Spatiotemporal variation of alpine gorge watershed landscape patterns via multi-scale metrics and optimal granularity analysis: a case study of Lushui City in Yunnan Province, China
{"title":"Spatiotemporal variation of alpine gorge watershed landscape patterns via multi-scale metrics and optimal granularity analysis: a case study of Lushui City in Yunnan Province, China","authors":"Yongshu Wang, Xiangdong Yan, Qingping Fang, Lan Wang, Dongbo Chen, Zhexiu Yu","doi":"10.3389/fevo.2024.1448426","DOIUrl":null,"url":null,"abstract":"IntroductionThe selection of an optimal scale or granularity in landscape analysis is pivotal for uncovering inherent patterns and changes driven by processes. Variations in spatial resolution can significantly alter the proportions and distributions of various landscape types, thereby impacting the assessment of landscape patterns. Despite its importance, the scale factor is frequently neglected in studies focusing on long-term landscape dynamics.MethodsBridging this gap, we utilized remote sensing imagery data from 1986 to 2020 for Lushui City, integrating remote sensing (RS) and geographic information system (GIS) technologies to generate land cover maps. Our focus centered on investigating the sensitivity of landscape pattern indices within the 30–1000m scale. Combining the first scale domain with an information loss assessment model, we identified the optimal granularity for the analysis, conducting a detailed spatiotemporal examination of landscape pattern from 1986 to 2020 using the index analysis method.Results and discussionThe results show that: (1) The dominance of forests in Lushui City, yet reveal a significant increase in construction land area over the study period, primarily driven by the conversion of forest and grassland. (2) Among the 10 examined indices, four (PD, ED, TE, and LSI) demonstrated predictable responses to changes in granularity, while three (PAFEAC, COHESION, AI) exhibited unpredictable stepwise reactions. Three indices (LPI, SHDI, PLAND) displayed minimal regularity to granularity changes. (3) The optimal long-term landscape analysis granularity for Lushui was identified as 100 m. (4) Before 1996, the city’s landscape exhibited characteristics of aggregation, good connectivity, and minimal anthropogenic disturbance. However, post-1996, the landscape experienced disruptions, leading to an overall increase in fragmentation. The expansion of cultivated land and construction land due to urbanization has intensified landscape fragmentation. However, policies such as converting cropland to forest and planned ecological civilization initiatives have restored forest coverage and improved landscape cohesion and connectivity in Lushui City. This research offers vital insights for ecological planning and resource management in alpine valley watershed cities, deepening our grasp of landscape pattern evolution.","PeriodicalId":12367,"journal":{"name":"Frontiers in Ecology and Evolution","volume":"122 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Ecology and Evolution","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.3389/fevo.2024.1448426","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
IntroductionThe selection of an optimal scale or granularity in landscape analysis is pivotal for uncovering inherent patterns and changes driven by processes. Variations in spatial resolution can significantly alter the proportions and distributions of various landscape types, thereby impacting the assessment of landscape patterns. Despite its importance, the scale factor is frequently neglected in studies focusing on long-term landscape dynamics.MethodsBridging this gap, we utilized remote sensing imagery data from 1986 to 2020 for Lushui City, integrating remote sensing (RS) and geographic information system (GIS) technologies to generate land cover maps. Our focus centered on investigating the sensitivity of landscape pattern indices within the 30–1000m scale. Combining the first scale domain with an information loss assessment model, we identified the optimal granularity for the analysis, conducting a detailed spatiotemporal examination of landscape pattern from 1986 to 2020 using the index analysis method.Results and discussionThe results show that: (1) The dominance of forests in Lushui City, yet reveal a significant increase in construction land area over the study period, primarily driven by the conversion of forest and grassland. (2) Among the 10 examined indices, four (PD, ED, TE, and LSI) demonstrated predictable responses to changes in granularity, while three (PAFEAC, COHESION, AI) exhibited unpredictable stepwise reactions. Three indices (LPI, SHDI, PLAND) displayed minimal regularity to granularity changes. (3) The optimal long-term landscape analysis granularity for Lushui was identified as 100 m. (4) Before 1996, the city’s landscape exhibited characteristics of aggregation, good connectivity, and minimal anthropogenic disturbance. However, post-1996, the landscape experienced disruptions, leading to an overall increase in fragmentation. The expansion of cultivated land and construction land due to urbanization has intensified landscape fragmentation. However, policies such as converting cropland to forest and planned ecological civilization initiatives have restored forest coverage and improved landscape cohesion and connectivity in Lushui City. This research offers vital insights for ecological planning and resource management in alpine valley watershed cities, deepening our grasp of landscape pattern evolution.
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