Green space must be increased in the development of new cities as green space can moderate temperature in the cities. In this study we estimated the land surface temperature (LST) and established relationships between LST and land cover and various vegetation and urban surface indices in the Iskandar Malaysia (IM) region. IM is one of the emerging economic gateways of Malaysia, and is envisaged to transform into a metropolis by 2025. This change may cause increased temperature in IM and therefore we conducted a study by using Landsat 5 image covering the study region (2,217 km2) to estimate LST, classify different land covers and calculate spectral indices. Results show that urban surface had highest LST (24.49 °C) and the lowest temperature was recorded in, forest, rubber and water bodies ( 20.69 to 21.02°C). Oil palm plantations showed intermediate mean LST values with 21.65 °C. We further investigated the relationship between vegetation and build up densities with temperature. We extracted 1000 collocated pure pixels of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), Urban Index (UI) and LST in the study area. Results show a strong and significant negative correlation with (R2= -0.74 and -0.79) respectively between NDVI, NDWI and LST . Meanwhile a strong positive correlation (R2=0.8 and 0.86) exists between NDBI, UI and LST. These results show the importance of increasing green cover in urban environment to combat any adverse effects of climate change.
The aerosol system is Southeast Asia is complex and the high concentrations are due to population growth, rapid urbanization and development of SEA countries. Nevertheless, only a few studies have been carried out especially at large spatial extent and on a continuous basis to study atmospheric aerosols in Malaysia. In this review paper we report the use of remote sensing data to study atmospheric aerosols in Malaysia and document gaps and recommend further studies to bridge the gaps. Satellite data have been used to study the spatial and seasonal patterns of aerosol optical depth (AOD) in Malaysia. Satellite data combined with AERONET data were used to delineate different types and sizes of aerosols and to identify the sources of aerosols in Malaysia. Most of the aerosol studies performed in Malaysia was based on station-based PM10 data that have limited spatial coverage. Thus, satellite data have been used to extrapolate and retrieve PM10 data over large areas by correlating remotely sensed AOD with ground-based PM10. Realising the critical role of aerosols on radiative forcing numerous studies have been conducted worldwide to assess the aerosol radiative forcing (ARF). Such studies are yet to be conducted in Malaysia. Although the only source of aerosol data covering large region in Malaysia is remote sensing, satellite observations are limited by cloud cover, orbital gaps of satellite track, etc. In addition, relatively less understanding is achieved on how the atmospheric aerosol interacts with the regional climate system. These gaps can be bridged by conducting more studies using integrated approach of remote sensing, AERONET and ground based measurements.
The amount of carbon sequestration by vegetation can be estimated using vegetation productivity. At present, there is a knowledge gap in oil palm net primary productivity (NPP) at a regional scale. Therefore, in this study NPP of oil palm trees in Peninsular Malaysia was estimated using remote sensing based light use efficiency (LUE) model with inputs from local meteorological data, upscaled leaf area index/fractional photosynthetically active radiation (LAI/fPAR) derived using UK-DMC 2 satellite data and a constant maximum LUE value from the literature. NPP values estimated from the model was then compared and validated with NPP estimated using allometric equations developed by Corley and Tinker (2003), Henson (2003) and Syahrinudin (2005) with diameter at breast height, age and the height of the oil palm trees collected from three estates in Peninsular Malaysia. Results of this study show that oil palm NPP derived using a light use efficiency model increases with respect to the age of oil palm trees, and it stabilises after ten years old. The mean value of oil palm NPP at 118 plots as derived using the LUE model is 968.72 g C m-2 year-1 and this is 188% - 273% higher than the NPP derived from the allometric equations. The estimated oil palm NPP of young oil palm trees is lower compared to mature oil palm trees (<10 years old), as young oil palm trees contribute to lower oil palm LAI and therefore fPAR, which is an important variable in the LUE model. In contrast, it is noted that oil palm NPP decreases with respect to the age of oil palm trees as estimated using the allomeric equations. It was found in this study that LUE models could not capture NPP variation of oil palm trees if LAI/fPAR is used. On the other hand, tree height and DBH are found to be important variables that can capture changes in oil palm NPP as a function of age.
Monitoring particulate matter less than 10 μm (PM10) near the ground routinely is critical for Malaysia for emergency management because Malaysia receives considerable amount of pollutants from both local and trans-boundary sources. Nevertheless, aerosol data covering major cities over a large spatial extent and on a continuous manner are limited. Thus, in the present study we aimed to estimate PM10 at 5 km spatial scale using AOD derived from MERIS sensor at 3 metropolitan cities in Malaysia. MERIS level 2 AOD data covering 5 years (2007-2011) were used to develop an empirical model to estimate PM10 at 11 locations covering Klang valley, Penang and Johor Bahru metropolitan cities. This study is different from previous studies conducted in Malaysia because in the current study we estimated PM10 by considering meteorological parameters that affect aerosol properties, including atmospheric stability, surface temperature and relative humidity derived from MODIS data and our product will be at ~5 km spatial scale. Results of this study show that the direct correlation between monthly averaged AOD and PM10 yielded a low and insignificant relationship (R2= 0.04 and RMSE = 7.06μg m-3). However, when AOD, relative humidity, land surface temperature and k index (atmospheric stability) were combined in a multiple linear regression analysis the correlation coefficient increased to 0.34 and the RMSE decreased to 8.91μg m-3. Among the variables k- index showed highest correlation with PM 10 (R2=0.35) compared to other variables. We further improved the relationship among PM10 and the independent variables using Artificial Neural Network. Results show that the correlation coefficient of the calibration dataset increased to 0.65 with low RMSE of 6.72μg m-3. The results may change when we consider more data points covering 10 years (2002- 2011) and enable the construction of a local model to estimate PM10 in urban areas in Malaysia.
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