Assessment of ecological quality in Northwest China (2000–2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality

Abstract

The ecological quality of inland areas is an important aspect of the United Nations Sustainable Development Goals (UN SDGs). The ecological environment of Northwest China is vulnerable to changes in climate and land use/land cover, and the changes in ecological quality in this arid region over the last two decades are not well understood. This makes it more difficult to advance the UN SDGs and develop appropriate measures at the regional level. In this study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) products to generate remote sensing ecological index (RSEI) on the Google Earth Engine (GEE) platform to examine the relationship between ecological quality and environment in Xinjiang during the last two decades (from 2000 to 2020). We analyzed a 21-year time series of the trends and spatial characteristics of ecological quality. We further assessed the importance of different environmental factors affecting ecological quality through the random forest algorithm using data from statistical yearbooks and land use products. Our results show that the RSEI constructed using the GEE platform can accurately reflect the ecological quality information in Xinjiang because the contribution of the first principal component was higher than 90.00%. The ecological quality in Xinjiang has increased significantly over the last two decades, with the northern part of this region having a better ecological quality than the southern part. The areas with slightly improved ecological quality accounted for 31.26% of the total land area of Xinjiang, whereas only 3.55% of the land area was classified as having a slightly worsen (3.16%) or worsen (0.39%) ecological quality. The vast majority of the deterioration in ecological quality mainly occurred in the barren areas Temperature, precipitation, closed shrublands, grasslands and savannas were the top five environmental factors affecting the changes in RSEI. Environmental factors were allocated different weights for different RSEI categories. In general, the recovery of ecological quality in Xinjiang has been controlled by climate and land use/land cover during the last two decades and policy-driven ecological restoration is therefore crucial. Rapid monitoring of inland ecological quality using the GEE platform is projected to aid in the advancement of the comprehensive assessment of the UN SDGs.

A regional assessment of ecological environment quality in Thailand special economic zone: Spatial heterogeneous influences and future prediction

“…The dominant factor varies widely among the regions because of regional unique characteristics such as climate, topography, major LULC, and development levels (Figure 7). For example, Wang, Ding, et al (2022) indicated the importance of climate factors to EEQ, while these influences were relatively fuzzy in a coal mining area (Nie et al, 2021). However, LULCC and natural conditions are the major agents influencing the EEQ that have been consistently detected in many studies (Wang, Ding, et al, 2022; Yuan et al, 2021; Zhang, Feng, et al, 2022; Zhang, She, et al, 2022).…”

“…The topographic features are expected to substantially differentiate the EEQ in the EEC as it is an area with diverse and complex topography (Figure 1). Temperature and precipitation represent climate factors, which greatly influence the ecological systems by affecting water availability, phenology, and ecosystem productivity (Sun et al, 2020; Wang, Ding, et al, 2022). For example, an increase in temperature and decrease in rainfall can lead to high barren land and degradation of EEQ (Bi et al, 2021).…”

“…More explicitly, the difference in RSEI between the considered year and the reference year at pixel is calculated by Equation . The threshold value of ±0.1 was proposed to identify the EEQ dynamics of significant degradation (2.2), no significant change (2.3), and significant improvement (2.4) (Wang, Ding, et al, 2022; Xu et al, 2019). With respect to trend analysis, the RSEI time series was analyzed by Mann–Kendall test and Thiel‐Sen slope to quantify its monotonic trend over years.…”

“…The RSEI is able to monitor the EEQ under various conditions, such as urban agglomeration, LULCC, land consolidation, mining activities, and geohazards (Airiken et al, 2022; Nie et al, 2021; Shan et al, 2019; Xu, Zhao, et al, 2021; Yan et al, 2021; Zhao et al, 2021). The EEQ depends on disparate factors at different extents, such as climate, terrain, soil properties, landscape patterns, and socioeconomic and anthropogenic elements (Geng et al, 2022; Tang et al, 2022; Wang, Ding, et al, 2022; Zhang, Feng, et al, 2022; Zhang, She, et al, 2022). However, the impacts of a certain factor on the EEQ in different regions and subregions may not be the same.…”

Land‒atmosphere coupling effects of soil temperature and moisture on extreme precipitation in the arid regions of Northwest China

“…Current predictions of extreme precipitation have higher uncertainty in dry regions than in wet regions (Kim et al, 2020). The ecological environment of the ANWC is vulnerable to climate and land use/land cover change, while the ecological quality is an important aspect of the UN Sustainable Development Goals (Wang et al, 2022).…”