2023
DOI: 10.1016/j.ecolind.2023.110211
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Spatiotemporal changes in landscape patterns in karst mountainous regions based on the optimal landscape scale: A case study of Guiyang City in Guizhou Province, China

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Cited by 38 publications
(8 citation statements)
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“…The advantage of this is that two indicators can confirm the study results more accurately than one indicator and can avoid excessive occurrence of indicators with the same ecological implications. Finally, compared with the studies of Wang [48] and Hu [50], the scale effect of LP was not considered in our study. Therefore, in future, similar studies, it is necessary to explore the differences in the effects of AD on LP at different scales.…”
Section: Contributions and Limitationsmentioning
confidence: 99%
“…The advantage of this is that two indicators can confirm the study results more accurately than one indicator and can avoid excessive occurrence of indicators with the same ecological implications. Finally, compared with the studies of Wang [48] and Hu [50], the scale effect of LP was not considered in our study. Therefore, in future, similar studies, it is necessary to explore the differences in the effects of AD on LP at different scales.…”
Section: Contributions and Limitationsmentioning
confidence: 99%
“…Xifeng, Xiuwen, Kaiyang, and Wudang had less LUI and ecological space as they were less urbanized overall. As a mountainous city, geographic factors and government policies influenced and restricted urban expansion and LUC in Guiyang [67]. In Guiyang, since 2000, economic development has been increasing, and its ESV was low in the central city.…”
Section: Dynamics Of Land Use Esv and Their Coupling Relationship In ...mentioning
confidence: 99%
“…Most rapidly growing populations dwell in cities and exert more pressure on urban forests, causing vegetation fragmentation and loss of landscape connectivity [24,44]. To quantify landscape configuration, the indexes referring to the relevant literature were calculated by employing the Fragstats 4.2 software as shown in Table 1 [45][46][47]. The dominance, complex, aggregation, fragmentation, and homogeneity were described by these typical indexes at class and landscape levels.…”
Section: Landscape Pattern Indexesmentioning
confidence: 99%