Remote Sensing-Based Analysis of Landscape Pattern Evolution in Industrial Rural Areas: A Case of Southern Jiangsu, China

Zhu, Yifan; Wang, Chengkang; Takeru, Sakai

doi:10.3390/su11184994

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…The present study confirmed that the settlement area, the shape index, and the spreading index had a more significant coupling relationship with the degree of the landscape fragmentation in woodlands and biological reserves, which was consistent with the findings of Zhu et al [92] and Xu et al [93]. However, the existing studies did not analyze the specific manifestations of the differences in the effects of settlement indicators on woodlands and biological reserves.…”

Section: Settlement Patterns and Fragmentation Of Woodlands And Biolo...supporting

confidence: 92%

Coupling Relationship between Rural Settlement Patterns and Landscape Fragmentation in Woodlands and Biological Reserves—A Case of Nanshan National Park

Ouyang

Wang

et al. 2023

Land

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Exploring the influence of settlement patterns on the landscape fragmentation in woodlands and biological reserves is key to achieving ecologically sustainable development. In this research, we chose the Nanshan National Park in Hunan Province, China, as a case study, to explore the influence mechanisms. First, we identified the biological reserves through the landscape security patterns of biological conservation. Second, we constructed a coupling coordination model to analyze the coupling relationship between the settlement patterns and landscape fragmentation in the woodlands and biological reserves. The analysis showed that, overall, the effect of the settlement area on the landscape fragmentation in the biological reserves was more pronounced, while the effect of the settlement spread and shape on the landscape fragmentation in the woodlands was more obvious. From a type-specific perspective, we analyzed the coupling relationship between the settlement patterns and (1) the landscape fragmentation in different woodlands and (2) the landscape fragmentation in the biological reserves, namely concerning Leiothrix lutea and Emberiza aureola. We found that the effect of the settlement patterns on the landscape fragmentation of the Leiothrix lutea biological reserve was more significant than that of the landscape fragmentation of its main habitat, the evergreen broad-leaved forest. The effect of settlement patterns on the landscape fragmentation of the Emberiza aureola biological reserve was more significant than that of the landscape fragmentation of its other habitats. In addition, the results demonstrated that the habitat protection of the woodlands was not a substitute for the systematic protection of biosecurity patterns. This research could assist in developing more efficient conservation measures for ecologically protected sites with rural settlements.

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Section: Settlement Patterns and Fragmentation Of Woodlands And Biolo...supporting

confidence: 92%

Coupling Relationship between Rural Settlement Patterns and Landscape Fragmentation in Woodlands and Biological Reserves—A Case of Nanshan National Park

Ouyang

Wang

et al. 2023

Land

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“…This land cover is somewhat sensitive to erosion [20], very slow permeability and compaction of topsoil [33] with the characteristics of soil types that have low fill weights [34], classification of humid rainfall intensity, and mountainous topography that is prone to failure surface slopes [35]. The evolution of landscape patterns leads to changes in spatial structure and landscape ecosystems which will ultimately affect ecological security [36]. Forests, plantation, and water bodies are the main source of ecological security [37], external forces (eg typhoons with heavy rainfall and earthquakes), and human activities (eg changes in land cover and deforestation result in complex interactions of various landslides [38].…”

Section: Resultsmentioning

confidence: 99%

A spatial study of landscape patterns in landslide-prone areas at cikakak sub-district, west java

Aisyah

Supriatna²,

Rustanto³

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Landslides have a record of deadliest impact in Indonesia during 2015 – 2017. Landslide may be caused by human activities which deteriorate the environment condition such as by reconstructing the landscapes that may reduce the ecological functions. Therefore, mapping the landslide-prone area is necessary as one of the efforts to mitigate the disaster that could be following. The methods used in this study are the landscape metrics with FRAGSTATS 4.2. and Index Storie. Landscape metrics is useful to map the landscape pattern while the Index Storie can be used to map the landslide-prone area, both Indexes then being used for further analysis to determine the relationship between landscape patterns and landslide-prone areas in the study area. Deforestation in the study area is indicated by the values of PD, LPI, and IJI, the Index Storie shows that areas of moderate landslide-prone areas dominate the study area and have specific characteristics that trigger the landslides. A combination of landscape metrics and Index Storie show that the relationship between landscape patterns and landslide-prone areas can be one of the priorities determining tools used for monitoring and planning land cover as an effort to mitigate landslides.

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“…When the landscape has only one patch, the SHDI equals 0, and when the patch type increases, the value of the SHDI also increases. The evolution process of landscape patterns can be determined by investigating the basic characteristics, morphological changes and spatiotemporal evolution of the various landscape categories [38]. Table 3.…”

Section: Landscape Indexesmentioning

confidence: 99%

Vegetation-Ice-Bare Land Cover Conversion in the Oceanic Glacial Region of Tibet Based on Multiple Machine Learning Classifications

Yang

Liu

et al. 2020

Remote Sensing

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Oceanic glaciers are one of the most sensitive indicators of climate change. However, remotely sensed evidence of land cover change in the oceanic glacial region is still limited due to the cloudy weather during the growing season. In addition, the performance of common machine learning classification algorithms is also worth testing in this cloudy, frigid and mountainous region. In this study, three algorithms, namely, the random forest, back-propagation neural network (BPNN) and convolutional neural network algorithms, were compared in their interpretation of the land cover change in south-eastern Tibet and resulted in three findings. (1) The BPNN achieves the highest overall accuracy and Kappa coefficient compared with the other two algorithms. The overall accuracy was 97.82%, 98.07%, 98.92%, and 94.63% in 1990, 2000, 2007, and 2016, and the Kappa coefficient was 0.958, 0.959, 0.980, and 0.918 in these four years, respectively. (2) From 1990 to 2000, the dominant land cover was ice at the landscape level. The landscape fragmentation decreased and the landscape aggregation increased. From 2000 to 2016, the dominant land cover transformed from ice to vegetation. The vegetation aggregation increased, while the ice aggregation decreased. (3) When the elevation was less than 4 km, the vegetation was usually transformed into bare land; otherwise, the probability of direct transformation between vegetation and ice increased. The findings on the land cover transformation in the oceanic glacial region by multiple classification algorithms can provide both long-term evidence and methodological indications to understand the recent environmental change in the “third pole”.

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Remote Sensing-Based Analysis of Landscape Pattern Evolution in Industrial Rural Areas: A Case of Southern Jiangsu, China

Cited by 10 publications

References 23 publications

Coupling Relationship between Rural Settlement Patterns and Landscape Fragmentation in Woodlands and Biological Reserves—A Case of Nanshan National Park

Coupling Relationship between Rural Settlement Patterns and Landscape Fragmentation in Woodlands and Biological Reserves—A Case of Nanshan National Park

A spatial study of landscape patterns in landslide-prone areas at cikakak sub-district, west java

Vegetation-Ice-Bare Land Cover Conversion in the Oceanic Glacial Region of Tibet Based on Multiple Machine Learning Classifications

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