• Energy Research

Land types play an important guiding role in human survival and production. Clarifying the division of land types is the basis for ensuring the sustainable and coordinated development of social-economic-natural complex ecosystems. To date, the land type classification system has not formed a unified standard, and the existing classification fails to highlight the natural background elements of land. Therefore, it is important to construct a classification system that can reflect natural background elements. Additionally, land type classification is often based on land resource surveys. Updating the land type is generally difficult and slow, mainly due to a lack of appropriate information. Hence, it is necessary to develop an automatic land type renewal method using multisource information. This study proposes the ecology-geomorphology cognition (Eco-geoC) approach for land type classification. The approach is realized by the segmentation of land units using remote sensing images, geographic information, vegetation, soil, DEM, and geoscience knowledge. This approach is an extension of the object-based image analysis method. The spatial objects segmented from different attribute data are integrated, and finally, a comprehensive land mapping unit representing a certain degree of geographical homogeneity and land use potential is generated. The results show that the Eco-geoC approach is an integrated approach with objectification cognition on remote sensing images and multisource information using geo-knowledge. The Eco-geoC approach is tested in the Altay region. From coarse to fine scales, the study area is divided into two kinds of natural belts, 27 land systems and 78 land units, and a 1:500,000 land-type map, which shows a good coupling relationship between the physiognomy, vegetation, and soil in the Altay region, is compiled. The results of this study show that the use of the Eco-geoC approach for land type classification is significant and has potential for land assessment and planning. This approach can provide a scientific basis for the restoration of the regional ecology and the comprehensive management and adjustment of land resources and the environment.

Land types play an important guiding role in human survival and production. Clarifying the division of land types is the basis for ensuring the sustainable and coordinated development of social-economic-natural complex ecosystems. To date, the land type classification system has not formed a unified standard, and the existing classification fails to highlight the natural background elements of land. Therefore, it is important to construct a classification system that can reflect natural background elements. Additionally, land type classification is often based on land resource surveys. Updating the land type is generally difficult and slow, mainly due to a lack of appropriate information. Hence, it is necessary to develop an automatic land type renewal method using multisource information. This study proposes the ecology-geomorphology cognition (Eco-geoC) approach for land type classification. The approach is realized by the segmentation of land units using remote sensing images, geographic information, vegetation, soil, DEM, and geoscience knowledge. This approach is an extension of the object-based image analysis method. The spatial objects segmented from different attribute data are integrated, and finally, a comprehensive land mapping unit representing a certain degree of geographical homogeneity and land use potential is generated. The results show that the Eco-geoC approach is an integrated approach with objectification cognition on remote sensing images and multisource information using geo-knowledge. The Eco-geoC approach is tested in the Altay region. From coarse to fine scales, the study area is divided into two kinds of natural belts, 27 land systems and 78 land units, and a 1:500,000 land-type map, which shows a good coupling relationship between the physiognomy, vegetation, and soil in the Altay region, is compiled. The results of this study show that the use of the Eco-geoC approach for land type classification is significant and has potential for land assessment and planning. This approach can provide a scientific basis for the restoration of the regional ecology and the comprehensive management and adjustment of land resources and the environment.

Regional ecosystem health is the basis for regular regional exploration, ecological protection, and sustainable development. This study explored ecosystem health at the southern end of the Hu Line (Sichuan and Yunnan provinces) using the pressure–state–response model and examined the spatial evolution of ecosystem health. The proportion of unhealthy and morbid cities decreased from 45.9% in 2000 to 35.1% in 2016. The imbalance of ecosystem health among cities has gradually increased since 2006, but more high-quality cities have emerged (Z of Moran’s Index < 1.96, p > 0.05). Overall, the regional ecosystem on the southeast side of the Hu Line was healthier than that on the northwest side. Differences in ecosystem health on both sides of the Hu Line showed decreasing trends over time except for the pressure score. The spatial pattern of ecosystem health moved along the Hu Line because the pressure and state scores of ecosystems were mainly determined by the natural environmental conditions. Based on the county-level assessment, the grade of imbalance within cities was divided, and those that were lagging were identified. To correct regional imbalances, a comprehensive and proactive policy framework for a smart development model was put forward in Sichuan and Yunnan.

Regional ecosystem health is the basis for regular regional exploration, ecological protection, and sustainable development. This study explored ecosystem health at the southern end of the Hu Line (Sichuan and Yunnan provinces) using the pressure–state–response model and examined the spatial evolution of ecosystem health. The proportion of unhealthy and morbid cities decreased from 45.9% in 2000 to 35.1% in 2016. The imbalance of ecosystem health among cities has gradually increased since 2006, but more high-quality cities have emerged (Z of Moran’s Index < 1.96, p > 0.05). Overall, the regional ecosystem on the southeast side of the Hu Line was healthier than that on the northwest side. Differences in ecosystem health on both sides of the Hu Line showed decreasing trends over time except for the pressure score. The spatial pattern of ecosystem health moved along the Hu Line because the pressure and state scores of ecosystems were mainly determined by the natural environmental conditions. Based on the county-level assessment, the grade of imbalance within cities was divided, and those that were lagging were identified. To correct regional imbalances, a comprehensive and proactive policy framework for a smart development model was put forward in Sichuan and Yunnan.

Flash floods are one of the most serious natural disasters, and have a significant impact on economic development. In this study, we employed the spatiotemporal analysis method to measure the spatial–temporal distribution of flash floods and examined the relationship between flash floods and driving factors in different subregions of landcover. Furthermore, we analyzed the response of flash floods on the economic development by sensitivity analysis. The results indicated that the number of flash floods occurring annually increased gradually from 1949 to 2015, and regions with a high quantity of flash floods were concentrated in Zhaotong, Qujing, Kunming, Yuxi, Chuxiong, Dali, and Baoshan. Specifically, precipitation and elevation had a more significant effect on flash floods in the settlement than in other subregions, with a high r (Pearson’s correlation coefficient) value of 0.675, 0.674, 0.593, 0.519, and 0.395 for the 10 min precipitation in 20-year return period, elevation, 60 min precipitation in 20-year return period, 24 h precipitation in 20-year return period, and 6 h precipitation in 20-year return period, respectively. The sensitivity analysis showed that the Kunming had the highest sensitivity (S = 21.86) during 2000–2005. Based on the research results, we should focus on heavy precipitation events for flash flood prevention and forecasting in the short term; but human activities and ecosystem vulnerability should be controlled over the long term.

Flash floods are one of the most serious natural disasters, and have a significant impact on economic development. In this study, we employed the spatiotemporal analysis method to measure the spatial–temporal distribution of flash floods and examined the relationship between flash floods and driving factors in different subregions of landcover. Furthermore, we analyzed the response of flash floods on the economic development by sensitivity analysis. The results indicated that the number of flash floods occurring annually increased gradually from 1949 to 2015, and regions with a high quantity of flash floods were concentrated in Zhaotong, Qujing, Kunming, Yuxi, Chuxiong, Dali, and Baoshan. Specifically, precipitation and elevation had a more significant effect on flash floods in the settlement than in other subregions, with a high r (Pearson’s correlation coefficient) value of 0.675, 0.674, 0.593, 0.519, and 0.395 for the 10 min precipitation in 20-year return period, elevation, 60 min precipitation in 20-year return period, 24 h precipitation in 20-year return period, and 6 h precipitation in 20-year return period, respectively. The sensitivity analysis showed that the Kunming had the highest sensitivity (S = 21.86) during 2000–2005. Based on the research results, we should focus on heavy precipitation events for flash flood prevention and forecasting in the short term; but human activities and ecosystem vulnerability should be controlled over the long term.

Mounting evidence suggests an increasing heatwave risk in the Chinese mainland, posing notable threats to public health and the socioeconomic landscape. In a comprehensive analysis, considering both climate and socioeconomic factors, including Gross Domestic Product (GDP) and population dynamics, we systematically evaluated the spatiotemporal distribution of heatwave socioeconomic exposure in the Chinese mainland from 2000 to 2019, utilizing a more comprehensive heatwave hazard index (HHI) that synthesizes heatwave intensity, frequency, and duration as climate factor for the first time. Results show that (1) Heatwave exposure is pronounced in eastern China, particularly in Southeast (SE), North China (NC), and Southwest (SW) regions. From 2000 to 2019, heatwave exposure showed an overall upward trend, with the most rapid escalation observed in the SE, NC, and SW regions. Population exposure manifests as a clustered expansion pattern, while GDP exposure demonstrates a more centralized distribution. (2) Climatic factors exert the most notable influence on population exposure, while GDP predominantly impacts economic exposure. The combination of climate and socioeconomic factors contributes less to exposure rates, except in the Northeast (NE) and Southwest (SW) regions where it impacts GDP exposure most. (3) High-risk hotspot cities include Shanghai, Beijing, Chongqing, Guangzhou, Wuhan, Zhengzhou, Hangzhou, Xi’an, Tianjin, and Nanjing. These findings underscore the urgent need for targeted interventions and mitigation strategies in these vulnerable areas.

Mounting evidence suggests an increasing heatwave risk in the Chinese mainland, posing notable threats to public health and the socioeconomic landscape. In a comprehensive analysis, considering both climate and socioeconomic factors, including Gross Domestic Product (GDP) and population dynamics, we systematically evaluated the spatiotemporal distribution of heatwave socioeconomic exposure in the Chinese mainland from 2000 to 2019, utilizing a more comprehensive heatwave hazard index (HHI) that synthesizes heatwave intensity, frequency, and duration as climate factor for the first time. Results show that (1) Heatwave exposure is pronounced in eastern China, particularly in Southeast (SE), North China (NC), and Southwest (SW) regions. From 2000 to 2019, heatwave exposure showed an overall upward trend, with the most rapid escalation observed in the SE, NC, and SW regions. Population exposure manifests as a clustered expansion pattern, while GDP exposure demonstrates a more centralized distribution. (2) Climatic factors exert the most notable influence on population exposure, while GDP predominantly impacts economic exposure. The combination of climate and socioeconomic factors contributes less to exposure rates, except in the Northeast (NE) and Southwest (SW) regions where it impacts GDP exposure most. (3) High-risk hotspot cities include Shanghai, Beijing, Chongqing, Guangzhou, Wuhan, Zhengzhou, Hangzhou, Xi’an, Tianjin, and Nanjing. These findings underscore the urgent need for targeted interventions and mitigation strategies in these vulnerable areas.