Abstract
Changes in land use and land cover (LULC) have major effects on biodiversity and ecosystem services. Land change models can simulate future trends of ecosystem services under different scenarios to inform the actions of decision makers towards building a more sustainable society. LULC data are essential inputs for predicting future land changes. It is now possible to derive high-resolution LULC maps from satellite data using remote sensing techniques. However, the classification of land categories in these maps is too limited to sufficiently assess biodiversity and ecosystem services. This study aims to develop land-use scenarios, using an appropriate LULC map, to enable assessment of biodiversity and ecosystem services at the national scale. First, we developed an LULC dataset using vegetation inventories based on field records of vegetation collected throughout the country in the periods 1978–1987, 1988–1998 and 1999–2014. The vegetation maps consist of over 905 vegetation categories, from which we aggregated the most prevalent categories into 9 LULC categories. Second, we created a business-as-usual scenario and plausible future scenarios on the land use change maps using the Land Change Model tool. In the process of developing the model, we considered key drivers including biophysical and socio-economic factors. The results showed some key land changes as consequences of intensive/extensive land-use interventions. These derived scenario maps can be used to assess the impacts of future land change on biodiversity and ecosystem services.
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Akasaka M, Takenaka A, Ishihama F, Kadoya T, Ogawa M, Osawa T, Yamakita T, Tagane S, Ishii R, Nagai S, Taki H, Akasaka T, Oguma H, Suzuki T, Yamano H (2014) Development of a national land-use/cover dataset to estimate biodiversity and ecosystem services. In: Nakano S, Yahara T, Nakashizuka T (eds) The biodiversity observation network in the Asia-Pacific region: integrative observations and assessments of Asian biodiversity. Springer, Berlin
Atkinson PM, Tatnall AL (1997) Introduction to neural networks in remote sensing. Int J Remote Sens 18(4):699–709
Brown DG, Verburg PH, Pontius RG, Lange MD (2013) Opportunities to improve impact, integration, and evaluation of land change models. Curr Opin Environ Sustain 5:452–457. https://doi.org/10.1016/j.cosust.2013.07.012
Burnham BO (1973) Markov intertemporal land use simulation model. South J Agric Econ 5(1):253–258
Eastman JR (2016) TerrSet tutorial: geospatial monitoring and modeling system. Clark Labs, Clark University, Worcester
Estoque RC, Murayama Y (2012) Examining the potential impact of land use/cover changes on the ecosystem services of Baguio city, the Philippines: a scenario-based analysis. Appl Geogr 35:316–326. https://doi.org/10.1016/j.apgeog.2012.08.006
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. https://doi.org/10.1126/science.1111772
Hashimoto S, DasGupta R, Kabaya K, Matsui T, Haga C, Saito O, Takeuchi K (2018) Scenario analysis of land-use and ecosystem services in the Noto Peninsula, Japan: implications of alternative development pathways under declining population. Sustain Sci
Himiyama Y (1998) Land use/cover change in Japan: from the past to the future. Hydrol Process 12:1995–2001
IPBES (2016) Scenarios and models of biodiversity and ecosystem services: the methodological assessment report on scenarios and models of biodiversity and ecosystem services. IPBES, UN Campus, Bonn
Lawler JJ, Lewis DJ, Nelson E, Plantinga AJ, Polasky S, Withey JC, Helmers DP, Martinuzzi S, Pennington D, Radeloff VC (2014) Projected land-use change impacts on ecosystem services in the United States. Proc Natl Acad Sci 111(20):7492–7497. https://doi.org/10.1073/pnas.1405557111
Lehsten V, Sykes MT, Scott AV, Tzanopoulos J, Kallimanis A, Mazaris A, Verburg PH, Schulp CJE, Potts SG, Vogiatzakis I (2015) Disentangling the effects of land-use change, climate and CO2 on projected future European habitat types: disentangling the drivers of habitat change. Glob Ecol Biogeogr 24(6):653–663
Matsui T, Ugata T, Machimura T (2014) A development of factor analyzing and predicting model of abandoned agricultural land with machine learning algorisms. J Jpn Soc Civ Eng G 70(6):131–139 (In Japanese with English abstract)
Matsui T, Haga C, Saito O, Hashimoto S (2018) Spatially explicit population distribution scenarios for projecting and assessing natural capital and ecosystem services in Japan. Sustain Sci. https://doi.org/10.1007/s11625-018-0605-y
Ministry of Agriculture, Forestry and Fisheries (2016) National forest and forestry plan. Ministry of Agriculture, Forestry, and Fisheries (MAFF), Government of Japan, Tokyo
Ministry of Land, Infrastructure Transport and Tourism (2015) National Spatial Strategy. Ministry of Land, Infrastructure Transport and Tourism, Government of Japan, pp 1–29
Ministry of the Environment of Japan (2015) Japan Biodiversity Outlook 2: result of comprehensive assessment of biodiversity and ecosystem services in Japan. Ministry of the Environment, Government of Japan, Tokyo
National Institute of Population and Social Security Research, Japan (2017) Population projections for Japan: 2016–2065. Population research series no. 336
Nelson E, Mendoza G, Regetz J, Polasky S, Tallis H, Cameron DR, Chan KM, Daily GC, Goldstein J, Kareiva PM, Lonsdorf E, Naidoo R, Ricketts TH, Shaw MR (2009) Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Front Ecol Environ 7(1):4–11. https://doi.org/10.1890/080023
Ogawa M, Takenaka A, Kadoya T, Ishihama F, Yamano H, Akasaka M (2013) Land-use classification and mapping at a whole scale of Japan based on a national vegetation map. Jpn J Conserv Ecol 18:69–76 (In Japanese with English abstract)
Ornetsmüller C, Verburg PH, Heinimann A (2016) Scenarios of land system change in the Lao PDR: transitions in response to alternative demands on goods and services provided by the land. Appl Geogr 75:1–11. https://doi.org/10.1016/j.apgeog.2016.07.010
Pistocchi A, Luzi L, Napolitano P (2002) The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol 41:765–775. https://doi.org/10.1007/s002540100440
Saito O, Kamiyama C, Hashimoto S, Matsui T, Shoyama K, Kabaya K, Uetake T, Taki H, Ishikawa Y, Matsushita K, Yamane F, Hori J, Ariga T, Takeuchi K (2018) Co-design of national scale future scenarios in Japan to predict and assess natural capital and ecosystem services. Sustain Sci. https://doi.org/10.1007/s11625-018-0587-9
Schulp CJE, Nabuurs GJ, Verburg PH (2008) Future carbon sequestration in Europe—effects of land use change. Agric Ecosyst Environ 127(3–4):251–264. https://doi.org/10.1016/j.agee.2008.04.010
Shoyama K, Kamiyama C, Morimoto J, Ooba M, Okuro T (2017) A review of modeling approaches for ecosystem services assessment in the Asian region. Ecosyst Serv 26:316–328. https://doi.org/10.1016/j.ecoser.2017.03.013
Statistics Bureau Ministry of Internal Affairs and Communications Japan (2016) Statistical handbook of Japan 2016. Statistics Bureau, Ministry of Internal Affairs and Communications, Japan
Takeuchi K, Brown RD, Washitani I, Tsunekawa A, Yokohari M (eds) (2006) Satoyama: the traditional rural landscape of Japan. Springer, Tokyo. https://doi.org/10.1007/978-4-431-67861-8
Thapa RB, Murayama Y (2012) Scenario based urban growth allocation in Kathmandu Valley, Nepal. Landsc Urban Plan 105:140–148. https://doi.org/10.1016/j.landurbplan.2011.12.007
Turner BL, Lambin EF, Reenberg A (2007) The emergence of land change science for global environmental change and sustainability. Proc Natl Acad Sci USA 104(52):20666–20671. https://doi.org/10.1017/CBO9781107415324.004
United Nations (2017) World population prospects: the 2017 revision—key findings and advance tables. Department of Economic and Social Affairs, Population Division, United Nations, New York. https://doi.org/10.1017/cbo9781107415324.004
Verburg PH, Eickhout B, Meijl H (2008) A multi-scale, multi-model approach for analyzing the future dynamics of European land use. Ann Reg Sci 42(1):57–77. https://doi.org/10.1007/s00168-007-0136-4
Yang W, Li F, Wang R, Hu D (2011) Ecological benefits assessment and spatial modeling of urban ecosystem for controlling urban sprawl in Eastern Beijing, China. Ecol Complex 8:153–160. https://doi.org/10.1016/j.ecocom.2011.01.004
Zhao S, Peng C, Jiang H, Tian D, Lei XD, Zhou XL (2006) Land use change in Asia and the ecological consequences. Ecol Res 21:890–896. https://doi.org/10.1007/s11284-006-0048-2
Acknowledgements
This research was funded by the Environment Research and Technology Development Fund (S-15 “Predicting and Assessing Natural Capital and Ecosystem Services” (PANCES), Ministry of the Environment, Japan). We also thank for helpful comments of anonymous reviewers and members of “Assessing land use functions for sustainable land management in Asia countries (CRRP2016-04MY-Zhen)” funded by Asia-Pacific Network for Global Change Research.
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Handled by Rajarshi DasGupta, The University of Tokyo, Japan.
Appendices: Transition matrices
Appendices: Transition matrices
1987–1998
Probability of changing to: | |||||||||
---|---|---|---|---|---|---|---|---|---|
Given: | LUC 1 | LUC 2 | LUC 3 | LUC 4 | LUC 5 | LUC 6 | LUC 7 | LUC 8 | LUC 9 |
1 | 0.987 | 0.000 | 0.004 | 0.003 | 0.001 | 0.000 | 0.000 | 0.000 | 0.004 |
2 | 0.040 | 0.940 | 0.005 | 0.001 | 0.002 | 0.000 | 0.005 | 0.004 | 0.003 |
3 | 0.033 | 0.008 | 0.919 | 0.003 | 0.002 | 0.001 | 0.012 | 0.014 | 0.007 |
4 | 0.024 | 0.005 | 0.007 | 0.946 | 0.002 | 0.001 | 0.005 | 0.006 | 0.004 |
5 | 0.042 | 0.010 | 0.022 | 0.021 | 0.726 | 0.013 | 0.054 | 0.087 | 0.025 |
6 | 0.003 | 0.001 | 0.007 | 0.006 | 0.019 | 0.910 | 0.007 | 0.043 | 0.005 |
7 | 0.025 | 0.013 | 0.007 | 0.005 | 0.016 | 0.003 | 0.822 | 0.090 | 0.020 |
8 | 0.013 | 0.005 | 0.009 | 0.005 | 0.010 | 0.006 | 0.016 | 0.926 | 0.012 |
9 | 0.030 | 0.001 | 0.004 | 0.001 | 0.001 | 0.000 | 0.004 | 0.010 | 0.949 |
BaU
Probability of changing to: | |||||||||
---|---|---|---|---|---|---|---|---|---|
Given: | LUC 1 | LUC 2 | LUC 3 | LUC 4 | LUC 5 | LUC 6 | LUC 7 | LUC 8 | LUC 9 |
1 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
2 | 0.000 | 0.870 | 0.000 | 0.000 | 0.130 | 0.000 | 0.000 | 0.000 | 0.000 |
3 | 0.000 | 0.000 | 0.869 | 0.000 | 0.131 | 0.000 | 0.000 | 0.000 | 0.000 |
4 | 0.000 | 0.000 | 0.000 | 0.868 | 0.132 | 0.000 | 0.000 | 0.000 | 0.000 |
5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.723 | 0.000 | 0.000 | 0.277 | 0.000 |
6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.999 | 0.000 | 0.001 | 0.000 |
7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.896 | 0.104 | 0.000 |
8 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 |
9 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
NC
Probability of changing to: | |||||||||
---|---|---|---|---|---|---|---|---|---|
Given: | LUC 1 | LUC 2 | LUC 3 | LUC 4 | LUC 5 | LUC 6 | LUC 7 | LUC 8 | LUC 9 |
1 | 0.862 | 0.000 | 0.000 | 0.000 | 0.138 | 0.000 | 0.000 | 0.000 | 0.000 |
2 | 0.000 | 0.917 | 0.000 | 0.000 | 0.083 | 0.000 | 0.000 | 0.000 | 0.000 |
3 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
4 | 0.000 | 0.000 | 0.000 | 0.972 | 0.028 | 0.000 | 0.000 | 0.000 | 0.000 |
5 | 0.000 | 0.000 | 0.078 | 0.000 | 0.779 | 0.000 | 0.128 | 0.016 | 0.000 |
6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 |
7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.013 | 0.987 | 0.000 | 0.000 |
8 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 |
9 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
ND
Probability of changing to: | |||||||||
---|---|---|---|---|---|---|---|---|---|
Given: | LUC 1 | LUC 2 | LUC 3 | LUC 4 | LUC 5 | LUC 6 | LUC 7 | LUC 8 | LUC 9 |
1 | 0.925 | 0.000 | 0.000 | 0.000 | 0.075 | 0.000 | 0.000 | 0.000 | 0.000 |
2 | 0.000 | 0.958 | 0.000 | 0.000 | 0.042 | 0.000 | 0.000 | 0.000 | 0.000 |
3 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
4 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
5 | 0.000 | 0.000 | 0.159 | 0.024 | 0.218 | 0.000 | 0.128 | 0.471 | 0.000 |
6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 |
7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.013 | 0.987 | 0.000 | 0.000 |
8 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 |
9 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
PC
Probability of changing to: | |||||||||
---|---|---|---|---|---|---|---|---|---|
Given: | LUC 1 | LUC 2 | LUC 3 | LUC 4 | LUC 5 | LUC 6 | LUC 7 | LUC 8 | LUC 9 |
1 | 0.932 | 0.000 | 0.000 | 0.000 | 0.068 | 0.000 | 0.000 | 0.000 | 0.000 |
2 | 0.000 | 0.757 | 0.000 | 0.000 | 0.243 | 0.000 | 0.000 | 0.000 | 0.000 |
3 | 0.000 | 0.000 | 0.864 | 0.000 | 0.136 | 0.000 | 0.000 | 0.000 | 0.000 |
4 | 0.000 | 0.000 | 0.000 | 0.803 | 0.197 | 0.000 | 0.000 | 0.000 | 0.000 |
5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.264 | 0.000 | 0.736 | 0.000 | 0.000 |
6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.999 | 0.001 | 0.000 | 0.000 |
7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 |
8 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.172 | 0.828 | 0.000 |
9 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
PD
Probability of changing to: | |||||||||
---|---|---|---|---|---|---|---|---|---|
Given: | LUC 1 | LUC 2 | LUC 3 | LUC 4 | LUC 5 | LUC 6 | LUC 7 | LUC 8 | LUC 9 |
1 | 0.964 | 0.000 | 0.000 | 0.000 | 0.036 | 0.000 | 0.000 | 0.000 | 0.000 |
2 | 0.000 | 0.820 | 0.000 | 0.000 | 0.000 | 0.000 | 0.180 | 0.000 | 0.000 |
3 | 0.000 | 0.000 | 0.935 | 0.000 | 0.065 | 0.000 | 0.000 | 0.000 | 0.000 |
4 | 0.000 | 0.000 | 0.000 | 0.870 | 0.130 | 0.000 | 0.000 | 0.000 | 0.000 |
5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.325 | 0.000 | 0.675 | 0.000 | 0.000 |
6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.999 | 0.001 | 0.000 | 0.000 |
7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 |
8 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.104 | 0.896 | 0.000 |
9 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
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Shoyama, K., Matsui, T., Hashimoto, S. et al. Development of land-use scenarios using vegetation inventories in Japan. Sustain Sci 14, 39–52 (2019). https://doi.org/10.1007/s11625-018-0617-7
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DOI: https://doi.org/10.1007/s11625-018-0617-7