, the difference is not significant between multispectral data and hyperspectral data. At the comparison of VPM and VIs, although the estimation of former is more accurate, the latter is more convenient for that the establishment of VIs just need several bands of remote sensing data. Our findings will help to improve future research on GPP estimation based on hyperspectral observations, which is being more important with increasing availability of hyperspectral satellite data products.

Vorheriger Artikel Estimating uniaxial compressive strength of carbonate building stones based on some intact stone properties after deterioration by freeze–thaw

Nächster Artikel Approximation of submarine groundwater discharge and allied nutrient fluxes to the Bay of Bengal, India using nutrient mass balance

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Anand A, Pandey PC, Petropoulos GP, Pavlides A, Srivastava PK, Sharma JK, Malhi RKM (2020) Use of Hyperion for mangrove forest carbon stock assessment in Bhitarkanika forest reserve: a contribution towards blue carbon initiative. Remote Sens. https://doi.org/10.3390/rs12040597CrossRef

Anthoni PM, Law BE, Unsworth MH (1999) Carbon and water vapor exchange of an open-canopied ponderosa pine ecosystem. Agric For Meteorol 95:151–168. https://doi.org/10.1016/s0168-1923(99)00029-5CrossRef

Baldocchi D (2008) Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust J Bot 56:1–26. https://doi.org/10.1071/bt07151CrossRef

Bar-On YM, Phillips R, Milo R (2018) The biomass distribution on Earth. Proc Natl Acad Sci USA 115:6506–6511. https://doi.org/10.1073/pnas.1711842115CrossRef

Benavides R, Roig S, Osoro K (2009) Potential productivity of forested areas based on a biophysical model. A case study of a mountainous region in northern Spain. Ann For Sci. https://doi.org/10.1051/forest/2008080CrossRef

Black TA et al (1996) Annual cycles of water vapour and carbon dioxide fluxes in and above a boreal aspen forest. Glob Change Biol 2:219–229. https://doi.org/10.1111/j.1365-2486.1996.tb00074.xCrossRef

Campbell PKE et al (2013) EO-1 Hyperion reflectance time series at calibration and validation sites: stability and sensitivity to seasonal dynamics. IEEE J Sel Top Appl Earth Observ Remote Sens 6:276–290. https://doi.org/10.1109/jstars.2013.2246139

Chen J, Zhang H, Liu Z, Che M, Chen B (2014) Evaluating parameter adjustment in the MODIS gross primary production algorithm based on eddy covariance tower measurements. Remote Sens 6:3321–3348. https://doi.org/10.3390/rs6043321CrossRef

Cheng Y-B, Middleton EM, Hilker T, Coops NC, Black TA, Krishnan P (2009) Dynamics of spectral bio-indicators and their correlations with light use efficiency using directional observations at a Douglas-fir forest. Meas Sci Technol 20. https://doi.org/10.1088/0957-0233/20/9/095107

Cook BD et al (2009) Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations. Remote Sens Environ 113:2366–2379. https://doi.org/10.1016/j.rse.2009.06.017CrossRef

Dhumal RK, Vibhute AD, Nagne AD, Kale KV, Mehrotra SC (2017) Performance analysis of spectral features based on narrowband vegetation indices for cotton and maize crops by EO-1 Hyperion dataset. In: Behera HS, Mohapatra DP (eds) Computational intelligence in data mining, Cidm 2016, vol 556. Advances in intelligent systems and computing, pp 581–590. https://doi.org/10.1007/978-981-10-3874-7_55

Dong T, Meng J, Wu B (2012) overview on methods of deriving fraction of absorbed photosynthetically active radiation(FPAR) using remote sensing. Acta Ecol Sin 32:7190–7201CrossRef

DuBois S et al (2018) Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape. Ecol Appl 28:1313–1324. https://doi.org/10.1002/eap.1733CrossRef

Finnigan J (2004) The footprint concept in complex terrain. Agric For Meteorol 127:117–129. https://doi.org/10.1016/j.agrformet.2004.07.008CrossRef

Franks S et al (2017) EO-1 data quality and sensor stability with changing orbital precession at the end of a 16 year mission. Remote Sens. https://doi.org/10.3390/rs9050412CrossRef

Goulden ML, Daube BC, Fan SM, Sutton DJ, Bazzaz A, Munger JW, Wofsy SC (1997) Physiological responses of a black spruce forest to weather. J Geophys Res-Atmos 102:28987–28996. https://doi.org/10.1029/97jd01111CrossRef

Guan D-X, Wu J-B, Zhao X-S, Han S-J, Yu G-R, Sun X-M, Jin C-J (2006) CO2 fluxes over an old, temperate mixed forest in northeastern China. Agric For Meteorol 137:138–149. https://doi.org/10.1016/j.agrformet.2006.02.003CrossRef

Horst TW (1999) The footprint for estimation of atmosphere-surface exchange fluxes by profile techniques. Bound-Layer Meteorol 90:171–188. https://doi.org/10.1023/a:1001774726067CrossRef

Hosseinjani Zadeh M, Tangestani MH, Velasco Roldan F, Yusta I (2014) Sub-pixel mineral mapping of a porphyry copper belt using EO-1 Hyperion data. Adv Space Res 53:440–451. https://doi.org/10.1016/j.asr.2013.11.029CrossRef

Houborg R, McCabe MF, Angel Y, Middleton EM, IEEE (2017) Time series from Hyperion to track productivity in pivot agriculture in Saudi Arabia. In: 2017 IEEE international geoscience and remote sensing symposium. IEEE international symposium on geoscience and remote sensing IGARSS, pp 3047–3050

Hsieh CI, Katul G, Chi T (2000) An approximate analytical model for footprint estimation of scaler fluxes in thermally stratified atmospheric flows. Adv Water Resour 23:765–772. https://doi.org/10.1016/s0309-1708(99)00042-1CrossRef

Huang X, Xiao J, Ma M (2019) Evaluating the performance of satellite-derived vegetation indices for estimating gross primary productivity using FLUXNET observations across the globe remote sensing, vol 11. https://doi.org/10.3390/rs11151823

Huemmrich KF, Campbell P, Landis D, Middleton E (2019) Developing a common globally applicable method for optical remote sensing of ecosystem light use efficiency. Remote Sens Environ 230:111190. https://doi.org/10.1016/j.rse.2019.05.009CrossRef

Huete AR, Liu HQ, Batchily K, vanLeeuwen W (1997) A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sens Environ 59:440–451. https://doi.org/10.1016/s0034-4257(96)00112-5CrossRef

Jiang Y, Zhang J, Xu X, Dong Z (2013) A GPP assimilation model for the southeastern Tibetan Plateau based on CO₂ eddy covariance flux tower and remote sensing data. Int J Appl Earth Observ Geoinf 23

Johnston C, Buongiorno J, Nepal P, Prestemon JP (2019) From source to sink: past changes and model projections of carbon sequestration in the global forest sector. J For Econ 34:47–72. https://doi.org/10.1561/112.00000442CrossRef

Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A (2018) Modern trends in hyperspectral image analysis: a review. IEEE Access 6:14118–14129. https://doi.org/10.1109/access.2018.2812999CrossRef

Kong B, He B, Yu H, Liu Y (2017) An integrated field and hyperspectral remote sensing method for the estimation of pigments content of Stipa Purpurea in Shenzha. Tibet Math Probl Eng 2017:1–10. https://doi.org/10.1155/2017/4787054CrossRef

Koptev SV, Tret'yakov SV, Bogdanov AP, Il'intsev AS, Demidenko SA (2017) Forest inventory norms of alder stands by the relascopic circular plot method. Lesnoy Zhurnal For J 5:53–63. https://doi.org/10.17238/issn0536-1036.2017.5.53

Kyparissis A, Markos N, Stagakis S, Levizou E, Sykioti O (2007) Ecosystem productivity and dynamics issued from multispectral and hyperspectral satellite imagery. In: Neale CMU, Owe M, Durso G (eds) Remote sensing for agriculture, ecosystems, and hydrology IX, vol 6742. Proceedings of SPIE. https://doi.org/10.1117/12.737688

Liu Z, Wu C, Peng D, Wang S, Gonsamo A, Fang B, Yuan W (2017) Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy. Agric For Meteorol 233:222–234. https://doi.org/10.1016/j.agrformet.2016.12.001CrossRef

LP DAAC-MOD09A1 (2021). https://lpdaac.usgs.gov/products/mod09a1v006/

Monteith JL (1972) Solar-radiation and productivity in tropical ecosystems. J Appl Ecol 9:747–766. https://doi.org/10.2307/2401901CrossRef

Peng Y, Gitelson AA (2012) Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sens Environ 117:440–448. https://doi.org/10.1016/j.rse.2011.10.021CrossRef

Porcar-Castell A et al (2015) EUROSPEC: at the interface between remote-sensing and ecosystem CO₂ flux measurements. Eur Biogeosci 12:6103–6124. https://doi.org/10.5194/bg-12-6103-2015CrossRef

Potter CS, Randerson JT, Field CB, Matson PA, Vitousek PM, Mooney HA, Klooster SA (1993) Terrestrial ecosystem production—a process model-based on global satellite and surface data. Glob Biogeochem Cycles 7:811–841. https://doi.org/10.1029/93gb02725CrossRef

Running SW, Baldocchi DD, Turner DP, Gower ST, Bakwin PS, Hibbard KA (1999) A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens Environ 70:108–127. https://doi.org/10.1016/s0034-4257(99)00061-9CrossRef

Soukhovolsky VG, Ivanova JD (2013) Estimation of forest-stand net primary productivity using fraction phytomass distribution model. Contemp Probl Ecol 6:700–707. https://doi.org/10.1134/s1995425513070111CrossRef

Suryoprayogo H, Ramdani F, Utaminingrum F (2017) Implementation of Landsat 7 ETM+ principal polar spectral indices based on EO-1 Hyperion wavebands selection. In: 2017 international symposium on geoinformatics. https://doi.org/10.1109/isyg.2017.8280664

Talbot J et al (2014) Methods to estimate aboveground wood productivity from long-term forest inventory plots. For Ecol Manag 320:30–38. https://doi.org/10.1016/j.foreco.2014.02.021CrossRef

Wofsy SC et al (1993) Net exchange of CO₂ in a midlatitude forest. Science 260:1314–1317. https://doi.org/10.1126/science.260.5112.1314CrossRef

Wu B, Zeng Y, Huang J (2004) Overview of LAI/FPAR retrieval from remotely sensed data. Adv Earth Sci 19:585–590

Wu JB, Xiao XM, Guan DX, Shi TT, Jin CJ, Han SJ (2009) Estimation of the gross primary production of an old-growth temperate mixed forest using eddy covariance and remote sensing. Int J Remote Sens 30:463–479. https://doi.org/10.1080/01431160802372143CrossRef

Wu JB, Zhou XY, Wang AZ, Yuan FH (2015) Comparative measurements of water vapor fluxes over a tall forest using open- and closed-path eddy covariance system. Atmos Meas Tech 8:4123–4131. https://doi.org/10.5194/amt-8-4123-2015CrossRef

Xiao XM, Hollinger D, Aber J, Goltz M, Davidson EA, Zhang QY, Moore B (2004) Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens Environ 89:519–534. https://doi.org/10.1016/j.rse.2003.11.008CrossRef

Xiao J et al (2019) Remote sensing of the terrestrial carbon cycle: a review of advances over 50 years. Remote Sens Environ. https://doi.org/10.1016/j.rse.2019.111383CrossRef

Yang H et al (2020) Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates. Glob Change Biol. https://doi.org/10.1111/gcb.15117CrossRef

Yu Q et al (2014) Narrowband bio-indicator monitoring of temperate forest carbon fluxes in Northeastern China. Remote Sens 6:8986–9013. https://doi.org/10.3390/rs6098986CrossRef

Yuan J, Niu Z, Fu W (2007) Model simulation for sensitivity of hyperspectral indices to LAI, leaf chlorophyll and internal structure parameter. In: Ju W, Zhao S (eds) Geoinformatics 2007: remotely sensed data and information, Pts 1 and 2, vol 6752. Proceedings of SPIE. https://doi.org/10.1117/12.760656

Zarco-Tejada PJ, Morales A, Testi L, Villalobos FJ (2013) Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sens Environ 133:102–115. https://doi.org/10.1016/j.rse.2013.02.003CrossRef

Zhang F, Zhou G (2017) Deriving a light use efficiency estimation algorithm using in situ hyperspectral and eddy covariance measurements for a maize canopy in Northeast China. Ecol Evolut 7:4735–4744. https://doi.org/10.1002/ece3.3051CrossRef

Zhang Q et al (2016) Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production. Remote Sens Environ 186:311–321. https://doi.org/10.1016/j.rse.2016.08.026CrossRef

Zhao X, Guan D, Wu J, Jin C, Han S (2005) Distribution of footprint and flux source area of the mixed forest of broad-leaved and Korean pine in Changbai Mountain. J Beijing For Univ 27:17–23

Zhao L, Liu Z, Xu S, He X, Ni Z, Zhao H, Ren S (2018) Retrieving the diurnal FPAR of a maize canopy from the jointing stage to the tasseling stage with vegetation indices under different water stresses and light conditions. Sensors. https://doi.org/10.3390/s18113965CrossRef

Zhou X, Xin Q (2019) Improving satellite-based modelling of gross primary production in deciduous broadleaf forests by accounting for seasonality in light use efficiency. Int J Remote Sens 40:931–955. https://doi.org/10.1080/01431161.2018.1519285CrossRef

Zhou Y, Zhang L, Xiao J, Chen S, Kato T, Zhou G (2014) A comparison of satellite-derived vegetation indices for approximating gross primary productivity of grasslands rangeland. Ecol Manag 67:9–18. https://doi.org/10.2111/rem-d-13-00059.1CrossRef

Titel: The application of EO-1 Hyperion hyperspectral data to estimate the GPP of temperate forest in Changbai Mountain, Northeast China
verfasst von: Yuan Zhang
Anzhi Wang
Fenghui Yuan
Dexin Guan
Jiabing Wu
Publikationsdatum: 01.05.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 9/2021
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-021-09639-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 9/2021

Limit equilibrium theory-based investigation on the critical tension crack depth in loess slope

Debris flow susceptibility assessment based on topo-hydrological factors at different unit scales: a case study of Mentougou district, Beijing

Aquifer hydraulic parameters estimation based on hydrogeophysical methods in West Nile Delta, Egypt

Time-lag characteristics of the response of karst springs to precipitation in the northern China

Assessment of terrain stability zones for human habitation in Himalayan Upper Pindar River Basin, Uttarakhand using AHP and GIS

Delineation of protection zones for springs in fractured volcanic media considering land use and climate change scenarios in central Mexico region