Identifying the changes and relationships between regional eco-environment quality and landscape pattern in an urban agglomeration have a great significance in realizing sustainable development goal. However, limited research has been performed to understand the spatiotemporal change of eco-environment quality, the variation of landscape pattern, and their relationship in an urban agglomeration. This study selected the Jing-Jin-Ji (JJJ) urban agglomeration as the study area. A comprehensive index, the remote sensing ecological index (RSEI), was utilized to understand the eco-environment spatiotemporal change and landscape pattern variation at class-level and landscape-level of JJJ during 2001~2015, then, their relationship was explored. The major conclusions were as follows: (1) The average RSEI value of JJJ increased from 0.43 to 0.46, which represented that the eco-environment of JJJ had improved in the fourteen years. Among it, the improved region was mainly located in Zhangjiakou city, while the degraded region was mainly distributed in the eastern Hebei plain. (2) The landscape characteristics of entire JJJ eco-environment were becoming more aggregated, connected, diverse, and regular. However, fair, moderate, and good grades were getting more concentrated and continuous; poor grade indicated a more fragmented and disconnected trend; excellent grade displayed an expanded and concentrated situation. (3) Human factors have an increasing influence on regional eco-environment changes. (4) Fair, moderate, and good grades showed a more dominant and stronger influence on the variation of landscape pattern in JJJ. Specifically, the fair grade had a positive correlation with the variation of landscape pattern, while moderate and good grades had a negative one. All of these conclusions could be valuable information for relevant decision-makers in managing or achieving the optimal eco-environment landscape pattern.

Exploring the regional eco-environmental quality (EEQ) and its driving factors is of great significance for regional management. Although existing studies have paid much attention to evaluate EEQ, few studies have been performed to investigate the spatiotemporal variations of EEQ and its driving factors in different ecosystem service regions (ESR) at an urban agglomeration scale. In this study, we selected Jing-Jin-Ji urban agglomeration (JJJ) as the study area to evaluate its EEQ, analyze its spatiotemporal variations, and investigate potential driving factors explanatory power based on the geographical detector methods in different ESR during 2001∼2015. The main conclusions were as follows: (1) The EEQ of JJJ had improved from 2001 to 2015, with the average RSEI increased from 0.43 to 0.46; among them, Bashang Plateau and Western Hebei Ecosystem Service Region (BWHE) had the highest RSEI change rate (±26.19%) and the highest NTEDI value (0.13), while Central Hebei Plain Ecosystem Service Region (CHPE) had the lowest RSEI change rate (-5.41%) and the lowest NTEDI value (-0.02). (2) The EEQ of JJJ had strong spatial agglomeration effects, with the global Moran’s I increased from 0.82 to 0.88. Spatially, the LL regions mainly changed into the HH regions in the northwestern part, while in the central and eastern areas, some isolated LL regions displayed an aggregated trend. (3) In terms of the driving factors, soil type and elevation were primary factors in explaining the variations of EEQ. Specifically, natural factors explained the highest variations in BWHE. The interaction of topographical and socio-economic factors had high explanatory power in Yanshan and Taihang Mountain Ecosystem Service Region (YTME) and CHPE; To Bohai and Coastal Ring Ecosystem Service Region (BCRE), the interaction of meteorological and socio-economic factors accounted for the high variations of EEQ. All these findings could provide more valuable advice for relevant policy-makers.

Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable.

According to the framework of “Pressure-State-Response”, this study established an indicator system which can reflect comprehensive risk of environment and health for an area at large scale. This indicator system includes 17 specific indicators covering social and economic development, pollution emission intensity, air pollution exposure, population vulnerability, living standards, medical and public health, culture and education. A corresponding weight was given to each indicator through Analytical Hierarchy Process (AHP) method. Comprehensive risk assessment of the environment and health of 58 counties was conducted in the Jiangsu province, China, and the assessment result was divided into four types according to risk level. Higher-risk counties are all located in the economically developed southern region of Jiangsu province and relatively high-risk counties are located along the Yangtze River and Xuzhou County and its surrounding areas. The spatial distribution of relatively low-risk counties is dispersive, and lower-risk counties mainly located in the middle region where the economy is somewhat weaker in the province. The assessment results provide reasonable and scientific basis for Jiangsu province Government in formulating environment and health policy. Moreover, it also provides a method reference for the comprehensive risk assessment of environment and health within a large area (provinces, regions and countries).

Measuring the regionally coordinated development degree quantitively at an urban agglomeration scale is vital for regional sustainable development. To date, existing studies mainly utilized statistical data to analyze coordinated development degrees between different subsystems, which failed to measure the development gap of subsystems between cities. This study integrated remote sensing and statistical data to evaluate the development degree from six subsystems. The coordinated index (CI) and coordinated development index (CDI) were then promoted to assess the coordinated degree and coordinated development degree. The main findings were: (1) The coordinated development degree of Jing-Jin-Ji (JJJ) had increased from 0.4616 in 2000 to 0.6099 in 2015, with the corresponding grade improvement from “moderate” to “good”; (2) JJJ and six subsystems’ development degree showed an increasing trend. JJJ’s whole development degree had improved from 0.34 to 0.52, and the grade had changed from “fair” to “moderate”; (3) The coordinated degree of JJJ displayed a “V” shape. However, the coordinated degree was lower in 2015 than in 2000.

The Chinese government has promulgated a de-capacity policy for economic growth and environmental sustainability, especially for the iron and steel industry. With these policies, this study aimed to monitor the economic activities and evaluate the production conditions of an iron and steel factory based on satellites via Landsat-8 Thermal Infrared Sensor (TIRS) data and high-resolution images from January 2013 to October 2017, and propel next economic adjustment and environmental protection. Our methods included the construction of a heat island intensity index for an iron and steel factory (ISHII), a heat island radio index for an iron and steel factory (ISHRI) and a dense classifying approach to monitor the spatiotemporal changes of the internal heat field of an iron and steel factory. Additionally, we used GF-2 and Google Earth images to identify the main production area, detect facility changes to a factory that alters its heat field and verify the accuracy of thermal analysis in a specific time span. Finally, these methods were used together to evaluate economic activity. Based on five iron and steel factories in the Beijing-Tianjin-Hebei region, when the ISHII curve is higher than the seasonal changes in a time series, production is normal; otherwise, there is a shut-down or cut-back. In the spatial pattern analyses, the ISHRI is large in normal production and decreases when cut-back or shut-down occurs. The density classifying images and high-resolution images give powerful evidence to the above-mentioned results. Finally, three types of economic activities of normal production, shut-down or cut-back were monitored for these samples. The study provides a new perspective and method for monitoring the economic activity of an iron and steel factory and provides supports for sustainable development in China.