The horizontal two-dimensional (2D) urban land use approach is not sufficient to trace rapid changes in urban environment. Hence, a three-dimensional (3D) approach that is different from the traditional geographical method is necessary to understand the mechanism of compound urban diversity. Using remote sensing data captured in 2010/2011 and geospatial tools and techniques, we quantified the urban volume (UV, consisting of urban built volume (UBV) and urban green volume (UGV)) and retrieved and mapped the land surface temperature (LST) of two cities in Japan (Tsukuba, a planned city, and Tsuchiura, a traditional city). We compared these two cities in terms of (1) UBV and UGV and their relationships with mean LST; and (2) the relationship of the UGV–UBV ratio with mean LST. Tsukuba had a total UBV of 74 million m³, while Tsuchiura had a total of 89 million m³. In terms of UGV, Tsukuba had a total of 52 million m³, while Tsuchiura had a total of 29 million m³. In both cities, UBV had a positive relationship with mean LST (Tsukuba: R² = 0.31, p < 0.001; Tsuchiura: R² = 0.42, p < 0.001), and UGV had a negative relationship with mean LST (Tsukuba: R² = 0.53, p < 0.001; Tsuchiura: R² = 0.19, p < 0.001). Tsukuba also had a higher UGV–UBV ratio of 54.9% in comparison with Tsuchiura, with 28.7%. Overall, the results indicate that mean LST was more intense in the traditional city (Tsuchiura). This could have been due to the difference in urban spatial structure. As a planned city, Tsukuba is still a relatively young city that has more dispersed green spaces and a well-spread (so far) built-up area.

The urban heat island (UHI) and its consequences have become a key research focus of various disciplines because of its negative externalities on urban ecology and the total livability of cities. Identifying spatial variation of the land surface temperature (LST) provides a clear picture to understand the UHI phenomenon, and it will help to introduce appropriate mitigation technique to address the advanced impact of UHI. Hence, the aim of the research is to examine the spatial variation of LST concerning the UHI phenomenon in rapidly urbanizing Lagos City. Four variables were examined to identify the impact of urban surface characteristics and socio-economic activities on LST. The gradient analysis was employed to assess the distribution outline of LST from the city center point to rural areas over the vegetation and built-up areas. Partial least square (PLS) regression analysis was used to assess the correlation and statistically significance of the variables. Landsat data captured in 2002 and 2013 were used as primary data sources and other gridded data, such as PD and FFCOE, were employed. The results of the analyses show that the distribution pattern of the LST in 2002 and 2013 has changed over the study period as results of changing urban surface characteristics (USC) and the influence of socio-economic activities. LST has a strong positive relationship with NDBI and a strong negative relationship with NDVI. The rapid development of Lagos City has been directly affected by conversion more green areas to build up areas over the time, and it has resulted in formulating more surface urban heat island (SUHI). Further, the increasing population and their socio-economic activities including industrialization and infrastructure development have also caused a significant impact on LST changes. We recommend that the results of this research be used as a proxy tool to introduce appropriate landscape and town planning in a sustainable viewpoint to make healthier and livable urban environments in Lagos City, Nigeria.

Urbanization addresses urban expansion, and it leads conversion of the green space into the built-up area. However, previous studies mainly focused on two-dimensional (2D) urban expansion rather than three-dimensional (3D) growth. Here, the purpose of this study is to examine the urban expansion, including built-up and green space for both horizontal and vertical dimensions using geospatial analysis including remote sensing (RS) and Geographic Information System (GIS) in the sub-Central Business District (CBD) area of Surabaya, Indonesia. The medium resolution remote sensing data for both image and Digital Surface Model (DSM) acquired by Advanced Land-Observing Satellite (ALOS) were applied for time-1 (2010). The orthophoto and DSM derived by LiDAR were used for time-2 (2016). We quantified the built-up and green expansions in 2D (area), which were extracted from land use/land cover (LU/LC) by applying hybrid classification. The built-up and green expansions in 3D (volume) were estimated by generating a surface feature model. The spatial configuration of area expansion was investigated using patch metric, while the volume growth was examined using the volume expansion rate. We got three findings. (1) The built-up and green area had expanded about 11.54% and 95.61%, respectively, from 2010 to 2016. The expansion of green area presented in a notable portion, which was mainly contributed by the conversion of bareland to playground or park. However, the expansion of built-up area was less than the volume expansion of 20.6%. It revealed that built-up growth led to vertical rather than horizontal development. (2) The built-up area expansion tended to scatter configuration, whereas, the green area expansion tended to aggregate in a linear pattern. (3) The ratio of built-up volume expansion to green volume expansion showed a mean of 3.7, indicating that the development of built-up and green volume was imbalanced. The built-up growth presented higher than the green growth, mainly in the areas with more vertical building establishment. The pressing need for higher green volume in the study area was identified in several sites located at surrounding artery and toll roads. Overall, our approach can be applied as a reference in monitoring neighborhood environment through greening programs for sustainable urban development.

Air pollution has emerged as a significant health, environmental, economic, and social problem all over the world. In this study, geospatial technologies coupled with a LUR (Land Use Regression) approach were applied to assess the spatial-temporal distribution of fine particulate (PM_2.5). In-situ observations of air pollutants from ground monitoring stations from 2016–2018 were used as dependent variables, while the land-use/land cover, a NDVI (Normalized Difference Vegetation Index) from a MODIS sensors, and meteorology data allocations surrounding the monitoring stations from 0.25–5 km buffer ranges were collected as spatial predictors from GIS and remote sensing databases. A linear regression method was developed for the LUR model and 10-fold cross-validation was used to assess the model robustness. The R² model obtained was 56% for DKI Jakarta, Indonesia, and 83% for Taipei Metropolis, Taiwan. According to the results of the PM_2.5 model, the essential predictors for DKI Jakarta were influenced by temperature, NDVI, humidity, and residential area, while those for the Taipei Metropolis region were influenced by PM₁₀, NO₂, SO₂, UV, rainfall, spring, main road, railroad, airport, proximity to airports, mining areas, and NDVI. The validation of the results of the estimated PM_2.5 distribution use 10-cross validation with indicated R² values of 0.62 for DKI Jakarta and 0.84 for Taipei Metropolis. The results of cross-validation show the strength of the model.

Deformation monitoring is one alternative methods that can become the indicators of brifge feasibility. The tools that can be applied for the deformation monitoring are GPS (Global Positioning System) technology and CRP (Close Range Photogrametry, since they are effective, easy, and can be used for multitemporal application. Suramadu bridge is a landmark building of East Java that is connecting Java and Madura islands. This objective of this study is to determine the deflection of Suramadu bridge.Global Positioning System (GPS) were employed to monitor the deflection in the middle of bridge by kinematic methods using precise ephemeris data for correcting coordinate. For the entire span bridge, CRP method is applied using a non-metric camera with self callibration and space resection processing in bundle adjustment technique.The results are still preliminary views, and at the moment this study is still underway. Based on these preliminary results, it appears that there are deflection in both horizontal and vertical axes based on 3 times of observation. Using CRP, the largest horizontal deformation inclined to the east at 80mm and a decline of 12mm.Using CRP, the smallest horizontal deformation is about 1mm to the east and no change to the Z axis. The next stage for the study consists of CRP entire span bridge, GPS mid-span bridge (4 points), and modeling deformation