Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics

Arora, Aman; Pandey, Manish; Mishra, Varun; Kumar, Rishi; Kumar, Praveen; Costache, Romulus; Punia, Milap; Di, Liping

doi:10.1016/j.ecolind.2021.107810

Cited by 54 publications

(20 citation statements)

References 70 publications

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“…Landscape pattern metrics have been widely used to reveal the landscape structure and spatial processes, including indices at three spatial levels: patches, categories and landscape [62][63][64][65]. To explore the spatial patterns of diverse land-use types within the county, this study selected the mean patch fragmentation dimension (MPED) and aggregation index (AI) to measure the morphology and spatial agglomeration degree of diverse land-use types at the class level [66,67].…”

Section: Landscape Pattern Metricsmentioning

confidence: 99%

Spatio-Temporal Variation and Driving Forces of Land-Use Change from 1980 to 2020 in Loess Plateau of Northern Shaanxi, China

Zhou

2021

Land

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Land-use cover is undergoing intense change under global climate change and rapid urbanization, especially in the Loess Plateau, where ecological restoration policies like Green for Grain Project (GFGP) have been vigorously implemented since the 1980s. The main objective of this study was to distinguish the difference of spatio-temporal variation of land-use change in the two study periods of 1980–2000 and 2000–2020 at the county scales. Geographically and temporally weighted regression (GTWR) was employed to handle both the spatial and temporal heterogeneity of the driving forces for land use change. The results showed that the quantity of construction land, woodland and grassland experienced continuous growth, but arable land declined substantially. The results of GTWR model showed that the dominant influencing factors of land-use change had temporal and spatial differences in the Loess Plateau. Specifically, the implementation of GFGP and precipitation accelerated the changes in arable land, grassland and woodland. For construction land, its growth was mainly promoted by gross domestic product (GDP) and population, both of which had more obvious positive effects in the last 20 years. The findings provide a scientific basis to put forward countermeasures emphasizing sustainable land use in the Loess Plateau.

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Section: Landscape Pattern Metricsmentioning

confidence: 99%

Spatio-Temporal Variation and Driving Forces of Land-Use Change from 1980 to 2020 in Loess Plateau of Northern Shaanxi, China

Zhou

2021

Land

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“…The root mean square error (RMSE) and mean relative error (MRE) are widely used to evaluate the accuracy of geospatial modeling [38], so these two indicators are used to evaluate the GWANN in this paper, as shown in Equations ( 15) and ( 16). The results of the two indicators are shown in Table 4, and the mean RMSE for the 11 years of GWANN in the whole study area is 0.0526, while the mean MRE is 0.1029.…”

Section: Accuracy Of Gwannmentioning

confidence: 99%

A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

Zhang

Zheng

et al. 2022

IJERPH

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Identifying the spatial range of mining disturbance on vegetation is of significant importance for the plan of environmental rehabilitation in mining areas. This paper proposes a method to identify the spatial range of mining disturbance (SRMD). First, a non-linear and quantitative relationship between driving factors and fractional vegetation cover (FVC) was constructed by geographically weighted artificial neural network (GWANN). The driving factors include precipitation, temperature, topography, urban activities, and mining activities. Second, the contribution of mining activities (Wmine) to FVC was quantified using the differential method. Third, the virtual contribution of mining activities (V-Wmine) to FVC during the period without mining activity was calculated, which was taken as the noise in the contribution of mining activities. Finally, the SRMD in 2020 was identified by the significance test based on the Wmine and noise. The results show that: (1) the mean RMSE and MRE for the 11 years of the GWANN in the whole study area are 0.0526 and 0.1029, which illustrates the successful construction of the relationship between driving factors and FVC; (2) the noise in the contribution of mining activities obeys normal distribution, and the critical value is 0.085 for the significance test; (3) most of the SRMD are inside the 3 km buffer with an average disturbance distance of 2.25 km for the whole SRMD, and significant directional heterogeneity is possessed by the SRMD. In conclusion, the usability of the proposed method for identifying SRMD has been demonstrated, with the advantages of elimination of coupling impact, spatial continuity, and threshold stability. This study can serve as an early environmental warning by identifying SRMD and also provide scientific data for developing plans of environmental rehabilitation in mining areas.

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“…Human activities thus greatly influence the landscape pattern of the Dapoling watershed. The ED value has an increase of 0.135 at the watershed scale, indicating that the edge density and total length of the landscape patches are increasing, the boundary between the patches is growing, and the shape of the landscape patches is becoming more irregular [65].…”

Section: Analysis Of Landscape Pattern Metricsmentioning

confidence: 99%

Response of Variation of Water and Sediment to Landscape Pattern in the Dapoling Watershed

et al. 2022

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The relationship between water-sediment processes and landscape pattern changes has currently become a research hotspot in low-carbon water and land resource optimization research. The SWAT-VRR model is a distributed hydrological model which better shows the effect of land use landscape change on hydrological processes in the watershed. In this paper, the hydrological models of the Dapoling watershed were built, the runoff and sediment yield from 2006 to 2011 were simulated, and the relationship between landscape patterns and water-sediment yield was analyzed. The results show that the SWAT-VRR model is more accurate and reasonable in describing runoff and sediment yield than the SWAT model. The sub-basins whose soil erosion is relatively light are mostly concentrated in the middle reaches with a slope mainly between 0–5°. The NP, PD, ED, SPIIT, SHEI, and SHDI of the watershed increased slightly, and the COHESION, AI, CONTAG, and LPI showed a certain decrease. The landscape pattern is further fragmented, with the degree of landscape heterogeneity increasing and the connection reducing. The runoff, sediment yield and surface runoff are all extremely significantly negatively correlated with forest, which implies that for more complicated patch shapes of forest which have longer boundaries connecting with the patches of other landscape types, the water and sediment processes are regulated more effectively. Therefore, it can be more productive to carry out research on the optimization of water and soil resources under the constraint of carbon emission based on the SWAT-VRR model.

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Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics

Cited by 54 publications

References 70 publications

Spatio-Temporal Variation and Driving Forces of Land-Use Change from 1980 to 2020 in Loess Plateau of Northern Shaanxi, China

Spatio-Temporal Variation and Driving Forces of Land-Use Change from 1980 to 2020 in Loess Plateau of Northern Shaanxi, China

A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

Response of Variation of Water and Sediment to Landscape Pattern in the Dapoling Watershed

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