Multi-Scenario Simulation of Production-Living-Ecological Space in the Poyang Lake Area Based on Remote Sensing and RF-Markov-FLUS Model

Abstract

With industrialization and urbanization, the competition among land production, living, and ecological (PLE) spaces has intensified. Particularly in ecological reserves, competition among various types of land use restricts the coordinated development of PLE space. To explore spatial sustainable development, this study starts from a PLE spatial perspective, based on Landsat long time series images. Object-based image analysis (OBIA) and landscape index analysis were selected to monitor the spatial and temporal land use and landscape pattern changes in the Poyang Lake region (PYL region) from 1989 to 2020. The RF-Markov-FLUS coupled model was used to simulate spatial changes in 2030 under four scenarios: production space priority (PSP), living space priority (LSP), ecological space priority (ESP), and an integrated development (ID). Finally, the goal-problem-principle was used to enhance PLE space. The results showed that: (1) production space and ecological spaces decreased in general from 1989 to 2020 by 3% and 7%, respectively; living space increased by 11%. (2) From 1989 to 2020, the overall landscape spread in the Poyang Lake (PYL) area decreased, connectivity decreased, fragmentation increased, landscape heterogeneity increased, and landscape geometry became more irregular. (3) Compared with the other three scenarios, the ID scenario maintained steady production space growth in 2030, the expansion rate of living space slowed, and the area of ecological space decreased the least. (4) Spatial pattern optimization should start with three aspects: the transformation of the agricultural industry, improving the efficiency of urban land use, and establishing communities of “mountains, water, forests, fields, lakes and grasses”. The results provide scientific planning and suggestions for the future ecological protection of Poyang Lake area with multiple scenarios and perspectives.

Land Use Transformation Based on Production-Living-Ecological Space and Associated Eco-Environment Effects: Evidence from the Upper Reaches of the Yangtze River, China

“…The theory of PLES has gradually attracted wide attention from the academic community and the understanding of its conceptual connotation has been deepened (Gao et al, 2016;Wang et al, 2010;Xu et al, 2022). On a national scale, the PLES correspond to the main functional areas, i.e., production space, living space, and ecological space correspond to key development zones, restricted development zones, and prohibited development zones, respectively (Li, H. et al, 2022;Li et al, 2021). However, this view does not apply to smallscale research at the county level in practice.…”

“…Subsequently, the research on land use transformation has continued to be integrated with other disciplines, achieving tremendous results in terms of research, methods and scales. Speci cally, the research content of land use is mainly involved in the overall or single land type transformation and change of agricultural land (Izquierdo and Grau, 2009), urban land (Bach et al, 2015), rural land (Zhou et al, 2020) and forest land (Ferreira et al, 2015;Shunzhou et al, 2018), to address the impact of land use transformation on the economy, society and natural environment (Lin et al, 2022;Tian et al, 2020), summarize the in uencing factors and development laws of land use transformation (Liu et al, 2016), and predict the number of land use transformation and future trends in the region (Li, H. et al, 2022;Zhang, S. et al, 2022). According to different research contents, comprehensive or single land use dynamic degree are chosen to study the variation characteristics of land quantity transformation for different regional land structure and quantity share (Zhou et al, 2017).…”

Spatial Identification and Change Analysis of Production-Living-Ecological Space Using Multi-Source Geospatial Data: A Case Study in Jiaodong Peninsula, China

“…Compared to the existing methods for spatially identifying PLES, the proposed method shows a higher level of generalizability. Specifically, in contrast to the existing methods that solely rely on RSI and LC data [13,22], the proposed method offers a finer-grained identification of PS and LS, making it effectively applicable to cities or regions with primary industries in agriculture, industry, or commercial and service sectors. Furthermore, in contrast to the existing methods that solely utilize social sensing data or combine multisource data [5,29,34,35], the proposed method effectively addressed the issues that limit the application of POI data in NUBUA, making it not only suit UBUA with pronounced human socioeconomic activities but also demonstrate robust applicability within NUBUA.…”

“…The convenience of LC data acquisition piqued scholars' interest in its application to PLES spatial identification. Li et al [22] utilized RSI to obtain LC data and identified PLES by establishing a mapping relationship between LC types and PLES types. Similar to the method solely reliant on RSI, this method also faces challenges in accurately and comprehensively identifying PS and LS due to the lack of human socioeconomic activity information.…”

Temporal and Spatial Evolution of Land Use in Gansu Section of The Yellow River Basin and Analysis of Driving Forces

“…The ecological environmental quality index [11] is often introduced into the study of the ecological effects of land use. GCAM-CA model [12], CLUE-S model [13] and grey prediction model [14] are utilized in predicting the quantity and structure of land use. The scale of research is gradually altering from a global scale to a regional scale [15].…”