Study on NDVI changes in Weihe Watershed based on CA–Markov model

Abstract

The simulation and prediction of the NDVI are conducive to regional ecological restoration. In this research, the Weihe Watershed is selected as the study area, which is located on the eastern edge of the Tianshan tectonic belt. Firstly, according to NDVI characters, we divide the values into five grades, including very low (<0.3), lower (0.3–0.4), medium (0.4–0.5), higher (0.5–0.6), and very high (>0.6), and analyse the NDVI tempo‐spatial variations in Weihe Watershed from 2006 to 2015. Then, we use CA–Markov model with parameters adjustment to simulate and predict the NDVI distributions in 2015 and 2020, respectively. The results show that (a) at temporal scales, the NDVI in Weihe Watershed increase year by year from 2006 to 2015, indicating a better vegetation recovery. At spatial scales, the NDVI increase from north‐west to south‐east gradually, which is related with the main climate factors such as temperature and precipitation. (b) By adjusting the parameters of state transition suitability atlas in the Markov model, we get the model with higher accuracy to simulate NDVI changes. And the Kappa coefficient between the simulation results and the true values is 0.6123. (c) According to the predictions in 2020, the regions with very high NDVI and higher NDVI mainly locate in Qinling Mountains, Guanzhong Plain, Ziwu Mountains, Jinghe Valley, and Luohe Valley. And these regional area percentages occupying the whole watershed increased by 13.62% and 2.64%, respectively, compared with the area percentages in 2015. The regions with medium NDVI, lower NDVI, and very low NDVI mainly distribute at the north Loess Plateau of the watershed. And these regional area percentages occupying the whole watershed decreased by 7.40%, 6.59%, and 2.28%, respectively, compared with the area percentages in 2015. The above results illustrate that the ecological environment of Weihe Watershed is improving, which can provide scientific reference for assessing the regional ecological risk and formulating ecological protection measures.

Identification and Prediction of Wetland Ecological Risk in Key Cities of the Yangtze River Economic Belt: From the Perspective of Land Development

“…Cellular space, cell and states, neighbors, and transition rules are the components of the model. CA is not a definite mathematical function, but a bottom-up research idea, that is, the simple behavior of local cellular individuals can produce a global, orderly pattern of complex systems [54]. So it can be expressed as follows:…”

Tianshan Orogen along the Silk Road (Volume 3): Orogen links, geochemistry, geochronology, mineral deposits, and environments

“…In the paper by Wei, Wang, and Wang ( ) , trends in spatial and temporal change of vegetation cover in NW China using remote sensing were analysed over an 11‐year period, from 2000 to 2010, with a view to suggesting pointers for sustainable ecological conservation and economic development in the ancient Silk Road region. In the final paper of this volume, Wang et al ( ) analysed vegetation dynamic changes in the Weihe watershed, which is located in the eastern edge of the Tianshan tectonic belt in Shaanxi Province, Gansu Province and Ningxia Autonomous Region, which forms the start of the Silk Road Economic Belt. The study utilized remote sensing and the normalized difference vegetation index is highly correlated with vegetative cover.…”

Driving Mechanism of Habitat Quality at Different Grid-Scales in a Metropolitan City

“…However, traditional field investigation and evaluation were no longer suitable for efficiently expressing the characteristics of habitat quality at urban or larger spatial scales, which has been the bottleneck in this field. With the development of electronic information science and technology, the mathematical methods of CA-Markov [12], MAXENT [13], SolVES [14], InVEST [15], and other models can be easily realize the quantitative analysis of the ecological environment at the meso-scale or macroscale. Among them, the InVEST model was tested by many scholars to assess the habitat quality of wetlands, forests, or other specific natural ecosystems because of its easy data acquisition, stronger visualization, higher accuracy, and simple operation [15][16][17][18][19].…”