A Time Series Land Ecosystem Classification Dataset of China in Five-Year Increments (1990–2010)

L, Xu X; Y, Liu J; Zhang, Z X

doi:10.3974/geodp.2017.01.08

Cited by 19 publications

(3 citation statements)

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“…To explore the effects of land use conversion on ALTC, 5‐year interval land use cover data (2000–2010) of Heilongjiang Province are used to develop a model of the effects of land use conversion on soil properties and agricultural infrastructure (Xu et al, 2017). The distribution of soil properties and agricultural infrastructure in 2000, 2005, and 2010 was obtained from the land use change data and the evaluation index of 2.4.…”

Section: Methodsmentioning

confidence: 99%

“…Moran's I can calculate the average similarity and dependence of spatial units. Local spatial autocorrelation (Anselin Local Moran's I, LISA) explores local characteristic differences in spatial distribution, which was proposed by Anselin in the 1950s to identify whether a unit forms clusters or significant outliers in space.The calculation for Moran's I and LISA is shown in Appendix A.7.2.6 | Land use change impact modelTo explore the effects of land use conversion on ALTC, 5-year interval land use cover data(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) of Heilongjiang Province are used to develop a model of the effects of land use conversion on soil properties and agricultural infrastructure(Xu et al, 2017). The distribution of soil properties and agricultural infrastructure in2000, 2005, and 2010 was obtained from the land use change data and the evaluation index of 2.4.…”

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confidence: 99%

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Land use evaluation considering soil properties and agricultural infrastructure in black soil region

Ren,

Song,

et al. 2023

Land Degrad Dev

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Systematic assessment of arable land use is a fundamental prerequisite to explore its sustainable development path. Agricultural infrastructure integrated with the tillage conditions and soil properties was used to evaluate the state of regional arable land use and its potential for sustainable productivity. We propose a combined weighting method integrating Delphi and entropy weights to consider both decision objectives and indicator attributes. The proposed approach takes into account both expertise and data feature, making the evaluating results more rational and applicable. The impacts of large‐scale land use change and regional urban distribution on soil properties and agricultural infrastructure were also explored to develop more rational and differentiated conservation strategies. Our evaluation showed that 44% and 48% of the soil properties of arable land in Heilongjiang Province, China, are in the excellent or good grades, respectively, meaning that no or only minor remediation measures are needed to achieve optimum conditions. Agricultural infrastructure deserves more attention from the management than soil properties, as only 16% and 24% of area have the same excellent and good grades. The results of the evaluation with a combination of subjective and objective weights are closer to a normal distribution curve than if only expert weights are used, which confirms our hypothesis that the new weighting method is more reasonable. The newly proposed weighted design method and index provide a better understanding of the sustainable productivity of agricultural areas and have a promising application in large‐scale black soil areas worldwide. The future rough growth strategy for resources will result in degradation and posed risks to regional ecological conservation. At the provincial level (up to 130 km), agricultural infrastructure declines and then rises as fields move away from cities, with the inflection point at 55 km. State‐owned farms are mainly responsible for this inflection point, which shift the agents of arable land from small farmers to large capital, with remote arable land receiving more investment. The impact mechanisms of urbanization should be deeper explored to address the challenges for arable land conservation.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Land use evaluation considering soil properties and agricultural infrastructure in black soil region

Ren,

Song,

et al. 2023

Land Degrad Dev

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“…The data for 2005 and 2010 were derived from Landsat-TM/ETM remote sensing image data respectively, and the data for 2015 were interpreted using Landsat 8 remote sensing image. After the data was corrected and manually interpreted, the comprehensive evaluation accuracy of the interpretation accuracy of the first-class types of cultivated land, woodland, grassland, water area, urban land, and unused land reached more than 94.30%, and the discrimination accuracy rate on the map patches reached 98.70% (Xu et al, 2017). Within the allowable error range, it can be used as the basic data for analyzing land use changes.…”

Section: Datamentioning

confidence: 99%

Multi-scenario urban flood risk assessment by integrating future land use change models and hydrodynamic models

Sun

Fang

Dang

et al. 2021

Preprint

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Abstract. Urbanization and climate change are the critical challenges in the 21st century. Flooding by extreme weather events and human activities can lead to catastrophic impacts in fast-urbanizing areas. However, high uncertainty in climate change and future urban growth limit the ability of cities to adapt to flood risk. This study presents a multi-scenario risk assessment method that couples the future land use simulation model (FLUS) and floodplain inundation model (LISFLOOD-FP) to simulate and evaluate the impacts of future urban growth scenarios with flooding under climate change (two representative concentration pathways (RCPs 2.6 and 8.5)). By taking Shanghai coastal city as an example, we then quantify the role of urban planning policies in future urban development to compare urban development under multiple policy scenarios (Business as usual, BU; Growth as planned, GP; Growth as eco-constraints, GE). Geospatial databases related to anthropogenic flood protection facilities, land subsidence, and storm surge are developed and used as inputs to the LISFLOOD-FP model to estimate flood risk under various urbanization and climate change scenarios. The results show that urban growth under the three scenario models manifests significant differences in expansion trajectories, influenced by key factors such as infrastructure development and policy constraints. Comparing the urban inundation results for the RCP2.6 and RCP8.5 scenarios, the urban inundation area under the GE scenario is less than that under the BU scenario, but more than that under the GP scenario. We also find that urban will tend to expand to areas vulnerable to floods under the restriction of ecological environment protection. The increasing flood risk information determined by the coupling model helps to understand the spatial distribution of future flood-prone urban areas and promote the re-formulation of urban planning in high-risk locations.

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A method for evaluating ecological quality levels and trends using natural evolution time series

Li,

Wei,

Xue

et al. 2024

Environ Sci Pollut Res

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A Time Series Land Ecosystem Classification Dataset of China in Five-Year Increments (1990–2010)

Cited by 19 publications

References 0 publications

Land use evaluation considering soil properties and agricultural infrastructure in black soil region

Land use evaluation considering soil properties and agricultural infrastructure in black soil region

Multi-scenario urban flood risk assessment by integrating future land use change models and hydrodynamic models

A method for evaluating ecological quality levels and trends using natural evolution time series

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