Due to urban expansion, economic development, and rapid population growth, land use/land cover (LULC) is changing in major cities around the globe. Quantitative analysis of LULC change is important for studying the corresponding impact on the ecosystem service value (ESV) that helps in decision-making and ecosystem conservation. Based on LULC data retrieved from remote-sensing interpretation, we computed the changes of ESV associated with the LULC dynamics using the benefits transfer method and geographic information system (GIS) technologies during the period of 1992–2018 following self-modified coefficients which were corrected by net primary productivity (NPP). This improved approach aimed to establish a regional value coefficients table for facilitating the reliable evaluation of ESV. The main objective of this research was to clarify the trend and spatial patterns of LULC changes and their influence on ecosystem service values and functions. Our results show a continuous reduction in total ESV from United States (US) $1476.25 million in 1992, to US $1410.17, $1335.10, and $1190.56 million in 2001, 2009, and 2018, respectively; such changes are attributed to a notable loss of farmland and forest land from 1992–2018. The elasticity of ESV in response to changes in LULC shows that 1% of land transition may have caused average changes of 0.28%, 0.34%, and 0.50% during the periods of 1992–2001, 2001–2009, and 2009–2018, respectively. This study provides important information useful for land resource management and for developing strategies to address the reduction of ESV.

Increasing trends of urbanization lead to vegetation degradation in big cities and affect the urban thermal environment. This study investigated (1) the cooling effect of urban green space spatial patterns on Land Surface Temperature (LST); (2) how the surrounding environment influences the green space cool islands (GCI), and vice versa. The study was conducted in two Asian capitals: Beijing, China and Islamabad, Pakistan by utilizing Gaofen-1 (GF-1) and Landsat-8 satellite imagery. Pearson’s correlation and normalized mutual information (NMI) were applied to investigate the relationship between green space characteristics and LST. Landscape metrics of green spaces including Percentage of Landscape (PLAND), Patch Density (PD), Edge Density (ED), and Landscape Shape Index (LSI) were selected to calculate the spatial patterns of green spaces, whereas GCI indicators were defined by Green Space Range (GR), Temperature Difference (TD), and Temperature Gradient (TG). The results indicate that both vegetation composition and configuration influence LST distributions; however, vegetation composition appeared to have a slightly greater effect. The cooling effect can be produced more effectively by increasing green space percentage, planting trees in large patches with equal distribution, and avoiding complex-shaped green spaces. The GCI principle indicates that LST can be decreased by increasing the green space area, increasing the water body fraction, or by decreasing the fraction of impervious surfaces. GCI can also be strengthened by decreasing the fraction of impervious surfaces and increasing the fraction of water body or vegetation in the surrounding environment. The cooling effect of vegetation and water could be explained based on their thermal properties. Beijing has already enacted the green-wedge initiative to increase the vegetation canopy. While designing the future urban layout of Islamabad, the construction of artificial lakes within the urban green spaces would also be beneficial, as is the case with Beijing.

Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district. Dengue cases from 2010 to 2014 at district level were collected from the Nepal government’s health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period. Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster. This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.