In recent decades, China has exhibited the fastest and most remarkable social-economic development in the world. As a result of such development, the forest cover of the country has undergone radical changes. This paper aims to develop a method for analyzing long-term and spatial changes in forest cover based on historical maps and remote sensing images. Moreover, we will focus on the reduction or restoration of forests distributed at different altitudes, slopes, soils, and lithologic types in different periods, to reveal the problems that should be paid attention to in forest restoration in karst areas. A typical county of China was selected as the study area. A historical military operation map was considered the principal source of basic data. These data were then combined with Landsat satellite images to conduct quantitative analysis on changes in the spatial area and location of forest cover with a long time series. The findings are as follows: in terms of time series, the forest area in the study area showed a trend of decreasing at first and then increasing, with the year 1986 as the turning point. In terms of spatial patterns, a considerable difference is observed in regions with changes in forest cover under different historical periods. Changes are obvious in limestone areas, rock soil areas, and areas with an elevation of 2000–2500 m and a slope gradient of 6°–15°. Spatial–temporal changes in forest cover reflect the effects of the war, national policy, and economic development to some extent. All these results indicate that, despite its limitations, a historical map is a valuable document for studying an ecological environment.

An understanding of the enrichment mechanisms of cadmium (Cd) and arsenic (As) in the process of rock weathering and soil formation is essential to develop agriculture according to local conditions. However, the enrichments of soil Cd and As under natural background conditions in karst areas are still uncertain. The enrichment factor, geo-accumulation index, redundancy analysis, and other methods were used to analyze the enrichment degree and the influencing factors of Cd and As on 5 rock–soil profiles and 15 topsoil samples, which were collected from a karst forest area in Libo County, Guizhou Province. The results showed that the enrichment process was divided into three stages. In the first stage, Cd and As were enriched in carbonate rocks, and their mean concentrations were 1.65 and 3.9 times those of the corresponding abundance of the crust. In the second stage, the enrichment of the parent rock into the soil, the enrichment factors of Cd and As in the parent material horizon relative to the bedrock horizon were 9.2 and 2.82, respectively. The third stage refers to the enrichments of Cd and As in the topsoil, where Cd enrichment was more obvious than that of As. Soil organic matter (SOM) and phosphorus (P) are important factors that influenced the enrichments of Cd and As in the topsoil. The functional groups of SOM were complexed with Cd and As; P easily formed precipitates with Cd, and the tree litter was fed back to the topsoil, which may be the reason for the surface enrichment of Cd and As. This study will help the scientific community understand the enrichment mechanisms of soil Cd and As in karst areas.

The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12–64%. The prediction ability of the model of the four metals is Cr > Co > Sb > Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.

The existence of global warming is common knowledge. However, it can be predicted that there may be cooling zones worldwide based on the mechanism of terrestrial biophysical processes. Here, the Theil–Sen median trend, the Mann–Kendall trend test method, continuous wavelet transformation, and the Hurst exponent were used to study the cooling trends, abrupt change times, periodicity, and future sustainability of temperature changes in different cooling zones since the 1900s based on the CRU dataset. We found an amazing result; 8,305,500 km² of land surface had been cooling since the 1900s, covering five continents and 32 countries, accounting for 86% of land area in China, and distributed over 16 zones. The average cooling rate of the cooling zones was −0.24 °C/century. The maximum cooling rate was −1.40 °C/century, and it was 1.43 times the average rate of global land warming (0.98 °C/century). The cooling zones near the sea were greatly influenced by ocean currents and were mainly affected by a small time scale periodicity of less than 30 years, whereas the cold zones located relatively far from the sea and less affected by ocean currents were mainly affected by medium time scales of more than 30 years. Moreover, 32.33% of the cooling zones, involving 2,684,900 km^2, will be continuously cooling in the future, and the rest will probably warm up in 2114, 2041, 2096, 2099, 2119, 2073, 2048, and 2101, respectively. The study will help us to further understand the essential characteristics of global climate change, and to find more theoretical bases for mitigating global warming and exploring surface cooling mechanisms.

Identifying the response of runoff changes to extreme climate evolution was of great scientific significance for the rational regulation of watershed water resources and the prevention of hydrological disasters. However, the time–frequency response relationships were not clear. The Yinjiang River watershed, a typical watershed with karst trough valley areas, was chosen to identify the impact of different climatic driving factors on runoff changes from 1984 to 2015. Continuous wavelet transform (CWT), cross-wavelet transform (XWT), and wavelet coherence transform (WTC) were performed to study the response relationship and time–frequency effect between runoff changes and extreme climate change at different time scales. The main results showed that: (1) Twelve extreme climate indices (ECIs) were detected to have a significant impact on runoff changes, mainly on a 6-year time scale; (2) The R10 and Rx1day in extreme precipitation index and SU34.4 and TNx in the extreme temperature index were the main driving factors of runoff changes, which had relatively large impacts on runoff changes in high and low energy vibration regions. However, the remaining eight ECIs that passed the 0.05 confidence level showed relatively large impacts on runoff changes only in low energy vibration regions; (3) The transition of the interaction between ECIs and runoff changes in high and low time–frequency scales was related to the abrupt change characteristics of the ECIs. The correlation of abrupt change was an important reason for the emergence of highly correlated regions that trigger high and low energy vibrations; (4) As a whole, the extreme precipitation events were ahead of runoff changes at the high time–frequency scale and exhibited small lag effects at the low time–frequency scale, while extreme temperature events were mainly ahead of runoff changes. This study has effectively revealed the impact of climate factors at different scales on runoff changes, and provides a theoretical understanding for regulating and managing water resources in karst basins.

Human well-being in many countries lags behind the gross domestic product (GDP) due to the rapid changes in the socio-economic environment that have occurred for decades. However, the mechanisms behind this complex phenomenon are still unclear. This study revealed the changes in human well-being in China from 1995 to 2017 by revising the genuine progress indicator (GPI) at the national level and further quantified the contribution of interfering factors that have driven the increase in the GPI. The results indicated that: (1) The per capita GPI of China showed an increasing trend with an annual growth rate of 12.43%. The changes in the GPI followed the same pattern as economic development, rather than presenting the phenomenon of economic growth combined with a decline in welfare that has been recorded in some countries and regions. (2) The increase in human well-being was mainly driven by economic growth, but it was most sensitive to social factors. (3) Increasing income inequality and the cost of lost leisure time contributed obvious negative impacts (24.69% and 23.35%, respectively) to the per capita GPI. However, the increase in personal consumption expenditures, the value of domestic labor, ecosystem service value, and net capital growth accelerated the rise in the GPI, with positive contribution rates of 30.69%, 23%, 20.54%, and 20.02%, respectively. (4) The continuous increase in economic investment and the strengthening of social management due to policy adjustments completely counteracted the negative impacts on human well-being, thus leading to a great increase in the per capita GPI. Such insights could provide theoretical support for decision making and policy implementation to improve global human well-being.