R-IMNet: Spatial-Temporal Evolution Analysis of Resource-Exhausted Urban Land Based on Residual-Intelligent Module Network

Abstract

The transformation of resource-exhausted urban land is an urgent problem for sustainable urban development in the world today. Obtaining the urban land use type and analyzing the changes in their land use can lead to better management of the relationship between economic development and resource utilization. In this paper, a residual-intelligent module network was proposed to solve the problems of low classification accuracy and missing objects edge information in traditional computer classification methods. The classification of four Landsat-TM/OLI images from 1993–2020 for Jiaozuo city (the first batch of resource-exhausted cities in China) was realized by this method. The results (overall accuracy was 98.61%, in 2020 images) were better than the comparison models (support vector machine, 2D-convolutional neural network, hybrid convolution networks; overall accuracy was 87.12%, 96.16%, 98.46%, respectively) and effectively reduced the loss of information on the edge of the ground objects. On this basis, six main land use types were constructed by combining field surveys and other methods. The characteristics and driving forces of spatial-temporal change in land use were explored from the aspect of social, economic and policy factors. The results showed that from 1993 to 2020 the cultivated land, forest land, water body and other land types in the study area decreased by 690.97 km2, 57.54 km2, 47.04 km2 and 59.43 km2, respectively. The construction land and bare land increased by 839.38 km2 and 15.57 km2, respectively. The transfer of land use types was mainly from cultivated land to construction land, with a cumulative conversion of 920.95 km2 within 27 years. The driving forces of land use in the study area were analyzed by principal component analysis (PCA) and regression analysis. The spatial-temporal evolution of land use types was affected by policy changes, the level of social development and the adjustment in the economy, industry and agriculture structure. The investment in fixed assets and per capita net income in rural areas were the top two influencing factors and their cumulative contribution rate was 94.62%. The findings of this study can provide scientific reference and theoretical support for land use planning, land reclamation in mining areas, ecological protection and sustainable development in Jiaozuo and other resource-exhausted cities in the world.

Spatial–Temporal Evolution Analysis of the Development of Jingdang and Famen Townships in the Vicinity of the Capital City Site of the Western Zhou Dynasty in China

“…The area and proportion of land use types in Jingdang and Famen towns in 1982, 1992, 2002, 2012, and 2022 were extracted by interpreting remote sensing images using ENVI 5.1 software. According to the purpose of the study and the research of scholars such as Chen [38,39], through the maximum likelihood classification method with reference to the classification criteria for current land use status in China (GB/T 21010-2017) (https: //max.book118.com/html/2018/1106/8024017017001132.shtm, accessed on 17 February 2024), the study area was divided into six categories, including CL, FL, GL, COL, UL, and WA. A confusion matrix was constructed to verify the accuracy.…”

Spatiotemporal Evolution of Habitat Quality in Typical Resource- Depleted Cities in China Based on Land Use Changes

“…The largest area transferred from water area to cropland was 97.74 km 2 , followed by 63.05 km 2 of cropland to construction land, while the rest of the land use changed relatively little. From 2000 to 2010, the largest area of construction land was transferred from cultivated land, 172.55 km 2 , followed by 69.52 and 36.34 km 2 from construction land and forestland to cultivated land, respectively. The area of cultivated land transferred to water was one category of land that decreased in area; cultivated land was mainly converted to construction land and water, with 172.55 and 43.40 km 2 , respectively.…”

Mapping and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of Zhoushan Island, China

“…The data sources used in previous studies include hyperspectral, light detection and ranging (Lidar), moderate resolution imaging spectroradiometer (MOD)IS data, and Landsat data [ [24] , [25] , [26] , [27] ]. Classification algorithms include the 2-D convolutional neural network, hybrid convolutional network, and RF [ [28] , [29] , [30] ]. Remote sensing indices can highlight a certain type of ground object, such as the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI).…”