Spatio-Temporal Changes in Land Use and Habitat Quality of Hobq Desert along the Yellow River Section

Abstract

As a key area in the Yellow River basin for sand control and management, the land change process in the Hobq Desert plays a crucial role in keeping the river and desert ecosystems and promoting the construction of ecological civilization in human systems. Based on multi-temporal remote sensing from 1991 to 2019 in the Hobq Desert along the Yellow River section, this study selected spatial statistical methods (land-use monitoring and landscape metrics) to examine land-use change dynamics. Then, we evaluated habitat quality using the InVEST model and quantitatively analyzed the factors causing spatial changes in habitat quality using geographic detectors. Finally, this paper predicted the pattern of land use and habitat quality in 2030 using the PLUS model. The results reveal that (1) from 1991 to 2019, the total area of forest grassland increased by 3572.5 km2, providing the most vegetation cover, and the sandy land and water area decreased continuously, while the cultivated land and construction land increased. There were 38.01% conversions of land types, with the land-use dynamic decreasing the greatest in sandy land (−12.66%) and increasing the greatest in construction land (9.26%); the comprehensive land-use dynamics were the highest in 2010–2019 (1.68%), which was the most active stage during our study period. (2) Both of the landscape indices NP and PD showed “N” type fluctuations during 1991–2019, and CONTAG and LSI rose from 69.19% to 70.29% and 36.01% to 38.89%, respectively, indicating that the land-use degree of landscape fragmentation increased, landscape connectivity turned better, and landscape dominance was enhanced, balanced, and developed evenly in overall landscape type. (3) From the overall region analysis, the average habitat quality in 1991, 2000, 2010, and 2019 was 0.3565, 0.5108, 0.5879, and 0.6482, respectively, with the overall habitat value showing a gradually increasing trend. Spatially, the habitat quality along the Yellow River section of the Hobq Desert has a certain regularity, and the overall pattern there is high in the south and low in the north, high in the east and west, and low in the middle. (4) The change in land use between 2019 and 2030 is similar to the previous period, but the change rate is generally lower. The habitat quality improved significantly, with the growth of high and medium habitat quality.

Characterization and Multi-Scenario Prediction of Habitat Quality Evolution in the Bosten Lake Watershed Based on the InVEST and PLUS Models

“…The InVEST model is widely used to evaluate the habitat quality of an ecosystem due to its high evaluation accuracy, good visualization effect, and excellent theoretical system [18]. For example, Meng et al [19] used the InVEST model to quantitatively evaluate the habitat quality of the Kubuzi Desert section of the Yellow River from 1991 to 2019, analyzing the factors affecting the spatial changes of habitat quality by using geoprobes. He et al [2] employed the habitat quality evaluation module of the model to assess the spatial and temporal evolution characteristics of habitat quality in Guilin from 2000 to 2020.…”

The windbreak effects of organic fence made from branches of Salix psammophila in Hobq Desert

“…The Hobq Desert, located at the northern boundary of the Ordos Plateau and adjacent to the Hetao Plain, is one of the primary sources of dust and sandstorms in Northern China (Yang et al, 2019). The region is delineated by the Yellow River on its western, northern, and eastern perimeters (Meng et al, 2023; Pan et al, 2015). Notably, the desert's natural shifting dunes are a natural source of sedimentation and have an impact on the sediment load of the Yellow River (Du et al, 2014; Pan et al, 2015).…”

Temporal and Spatial Variations in Landscape Habitat Quality under Multiple Land-Use/Land-Cover Scenarios Based on the PLUS-InVEST Model in the Yangtze River Basin, China

“…The In-VEST model has the advantages of easy data access, minimal requirements, accurate analytics, and simple operation and data processing [17,18]. The model has been successfully used to assess HQ [19], the impact of urban expansion on the HQ [20], and predicted future HQ through simulations [21,22]. It has also explored the relationship between landscape pattern intensity, population density, and HQ [23].…”