Jakarta is the government and economic capital of Indonesia with a consistently increasing population, resulting in high demand for infrastructure development, which has several socio-economic benefits. However, the environmental issues related to an increasing population and built-up area have come to the public’s attention in recent years. This research analyses the effects of land use and land cover changes (LULCC) on land surface temperature (LST), the development of urban heat island (UHI), and weather conditions from 2000-2019. Focusing the analysis on remote sensing data, satellite images at two time points (2000 and 2019) were used to investigate the LULCC and its impact on UHI development that were associated with meteorological data from ground-based stations. The normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were used to describe the spatial distribution evolution of LULCC along with the urban thermal field variance index (UTFVI) that illustrates the potential impacts of UHI on the quality of life in the urban area. Supervised classification was employed to describe LULCC together with the accuracy of the classification result. UHI areas were extended to the southern and eastern part of Jakarta during the time with an increment of 13%, followed by increasing urban heat island index over a selected urban and rural area. Meanwhile, the decreased number of waters, vegetation, and agriculture area was observed during 2000-2019, followed by the increased number of residence and industry areas. The overall results indicate LULCC plays a critical role in defining the change of LST and meteorological conditions.
Numerous studies employed remote sensing techniques on regional air quality in terms of aerosols, in particular, the observations from polar-orbiting satellites offer more detail on spatial distribution. Since the air pollutants/aerosols dramatically vary in location with time, diurnal observations on a timescale are restricted by the temporal resolution of the polar-orbiting satellite. To address this issue, this research proposes a spatially and temporally adaptive reflectance fusion model for measuring atmospheric properties to synthesize high-spatial–temporal resolution images from polar and geostationary satellite imagery for air quality monitoring. The reflectivity from short-wave infrared is employed to preserve the atmospheric effect within the fused image in the green band for further aerosol optical depth (AOD) retrieval. Taking the Landsat-8 Operational Land Imager as the reference, the spatial resolution of the Himawari-8 Advanced Himawari Imager (in kilometers) can thus be resampled into 30 m every 10 min during the daytime, by considering the surface bidirectional reflectivity from the variation of the solar zenith angle. The AOD retrieved with fused images containing atmospheric effect could have a better performance after comparison with in situ measurements, and therefore, be suggested for high-spatial–temporal aerosol monitoring.
Many people in Asia regions have been suffering from disastrous rainfalls year by year. The rainfall from typhoons or tropical cyclones (TCs) is one of their key water supply sources, but from another perspective such TCs may also bring forth unexpected heavy rainfall, thereby causing flash floods, mudslides or other disasters. So far we cannot stop or change a TC route or intensity via present techniques. Instead, however we could significantly mitigate the possible heavy casualties and economic losses if we can earlier know a TC’s formation and can estimate its rainfall amount and distribution more accurate before its landfalling. In light of these problems, this short article presents methods to detect a TC’s formation as earlier and to delineate its rainfall potential pattern more accurate in advance. For this first part, the satellite-retrieved air-sea parameters are obtained and used to estimate the thermal and dynamic energy fields and variation over open oceans to delineate the high-possibility typhoon occurring ocean areas and cloud clusters. For the second part, an improved tropical rainfall potential (TRaP) model is proposed with better assumptions then the original TRaP for TC rainfall band rotations, rainfall amount estimation, and topographic effect correction, to obtain more accurate TC rainfall distributions, especially for hilly and mountainous areas, such as Taiwan.
This study used the spectral features of the geostationary satellite infrared window channel and the water vapor channel data to calculate a new parameter, normalized difference convection index (NDCI), to help determine the overshooting areas in typhoon cloud systems and the centers and intensity of typhoons. The results showed that the two-dimensional NDCI analysis helped to identify typhoon convective cloud systems and the positions of overshooting areas. In addition, because the NDCI values near a typhoon eye were rather significant, if a typhoon eye was formed, the NDCI cross-section analysis could help to confirm its position. When the center of a typhoon was covered by the high anvils and cirrus layers, it could still be qualitatively found through two-dimensional analysis. As for determining the intensity of typhoons, this study also tried to perform correlation analyses with NDCI and maximum sustained wind speed. The result showed that in the ranges within circles of 200 to 250 km radii with a typhoon eye as the center, the correlation between the area with the NDCI values <0 and the maximum sustained wind speed is high with a coefficient 0.7. Thus, the NDCI value could be a referential index to determine the intensity of a typhoon.
The effect of black carbon on the optical properties of polluted mineral dust is studied from a satellite remote-sensing perspective. By including the auxiliary data of surface reflectivity and aerosol mixing weight, the optical properties of mineral dust, or more specifically, the aerosol optical depth (AOD) and single-scattering albedo (SSA), can be retrieved with improved accuracy. Precomputed look-up tables based on the principle of the Deep Blue algorithm are utilized in the retrieval. The mean differences between the retrieved results and the corresponding ground-based measurements are smaller than 1% for both AOD and SSA in the case of pure dust. However, the retrievals can be underestimated by as much as 11.9% for AOD and overestimated by up to 4.1% for SSA in the case of polluted dust with an estimated 10% (in terms of the number-density mixing ratio) of soot aggregates if the black carbon effect on dust aerosols is neglected.
This paper presents a systematic approach to utilize multi-temporal remote sensing images and spatial analysis for the
detection, investigation, and long-term monitoring of landslide hazards in Taiwan. Rigorous orthorectification of satellite
images are achieved by correction of sensor orbits and backward projections with ground control points of digital elevation
models. Individual images are also radiometrically corrected according to sensor calibration factors. In addition, multi-temporal
images are further normalized based on pseudo-invariant features identified from the images. Probable landslides
are automatically detected with a change-detection procedure that combines NDVI filtering and Change-Vector Analysis.
A spatial analysis system is also developed to further edit and analyze detected landslides and to produce landslide maps
and other helpful outputs such as field-investigation forms and statistical reports. The developed landslide detection and
monitoring system was applied to a study of large-scale landslide mapping and analysis in southern Taiwan and to the
long-term monitoring of landslides in the watershed of Shimen Reservoir in northern Taiwan. Both application examples
indicate that the proposed approach is viable. It can detect landslides effectively and with high accuracy. The data produced
with the developed spatial analysis system are also helpful for hazard investigation, reconstruction, and mitigation.
Long range transport leads mineral dusts to internally/externally mix with the ambient aerosols, such as soot particles,
naturally. The physicochemical characteristics of dust particles thus are dramatically altered after mixing with soot
aggregates. Therefore, the investigation on the optical properties of mineral dust along with their pathway causes a
significant topic for understanding the impacts of Asian dust storm on regional air quality, environment and climate.
Unfortunately, the previous researches regarding to the optical properties of dust/soot mixture for satellite remote sensing
are scarce. Consequently, the objective of this study is to simulate the effects of mixing with soot aggregates on the
optical properties of dust particles for satellite observations based on the well developed models. A tri-axial ellipsoidal
model for dust particles by introducing the third morphological freedom to improve the symmetry of spheroids has been
developed and showed in good agreement for the retrievals of dust optical properties from remote sensing measurements
and ground based observations. For the model of soot aggregation, the scattering properties of fractal aggregates can be
obtained with the Rayleigh-Debye-Gans (RDG), superposition T-matrix and Generalized Multiple Mie (GMM) methods.
The results show that the AOD (aerosol optical depth) retrievals of dust particle will be underestimated while the SSA
(single scattering albedo) will be overestimated when neglecting the combination of soot aggregates. The simulations
also suggest that simultaneously retrieve AOD and SSA based on the apparent reflectance may induce large uncertainty
for the dust/soot mixtures.
The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is considered a very versatile tool in studying environmental changes. The multi-spectral sensor owns a high revisit period, a large scanning area, plus a handful of other advantages. The main purpose of this study is to employ reflectance data retrieved by the MODIS sensor in detecting smoke plumes, estimating their respective intensity and retrieving the AOD (Aerosol Optical Depth). Specifically, in the detection of the smoke plumes, biomass burning cases are studied in delineating the reflective characteristics. Following the detection, the Deep-Blue Aerosol Index (DAI) is utilized to evaluate the intensity. Relevant AOD information is retrieved by analyzing the relationship between the DAI and AOD. Results show a high correlation between the satellite-retrieved AOD and Sun Photometer-observed AOD data, thus demonstrating the feasibility in obtaining the aerosol distribution over highly reflective areas. As the proposed approach in this study is capable of accurately portraying the spatial distribution and intensity of smoke plumes, it can be effectively used in monitoring biomass burning hazards.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.