Effects of Urbanization on Landscape Patterns in the Middle Reaches of the Yangtze River Region

Abstract

The middle reaches of the Yangtze River region (MRYRR) are China’s first trans-regional urban agglomeration, located in the center of the Yangtze River Economic Belt. The MRYRR is an important ecological reserve, and its land cover changes are affected by both socio-economic development and geographical environment. In this paper, Landsat ETM/TM/OLI remote sensing images were used to monitor land use and landscape patterns from 1990 to 2015. Through supervised classification, land use transfer matrix, landscape pattern metrics and correlation analysis, the spatial-temporal patterns of land use change and its relationship with socio-economic in the study area were revealed. The results showed that: (1) the main land use types in the study area were cropland (CL) and forestland (FL), accounting for more than three-quarters of the study area. During the study period, built-up land (BL) increased, CL decreased, FL increased first and then decreased; (2) the BL expanded mainly by occupying CL and FL, and regional landscape pattern was gradually fragmented, with complex patch shape and increasing diversity and heterogeneity. Among them, the BL is gradually gathered, and the FL and CL are gradually fragmented; (3) in the past 25 years, the urbanization process in this region has been obvious, and the Gross Domestic Product (GDP) has increased by 36 times. The socioeconomic variables were positively correlated with BL, orchard (OL) and Shannon diversity index (SHID), and negatively correlated with CL, Wasteland (WL), mean patch size (MPS) and contagion size (CONTAG). The results showed that the urbanization development has a great impact on the region, and the ecological protection task is still challenging. It is necessary to protect high-quality cropland and draw a red line for ecological protection. We should strengthen the construction of ecological corridors and ecological nodes to adapt to regional sustainable development.

Actor Resistance Influences Effectiveness of Ostrom’s Design Principles for Governing Contested Landscapes

“…In theory, this could distort the true relationship between the response variable (landscape metrics) and the predictor variable (the level of resistance). Urbanization and changes in landscape metrics are interconnected (Yi et al, 2021) and the rate of urbanization may influence the results of the model. We checked for an influence of rate of urbanization on the relationship between landscape metrics and levels of resistance using linear regression (Pourhoseingholi, Baghestani, & Vahedi, 2012).…”

Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing

“…To assess changes in the structural characteristics of landscape patterns at the sub-watershed level, we selected the number of patches (NP), mean patch size (MPS), patch density (PD) and patch cohesion index (COHESION), to characterize landscape patterns ( Table 1 ) [ 62 , 63 ]. All calculations were extracted from the FRAGSTATS 4.2 software (Developed by the Clark Labs, Clark University).…”

“…The kappa coefficient is calculated as follows [ 62 ]: where is the observed consistency rate between the reference data and the simulation data, is the expected correct simulation proportion in the case of randomness and is the correct simulation proportion in the case of ideal classification, which is generally 1.…”

The External Characteristics and Mechanism of Urban Road Corridors to Agglomeration: Case Study for Guangzhou, China

“…From 1950 to the present, theories on the interaction between urban roads and urban society and other elements mainly focused on the biological environment and landscape [1][2][3][4][5][6], urban agglomeration and urbanization [7][8][9][10][11][12], industrial transformation [13][14][15][16], land agglomeration use [17][18][19][20][21], real estate development [22][23][24][25][26][27][28], location [29][30][31][32][33][34][35][36][37], mixed land use [38][39][40][41], and property right [42,43]. Based on quantitative models, scholars have conducted a lot of research mainly on the interaction between economy, social demand [44,45], land and transportation [46].…”