Development of land-use scenarios using vegetation inventories in Japan

Shoyama, Kikuko; Matsui, Takanori; Hashimoto, Shizuka; Kabaya, Kei; Oono, Akiko; Saito, Osamu

doi:10.1007/s11625-018-0617-7

Cited by 28 publications

(25 citation statements)

References 23 publications

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“…The legend indicates the two scenarios, namely overlaid population distribution map and LULC map. The reason for the change in the number of grids is due to changes to certain types of land use, such as paddy fields or plantation forests, based on scenario assumptions [see Shoyama et al ( 2019 ) for details]. The grids which were intended to hold a population of only 0–20 people and be covered by the MNC were concerned to face possible shortages in labor required to maintain MNC.…”

Section: Resultsmentioning

confidence: 99%

“…6 , Noto Digital Archive Corporation). Natural capital-based scenarios for the LULC map assumed that naturally produced rice would be preferred and larger areas of paddy field would not be abandoned compared to BAU (Shoyama et al 2019 ). Ouchigata reclaimed land was also projected to be maintained as paddy fields, but in the compact scenario, more than half of the residents of this area were projected to be over 65 in 2050.…”

Section: Resultsmentioning

confidence: 99%

“…2050 LULC maps, developed according to Shoyama et al’s ( 2019 ) method, were used to conduct the overlay analysis. The maps correspond to four PANCES scenarios at the 500 m grid resolution.…”

Section: Methodsmentioning

confidence: 99%

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Projecting population distribution under depopulation conditions in Japan: scenario analysis for future socio-ecological systems

et al. 2020

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This study develops a projection model of future population distribution on the basis of Japan’s current depopulation trend and applies this model to scenario analyses that assume population compactification and dispersion. The model enables a description of population migration at two levels. First, municipal populations are projected using the cohort-component method, and second, the spatial distribution of populations within municipalities is projected at a 500 m grid resolution with the use of the gravity model. Based on the Japanese depopulation context and the country’s National Spatial Strategy, the compact scenario predicts the formation of medium-scale regional urban areas (population centers located across Japan) and the concentration of people on high-density population areas within municipalities. Meanwhile, the dispersed scenario predicts the formation of more but smaller regional urban areas and the dispersion of the population to low-density areas. The simulated population distribution for 2050 reveals spatial change in population density and age structure, as well as an abundance of areas that were inhabited in 2015 but will be zero population areas by 2050. Overlay analysis of future land use maps and the simulated population distribution maps can contribute toward identifying areas where natural capital such as farmland and forest plantation should be managed but where there will be significant population loss by 2050. Electronic supplementary material The online version of this article (10.1007/s11625-020-00835-5) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Projecting population distribution under depopulation conditions in Japan: scenario analysis for future socio-ecological systems

et al. 2020

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“…Section 3.1 conducts future predictions of natural capital at the municipality level while section 3.2 presents the results of the prediction. We predict the future of terrestrial natural capital up to 2050 in Japan by incorporating the value per unit of each natural capital estimated in section 2 and data on the Japanese population and land use prediction Shoyama et al, 2019). This subsection clarifies how much future natural capital will be affected by the type of population and land use scenarios Japan will follow, considering the differences between urban and rural areas.…”

Section: Introductionmentioning

confidence: 99%

Natural capital for nature’s contributions to people: the case of Japan

et al. 2021

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Recently, natural capital has gained the attention of researchers and policymakers to promote sustainability. Previous studies have investigated the value of ecosystem services with respect to specific areas or species. Other studies have investigated the value of various types of ecosystem services and natural capital by integrating a number of findings using meta-analyses at the global level. Although these studies have provided information on either the global value of natural capital or the local value of specific subjects, there is little evidence on the country-specific values of natural capital in Japan, which will provide useful information for national environmental policies. We investigated the perceived values of terrestrial and marine natural capital in Japan using internet surveys and payment card methods. Data on various natural forms of capital were collected in a unified format and comparable manner. We found that some explanatory variables, such as perceived importance and visit frequency, as well as sociodemographic characteristics, are significant drivers of the willingness to pay (WTP), which maintains each aspect of natural capital. In addition, we conducted future predictions of terrestrial and marine natural capital using a scenario developed in a previous study. Our results indicate that Japan should follow a population-dispersed scenario for the sustainable management of natural capital up to 2050.

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“…In the present study, MLPNN-based MCM was adopted to predict the forthcoming pattern of LULC. MLPNN-MCM is a hybrid type of model which was successfully used and tested by numerous researchers [47][48][49][50]. The capability of MLPNN to model numerous or even entire transitions at one time makes MLPNN-MCM better than other models to predict the future pattern of LULC [27].…”

Section: Lulc Simulation and Predictionmentioning

confidence: 99%

Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

2021

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In this study, prediction of the future land use land cover (LULC) changes over Mumbai and its surrounding region, India, was conducted to have reference information in urban development. To obtain the historical dynamics of the LULC, a supervised classification algorithm was applied to the Landsat images of 1992, 2002, and 2011. Based on spatial drivers and LULC of 1992 and 2002, the multiple perceptron neural network (MLPNN)-based Markov chain model (MCM) was applied to simulate the LULC in 2011, which was further validated using kappa statistics. Thereafter, by using 2002 and 2011 LULC, MLPNN-MCM was applied to predict the LULC in 2050. This study predicted the prompt urban growth over the suburban regions of Mumbai, which shows, by 2050, the Urban class will occupy 46.87% (1328.77 km2) of the entire study area. As compared to the LULC in 2011, the Urban and Forest areas in 2050 will increase by 14.31% and 2.05%, respectively, while the area under the Agriculture/Sparsely Vegetated and Barren land will decline by 16.87%. The class of water and the coastal feature will experience minute fluctuations (<1%) in the future. The predicted LULC for 2050 can be used as a thematic map in various climatic, environmental, and urban planning models to achieve the aims of sustainable development over the region.

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Development of land-use scenarios using vegetation inventories in Japan

Cited by 28 publications

References 23 publications

Projecting population distribution under depopulation conditions in Japan: scenario analysis for future socio-ecological systems

Projecting population distribution under depopulation conditions in Japan: scenario analysis for future socio-ecological systems

Natural capital for nature’s contributions to people: the case of Japan

Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

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