Assessment of MODIS NPP algorithm-based estimates using soil fertility and forest inventory data in mixed hemiboreal forests

Open access

Abstract

Optical remote sensing data-based estimates of terrestrial net primary production (NPP) are released by different projects using light use efficiency-type models. Although spatial resolution of the NPP data sets is still too coarse (500–1000 m) for single forest stands, regional monitoring of forest management and growth with 25–100 ha sampling units is feasible if the NPPSAT estimates are sensitive to forest growth differences depending on soil fertility in the area of interest. In this study, NPP estimates for 2,914 mixed forest class pixels (according to the MODIS land cover map) located in Estonia were (1) obtained from three different NPPSAT products, (2) calculated using an empirical soil potential phytoproductivity (SPP) model applied to a 1:10,000 soil map (NPPSPP), and (3) calculated using stem volume increment estimates given in a forest management inventory data base (NPPFIDB). A linear multiple regression model was then used to explore the relationships of NPPSAT with the proportion of coniferous forests, the NPPSPP and distance of the pixels from the Baltic Sea coast – the variables that have been found informative in previous studies. We found a positive moderate correlation (0.57, p < 0.001) between NPPSPP and NPPFIDB. The local or downscaled meteorological data-based NPPSAT estimates were more consistent with the NPPSPP and NPPFIDB, but the correlation with NPPSAT was weak and sometimes even negative. The range of NPP estimates in NPPSAT data sets was much narrower than the range of NPPSPP or NPPFIDB. Errors in land cover maps and in estimates of absorbed photosynthetically active radiation were identified as the main reasons for NPPSAT inconsistencies.

Adermann, V. 2009. Forests of Estonia. (Eesti Metsad 2008). Metsakaitse-ja Metsauuendus-keskus, Tallinn. (In Estonian).

Bontemps, J.D., Bouriaud, O. 2014. Predictive approaches to forest site productivity: recent trends, challenges and future perspectives. – Forestry, 87, 109–128.

DeLucia, E.H., Drake, J.E., Thomas, R.B., Gonzalez-Meler, M. 2007. Forest carbon use efficiency: Is respiration a constant fraction of gross primary production? – Global Change Biology, 13, 1157–1167.

D’Odorico, P., Gonsamo, A., Pinty, B., Gobron, N., Coops, N., Mendez, E., Schaepman, M.E. 2014. Intercomparison of fraction of absorbed photosynthetically active radiation products derived from satellite data over Europe. – Remote Sensing of Environment, 142, 141–154.

Eenmäe, A., Nilson, T., Lang, M. 2011. A note on meteorological variables related trends in the MODIS NPP product for Estonia. – Forestry Studies / Metsanduslikud Uurimused, 55, 60–63.

FAO, IIASA, ISRIC, ISS-CAS, JRC. 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria.

Finér, L., Ohashi, M., Noguchi, K., Hirano, Y. 2011. Fine root production and turnover in forest ecosystems in relation to stand and environmental characteristics. – Forest Ecology and Management, 262, 2008–2023.

GFOI. 2013. Integrating remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests: Methods and Guidance from the Global Forest Observations Initiative: Pub: Group on Earth Observations, Geneva, Switzerland, 2014. ISBN 978-92-990047-4-6.

Härkönen, S., Lehtonen, A., Eerikäinen, K., Peltoniemi, M., Mäkelä, A. 2011. Estimating forest carbon fluxes for large regions based on process-based modelling, NFI data and Landsat satellite images. – Forest Ecology and Management, 262, 2364–2377.

Härkönen, S., Pulkkinen, M., Duursma, R., Mäkelä, A. 2010. Estimating annual GPP, NPP and stem growth in Finland using summary models. – Forest Ecology and Management, 259, 524–533.

Härkönen, S., Tokola, T., Packalén, P., Korhonen, L., Mäkelä, A. 2013. Predicting forest growth based on airborne light detection and ranging data, climate data, and a simplified processbased model. – Canadian Journal of Forest Research, 43, 364–375.

IDB. 2016. State register for accounting of forest resource. (Metsaressursi arvestuse riikliku registri põhimäärus). – Riigi Teataja, RT I, 12.01.2016, 2. (In Estonian).

IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. – Eggelston. S., Buendia. L., Miwa. K., Ngara. T., Tanabe. K (eds.). Hayama: Institute of Global Environmental Strategies (IGES). Volume 4: Agriculture, Forestry and Other Land Use.

IUSS Working Group WRB. 2014. World Reference Base for Soil Resources 2014. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. FAO, Rome.

Kandare, K., Ørka, H.O., Dalponte, M., Næsset, E., Gobakken, T. 2017. Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data. – International Journal of Applied Earth Observation and Geoinformation, 60, 72–82.

Kiviste, A., Hordo, M., Kangur, A., Kardakov, A., Laarmann, D., Lilleleht, A., Metslaid, S., Sims, A., Korjus, H. 2015. Monitoring and modeling of forest ecosystems: the Estonian Network of Forest Research Plots. – Forestry Studies / Metsanduslikud Uurimused, 62, 26–38.

Kliimaatlas. 1970. Climate data in Estonia (Eesti NSV kliimaatlas). Eesti NSV Hüdrometeoroloogia teenistuse valitsus. Tallinn.

Kõlli, R. 1988. Phytoproductivity of Estonian soils in forest land. – Почвоведение, 4, 96−107. (In Russian).

Kõlli, R., Asi, E., Köster, T. 2004. Organic carbon pools in Estonian forest soils. – Baltic Forestry, 10(1), 19–26.

Kõlli, R., Ellermäe, O., Köster, T., Lemetti, I., Asi, E., Kauer, K. 2009. Stocks of organic carbon in Estonian soils. – Estonian Journal of Earth Sciences, 58, 95–108.

Kõlli, R., Kanal, A. 2010. The management and protection of soil cover: an ecosystem approach. – Forestry Studies / Metsanduslikud Uurimused, 53, 25–34.

Kuusk, A., Kuusk, J., Lang, M. 2016. Albedo of the forested landscape at the SMEAR-Estonia research station. – Baltic Forestry, 22(2), 390−395.

Lang, M., Traškovs, A., Gulbe, L. 2013. Assessment of NTSG MODIS NPP product for forests in Kurzeme region, Latvia. – Forestry Studies / Metsanduslikud Uurimused, 58, 26–36.

Liu, C., Westman, C.J., Berg, B., Kutsch, W., Wang, G.Z., Man, R., Ilvesniemi, H. 2004. Variation in litterfall-climate relationships between coniferous and broadleaf forests in Eurasia. – Global Ecology and Biogeography, 13, 105–114.

Liu, J., Chen, J.M., Cihlar, J., Park, W.M. 1997. A process-based boreal ecosystem productivity simulator using remote sensing inputs. – Remote Sensing of Environment, 62, 158–175.

Mäkelä, A., Valentine, H.T. 2001. The ratio of NPP to GPP: evidence of change over the course of stand development. – Tree Physiology, 21, 1015-1030.

Makkonen, K., Helmisaari, H.S. 2001. Fine root biomass and production in Scots pine stands in relation to stand age. – Tree Physiology, 21, 193–198.

Metsakorralduse. 2017. Forest inventory act. (Metsa korraldamise juhend). – RT I, 22.02. 2017, 11. (In Estonian).

Moreno, A., Hasenauer, H. 2016. Spatial down-scaling of European climate data. – International Journal of Climatology, 36, 1444–1458.

Moreno, M., Neumann, M., Hasenauer, H. 2016. Optimal resolution for linking remotely sensed and forest inventory data in Europe. – Remote Sensing of Environment, 183, 109–119.

Mullakaardi. 2001. The fine-scale map of Estonian soils. (Vabariigi digitaalse suuremõõtkavalise mullastiku kaardi seletuskiri). Maa-amet, Tallinn. [WWW document]. – http://geoportaal.maaamet.ee/est/Andmed-jakaardid/Mullastiku-kaart-p33.html [Accessed 16 April 2016]. (In Estonian).

Neumann, M., Moreno, A., Thurnher, C., Mues, V., Härkönen, S., Mura, M., Bouriaud, O., Lang, M., Cardellini, G., Thivolle-Cazat, A., Bronisz, K., Merganic, J., Alberdi, I., Astrup, R., Mohren, F., Zhao, M., Hasenauer, H. 2016. Creating a regional MODIS satellite-driven net primary production dataset for European forests. – Remote Sensing, 8(7), 554, doi:10.3390/rs8070554.

Neumann, M., Mues, V., Moreno, A., Hasenauer, H., Seidl, R. 2017. Climate variability drives recent tree mortality in Europe. Global Change Biology, 00, 1–10, https://doi.org/10.1111/gcb.13724 (In press).

Nilson, T., Rennel, M., Luhamaa, A., Hordo, M., Olesk, A., Lang, M. 2012. MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation. – Forestry Studies / Metsanduslikud Uurimused, 56, 56–78.

Noe, S.M., Niinemets, Ü., Krasnova, A., Krasnov, D., Motallebi, A., Kängsepp, V., Jõgiste, K., Hõrrak, U., Komsaare, K., Mirme, S., Vana, M., Tammet, H., Bäck, J., Vesala, T., Kulmala, M., Petäjä, T., Kangur, A. 2015. SMEAR Estonia: Perspectives of a large-scale forest ecosystem – atmosphere research infrastructure. – Forestry Studies / Metsanduslikud Uurimused, 63, 56–84.

Olofsson, P., Eklundh, L., Lagergren, F., Jonsson, P., Lindroth, A. 2007. Estimating net primary production for Scandinavian forests using data from Terra/MODIS. – Advances in Space Research, 39, 125–130.

Olson, R.J., Johnson, K.R., Zheng, D.L., Scurlock, J.M.O. 2001. Global and regional ecosystem modeling: Databases of model drivers and validation measurements. Oakridge Laboratory: Oak Ridge, TN, USA.

Ostonen, I., Lõhmus, K., Pajuste, K. 2005. Fine root biomass, production and its proportion of NPP in a fertile middle-aged Norway spruce forest: Comparison of soil core and ingrowth core methods. – Forest Ecology and Management, 212, 264–277.

Quaife, T., Quegan, S., Disney, M., Lewis, P., Lomas, M., Woodward F.I. 2008. Impact of land cover uncertainties on estimates of biospheric carbon fluxes. – Global Biogeochemical Cycles, 22, 1–12.

Raich, J.W., Nadelhoffer, K.J. 1989. Belowground carbon allocation in forest ecosystems: global trends. – Ecology, 70, 1346–1354.

Raudsaar, M., Pärt, E., Adermann, V. 2016. Forest resources. In: Yearbook Forest 2014. Tallinn, Keskkonnaagentuur. 1–61.

Schubert, P., Lagergren, F., Aurela, M., Christensen, T., Grelle, A., Heliasz, M., Klemedtsson, L., Lindroth, A., Pilegaard, K., Vesala, T., Eklundh, L. 2012. Modeling GPP in the Nordic forest landscape with MODIS time series data – comparison with the MODIS GPP product. – Remote Sensing of Environment, 126, 136–147.

Sims, A., Mändma, R., Laarmann, D., Korjus, H. 2014. Assessment of tree mortality on the Estonian Network of Forest Research Plots. – Forestry Studies / Metsanduslikud Uurimused, 60, 57–68.

Stagakis, S., Markos, N., Vanikiotis, T., Tzotsos, A., Sykioti, O., Kyparissis, A. 2015. sCASE: A primary productivity monitoring system for the forests of North Pindus National Park (Epirus, Greece). – European Journal of Remote Sensing, 48, 223–243.

Tan, B., Woodcock, C.E, Hu, J., Zhang, P., Ozdogan, M., Huang, D., Yang, W., Knyazikhin, Y., Myneni, R.B. 2006. The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions. – Remote Sensing of Environment, 105, 98–114.

Tang, J., Luyssaert, S., Richardson, A.D., Kutsch, W., Janssens, I.A. 2014. Steeper declines in forest photosynthesis than respiration explain age-driven decreases in forest growth. – Proceedings of the National Academy of Sciences, 111, 8856–8860.

Tomppo, E., Gschwantner, T., Lawrence, M., McRoberts, R. 2010. National Forest Inventories: Pathways for common reporting. Springer: Berlin, Germany.

Tooming, H. 1977. Solar radiation and yield formation. Gidrometeoizdat, Leningrad, Russia.

Turner, D.P., Ritts, W.D., Cohen, W.B., Gower, S.T., Running, S.W., Zhao, M., Costa, M.H., Kirschbaum, A.A., Ham, J.M., Saleska, S.R., Ahl, D.E. 2006. Evaluation of MODIS NPP and GPP products across multiple biomes. – Remote Sensing of Environment, 102, 282–292.

Vanninen, P., Mäkelä, A. 1999. Fine root biomass of Scots pine stands differing in age and soil fertility in southern Finland. – Tree Physiology, 19, 823–830.

Varik, M., Aosaar, J., Uri, V. 2009. Biomass production in silver birch stands in Oxalis site type. – Forestry Studies / Metsanduslikud Uurimused, 51, 5–16.

Waring, R.H., Milner K.S., Jolly, W.M., Phillips, L., McWethy, D. 2006. Assessment of site index and forest growth capacity across the Pacific and Inland Northwest U.S.A. with a MODIS satellite-derived vegetation index. – Forest Ecology and Management, 228, 285–291.

Xin, Q., Olofsson, P., Zhu, Z., Tan, B., Woodcock, C.E. 2013. Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. – Remote Sensing of Environment, 135, 234–247.

Yuan, Z.Y., Chen, H.Y.H. 2012a. Indirect methods produce higher estimates of fine root production and turnover rates than direct methods. – PLoS ONE 7(11): e48989. doi:10.1371/journal.pone.004898.

Yuan, Z.Y., Chen, H.Y.H. 2012b. A global analysis of fine root production as affected by soil nitrogen and phosphorus. – Proceedings of the Royal Society B: Biological Sciences, 279, 3796–3802.

Zhao, M., Heinsch, F.A., Nemani, R.R., Running, S.W. 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. – Remote Sensing of Environment, 95, 164–176.

Zhao, M., Running S.W., Heinsch F.A., Nemani R.R. 2011. MODIS-derived terrestrial primary production. – Ramachandran, B., Justice, C.O., Abrams, M.J. (eds.). Land Remote Sensing and Global Environmental Change: NASA’s Earth Observing System and the Science of ASTER and MODIS. New York, Springer-Verlag, 635-660.

Forestry Studies

Metsanduslikud Uurimused; The Journal of Estonian University of Life Sciences

Journal Information


CiteScore 2017: 0.30

SCImago Journal Rank (SJR) 2017: 0.209
Source Normalized Impact per Paper (SNIP) 2017: 0.187

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 235 235 38
PDF Downloads 118 118 38